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In a recent study published in Nature Communications, a team of researchers at the Carl R. Woese Institute for Genomic Biology reports a new, robust computational toolset to extract biological relationships from large transcriptomics datasets. These efforts will help scientists better investigate cellular processes.

Living organisms are governed by their genome—an instruction manual written in the language of DNA that dictates how an organism grows, survives, and reproduces. By regulating the abundance of different RNA transcripts, cells control their protein expression level, thereby shaping their functions and responses to the environment.

Transcriptomics is the study of gene expression through cataloging the presence and abundance levels of active RNA transcripts generated from the genome under different conditions. Through the lens of RNA, transcriptomics technologies allow scientists to study the that enable life and cause disease, as well as assess the biological effects of therapeutics.

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Unlocking Real-Time Inflammation Monitoring with Active-Reset Protein Sensors.

Imagine a tiny device that could continuously monitor your body’s inflammation levels, offering insights in real time to help manage diseases. While sensors for small molecules like glucose have existed for years, tracking proteins—a critical component in understanding inflammation—has been a major challenge. Proteins are present at much lower levels in the body, and traditional sensors struggle with slow response times due to their high-affinity binding to these molecules.

A team led by Zargartalebi has now overcome this barrier by introducing active-reset protein sensors. These sensors employ high-frequency oscillations of positive voltage to rapidly release bound protein molecules from their sensing electrodes. This breakthrough allows the sensors to reset in under a minute, enabling continuous tracking of protein levels.

The researchers incorporated these sensors into a microneedle device, which was successfully tested in mice to monitor cytokines—key markers of inflammation associated with diabetes. Unlike previous approaches, this method is not only simple to implement but can also be adapted to various sensor designs, potentially revolutionizing how we monitor and respond to conditions like chronic inflammation.

By bringing real-time protein monitoring closer to reality, active-reset sensors could pave the way for more dynamic and responsive healthcare, ensuring better disease management and prevention.

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Ingestible devices are often used to study and treat hard-to-reach tissues in the body. Swallowed in pill form, these capsules can pass through the digestive tract, snapping photos or delivering drugs.

While in their simplest form, these devices are passively transported through the gut, there are a wide range of applications where you may want a device to attach to the tissue or other flexible materials. A rich history of biologically inspired solutions exist to address this need, ranging from cocklebur-inspired Velcro to slug-inspired medical adhesives, but the creation of on-demand and reversible attachment mechanisms that can be incorporated into millimeter-scale devices for biomedical sensing and diagnostics remains a challenge.

A new interdisciplinary effort led by Robert Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and James Weaver, of Harvard’s Wyss Institute, has drawn inspiration from an unexpected source: the world of parasites.

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Dear Colleagues.

A good number of insect pests are known for harboring various bacterial symbionts. Herbivore-associated bacterial symbionts such as Wolbachia and Spiroplasma are widespread in diverse arthropods and mostly act as reproductive parasites. Both have been reported to significantly improve reproductive performance of various insects, including spider mites, aphids, Drosophila, etc. These insect-associated symbionts influence herbivore fitness, growth, and development, as well as interfere with plant defenses by changing plant physiology. Both Wolbachia and Spiroplasma are maternally inherited endosymbionts in arthropods and are able to co-exist and infect the same host. Understanding these complex interactions is very important for the development of an effective insect pest management program. We look forward to receiving your contributions to this Special Issue in the form of original research papers and review articles focusing on, but not limited to, the latest research on the occurrence, genetic diversity, and physiological functions of Wolbachia and/or Spiroplasma symbionts in various primary hosts. Although our focus will be on these two major facultative insect symbionts, articles reporting work carried out on other bacterial species are also welcome.

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Proteins are so much more than nutrients in food. Virtually every reaction in the body that makes life possible involves this large group of molecules. And when things go wrong in our health, proteins are usually part of the problem.

In certain types of heart disease, for instance, the proteins in cardiac tissue, seen with , are visibly disordered. Alex Dunn, professor of chemical engineering, describes proteins like the beams of a house: “We can see that in unhealthy heart muscle cells, all of those beams are out of place.”

Proteins are the workhorses of the cell, making the biochemical processes of life possible. These workhorses include enzymes, which bind to other molecules to speed up reactions, and antibodies that attach to viruses and prevent them from infecting cells.

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Professor Carlos Duarte, Ph.D. is Distinguished Professor, Marine Science, and Executive Director, Coral Research \& Development Accelerator Platform (CORDAP — https://cordap.org/), Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST — https://www.kaust.edu.sa/en/study/fac…), in Saudi Arabia, as well as Chief Scientist of Oceans2050, OceanUS, and E1Series.

Prior to these roles Professor Duarte was Research Professor with the Spanish National Research Council (CSIC) and Director of the Oceans Institute at The University of Western Australia. He also holds honorary positions at the Arctic Research Center in Aarhus University, Denmark and the Oceans Institute at The University of Western Australia.

Professor Duarte’s research focuses on understanding the effects of global change in marine ecosystems and developing nature-based solutions to global challenges, including climate change, and developing evidence-based strategies to rebuild the abundance of marine life by 2050.

Building on his research showing mangroves, seagrasses and salt-marshes to be globally-relevant carbon sinks, Professor Duarte developed, working with different UN agencies, the concept of Blue Carbon, as a nature-based solution to climate change, which has catalyzed their global conservation and restoration.

Continue reading “Prof. Carlos Duarte, Ph.D. — Executive Director, Coral Research & Development Accelerator Platform” | >

Accurately forecasting weather remains a complex challenge due to the inherent uncertainty in atmospheric dynamics and the nonlinear nature of weather systems. As such, methodologies developed ought to reflect the most probable and potential outcomes, especially in high-stakes decision-making over disasters, energy management, and public safety. While numerical weather prediction (NWP) models offer probabilistic insights through ensemble forecasting, they are computationally expensive and prone to errors. Although ML models have been very promising in giving faster and more accurate predictions, they fail to represent forecast uncertainty, especially in extreme events. This makes ML-based models less useful in actual real-world applications.

The physics-based ensemble models, for example, the ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), rely on these simulations to produce probabilistic forecasts. These models properly represent the forecast distributions and joint spatiotemporal dependencies and require high computational resources and manual engineering. Conversely, the ML-based method, like GraphCast or FourCastNet, focuses only on deterministic forecasts and will minimize the errors in the mean outcome without considering any uncertainty. None of the attempts to generate probabilistic ensembles by MLWP produced realistic samples or competed with the accuracy of operational ensemble forecasts. Hybrid approaches like NeuralGCM embed ML-based parameterizations within traditional frameworks but have poor resolution and limited performance.

Researchers from Google DeepMind released GenCast, a probabilistic weather forecasting model that generates accurate and efficient ensemble forecasts. This machine learning model applies conditional diffusion models to produce stochastic trajectories of weather, such that the ensembles consist of the entire probability distribution of atmospheric conditions. In systematic ways, it creates forecast trajectories by using the prior states through autoregressive sampling and uses a denoising neural network, which is integrated with a graph-transformer processor on a refined icosahedral mesh. Utilizing 40 years of ERA5 reanalysis data, GenCast captures a rich set of weather patterns and provides high performance. This feature allows it to generate a 15-day global forecast at 0.25° resolution within 8 minutes, which is state-of-the-art ENS in terms of both skill and speed.

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