Miracle-dz/UniFAR-FTUs huggingface dataset:
Series Solution of the Time-Dependent Schrödinger-Newton Equations in the Presence of Dark Energy via the Adomian Decomposition Method

The Schrödinger–Newton model is a nonlinear system obtained by coupling the linear Schrödinger equation of canonical quantum mechanics with the Poisson equation of Newtonian mechanics. In this paper, we investigate the effects of dark energy on the time-dependent Schrödinger–Newton equations by including a new source term with energy density proportional to the cosmological constant Λ, in addition to the particle-mass source term. The resulting Schrödinger–Newton–Λ (S-N-Λ) system cannot be solved exactly, in closed form, and one must resort to either numerical or semianalytical (i.e., series) solution methods. We apply the Adomian Decomposition Method, a very powerful method for solving a large class of nonlinear ordinary and partial differential equations, to obtain accurate series solutions of the S-N-Λ system, for the first time. The dark energy dominated regime is also investigated in detail. We then compare our results to existing numerical solutions and analytical estimates and show that they are consistent with previous findings. Finally, we outline the advantages of using the Adomian Decomposition Method, which allows accurate solutions of the S-N-Λ system to be obtained quickly, even with minimal computational resources. The extensive use of the Adomian Decomposition Method in the field of quantum mechanics and quantum field theory may open new mathematical, and physical, perspectives on obtaining semi-analytical solutions for some complex problems of quantum theory.
Discrete time multi-period mean-variance model: Bellman type strategy and Empirical analysis.

In this paper, we attempt to introduce the Bellman principle for a discrete time multi-period mean-variance model. Based on this new take on the Bellman principle, we obtain a dynamic time-consistent optimal strategy and related efficient frontier. Furthermore, we develop a varying investment period discrete time multi-period mean-variance model and obtain a related dynamic optimal strategy and an optimal investment period. This paper compares the highlighted dynamic optimal strategies of this study with the 1/n equality strategy, and shows that we can secure a higher return with a smaller risk based on the dynamic optimal strategies.
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations

Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists, and continuing to optimize ELBO after this value will not improve inference accuracy. On the basis of the experiment results, we propose SHOT-VAE to address these problems without introducing additional prior knowledge. The SHOT-VAE offers two contributions: (1) A new ELBO approximation named smooth-ELBO that integrates the label predictive loss into ELBO. (2) An approximation based on optimal interpolation that breaks the ELBO value bottleneck by reducing the margin between ELBO and the data likelihood. The SHOT-VAE achieves good performance with 25.30% error rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on CIFAR-10 with 4k labels.
Doubly weighted M-estimation for nonrandom assignment and missing outcomes

This article proposes a class of M-estimators that double weight for the joint problems of nonrandom treatment assignment and missing outcomes. Identification of the main parameter of interest is achieved under unconfoundedness and missing at random assumptions with respect to the treatment and sample selection problems, respectively. Given the parametric framework, the asymptotic theory of the proposed estimator is outlined in two parts: first, when the parameter solves an unconditional problem, and second, when it solves a stronger conditional problem. The two parts help to summarize the misspecification scenarios permissible under the given framework and the role played by double weighting in each. As illustrative examples, the article also discusses the estimation of causal parameters like average and quantile treatment effects. With respect to the average treatment effect, this article shows that the proposed estimator is doubly robust. Finally, a detailed application to Calónico and Smith’s (The women of the national supported work demonstration. J Labor Econom. 2017;35(S1):S65–S97. ) reconstructed sample from the National Supported Work training program is used to demonstrate the estimator’s performance in empirical settings.
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition

A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames. Recent works suggest establishing accurate correspondences between neighboring frames before fusing temporal information. However, the generated results heavily depend on the quality of correspondence estimation. This paper proposes a more robust solution: sampling and fusing multi-level features across neighborhood frames to generate the results. Based on this idea, we introduce a new module to improve the capability of 3D convolution, namely, learnable sampling 3D convolution (LS3D-Conv). We add learnable 2D offsets to 3D convolution, aiming to sample locations on spatial feature maps across frames. The offsets can be learned for specific tasks. The LS3D-Conv can flexibly replace 3D convolution layers in existing 3D networks and get new architectures, which learns the sampling at multiple feature levels. The experiments on video interpolation, video super-resolution, video denoising, and action recognition demonstrate the effectiveness of our approach.
An Effective Anti-Aliasing Approach for Residual Networks

Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning. However, with the advent of large datasets, many practitioners concluded that this was unnecessary due to the belief that these priors can be learned from the data itself. Frequency aliasing is a phenomenon that may occur when sub-sampling any signal, such as an image or feature map, causing distortion in the sub-sampled output. We show that we can mitigate this effect by placing non-trainable blur filters and using smooth activation functions at key locations, particularly where networks lack the capacity to learn them. These simple architectural changes lead to substantial improvements in out-of-distribution generalization on both image classification under natural corruptions on ImageNet-C [10] and few-shot learning on Meta-Dataset [17], without introducing additional trainable parameters and using the default hyper-parameters of open source codebases.
Investigation of superspreading COVID-19 outbreak events in meat and poultry processing plants in Germany: A cross-sectional study

Since May 2020, several COVID-19 outbreaks have occurred in the German meat industry despite various protective measures, and temperature and ventilation conditions were considered as possible high-risk factors. This cross-sectional study examined meat and poultry plants to assess possible risk factors. Companies completed a self-administered questionnaire on the work environment and protective measures taken to prevent SARS-CoV-2 infection. Multivariable logistic regression analysis adjusted for the possibility to distance at least 1.5 meters, break rules, and employment status was performed to identify risk factors associated with COVID-19 cases. Twenty-two meat and poultry plants with 19,072 employees participated. The prevalence of COVID-19 in the seven plants with more than 10 cases was 12.1% and was highest in the deboning and meat cutting area with 16.1%. A subsample analysis where information on maximal ventilation rate per employee was available revealed an association with the ventilation rate (adjusted odds ratio (AOR) 0.996, 95% CI 0.993–0.999). When including temperature as an interaction term in the working area, the association with the ventilation rate did not change. When room temperatures increased, the chance of testing positive for COVID-19 (AOR 0.90 95% CI 0.82–0.99) decreased, and the chance for testing positive for COVID-19for the interaction term (AOR 1.001, 95% CI 1.000–1.003) increased. Employees who work where a minimum distance of less than 1.5 m between workers was the norm had a higher chance of testing positive (AOR 3.61; 95% CI 2.83–4.6). Our results further indicate that climate conditions and low outdoor air flow are factors that can promote the spread of SARS-CoV-2 aerosols. A possible requirement for pandemic mitigation strategies in industrial workplace settings is to increase the ventilation rate.
Bridging from single to collective cell migration: A review of models and links to experiments

Mathematical and computational models can assist in gaining an understanding of cell behavior at many levels of organization. Here, we review models in the literature that focus on eukaryotic cell motility at 3 size scales: intracellular signaling that regulates cell shape and movement, single cell motility, and collective cell behavior from a few cells to tissues. We survey recent literature to summarize distinct computational methods (phase-field, polygonal, Cellular Potts, and spherical cells). We discuss models that bridge between levels of organization, and describe levels of detail, both biochemical and geometric, included in the models. We also highlight links between models and experiments. We find that models that span the 3 levels are still in the minority.
Community Health Behaviors and Geographic Variation in Early-Onset Colorectal Cancer Survival Among Women

INTRODUCTION: Despite overall reductions in colorectal cancer (CRC) morbidity and mortality, survival disparities by sex persist among young patients (age <50 years). Our study sought to quantify variance in early-onset CRC survival accounted for by individual/community-level characteristics among a population-based cohort of US women. METHODS: Geographic hot spots—counties with high early-onset CRC mortality rates among women—were derived using 3 geospatial autocorrelation approaches with Centers for Disease Control and Prevention national mortality data. We identified women (age: 15–49 years) diagnosed with CRC from 1999 to 2016 in the National Institutes of Health/National Cancer Institute's Surveillance, Epidemiology, and End Results program. Patterns of community health behaviors by hot spot classification were assessed by Spearman correlation (ρ). Generalized R2 values were used to evaluate variance in survival attributed to individual/community-level features. RESULTS: Approximately 1 in every 16 contiguous US counties identified as hot spots (191 of 3,108), and 52.9% of hot spot counties (n = 101) were located in the South. Among 28,790 women with early-onset CRC, 13.7% of cases (n = 3,954) resided in hot spot counties. Physical inactivity and fertility were community health behaviors that modestly correlated with hot spot residence among women with early-onset CRC (ρ = 0.21 and ρ = −0.23, respectively; P < 0.01). Together, individual/community-level features accounted for distinct variance patterns in early-onset CRC survival among women (hot spot counties: 33.8%; non–hot spot counties: 34.1%). DISCUSSION: Individual/community-level features accounted for approximately one-third of variation in early-onset CRC survival among women and differed between hot spot vs non–hot spot counties. Understanding the impact of community health behaviors—particularly in regions with high early-onset CRC mortality rates—is critical for tailoring strategies to reduce early-onset CRC disparities.
BCG Vaccine Derived Peptides Induce SARS-CoV-2 T Cell Cross-Reactivity

Epidemiological studies and clinical trials suggest Bacillus Calmette-Guérin (BCG) vaccine has protective effects against coronavirus disease 2019 (COVID-19). There are now over 30 clinical trials evaluating if BCG vaccination can prevent or reduce the severity of COVID-19. However, the mechanism by which BCG vaccination can induce severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell responses is unknown. Here, we identify 8 novel BCG-derived peptides with significant sequence homology to either SARS-CoV-2 NSP3 or NSP13-derived peptides. Using an in vitro co-culture system, we show that human CD4+ and CD8+ T cells primed with a BCG-derived peptide developed enhanced reactivity to its corresponding homologous SARS-CoV-2-derived peptide. As expected, HLA differences between individuals meant that not all persons developed immunogenic responses to all 8 BCG-derived peptides. Nevertheless, all of the 20 individuals that were primed with BCG-derived peptides developed enhanced T cell reactivity to at least 7 of 8 SARS-CoV-2-derived peptides. These findings provide an in vitro mechanism that may account, in part, for the epidemiologic observation that BCG vaccination confers some protection from COVID-19.
Is Standard Personal Protective Equipment Effective Enough To Prevent COVID-19 Transmission During Aerosol Generating Dental, Oral and Maxillofacial Procedures ? A Systematic Review

A systematic review was performed to answer the following questions: 1) Do dental , oral and maxillofacial (OMF) surgical procedures generate bioaerosols (and if so, which ones), which can result in transmission of COVID-19? ; 2) Are aerosolized airborne droplets (and to which extent is splatter) in dental and OMF procedures infective? ; 3) Is enhanced personal protective equipment (PPE) an essential to prevent spreading of COVID-19 during dental and OMF aerosol generating procedures (AGPs)? Authors performed a systematic review to retrieve all pertinent literature that assessed effectiveness of surgical mask vs respirators for protecting dental health care workers during dental and OMF AGPs surgical procedures. Additionally, studies which assessed potential aerosolization during dental, OMF and orthopaedic surgeries were retrieved. There is moderate evidence showing that ultrasonic scaling and bone drilling using high speed rotary instruments produces respirable aerosols. Additionally, there is very weak/inconclusive evidence to support the creation of infectious aerosols during dental procedures. According to available very weak/inconclusive evidence, transmission of SARS-CoV-2 via infective aerosol during AGPS, so far, must remain speculative and controversial. As, however, this is a probable opportunistic way of transmission which at least cannot be sufficiently excluded and therefore should not be dismissed out of hand prematurely, proper and equally important properly applied protective equipment (i.e., N95 respirators or FFP-2 masksv or above regarding mouth and nose protection) should always be used during AGPs.
Short-Term Quality of Life, Functional Status, and Their Predictors in Tongue Cancer Patients After Anterolateral Thigh Free Flap Reconstruction: A Single-Center, Prospective, Comparative Study

Purpose To assess the quality of life, functional status, and their predictors in tongue cancer patients up to three months after anterolateral thigh free flap (ALTFF) reconstruction. Patients and Methods Tongue cancer patients were examined before and one and three months after ALTFF reconstruction using three validated questionnaires: the MD Anderson Dysphagia Inventory (MDADI), Eating Assessment Tool (EAT-10), and Functional Assessment of Cancer Therapy-Head and Neck (FACT-H&N). Mean scores were compared using the Friedman test. Forward selection and backward elimination methods of multiple linear regression analysis were used to identify the predictors of quality of life and functional status using SPSS at a 95% significance level. Results The present study included 265 participants (88.68% males, mean age 46.7 ± 11.05 years). The mean MDADI and EAT-10 scores were highest at one month after the surgery and lowest at three months after the surgery (p < 0.05). The mean FACT-H&N score was lowest at one month after the surgery (p < 0.05); however, post hoc analysis revealed that the difference in the scores before and three months after the surgery was non significant (p > 0.05). Denture use, occupation, age, tumor site, illness duration, drinking habit, and diet predicted the patients’ quality of life, while denture use, betel nut consumption, age, marital status, and diet predicted their functional status. Conclusion Although tongue cancer patients have a poor quality of life and functional status in the first month after ALTFF reconstruction, their quality of life and functional status improve thereafter. We recommend the implementation of swallowing training programs and case-oriented psychological interventions to assist patients in coping with temporary deterioration during the first month after the surgery.
Interstellar and Circumgalactic Properties of an Unseen $z=6.84$ Galaxy: Abundances, Ionization, and Heating in the Earliest Known Quasar Absorber.

We analyze relative abundances and ionization conditions in a strong absorption system at z=6.84, seen in the spectrum of the z=7.54 background quasar ULAS J134208.10+092838.61. Singly ionized C, Si, Fe, Mg, and Al measurements are consistent with a warm neutral medium that is metal-poor but not chemically pristine. Firm non-detections of C IV and Si IV imply that any warm ionized phase of the IGM or CGM has not yet been enriched past the ultra-metal-poor regime (<0.001Z_{solar}), unlike lower redshift DLAs where these lines are nearly ubiquitous. Relative abundances of the heavy elements 794 Myr after the Big Bang resemble those of metal-poor damped Lyman Alpha systems at intermediate redshift and Milky Way halo stars, and show no evidence of enhanced [alpha/Fe], [C/Fe] or other signatures of yields dominated by massive stars. A detection of the CII* fine structure line reveals local sources of excitation from heating, beyond the level of photo-excitation supplied by the CMB. We estimate the total and [CII] cooling rates, balancing against ISM heating sources to develop an heuristic two-phase model of the neutral medium. The implied heating requires a surface density of star formation slightly exceeding that of the Milky Way but not at the level of a strong starburst. For a typical (assumed) NHI=10^{20.6}, an abundance of [Fe/H]=-2.2 matches the columns of species in the neutral phase. To remain undetected in C IV, a warm ionized phase would either need much lower [C/H]<-4.2 over an absorption path of 1 kpc, or else a very small absorption path (a few pc). While still speculative, these results suggest a significant reduction in heavy element enrichment outside of neutral star forming regions of the ISM, as would be expected in early stages of galactic chemical evolution.
mRNAs in urinary nano-extracellular vesicles as potential biomarkers for non-invasive kidney biopsy

Urinary nano-extracellular vesicles (NVs), including exosomes and microvesicles, are considered potential biomarkers for kidney diseases using liquid biopsies. However, clinical application of urinary NVs has not yet been validated. In the present study, the levels of mRNAs in urinary NVs in animal models of kidney disease were assessed. Urine samples were collected from the animal models and urinary NVs were isolated by ultracentrifugation. Gene expression levels of kidney injury markers in urinary NVs and renal tissue were quantified by reverse transcription-quantitative PCR. The mRNA levels of desmin, a podocyte injury marker, in urinary NVs was markedly increased in the puromycin aminonucleoside (PAN) nephritis model, in parallel with enhanced desmin expression in kidney tissues. The expression of regulator of calcineurin 1 and the podocin to nephrin ratio (PNR) were also increased in the PAN nephritis model. Treatment with prednisolone mitigated these changes in gene expression as well as proteinuria. PNR, which is considered a predictive marker of glomerular dysfunction, in urinary NVs was highly correlated with urinary protein excretion (P<0.01). Furthermore, PNR in urinary NVs of Zucker diabetic fatty rats, a diabetic kidney disease model, was correlated with urinary albumin excretion (P<0.01). These results suggest that changes in mRNA levels of urinary NVs reflect the disease status of kidney tissues and their functional alterations. Collectively, mRNA analysis of urinary NVs may be used as a liquid biopsy tool for improved classification and performance of risk prediction to determine the severity of kidney diseases.
Ion channels : from structure to function

INTRODUCTION 1. Introduction to Ion Channel Structure and Function VOLTAGE GATED ION CHANNELS 2.1 Voltage-Gated K+ channels 2.2 Kv1-Kv6 \ Kv8-Kv9 family 2.3 Kv7 family 2.4 Kv10-Kv12 families 2.5 K2P families 2.6 Voltage-Gated Ca2+ Channels 2.7 Voltage-Gated Na+ Channels 2.8 Voltage-Gated Chloride Channels 2.9 Hyperpolarization-Activated Channels EXTRACELLULAR LIGAND-GATED ION CHANNELS 3.1 Cys Loop Receptors 3.2 Nicotinic Acetylcholine Receptors 3.3 5-HT3 receptors 3.4 Glycine Receptors 3.5 GABAA receptors 3.6 Glutamate Receptors 3.7 AMPA Receptors 3.8 Kainate Receptors 3.9 NMDA Receptors 3.10 ATP Receptors 3.11 P2X Receptors 3.12 Others 3.13 Acid Sensing Ion Channels (ASICs) 3.14 Epithelial Sodium Channels INTRACELLULAR LIGAND-GATED ION CHANNELS 4.1 Chloride Channels Activated by Intracellular Ligands 4.2 Cyclic Nucleotide-Gated Cation Channels 4.3 KCa1- KCa5 Families 4.4 KIR family 4.5 KIR1,2,4,5,7 families 4.6 KIR3 family 4.7 KIR6 (KATP) family 4.8 Ryanodine Receptors 4.9 IP3 receptor POLYMODAL GATED ION CHANNELS 5.1 Transient Receptor Potential Channels 5.2 Connexins - Gap Junctions
Adhesive Shear Strength of Ice from Nanostructured Graphite Surfaces by Molecular Dynamics Simulations.

The issue of ice accumulation at low-temperature circumstances causes multiple problems and serious damages in many civil infrastructures which substantially influence human daily life. However, despite the significant consideration in manufacturing anti-icing or icephobic surfaces, it is still demanding to design surfaces with well ice-repellent properties. Here in this study, we used all-atom molecular dynamics (MD) simulations to investigate ice shearing mechanism on atomistically smooth and nanotexture graphite substrates. We find that ice shearing strength strongly depends on ice temperature, the lattice structure of the surface substrate, the size of the surface nanotexture structure, and the depth of interdigitated water molecules. Our results indicate nanoscale surface roughness and depth of interdigitated water molecules tend to increase ice shear failure stress and for corrugated substrates, this is further raised with increasing the depth of interdigitated water molecules which is a result of strain being distributed well into the ice cube away from the interface. These results supply an in-depth understanding of the effect of surface nanotexture on ice shearing mechanism that provides useful information in designing anti-icing surfaces and provide for the first-time theoretical references in understanding the effect of surface nanotexture structure and depth of interlocked water on adhesive ice shear strength on nanotextured surfaces. Keywords: Ice, Graphene, Shear strength, Molecular dynamics simulation