Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast, accurate local ancestry inference with FLARE

Local ancestry is the source ancestry at each point in the genome of an admixed individual. Inferred local ancestry is used for admixture mapping and population genetic analyses. We present FLARE (Fast Local Ancestry Estimation), a new method for local ancestry inference. FLARE achieves high accuracy through the use of an extended Li and Stephens model, and it achieves exceptional computational performance through incorporation of computational techniques developed for genotype imputation. Memory requirements are reduced through on-the-fly compression of reference haplotypes and stored checkpoints. Computation time is reduced through the use of composite reference haplotypes. These techniques allow FLARE to scale to data sets with hundreds of thousands of sequenced individuals and to provide superior accuracy on large-scale data. FLARE is freely available at https://github.com/browning-lab/flare.
Confirming the Pro-longevity Effects of H3K4me3-deficient set-2 Mutants in Extending Lifespan in C. elegans

The COMPASS chromatin complex, which trimethylates lysine 4 on histone H3 (H3K4me3), regulates lifespan in Caenorhabditis elegans. Knockdown or partial loss-of-function of SET-2, a member of the COMPASS complex, extends the lifespan of worms. However, recent observations suggested that full loss of SET-2 methyltransferase activity via deletion of its active site reduces lifespan, indicating the degree of COMPASS inhibition may be critical to its effects on aging. To further explore these inconsistencies, we examined set-2 longevity across a range of interventions from weak to full inhibition. Via CRISPR/Cas9 genome-editing, we made two new set-2 mutants, a new genocopy of the most commonly used set-2(ok952) allele for mild set-2 inhibition, and a full set-2 genomic deletion. We found that both new strains (partial and null) and RNAi show a lifespan extension in C. elegans fed HT115 E. coli. However, neither mutant was long-lived in C. elegans fed OP50-1 E. coli. These data confirm that the previous lifespan extension observed in set-2(ok952) mutants was indeed the result of set-2 inhibition (and not a secondary linked mutation generated in the original strain). These data also indicate that a diet-dependent mechanism might contribute to the regulation of lifespan under H3K4me3 deficiency and highlight how COMPASS-mediated longevity involves a complex interaction between chromatin state and environment.
Pleiotropy promotes the evolution of inducible immune responses in a model of host-pathogen coevolution

Components of immune systems face significant selective pressure to efficiently use organismal resources, mitigate infection, and resist parasitic manipulation. A theoretically optimal immune defense balances investment in constitutive and inducible immune components depending on the kinds of parasites encountered, but genetic and dynamic constraints can force deviation away from theoretical optima. One such potential constraint is pleiotropy, the phenomenon where a single gene affects multiple phenotypes. Although pleiotropy can prevent or dramatically slow adaptive evolution, it is prevalent in the signaling networks that compose metazoan immune systems. We hypothesized that pleiotropy is maintained in immune signaling networks despite slowed adaptive evolution because it provides some other advantage, such as forcing network evolution to compensate in ways that increase host fitness during infection. To study the effects of pleiotropy on the evolution of immune signaling networks, we used an agent-based modeling approach to evolve a population of host immune systems infected by simultaneously co-evolving parasites. Four kinds of pleiotropic restrictions on evolvability were incorporated into the networks, and their evolutionary outcomes were compared to, and competed against, non-pleiotropic networks. As the networks evolved, we tracked several metrics of immune network complexity, relative investment in inducible and constitutive defenses, and features associated with the winners and losers of competitive simulations. Our results suggest non-pleiotropic networks evolve to deploy highly constitutive immune responses regardless of parasite prevalence, but some implementations of pleiotropy favor the evolution of highly inducible immunity. These inducible pleiotropic networks are no less fit than non-pleiotropic networks and can out-compete non-pleiotropic networks in competitive simulations. These provide a theoretical explanation for the prevalence of pleiotropic genes in immune systems and highlight a mechanism that could facilitate the evolution of inducible immune responses.
RELIEF: A structured multivariate approach for removal of latent inter-scanner effects

Combining data collected from multiple study sites is becoming common and is advantageous to researchers to increase the generalizability and replicability of scientific discoveries. However, at the same time, unwanted inter-scanner biases are commonly observed across neuroimaging data collected from multiple study sites or scanners, rendering difficulties in integrating such data to obtain reliable findings. While several methods for handling such unwanted variations have been proposed, most of them use univariate approaches that could be too simple to capture all sources of scanner-specific variations. To address these challenges, we propose a novel multivariate harmonization method, called RELIEF (REmoval of Latent Inter-scanner Effects through Factorization) for estimating and removing both explicit and latent scanner effects. Our method is the first approach to introduce the simultaneous dimension reduction and factorization of interlinked matrices to a data harmonization context, which provides a new direction in methodological research for correcting inter-scanner biases. Analyzing diffusion tensor imaging (DTI) data from the Social Processes Initiative in Neurobiology of the Schizophrenia (SPINS) study and conducting extensive simulation studies, we show that RELIEF outperforms existing harmonization methods in mitigating inter-scanner biases and retaining biological associations of interest to increase statistical power. RELIEF is publicly available as an R package.
Rhometa: Population recombination rate estimation from metagenomic read datasets

Bacterial evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a bacterial population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes.It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. In simulated datasets, the deviation from the expected value decreased as the number of genomes increased and we show that 80 genomes are sufficient to reduce these variations below 30%. Testing on an S. pneumoniae transformation experiment dataset we show that Rhometa accurately estimate the expected levels of recombination in a real world dataset.
Cellular Uptake of His-Rich Peptide Coacervates Occurs by a Macropinocytosis-Like Mechanism

Coacervates are dense microdroplets formed by liquid-liquid phase separation (LLPS) of macromolecules that have gained increasing attention as drug delivery vehicles. Recently, we have reported a new intracellular delivery system based on self-coacervating histidine (His)-rich beak peptides (HBpep and HBpep-SP) inspired by beak proteins of the Humboldt squid. These peptide microdroplets combine excellent encapsulation efficiency of therapeutics with high transfection rate and low cytotoxicity. However, the mechanism by which they cross the cell membrane remains elusive. Previous inhibitor studies provided incomplete clues into the detail uptake pathway, although they suggested a cholesterol-dependent and, possibly, an energy-independent non-classical mechanism of internalization. In this study, we improved our understanding of coacervates/cell membrane interactions using model membranes, namely Giant Unilamellar Vesicles (GUVs) and Giant Plasma Membrane Vesicles (GPMVs). We also employ a combination of electron microscopy techniques to gain detailed structural insights into the cell uptake of HBpep and HBpep-SP coacervates. We demonstrate that modulating lipid charge and cholesterol level influence coacervate attachment to GUVs. However, they are not able to cross the GUV’s lumen in an energy-independent manner. We then show that the coacervates enter HeLa and HepG2 cells via a mechanism sharing morphological features of macropinocytosis and phagocytosis, in particular involving cytoskeleton rearrangement and capture by filipodia. Our study provides key insights into the interaction of HPpep and HBpep-SP coacervates with model membranes as well as their cellular uptake pathway.
Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the mean expression is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution.
Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage

Background Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute spontaneous intracerebral hemorrhage. Aims To help clinicians explore the benefit of time-dependent treatments for unclear-onset patients, we presented artificial intelligence models to identify onset time using non-contrast computed tomography (NCCT) based on weakly supervised multitask learning (WS-MTL) structure. Methods The patients with reliable symptom onset time (strong label) or repeat CT (weak label) were included and split into training set and test set (internal and external). The WS-MTL structure utilized strong and weak labels simultaneously to improve performance. The models included three binary classification models for classifying whether NCCT acquired within 6, 8 or 12 h for different treatments measured by area under curve, and a regression model for determining the exact onset time measured by mean absolute error. The generalizability of models was also explored in comprehensive analysis. Results A total of 4004 patients with 10,780 NCCT scans were included. The performance of WS-MTL classification model showed high accuracy, and that of regression model was satisfactory in ≤6 h subgroup. In comprehensive analysis, the WS-MTL showed better performance for larger hematomas and thinner scans. And the performance improved effectively as training amounts increasing and could be improved steadily through retraining. Conclusions The WS-MTL models showed good performance and generalizability. Considering the large number of unclear-onset spontaneous intracerebral hemorrhage patients, it may be worth to integrate the WS-MTL model into clinical practice to identify the onset time.
Effects of Stimulus Frequency, Intensity, and Sex on the Autonomic Response to Transcutaneous Vagus Nerve Stimulation

This study aimed to determine how transcutaneous vagus nerve stimulation (tVNS) alters autonomic nervous activity by comparing the effects of different tVNS frequencies and current intensities. We also investigated the sex-dependent autonomic response to tVNS. Thirty-five healthy adult participants were stimulated using a tVNS stimulator at the left cymba conchae while sitting on a reclining chair; tVNS-induced waveform changes were then recorded for different stimulus frequencies (Experiment 1: 3.0 mA at 100 Hz, 25 Hz, 10 Hz, 1 Hz, and 0 Hz (no stimulation)) and current intensities (Experiment 2: 100 Hz at 3.0 mA, 1.0 mA, 0.2 mA (below sensory threshold), and 0 mA (no stimulation)) using an electrocardiogram. Pulse widths were set at 250 µs in both experiment 1 and 2. Changes in heart rate (HR), root-mean-square of the difference between two successive R waves (RMSSD), and the ratio between low-frequency (LF) (0.04–0.15 Hz) and high-frequency (HF) (0.15–0.40 Hz) bands (LF/HF) in spectral analysis, which indicates sympathetic and parasympathetic activity, respectively, in heart rate variability (HRV), were recorded for analysis. Although stimulation at all frequencies significantly reduced HR (p = 0.001), stimulation at 100 Hz had the most pronounced effect (p = 0.001) in Experiment 1 and was revealed to be required to deliver at 3.0 mA in Experiment 2 (p = 0.003). Additionally, participants with higher baseline sympathetic activity experienced higher parasympathetic response during stimulation, and sex differences may exist in the autonomic responses by the application of tVNS. Therefore, our findings suggest that optimal autonomic changes induced by tVNS to the left cymba conchae vary depending on stimulating parameters and sex.
Online Social Network Analysis Using Machine Learning Techniques

People spent most of the time in Social Networks.People express their views and opinions in Social Networks.Opinions influence the behaviors of the people. Opinion is the subject of study of Sentiment analysis and Opinion Mining. Opinion expressed in Social network can be analyzed and assist in making decision choosing the most popular brands. Sometime predicting the future results too. Twitter is the most popular Social networking site where peopletweet about particular topics. Different Machine Learning Algorithms such as Naive Bayes, Support Vector Machine and Logistic Regression are used to extract and analyses the tweets data to provide the required results. In this paper, tweets are analyzed about the Android and IOS platform smartphone to determine and predict the most popular brand in market. Sentiment Index, Relative Strength and Post Rate approach are used for Prediction. The finding of this paper is useful in providing correct review for the Smartphones users to select the best Smartphones.
Use of a Portable Electronic Interface Improves Clinical Handoffs and Adherence to Lung Protective Ventilation

Background Mechanical ventilation (MV) is used to support patients with respiratory impairment. Evidence supports the use of lung-protective ventilation (LPV) during MV to improve outcomes. However, studies have demonstrated poor adherence to LPV guidelines. We hypothesized that an electronic platform adapted to a hand-held tablet receiving real-time ventilatory parameters could increase clinician awareness of key LPV parameters. Furthermore, we speculated that an electronic shift-change tool could improve the quality of clinician handoffs. Methods Using a specially designed Wi-Fi dongle to transmit data from three ventilators and a respiratory monitor, we implemented a system that displays data from all ventilators under the care of a Respiratory Care Practitioner (RCP) on an electronic tablet. In addition, the tablet created a handoff checklist to improve shift-change communication. In a simulated ICU environment, we monitored the performance of eight RCPs at baseline and while using the system. Results Using the system, the time above guideline Pplat decreased by 74% from control, and the time outside the VT range decreased by 60% from control, p = 0.007 and 0.015, respectively. The handoff scores improved quality significantly from 2.8 to 1.6 on a scale of 1 to 5 (1 being best), p = 0.03. Conclusion In a simulated environment, an electronic RT tool can significantly improve shift-change communication and increase the RCP’s level of LPV adherence.
Toric degenerations of weight varieties and applications

We show that a weight variety, which is a quotient of a flag variety by the maximal torus, admits a flat degeneration to a toric variety. In particular, we show that the moduli spaces of spatial polygons degenerate to polarized toric varieties with the moment polytopes defined by the lengths of their diagonals. We extend these results to more general Flaschka-Millson hamiltonians on the quotients of products of projective spaces. We also study boundary toric divisors and certain real loci.
Of mice and men: Laboratory murine models for recapitulating the immunosuppression of human sepsis

Prolonged immunosuppression is increasingly recognized as the major cause of late phase and long-term mortality in sepsis. Numerous murine models with different paradigms, such as lipopolysaccharide injection, bacterial inoculation, and barrier disruption, have been used to explore the pathogenesis of immunosuppression in sepsis or to test the efficacy of potential therapeutic agents. Nonetheless, the reproducibility and translational value of such models are often questioned, owing to a highly heterogeneric, complex, and dynamic nature of immunopathology in human sepsis, which cannot be consistently and stably recapitulated in mice. Despite of the inherent discrepancies that exist between mice and humans, we can increase the feasibility of murine models by minimizing inconsistency and increasing their clinical relevance. In this mini review, we summarize the current knowledge of murine models that are most commonly used to investigate sepsis-induced immunopathology, highlighting their strengths and limitations in mimicking the dysregulated immune response encountered in human sepsis. We also propose potential directions for refining murine sepsis models, such as reducing experimental inconsistencies, increasing the clinical relevance, and enhancing immunological similarities between mice and humans; such modifications may optimize the value of murine models in meeting research and translational demands when applied in studies of sepsis-induced immunosuppression.
Reduced False Alarm for Forest Fires Detection and Monitoring using Fuzzy Logic Algorithm

—The purpose of this research was to detect forest fires in efforts to minimize the incidence of false alarms. The research method used is divided into several stages, namely planning, analysis, design, implementation, testing, and maintenance. Arduino acts as a data collector on the field, which will later be used to detect forest fires and false alarms. Fuzzy logic is used as the essence of the algorithm and will provide a higher level of accuracy for forest fire detection and false alarms. In testing the fuzzy program, the fuzzy output between Arduino and the fuzzy on the monitoring dashboard has a small difference of 0.99%. It can be concluded; the application can minimize the occurrence of false alarms to reduce the user's workload.
Sentiment Analysis using Term based Method for Customers’ Reviews in Amazon Product

—Customers’ review in Amazon platform plays an important role for making online purchase decision making, however the reviews are snowballing in E-commerce day by day. The active sharing of customers’ experience and feedback helps to predict the products and retailers’ quality by using natural language processing. This paper will focus on experimental discussion on Amazon products reviews analysis coupled with sentiment analysis using term-based method and N-gram to achieve best findings. The investigation of sentiment analysis on amazon product gain more valuable information on related text to solve problem related services, products information and quality. The analysis begins with data pre-processing of Amazon products reviews then feature extraction with POS tagging and term-based concept. e-Commerce customer’s reviews normally classify different experience into positive, negative and neutral to judge human behavior and emotion towards the purchase products. The major findings discussed in this journal will be using four different classifier and N-grams methods by computing accuracy, precision, recall and F1-Score. TF-IDF method with N-gram shows unigram with Support Vector Machine learning with highest accuracy results for Amazon product customers’ reviews. The score reveals that Support Vector Machine for unigram achieved 82.27% for accuracy, 82% precision, 80% Re-call and 72% F1-Score.
The Role of Cardiac Resynchronization Therapy for the Management of Functional Mitral Regurgitation

Valve leaflets and chordae structurally normal characterize functional mitral regurgitation (FMR), which in heart failure (HF) setting results from an imbalance between closing and tethering forces secondary to alterations in the left ventricle (LV) and left atrium geometry. In this context, FMR impacts the quality of life and increases mortality. Despite multiple medical and surgical attempts to treat FMR, to date, there is no univocal treatment for many patients. The pathophysiology of FMR is highly complex and involves several underlying mechanisms. Left ventricle dyssynchrony may contribute to FMR onset and worsening and represents an important target for FMR management. In this article, we discuss the mechanisms of FMR and review the potential therapeutic role of CRT, providing a comprehensive review of the available data coming from clinical studies and trials.
Emerging Concepts on Infection of Novel Cardiac Implantable Devices

Novel cardiac devices, including the MitraClip system, occluder devices, leadless pacemakers, and subcutaneous implantable cardioverter defibrillators (S-ICD), are mostly used in the management of patients who are at high risk for surgery and/or developing infections. Several mechanisms render most of these devices resistant to infection, including avoiding long transvenous access and novel manufacturing material. Since subjects who use these devices already endure several comorbid conditions, uncommon cases of device-associated infection could result in serious complications and increased mortality. In this review, we aim to summarize the current state of evidence on the incidence, clinical presentation, management, and prognosis of new cardiac devices’ associated infection.