Miracle-dz/UniFAR-FTUs huggingface dataset:
Environmental risk factors and genetic markers of Kaposi's sarcoma‐associated herpesvirus infection among Uygur population in Xinjiang, China

Kaposi sarcoma‐associated herpesvirus (KSHV) is endemic in Xinjiang, China. Determinants of KSHV seropositivity among high‐risk groups are not well understood. We seek to identify genetic and environmental predisposing factors for KSHV infection among Uygurs in this endemic region. A cross‐sectional study was performed among the Uygur population in Xinjiang, China. KSHV‐antibodies were detected using immunofluorescence assay (IFA) and human leukocyte antigen (HLA) alleles were genotyped. Univariate and multivariate logistic regression analyses were applied to explore the environmental and genetic risk factors of KSHV seropositivity. Finally, a total of 721 participants were included. The seroprevalence of KSHV was 24.1% among this population. Sweet‐food preference (odds ratio [OR] 1.85, 95% confidence interval [CI] 1.03–3.34), and coronary heart disease (OR 1.91, 95%CI 1.24–2.94) were statistically correlated with KSHV infection. HLA‐DQB1*06:09 were found to significantly increase the risk of KSHV infection under all 3 models (ORAllelic = 4.06; ORDominant = 3.27; and ORRecessive = 8.06). Six SNPs (SNP0260, SNP0361, SNP0797, SNP0852, SNP1159, and SNP1375) in the DQB1 and DRB1 region and haploid type GTCTAACTAATC in block 17 were statistically associated with KSHV infection. We demonstrated that genetic variations in HLA‐DQB1/DRB1 and environmental risk factors were strongly associated with KSHV infection among this population.
Preferential interactions of a crowder protein with the specific binding site of a native protein complex

The crowded cellular environments provide ample opportunities for proteins to interact with bystander macromolecules, yet direct evidence, let alone residue-specific information, for such nonspecific binding is rare. Here, by combining NMR spectroscopy and atomistic modeling, we investigated how crowders influence the association equilibrium and kinetics of two protein partners, EIN and HPr. Ficoll-70 increases the EIN-HPr binding affinity whereas bovine serum albumin (BSA) decreases the affinity. The opposite effects of the two crowders are quantitatively explained by atomistic modeling, which shows that the stabilizing effect of Ficoll-70 arises from volume exclusion favoring the bound state. In contrast, the destabilizing effect of BSA arises from preferential soft interactions with the free state; notably, BSA has favorable electrostatic interactions with positively charged HPr residues within the EIN-binding site. Some of the residues from this site indeed experience significant chemical shift perturbation when titrated with BSA, while the relaxation rates of HPr backbone amides exhibit overall elevation. Furthermore, relaxation dispersion data indicate that Ficoll-70 and BSA both slow down the EIN-HPr association rate, but change the dissociate rate in opposite directions. The observations on kinetics are accounted for by two effects of the crowders: increasing the solution microviscosity and reshaping the EIN-HPr interaction energy surface. The kind of preferential interactions between BSA and HPr that leads to competition with EIN should be prevalent in cellular environments. Our NMR results and atomistic modeling provide benchmarks, at both qualitative and quantitative levels, for the effects of crowded cellular environments on protein-protein specific interactions. Significance Statement Although nonspecific binding of crowder macromolecules with functional proteins is likely prevalent in vivo, direct evidence is rare. Here we present NMR characterizations showing that bovine serum albumin preferentially interacts with a specific binding site on HPr, leading to competition with the latter’s partner EIN. The preferential interactions result in destabilization of the EIN-HPr native complex and speedup of its dissociation, contrary to expectations from excluded-volume and viscosity effects. Atomistic modeling of macromolecular crowding rationalizes the experimental observations, and provides qualitative and quantitative insight into the influences of the crowded cellular environment on protein-protein specific interactions. Our work also has implications for evolution, regarding how nonspecific binding can be either minimized or exploited for gaining new functions.
A Clinical Update on Gestational Diabetes Mellitus

Abstract Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups’ criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks’ gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.
Musculoskeletal physiotherapy in physical sequelae of SARS-CoV-2 infection: A case report.

BACKGROUND Patients infected with severe acute respiratory syndrome-CoV-2 can present physical complications that can make the return to their daily physical activities difficult. Despite the positive results in different pathologies through musculoskeletal physiotherapy, there are few reports in post-COVID-19 patients. This case describes an intervention based on musculoskeletal physiotherapy in a post-COVID-19 adult woman with physical sequelae. METHODS As part of an initial evaluation in a woman with physical sequelaes post-COVID-19, it was decided to conduct a musculoskeletal physiotherapy over a course of 5 weeks distributed in 15 visits. To evaluate her progress the joint ranges were evaluated according to the American Academy of Orthopedic Surgeons (AAOS), the Numerical pain scale was used, the muscle strength evaluated according to the Daniel Scale, and finally the Unipodal Station Test used. RESULTS Musculoskeletal physiotherapy improved the patient's physical symptoms. The patient's pain was reduced from a value of 10 to 3; the strength of the musculature increased from a value of 2 to 4; initial joint ranges were expanded from 67% to 100% in various body segments. Finally, the woman managed to stay up to 8 s in the monopodal position. DISCUSSION Using musculoskeletal physical therapy and measuring progress through quantitative measurements at each visit, a post-COVID-19 woman was able to resume her daily physical activities. This therapy is a valuable tool to recover the musculoskeletal system that proves to be an important advance in the recovery of the quality of life in patients with musculoskeletal sequelae from COVID-19.
Inference for nonparanormal partial correlation via regularized rank‐based nodewise regression

Partial correlation is a common tool in studying conditional dependence for Gaussian distributed data. However, partial correlation being zero may not be equivalent to conditional independence under non‐Gaussian distributions. In this paper, we propose a statistical inference procedure for partial correlations under the high‐dimensional nonparanormal (NPN) model where the observed data are normally distributed after certain monotone transformations. The NPN partial correlation is the partial correlation of the normal transformed data under the NPN model, which is a more general measure of conditional dependence. We estimate the NPN partial correlations by regularized nodewise regression based on the empirical ranks of the original data. A multiple testing procedure is proposed to identify the nonzero NPN partial correlations. The proposed method can be carried out by a simple coordinate descent algorithm for lasso optimization. It is easy‐to‐implement and computationally more efficient compared to the existing methods for estimating NPN graphical models. Theoretical results are developed to show the asymptotic normality of the proposed estimator and to justify the proposed multiple testing procedure. Numerical simulations and a case study on brain imaging data demonstrate the utility of the proposed procedure and evaluate its performance compared to the existing methods. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Implications of a patent foramen ovale for environmental physiology and pathophysiology: do we know the ‘hole’ story?

The foramen ovale is an essential component of the fetal circulation contributing to oxygenation and carbon dioxide elimination that remains patent under certain circumstances in ∼30% of the healthy adult population, without major negative sequelae in most. Adults with a patent foramen ovale (PFO) have a greater tendency to develop symptoms of acute mountain sickness and high‐altitude pulmonary oedema upon ascent to high altitude, and PFO presence is associated with worse cardiopulmonary function in chronic mountain sickness. This increase in altitude illness prevalence may be related to dysregulated cerebral blood flow associated with altered respiratory chemoreflex sensitivity; however, the mechanisms remain to be elucidated. Interestingly, men with a PFO appear to have a shift in thermoregulatory control to higher internal temperatures, both at rest and during exercise, and they have blunted thermal hyperpnoea. The teleological ‘reason’ for this thermoregulatory shift is unclear, but the shift of ∼0.5°C in core body temperature does not appear to be sufficient to have any significant negative consequences in terms of risk of heat illness. Further work in this area is needed, particularly in women, to evaluate mechanisms of heat storage and dissipation in these individuals compared to people without a PFO. Consequences of a PFO in SCUBA divers include a greater incidence of unprovoked decompression sickness, but whether PFO is beneficial or detrimental to breath hold diving remains unexplored. Whether PFO presence will explain interindividual variability in responses to, and consequences from, other environmental stressors such as spaceflight remain entirely unknown.
Design and evaluation of an achievability contour display for piloted lunar landing

Landing on the moon requires the selection and identification of a location that is level and free of hazards, along with a stable, controlled descent to the lunar surface through the use of automated systems and manual control. Spatial disorientation may occur upon reentering a gravitational field after vestibular adaptation to microgravity during lunar transit. The workload associated with selecting a suitable landing point based on the remaining fuel and current vehicle states is a concern. In Apollo, visual out-the-window information was heavily relied upon to support the selection of a landing point, and there was little support information available to indicate whether the desired site was achievable. A novel achievability contour display element showing the dynamic achievable landing area was developed based on a Goal-Directed Task Analysis and usability testing. A subject experiment was conducted in a lunar landing simulation environment to test the effects of the achievability contour on pilot performance, situation awareness, and workload in simulated approach and terminal descent scenarios as compared to an Apollo-style auditory display. Two control modes were used: supervisory control and roll, pitch, and yaw rate-control/attitude-hold (RCAH) manual control. The experiment also investigated differences in display effect with and without a required redesignation. Results of the subject experiment (N = 10) indicate that the achievability contour display showed significant improvement in subjective situation awareness and workload ratings. The results also indicate a change in decision-making behavior with the use of the achievability contour display. There was no measurable difference in flight and landing performance measures between the two display conditions. The results of the experiment suggest that providing the achievability contour display may have beneficial effects on pilot situation awareness and workload during the final approach and terminal descent maneuvers. Additional research is needed to determine the optimal implementation and pilot interaction methods in the use of this display.
Distributed Network Music Workshop with Soundjack

Playing live with someone abroad can be considered as a major challenge for musicians and sound engineers likewise. Due to cognitive, technical and purely musical problems and restrictions it had so far been impossible to reproduce a realistic rehearsing scenario as if in the same room. However, since nowadays the Internet oers suciant bandwidth and reliability it can satisfy the high demands and actually fulll the extreme time critical restrictions. In order to achieve the most decent latency and quality of a network, the Soundjack software has been developed and applied in terms of a musical telepresence. In this workshop the authors will demonstrate what problems sound engineers and musicians have to overcome in context with audio- and network delays and how alternative high delay solutions can be applied. As a practical showcase three musicians
Heritability in corpus callosum morphology and its association with tool use skill in chimpanzees (Pan troglodytes): Reproducibility in two genetically isolated populations

The corpus callosum (CC) is the major white matter tract connecting the left and right cerebral hemispheres. It has been hypothesized that individual variation in CC morphology is negatively associated with forebrain volume (FBV) and this accounts for variation in behavioral and brain asymmetries as well as sex differences. To test this hypothesis, CC surface area and thickness as well as FBV was quantified in 221 chimpanzees with known pedigrees. CC surface area, thickness and FBV were significantly heritable and phenotypically associated with each other; however, no significant genetic association was found between FBV, CC surface area and thickness. The CC surface area and thickness measures were also found to be significantly heritable in both chimpanzee cohorts as were phenotypic associations with variation in asymmetries in tool use skill, suggesting that these findings are reproducible. Finally, significant phenotypic and genetic associations were found between hand use skill and region‐specific variation in CC surface area and thickness. These findings suggest that common genes may underlie individual differences in chimpanzee tool use skill and interhemispheric connectivity as manifest by variation in surface area and thickness within the anterior region of the CC.
Opinion dynamics in financial markets via random networks

Significance Financial markets reflect the economic activity of nations, industries, companies, and societies. The recent housing market crash, COVID-19 pandemic, high inflation indices, and cryptocurrency frenzy have illustrated the decisive influence of such systems in modern culture. This work uses an agent-based model within the framework of a complex network to describe asset price dynamics in stock markets. We assume that a financial market comprises two main categories of investors—noise traders and fundamentalists—with the former investing based on its acquaintances and the latter acting based on the market index. Our model reproduces real-world market features for a finite level of social anxiety, denoted by a social temperature, and for a small number of interacting fundamentalist agents in a random network.
Biophysical and Biochemical mechanisms underlying Collective Cell Migration in Cancer Metastasis

Multicellular collective migration is an ubiquitous strategy of cells to translocate spatially in diverse tissue environments to accomplish a wide variety of biological phenomena, viz. embryonic development, wound healing and tumor progression. Diverse cellular functions and behaviours, for instance cell protrusions, active contractions, cell–cell adhesion, biochemical signaling, remodeling of tissue micro-environment etc., play their own role concomitantly to have a single concerted consequence of multicellular migration. Thus unveiling the driving principles, both biochemical and biophysical, of the inherently complex process of collective cell migration is an insurmountable task. Mathematical and computational models, in tandem with experimental data, helps in shedding some light on it. Here we review different factors influencing Collective Cell Migration and then focus on different mathematical and computational models discrete, hybrid and continuum which helps in revealing different aspects of multicellular migration. Finally we discuss the applications of these modeling frameworks specific to cancer.
A Concise Tutorial on Approximate Message Passing

High-dimensional signal recovery of standard linear regression is a key challenge in many engineering fields, such as, communications, compressed sensing, and image processing. The approximate message passing (AMP) algorithm proposed by Donoho \textit{et al} is a computational efficient method to such problems, which can attain Bayes-optimal performance in independent identical distributed (IID) sub-Gaussian random matrices region. A significant feature of AMP is that the dynamical behavior of AMP can be fully predicted by a scalar equation termed station evolution (SE). Although AMP is optimal in IID sub-Gaussian random matrices, AMP may fail to converge when measurement matrix is beyond IID sub-Gaussian. To extend the region of random measurement matrix, an expectation propagation (EP)-related algorithm orthogonal AMP (OAMP) was proposed, which shares the same algorithm with EP, expectation consistent (EC), and vector AMP (VAMP). This paper aims at giving a review for those algorithms. We begin with the worst case, i.e., least absolute shrinkage and selection operator (LASSO) inference problem, and then give the detailed derivation of AMP derived from message passing. Also, in the Bayes-optimal setting, we give the Bayes-optimal AMP which has a slight difference from AMP for LASSO. In addition, we review some AMP-related algorithms: OAMP, VAMP, and Memory AMP (MAMP), which can be applied to more general random matrices.
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.
Decomposing LIBOR in transition: evidence from the futures markets

Applying historical data from the USD LIBOR transition period, we estimate a joint model for SOFR, Federal Funds, and Eurodollar futures rates as well as spot USD LIBOR and term repo rates. The framework endogenously models basis spreads between each of the benchmark rates and allows for the decomposition of spreads. Modelling the LIBOR-OIS spread as credit and funding-liquidity roll-over risk, we find that the spike in the LIBOR-OIS spread during the onset of COVID-19 was mainly due to credit risk, while on average credit and funding-liquidity risk contribute equally to the spread.
Exploring Kervolutional Neural Networks

A paper published in the CVPR 2019 conference outlines a new technique called 'kervolution' used in a new type of augmented convolutional neural network (CNN) called a 'kervolutional neural network' (KNN). The paper asserts that KNNs achieve faster convergence and higher accuracies than CNNs. This"mini paper"will further examine the findings in the original paper and perform a more in depth analysis of the KNN architecture. This will be done by analyzing the impact of hyper parameters (specifically the learning rate) on KNNs versus CNNs, experimenting with other types of kervolution operations not tested in the original paper, a more rigourous statistical analysis of accuracies and convergence times and additional theoretical analysis. The accompanying code is publicly available.
Bregman Deviations of Generic Exponential Families

We revisit the method of mixture technique, also known as the Laplace method, to study the concentration phenomenon in generic exponential families. Combining the properties of Bregman divergence associated with log-partition function of the family with the method of mixtures for super-martingales, we establish a generic bound controlling the Bregman divergence between the parameter of the family and a finite sample estimate of the parameter. Our bound is time-uniform and makes appear a quantity extending the classical information gain to exponential families, which we call the Bregman information gain. For the practitioner, we instantiate this novel bound to several classical families, e.g., Gaussian, Bernoulli, Exponential, Weibull, Pareto, Poisson and Chi-square yielding explicit forms of the confidence sets and the Bregman information gain. We further numerically compare the resulting confidence bounds to state-of-the-art alternatives for time-uniform concentration and show that this novel method yields competitive results. Finally, we highlight the benefit of our concentration bounds on some illustrative applications.
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.