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Received — 11 October 2026 ⏭

Epigenetic Drug Valemetostat Shows Dual Action Against HTLV-1 Eye Inflammation

✇Science Mag
By: Juliet Wilcox
11 October 2026 at 09:21

Human T cell leukemia virus type 1, the first human retrovirus ever identified, infects an estimated 5 to 10 million people worldwide, a figure extrapolated from surveys covering only about one-seventh of the global population. The virus inserts a DNA copy of its RNA genome into the chromosomes of host T cells, establishing a lifelong infection that can drive a spectrum of diseases, including adult T cell leukemia/lymphoma, HTLV-1-associated myelopathy, and HTLV-1-associated uveitis, a chronic inflammation inside the eye that is particularly common in endemic regions such as Japan. Despite the scale of the problem, coordinated global action has been slow; the World Health Organization only published technical reports aimed at reducing HTLV-1-related morbidity and mortality in 2021, and no broadly effective intervention has emerged in decades of research.

Current treatment for the ocular disease relies heavily on corticosteroids, delivered as topical drops, injections into the eye, or oral prednisone. This approach has serious shortcomings. Studies indicate that 30 to 60 percent of patients with HTLV-1-associated uveitis experience recurrent episodes, and long-term steroid exposure can trigger cataracts, glaucoma, macular edema, epiretinal membranes, retinal degeneration, optic nerve atrophy, and bullous keratopathy. Most importantly, corticosteroids suppress inflammation without reducing the number of virus-infected cells or halting disease progression. Newer molecular targeted agents, including the anti-CCR4 antibody mogamulizumab and the immunomodulatory drug lenalidomide, have been explored, but treatment-related inflammation and adverse effects remain significant obstacles. This therapeutic gap motivated a team led by Yaru Zou and Koju Kamoi of Tokyo Medical and Dental University to investigate whether an epigenetic drug already approved for blood cancer could address the root of the ocular inflammation.

The drug in question is valemetostat, marketed as EZHARMIA, a dual inhibitor of the enzymes EZH1 and EZH2 that was approved in 2022 for adult T cell leukemia/lymphoma. Both enzymes are the catalytic components of polycomb repressive complex 2, a molecular machine that silences genes by adding three methyl groups to lysine 27 on histone H3, producing the repressive chromatin mark known as H3K27me3. Previous work had shown that EZH1 and EZH2 are overexpressed, and H3K27me3 deposition increased, in ATL cells and in cells expressing the viral oncoprotein Tax. Dual EZH1/2 inhibitors have also been reported to curb the spontaneous proliferation and exaggerated immune responses of HTLV-1-infected cells in patients with HTLV-1-associated myelopathy. What remained unknown was whether such inhibition could benefit the inflamed eye.

To find out, the researchers built a series of laboratory models centered on ARPE-19 cells, a human retinal pigment epithelial cell line chosen because the retinal pigment epithelium forms the outer blood-retinal barrier and governs retinal immune homeostasis. They exposed these ocular cells to three kinds of T cells: MT-2 cells, which carry transcriptionally active HTLV-1 and express the viral protein Tax; TL-Om1 cells, an ATL-derived line in which the virus is largely silent and Tax is low or absent; and Jurkat cells, which are uninfected and served as controls. In parallel, they treated the T cell lines themselves. Western blotting and quantitative PCR revealed that HTLV-1 infection drove significant upregulation of both EZH1 and EZH2, along with elevated H3K27me3, in infected retinal pigment epithelial cells and in MT-2 cells, with EZH1 protein rising roughly 2.5-fold in infected ocular cells and six-fold in MT-2 cells. This was the first demonstration that HTLV-1 infection induces polycomb-mediated epigenetic changes in retinal cells.

The team then treated the models with valemetostat at 0.01, 0.1, and 1 micromolar for 96 hours, concentrations selected in preliminary experiments because they harmed infected T cells while leaving normal ocular cells, including primary human trabecular meshwork cells, unaffected below 10 micromolar. The results were strikingly dependent on the model. In co-cultures of retinal cells with Tax-positive MT-2 cells, and in retinal cells directly infected with HTLV-1, the drug produced modest but statistically significant reductions in cell viability at the highest dose, accompanied by small increases in late-stage apoptosis. No such effects appeared in co-cultures with TL-Om1 or Jurkat cells. Crucially, the absolute magnitude of apoptosis remained limited, typically only 1 to 3 percent above baseline, indicating that valemetostat is not broadly toxic to retinal cells, an important safety consideration given their role in maintaining vision.

The virological findings were equally nuanced. Valemetostat did not significantly reduce the HTLV-1 proviral load in co-culture systems or in directly infected retinal pigment epithelial cells. However, in MT-2 cells themselves, 1 micromolar valemetostat significantly lowered proviral load and suppressed the messenger RNA levels of both Tax and HBZ, the two hallmark transcripts of the virus. Because MT-2 cells represent the transcriptionally active, highly infectious state of HTLV-1, while TL-Om1 represents latency, the results suggest that the drug’s antiviral effect is gated by the transcriptional status of the infected cell. Within the four-day treatment window, the authors conclude, valemetostat’s benefit to the ocular environment is likely indirect, acting primarily by quieting the most virally active cells rather than by eradicating provirus everywhere.

The most clinically relevant effects emerged in the inflammatory readouts. HTLV-1-infected T cells recovered from the eyes of patients with uveitis are known to secrete copious interleukin-6, interleukin-8, and interleukin-10, all implicated in disease. In the new study, valemetostat produced a dose-dependent increase in the anti-inflammatory cytokine IL-10 in MT-2-containing conditions, significant at 1 micromolar in the co-culture system and across all doses in MT-2 cells. This fits a mechanistic expectation: blocking EZH1/2 removes H3K27me3 marks that repress the IL-10 gene, and prior genetic studies showed that loss of EZH2 directly activates IL-10 transcription in several CD4-positive T cell subsets. Meanwhile, pro-inflammatory signals fell in a context-dependent pattern. IL-12p70 and the chemokine CXCL-10 dropped in co-cultures, IL-6 and IL-8 declined mainly in MT-2 cells, and CCL-2 fell at low doses in the MT-2 co-culture, whereas the chemokine CCL-5 was unchanged in every model.

The drug’s influence on NF-kappaB, the central inflammatory pathway that Tax hijacks to drive both viral replication and cytokine production, was more subdued. Valemetostat modestly reduced NF-kappaB p65 phosphorylation in the MT-2 co-culture at the lowest dose and slightly decreased the nuclear-to-cytoplasmic ratio of p65 in retinal cells stimulated with TNF-alpha, but it did not abolish nuclear translocation. The authors attribute this to the overwhelming strength of Tax-driven NF-kappaB activation, which a 96-hour course of EZH1/2 inhibition may be insufficient to counteract, and to the fact that NF-kappaB and cytokine networks are regulated by many factors beyond polycomb complexes. Immunofluorescence confirmed that valemetostat significantly lowered H3K27me3 in retinal cells exposed to MT-2 cells or directly infected, but not in cells co-cultured with TL-Om1 or Jurkat cells, again underscoring the model dependence of the epigenetic response.

The study also carries a cautionary note about IL-10. Although the cytokine is anti-inflammatory, it can promote the survival of HTLV-1-infected T cells and facilitate immune evasion by impairing dendritic cell antigen presentation and downregulating MHC class I molecules, and patients with aggressive ATL often show abnormally elevated IL-10. The authors therefore suggest that combining EZH1/2 inhibition with IL-10 pathway blockade could prove more effective, noting evidence that IL-10 neutralization sensitizes ATL cells to standard therapies. In the TL-Om1 model of ATL-related ocular infiltration, valemetostat failed to reduce H3K27me3, proviral load, or NF-kappaB activation within the malignant cells, yet it suppressed TL-Om1-induced CXCL10 secretion and induced dose-dependent apoptosis, pointing to a benefit mediated through the inflammatory microenvironment rather than direct tumor killing.

The authors are candid about the limitations. The work relied entirely on in vitro co-cultures and established cell lines, which cannot reproduce the blood-retinal barrier, retinal architecture, or local immune regulation of a living eye, and the intraocular pharmacokinetics of valemetostat after systemic administration remain undefined. Sex as a biological variable could not be assessed because donor-defined primary cells were not used. Even so, the findings establish a proof of concept that an approved epigenetic therapy can counteract HTLV-1-induced chromatin changes in retinal cells, dampen viral transcription in the most active infected cells, and reshape the cytokine milieu that sustains uveitis. Longer treatment studies, in vivo models, and patient-derived systems will be needed to determine whether these modest, context-dependent effects can be translated into a genuine steroid-sparing therapy for one of the most burdensome manifestations of a virus that medicine has long neglected.

Subject of Research: EZH1/2-targeted epigenetic therapy with valemetostat for HTLV-1-associated uveitis

Article Title: Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms

Article References: Zou, Y., Kamoi, K., Yang, M., Zong, Y., Zhang, J., & Ohno-Matsui, K. (2026). Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms. iScience, 29(11), Article 117775. https://doi.org/10.1016/j.isci.2026.117775

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117775

Keywords: HTLV-1, valemetostat, EZH1, EZH2, H3K27me3, uveitis, epigenetics, PRC2, Tax, IL-10, NF-kappaB, retinal pigment epithelium

Manta rays inspire a smarter algorithm for cleaner, cheaper power grids

✇Science Mag
By: Denise Maddox
11 October 2026 at 09:14

Every time a power grid operator decides how much electricity each generator should produce, a quiet balancing act takes place. Burning less fuel saves money, but it often means allowing more pollution, and cutting emissions usually drives costs up. Engineers have long searched for a way to satisfy both goals at once, a challenge known as combined economic emission dispatch, or CEED. Now, a team of researchers has turned to one of the ocean’s most graceful feeders, the manta ray, for inspiration, and the result is an optimization algorithm that outperforms a crowded field of competitors on both cost and cleanliness.

The study, published in the journal Results in Engineering by Yu-Feng Sun, Jie-Sheng Wang and colleagues, describes an enhanced version of the Manta Ray Foraging Optimization algorithm, or MRFO. The original algorithm, first introduced a few years ago, mimics the cooperative hunting strategies that manta rays use to harvest plankton. Because plankton drifts unpredictably with seasons and weather, manta rays have evolved three distinct foraging behaviors: chain foraging, in which animals line up in arcs so that each one catches food missed by the one ahead; spiral foraging, where the group coils around a dense patch of food; and somersaulting, in which individuals flip back and forth around a promising spot. Translated into mathematics, these behaviors become rules that move a population of candidate solutions around a search space, gradually converging on the best answer.

What makes the CEED problem so stubborn is its mathematical character. The relationship between a generator’s output and its fuel consumption follows a cubic curve, and the emissions of carbon dioxide, nitrogen oxides and sulfur dioxide each follow their own cubic functions. The combined objective is nonlinear, nonconvex and discontinuous, riddled with local minima that can trap classical techniques such as lambda iteration or gradient-based methods. To make matters harder, the problem is genuinely multi-objective, so the researchers converted it into a single objective by applying price penalty factors that fold the cost of each pollutant into one total figure in dollars per hour.

Constraint handling is where many algorithms stumble. Traditional approaches add a fixed penalty whenever a solution violates the power balance requirement, the equation demanding that total generation minus transmission losses equals demand. But a fixed penalty is a blunt instrument. Set it too low and the algorithm happily returns infeasible schedules; set it too high and the search becomes so restricted that it cannot explore properly. Finding the right value through trial and simulation is time-consuming and inefficient. The team’s answer was to make the penalty itself dynamic, drawing on five different mathematical functions: sinusoidal, hyperbolic tangent, arctangent, linear and hyperbolic secant. Each function is raised to an order that changes during the run, so the penalty strength adapts to both the size of the violation and the stage of the optimization. Early on, mild penalties preserve exploration; later, penalties tighten to push solutions toward feasibility.

The second major innovation targets the heart of the MRFO algorithm. In the original design, a parameter called C governs the switch between exploration, casting the net wide across the search space, and exploitation, homing in on the best solution found so far. This parameter rises monotonically from zero to one, a predictable march that can leave the algorithm poorly balanced at certain stages. The researchers replaced it with a chaotic switching factor, drawn from nine well-known chaotic maps including Chebyshev, logistic, tent, sine, Gauss and singer maps, combined with an exponential adjustment function. The result is a parameter that still trends upward but oscillates along the way, periodically reopening the search space and preventing the population from settling prematurely into a local optimum.

To find out which chaotic map worked best, the team ran all nine enhanced variants, named C1MRFO through C9MRFO, through the CEC-2022 benchmark suite of twelve test functions, using a population of thirty and five hundred iterations averaged over thirty independent runs. The Chebyshev-based variant, C1MRFO, emerged as the overall winner, ranking first among the nine and beating the original MRFO, which finished last. Wilcoxon rank-sum tests confirmed that most of the improvements were statistically significant, with p-values below the conventional 0.05 threshold. When C1MRFO was pitted against a roster of state-of-the-art metaheuristics, including the dhole optimization algorithm, evolutionary mating algorithm, dung beetle optimizer, snow geese algorithm, particle swarm optimization, grey wolf optimizer, sparrow search algorithm and marine predators algorithm, it achieved the best final rank, taking first place on many of the test functions while keeping computational times competitive.

The real test, however, came when the algorithm was applied to actual dispatch problems. The researchers simulated a six-unit power system under four load demands of 150, 175, 200 and 225 megawatts. Across all four scenarios, C1MRFO produced the lowest comprehensive total cost, which combines fuel cost with the weighted penalties for carbon dioxide, nitrogen oxide and sulfur dioxide emissions. At 150 megawatts, for example, it reached a total cost of about 10,137 dollars per hour, edging out the grey wolf optimizer and clearly beating algorithms such as the arithmetic optimization algorithm, Harris hawks optimization, whale optimization algorithm, sine cosine algorithm, particle swarm optimization and the lambda iteration method. The pattern held at every load level, with the margin over the weakest classical method growing to thousands of dollars per hour at 225 megawatts.

The dynamic penalty functions delivered a further, striking gain. When the sinusoidal penalty function was applied with the parameter order set to dy5, the total cost at 150 megawatts dropped from roughly 10,137 to 9,065.71 dollars per hour, and similar reductions appeared at the higher loads, reaching 14,621.45 dollars per hour at 225 megawatts. Comparing the five penalty shapes, the sinusoidal function consistently produced the lowest minimum costs, beating the hyperbolic tangent, arctangent, linear and hyperbolic secant alternatives by margins ranging from hundreds to nearly two thousand dollars per hour depending on the load. Notably, when the penalty order was fixed rather than dynamic, performance was worst across the board, confirming that the adaptivity of the penalty, not merely its presence, is what matters.

To verify that each improvement contributes on its own, the team conducted an ablation study on a much larger twenty-unit system with a total demand of 2,500 megawatts. The results formed a clear hierarchy: the original MRFO achieved 61,835.16 dollars per hour, the chaos-enhanced C1MRFO reached 61,826.11, and the fully improved version combining chaos with the optimal sinusoidal dynamic penalty, sin(dy5)MRFO, achieved the best figure of 61,807.36 dollars per hour. All three outperformed the phasor particle swarm optimization benchmark, which came in at 61,895.57 dollars per hour. The stepwise improvement demonstrates that the two enhancements are not redundant but superimpose their benefits, and that the framework scales to systems far larger than the six-unit case.

The authors are careful to note where the work goes next. The current approach converts the multi-objective problem into a single objective through price penalty factors, but future versions could adopt Pareto-based multi-objective optimization to generate full sets of non-dominated trade-off solutions. The test cases also assume deterministic demand, so extending the algorithm to stochastic dispatch with wind and solar integration is another goal, as is combining the dynamic penalty with feasibility-first constraint-handling strategies. For now, the study offers a compelling demonstration that borrowing the foraging logic of a filter-feeding fish, perturbing it with chaos, and teaching it to calibrate its own punishments can squeeze meaningful savings and emission reductions out of one of power engineering’s hardest scheduling problems.

Subject of Research: An improved manta ray foraging optimization algorithm with chaotic switching factors and dynamic penalty functions for solving the combined economic emission dispatch problem in power systems.

Article Title: Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem

Article References: Sun, Y.-F., Wang, J.-S., Zhang, X.-Y., Li, Y.-X., Guan, X.-Y., & Liu, X. (2026). Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem. Results in Engineering, 32, Article 113383. https://doi.org/10.1016/j.rineng.2026.113383

Image Credits: AI Generated

DOI: Not provided

Keywords: manta ray foraging optimization, combined economic emission dispatch, metaheuristic algorithms, chaotic maps, dynamic penalty function, power system optimization, emission reduction, fuel cost minimization, CEC-2022 benchmarks, swarm intelligence, constraint handling, Results in Engineering

AI Paints Building Schedules Out of Noise, Cutting Energy Use by Nearly 29 Percent

✇Science Mag
By: Blake Davidson
11 October 2026 at 09:05

Buildings are responsible for an enormous share of global electricity demand, and the way their air conditioners, lights, and heating systems are scheduled can make the difference between a grid under strain and one running smoothly. For years, engineers have tried to optimize these schedules with evolutionary algorithms and reinforcement learning, but the problem becomes brutally hard when dozens of buildings share a single electrical network and must be coordinated simultaneously. A new study published in Discover Artificial Intelligence proposes a strikingly different approach: instead of searching step by step for the best schedule, a generative artificial intelligence model learns to paint one directly out of pure noise, guided by a map of how the buildings are physically wired together.

The framework, called DMCBO, short for Diffusion-driven Multi-objective Collaborative Optimization for Building Clusters, was developed by Yalan Zheng of Zhengzhou Technical College and Taixiang Yin of North China University of Water Resources and Electric Power. Their central insight is that the joint scheduling of equipment settings and load distribution across an entire cluster of buildings can be treated as a high-dimensional data sample, much like an image. Diffusion models, the same family of generative techniques behind modern text-to-image systems, work by progressively corrupting data with Gaussian noise and then learning to reverse that corruption. The researchers reformulated multi-objective building optimization as exactly this kind of conditional denoising process: starting from random noise, the model strips away noise step by step until a complete, Pareto-optimal collaborative schedule for every piece of equipment in every building emerges.

Technically, the forward diffusion process is a fixed Markov chain that adds tiny amounts of Gaussian noise to real scheduling data over one hundred steps, with the noise coefficient increasing linearly from 0.0001 to 0.02 until the original data becomes indistinguishable from standard Gaussian noise. A neural network is then trained to predict the noise that was added at each step, given the noisy schedule and a conditional encoding vector describing the current situation. Once trained, the model can generate fresh solutions at inference time by sampling pure noise and running the reverse process, refining it over one hundred denoising steps into a schedule tailored to the prevailing weather, occupancy, and grid conditions. Because this generation is a gradient-based learning process rather than an iterative search, it avoids the local optima traps that plague traditional multi-objective evolutionary algorithms and the reward oscillations that destabilize deep reinforcement learning.

What makes the system genuinely cluster-aware is its use of graph neural networks. The researchers model the building cluster as a graph mapped onto a radial distribution microgrid topology, where nodes represent buildings and edges encode physical constraints such as maximum line thermal capacities and transformer limits. A two-layer graph convolutional network passes messages between neighboring buildings, fusing spatial-topological dependencies and network constraints into a single graph embedding. This embedding is concatenated with an adjustable preference weight vector to form the condition that steers the denoising trajectory. In effect, when the model generates a control strategy for one building, it simultaneously perceives the state of its neighbors and the limits of the shared electrical infrastructure, suppressing systemic coordination errors from 0.18 down to 0.06 in the team’s experiments.

The multi-objective aspect is handled through an elegant scalarization trick. Each objective, such as energy consumption, operating cost, or thermal comfort, is normalized to a dimensionless value between zero and one using Min-Max normalization based on historical operational bounds. The normalized objectives are then blended into a single scalar condition via a weight vector whose components sum to one. During training, these weights are sampled from a uniform Dirichlet distribution, exposing the model to the entire space of possible preferences so it learns the distribution of the full Pareto front. At deployment, a decision maker can slide the weight vector like an adjustment knob, instantly generating schedules that emphasize energy saving, comfort, or a balanced compromise, all within the same trained model.

The denoising backbone itself is a U-Net adapted to process one- and two-dimensional building scheduling tensors. Diffusion time steps are embedded through sinusoidal positional encodings, while the graph-derived condition is injected into every residual block via Adaptive Layer Normalization. A bottleneck layer with multi-head self-attention captures long-range dependencies across the entire daily scheduling horizon, and symmetric skip connections preserve high-resolution information during reconstruction. The authors trained the model for 300 epochs on dual NVIDIA Tesla V100 GPUs, and the loss curves dropped from 0.75 to below 0.03 with training and validation curves closely coinciding, indicating no overfitting. As the reverse diffusion step count decreased from 90 to 10 during analysis, the noise-prediction error distribution became sharply concentrated at zero, demonstrating extremely high denoising precision at the end of generation.

Validation was carried out on the open-access Building Data Genome Project 2 dataset, which supplied real hourly weather records, operational schedules, and annual electricity, cooling, and heating load profiles across diverse climate zones. Because that dataset provides metered energy data rather than physical building models, the team used United States Department of Energy Commercial Reference Buildings as templates and calibrated them in EnergyPlus, a high-fidelity thermodynamic simulation platform, to create a digital twin consistent with the real data. All results were benchmarked against a rule-based control strategy with static thermostat setpoints of 25 degrees Celsius in summer and 22 degrees in winter, and every experiment was repeated ten independent times to confirm statistical significance.

The headline numbers are impressive. In high-demand summer conditions, DMCBO achieved an average energy-saving rate of 28.6 percent and cut peak load by 38.5 percent, shaving the afternoon peak from roughly 560 kilowatts down to about 440 kilowatts and saving approximately 142.80 US dollars per day. Cumulative daily power savings exceeded 1,000 kilowatt-hours, compared with 400 kilowatt-hours for a traditional collaborative strategy and just 120 kilowatt-hours without coordination. In winter, peak shaving and valley filling reached about 39 and 42 percent respectively, far above the 22 and 25 percent of conventional methods. When the solution sets were projected into objective space, DMCBO dominated the baselines: it reached a hypervolume indicator of 0.95 within 12,000 function evaluations, versus 0.85 for NSGA-II and 0.70 for MOPSO, while keeping operating costs between 140 and 160 dollars against more than 210 dollars for deep reinforcement learning and 260 to 300 dollars for a GAN-based generative approach.

Robustness and scalability proved equally compelling. Under injected stochastic disturbances mimicking sudden weather changes and occupancy swings, the framework confined power fluctuations to a narrow band of plus or minus 5 kilowatts, whereas the deep reinforcement learning baseline swung between minus 15 and plus 20 kilowatts. Scaling from 5 to 50 buildings, the global energy-saving rate actually rose from 28.5 to 31.2 percent, because larger clusters unlock more load-complementary opportunities that the graph encoder can safely exploit within encoded grid boundaries. Computation time grew to 9.8 seconds at the 50-building scale, but that remains comfortably within the 15-minute control horizon used by real building management systems, and the hypervolume index slipped only slightly to 0.88. Ablation experiments confirmed that both the graph neural network module and the choice of 100 diffusion steps, which yields a 50-millisecond inference latency, were essential to this balance of speed, accuracy, and stability.

The authors are candid about limitations. The framework has so far been validated only in simulation, and EnergyPlus simplifies sub-minute mechanical actuation latencies that real HVAC hardware would introduce; they propose cascading PID loops and predictive safety margins to absorb those delays in practice. Data quality, generalization to residential buildings and extreme climates, the computational cost of scaling to city-sized networks, and the black-box nature of the generative process all remain open challenges, with explainable AI and transfer learning flagged as future work. Still, the architecture is designed to slot into standard building management systems as a supervisory layer, transmitting setpoints over protocols such as BACnet or Modbus. If the simulation gains survive contact with physical buildings, the idea of generating optimal energy schedules from noise, guided by a graph of the grid itself, could become a powerful new tool in the push toward decarbonized cities.

Subject of Research: Conditional diffusion models with graph neural networks for multi-objective energy optimization in building clusters

Article Title: Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

Article References: Zheng, Y., & Yin, T. (2026). Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks. Discover Artificial Intelligence, 6(1), Article 1383. https://doi.org/10.1007/s44163-026-02140-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02140-z

Keywords: diffusion models, graph neural networks, building energy management, multi-objective optimization, EnergyPlus, smart buildings, Pareto optimization, deep learning, energy efficiency, HVAC control, microgrids, generative AI

Received — 25 September 2026 ⏭

Enlarged Breasts Linked to Migraine, Neck Pain and Sleep Apnea

✇Knowridge Science
By: Knowridge
25 September 2026 at 01:01

Women with unusually enlarged breast tissue may face a higher likelihood of several health problems, including chronic migraine, neck pain and sleep apnea, according to new research. The condition, medically known as macromastia, is often discussed in terms of discomfort or appearance. However, researchers from Wake Forest University School of Medicine say it may be […]

The post Enlarged Breasts Linked to Migraine, Neck Pain and Sleep Apnea appeared first on Knowridge Science Report.

Received — 17 September 2026 ⏭

Salesforce’s massive outage exposes the hidden risks of cloud dependencies

✇Computerworld
17 September 2026 at 02:05

While its flagship Dreamforce event was in full swing on Wednesday, Salesforce was triaging a roughly seven and a half hour-long service outage that disrupted user access and caused “severe delays” and intermittent errors. Some customers were also unable to submit new support cases.

The service outage hit at 3:50 a.m. EDT, impacting “multiple instances across all regions,” Salesforce reported. It was marked resolved right around 3 p.m. EDT, after several hours of monitoring to determine that fixes had been successful.

Salesforce initially pegged the issue to an “external dependency failure” impacting the legacy login server. A core system component experienced increased load, limiting its capacity to process requests. The company confirmed that there were no issues with third-party infrastructure.

Beyond the obvious embarrassment from the outage occurring during Dreamforce, an analyst said the incident highlights a cloud resilience problem, rather than purely a legacy one.

“Cloud does not eliminate architectural dependencies,” said Abbas Jaffery, a principal advisory director at Info-Tech Research Group. “It can sometimes make them less visible. And when the platform is your system of record, those hidden dependencies become an enterprise risk rather than simply a technology risk.”

Rolling restarts, persistent issues throughout the day

Salesforce began experiencing service issues at 3:50 a.m. EDT on September 16, and initial investigation determined that requests were stalling while waiting on responses from an internal login service that was using up available server resources.

Initially, Salesforce blocked application programming interface (API) endpoints and attempted rolling restarts to revive the service, then pushed out fixes region-by-region. At 7:20 a.m., some customers were seeing service return to normal, and Salesforce was working on a “code-level permanent fix.”

However, the rollout did not complete for a number of instances, and some automated fixes didn’t fully resolve the issue. For example, customers reported that scheduled jobs were not running as expected, even after service was restored. Salesforce manually restarted those ‌instances.

Salesforce later reported that the impact radius was “narrower than initially understood” and saw signs of recovery by around 11 a.m. EDT, with most customers coming back online.

A sub-set of Hyperforce instances were the last to be restored. Mitigations were in place across all instances by 11:39 a.m. EDT, and Salesforce continued to monitor the issue until marking the incident resolved at 2:59 p.m. EDT.

“We apologize for how this incident affected you and your business,” Salesforce posted on its incident blog. “We will undertake a full investigation of the incident, establishing the technical trigger, the underlying cause, and preventive action to avoid a repeat in the future.”

Creating a ‘temporal’ data problem


For customers for whom Salesforce is a system of record, several hours of authentication and service disruption can create a “temporal data problem,” Info-Tech’s Jaffery explained. “Events that should have happened at different points in time may occur later, fail altogether, or arrive out of sequence,” he said.

For instance, a customer interaction may occur through another channel while Salesforce is unavailable, but an integration, workflow, or scheduled process that normally records or propagates that event is unable to run.

This has several potential consequences, Jaffery said. Transactions and customer service processes are delayed; APIs and middleware may accumulate retries, timeouts, and queues. Records can become temporarily inconsistent, and scheduled jobs and workflows may be missed. Employees could lose visibility into customer history or case status, even when the underlying data has not been lost, creating a “data divergence.”

The first mistake would be to assume that just because users can log in, the incident is over, he noted. “Enterprises should move immediately into a reconciliation and integrity phase,” Jaffery advised. This means not only verifying interactive access, but APIs, integrations, scheduled jobs, queues, workflows, automation, authentication flows, and downstream systems.

Enterprises should ask what transactions failed, partially completed, or were duplicated during the outage? Which scheduled or asynchronous processes did not execute? Did integrations retry successfully, or did they create a backlog or retry storm? Are downstream systems now consistent with Salesforce?

Security teams should also validate authentication and session behavior, privileged access, integration credentials, and any emergency changes made during recovery, Jaffery explained. “The most important question is not simply ‘Is Salesforce back?’, but ‘What did the business expect to happen during the outage, and can we prove that it actually happened?,’” he said.

What to look for in post-incident reports

A credible post-incident review from Salesforce should establish a causal chain: The trigger, dependency failure, technical propagation, customer impact, detection, mitigation, recovery, and permanent corrective action, Jaffery said.

The company should be able to answer these questions, he said:

  • What was the actual initiating failure and why did the failure propagate into the login path?
  • Why could the affected dependency consume sufficient capacity to affect core services?
  • Why didn’t isolation or failover prevent the impact?
  • Why did initial remediation attempts fail and why did the subsequent rollout require additional intervention?
  • What safeguards are being added to prevent recurrence?
  • How will Salesforce demonstrate that the corrective action actually works under failure conditions?

Service restoration simply tells customers: “We got it working again,” he noted. But root cause analysis tells customers: “We understand why it failed, why our controls didn’t prevent it, and what has changed so that the same failure mode is less likely to recur.”

It’s not just about ‘legacy’ pieces in the stack

One architectural lesson is that a legacy component does not have to be large to be critical, Jaffery pointed out. An older authentication service can remain part of a modern stack, and therefore become a dependency for newer services.

“The component’s age matters less than its position in the dependency graph, its blast radius, and the quality of its isolation and failure handling,” he said, pointing to this incident’s progression: Requests stalled waiting on an internal login service due to increased resource consumption led to investigation into an external dependency failure, which in turn revealed impact on a legacy login server. Finally, Salesforce said, “core system components experienced increased load, which limited its capacity to process request”.

That is a classic resilience question, Jaffery pointed out: Can a failure in one dependency remain in that one dependency, or does it become a platform-wide failure?

Modernization should not be identified simply by how much old technology has been replaced, he noted, it should also measure dependency concentration, isolation, “graceful degradation,” recovery paths, and failure blast radius.

“For enterprise architects, that is the real takeaway,” he said.

Maybe driven by agentic AI, exacerbated by layoffs

At this point, there are no obvious signs that this was a security incident, noted David Shipley, CEO of Beauceron Security. “Right now, this bears all the hallmarks of an update gone horribly wrong.”

He pointed to an incident in December 2025 when Amazon’s internal AI coding agent, Kiro, caused a 13-hour AWS outage in a mainland China region, noting, “I’m not going to be shocked if we don’t see some kind of agent role in this kind of scale disaster.”

Significant Salesforce layoffs over the last few years could also have had a negative impact on the outage and recovery, he added. “Having it happen during Dreamforce had to be all kinds of hell, though, for their sales and customer support teams,” he said. “Pour one out for them as they work on rebuilding relationships, face-to-face.”

This article originally appeared on CIO.com.

Received — 16 September 2026 ⏭

How to keep AI-generated code aligned with your standards

✇InfoWorld
16 September 2026 at 10:00

One of the first questions I ask devops organizations is to walk me through their development and operations standards. What are the non-negotiable devops practices? What are the data governance first principles? What observability standards are in place? How is security embedded in devops? This is the starting point. From there, we might review standards on how functional requirements are written. For businesses developing AI agents, I’ll ask about their non-functional requirements (NFRs) and how testing is performed.

Now that many devops organizations are using AI code generators, vibe coding, and applying spec-driven development practices, the questions increase. How does the AI know your organization’s standards? What processes monitor and enforce these standards? What are the developer responsibilities for owning the outcomes?

“Generative AI will happily hand you the movie set of an app with looks of a house from the street. The doors and windows open, and the demo runs clean,” says Mike Toole, director of security and IT at Blumira. “Behind the facade, there’s no plumbing and no wiring: no input validation, no auth boundaries, nothing actually holding it up. If your only gate is ‘does it run and look right,’ you’re shipping a set, not a building.”

One report shows that 92% of developers use AI coding tools daily in 2026 and that 41% of all global code is now AI-generated. Will all that code deliver value, operational issues, or mounting AI debt? I spoke to several experts about how to keep AI-generated code aligned with organizational standards and how to define an AI code generator’s context beyond just functional requirements.

Document like a construction specification

A general contractor in the construction industry will review building information models that provide a view of what they are developing. But many of the details are in the construction specifications, including component stipulations and performance requirements. Devops teams without equivalent documentation for their organization, architectures, and applications may have gotten away with this omission in the pre-AI era. But institutional knowledge and compliance requirements are the context AI code generators need before you share the first functional prompts with them. 

“AI-generated code stays aligned with engineering standards only when those standards are explicit, available, and difficult to bypass,” says Eddie Zhou, founding engineer at Glean. “Put architecture, approved libraries, data-handling rules, security controls, naming conventions, observability, test expectations, and non-functional requirements into specs and automated checks. Then, ensure you give the model that context but treat its output as untrusted until it passes the same reviews, tests, scans, and production monitoring as human-written code.”

Overcoming tribal knowledge was always challenging as devops teams had too much pressure to push features and little incentive to maintain documentation. Two of my top AI leadership skills are change management and agile collaborative leadership, because devops teams need practice alignment while introducing AI capabilities.  

Zhou adds, “AI amplifies ambiguity and weak governance as efficiently as good engineering. If standards live only in tribal knowledge, AI will expose that problem, not solve it.”

Encode non-functional requirements as automated tests

The problem with requirement documents and reference architectures is they aren’t directly enforceable with human or AI developers. Once a devops team agrees on standards and documents them, the next step is to develop approaches to encode them as automated acceptance criteria in CI/CD pipelines.

“AI code generators and human developers optimize for functional correctness—making the feature work—and quietly skip the non-functional requirements where most of your real standards live, including performance, security posture, error handling, observability, and auditability,” says Michael Bevilacqua, vice president of AI product management at Adeptia. “Encode your NFRs as executable acceptance criteria that generated code must pass before it merges, so ‘aligned with our standards’ becomes a gate the pipeline enforces on every change.”

An example: Performance requirements stating that all web pages should have a total page weight under 2MB can be validated during the pull request, and requiring the time to first byte (TTFB) to be under 800ms can be automated in CI/CD in a staging environment.

Requirements for stakeholders and AI

Architecture and automated non-functional requirements covering performance, security, and other organization-level compliance requirements should become the base-level context standards shared with AI-code generators. The next set of considerations for setting accurate contexts starts with application- and agent-specific requirements, their intended use cases, and specific compliance guardrails.

“CIOs are mandating AI to build apps faster, but many are realizing speed isn’t the bottleneck because more code doesn’t automatically create business value,” says Matt Healy, senior director of product strategy and marketing at Pegasystems. “Before developers write any code, organizations must understand customer needs, assess legacy systems, stay current with regulations and best practices, map enterprise architectures and integrations, and align business and IT.”

Consider three stages of understanding requirements. Once stakeholders set business value and intent, developers must first align on an implementation strategy and discuss performance, cost, and other trade-offs. Then, developers should reconvene with stakeholders and review implementation trade-offs. Healy adds, “On large projects, defining the solution and success metrics can take as much time as building it. Leaders aren’t just using AI to accelerate development, but to help executives, business analysts, product owners, and IT teams collaborate more effectively.”

The third stage is gathering the goals, user requirements, and implementation approach into a development-specific context. But we’re not done yet, because data requirements and operations considerations must also be included in the context fed to AI code generators. 

Build governance into the specs

How the application or AI agent being developed can use the data it has access to requires clear specifications. Data governance programs should have standards based on data sets, defined roles, entitlements, and usage specifications.

Kash Mehdi, vice president and field CTO at Reltio, says the teams that are pulling ahead treat data governance as an input to AI, not an audit after it. “They ground their tools in trusted, semantically consistent data models, approved specs, and security policies, so the AI inherits the organization’s standards instead of inventing its own. Without that governed context layer, you don’t scale from a handful of AI-assisted workflows to thousands; you just accumulate inconsistent code faster,” Mehdi says.

Here are two options to consider in creating standards: First, establish repurposable data sets as data products with defined integrations and usage rules. Organizations with many data sets and integration points should consider data fabrics. The second (and key requirement) is to standardize the format of data contracts, using human- and machine-readable formats so that data owners, users, and AI code generators operate from a consistent set of principles.  

“AI writes code that compiles and passes tests but quietly ignores your data contracts, so the failures show up in production as corrupted state, not build errors,” says Ed Huang, cofounder and CEO of TiDB. “The fix is to make governance machine-checkable, including schema constraints, access policies, and data-quality rules enforced at the database layer where the AI can’t route around them, not documented in a wiki the model never reads. Treat the database as the enforcement point for your standards, and it stops mattering whether a human or an agent wrote the query.”

Observability and testing first

When AI generates the code, a best practice is to use static application security testing (SAST) and other code analysis tools to perform automated code reviews. Another best practice is to put applications through penetration testing and other application security validations. But many of these tools are outside in view of what the AI developed. To get an inside view, observability and testing requirements must be included as a standard, with specific requirements around the key transactions you’re asking the AI to develop.

Sanjay Gidwani, CEO and founder at Kosmos, says, “More code volume just means more time spent investigating, and the fix isn’t reading the code more carefully before it ships. It’s connecting the observability signal, the thing that broke, back to the specific change that caused it. Without that link, you’re doing the same manual investigation as before, just with a bigger haystack.”

Testing requirements must go beyond generating automated tests, using synthetic data to expand the test patterns, and implementing continuous testing. “Instead of testing one input against one output, test whether an invariant holds across a wide range of generated inputs, which is a good fit for AI’s unbounded input space and non-deterministic outputs,” says Lior Gavish, cofounder and CTO at Monte Carlo. “I treat it as one layer in a stack with evals mapping the input space, property-based testing stress-testing it for edge cases and brittleness, and observability on live traffic checking whether production is drifting from the properties you defined as non-negotiable.”

Package and evolve the standards

Ed Frederici, CTO at Appfire, recommends codifying standards as machine-readable rules, including linters, formatters, and type checks. Merge the organizational and application standards into one context shared with the AI code-generating tool. His other recommendations:

  • Enforce standards in continuous integration so violations block merges.
  • Give the AI persistent context, such as CLAUDE.md, style guides, and architecture docs.
  • Keep changes small and reviewable.
  • Require human review of every pull request because AI-generated code must get the same scrutiny as any contributor’s. No exceptions.
  • Back this approach with solid test coverage to catch regressions.
  • Periodically audit for drift as your standards evolve.

It’s also important to remember that as code must be extended and maintained, so must the underlying development standards. “AI-generated code only stays aligned with your standards when it’s powered by a context engine that keeps learning, not a static snapshot of your rules,” says Itamar Friedman, CEO and cofounder at Qodo. “That means an engine that continuously adapts to your codebase’s evolving standards, your team’s PR history, and the prior decisions your engineers have already made, so the AI isn’t just following instructions but actually understands how your team works.”

As more devops organizations rely on AI code-generating tools, developers have an important opportunity to develop the standards and automated validations required to ensure what is built meets engineering and compliance requirements.

Oracle forecasts 33% increase in restructuring costs as new round of layoffs hits

✇Computerworld
15 September 2026 at 17:22

Oracle began a new round of layoffs this week, sending early-morning termination emails to staff for the second time in six months.

The latest wave began Monday with affected staff being told, “After careful consideration of Oracle’s current business needs, we have made the decision to eliminate your role as part of a broader organizational change,” according to BusinessInsider. Termination was immediate.

That’s the same wording as in the previous wave of layoffs, which took place on March 31 and affected workers in the US, India, Canada, Mexico and Uruguay. In the 12 months to May 31, Oracle cut its global workforce from about 162,000 to about 141,000, a decline of roughly 13%.

Oracle has made no public statement confirming the latest round of job cuts, but in a regulatory filing days earlier the company said it had set aside a further $700 million for restructuring charges, bringing the total charges this year to roughly $2.8 billion.

What the new termination emails say

Staff receiving the latest termination emails were told termination and compensation details would follow by DocuSign, Business Insider wrote. An internal document reviewed by the publication said severance terms varied by role and region, and some teams facing double-digit percentage cuts.

Affected employees took to social media to vent.

Eric Brunson, a senior principal offensive security researcher at Oracle, described in a LinkedIn post how he had lost access to corporate communication tools before receiving any formal notice. “I woke up to not being able to log back into Slack,” he wrote. “No new emails or notification in email and I’m locked out of there. I was able to get ahold of my manager on LinkedIn and she confirmed.” Brunson said the timing fell two days before a scheduled RSU vesting date, adding, “Probably part of the plan.”

The filing that backs up the layoff accounts

While Oracle is not talking about the layoffs, its 10-Q quarterly report states that management approved and supplemented restructuring plans “to implement certain strategic measures and further improve operational efficiencies, including through the adoption and integration of artificial intelligence technologies across certain functions.” It adds that “subsequent to August 31, 2026, our management supplemented the 2026 Restructuring Plan by approximately $700 million to reflect additional actions that we expect to take.”

Oracle has already spent $1.97 billion of the $2.1 billion restructuring charges it originally budgeted, it reported.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the filing should be read carefully rather than as confirmation of a headcount. “The supplement is an estimate, not a bill,” he said, noting it raises the program’s estimated cost by about a third without committing Oracle to a timetable.

Gogia said the more significant shift is in the filing’s language rather than the dollar figure. Oracle’s August 2025 and February 2026 filings had described the plan as tied to “acquisitions and certain other operational activities.”

AI was named as a driver of restructuring for the first time in the Sept. 11 filing.

Why the headcount stays unconfirmed

Gogia said no verified figure exists yet for how many employees the September round affected.

He noted that 30,000 was a January forecast of the total 2026 program. Twenty-one thousand is the confirmed net decline in Oracle’s global workforce across the full fiscal year. A reported 3,000 job cuts in India on Sept. 1 remains unconfirmed by Oracle.

The $700 million restructuring supplement “cannot be divided into people,” Gogia said, since it covers termination benefits, contract termination costs and other exit costs across a program spanning multiple countries. “There is no solid headcount for the September round,” he said.

A pattern that began in March

Oracle’s first 2026 layoff wave began March 31, when the Revenue and Health Sciences unit, the SaaS and Virtual Operations Services group, and NetSuite’s India Development Centre saw some of the deepest reductions.

Figures published by Oracle for its fiscal year ending May 31, 2026, show research and development headcount fell from 50,000 to 43,000 employees during the year, sales and marketing fell from 31,000 to 25,000, and services fell from 37,000 to 34,000, according to Gogia’s analysis of the company’s own reporting.

International staff, at 92,000, saw a larger reduction than the 49,000-strong U.S. workforce, he said.

Oracle did not respond to a request for comment.

This article first appeared on CIO.

Tech layoffs: A 2026 timeline

✇Computerworld
15 September 2026 at 17:21

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

  • Oracle (again)
  • Monday.com
  • Microsoft
  • Meta
  • Cisco
  • Cloudflare
  • Oracle
  • Atlassian
  • Salesforce
  • Amazon
  • Ericsson

Sept. 15, 2026: Oracle forecasts 33% increase in restructuring costs as new round of layoffs hits

Oracle began a new round of layoffs this week, sending early-morning termination emails to staff for the second time in six months. Oracle has made no public statement confirming the latest round of job cuts, but in a regulatory filing days earlier the company said it had set aside a further $700 million for restructuring charges, bringing the total charges this year to roughly $2.8 billion.

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

Feb. 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

Jan. 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

Jan. 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

Jan. 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

  • Cisco
  • Oracle
  • Windsurf
  • Intel
  • Microsoft
  • Crowdstrike
  • HPE
  • Autodesk
  • HPE
  • CISA
  • Workday
  • Salesforce
  • Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

  • Equinix
  • AMD
  • Freshworks
  • Cisco
  • General Motors
  • Intel
  • OpenText
  • Microsoft
  • AWS
  • Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

Cockroach Labs bets on database pooling to cut cost of AI-era workloads

✇InfoWorld
15 September 2026 at 14:39

Cockroach Labs, the company behind distributed SQL database CockroachDB, is betting that the rise of agentic applications will make traditional database provisioning, where systems are sized for peak demand, increasingly difficult to justify as agents create highly concurrent, unpredictable and often short-lived database workloads that can leave provisioned compute idle between bursts.

Its solution to that problem is Cockroach Continuum, a database fleet management platform that pools compute and storage across isolated CockroachDB databases in an enterprise to allow capacity to be shared across workloads rather than reserved for individual systems.

That shared model can help enterprises accommodate sudden bursts of agent activity without maintaining dedicated infrastructure for every database, while paying only for consumption rather than peak capacity, the company said Tuesday.

Continuum does this by combining database virtualization with Cockroach Plenum, a feature that separates compute from storage so that the two capacities can scale independently.

It also draws on Cockroach Aegis, which uses an AI-assisted operations layer to diagnose issues and recommend actions across an enterprise’s database estate to help database administrators (DBAs) reduce the manual work involved in monitoring, troubleshooting and tuning databases while retaining control through policies, the company said.

What Continuum could mean for CIOs

That reduction in manual work for DBAs is likely to benefit CIOs in terms of operational cost.

“Enterprises don’t just pay for database capacity. They pay people to provision, monitor, tune, troubleshoot and scale these environments. If Cockroach can automate a meaningful portion of that work, there is value there and a chance to offset costs,” said Ashish Chaturvedi, executive research leader at HFS Research.

For more direct infrastructure-related cost savings via Continuum, though, CIOs will need to look at how their database estates are provisioned.

Stephanie Walter, practice lead of AI stack at HyperFrame Research, said the greatest savings will come from large fleets of independently provisioned databases that spend much of their time underused: “Sharing capacity across workloads with different demand patterns could substantially improve utilization,” she said.

Not all enterprises will benefit: “Cost savings will be less meaningful for databases that already run near capacity or require tightly dedicated resources,” she said.

One caveat is that, because Continuum shifts the calculation of database costs from provisioned capacity to consumption, unexpected bursts of activity could make it harder for CIOs to forecast database costs.

CIOs will therefore need to assess their own workload patterns and test whether the higher utilization and reduced idle capacity actually translate into lower overall database costs, rather than assuming that consumption-based pricing will automatically deliver savings, Walter said.

While Continuum may simplify database management, it could also deepen dependence on CockroachDB and make workloads harder to move elsewhere, she said — with the added risk that, by coupling independent databases together onto one platform, it creates a new single point of failure.

What Continuum could mean for developers

For developers though, the shift to Continuum could reduce the time and effort required to build applications, especially agentic ones.

Development teams using Continuum can design applications without having to worry as much about the database compute and storage capacity those applications will need, when that capacity needs to scale, or the operational work required to provision and manage it, said HFS’s Chaturvedi.

But, said Walter, those advantages don’t come without challenges.

“Pooling infrastructure as Continuum does is more likely to create questions around workload isolation, performance predictability, and debugging,” she said. “Enterprise teams will want to know whether one demanding workload can affect others and whether they can trace a performance problem through the shared environment.”

Who is likely to adopt Continuum

Existing CockroachDB customers with large fleets of distributed transactional databases are likely to be among the earliest adopters of Continuum, Walter said.

SaaS companies, financial services firms, digital platforms and AI-native businesses could also be strong candidates, particularly where applications generate unpredictable demand and database capacity needs to scale quickly, she said.

For those enterprises, though, one of the capabilities that could make Continuum most useful for unpredictable workloads is yet to arrive.

Automatic compute scaling, which will help scale compute up or down as necessary to maximize cost savings without compromising availability, is “coming soon,” the company said.

Why Every Theory of Life’s Origin that Excludes Design Must Fail

✇Science and Culture Today
By: Brian Miller
15 September 2026 at 01:35
The proposal ceases to be an inference from observations and known physics and becomes an appeal to the absurd.

Source

How Microsoft connects your data across the enterprise

✇InfoWorld
14 September 2026 at 19:55

For CIOs and CDOs heading into Microsoft Fabric and SQL Community Conference Europe, the main question is not which data technology sits at the center. It is whether the data platform improves revenue, productivity, and decision speed while creating a trusted foundation for AI at scale. That should drive the data strategy.

Enterprise AI is forcing companies to tackle a familiar problem, with data spread across ERP, CRM, operational systems, databases, applications, warehouses, and analytics platforms built over years. That was manageable for reporting and point-to-point integration. Copilots and agents raise the stakes because they need to reason across customers, orders, inventory, financial implications, operating constraints, permissions, and business rules at the same time.

That moves the discussion from architecture into CIO and CDO strategy. What outcomes are we trying to improve? What data and context are required? What needs to move versus connect? Where do Microsoft Fabric, Azure Databases, and other platforms fit? How will governance and authority follow the data? Where should the data be stored and what sovereignty or regulatory restrictions apply? And how will we measure results?

Microsoft is positioning its broader data platform around AI business value. That framing is useful when it translates into an operating strategy. As I wrote in Forbes about Microsoft Build in June, the next phase of AI depends less on the model and more on data readiness, governance, interoperability, and operational execution. The conference should give customers a closer view of how those pieces come together. The question for CIOs and CDOs is how that direction fits their existing data estate.

The AI problem is usually a data problem

Many AI initiatives stall on data readiness rather than the model itself. The issue is not one statistic. It is the combination of fragmented data, inconsistent definitions, and the effort needed to prepare and govern information before AI can use it reliably.

Copilots and agents need trusted context around customers, financials, inventory, operations, permissions, and business rules. AI exposes existing weaknesses. Duplicate customers, poor product data, and inaccurate inventory become more consequential when AI is recommending or taking action. AI does not create the data quality problem; it makes the operating cost harder to ignore.

The goal is to make more of the existing data estate useful for AI without turning modernization into a wholesale rebuild.

Microsoft Fabric and Azure Databases play different roles

For customers, the question is how Microsoft’s broad portfolio translates into architectural choices, including what should move, what should connect, and what can stay in place without limiting the result the business is trying to achieve.

Azure Databases support transactional and operational workloads, each with distinct performance and application requirements. Fabric provides a unified SaaS experience across data integration, engineering, analytics, real-time intelligence, data science, and business intelligence. Fabric IQ adds a semantic intelligence layer that provides shared business context for Copilot and agents. Some workloads may belong in Fabric, others in Azure Databases, and some on third-party platforms; moving them adds cost without creating enough business value.

The architecture should follow workload, data maturity, economics, and business value, not a single destination.

Connected data has to improve the decision

Microsoft’s broader data platform can connect systems of record and operational applications with analytics and AI. Azure Databases, Fabric, OneLake, Power BI, and governance can play different roles. What matters is whether those connections shorten the path from operational data to a decision, not how neatly the products line up.

A customer record may live in CRM, orders in ERP, inventory in operational databases, and demand models in an analytical environment. Fabric Real-Time Intelligence can add live events to that picture. A manufacturer can stream equipment events or production events, detect a developing issue, and alert teams while there is still time to change the outcome. The value comes from reasoning across that context so the business can understand what is happening, why it matters, and what action makes sense next.

Trusted context turns data into AI business value

I recently wrote in InfoWorld that trusted context is becoming the currency for enterprise AI. That is especially relevant here because once copilots and agents rely on enterprise data, then quality, permissions, lineage, and security become operating requirements, not background data issues.

Knowing inventory is 8,000 units is data. Knowing whether those units are available, committed elsewhere, in the wrong distribution center, or on quality hold is business context. As AI moves from answering questions to taking action, that context determines whether the result can be trusted.

Microsoft frames AI business value around growth, insight, and scale. Each needs an operating definition. Growth can mean new revenue, better customer responsiveness, or growing without adding cost at the same rate. Insight means combining context across Microsoft 365, Fabric, and operational systems to make better decisions faster. Scale means expanding AI without rebuilding governance for every use case.

A reusable data foundation can also reduce pipeline and management effort while improving decisions around pricing, inventory, customers, supply chain, and finance. Microsoft cites coverage of more than 100 standards. The practical question is whether AI can scale without risk and complexity growing at the same pace. For multinational or regulated organizations, that also includes where data is stored and processed and which sovereignty requirements apply.

Agentic workloads raise the bar. Organizations need to know what information informed a decision, who or what had authority to act, which policies applied, and how a wrong action can be reviewed or reversed.

That is where the data platform becomes part of what I describe as decision architecture. Context explains what is happening. Decision logic determines what should happen next. Authority defines what a person or agent can do. Orchestration coordinates systems and people. Execution completes the action and provides a path to review or reverse it. Trusted data feeds that architecture. Without trusted data, automation will be difficult to trust at scale.

Microsoft’s breadth is most useful when it reduces complexity

Microsoft’s advantage may be its enterprise footprint. Azure Databases, Fabric, OneLake, Power BI, Microsoft 365, business applications, developer tools, identity, security, and partners touch many of the places where information is created and work gets done.

Most customers will keep data across multiple systems. Microsoft’s opportunity is to use that breadth to create a simpler path from data to workflow to business outcome without requiring everything to live in one place.

Customer proof points matter because data strategies can get abstract. The KPMG Australia example is useful because KymChat moved beyond an internal productivity tool into a client-facing offering, creating a path from internal AI investment to new business. Microsoft also reports that improvements to the data foundation increased KymChat search quality from 50% to 91%, with results delivered in under a second.

BMW Group provides a more recognizable operating example. Microsoft says Azure helped BMW make vehicle data delivery and analysis 10 times faster, cutting the lead time for engineering insights from days to hours or minutes. Audi adds an AI deployment example. It used Azure AI Foundry, Azure App Service, and Azure Cosmos DB to launch a secure HR assistant in two weeks and is extending the same framework to eight additional agents across the enterprise. Levi Strauss & Co. offers another angle. It consolidated nine ERP systems on Azure, reported a twofold improvement in latency and a 60% improvement in IOPS and now uses Fabric IQ for companywide cost reporting. These are the kinds of proof points that connect platform architecture to time, cost, productivity, and decision quality.

Interoperability matters across the enterprise estate

Microsoft sits in a market with Snowflake, AWS, Google Cloud, Oracle, and others, each with different strengths across infrastructure, databases, analytics, openness, and AI. Databricks also plays an important role, but the connection is different because Azure Databricks is provided as a first-party service directly on Azure. Most large enterprises will remain heterogeneous, making interoperability, cross-platform governance, and incremental modernization central to strategy.

The opportunity is to make Microsoft’s data platform useful across the mix of systems that customers already run. Most enterprises are not starting from a blank sheet, so the value is in helping them connect, govern, and modernize over time without forcing everything into one place.

CIO and CDO strategy should start with the business outcome

This is where the conversation becomes most useful at the Microsoft Fabric and SQL Community Conference Europe. For CIOs and CDOs, an architecture diagram matters more when it is backed by a decision model. A useful data and AI strategy should work backward from the business outcome through five decisions.

  1. Start with the business outcome. Define what needs to improve, including revenue, margin, productivity, customer experience, decision speed, working capital, or time to value. Let the technology choice follow the objective.
  2. Map the data and context required. Identify the operational, transactional, and analytical information, semantics, and business rules that influence the outcome.
  3. Decide what moves and what connects. Determine where Fabric, Azure Databases, and non-Microsoft platforms fit. Let workload, economics, and value drive modernization rather than a single destination.
  4. Design governance, authority, and economics together. Define how security, permissions, lineage, and controls span operational, analytical, and AI workloads, and identify opportunities to simplify pipeline, integration, and management efforts.
  5. Measure the operating result. Set KPIs before scaling. Technology adoption is not the outcome. CIOs and CDOs should know which business measures should change, by how much, and over what period.

That is how the data platform becomes enterprise strategy, tying architecture, governance, AI, and economics to operating outcomes and bringing business leaders into the discussion before the architecture is set.

What customers will still want to understand

Microsoft has many of the pieces required to connect enterprise data, analytics, and AI. Customers will keep looking for guidance on the roles of Azure Databases, Fabric, and the broader data platform, along with cost, licensing, migration, and governance across heterogeneous environments. They will also want more proof points from KPMG, BMW Group, Audi, and Levi Strauss & Co. that start with a business result.

That is where Microsoft’s positioning around a data platform for AI business value comes together. The value is in helping customers connect platform choices to better decisions, faster action, and measurable operating results.

After three decades in enterprise technology, I keep coming back to the same point. The technology is only part of the answer. For CIOs and CDOs, the harder part is deciding how the data platform fits into the way the business operates, how decisions get made, and where AI can improve the outcome. That is really the question behind how Microsoft connects your data across the enterprise. The conference should give customers a useful view of how Microsoft is bringing those pieces together and where that can create better business outcomes.

Govern AI agents like workers. Just don’t pretend they’re human

✇Computerworld
14 September 2026 at 12:01

The newest member of the corporate workforce never sleeps, never asks for a raise, and can be cloned before lunch. It can also confidently make the wrong decision thousands of times before anyone notices.

That helps explain why some technology leaders have started talking about AI agents as employees, co-workers, or interns rather than software. Once an agent can update records, issue refunds, send communications, or act across multiple enterprise systems, deploying it no longer looks much like installing another application.

Recent Harvard Business Review articles have pushed that analogy further. One argues that agents should be treated like a new workforce, with defined roles, authority, sources of truth, supervision, and audit trails. Another recommends giving agents names and job descriptions, onboarding them, evaluating their performance, and increasing their responsibilities after they prove themselves.

But there is a catch: People may start believing the metaphor.

A separate HBR study of 1,261 managers found that when AI was framed as an employee rather than a tool, managers with experience working around AI agents took less personal responsibility for results and assigned more responsibility to the AI. They also escalated more problems and caught fewer errors. In the study, 31% of respondents said their companies already framed AI as a teammate or employee, while 23% said agents appeared on organizational or work charts.

For IT leaders, the answer may therefore be to borrow freely from the employee-management playbook — without extending the metaphor too far.

Don’t put software on the org chart

Amy Loomis, group vice president at IDC, draws a hard line around one word: responsibility.

Humans, she says, have obligations to their companies and to one another, and they bring an understanding of corporate culture and values that an agent can mimic linguistically without possessing.

That makes calling an agent a co-worker potentially more than harmless anthropomorphism. The label suggests a peer relationship and can encourage people to cede judgment to a system that’s very good at giving what was requested, Loomis says, but not necessarily at recognizing what was actually needed.

“Accountability is something that has qualities associated with it that are uniquely human,” she says. “Agents may be given permission to access data, but the accountability for what they do with that access lies with the person who gave the agent access, not the agent itself.”

That distinction still leaves plenty to borrow from employee management. It makes sense to give an agent traceable access and tightly scoped permissions — and to institute a kill switch to prevent the agent from having continued access. But Loomis finds it more effective to think in terms of giving an agent an operations charter rather than a human identity: define what the system does, what it may access, and what humans remain responsible for.

In other words, give the agent a security badge. Just don’t put its picture on it.

The employee metaphor has a job to do

Raja Iqbal, founder of agentic AI governance company Ejento AI and co-author of the HBR article “To Scale AI Agents Successfully, Think of Them Like Team Members,” is closer to Loomis than the headline suggests.

Iqbal says the metaphor is intended to put CIOs, CISOs, and other technology leaders into the right “cognitive posture.” Thinking about an agent as a team member, he argues, can remind an enterprise to impose familiar management disciplines: a unique identity, clear human oversight, defined authority, spending limits, approved information sources, and an audit trail.

If another term produces the same discipline, he’s fine with that too.

In fact, Iqbal explicitly rejects synthetic employees with human personas and seats on the org chart. That creates exactly the accountability problem critics worry about: “Bob did it” becomes an excuse even though Bob is software.

“The metaphor is dangerous when it’s decorative,” Iqbal says. By that, he means companies get into trouble when they adopt the human trappings — names, personas, or the language of colleagues — without also imposing the supervision, limits, and accountability that make the analogy useful.

His preferred distinction is simpler: the agent needs a technical identity so the enterprise can reconstruct what it did, while a human must remain responsible for those actions. “The identity is the audit primitive, and the human owner is the accountability primitive,” he says. Put more plainly, the agent identity answers what acted; the human owner answers who is accountable.

The employee analogy becomes more useful again when deciding how much freedom an agent should receive. Iqbal recommends graduated autonomy. An agent might begin with a human approving every action, progress to performing low-risk actions independently, and eventually receive bounded autonomy if its observed performance justifies it. A serious error can send it back down the ladder — or out the door entirely.

That sounds a lot like a probation period for a new employee. It’s also simply good software governance for a probabilistic system.

At DXC, professional agents are software

Russell Jukes has had plenty of opportunity to test that distinction. As chief digital and information officer at DXC Technology, he oversees AI in an IT services organization with roughly 115,000 employees.

“We think about [an AI agent] as a superpower, not as a person,” he says.

DXC separates agents into two categories. Personal agents work for individual employees and generally operate using the employee’s identity and access. Professional agents are enterprise systems built for broader workflows. On one of DXC’s major AI platforms, employees have so far created roughly 8,000 personal agents, compared with about 100 professional agents.

The professional variety receives much stricter oversight. It can have its own nonhuman identity, credentials, permissions, and authority boundaries. Activity passes through control planes that allow DXC to see what agents are doing and, when necessary, shut them down.

Jukes thinks many of these agents will eventually disappear from users’ view altogether. An agent might scan internal resource requirements overnight and recommend employees for projects. Nobody needs to greet it in the morning or consider it a colleague; they simply receive the result.

That’s where he parts company with the worker metaphor.

Jukes says he learned early on that AI cannot be deployed like conventional SaaS. His first attempt followed the familiar software model: make the application available, let employees use it within the prescribed workflow, and expect adoption to follow. “I did that, and nobody used it,” he says. The experience convinced him that AI requires a different approach, and he vowed to “never deploy AI like it’s SaaS again.”

Conventional SaaS is largely deterministic: the software constrains users to a defined workflow and produces predictable outputs. Agentic AI is different. It can interpret a goal, choose among tools and data sources, and decide how to get to an outcome. For Jukes, that means CIOs have to design the policies, permissions, data access, guardrails, and authority around the agent rather than simply deploy another application.

That also creates a clearer chain of responsibility. If a person approves the agent’s action, Jukes says that person owns the decision. If an autonomous agent behaves according to a business policy, accountability belongs with the people responsible for that policy. If the technology fails to operate as designed, responsibility moves to IT.

The agent itself never gets handed the blame.

Maybe treat it like an intern after all

Nina Tatsiy, global CIO of Quadient, an automation technology company, takes a position somewhere between Iqbal and Jukes. She calls agents interns, but deliberately uses the term as an analogy, not an organizational designation.

Agents must be managed as software, with security, governance, and technical controls, while recognizing that their probabilistic behavior requires additional supervision. “You have to do both,” Tatsiy says.

An intern does not receive every password and authorization on the first morning. Neither should an agent. It has to be onboarded, trained, watched, retrained when its environment changes, and eventually retired when it’s obsolete.

Quadient generally avoids giving agents human names. Tatsiy worries that doing so encourages people to see them as real colleagues capable of judgment.

“Overtrust is a problem,” she says, particularly because AI tends to state conclusions with such assurance. Tatsiy prefers using AI to challenge a hypothesis or surface questions people haven’t considered rather than allowing it to make the conclusion for them.

Yet the intern analogy has proved useful operationally.

In one accounts-payable deployment, Quadient had an agent effectively shadow human workers and learn from their feedback across different scenarios. Only after its performance had been tested across increasing levels of complexity did it begin providing recommendations. Humans still made the final decision.

The agent, in other words, earned more trust. It didn’t earn accountability.

Borrow from HR, but keep humans in charge

The emerging divide may therefore be less about whether agents are employees or software than about which parts of each management model IT leaders should retain.

Agents need individual technical identities, but not human identities. They need defined responsibilities, but not jobs in the human sense. They need monitoring and evaluation, but not annual performance reviews. They can receive progressively greater authority, but not progressively greater accountability.

And the stronger they become, the more important that distinction becomes.

Treating an autonomous agent like ordinary SaaS ignores the fact that it can interpret, choose, and act in ways conventional software cannot. Treating it like Steve from Accounting creates a different problem: sooner or later somebody may start assuming “Steve” knows what he’s doing.

IT leaders may indeed need to borrow heavily from HR as agentic AI spreads across the enterprise. They just shouldn’t start issuing employee badges.

Related reading:

Why DBAs are right to be skeptical of AI — and where they’re wrong

✇InfoWorld
14 September 2026 at 10:00

Postgres just turned 30. Over those three decades, I’ve watched it pick up hot standby replication, logical replication, asynchronous IO, just-in-time compilation, and dozens of other features built for enterprise workloads. Every one of them solved a real problem and raised the floor on what a DBA needs to know just to do the job well.

I don’t think most people outside this world fully register how much that floor has risen. In the early days, improvements were small and mostly about stability. Now, 30 years in, the focus is squarely on enterprise functionality, and that functionality comes with complexity attached. The more you build, the more there is to understand and manage, and that increases every year.

Hiring more people doesn’t solve this

Most organizations treat a scaling database problem as a staffing problem. Hire enough DBAs and you’re covered. I think that’s the wrong way to look at it entirely.

It’s not far off from the old “too many cooks spoil the broth” problem. You don’t make better soup by adding cooks. You make better soup with a better chef. A 100-terabyte database with serious performance issues doesn’t get fixed by putting 10 DBAs on it instead of one. It gets fixed by someone who has seen that failure before and knows exactly where to look.

Experienced DBAs are expensive and hard to find, and the problem is only getting worse. A few years ago, it was IoT driving demand for time series expertise at scale. Now it’s AI, and organizations are building enormous databases to support retrieval pipelines and large language model (LLM) training, often without the experience on hand to run them safely.

Where human error comes from

Humans are unpredictable. We make assumptions. Sometimes, we think the process is getting in the way, so we take a shortcut around it. And we make mistakes because that’s simply human nature.

I don’t think that’s something we’re ever going to fully engineer away as long as a person is in the loop, and honestly, we want a person in the loop making the real decisions. So it becomes a balancing act. You have to understand the mistakes people tend to make, build the discipline to follow good processes consistently, and still know when a situation calls for judgment rather than a checklist.

Why AI helps more than people expect

I think this is where AI has earned its place. It’s not as a replacement for the DBA, but as something that follows a process more consistently than a person under pressure tends to.

Modern AI systems still have problems with hallucination and getting the full picture right. However, with the right guardrails, auditing, and tracking in place, they are able to follow the process very well. It is much easier for me to say, “Forget it, I know a shortcut, I’ll just push this through,” than it is for a well-constrained model to deviate from its instructions. As long as the instructions are clearly defined, LLMs tend to stick to them.

Another advantage worth naming is if an LLM is doing the diagnostic work, you can put much stronger gating around its ability to actually make changes. You can let it follow a tight, well-defined process to diagnose a problem while still requiring a human to approve anything that touches the live system, whether that’s creating an index, dropping one, or changing a configuration value.

That combination gets you to a solution far faster than the old way, using the model’s training plus whatever supplemental knowledge you feed it through retrieval-augmented generation (RAG), while keeping a person at the one point where it actually matters. Did the model get the diagnosis right? Does the proposed fix line up with the actual problem? That’s the question a human needs to answer before anything gets applied.

The industry hasn’t figured out the limits yet

I am not sure we are far enough along the path as an industry to really know what we’re getting wrong. I am working through these questions myself right now, looking at autonomous agents for Postgres operations and where the real risk is.

What’s clear to me already is that strong guardrails matter in two places, not one. You need guardrails on the prompts so the model verifies its findings and doesn’t hallucinate into a bad recommendation. And you also need guardrails on what the model is actually allowed to do. Letting an agent create an index carries a manageable risk. Letting it truncate your orders table does not. Auditing matters just as much; when something does go wrong, you need to see exactly what happened and undo it. The fallback needs to stay in place until the industry broadly trusts these systems more than it trusts a human.

Picture a load spike that hits overnight, the kind where batch processing that normally runs fine suddenly does not. The old way means spending hours trawling logs, index stats, table stats, and query performance before you land on a likely cause and you can apply a fix.

Put an LLM on that same problem and the hours-long process compresses into minutes. It’s the same productivity shift we’ve already seen in software development, where an experienced user with the right skills and guardrails can get LLM output that matches or beats a junior developer’s work. The quality comes down to this process: Structure the work, define a clear spec, build the implementation plan, then verify each stage before moving on.

The same discipline applies to database operations. The worst thing you can do is rush in and say to the model, “This happened, go fix it.” You need to guide the tools properly to get good results from them. When it’s done right, it works.

Where this goes from here

A few things need to be true for this to scale across the enterprise. Frontier models already understand Postgres well, but they don’t know everything, so you need to feed them supplemental knowledge alongside that training. Full documentation matters here, so do internal runbooks. The process has to require the model to actually consult that material rather than coast on what it already knows.

I expect the same kind of structured skills that are common in AI-assisted development, the step-by-step approach to planning and verification, to show up specifically for Postgres operations. That will make the job easier for DBAs, and easier is what matters.

We’re probably not far from agents that sit and watch a Postgres instance around the clock and rarely need a human to step in. The slower part is trust. For example, look at how long it took people to actually believe AI-written code was any good. I would guess it is the same pattern here, but I don’t doubt for a second that we will get there.

In the meantime, you do not need to hand an LLM the keys to your database tomorrow. Start smaller and begin with read access to your monitoring data. It’s a low-risk place to start.

An agent watching constantly can catch the kind of slow, compounding anomaly that threshold-based alerting tends to miss, a gradual rise in IO alongside a gradual rise in memory, and flag it before it becomes the kind of outage that costs real money every hour it runs.

That’s not a small thing. The DBA shortage is not going away on its own, and waiting for the industry to fully trust autonomous agents before using AI at all is the same as falling behind. The teams that figure out where the human belongs in this process are the ones who’ll be ready when the rest of the industry catches up.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

Received — 11 September 2026 ⏭

A New Way to Stop Pain Without Opioids

✇Knowridge Science
By: Knowridge
11 September 2026 at 12:10

For decades, one of the biggest problems in pain medicine has been surprisingly simple: the strongest medicines often come with the greatest risks. Opioids can make severe pain manageable, but they can also cause dependence, addiction, overdose and troublesome side effects. A new drug called LTG-001 is being developed to see whether powerful pain relief […]

The post A New Way to Stop Pain Without Opioids appeared first on Knowridge Science Report.

New Treatment Reverses Nerve Damage in Diabetes

✇Knowridge Science
By: Knowridge
11 September 2026 at 12:05

Diabetes can slowly damage nerves throughout the body, leaving some people with pain, burning, tingling, or numbness that can become difficult to manage. Now, scientists at UTHealth Houston have developed an experimental treatment that slowed and even reversed this type of nerve damage in mice. The research was published in Science Translational Medicine. Although the […]

The post New Treatment Reverses Nerve Damage in Diabetes appeared first on Knowridge Science Report.

Vitamin B12 May Help Keep Inflammation in Check

✇Knowridge Science
By: Knowridge
11 September 2026 at 12:00

A study has found a possible link between vitamin B12 and inflammation in the body. People with higher levels of vitamin B12 in their blood tended to have lower levels of two important signs of inflammation. The research was published in the Journal of the Science of Food and Agriculture. The findings add to growing […]

The post Vitamin B12 May Help Keep Inflammation in Check appeared first on Knowridge Science Report.

How systemic inflammation drives muscle dysfunction in COPD exercise

✇Science Mag
By: Ophelia Keating
10 September 2026 at 12:59

A sweeping new narrative review published in the Journal of Translational Medicine is pulling back the curtain on one of the most overlooked aspects of chronic obstructive pulmonary disease: the devastating toll the disease takes on the body’s muscles, and the crucial — but complicated — role that exercise plays in fighting back. Led by Domenico Di Raimondo and colleagues at the University of Palermo, the review synthesizes decades of mechanistic research into how COPD reshapes both limb and respiratory muscles, how systemic inflammation may or may not be driving that damage, and why rehabilitation remains the most powerful non-drug therapy available despite nagging scientific uncertainties.

COPD has long been framed as a lung disease, defined by airway obstruction, chronic bronchitis, and emphysematous destruction of lung tissue. But clinicians have increasingly recognized that its most disabling features extend far beyond the chest. Patients with COPD frequently suffer from profound muscle wasting, particularly in the quadriceps, and this skeletal muscle dysfunction has emerged as a clinically relevant systemic manifestation of the disease. It contributes directly to the breathlessness that defines daily life for patients, reduces exercise tolerance, erodes quality of life, and — perhaps most tellingly — predicts mortality. A patient’s ability to stand from a chair or climb a flight of stairs, the review argues, may reveal as much about their prognosis as any spirometry reading.

The muscle changes in COPD are not simply the result of disuse, though physical inactivity certainly plays a role. Under the microscope, the peripheral muscles of these patients tell a story of deep biological remodeling. Muscle fibers shift in type, favoring fast-twitch, glycolytic fibers over the oxidative, fatigue-resistant fibers that sustain everyday activity. The oxidative capacity of the muscle falls, undermining the tissue’s ability to generate adenosine triphosphate efficiently and causing lactic acid to accumulate early during exertion — a process that patients experience as overwhelming leg fatigue long before their lungs reach their limits. Capillarization is impaired, reducing the delivery of oxygen and nutrients to working fibers. Mitochondria, the cellular power plants, show structural and functional dysfunction. Oxidative stress, driven by an excess of reactive oxygen species, damages proteins and membranes. And catabolic pathways, including the ubiquitin-proteasome system, are activated, actively breaking down muscle protein faster than the body can rebuild it.

One of the review’s central arguments is that not all muscle in COPD suffers equally, or in the same way. The quadriceps and other limb muscles typically show the destructive changes described above, but the diaphragm follows a different trajectory. The diaphragm in COPD patients is caught in a double bind: it is subject to intrinsic remodeling driven by chronic overload, yet it also works under a severe mechanical disadvantage caused by lung hyperinflation. As the lungs overinflate, the diaphragm flattens, shortens, and loses its mechanical leverage, making every breath more costly. In response, the diaphragm adapts in ways that differ from limb muscles — sometimes showing adaptive, rather than purely destructive, features as it struggles to meet the relentless demands of breathing through compromised lungs. This distinction between limb and respiratory muscle biology, the authors emphasize, matters enormously for understanding why patients with severe airflow obstruction can still have relatively preserved diaphragm function even as their legs waste away.

If the muscle pathology is the crime scene, systemic inflammation has long stood as the prime suspect. Elevated levels of inflammatory mediators such as tumor necrosis factor-alpha, interleukin-6, and C-reactive protein are commonly detected in COPD patients, and these signaling molecules are known to promote muscle protein breakdown, suppress anabolic signaling, and activate catabolic cascades. The review details the molecular routes by which inflammation can sabotage muscle: activation of nuclear factor-kappa B, engagement of the ubiquitin-proteasome system, interference with insulin-like growth factor-1 signaling, and disruption of insulin receptor substrate pathways that normally support muscle growth and glucose uptake. Yet the authors are refreshingly candid about the limits of this story. The true source of systemic inflammation in COPD is still debated — is it the lungs themselves, comorbid conditions, altered gut microbiota, or something else? — and, crucially, the causal contribution of systemic inflammation to muscle loss and dysfunction in COPD patients remains incompletely proven. Inflammation may be an amplifier rather than the sole architect of muscle decline.

Indeed, the review paints muscle wasting in COPD as a multifactorial process in which inflammation is only one ingredient. Ageing naturally erodes muscle mass and regenerative capacity. Multimorbidity — the coexistence of heart failure, chronic kidney disease, and other chronic conditions — compounds the burden. Malnutrition and inadequate caloric intake starve muscle tissue of the building blocks it needs. Hypoxemia, the low blood oxygen characteristic of advanced disease, impairs mitochondrial function and pushes muscle fibers toward glycolytic metabolism. Corticosteroid exposure, a frequent feature of COPD treatment during exacerbations, can further promote protein breakdown and impair anabolic signaling, a process partly mediated by enzymes such as 11 beta-hydroxysteroid dehydrogenase 1 that amplify glucocorticoid activity within muscle tissue. And physical inactivity, both a cause and a consequence of muscle decline, completes a vicious circle in which weakness breeds inactivity, which in turn breeds further weakness. Acute exacerbations of COPD accelerate this cycle dramatically, with each hospitalization potentially stripping away months of hard-won muscle mass and function.

Against this grim mechanistic backdrop, the review delivers its most clinically significant message: exercise training works. Pulmonary rehabilitation, built around structured exercise programs, is firmly supported as a cornerstone of non-pharmacological COPD management. The evidence base shows that exercise training improves muscle function, increases exercise capacity, alleviates symptoms, and restores functional autonomy — the ability of patients to manage their own daily lives. The mechanisms behind these gains are well understood from exercise physiology. Endurance and resistance training stimulate mitochondrial biogenesis through pathways involving peroxisome proliferator-activated receptor-gamma coactivator 1-alpha, the master regulator of oxidative metabolism. Training restores capillary density, shifts muscle fiber types back toward oxidative phenotypes, strengthens the antioxidant defense system against reactive oxygen species, and tips the balance between protein synthesis and breakdown back toward growth. Resistance training in particular offers a direct counterweight to catabolic signaling, mechanically stimulating muscle protein synthesis even in the presence of inflammatory stress.

But the review does not let exercise off the hook without scrutiny. One of its most provocative observations concerns inflammation. If exercise is so beneficial, and if inflammation is a key driver of muscle damage, one might expect training to measurably reduce circulating inflammatory biomarkers such as C-reactive protein, tumor necrosis factor-alpha, or interleukin-6. Yet the current evidence, the authors find, does not consistently demonstrate such a parallel reduction after training programs. Patients get stronger and more functional, but their blood inflammatory profiles often remain stubbornly unchanged. This disconnect raises a fundamental question about how exercise exerts its benefits in COPD: are the improvements driven by chronic adaptations within the cardiac, respiratory, and muscular systems themselves — local remodeling of muscle fibers, capillaries, and mitochondria — or do they also depend on systemic anti-inflammatory effects that clinical trials have so far failed to detect reliably?

The authors frame the resolution of this question as the field’s main unresolved challenge. Beyond the inflammation puzzle, the review identifies a set of pressing practical questions that clinicians and researchers have yet to answer definitively. What is the optimal training modality — endurance, resistance, interval training, or some combination? What intensity and duration produce the greatest and most durable muscle outcomes? And how should rehabilitation be tailored to the phenotype of the individual patient, given that a cachectic, hypoxemic patient with severe hyperinflation may need a fundamentally different program from a breathless but well-nourished patient with preserved muscle mass? The notion of phenotype-specific rehabilitation — matching the training prescription to the patient’s muscle biology, nutritional status, and inflammatory profile — emerges as a promising but still untested frontier.

The review also situates these findings within the broader translational landscape. Many of the molecular pathways it describes — the ubiquitin-proteasome system, the NOD-like receptor pyrin domain-containing protein 3 inflammasome, myostatin-related signaling, and the interplay between hypoxia-inducible factor 1 alpha and mitochondrial function — are potential drug targets. But the authors’ implicit conclusion is that no pharmacological shortcut will soon replace the accumulated, adaptive power of regular physical training. Muscle is plastic tissue, and the review suggests that the therapeutic leverage lies precisely in that plasticity, harnessed through carefully dosed and progressive exercise.

What makes the review timely is its honesty. Rather than inflating the anti-inflammatory credentials of exercise or overselling any single mechanism, it maps the terrain as it actually stands: muscle dysfunction in COPD is real, multifactorial, and deadly serious; exercise is genuinely effective; and the causal chain linking inflammation to muscle loss remains, frustratingly, an open question. For the millions of patients living with COPD worldwide, the practical takeaway is clear. Rehabilitation is not an optional add-on to inhaler therapy — it is a biological intervention that works at the level of the muscle fiber, the mitochondrion, and the capillary, even when the exact systemic chemistry of its benefits remains to be fully explained.

For researchers, the review is a call to arms. Future studies must disentangle the contribution of inflammation from the many other forces eroding muscle in COPD, identify biomarkers that predict who will respond best to which training modality, and design trials that test phenotype-matched rehabilitation strategies rather than one-size-fits-all programs. Until then, the message for clinical practice stands on remarkably firm ground: in COPD, what the lungs cannot fix, the muscles — given the right training — may yet do.

Subject of Research: Mechanisms linking COPD to skeletal and respiratory muscle dysfunction, systemic inflammation, and the effects of exercise-based rehabilitation

Article Title: Muscle dysfunction, systemic inflammation and exercise in COPD – a narrative review

Article References: Di Raimondo, D., Siragusa, P., Pirera, E., Romito, G., Calcullo, D., Scaglione, S., Daidone, M., Ferrantelli, S., Siscaro, G., & Tuttolomondo, A. (2026). Muscle dysfunction, systemic inflammation and exercise in COPD – a narrative review. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08961-x

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08961-x

Keywords: Chronic obstructive pulmonary disease, Muscle dysfunction, Systemic inflammation, Exercise, Rehabilitation, Quadriceps, Diaphragm, Cachexia, Mitochondrial dysfunction, Oxidative stress, Hyperinflation, Pulmonary rehabilitation

Advanced oxidation process reduces micropollutant toxicity in wastewater for agricultural reuse

✇Science Mag
By: Violet Maxwell
10 September 2026 at 12:46

Researchers in Brazil have demonstrated that a hybrid advanced oxidation process can transform pharmaceutical-laden wastewater into an effluent that is dramatically safer for plants, earthworms, onion root cells, and beneficial microorganisms, offering a rigorous ecotoxicological case for reusing treated sewage in agriculture. The study, published in Environmental Science and Pollution Research, addresses a long-standing blind spot in water treatment: proving that a chemically “clean” effluent is also biologically compatible.

Conventional wastewater treatment plants were never designed to strip out recalcitrant organic micropollutants such as pharmaceuticals, and trace amounts routinely slip through into receiving waters. Advanced oxidation processes (AOPs) attack these stubborn compounds by generating highly reactive free radicals that fragment contaminant molecules. But chemical degradation alone does not guarantee safety. As contaminants break down, they spawn transformation products (TPs) that can, in some cases, be as toxic as—or more toxic than—the parent compounds. The research team, led by Lucas Gustavo da Costa and Alam Gustavo Trovó of the Federal University of Uberlândia, together with collaborators at the Oswaldo Cruz Institute, set out to answer a deceptively simple question: does measurable chemical removal of micropollutants actually translate into reduced biological harm?

The centerpiece of their work is the H₂O₂/S₂O₈²⁻/UVC process, a hybrid system that combines hydrogen peroxide and persulfate with short-wave ultraviolet C radiation. UVC photons cleave both oxidants simultaneously, generating hydroxyl radicals (HO•) and sulfate radicals (SO₄•⁻) in the same reaction volume. This dual-radical strategy outperforms systems relying on either oxidant alone, or on peroxymonosulfate (HSO₅⁻), which is costlier and demands more energy for activation. The hybrid route also resists interference from inorganic species commonly found in real effluents, making it an economically attractive candidate for deployment at full-scale treatment plants. The process had already been chemically optimized in the team’s earlier work using multivariate mixture design and rotatable central composite design experiments; the new study is its first integrated biological stress test.

The experiments used real municipal effluent from a wastewater treatment plant in Uberlândia, Minas Gerais, sampled after the plant’s final tertiary treatment stage of coagulation-flocculation with ferric chloride and flotation. The effluent was enriched with three pharmaceuticals representing different therapeutic classes: colchicine (COL, an antimitotic agent), nitazoxanide (NTZ, an antiparasitic), and sulfamethoxazole (SMX, a widely detected antibiotic), each at 325 nmol L⁻¹—equivalent to 130, 100, and 82 micrograms per liter, respectively. That concentration was deliberately chosen as high enough for direct HPLC–DAD analysis without preconcentration, yet low enough to be environmentally representative. Treatment was carried out in an amber glass reactor irradiated by two 8-watt UVC mercury lamps emitting at 254 nm, with a measured irradiance of 4.9 W m⁻². Residual oxidants were neutralized with sodium thiosulfate before any biological testing, ensuring that observed effects could not be attributed to leftover peroxide chemistry.

The ecotoxicological battery spanned multiple trophic levels and levels of biological organization. Oxidative stress was assessed in the earthworm Eisenia andrei by measuring malondialdehyde (MDA), a marker of lipid peroxidation, and protein carbonylation, an irreversible oxidative modification of proteins. Phytotoxicity was evaluated using lettuce (Lactuca sativa) seeds and a growth index combining germination rate and root elongation. Cytotoxicity and genotoxicity were quantified in onion (Allium cepa) root meristems through the mitotic index and the frequency of chromosomal and nuclear aberrations—micronuclei, chromosome breaks, stickiness, bridges, and nuclear buds—across 5,000 cells per sample. Finally, growth inhibition was tested in two environmentally relevant microbes: Azospirillum brasilense, a plant growth-promoting bacterium used in soybean cultivation, and Saccharomyces cerevisiae, a yeast that persists in soils and fermentative niches.

The untreated enriched effluent told a worrying story. It exhibited substantial genotoxicity in onion cells—21%, approaching the 24% seen with the positive control, methyl methanesulfonate—and severe phytotoxicity, with lettuce growth indices far below the 80% threshold that signals absence of toxicity. Even the unspiked effluent itself suppressed lettuce growth, achieving a growth index of only 47%, evidence that bioactive compounds survive conventional treatment. In earthworms, individual aqueous solutions of NTZ and SMX raised MDA levels by 37.2% and 23.5% respectively, while the three-compound mixture increased lipid peroxidation by 38.0%, pointing to additive or synergistic oxidative stress mechanisms involving reactive oxygen species and Fenton-type chemistry within cells.

The picture changed decisively after oxidation. Following 10 minutes of UVC-driven treatment—the point at which 80% chemical degradation had been achieved, matching the minimum removal target proposed in the European Union’s COM(2022)541 directive—genotoxicity fell to 10%, and after 20 minutes, corresponding to the limit of quantification for the parent compounds, it dropped further to 6%, a statistically significant reduction. The plant growth index climbed above 80%, crossing from toxic territory into biocompatibility. The mitotic index in onion cells remained statistically indistinguishable from the deionized-water control (around 39–40% versus 40%), demonstrating that the process generated no cytotoxic byproducts capable of arresting cell division. And crucially for agricultural applications, neither the treated effluent nor its transformation products inhibited growth of A. brasilense or S. cerevisiae.

The mechanistic details are instructive. In the untreated enriched effluent, micronuclei—membrane-bound DNA fragments expelled from the nucleus during flawed cell division—were among the most frequent aberrations, indicating clastogenic and aneugenic damage. Colchicine exposure predictably produced C-metaphase accumulation, a signature of its tubulin-binding, spindle-disrupting mechanism first described in Allium nearly a century ago. SMX depressed the mitotic index and induced chromosomal damage, consistent with prior findings in Vicia faba. NTZ, whose genotoxicity had never before been reported, produced chromosome breaks, stickiness, and nuclear buds. After oxidation, the overall aberration burden fell sharply, particularly micronuclei and stickiness, although a few bridges and polyploid cells persisted even at 20 minutes—a reminder that some transformation products or resistant residuals may linger.

The microbial results came with a subtlety. Reduced A. brasilense growth after treatment initially looked like a red flag, but the team attributes it to depletion of assimilable organic matter, which serves as radical scavenger during oxidation and as a nutrient source afterward—not to toxicity. The negative control likewise grew slowly. This distinction matters because AOPs are designed to mineralize organic carbon, and a nutrient-poorer medium should not be misread as a more toxic one. For S. cerevisiae, no inhibition occurred at any stage of treatment, although NTZ alone significantly depressed yeast optical density, reinforcing the compound’s cell-cycle interference potential even at nanomolar-scale exposures.

The study’s broader significance lies in its methodological stance. By adopting an “effect-driven approach,” the team evaluated the toxicity of whole reaction mixtures without needing to isolate and identify individual transformation products—an impractical task given that many degradation byproducts are not commercially available. Their findings align with a growing consensus that treatment efficacy must be judged not merely by parent-compound removal but by the nature and reactivity of the resulting transformation products. Biochemical biomarkers such as MDA and protein carbonylation detected sublethal disturbances that mortality-based endpoints would have missed entirely, providing early warnings of cellular distress at environmentally relevant concentrations.

One caveat deserves attention: while lipid peroxidation declined after treatment, protein carbonylation rose slightly—1.05-fold above control—suggesting some transformation products may still provoke protein oxidation even as lipid damage subsides. The authors flag this discrepancy and call for future identification of the specific TPs responsible, alongside expanded organism panels, longer-term exposure studies, and pilot-scale validation across different environmental matrices.

The regulatory context sharpens the urgency. The EU’s proposed urban wastewater treatment directive mandates at least 80% removal of specified organic micropollutants and microbiological control for agricultural reuse, yet it conspicuously omits eco-compatibility assessment of the treated water itself. This study supplies exactly that missing dimension, showing that a process satisfying the chemical benchmark also delivers measurable biological benefit—lower genotoxicity, restored plant growth, intact cell division, and unharmed beneficial microbes. As water scarcity intensifies globally and reuse becomes less optional, the Brazilian team’s integrated bioassay framework offers a template for ensuring that the water farmers irrigate with is not merely chemically compliant, but genuinely ecologically safe.

Subject of Research: Mitigation of micropollutant toxicity in treated wastewater using the H₂O₂/S₂O₈²⁻/UVC advanced oxidation process, assessed through integrated ecotoxicological bioassays for potential agricultural reuse.

Article Title: Mitigation of micropollutant toxicity in treated wastewater using the H2O2/S2O82−/UVC process: An ecotoxicological perspective for agricultural reuse

Article References: da Costa, L. G., dos Santos, G. M., Marson, E. O., de Lima, M. G. F., de Souza Bessa, M. A., Scarafiz, G., Junior, S. F. S., Saggioro, E. M., de Carvalho, S. R., de Siqueira Ferreira, A., Nilin, J., Neto, W. B., & Trovó, A. G. (2026). Mitigation of micropollutant toxicity in treated wastewater using the H2O2/S2O82−/UVC process: An ecotoxicological perspective for agricultural reuse. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-026-38218-5

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38218-5

Keywords: Advanced oxidation process, Micropollutants, Wastewater reuse, Ecotoxicity, Genotoxicity, Phytotoxicity, Oxidative stress, Hydroxyl radicals, Sulfate radicals, UVC treatment, Agricultural irrigation, Transformation products

A Soft Back Belt May Ease Common Low Back Pain

✇Knowridge Science
By: Knowridge
10 September 2026 at 12:02

Low back pain is something most people experience at some point in life. Long hours at a desk, repeated lifting, reduced activity and many other factors can contribute, but in most cases doctors cannot identify one specific injury or disease that explains the pain. This common form is called nonspecific low back pain. It accounts […]

The post A Soft Back Belt May Ease Common Low Back Pain appeared first on Knowridge Science Report.

Received — 9 September 2026 ⏭

AI predicts prolonged air leak after lung surgery in elderly patients

✇Science Mag
By: Nathaniel Bowman
9 September 2026 at 18:09

When surgeons remove a portion of a cancerous lung, one of the most frustrating complications they can face is air. A persistent leak from the lung’s cut surface or staple lines — known as prolonged air leak — keeps chest tubes in place far longer than anyone would like, extends hospital stays, inflates costs, and elevates the risk of infection and other downstream problems. Now, a research team in China has developed a machine learning tool that predicts, with striking accuracy, which older lung cancer patients are most likely to develop this complication before it ever occurs. The study, published in BMC Cancer, combines cardiopulmonary exercise testing data with an interpretable artificial intelligence model and could reshape how thoracic surgical teams prepare for and manage one of the most common postoperative challenges in lung surgery.

The research was led by Zixiao Wang, Jiabao Ding, Huagang Liang and colleagues at the First Hospital of Qinhuangdao, in collaboration with Kai Wang of Tianjin Chest Hospital. It addressed a well-recognized gap in thoracic surgical practice. While prolonged air leak — typically defined as air leaking from the chest drainage system for more than five days after surgery — is among the most frequent complications following video-assisted thoracoscopic anatomical lung resection, the prediction models available to clinicians have historically lacked rigorous external validation and, critically, interpretability. Surgeons have been asked to trust risk scores without understanding where those risks come from, a barrier that has limited clinical uptake of computational tools in the operating room planning process.

To build their model, the investigators enrolled patients aged 60 and older with lung cancer who underwent thoracoscopic anatomical lung resection — either lobectomy, the removal of an entire lung lobe, or segmentectomy, the removal of a smaller anatomical segment. The study was designed as a dual-centre cohort investigation with both retrospective and prospective components. A total of 471 patients treated at Qinhuangdao First Hospital between January 2024 and June 2025 formed the model development cohort. A separate group of 140 patients from Tianjin Chest Hospital, enrolled prospectively between June and August 2025, provided the external validation set — a crucial test of whether the model generalized beyond the institution where it was built.

The methodology followed the emerging best-practice playbook for clinical machine learning. After preliminary data processing, the development cohort was randomly split into a training set containing 70 percent of patients and an internal validation set holding the remaining 30 percent. Because prolonged air leak affects only a minority of surgical patients, the training data were inherently imbalanced, with far more non-leak cases than leak cases — a condition that can fool machine learning algorithms into simply predicting that no one is at risk. The researchers countered this with SMOTE, the Synthetic Minority Over-sampling Technique, which generates synthetic examples of the underrepresented class to rebalance the dataset and force the algorithms to learn the genuine signature of risk.

Feature selection was performed using LASSO regression — least absolute shrinkage and selection operator — a statistical technique that penalizes model complexity and drives the coefficients of irrelevant variables to zero, effectively filtering a large panel of candidate variables down to the most predictive few. From this process, four predictors of prolonged air leak emerged: a history of emphysema, the smoking index, a previous history of thoracic surgery or chest trauma, and the ventilatory equivalent for carbon dioxide, abbreviated VE/VCO₂. This last variable is drawn directly from cardiopulmonary exercise testing, a stress test performed on a bicycle or treadmill that measures how the heart, lungs, and circulatory system respond together to increasing exertion. VE/VCO₂ reflects the efficiency of ventilation during exercise — how much air a patient must move to eliminate a given amount of carbon dioxide — and elevated values signal impaired pulmonary gas exchange and often underlying parenchymal lung disease.

With the four features in hand, the team constructed six competing machine learning models: logistic regression, support vector machine, decision tree, extreme gradient boosting, and artificial neural networks, among others. Each was evaluated using a battery of metrics including the area under the receiver operating characteristic curve, or AUC, the F1 score, and the Brier score, which measures the accuracy of probabilistic predictions. The winner was XGBoost, an ensemble method that builds a sequence of decision trees, with each new tree trained to correct the residual errors of its predecessors. In internal validation, XGBoost achieved an AUC of 0.903 (95 percent confidence interval, 0.834 to 0.960) — well within the range conventionally considered excellent discrimination — along with the highest F1 score of 0.717 and the lowest Brier score of 0.092 among all six candidates.

The external validation was arguably the study’s most important test. A model can memorize the quirks of its home institution’s patient population, equipment, and surgical practices, only to falter elsewhere. Yet when applied to the 140 previously unseen patients from Tianjin Chest Hospital, the XGBoost model maintained an AUC of 0.800 (95 percent confidence interval, 0.656 to 0.913), with a sensitivity of 0.700, a specificity of 0.833, and a Brier score of 0.133. In practical terms, the model correctly flagged seven out of ten patients who went on to develop prolonged air leak while correctly reassuring more than eight out of ten of those who did not — performance that, if replicated in routine practice, would give surgeons a meaningful window to intervene preemptively.

Interpretability was treated not as an afterthought but as a core design requirement. The researchers applied SHAP — Shapley Additive Explanations — a technique borrowed from cooperative game theory that assigns each feature a contribution value for every individual prediction. Rather than presenting clinicians with an opaque probability, SHAP reveals how much each of the four variables pushed a given patient’s risk up or down, and in which direction. Through this analysis, VE/VCO₂ emerged as the single most influential feature, followed by the other three predictors ranked by correlation. The finding is physiologically plausible: patients whose lungs ventilate inefficiently under the stress of exercise are precisely those whose lung tissue is most fragile and least likely to seal spontaneously after surgical manipulation. A history of emphysema, a heavy smoking history, and prior chest surgery or trauma each independently compound this tissue vulnerability.

The clinical utility of the work extends beyond prediction to prevention. Armed with a preoperative risk estimate, surgical teams can tailor their approach for high-risk patients. Options include more meticulous staple-line reinforcement, the use of sealants or pledgeted sutures on fragile lung tissue, choosing segmentectomy over lobectomy where oncologically appropriate, planning for ambulatory drainage systems on discharge, or scheduling earlier follow-up to catch and manage leaks before they become complicated. To lower the barrier to bedside use, the team built a web-based risk calculator that allows clinicians to enter a patient’s four variables and receive an individualized probability of prolonged air leak — no programming expertise required.

The significance of incorporating cardiopulmonary exercise testing into a surgical prediction model deserves particular emphasis. Traditional preoperative pulmonary assessment in lung cancer surgery has relied on static spirometric measures such as forced expiratory volume in one second, or FEV1, and the diffusing capacity for carbon monoxide, DLCO. These tests measure lung function at rest and provide only a partial picture of cardiopulmonary reserve. CPET, by contrast, captures the integrated response of multiple organ systems under physiological stress, and the VE/VCO₂ slope in particular has gained recognition as a sensitive marker of ventilatory inefficiency in chronic obstructive pulmonary disease and heart failure. The present study suggests that this dynamic measure carries prognostic information for surgical outcomes that static tests do not fully capture — a finding with implications well beyond air leak prediction.

Several caveats frame the work appropriately. The study population was limited to Chinese patients aged 60 and above undergoing video-assisted thoracoscopic resection, and generalization to younger patients, other surgical techniques such as robotic-assisted resection, or different health care systems remains to be demonstrated. The external validation cohort, while methodologically essential, comprised 140 patients, and wider multicenter prospective trials would strengthen confidence in the model’s performance. The published article is an accepted, peer-reviewed author manuscript shared early ahead of the final version of record. The study was funded by the Beijing Kechuang Medical Development Foundation, with the funder playing no role in study design, analysis, or manuscript preparation, and the authors declare no competing interests.

Even so, the study stands as a compelling example of where predictive medicine is heading. By pairing a rigorous dual-centre validation strategy with explainable artificial intelligence and a physiologically grounded predictor, the researchers have moved machine learning in thoracic surgery closer to genuine clinical utility rather than proof-of-concept novelty. For the growing population of older patients facing lung cancer surgery, a simple preoperative test and a few clicks in a web calculator may soon determine who needs extra surgical vigilance — and spare many of them the days of tethered chest drainage that have long been an accepted, if unwelcome, part of recovery.

Subject of Research: Machine learning-based prediction of prolonged air leak after video-assisted thoracoscopic anatomical lung resection in older lung cancer patients using cardiopulmonary exercise testing

Article Title: Machine learning-based prediction of prolonged air leak after video-assisted thoracoscopic anatomical lung resection in older patients using cardiopulmonary exercise testing: a dual-centre development and validation study

Article References: Wang, Z., Wang, K., Zhu, N., liu, H., Li, J., Liang, H., & Ding, J. (2026). Machine learning-based prediction of prolonged air leak after video-assisted thoracoscopic anatomical lung resection in older patients using cardiopulmonary exercise testing: a dual-centre development and validation study. BMC Cancer. https://doi.org/10.1186/s12885-026-16933-z

Image Credits: AI Generated

DOI: 10.1186/s12885-026-16933-z

Keywords: Machine learning, Prolonged air leak, Cardiopulmonary exercise testing, XGBoost, Lung cancer surgery, Video-assisted thoracoscopic surgery, Prediction models, VE/VCO₂, SHAP interpretability, Thoracic surgery, Predictive medicine, COPD

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