Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability of a Novel Automated Ultrasound Technology for Body Composition Assessment and Comparisons with Dual Energy X-Ray Absorptiometry.

Body composition tools vary in reliability, portability, and accessibility. The purpose of this study was to evaluate test-retest reliability of MuscleSound® (MS) and dual-energy x-ray absorptiometry (DXA) for both two compartment (region) and three compartment (tissue) models. A secondary aim was to compare body composition values produced by both devices. Fifty participants (n = 25 male, n = 25 female) aged 18-39 years completed two body composition assessments, twice in a single session. Participants arrived at the lab after a 12-hour fast. DXA required participants to lay supine for 10-15 minutes during the scanning process. Thereafter, MS was utilized to measure subcutaneous adipose tissue thickness at seven sites: chest, subscapula, triceps, axilla, suprailium, abdomen, and mid-thigh. MS automatically estimated body composition utilizing a modified Jackson-Pollock equation and the Siri equation within the software. The sequence of assessments was then repeated. Statistical analysis included paired T-tests with Pearson correlations, intraclass correlation coefficients (ICC), and least significant change (LSC). Both methods were strongly reliable (ICCMS = .997, ICCDXA-region = .999, ICCDXA-tissue = .999). MS and DXA-region body fat percentages were significantly different (mean difference (%): 2.60 ± 1.32, p < . 001) but highly correlated (r = .928, p < .001). Notably, the mean difference was within DXA-region's calculated least significant change of 3.24%. MS is reliable for assessing body fat percentage in young and middle-aged adults and operators can utilize MS to collect body composition data in the field.
Is coaches’ perceived team performance associated with their intentions to persist in coaching? The mediating role of coaches’ psychological needs and motivation

ABSTRACT This study examined the association between team performance perceived by coaches, coaches’ basic psychological needs, motivation, and their intentions to persist in coaching. Participants were 719 coaches (80 females, M = 33.49 years, SD = 10.27, range = 18–67) of different team sports (soccer = 514; basketball = 165; volleyball = 23; handball = 14; and hockey = 3) who completed questionnaire measures at the end of the season. Structural equation modelling adjusted for sex, coaches’ experience, age categories, and type of sport revealed that coaches’ perceptions of team performance were positively related to their need satisfaction and negatively to their need frustration. In turn, coaches’ need satisfaction was positively associated with their autonomous motivation, whereas their need frustration was positively related to controlled motivation and amotivation. Finally, coaches’ autonomous motivation was a positive predictor of their intentions to persist, and conversely, coaches’ amotivation was a negative predictor of their intentions to persist. We found a significant positive and indirect effect between team performance perceived by coaches and their intention to persist through their need satisfaction and autonomous motivation. These findings represent the first quantitative insight into how coach-perceived team performance could function as a new antecedent relevant to coaches’ motivational processes and persistence.
Challenges in Estimating the Effectiveness of COVID-19 Vaccination Using Observational Data

The COVID-19 vaccines were rigorously evaluated in randomized trials, but important questions, such as the magnitude and duration of protection, effectiveness against new variants, and effectiveness of booster vaccination, could not be answered by trials and have been addressed in observational studies. Emulating a hypothetical “target trial” using observational data assembled during vaccine rollouts can help manage potential sources of bias in observational studies. This article describes 2 approaches to target trial emulation using observational data that can help design studies that provide robust evidence to guide clinical and policy decisions.
Clinical decision rules in primary care: necessary investments for sustainable healthcare

Abstract Clinical judgement in primary care is more often decisive than in the hospital. Clinical decision rules (CDRs) can help general practitioners facilitating the work-through of differentials that follows an initial suspicion, resulting in a concrete ‘course of action’: a ‘rule-out’ without further testing, a need for further testing, or a specific treatment. However, in daily primary care, the use of CDRs is limited to only a few isolated rules. In this paper, we aimed to provide insight into the laborious path required to implement a viable CDR. At the same time, we noted that the limited use of CDRs in primary care cannot be explained by implementation barriers alone. Through the case study of the Oudega rule for the exclusion of deep vein thrombosis, we concluded that primary care CDRs come out best if they are tailor-made, taking into consideration the specific context of primary health care. Current CDRs should be evaluated frequently, and future decision rules should anticipate the latest developments such as the use of point-of-care (POC) tests. Hence, such new powerful diagnostic CDRs could improve and expand the possibilities for patient-oriented primary care.
Regression using Fractional Polynomials of Continuous Covariates: Parsimonious

SUMMARY The relationship between a response variable and one or more continuous covariates is often curved. Attempts to represent curvature in single- or multiple-regression models are usually made by means of polynomials of the covariates, typically quadratics. However, low order polynomials offer a limited family of shapes, and high order polynomials may fit poorly at the extreme values of the covariates. We propose an extended family of curves, which we call fractional polynomials, whose power terms are restricted to a small predefined set of integer and non-integer values. The powers are selected so that conventional polynomials are a subset of the family. Regression models using fractional polynomials of the covariates have appeared in the literature in an ad hoc fashion over a long period; we provide a unified description and a degree of formalization for them. They are shown to have considerable flexibility and are straightforward to fit using standard methods. We suggest an iterative algorithm for covariate selection and model fitting when several covariates are available. We give six examples of the use of fractional polynomial models in three types of regression analysis: normal errors, logistic and Cox regression. The examples all relate to medical data: fetal measurements, immunoglobulin concentrations in children, diabetes in children, infertility in women, myelomatosis (a type of leukaemia) and leg ulcers.
General particle swarm optimization algorithm for job-shop scheduling problem

To overcome the limitations of traditional Particle Swarm Optimization(PSO) on solutions to the combinatorial optimization problems,a General PSO(GPSO) model was proposed after analyzing the optimization mechanism of the traditional PSO. Based on this model,a GPSO algorithm was presented to solve the Job-shop Scheduling Problem(JSP). In GPSO,crossover and mutation operations in genetic algorithm were respectively utilized by particles to exchange information and search randomly.Besides,Tabu Search was used for particles' local search.To control the local search and ensure its convergence to the global optimum solution,time-varying crossover probability and time-varying maximum step size of Tabu Search were introduced.The experimental results showed that JSP could be solved by GPSO effectively.The feasibility of the proposed GPSO model was also demonstrated.
Frequency shift of a nanowaveguide resonator driven by the tunable optical gradient force

A nanowaveguide resonator driven by a tunable optical gradient force can easily enter into the nonlinear oscillation regime, where the resonance frequency will shift. In this work, a continuum elastic model of the optoresonator is presented and solved analytically using the method of multiple scales. The effects of the optical gradient force on the resonance frequency and dynamic behavior are investigated. The results theoretically figure out why and when the nonlinear behavior of spring softening and spring hardening can occur. It is shown that the nonlinear phenomenon of spring softening is generally more dominant than the hardening effect when the optical gradient force is strong. However, the nonlinear cubic mechanical stiffness of the waveguide makes the dynamic behavior of spring softening dominant when the optical force is not strong enough. Based on the logical derivation of the closed-form solution, it can be found that the decrease of resonance frequency is due to the bias term, which is inherent in the nature of the tunable optical gradient force. Additionally, the complex variations of the resonance frequency and maximum vibration amplitude with different waveguide widths, lengths, and initial gaps are investigated and discussed. The proposed solutions are also verified with the reported experimental results.
Using Graphical Perception in Visualization Recommendation

different encoding choices, which could inform the development of visualization recommendation tools. However, when we surveyed current tools [2], we noticed a surprising pattern: They seem to reference few if any findings from graphical perception when recommending visual encodings. This result led us to another important question: Why aren’t current visualization recommendation tools incorporating experiment results and guidelines from graphical perception research? A natural starting point is to review the graphical perception literature and figure out which parts are most relevant to visualization recommendation tools. This led us to review 132 interesting works in graphical perception [3], from visualization textbooks to decadesold experiments of how people perceive bar charts to studies of what happens when you add iconography or other embellishments to visualizations, among others. The sheer breadth and depth of work was at times overwhelming, and we started to see the problems that developers were running into. For example, it’s a struggle to separate the papers (and textbooks) that are relevant to visualization recommendation from those that are A s data continues to grow at unprecedented rates, we encounter unique challenges in helping analysts make sense of it. A prime example involves visualizing the data, where an analyst may have to reduce thousands of data columns and billions of data records to a single visualization. This often involves selecting which columns to visualize; sampling, filtering, or aggregating the data down to a manageable number of records; and mapping the results to intuitive visual encodings such as positional axes, bar heights, or color hues. Every step of the way, the analyst must grapple with what to focus on and how to translate the focus into a compelling image. We see a small slice of this problem in Figure 1: We can generate many different visualizations for a movie dataset, but the default design choices can be problematic. For example, the line chart in Figure 1 is just a blob of blue pixels. How can visualization tools help analysts navigate this complex and even frustrating web of interconnected design decisions? We have seen an explosion of visualization recommendation tools responding to this challenge. These tools aim to reduce decision fatigue by automating part or even all of the visualization design process. We summarize how these tools behave based on what they aim to automate [2]: which parts of the data to focus on (recommending data columns, rows, queries, etc.), which visual encodings to apply (recommending scales, colors, shapes, etc.), or both. Graphical perception research Why aren’t current tools incorporating experiment results and guidelines from graphical perception research? Using Graphical Perception in Visualization Recommendation
MitoSort: robust demultiplexing of pooled single-cell genomics data using endogenous germline mitochondrial variants

Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. Using endogenous genetic barcodes eliminates the need for additional experimental processing steps. Among the available choices for endogenous barcodes, the unique features of mtDNA variants render them a more computationally efficient and robust option compared to genome variants. Here we present MitoSort, a method that uses mtDNA germline variants to assign cells to their donor of origin and identify cross-genotype doublets. We evaluated the performance of MitoSort by in silico pooled mtscATAC-seq libraries and experimentally multiplexed data using cell hashing method. MitoSort achieve both high accuracy and efficiency on genotype clustering and doublet detection for mtscATAC-seq data, which fills a void left by the inadequacies of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq, as long as the mtDNA variants can be reliably detected. Furthermore, through a case study, we demonstrated that demultiplexing 8 individuals assayed at the same time with MitoSort, enables the comparison of cell composition without batch effects.
Language experience shapes predictive coding of rhythmic sound sequences

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and contextindependent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been welldocumented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial versus Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language’s word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker’s linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term “duration” prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.
WNK1 enforces macrophage lineage fidelity

The appropriate development of macrophages, the body’s professional phagocyte, is essential for organismal development, especially in mammals. This dependence is exemplified by the observation that loss-of-function mutations in colony stimulating factor 1 receptor (CSF1R) results in multiple tissue abnormalities owing to an absence of macrophages. Despite this importance, little is known about the molecular and cell biological regulation of macrophage development. Here, we report the surprising finding that the chloride-sensing kinase With-no-lysine 1 (WNK1) is required for development of tissue-resident macrophages (TRMs). Myeloid-specific deletion of Wnk1 resulted in a dramatic loss of TRMs, disrupted organ development, systemic neutrophilia, and mortality between 3 and 4 weeks of age. Strikingly, we found that myeloid progenitors or precursors lacking WNK1 not only failed to differentiate into macrophages, but instead differentiated into neutrophils. Mechanistically, the cognate CSF1R cytokine macrophage-colony stimulating factor (M-CSF) stimulates macropinocytosis by both mouse and human myeloid progenitors and precursor cells. Macropinocytosis, in turn, induces chloride flux and WNK1 phosphorylation. Importantly, blocking macropinocytosis, perturbing chloride flux during macropinocytosis, and inhibiting WNK1 chloride-sensing activity each skewed myeloid progenitor differentiation from macrophages into neutrophils. Thus, we have elucidated a role for WNK1 during macropinocytosis and discovered a novel function of macropinocytosis in myeloid progenitors and precursor cells to ensure macrophage lineage fidelity. Highlights - Myeloid-specific WNK1 loss causes failed macrophage development and premature death - M-CSF-stimulated myeloid progenitors and precursors become neutrophils instead of macrophages - M-CSF induces macropinocytosis by myeloid progenitors, which depends on WNK1 - Macropinocytosis enforces macrophage lineage commitment
The Drosophila drop-dead gene is required for eggshell integrity

The eggshell of the fruit fly Drosophila melanogaster is a useful model for understanding the synthesis of a complex extracellular matrix. The eggshell is synthesized during mid-to-late oogenesis by the somatic follicle cells that surround the developing oocyte. We previously reported that female flies mutant for the gene drop-dead (drd) are sterile, but the underlying cause of the sterility remained unknown. In this study, we examined the role of drd in eggshell synthesis. We show that eggs laid by drd mutant females are fertilized but arrest early in embryogenesis, and that the innermost layer of the eggshell, the vitelline membrane, is abnormally permeable to dye in these eggs. In addition, the major vitelline membrane proteins fail to become crosslinked by nonreducible bonds, a process that normally occurs during egg activation following ovulation, as evidenced by their solubility and detection by Western blot in laid eggs. In contrast, the Cp36 protein, which is found in the outer chorion layers of the eggshell, becomes crosslinked normally. To link the drd expression pattern with these phenotypes, we show that drd is expressed in the ovarian follicle cells beginning in mid-oogenesis, and, importantly, that all drd mutant eggshell phenotypes could be recapitulated by selective knockdown of drd expression in the follicle cells. To determine whether drd expression was required for the crosslinking itself, we performed in vitro activation and crosslinking experiments. The vitelline membranes of control egg chambers could become crosslinked either by incubation in hyperosmotic medium, which activates the egg chambers, or by exogenous peroxidase and hydrogen peroxide. In contrast, neither treatment resulted in the crosslinking of the vitelline membrane in drd mutant egg chambers. These results indicate that drd expression in the follicle cells is necessary for vitelline membrane proteins to serve as substrates for peroxidase-mediated cross-linking at the end of oogenesis.
DEGAP: Dynamic Elongation of a Genome Assembly Path

Genome assembly remains to be a major task in genomic research. Despite the development over the past decades of different assembler software programs and algorithms, it is still a great challenge to assemble a complete genome with no gaps. With the latest DNA Circular Consensus Sequencing (CCS) technology, several assembler programs now can build a genome from raw sequencing data to contigs, however, some complex sequence regions remain as unresolved gaps. Here, we present a novel gap-filling software DEGAP that can resolve gap regions in genomes by utilizing the dual advantages of accuracy and length of high-fidelity (HiFi) reads. DEGAP optimizes HiFi reads by identifying the differences between reads and provides ‘GapFiller’ or ‘CtgLinker’ modes to eliminate or shorten gaps in genomes. DEGAP adopts a cyclic elongation strategy that automatically and dynamically adjusts parameters according to the complexity of the sequences and selects the optimal extension path. DEGAP has already been successfully applied to decipher complex genomic regions in several projects and may be widely employed to generate more gap-free genomes.
Bcl-xL is translocated to the nucleus via CtBP2 to epigenetically promote metastasis

Besides its mitochondria-based anti-apoptotic role, Bcl-xL also travels to the nucleus to promote cancer metastasis by upregulating global histone H3 trimethyl Lys4 (H3K4me3) and TGFβ transcription. How Bcl-xL is translocated into the nucleus and how nuclear Bcl-xL regulates H3K4me3 modification are not understood. Here, we report that C-terminal Binding Protein 2 (CtBP2) binds Bcl-xL via its N-terminus and translocates Bcl-xL into the nucleus. Knockdown of CtBP2 by shRNA decreases the nuclear portion of Bcl-xL and reverses Bcl-xL-induced cell migration and metastasis in mouse models. Furthermore, knockout of CtBP2 suppresses Bcl-xL transcription. The binding between Bcl-xL and CtBP2 is required for their interaction with MLL1, a histone H3K4 methyltransferase. Pharmacologic inhibition of MLL1 enzymatic activity reverses Bcl-xL-induced H3K4me3 and TGFβ mRNA upregulation as well as cell invasion. Moreover, cleavage under targets and release using nuclease (CUT&RUN) coupled with next generation sequencing reveals that H3K4me3 modifications are particularly enriched in the promotor region of genes encoding TGFβ and its signaling pathway in the cancer cells overexpressing Bcl-xL. Altogether, the metastatic function of Bcl-xL is mediated by its interaction with CtBP2 and MLL1.
Aesthetic experience models human learning

Aesthetic experiences have the potential to promote learning and creativity by enhancing the ability to understand complexity and to integrate novel or disparate information. Offering a theoretical framework for understanding the cognitive benefits of aesthetic experiences, this paper argues they are the necessary outcome of human learning, in which natural objects or artworks are evaluated in a multi-dimensional preference space shaped by Bayesian prediction. In addition, it contends that the brain-states underlying aesthetic experiences harness configurations of the apex three transmodal neural systems—the default mode network, the central executive network, and the salience network—that may offer information-processing advantages by recruiting the brain’s high-power communication hubs, thus enhancing potential for learning gain.
Existence and nonexistence of traveling pulses in a lateral inhibition neural network

We study the spatial propagating dynamics in a neural network of excitatory and inhibitory populations. Our study demonstrates the existence and nonexistence of traveling pulse solutions with a nonsaturating piecewise linear gain function. We prove that traveling pulse solutions do not exist for such neural field models with even (symmetric) couplings. The neural field models only support traveling pulse solutions with asymmetric couplings. We also show that such neural field models with asymmetric couplings will lead to a system of delay differential equations. We further compute traveling 1--bump solutions using the system of delay differential equations. Finally, we develop Evans functions to assess the stability of traveling 1--bump solutions.