![Distribution of prophages in the <i>Streptococcus</i> bacteria genus and their role in increasing host pathogenicity](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBdGdNIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--f5aa3b6d37a485fbbb3ca5c4d15650a05e26baa9/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJYW5CbkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--a3b53ba1a0f83efef18f6e75a8d4ce784384bee2/cdc-QEU-QgIOJKA-unsplash.jpg)
The authors investigated prophages present in Streptococcus bacteria that may increase their survival in different environments.
Read More...Distribution of prophages in the Streptococcus bacteria genus and their role in increasing host pathogenicity
The authors investigated prophages present in Streptococcus bacteria that may increase their survival in different environments.
Read More...Recognition of animal body parts via supervised learning
The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.
Read More...Deciphering correlation and causation in risk factors for heart disease with Mendelian randomization
Here, seeking to identify the risk of coronary artery disease (CAD), a major cause of cardiovascular disease, the authors used Mendelian randomization. With this method they identified several traits such as blood pressure readings, LDL cholesterol and BMI as significant risk factors. While other traits were not found to be significant risk factors.
Read More...TGFβ1 Codon 10 Polymorphism and its Association with the Prevalence of Low Myopia
The goal of this project was to assess the relationships among low myopia, behavioral and demographic factors, and a single-nucleotide polymorphism (SNP) in the TGFβ1 gene.
Read More...Innovative Treatment for Reducing Senescence and Revitalizing Aging Cells through Gene Silencing
Cellular senescence plays a key role in aging cells and is attributed to a number of disease and pathology. These authors find that genetic editing of both RPS6KB1 and PPARGC1A revitalizes a human skin fibroblast cell line.
Read More...Pruning replay buffer for efficient training of deep reinforcement learning
Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.
Read More...Quantitative analysis and development of alopecia areata classification frameworks
This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.
Read More...The Impacts of Varying Types of Light on the Growth of Five Arabidopsis Varieties
Arabadopsis, “the fruit fly of plants”, is an easy to grow plant system for genetic manipulation. Here, researchers tested the effects of varied light conditions on plants with specific mutations in the light sensing pathways.
Read More...Artificial intelligence assisted violin performance learning
In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.
Read More...Utilizing a Wastewater-Based Medium for Engineered Saccharomyces cerevisiae for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals
Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.
Read More...Search articles by title, author name, or tags