Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog – Page 116

Get the latest international news and world events from around the world.

Log in for authorized contributors

Clinically informed AI outperforms foundation models in spinal cord disease prediction

Cervical spondylotic myelopathy (CSM) refers to spinal cord compression from arthritis in the neck and is the leading cause of spinal cord dysfunction in older adults. CSM is a chronic, progressive condition that can cause neck pain, muscle weakness, difficulty walking and other debilitating symptoms. While the diagnosis is sometimes clear, often the diagnosis can take years because symptoms aren’t recognized until the later stages, and by then, treatment options are limited.

A multidisciplinary team of surgeon-scientists, computer scientists and researchers at WashU developed an artificial intelligence (AI)-based approach that could help clinicians screen for and diagnose CSM up to 30 months earlier, opening new opportunities for earlier treatment. The findings are published in npj Digital Medicine.

Salim Yakdan, MD, a postdoctoral research fellow in the Taylor Family Department of Neurosurgery at WashU Medicine, and Ben Warner, a doctoral student in computer science and engineering at the McKelvey School of Engineering, co-first authors on the research, used seven different AI models to analyze large datasets containing electronic health record data of more than 2 million people with and without CSM. The models examined patterns of health-care interactions, such as tests and diagnoses, recorded in electronic health records to spot patients whose medical histories resemble those already diagnosed with CSM, helping to flag individuals who may be at higher risk.

Two lipids that help switch on STING open doors in fight against autoimmune disorders and cancer

UT Southwestern Medical Center researchers have identified two lipids that work together with a quintessential protein known as stimulator of interferon genes (STING) to launch an immune response in the human body. Their findings, detailed in two papers published concurrently in Nature, could lead to new ways to manipulate the immune system to fight infections, cancer, autoimmune disorders, and neurodegenerative diseases.

“These studies reveal additional levels of regulation of the cGAS-STING pathway, underscoring the importance of controlling the activity of this pathway so the body can mount an effective immune response against infections while avoiding autoimmune reactions to self-tissues. Dysregulation of this pathway has been shown to cause a variety of autoimmune and inflammatory diseases,” said Zhijian “James” Chen, Ph.D., Professor of Molecular Biology and Director of the Center for Inflammation Research at UT Southwestern.

Dr. Chen, one of the world’s leading researchers on innate immunity, is a co-author on one study and senior author on the other. His discovery of cGAS, an enzyme that produces a molecule called cGAMP to activate STING, has been recognized with numerous top honors including the 2026 Japan Prize in Life Sciences, the 2024 Albert Lasker Basic Medical Research Award, and the 2019 Breakthrough Prize in Life Sciences.

Microplastic presence in dog and human testis and its potential association with sperm count and weights of testis and epididymis

The ubiquitous existence of microplastics and nanoplastics raises concerns about their potential impact on the human reproductive system. Limited data exists on microplastics within the human reproductive system and their potential consequences on sperm quality. Our objectives were to quantify and characterize the prevalence and composition of microplastics within both canine and human testes and investigate potential associations with the sperm count, and weights of testis and epididymis. Using advanced sensitive pyrolysis-gas chromatography/mass spectrometry, we quantified 12 types of microplastics within 47 canine and 23 human testes. Data on reproductive organ weights, and sperm count in dogs were collected.

Algal Swimming Patterns Change with Light Intensity

In response to changes in illumination, a swimming microorganism reverses the direction of its circular trajectory by tilting its flagella’s planes of motion.

Many microorganisms adjust their swimming trajectories in response to environmental signals such as nutrients or light. Researchers have now discovered a new mode of such behavior in a species of green algae [1]. The microbes swim in wide circles when illuminated and switch from counterclockwise (CCW) to clockwise (CW) swimming when the light intensity is above a threshold value. The researchers determined how this change is generated by the algae’s two whip-like flagella. They say that the results reveal a new navigation strategy that microorganisms can use to find optimal environments.

The single-celled green alga Chlamydomonas reinhardtii is photosynthetic and moves toward light by beating its two flagella, situated close together on its front surface, in a breaststroke pattern. In 2021, Kirsty Wan and Dario Cortese of the University of Exeter in the UK figured out the beating pattern that produces the microbe’s typical corkscrew-shaped trajectory, which follows a tight helix [2]. They showed how changing the frequency, amplitude, and synchronization of the flagellar beating allows the cell to change the overall direction of motion, perhaps to steer it toward or away from a light source and optimize the intensity of light it receives.

Viewing Neural Networks Through a Statistical-Physics Lens

Statistical physics is shedding light on how network architecture and data structure shape the effectiveness of neural-network learning.

Machine-learning technologies have profoundly reshaped many technical fields, with sweeping applications in medical diagnosis, customer service, drug discovery, and beyond. Central to this transformation are neural networks (NNs), models that learn patterns from data by combining many simple computational units, or neurons, linked by weighted connections. Acting collectively, these neurons can process data to learn complex input–output relationships. Despite their practical success, the fundamental mechanisms by which NNs learn remain poorly understood at a theoretical level. Statistical physics offers a promising framework for exploring central questions in machine-learning theory, potentially clarifying how learning depends on the layout of the network—the NN architecture—and on statistics of the data—the data structure (Fig. 1).

Three recent papers in a special Physical Review E collection (See Collection: Statistical Physics Meets Machine Learning — Machine Learning Meets Statistical Physics) provide significant insights into these questions. Francesca Mignacco of City University of New York and Princeton University and Francesco Mori of the University of Oxford in the UK derived analytical results on the optimal fraction of neurons that should be active at a given time [1]. Abdulkadir Canatar and SueYeon Chung of the Flatiron Institute in New York and New York University investigated the influence of the precision with which a network is “trained” on the amount of data the NN can reliably decode [2]. Francesco Cagnetta at the International School for Advanced Studies in Italy and colleagues showed that NNs whose structure mirrors that of the data learn faster [3].

How many bee species exist? New global count puts the total near 26,000

The world has far more bees than anyone realized. Scientists have, for the first time, estimated just how many species of bees are out there on a global scale, offering a clearer look at how these vital pollinators are distributed around the planet. The landmark study, led by University of Wollongong (UOW) evolutionary biologist Dr. James Dorey, provides the most comprehensive count to date—broken down by continent and country—calculating there are, at a minimum, between 3,700 and 5,200 more bee species buzzing around the world than currently recognized.

The research, outlined in a new paper published Tuesday, February 24, in Nature Communications, lifts global estimates to between 24,705 and 26,164 bee species and reveals a richer and more complex picture of the world’s bees than ever before. The findings highlight how many bee species remain unclassified or overlooked, showing that even our much-loved pollinators are not fully understood, and that closing these knowledge gaps is crucial for conservation and food security.

“Knowing how many species exist in a place, or within a group like bees, really matters. It shapes how we approach conservation, land management, and even big-picture science questions about evolution and ecosystems,” Dr. Dorey said. “Bees are a perfect example. They’re keystone species; their diversity underpins healthy environments and resilient agriculture. If we don’t understand how many bee species there are, we’re missing a key part of the puzzle for protecting both nature and farming.”

/* */