Miracle-dz/UniFAR-FTUs huggingface dataset:
Muscle fibrosis and maladaptation occur progressively in CKD and are rescued by dialysis

BACKGROUND Skeletal muscle maladaptation accompanies chronic kidney disease (CKD) and negatively affects physical function. Emphasis in CKD has historically been placed on muscle fiber–intrinsic deficits, such as altered protein metabolism and atrophy. However, targeted treatment of fiber-intrinsic dysfunction has produced limited improvement, whereas alterations within the fiber-extrinsic environment have scarcely been examined. METHODS We investigated alterations to the skeletal muscle interstitial environment with deep cellular phenotyping of biopsies from patients with CKD and age-matched controls and performed transcriptome profiling to define the molecular underpinnings of CKD-associated muscle impairments. We examined changes in muscle maladaptation following initiation of dialysis therapy for kidney failure. RESULTS Patients with CKD exhibited a progressive fibrotic muscle phenotype, which was associated with impaired regenerative capacity and lower vascular density. The severity of these deficits was strongly associated with the degree of kidney dysfunction. Consistent with these profound deficits, CKD was associated with broad alterations to the muscle transcriptome, including altered ECM organization, downregulated angiogenesis, and altered expression of pathways related to stem cell self-renewal. Remarkably, despite the seemingly advanced nature of this fibrotic transformation, dialysis treatment rescued these deficits, restoring a healthier muscle phenotype. Furthermore, after accounting for muscle atrophy, strength and endurance improved after dialysis initiation. CONCLUSION These data identify a dialysis-responsive muscle fibrotic phenotype in CKD and suggest the early dialysis window presents a unique opportunity of improved muscle regenerative capacity during which targeted interventions may achieve maximal impact. TRIAL REGISTRATION NCT01452412 FUNDING NIH, NIH Clinical and Translational Science Awards (CTSA), and Einstein-Mount Sinai Diabetes Research Center
Nomograms Based on the Advanced Lung Cancer Inflammation Index for the Prediction of Coronary Artery Disease and Calcification

Aim To develop and validate 3 nomograms incorporating the advanced lung cancer inflammation index (ALI) that can aid in predicting the risk of coronary artery disease (CAD) and coronary artery calcification (CAC). Methods The study enrolled 562 consecutive patients with suspected CAD who underwent coronary computed tomographic angiography between September 2015 and June 2017. Independent risk factors for CAD, CAC, and CAD with CAC were identified via univariate and multivariate analysis, and nomograms were established based on the independent predictors identified. The area under the curve (AUC), calibration curve, and decision curve analysis were used to evaluate the nomograms. Correlations between ALI and other clinical indicators were examined via Spearman correlation analysis. Results In total, 549 patients with suspected CAD who underwent coronary computed tomographic angiography were included. Male sex, hypertension, diabetes, dyslipidemia, ischemic stroke, and ALI were independent predictors of both CAD and CAC. Male sex, hypertension, diabetes, dyslipidemia, and ALI were also identified as independent predictors of CAD with CAC. The AUC values for the nomograms developed using these risk factors were 0.739 (95% confidence interval [CI], 0.693-0.785), 0.728 (95% CI, 0.684-0.772), and 0.717 (95% CI 0.673-0.761), respectively. ALI was negatively correlated with neutrophil-to-lymphocyte ratio and CAC score and positively correlated with serum albumin levels and body mass index (all P < .05). Conclusions ALI is an independent predictor of CAD, CAC, and CAD with CAC. Our ALI-based nomograms can provide accurate and individualized risk predictions for patients with suspected CAD.
Adaptive Lookahead Pure-Pursuit for Autonomous Racing

This paper presents an adaptive lookahead pure-pursuit lateral controller for optimizing racing metrics such as lap time, average lap speed, and deviation from a reference trajectory in an autonomous racing scenario. We propose a greedy algorithm to compute and assign optimal lookahead distances for the pure-pursuit controller for each waypoint on a reference trajectory for improving the race metrics. We use a ROS based autonomous racing simulator to evaluate the adaptive pure-pursuit algorithm and compare our method with several other pure-pursuit based lateral controllers. We also demonstrate our approach on a scaled real testbed using a F1/10 autonomous racecar. Our method results in a significant improvement (20%) in the racing metrics for an autonomous racecar.
Isolation and genomic characterization of a Chinese NADC34-like PRRSV isolated from Jiangsu province.

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important causative agents to swine industry, which has been epidemic more than 30 years. The emergence and recombination of new virus strains bring great challenges to the prevention and control of PRRSV. In the present study, we reported and characterized a novel PRRSV strain, designated as JS2021NADC34, which was for the first time isolated from clinical samples in Jiangsu province, China. Phylogenetic analysis demonstrated that JS2021NADC34 belonging to sublineage 1.5 of PRRSV-2 and was highly related to NADC34-like strains. Genetically, JS2021NADC34 strain had a continuous 100 aa depletion in NSP2, as compared to VR-2332 strain, which was consistent with most reported NADC34-like strains. Moreover, there were several amino acid substitutions occurred in the antigenic regions of GP2-GP5. Similar to other reported NADC34-like PRRSV in China, JS2021NADC34 had no recombination with other domestic strains, which indicates this sublineage of PRRSV may be directly transported from the United States of America and have not undergone extensive mutation and recombination with local strains yet. This article is protected by copyright. All rights reserved.
Protease-dependent defects in N-cadherin processing drive PMM2-CDG pathogenesis

The genetic bases for the congenital disorders of glycosylation (CDG) continue to expand, but how glycosylation defects cause patient phenotypes remains largely unknown. Here, we combined developmental phenotyping and biochemical studies in a potentially new zebrafish model (pmm2sa10150) of PMM2-CDG to uncover a protease-mediated pathogenic mechanism relevant to craniofacial and motility phenotypes in mutant embryos. Mutant embryos had reduced phosphomannomutase activity and modest decreases in N-glycan occupancy as detected by matrix-assisted laser desorption ionization mass spectrometry imaging. Cellular analyses of cartilage defects in pmm2sa10150 embryos revealed a block in chondrogenesis that was associated with defective proteolytic processing, but seemingly normal N-glycosylation, of the cell adhesion molecule N-cadherin. The activities of the proconvertases and matrix metalloproteinases responsible for N-cadherin maturation were significantly altered in pmm2sa10150 mutant embryos. Importantly, pharmacologic and genetic manipulation of proconvertase activity restored matrix metalloproteinase activity, N-cadherin processing, and cartilage pathology in pmm2sa10150 embryos. Collectively, these studies demonstrate in CDG that targeted alterations in protease activity create a pathogenic cascade that affects the maturation of cell adhesion proteins critical for tissue development.
Revised nomenclature and SNP barcode for Mycobacterium tuberculosis lineage 2

Mycobacterium tuberculosis (Mtb) lineage 2 (L2) strains are present globally, contributing to a widespread tuberculosis (TB) burden, particularly in Asia where both prevalence of TB and numbers of drug resistant TB are highest. The increasing availability of whole-genome sequencing (WGS) data worldwide provides an opportunity to improve our understanding of the global genetic diversity of Mtb L2 and its association with the disease epidemiology and pathogenesis. However, existing L2 sublineage classification schemes leave >20 % of the Modern Beijing isolates unclassified. Here, we present a revised SNP-based classification scheme of L2 in a genomic framework based on phylogenetic analysis of >4000 L2 isolates from 34 countries in Asia, Eastern Europe, Oceania and Africa. Our scheme consists of over 30 genotypes, many of which have not been described before. In particular, we propose six main genotypes of Modern Beijing strains, denoted L2.2. M1–L2.2. M6. We also provide SNP markers for genotyping L2 strains from WGS data. This fine-scale genotyping scheme, which can classify >98 % of the studied isolates, serves as a basis for more effective monitoring and reporting of transmission and outbreaks, as well as improving genotype-phenotype associations such as disease severity and drug resistance. This article contains data hosted by Microreact.
Max-Min Grouped Bandits

In this paper, we introduce a multi-armed bandit problem termed max-min grouped bandits, in which the arms are arranged in possibly-overlapping groups, and the goal is to find a group whose worst arm has the highest mean reward. This problem is of interest in applications such as recommendation systems, and is also closely related to widely-studied robust optimization problems. We present two algorithms based successive elimination and robust optimization, and derive upper bounds on the number of samples to guarantee finding a max-min optimal or near-optimal group, as well as an algorithm-independent lower bound. We discuss the degree of tightness of our bounds in various cases of interest, and the difficulties in deriving uniformly tight bounds.
Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features

With the continuous development of underwater vision technology, more and more remote sensing images could be obtained. In the underwater scene, sonar sensors are currently the most effective remote perception devices, and the sonar images captured by them could provide rich environment information. In order to analyze a certain scene, we often need to merge the sonar images from different periods, various sonar frequencies and distinctive viewpoints. However, the above scenes will bring nonlinear intensity differences to the sonar images, which will make traditional matching methods almost ineffective. This paper proposes a non-linear intensity sonar image matching method that combines local feature points and deep convolution features. This method has two key advantages: (i) we generate data samples related to local feature points based on the self-learning idea; (ii) we use the convolutional neural network (CNN) and Siamese network architecture to measure the similarity of the local position in the sonar image pair. Our method encapsulates the feature extraction and feature matching stage in a model, and directly learns the mapping function from image patch pairs to matching labels, and achieves matching tasks in a near-end-to-end manner. Feature matching experiments are carried out on the sonar images acquired by autonomous underwater vehicle (AUV) in the real underwater environment. Experiment results show that our method has better matching effects and strong robustness.
LES of hypersonic flow over a 3D single-fin

LES of hypersonic flow passing a 3D single-fin at Mach 5 and Re∞=3.7×107/m was conducted using a newly developed 7th-order low-dissipation monotonicity-preserving NS solver with dynamic Smagorinsky subgrid model. The simulation captures salient flow phenome such as shock systems, flow separation structures, and turbulence characteristics. It was found that in the reverse flow region, the streamwise elongated coherent structures are regenerated beneath the main separation vortex, almost immediately after the flow reattachment.
Self-Learning Tuning for Post-Silicon Validation

Increasing complexity of modern chips makes design validation more difficult. Existing approaches are not able anymore to cope with the complexity of tasks such as robust performance tuning in post-silicon validation. Therefore, we propose a novel approach based on learn-to-optimize and reinforcement learning in order to solve complex and mixed-type tuning tasks in a efficient and robust way.
A Note on the Gravitoelectromagnetic Analogy

We discuss the linear gravitoelectromagnetic approach used to solve Einstein’s equations in the weak-field and slow-motion approximation, which is a powerful tool to explain, by analogy with electromagnetism, several gravitational effects in the solar system, where the approximation holds true. In particular, we discuss the analogy, according to which Einstein’s equations can be written as Maxwell-like equations, and focus on the definition of the gravitoelectromagnetic fields in non-stationary conditions. Furthermore, we examine to what extent, starting from a given solution of Einstein’s equations, gravitoelectromagnetic fields can be used to describe the motion of test particles using a Lorentz-like force equation.
Survey on Symbiotic Radio: A Paradigm Shift in Spectrum Sharing and Coexistence

Sixth-generation (6G) of mobile communication aims to connect this world digitally through green communication networks that provide secure, ubiquitous, and unlimited connectivity in an attempt to improve the overall quality of life.The driving force behind the development of these networks is the rapid evolution of Internet-of-Things (IoT), which has stimulated the proliferation of wireless applications in health,education, agriculture, utilities, etc. However, these applications are accompanied by the deployment of a massive number of IoT devices that require a significant radio spectrum for wireless connectivity. IoT devices usually have low data rate requirements and limited power provision but desirably a long life.Recently, the development of passive radio systems has opened new paradigms of spectrum sharing and coexistence. These systems utilize the radio resources and infrastructure of the active radio systems to perform their functionalities. By enabling the dependent coexistence, a new technology named symbiotic radio (SRad) enables the symbiotic relationships between the different radio systems ranging from mutual benefits or competition in terms of sharing the resources, in particular for IoT devices. This survey first provides the motivation for dependent coexistence and background of spectrum sharing through coexistence along with existing literature. Then, it describes the active and passive radio systems definition and a brief overview. Afterward, the history of symbiosis and the role of SRad technology in spectrum sharing and coexistence are defined while focusing on symbiotic communication. Lastly, we discuss research challenges, future directions, and applications scenarios.
The effects of the quality nursing mode intervention on the psychological moods, postoperative complications, and nursing satisfaction of breast cancer surgery patients.

OBJECTIVE To evaluate the influence of high-quality care on the psychological well-being, the postoperative complications, and patient satisfaction following a radical mastectomy. METHODS One hundred and eighteen breast cancer patients who underwent radical mastectomies from February, 2017 to May, 2019 in the Cancer Hospital of China Medical University were enrolled. Among them, 56 patients treated with regular care were included in the regular group, while the other 62 patients who underwent high-quality care were included in the high-quality group. The postoperative recoveries and complications were compared between the two groups. The visual analogue scale (VAS), the disabilities of the arm, shoulder and hand (DASH) questionnaire, the self-rating anxiety scale (SAS), the self-rating depression scale (SDS), the quality of life (QOL) assessment scale, and a patient satisfaction questionnaire were used to evaluate the degrees of pain, the upper limb recovery, the negative emotions, the QOL, the patient satisfaction and the two-year survival rate, respectively. RESULTS The ambulation times, the regression times of upper limb swelling, and the hospital stays in the high-quality group were remarkably shorter than they were in the regular group (P<0.05). The postoperative complications were less frequent in the high-quality group (P<0.05). The VAS scores in the high-quality group were lower than they were in the regular group at 1, 3, and 5 days after surgery, and the DASH scores in the high-quality group were lower at 1 month after discharge (P<0.05). The high-quality group showed lower SAS and SDS scores and higher QOL and patient satisfaction than the regular group (all P<0.05). There was no difference in the two-year survival rates between the two groups after the intervention (P>0.05). CONCLUSION High-quality care following a radical mastectomy contributes to postoperative recovery, the relief of negative emotions, the reduction of complications, as well as the enhancement of QOL and patient satisfaction, so it is worthy of clinical promotion.
Platform replacement planning for management of product family obsolescence

In our rapidly changing world, existing technologies quickly obsolesce and new technologies continually emerge. While it is desirable that product platforms are designed for long life spans, state-of-the-art products cannot be developed on platforms with obsolete technologies. The purpose of this study is to develop a model to determine the optimal lifetime of platforms trading-off the cost efficiency of platform development and lost sales due to obsolete technologies. In order to predict sales revenue with respect to platform lifetime, a stochastic product introduction model is developed based on a diffusion model. A computational analysis is performed to reveals the influence of platform features and the market situation on the optimal lifetime and profitability of platforms. Furthermore, the economic value of retarding the speed of obsolescence is assessed in order to glean insights into the design of a robust or flexible platform.
Eccentric Gap Induced by a Super-Jupiter-mass Planet

A giant planet embedded in a protoplanetary disk opens a gap by tidal interaction, and properties of the gap strongly depend on the planetary mass and disk parameters. Many numerical simulations of this process have been conducted, but detailed simulations and analysis of gap formation by a super-Jupiter-mass planet have not been thoroughly conducted. We performed two-dimensional numerical hydrodynamic simulations of the gap formation process by a super-Jupiter-mass planet and examined the eccentricity of the gap. When the planet is massive, the radial motion of gas is excited, causing the eccentricity of the gap’s outer edge to increase. Our simulations showed that the critical planetary mass for the eccentric gap was ∼ 3 M J in a disk with α = 4.0 × 10−3 and h/r = 0.05, a finding that was consistent with that reported in a previous work. The critical planetary mass for the eccentric gap depends on the viscosity and the disk scale height. We found that the critical mass could be described by considering a dimensionless parameter related to the gap depth. The onset of gap eccentricity enhanced the surface density inside the gap, shallowing the gap more than the empirical relation derived in previous studies for a planet heavier than the critical mass. Therefore, our results suggest that the mass accretion rate, which strongly depends on the gas surface density in the gap, is also enhanced for super-Jupiter-mass planets. These results may substantially impact the formation and evolution processes of super-Jupiter-mass planets and population synthesis calculations.
Stories or Directives: A Cross Cultural Comparison of Governmental Messages to Their Constituents during COVID-19

ABSTRACT This cross-sectional investigation examines the message strategies employed by the CDC and the NHC regarding the COVID-19 pandemic and established that messages sent by the CDC via Twitter differed significantly from the messages posted by the NHC via Weibo. Within a random sample (n = 200) of CDC and NHC messaging, six common themes emerged. They were: offering general advice, offering advice for professionals, pandemic progress, organizational efforts, knowledge popularization, and event notification. Results suggest the CDC offered advice to the general public (n = 50) more often than the NHC (n = 19). Similarly, the CDC offered more advice oriented toward professionals (n = 20) than the NHC (n = 9). The NHC, was more likely to discuss the role of government in remedying the pandemic (n = 12) than the CDC (n = 0) and more likely to employ a narrative style in their messaging (n = 35) than the CDC (n = 1).
Zero-shot Singing Technique Conversion

In this paper we propose modifications to the neural network framework, AutoVC for the task of singing technique conversion. This includes utilising a pretrained singing technique encoder which extracts technique information, upon which a decoder is conditioned during training. By swapping out a source singer's technique information for that of the target's during conversion, the input spectrogram is reconstructed with the target's technique. We document the beneficial effects of omitting the latent loss, the importance of sequential training, and our process for fine-tuning the bottleneck. We also conducted a listening study where participants rate the specificity of technique-converted voices as well as their naturalness. From this we are able to conclude how effective the technique conversions are and how different conditions affect them, while assessing the model's ability to reconstruct its input data.
Noise-Induced Aggregation of Swimmers in the Kolmogorov Flow

We investigate a model for the dynamics of ellipsoidal microswimmers in an externally imposed, laminar Kolmogorov flow. Through a phase-space analysis of the dynamics without noise, we find that swimmers favor either cross-stream or rotational drift, depending on their swimming speed and aspect ratio. When including noise, i.e., rotational diffusion, we find that swimmers are driven into certain parts of phase space, leading to a nonuniform steady-state distribution. This distribution exhibits a transition from swimmer aggregation in low-shear regions of the flow to aggregation in high-shear regions as the swimmer’s speed, aspect ratio, and rotational diffusivity are varied. To explain the nonuniform phase-space distribution of swimmers, we apply a weak-noise averaging principle that produces a reduced description of the stochastic swimmer dynamics. Using this technique, we find that certain swimmer trajectories are more favorable than others in the presence of weak rotational diffusion. By combining this information with the phase-space speed of swimmers along each trajectory, we predict the regions of phase space where swimmers tend to accumulate. The results of the averaging technique are in good agreement with direct calculations of the steady-state distributions of swimmers. In particular, our analysis explains the transition from low-shear to high-shear aggregation.