Fungal diseases are a major threat to the global banana supply. Breakthroughs, discoveries, and DIY tips sent six days a week. The popular fruit is threatened by a fungal disease called Fusarium wilt of banana (FWB), which blocks the flow of nutrients and makes it wilt. In the 1950s, the pathogen even made one species-Gros Michel bananas-functionally extinct. Fear not though, scientists are on it.

Lifting weights can keep you carrying groceries and riding bikes even as you get older. To join or not to join a gym: That is the question. If you opt out of building a home gym, you can join a club and have access to more weights and machines. Friends and classes motivate you to keep coming, and that monthly bill keeps you disciplined. On the other hand, gym memberships are steep, workouts can get hijacked by bullies, and going to the gym is an additional commute.

Breakthroughs, discoveries, and DIY tips sent six days a week. Whether it's the ocean's deepest hydrothermal vents or tall mountain peaks, bacteria is likely surviving and thriving. Ice caves can host a wide variety of microorganisms and offer biologists a bevy of genetic diversity that still has to be studied. And it could help save lives. A team of scientists in Romania tested antibiotic resistance profiles with a bacterial strain that was hidden in a 5,000-year-old layer of ice inside an underground ice cave.

Effective resistances have a broad range of algorithmic implications.

César de la Fuente is on a mission to combat antimicrobial resistance by looking at nature's own solutions. César de la Fuente is an associate professor at the University of Pennsylvania, where he leads the Machine Biology Group. When he was just a teenager trying to decide what to do with his life, César de la Fuente compiled a list of the world's biggest problems. He ranked them inversely by how much money governments were spending to solve them. Antimicrobial resistance topped the list. Twenty years on, the problem has not gone away.

Large language models (LLMs) are increasingly integrated into multi-agent systems (MAS), where peer interactions shape individual decisions. While prior work has mainly examined conformity bias, we broaden the view to include how LLMs build rapport from prior interactions, discern and integrate high-quality peer information, and resist misleading inputs-abilities essential for achieving collective intelligence under complex social dynamics. We introduce KAIROS, a benchmark that simulates quiz-style collaboration with peer agents whose rapport levels and behaviours can be precisely controlled in both historical interactions and the current round. This unified setup enables systematic analysis of how rapport, peer actions, and the model's self-confidence jointly influence decision-making. Using KAIROS, we evaluate prompting, supervised fine-tuning, and reinforcement learning via Group Relative Policy Optimisation (GRPO). Results show that model scale is a primary factor moderating susceptibility to social influence: larger models are more resilient and benefit from prompting-based mitigation, whereas smaller models remain vulnerable. Only carefully configured GRPO training yields consistent robustness and performance gains for small models.

MiRNAs, due to their role in gene regulation, have paved a new pathway for pharmacology, focusing on drug development that targets miRNAs. However, traditional wet lab experiments are limited by efficiency and cost constraints, making it difficult to extensively explore potential associations between developed drugs and target miRNAs. Therefore, we have designed a novel machine learning model based on a multi-layer transformer-based graph neural network, DMAGT, specifically for predicting associations between drugs and miRNAs. This model transforms drug-miRNA associations into graphs, employs Word2Vec for embedding features of drug molecular structures and miRNA base structures, and leverages a graph transformer model to learn from embedded features and relational structures, ultimately predicting associations between drugs and miRNAs. To evaluate DMAGT, we tested its performance on three datasets composed of drug-miRNA associations: ncDR, RNAInter, and SM2miR, achieving up to AUC of $95.24\pm0.05$. DMAGT demonstrated superior performance in comparative experiments tackling similar challenges. To validate its practical efficacy, we specifically focused on two drugs, namely 5-Fluorouracil and Oxaliplatin. Of the 20 potential drug-miRNA associations identified as the most likely, 14 were successfully validated. The above experiments demonstrate that DMAGT has an excellent performance and stability in predicting drug-miRNA associations, providing a new shortcut for miRNA drug development.

The Download: spotting crimes in prisoners' phone calls, and nominate an Innovator Under 35 A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on models for other states and counties. However, prisoner rights advocates say that the new AI system enables a system of invasive surveillance, and courts have specified few limits to this power. We have some exciting news: Nominations are now open for MIT Technology Review's 2026 Innovators Under 35 competition. This annual list recognizes 35 of the world's best young scientists and inventors, and our newsroom has produced it for more than two decades.

As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain vulnerable to forging and overwriting attacks. To address those challenges, we propose NeuralMark, a robust method built around a hashed watermark filter. Specifically, we utilize a hash function to generate an irreversible binary watermark from a secret key, which is then used as a filter to select the model parameters for embedding. This design cleverly intertwines the embedding parameters with the hashed watermark, providing a robust defense against both forging and overwriting attacks. An average pooling is also incorporated to resist fine-tuning and pruning attacks. Furthermore, it can be seamlessly integrated into various neural network architectures, ensuring broad applicability. Theoretically, we analyze its security boundary. Empirically, we verify its effectiveness and robustness across 13 distinct Convolutional and Transformer architectures, covering five image classification tasks and one text generation task. The source codes are available at https://github.com/AIResearch-Group/NeuralMark.