James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR).

The research project is sponsored by Jameel Research, part of the Abdul Latif Jameel International network. The initial three-year, $3 million research project in MIT’s Department of Biological Engineering and Institute of Medical Engineering and Science focuses on developing and validating programmable antibacterials against key pathogens.

AMR — driven by the overuse and misuse of antibiotics — has accelerated the rise of drug-resistant infections, while the development of new antibacterial tools has slowed. The impact is felt worldwide, especially in low- and middle-income countries, where limited diagnostic infrastructure causes delays or ineffective treatment.

The project centers on developing a new generation of targeted antibacterials using AI to design small proteins to disable specific bacterial functions. These designer molecules would be produced and delivered by engineered microbes, providing a more precise and adaptable approach than traditional antibiotics.

“This project reflects my belief that tackling AMR requires both bold scientific ideas and a pathway to real-world impact,” Collins says. “Jameel Research is keen to address this crisis by supporting innovative, translatable research at MIT.”

Mohammed Abdul Latif Jameel ’78, chair of Abdul Latif Jameel, says, “antimicrobial resistance is one of the most urgent challenges we face today, and addressing it will require ambitious science and sustained collaboration. We are pleased to support this new research, building on our long-standing relationship with MIT and our commitment to advancing research across the world, to strengthen global health and contribute to a more resilient future.”

Interesting research: “CHAI: Command Hijacking Against Embodied AI.”

Abstract: Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a new class of prompt-based attacks that exploit the multimodal language interpretation abilities of Large Visual-Language Models (LVLMs). CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents; drone emergency landing, autonomous driving, and aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness...

The Department of Energy has identified more than three dozen coal plants that could power military installations, according to one official.

With its plans to revoke the endangerment finding, the administration is betting the Supreme Court will allow it to completely avoid regulating greenhouse gases.

It would be the first time a country attaches carbon reduction numbers to technologies that can pull climate pollution from the sky and seas.

Optimistic projections that minimize climate change are misleading institutional investors and governments, U.K. researchers have found.

The agency would be tasked with investigating suspected “weather modification” under new legislation.

The South American country is hosting an informal climate summit as progress stalls at U.N.-organized conferences.

Glaciers, snow and ice covered in the soot emitted by ships have less ability to reflect the sun.

Peter Liese, the European People's Party's lead on environmental issues, said his group wanted a “balanced” approach to overhauling the emissions cap-and-trade program.

Beekeepers are persisting in the face of floods, more powerful monsoons and extreme heat, as well as development that changes the landscapes bees rely on.

An Apparel Impact Institute report flagged three pressure points: higher carbon prices, rising raw-material costs and more expensive energy.

Nature Climate Change, Published online: 11 February 2026; doi:10.1038/s41558-026-02564-6

Early life stages are particularly critical for human brain development. A large-scale study in China shows that heat exposure in early life is associated with increased risks of delayed neurodevelopment in preschool children.

Nature Climate Change, Published online: 11 February 2026; doi:10.1038/s41558-026-02555-7

National greenhouse gas inventories systematically undercount methane and nitrous oxide emissions from wastewater due to outdated methods and incomplete coverage. Addressing these discrepancies is essential to strengthen transparency in global climate efforts and improving the effectiveness of national mitigation strategies.

Nature Climate Change, Published online: 11 February 2026; doi:10.1038/s41558-025-02540-6

The wastewater sector is a major source of non-CO2 emissions, but accurate emissions accounting remains challenging, despite guidelines for basic inventories provided by the IPCC. This research reveals that national inventories widely omit key sources and apply inconsistent methods, which lead to greatly under-reported emissions.

Nature Climate Change, Published online: 11 February 2026; doi:10.1038/s41558-026-02560-w

The authors use data from over 100,000 children (3–5.5 years old) across 551 Chinese cities, to show that risk of suspected neurodevelopmental delay increases under both prenatal and postnatal heat exposure. Steep increases at high temperatures highlight future potential vulnerability increases.

We are calling on technology companies like Meta and Google to stand up for their users by resisting the Department of Homeland Security's (DHS) lawless administrative subpoenas for user data. 

In the past year, DHS has consistently targeted people engaged in First Amendment activity. Among other things, the agency has issued subpoenas to technology companies to unmask or locate people who have documented ICE's activities in their community, criticized the government, or attended protests.   

These subpoenas are unlawful, and the government knowns it. When a handful of users challenged a few of them in court with the help of ACLU affiliates in Northern California and Pennsylvania, DHS withdrew them rather than waiting for a decision. 

These subpoenas are unlawful, and the government knowns it.

But it is difficult for the average user to fight back on their own. Quashing a subpoena is a fast-moving process that requires lawyers and resources. Not everyone can afford a lawyer on a moment’s notice, and non-profits and pro-bono attorneys have already been stretched to near capacity during the Trump administration.  

 That is why we, joined by the ACLU of Northern California, have asked several large tech platforms to do more to protect their users, including: 

1.  Insist on court intervention and an order before complying with a DHS subpoena, because the agency has already proved that its legal process is often unlawful and unconstitutional;  
2. Give users as much notice as possible when they are the target of a subpoena, so the user can seek help. While many companies have already made this promise, there are high-profile examples of it not happening—ultimately stripping users of their day in court;  
3. Resist gag orders that would prevent companies from notifying their users that they are a target of a subpoena. 

 We sent the letter to Amazon, Apple, Discord, Google, Meta, Microsoft, Reddit, SNAP, TikTok, and X.  

 Recipients are not legally compelled to comply with administrative subpoenas absent a court order 

 An administrative subpoena is an investigative tool available to federal agencies like DHS. Many times, these are sent to technology companies to obtain user data. A subpoena cannot be used to obtain the content of communications, but they have been used to try and obtain some basic subscriber information like name, address, IP address, length of service, and session times.  

Unlike a search warrant, an administrative subpoena is not approved by a judge. If a technology company refuses to comply, an agency’s only recourse is to drop it or go to court and try to convince a judge that the request is lawful. That is what we are asking companies to do—simply require court intervention and not obey in advance. 

It is unclear how many administrative subpoenas DHS has issued in the past year. Subpoenas can come from many places—including civil courts, grand juries, criminal trials, and administrative agencies like DHS. Altogether, Google received 28,622 and Meta received 14,520 subpoenas in the first half of 2025, according to their transparency reports. The numbers are not broken out by type.   

DHS is abusing its authority to issue subpoenas 

In the past year, DHS has used these subpoenas to target protected speech. The following are just a few of the known examples. 

On April 1, 2025, DHS sent a subpoena to Google in an attempt to locate a Cornell PhD student in the United States on a student visa. The student was likely targeted because of his brief attendance at a protest the year before. Google complied with the subpoena without giving the student an opportunity to challenge it. While Google promises to give users prior notice, it sometimes breaks that promise to avoid delay. This must stop.   

In September 2025, DHS sent a subpoena and summons to Meta to try to unmask anonymous users behind Instagram accounts that tracked ICE activity in communities in California and Pennsylvania. The users—with the help of the ACLU and its state affiliates— challenged the subpoenas in court, and DHS withdrew the subpoenas before a court could make a ruling. In the Pennsylvania case, DHS tried to use legal authority that its own inspector general had already criticized in a lengthy report.  

In October 2025, DHS sent Google a subpoena demanding information about a retiree who criticized the agency’s policies. The retiree had sent an email asking the agency to use common sense and decency in a high-profile asylum case. In a shocking turn, federal agents later appeared on that person’s doorstep. The ACLU is currently challenging the subpoena.  

Read the full letter here

The signals that drive many of the brain and body’s most essential functions — consciousness, sleep, breathing, heart rate, and motion — course through bundles of “white matter” fibers in the brainstem, but imaging systems so far have been unable to finely resolve these crucial neural cables. That has left researchers and doctors with little capability to assess how they are affected by trauma or neurodegeneration. 

In a new study, a team of MIT, Harvard University, and Massachusetts General Hospital researchers unveil AI-powered software capable of automatically segmenting eight distinct bundles in any diffusion MRI sequence.

In the open-access study, published Feb. 6 in the Proceedings of the National Academy Sciences, the research team led by MIT graduate student Mark Olchanyi reports that their BrainStem Bundle Tool (BSBT), which they’ve made publicly available, revealed distinct patterns of structural changes in patients with Parkinson’s disease, multiple sclerosis, and traumatic brain injury, and shed light on Alzheimer’s disease as well. Moreover, the study shows, BSBT retrospectively enabled tracking of bundle healing in a coma patient that reflected the patient’s seven-month road to recovery.

“The brainstem is a region of the brain that is essentially not explored because it is tough to image,” says Olchanyi, a doctoral candidate in MIT’s Medical Engineering and Medical Physics Program. “People don't really understand its makeup from an imaging perspective. We need to understand what the organization of the white matter is in humans and how this organization breaks down in certain disorders.”

Adds Professor Emery N. Brown, Olchanyi’s thesis supervisor and co-senior author of the study, “the brainstem is one of the body’s most important control centers. Mark’s algorithms are a significant contribution to imaging research and to our ability to the understand regulation of fundamental physiology. By enhancing our capacity to image the brainstem, he offers us new access to vital physiological functions such as control of the respiratory and cardiovascular systems, temperature regulation, how we stay awake during the day and how sleep at night.”

Brown is the Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering in The Picower Institute for Learning and Memory, the Institute for Medical Engineering and Science, and the Department of Brain and Cognitive Sciences at MIT. He is also an anesthesiologist at MGH and a professor at Harvard Medical School.

Building the algorithm

Diffusion MRI helps trace the long branches, or “axons,” that neurons extend to communicate with each other. Axons are typically clad in a sheath of fat called myelin, and water diffuses along the axons within the myelin, which is also called the brain’s “white matter.” Diffusion MRI can highlight this very directed displacement of water. But segmenting the distinct bundles of axons in the brainstem has proved challenging, because they are small and masked by flows of brain fluids and the motions produced by breathing and heart beats.

As part of his thesis work to better understand the neural mechanisms that underpin consciousness, Olchanyi wanted to develop an AI algorithm to overcome these obstacles. BSBT works by tracing fiber bundles that plunge into the brainstem from neighboring areas higher in the brain, such as the thalamus and the cerebellum, to produce a “probabilistic fiber map.” An artificial intelligence module called a “convolutional neural network” then combines the map with several channels of imaging information from within the brainstem to distinguish eight individual bundles.

To train the neural network to segment the bundles, Olchanyi “showed” it 30 live diffusion MRI scans from volunteers in the Human Connectome Project (HCP). The scans were manually annotated to teach the neural network how to identify the bundles. Then he validated BSBT by testing its output against “ground truth” dissections of post-mortem human brains where the bundles were well delineated via microscopic inspection or very slow but ultra-high-resolution imaging. After training, BSBT became proficient in automatically identifying the eight distinct fiber bundles in new scans.

In an experiment to test its consistency and reliability, Olchanyi tasked BSBT with finding the bundles in 40 volunteers who underwent separate scans two months apart. In each case, the tool was able to find the same bundles in the same patients in each of their two scans. Olchanyi also tested BSBT with multiple datasets (not just the HCP), and even inspected how each component of the neural network contributed to BSBT’s analysis by hobbling them one by one.

“We put the neural network through the wringer,” Olchanyi says. “We wanted to make sure that it’s actually doing these plausible segmentations and it is leveraging each of its individual components in a way that improves the accuracy.”

Potential novel biomarkers

Once the algorithm was properly trained and validated, the research team moved on to testing whether the ability to segment distinct fiber bundles in diffusion MRI scans could enable tracking of how each bundle’s volume and structure varied with disease or injury, creating a novel kind of biomarker. Although the brainstem has been difficult to examine in detail, many studies show that neurodegenerative diseases affect the brainstem, often early on in their progression.

Olchanyi, Brown and their co-authors applied BSBT to scores of datasets of diffusion MRI scans from patients with Alzheimer’s, Parkinson’s, MS, and traumatic brain injury (TBI). Patients were compared to controls and sometimes to themselves over time. In the scans, the tool measured bundle volume and “fractional anisotropy,” (FA) which tracks how much water is flowing along the myelinated axons versus how much is diffusing in other directions, a proxy for white matter structural integrity.

In each condition, the tool found consistent patterns of changes in the bundles. While only one bundle showed significant decline in Alzheimer’s, in Parkinson’s the tool revealed a reduction in FA in three of the eight bundles. It also revealed volume loss in another bundle in patients between a baseline scan and a two-year follow-up. Patients with MS showed their greatest FA reductions in four bundles and volume loss in three. Meanwhile, TBI patients didn’t show significant volume loss in any bundles, but FA reductions were apparent in the majority of bundles.

Testing in the study showed that BSBT proved more accurate than other classifier methods in discriminating between patients with health conditions versus controls.

BSBT, therefore, can be “a key adjunct that aids current diagnostic imaging methods by providing a fine-grained assessment of brainstem white matter structure and, in some cases, longitudinal information,” the authors wrote.

Finally, in the case of a 29-year-old man who suffered a severe TBI, Olchanyi applied BSBT to a scans taken during the man’s seven-month coma. The tool showed that the man’s brainstem bundles had been displaced, but not cut, and showed that over his coma, the lesions on the nerve bundles decreased by a factor of three in volume. As they healed, the bundles moved back into place as well.

The authors wrote that BSBT “has substantial prognostic potential by identifying preserved brainstem bundles that can facilitate coma recovery.”

The study’s other senior authors are Juan Eugenio Iglesias and Brian Edlow. Other co-authors are David Schreier, Jian Li, Chiara Maffei, Annabel Sorby-Adams, Hannah Kinney, Brian Healy, Holly Freeman, Jared Shless, Christophe Destrieux, and Hendry Tregidgo.

Funding for the study came from the National Institutes of Health, U.S. Department of Defense, James S. McDonnell Foundation, Rappaport Foundation, American SidS Institute, American Brain Foundation, American Academy of Neurology, Center for Integration of Medicine and Innovative Technology, Blueprint for Neuroscience Research, and Massachusetts Life Sciences Center.

3D bioprinting, in which living tissues are printed with cells mixed into soft hydrogels, or “bio-inks,” is widely used in the field of bioengineering for modeling or replacing the tissues in our bodies. The print quality and reproducibility of tissues, however, can face challenges. One of the most significant challenges is created simply by gravity — cells naturally sink to the bottom of the bioink-extruding printer syringe because the cells are heavier than the hydrogel around them.

“This cell settling, which becomes worse during the long print sessions required to print large tissues, leads to clogged nozzles, uneven cell distribution, and inconsistencies between printed tissues,” explains Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Existing solutions, such as manually stirring bioinks before loading them into the printer, or using passive mixers, cannot maintain uniformity once printing begins.”

In a study published Feb. 2 in the journal Device, Raman’s team introduces a new approach that aims to solve this core limitation by actively preventing cell sedimentation within bioinks during printing, allowing for more reliable and biologically consistent 3D printed tissues.

“Precise control over the bioink’s physical and biological properties is essential for recreating the structure and function of native tissues,” says Ferdows Afghah, a postdoc in mechanical engineering at MIT and lead author of the study.

“If we can print tissues that more closely mimic those in our bodies, we can use them as models to understand more about human diseases, or to test the safety and efficacy of new therapeutic drugs,” adds Raman. Such models could help researchers move away from techniques like animal testing, which supports recent interest from the U.S. Food and Drug Administration in developing faster, less expensive, and more informative new approaches to establish the safety and efficacy of new treatment paths.

“Eventually, we are working towards regenerative medicine applications such as replacing diseased or injured tissues in our bodies with 3D printed tissues that can help restore healthy function,” says Raman.

MagMix, a magnetically actuated mixer, is composed of two parts: a small magnetic propeller that fits inside the syringes used by bioprinters to deposit bioinks, layer by layer, into 3D tissues, and a permanent magnet attached to a motor that moves up and down near the syringe, controlling the movement of the propeller inside. Together, this compact system can be mounted onto any standard 3D bioprinter, keeping bioinks uniformly mixed during printing without changing the bioink formulation or interfering with the printer’s normal operation. To test the approach, the team used computer simulations to design the optimal mixing propeller geometry and speed and then validated its performance experimentally.

“Across multiple bioink types, MagMix prevented cell settling for more than 45 minutes of continuous printing, reducing clogging and preserving high cell viability,” says Raman. “Importantly, we showed that mixing speeds could be adjusted to balance effective homogenization for different bioinks while inducing minimal stress on the cells. As a proof-of-concept, we demonstrated that MagMix could be used to 3D print cells that could mature into muscle tissues over the course of several days.”

By maintaining uniform cell distribution throughout long or complex print jobs, MagMix enables the fabrication of high-quality tissues with more consistent biological function. Because the device is compact, low-cost, customizable, and easily integrated into existing 3D printers, it offers a broadly accessible solution for laboratories and industries working toward reproducible engineered tissues for applications in human health including disease modeling, drug screening, and regenerative medicine.

This work was supported, in part, by the Safety, Health, and Environmental Discovery Lab (SHED) at MIT, which provides infrastructure and interdisciplinary expertise to help translate biofabrication innovations from lab-scale demonstrations to scalable, reproducible applications.

“At the SHED, we focus on accelerating the translation of innovative methods into practical tools that researchers can reliably adopt,” says Tolga Durak, the SHED’s founding director. “MagMix is a strong example of how the right combination of technical infrastructure and interdisciplinary support can move biofabrication technologies toward scalable, real-world impact.”

The SHED’s involvement reflects a broader vision of strengthening technology pathways that enhance reproducibility and accessibility across engineering and the life sciences by providing equitable access to advanced equipment and fostering cross-disciplinary collaboration.

“As the field advances toward larger-scale and more standardized systems, integrated labs like SHED are essential for building sustainable capacity,” Durak adds. “Our goal is not only to enable discovery, but to ensure that new technologies can be reliably adopted and sustained over time.”

The team is also interested in non-medical applications of engineered tissues, such as using printed muscles to power safer and more efficient “biohybrid” robots.

The researchers believe this work can improve the reliability and scalability of 3D bioprinting, making the potential impacts on the field of 3D bioprinting and on human health significant. Their paper, “Advancing Bioink Homogeneity in Extrusion 3D Bioprinting with Active In Situ Magnetic Mixing,” is available now from the journal Device. 

Amazon Ring’s Super Bowl ad offered a vision of our streets that should leave every person unsettled about the company’s goals for disintegrating our privacy in public.

In the ad, disguised as a heartfelt effort to reunite the lost dogs of the country with their innocent owners, the company previewed future surveillance of our streets: a world where biometric identification could be unleashed from consumer devices to identify, track, and locate anything — human, pet, and otherwise.

The ad for Ring’s “Search Party” feature highlighted the doorbell camera’s ability to scan footage across Ring devices in a neighborhood, using AI analysis to identify potential canine matches among the many personal devices within the network. 

Amazon Ring already integrates biometric identification, like face recognition, into its products via features like "Familiar Faces,” which depends on scanning the faces of those in sight of the camera and matching it against a list of pre-saved, pre-approved faces. It doesn’t take much to imagine Ring eventually combining these two features: face recognition and neighborhood searches. 

Ring’s “Familiar Faces” feature could already run afoul of biometric privacy laws in some states, which require explicit, informed consent from individuals before a company can just run face recognition on someone. Unfortunately, not all states have similar privacy protections for their residents. 

Ring has a history of privacy violations, enabling surveillance of innocents and protestors, and close collaboration with law enforcement, and EFF has spent years reporting on its many privacy problems.

The cameras, which many people buy and install to identify potential porch pirates or get a look at anyone that might be on their doorstep, feature microphones that have been found to capture audio from the street. In 2023, Ring settled with the Federal Trade Commission over the extensive access it gave employees to personal customer footage. At that time, just three years ago, the FTC wrote: “As a result of this dangerously overbroad access and lax attitude toward privacy and security, employees and third-party contractors were able to view, download, and transfer customers’ sensitive video data for their own purposes.”

The company has made law enforcement access a regular part of its business. As early as 2016, the company was courting police departments through free giveaways. The company provided law enforcement warrantless access to people’s footage, a practice they claimed to cut off in 2024. Not long after, though, the company established partnerships with major police companies Axon and Flock Safety to facilitate the integration of Ring cameras into police intelligence networks. The partnership allows law enforcement to again request Ring footage directly from users without a warrant. This supplements the already wide-ranging apparatus of data and surveillance feeds now available to law enforcement. 

This feature is turned on by default, meaning that Ring owners need to go into the controls to change it. According to Amazon Ring’s instructions, this is how to disable the “search party” feature: 

1. Open the Ring app to the main dashboard.
2. Tap the menu (☰).
3. Tap Control Center.
4. Select Search Party.
5. Tap Disable Search for Lost Pets. Tap the blue Pet icon next to "Search for Lost Pets" to turn the feature off for each camera. (You also have the option to "Disable Natural Hazards (Fire Watch)" and the option to tap the blue Flame icon next to Natural Hazards (Fire Watch) to turn the feature on or off for each camera.)

The addition of AI-driven biometric identification is the latest entry in the company’s history of profiting off of public safety worries and disregard for individual privacy, one that turbocharges the extreme dangers of allowing this to carry on. People need to reject this kind of disingenuous framing and recognize the potential end result: a scary overreach of the surveillance state designed to catch us all in its net.