Modelling the mass accretion histories of dark matter haloes using a Gamma formalism

We present a physical model of the Mass Accretion Histories (MAH) of haloes in concordance with the {\it observed} cosmic star formation rate density (CSFRD). We model the MAHs of dark matter haloes using a Gamma ($\Gamma$) functional form: $M_h(T) = \frac{M_0}{f_{0}} \, \times \frac{\gamma(\alpha_h, ~\beta_h \times (T-Th))}{\Gamma(\alpha_h)}$, where $M_0$ is the halo mass at present time, $T$ is time, $\alpha_h$ and $\beta_h$ are parameters we explore, $f_{0}$ is the percentage of the mass of the halo at z = 0 with respect to the final mass of the halo achieved at $T = \infty$. We use the MAHs of haloes obtained from cosmological simulations and analytical models to constrain our model. $f_{0}$ can be described by a power-law ($f_{0} = 1- c \times M_{0}^{d}$). Haloes with small masses have already on average attained most of their final masses. The average of haloes in the Universe is $ > 0.95$ pointing to the direction that the cosmic MAH/CSFRD is saturated at our era. The average parameter (the depletion rate of the available dark matter for halo growth) is related to the dynamical timescales of haloes. The $\alpha$ parameter is a power-law index of $M_{0}$ and represents the early growth a halo experiences before the expansion of the Universe starts to slow it down. Finally, $T_{h}$ (the time that marks the co-evolution/growth of galaxies and haloes after the Big Bang) is found to be 150-300 million years.Comment: 18 pages, 8 figures, Accepted at MNRA

Sharing and Preserving Computational Analyses for Posterity with encapsulator

Open data and open-source software may be part of the solution to science's "reproducibility crisis", but they are insufficient to guarantee reproducibility. Requiring minimal end-user expertise, encapsulator creates a "time capsule" with reproducible code in a self-contained computational environment. encapsulator provides end-users with a fully-featured desktop environment for reproducible research.Comment: 11 pages, 6 figure

A Phylogenomic Approach to Vertebrate Phylogeny Supports a Turtle-Archosaur Affinity and a Possible Paraphyletic Lissamphibia

In resolving the vertebrate tree of life, two fundamental questions remain: 1) what is the phylogenetic position of turtles within amniotes, and 2) what are the relationships between the three major lissamphibian (extant amphibian) groups? These relationships have historically been difficult to resolve, with five different hypotheses proposed for turtle placement, and four proposed branching patterns within Lissamphibia. We compiled a large cDNA/EST dataset for vertebrates (75 genes for 129 taxa) to address these outstanding questions. Gene-specific phylogenetic analyses revealed a great deal of variation in preferred topology, resulting in topologically ambiguous conclusions from the combined dataset. Due to consistent preferences for the same divergent topologies across genes, we suspected systematic phylogenetic error as a cause of some variation. Accordingly, we developed and tested a novel statistical method that identifies sites that have a high probability of containing biased signal for a specific phylogenetic relationship. After removing putatively biased sites, support emerged for a sister relationship between turtles and either crocodilians or archosaurs, as well as for a caecilian-salamander sister relationship within Lissamphibia, with Lissamphibia potentially paraphyletic.Organismic and Evolutionary Biolog

Utilizing Bibliometrics to Understand the Role of Machine Learning in the Current Orthopedic Arthroplasty Literature

Background: Machine learning technology has been demonstrated to be a very useful tool in current orthopedic research. Furthermore, machine learning has shown to be quite impactful in the field of arthroplasty solving many clinical and scientific problems, leading to greater utilization in retrospective studies. This current study aims to identify machine learning arthroplasty research and predict future hotspots. We hypothesize that the production of current scientific literature on machine learning will be produced by US-based national institutions and will have exponentially grown in the past 5 years. Methods: Machine learning arthroplasty publications between 1996 and 2023 were identified using the Web of Science Core Collection of Clarivate Analytics. Then bibliometric indicators were obtained and imported for further analysis with VOSviewer and Bibliometreix to identify previous and ongoing trends within this field. Results: The bibliometric sourcing identified a total of 235 documents that were associated with machine learning applications to arthroplasty. 34 countries published articles on the topic and the United States demonstrated to be the largest contributor. The year 2022 had the highest number of publications produced in a year, totaling 66 articles. A total of 405 institutions across the world had published articles, the most relevant institutions with the highest production were Harvard University and Harvard Medical School with 41 and 34 articles produced respectively. Kwon YM was the most productive author while Haeberle HS and Ramkumar PN are the most impactful based on h-index. Co-occurrence visualization and thematic map identified niche and major themes within the literature. Conclusions: Machine learning in arthroplasty research continues to show an increasing trend since 2021 with contributions from authors and institutions globally. United States institutions and authors are the leading contributors to machine learning applications in arthroplasty research. This study identifies previous, current, and developing trends within this field for future hotspot development

Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19

We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions

PSAT: A web tool to compare genomic neighborhoods of multiple prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved. Although some tools are available for visualizing and comparing the order of genes between genomes of study, few support an efficient and organized analysis between large numbers of genomes. The Prokaryotic Sequence homology Analysis Tool (PSAT) is a web tool for comparing gene neighborhoods among multiple prokaryotic genomes.</p> <p>Results</p> <p>PSAT utilizes a database that is preloaded with gene annotation, BLAST hit results, and gene-clustering scores designed to help identify regions of conserved gene order. Researchers use the PSAT web interface to find a gene of interest in a reference genome and efficiently retrieve the sequence homologs found in other bacterial genomes. The tool generates a graphic of the genomic neighborhood surrounding the selected gene and the corresponding regions for its homologs in each comparison genome. Homologs in each region are color coded to assist users with analyzing gene order among various genomes. In contrast to common comparative analysis methods that filter sequence homolog data based on alignment score cutoffs, PSAT leverages gene context information for homologs, including those with weak alignment scores, enabling a more sensitive analysis. Features for constraining or ordering results are designed to help researchers browse results from large numbers of comparison genomes in an organized manner. PSAT has been demonstrated to be useful for helping to identify gene orthologs and potential functional gene clusters, and detecting genome modifications that may result in loss of function.</p> <p>Conclusion</p> <p>PSAT allows researchers to investigate the order of genes within local genomic neighborhoods of multiple genomes. A PSAT web server for public use is available for performing analyses on a growing set of reference genomes through any web browser with no client side software setup or installation required. Source code is freely available to researchers interested in setting up a local version of PSAT for analysis of genomes not available through the public server. Access to the public web server and instructions for obtaining source code can be found at <url>http://www.nwrce.org/psat</url>.</p

Physical evolution of dark matter halo around the depletion boundary

We investigate the build-up of the halo profile out to large scale in a cosmological simulation, focusing on the roles played by the recently proposed depletion radii. We explicitly show that halo growth is accompanied by the depletion of the environment, with the inner depletion radius demarcating the two. This evolution process is also observed via the formation of a trough in the bias profile, with the two depletion radii identifying key scales in the evolution. The ratio between the inner depletion radius and the virial radius is approximately a constant factor of 2 across redshifts and halo masses. The ratio between their enclosed densities is also close to a constant of 0.18. These simple scaling relations reflect the largely universal scaled mass profile on these scales, which only evolves weakly with redshift. The overall picture of the boundary evolution can be broadly divided into three stages according to the maturity of the depletion process, with cluster halos lagging behind low mass ones in the evolution. We also show that the traditional slow and fast accretion dichotomy of halo growth can be identified as accelerated and decelerated depletion phases respectively.Comment: 14 pages, 10 figures, accepted by Ap

Bridging the Gap in Medication Access

https://digitalcommons.pcom.edu/bridging_gaps2016/1000/thumbnail.jp