Lifeboat Foundation

Since 2014, the ALS Ice Bucket Challenge has inspired more than 17 million people to raise $115 million for The ALS Association, which has funded over 500 research projects with the money. Because of that boost, the first drug to treat ALS has been approved by the FDA, other new treatments are in testing, and scientists have been able to identify several genes that are connected to the disease.

While mutations in a gene called NEK1 have only been associated with around two percent of ALS cases, it is one of the primary genetic causes of ALS that have been revealed so far. Now investigators have learned more about how NEK1 mutations can lead to ALS, a disease in which the motor neurons that control movement degenerate and die, which causes paralysis and eventually, death. The work has been reported in Science Advances.

Researchers at the National Heart, Lung, and Blood Institute at NIH, Bethesda, have discovered a potential breakthrough for people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), marked by extreme exhaustion, post-exertional malaise and cognitive issues.

In a paper, “WASF3 disrupts mitochondrial respiration and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome,” published in PNAS, the team details the influence of increased WASF3 proteins on the assembly of mitochondrial proteins, hampering energy production.

The study focused on a woman (S1) who experienced severe long-term fatigue. Measuring her muscles for phosphocreatine regeneration after exercise revealed a significant delay in mitochondrial ATP synthesis capacity. This discovery was followed up with a cell assay which found increased phospho-activation of an enzyme in a signaling pathway (MPAK).

A recent study published in Nature Machine Intelligence examines a novel deep-learning method known as BigMHC, which can predict when the immune system will respond to triggers from cancer-related protein fragments, thus killing the tumors. This study was led and conducted by a team of researchers at Johns Hopkins University and holds the potential to develop personalized cancer immunotherapies and vaccines.

Rendition of cytotoxic CD8+ T-cells identifying cancer cells via receptor binding neoantigens. (Credit: Image generated by DALL-E 2 from OpenAI)

“Cancer immunotherapy is designed to activate a patient’s immune system to destroy cancer cells,” said Dr. Rachel Karchin, who is a professor of biomedical engineering, oncology and computer science at Johns Hopkins University, and a co-author on the study. “A critical step in the process is immune system recognition of cancer cells through T-cell binding to cancer-specific protein fragments on the cell surface.”

A new study led by University of Maryland physicists sheds light on the cellular processes that regulate genes. Published in the journal Science Advances, the paper explains how the dynamics of a polymer called chromatin—the structure into which DNA is packaged—regulate gene expression.

Through the use of machine learning and statistical algorithms, a research team led by Physics Professor Arpita Upadhyaya and National Institutes of Health Senior Investigator Gordon Hager discovered that chromatin can switch between a lower and higher mobility state within seconds. The team found that the extent to which chromatin moves inside cells is an overlooked but important process, with the lower mobility state being linked to gene expression.

Notably, transcription factors (TFs)—proteins that bind specific DNA sequences within the chromatin polymer and turn genes on or off—exhibit the same mobility as that of the piece of chromatin they are bound to. In their study, the researchers analyzed a group of TFs called nuclear receptors, which are targeted by drugs that treat a variety of diseases and conditions.

We’ve explored bioelectricity in cells. We’ve looked at bioelectricity within the human body. Now, functional use of “electrical engineering” is being found in the realms between.

Physicists learn about electrostatics, the laws governing stationary charges. Then they learn about electrodynamics, the laws governing moving charges. Biologists are finding that life utilizes both systems of laws at all scales, from within the cell to tissues, organs, and entire organisms. Here are some recent discoveries in the emerging science of bioelectricity.

How does that tick jump from its twig onto your clothing as you walk through brush? The answer, says Current Biology, is by hopping on an electrostatic bullet train. A cow or other host animal walking through the bushes carries a net static charge. The tick, regardless of its own charge polarity, is “pulled by these electric fields across air gaps of several body lengths.”

Researchers have determined a new feature of how the natural ends of our chromosomes are protected from harmful outcomes.

In a new study, University of Michigan researchers looked at how the DNA damage recognition process seems to know the difference between harmful DNA breaks that need repair versus the natural ends of chromosomes, called telomeres, that need to be left alone.

“If possible, you repair it, and if you can’t repair it, then the cell dies. You don’t want to keep dividing with broken DNA. That’s what happens in a normal cell, and that’s a good thing,” said Jayakrishnan Nandakumar, a professor of molecular, cellular and developmental biology.

O.o!!!!!

In the last 28-day period (July 17 to August 13), over 1.4 million new COVID-19 cases and over 2,300 deaths were reported from the World Health Organization’s (WHO) six regions, an increase of 63% and a decrease of 56%, respectively, compared to the previous 28 days, noted the latest WHO report.

As of August 13, over 769 million confirmed cases and over 6.9 million deaths have been reported globally. While four WHO regions have reported decreases in the number of both cases and deaths, the Western Pacific Region has reported an increase in cases and a decrease in deaths.

Continue reading “WHO: 63% increase recorded in number of COVID-19 cases worldwide in last 28 days” »

Google DeepMind researchers have finally found a way to make life coaching even worse: infuse it with generative AI.

According to internal documents obtained by The New York Times reports, Google and the Google-owned DeepMind AI lab are working with “generative AI to perform at least 21 different types of personal and professional tasks.” And among those tasks, apparently, is an effort to use generative AI to build a “life advice” tool. You know, because an inhuman AI model knows everything there is to know about navigating the complexities of mortal human existence.

As the NYT points out, the news of the effort notably comes months after AI safety experts at Google said, back in just December, that users of AI systems could suffer “diminished health and well-being” and a “loss of agency” as the result of taking AI-spun life advice. The Google chatbot Bard, meanwhile, is barred from providing legal, financial, or medical advice to its users.

Recent advancements in deep learning have significantly impacted computational imaging, microscopy, and holography-related fields. These technologies have applications in diverse areas, such as biomedical imaging, sensing, diagnostics, and 3D displays. Deep learning models have demonstrated remarkable flexibility and effectiveness in tasks like image translation, enhancement, super-resolution, denoising, and virtual staining. They have been successfully applied across various imaging modalities, including bright-field and fluorescence microscopy; deep learning’s integration is reshaping our understanding and capabilities in visualizing the intricate world at microscopic scales.

In computational imaging, prevailing techniques predominantly employ supervised learning models, necessitating substantial datasets with annotations or ground-truth experimental images. These models often rely on labeled training data acquired through various methods, such as classical algorithms or registered image pairs from different imaging modalities. However, these approaches have limitations, including the laborious acquisition, alignment, and preprocessing of training images and the potential introduction of inference bias. Despite efforts to address these challenges through unsupervised and self-supervised learning, the dependence on experimental measurements or sample labels persists. While some attempts have used labeled simulated data for training, accurately representing experimental sample distributions remains complex and requires prior knowledge of sample features and imaging setups.

To address these inherent issues, researchers from the UCLA Samueli School of Engineering introduced an innovative approach named GedankenNet, which, on the other hand, presents a revolutionary self-supervised learning framework. This approach eliminates the need for labeled or experimental training data and any resemblance to real-world samples. By training based on physics consistency and artificial random images, GedankenNet overcomes the challenges posed by existing methods. It establishes a new paradigm in hologram reconstruction, offering a promising solution to the limitations of supervised learning approaches commonly utilized in various microscopy, holography, and computational imaging tasks.