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A research team develops disinfection robot combining physical wiping and UV-C sterilization.
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Author: WeWorldMedia-admin
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Researchers have outlined a bold new roadmap for harnessing heterogeneous catalysis to destroy per- and polyfluoroalkyl substances (PFAS), the so-called ‘forever chemicals’ that have contaminated water supplies worldwide.
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The state of Tennessee and FEMA have awarded nearly $2 million for emergency and permanent work to repair bridges, restore utilities, remove debris and take measures to protect 14 Eastern Tennessee counties affected by Tropical Storm Helene.
The major presidential declaration designated Carter, Claiborne, Cocke, Grainger, Greene, Hamblen, Hancock, Hawkins, Jefferson, Johnson, Sevier, Sullivan, Unicoi and Washington counties for FEMA Public Assistance, a program that helps communities as they respond to declared disasters or emergencies.
In Cocke County, the City of Newport cleared more than 4,726 cubic yards of sand, soil and mud from streets and sidewalks after the storm. The city also contracted to have 4,156 cubic yards of vegetative debris and 570 cubic yards of mud, soil and sand removed from the public rights-of-way.
FEMA’s share for this project is $83,731; the nonfederal share is $27,910.
The Public Assistance program reimburses communities for emergency work to save lives and protect property; remove debris; and repair roads, bridges, public buildings, utilities and parks. Because this is a cost-sharing program, FEMA reimburses state applicants 75% of eligible costs. The remaining 25% represents nonfederal funds.
Below is a list of community projects, the total dollars awarded, and the category of Public Assistance funding:
Carter County: First Utility District $4,966 for emergency protective measures.
Hawkins County: Emergency Communications District $4,632 for emergency protective measures.
Johnson County: $624,640 for Morefield Bridge repairs; $617,344 for Furnace Creek Bridge
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A new computational tool developed with support from the U.S. National Science Foundation could greatly speed up determining the 3D structure of RNAs, a critical step in developing new RNA-based drugs, identifying drug-binding sites and using RNAs in other biotechnology and biomedicine applications.
The tool, NuFold, leverages state-of-the-art machine learning techniques to predict the structure of a wide variety of RNA molecules from their sequences. This new capability will allow researchers to visualize what a given RNA structure could look like based on its sequence and identify its potential use in drug delivery, disease treatment and other applications. The research leading to NuFold was published in Nature Communications.
RNAs are critical biological molecules — encoding information, like DNA, and performing cellular functions, like proteins — but relatively few RNA structures have been determined through experimentation thus far, which severely limits understanding of their functions. For example, RNAs in the NSF-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) represent only about 3% of total entries. Experimentally determining RNA structures is often time-consuming and costly. By providing a path to reliably predicting RNA structure from sequence, NuFold could greatly expedite the discovery of RNA function and enable quicker development of RNA-based therapeutics and technologies.