Universality, limits and predictability of gold-medal performances at the Olympic Games

Inspired by the Games held in ancient Greece, modern Olympics represent the world's largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics-including running, jumping, and throwing-and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games.Comment: 8 pages, 3 figures, 1 table. Supporting information files and data are available at filrad.homelinux.or

Machine Learning Approach to Forecast Average Weather Temperature of Bangladesh

Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies It is essential to predict the temperature of the weather for some time In this research paper we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season We used different machine learning algorithms to predict the future temperature of the Bangladesh region In the experiment we used machine learning algorithms such as Linear Regression Polynomial Regression Isotonic Regression and Support Vector Regressor Isotonic Regression algorithm predicts the training dataset most accurately but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accuratel

Sandspur, Vol 106 No 13, February 11, 2000

Rollins College student newspaper, written by the students and published at Rollins College. The Sandspur started as a literary journal.https://stars.library.ucf.edu/cfm-sandspur/1134/thumbnail.jp

Effects of common mutations in the SARS-CoV-2 Spike RBD and its ligand the human ACE2 receptor on binding affinity and kinetics

The interaction between the SARS-CoV-2 virus Spike protein receptor binding domain (RBD) and the ACE2 cell surface protein is required for viral infection of cells. Mutations in the RBD are present in SARS-CoV-2 variants of concern that have emerged independently worldwide. For example, the B.1.1.7 lineage has a mutation (N501Y) in its Spike RBD that enhances binding to ACE2. There are also ACE2 alleles in humans with mutations in the RBD binding site. Here we perform a detailed affinity and kinetics analysis of the effect of five common RBD mutations (K417N, K417T, N501Y, E484K, and S477N) and two common ACE2 mutations (S19P and K26R) on the RBD/ACE2 interaction. We analysed the effects of individual RBD mutations and combinations found in new SARS-CoV-2 Alpha (B.1.1.7), Beta (B.1.351), and Gamma (P1) variants. Most of these mutations increased the affinity of the RBD/ACE2 interaction. The exceptions were mutations K417N/T, which decreased the affinity. Taken together with other studies, our results suggest that the N501Y and S477N mutations enhance transmission primarily by enhancing binding, the K417N/T mutations facilitate immune escape, and the E484K mutation enhances binding and immune escape

Cryo-EM structure of the complete and ligand-saturated insulin receptor ectodomain

Glucose homeostasis and growth essentially depend on the hormone insulin engaging its receptor. Despite biochemical and structural advances, a fundamental contradiction has persisted in the current understanding of insulin ligand-receptor interactions. While biochemistry predicts two distinct insulin binding sites, 1 and 2, recent structural analyses have resolved only site 1. Using a combined approach of cryo-EM and atomistic molecular dynamics simulation, we present the structure of the entire dimeric insulin receptor ectodomain saturated with four insulin molecules. Complementing the previously described insulin-site 1 interaction, we present the first view of insulin bound to the discrete insulin receptor site 2. Insulin binding stabilizes the receptor ectodomain in a T-shaped conformation wherein the membrane-proximal domains converge and contact each other. These findings expand the current models of insulin binding to its receptor and of its regulation. In summary, we provide the structural basis for a comprehensive description of ligand-receptor interactions that ultimately will inform new approaches to structure-based drug design.Peer reviewe

Probing Amyloid-beta protein structure and dynamics with a selective antibody

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. The AD brain is characterized by significant neuronal loss and accumulation of insoluble fibrillar amyloid-β protein (Aβ) plaques and tau protein neurofibrillary tangles in the brain. However, over the last decade, many studies have shown that the neurodegenerative effect of Aβ may in fact be caused by various soluble oligomeric forms as opposed to the insoluble fibrils. Furthermore, the data suggest that a pre-fibrillar aggregated form, termed protofibrils, mediates direct neurotoxicity, and triggers a robust neuroinflammatory response. Antibodies targeting the various conformation of Aβ are important therapeutic agents to prevent the progression of AD. We have generated conformationally-selective monoclonal antibody St. Louis (mAbSL) that selectively targets Aβ42 protofibrils compared to Aβ42 monomers and fibrils. The development aspects of these antibodies include the cloning of HC and LC variable fragments into the plasmid vector, transfection of the plasmids into 293 F cells, collection of the supernatant and purification using Protein A or protein G affinity chromatography. Sequencing of the heavy and light chain variable regions for multiple antibodies identified sequence characteristics that may impart conformational selectivity to the antibodies. Thus, I have successfully developed, expressed, and characterized these conformationally selective antibodies using various ELISA formats. Exploration of Aβ42 aggregation in the presence a selective (mAbSL 113) and a non-selective antibody (mAb Ab 513) using spectroscopic and microscopic techniques is quintessential to looking at the effect of these antibodies on Aβ42 monomer aggregation and protofibril dynamics. It yielded a unique inhibitory mechanism on Aβ42 monomer aggregation offered by mAbSL antibodies. Aβ42 protofibril dynamics were prominently altered in the presence of mAbSL 113 with an insoluble complex formation by the antibody at low sub-stoichiometric molar ratios. We focused on accurately determining the conformational epitope of our developed antibodies on Aβ42 protofibrils. The conformational epitope on Aβ42 protofibril was detected using a monoclonal antibody in various experimental formats like antibody competition ELISA, HDX-MS, and FPOP analysis. Our findings demonstrated new insights into monoclonal antibodies that target AD progression