Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide chromatin contacts of super-enhancer-associated lncRNA identify LINC01013 as a regulator of fibrosis in the aortic valve

Calcific aortic valve disease (CAVD) is characterized by a fibrocalcific process. The regulatory mechanisms that drive the fibrotic response in the aortic valve (AV) are poorly understood. Long noncoding RNAs derived from super-enhancers (lncRNA-SE) control gene expression and cell fate. Herein, multidimensional profiling including chromatin immunoprecipitation and sequencing, transposase-accessible chromatin sequencing, genome-wide 3D chromatin contacts of enhancer-promoter identified LINC01013 as an overexpressed lncRNA-SE during CAVD. LINC01013 is within a loop anchor, which has contact with the promoter of CCN2 (CTGF) located at ~180 kb upstream. Investigation showed that LINC01013 acts as a decoy factor for the negative transcription elongation factor E (NELF-E), whereby it controls the expression of CCN2. LINC01013-CCN2 is part of a transforming growth factor beta 1 (TGFB1) network and exerts a control over fibrogenesis. These findings illustrate a novel mechanism whereby a dysregulated lncRNA-SE controls, through a looping process, the expression of CCN2 and fibrogenesis of the AV.
Metformin Alleviates Neuroinflammation Following Intracerebral Hemorrhage in Mice by Regulating Microglia/Macrophage Phenotype in a Gut Microbiota-Dependent Manner

The gut microbiota plays a key role in regulating intracerebral hemorrhage (ICH)-induced neuroinflammation. The anti-neuroinflammatory effects of metformin (Met) have been reported in many central nervous system (CNS) diseases. However, whether Met regulates neuroinflammation through the gut microbiota in ICH-induced brain injury remains unknown. We found that Met treatment substantially alleviated neurological dysfunction and reduced neuroinflammation by inhibiting pro-inflammatory polarization of microglia/macrophages in mice with ICH. Moreover, Met treatment altered the microbiota composition and improved intestinal barrier function. The expression of lipopolysaccharide-binding protein (LBP), a biomarker of intestinal barrier damage, was also significantly reduced by Met treatment. Neuroinflammation was also potently ameliorated after the transplantation of fecal microbiota from Met-treated ICH mice. The neuroprotective effects of fecal microbiota transplantation (FMT) were similar to those of oral Met treatment. However, suppression of the gut microbiota negated the neuroprotective effects of Met in ICH mice. Therefore, Met is a promising therapeutic agent for neuroinflammation owing to ICH-induced imbalance of the gut microbiota.
Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.
A novel signature based on autophagy-related lncRNA for prognostic prediction and candidate drugs for lung adenocarcinoma

Background Autophagy inhibits tumorigenesis by limiting inflammation. Various lncRNAs are associated with tumour biological processes, including lung adenocarcinoma (LUAD), but the role of autophagy-related lncRNAs (ARlncRNAs) in LUAD has not been fully elucidated. Thus, this study aimed to construct a prognostic signature based on ARlncRNAs for LUAD. Methods The RNA-seq (FPKM) data and clinical information of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. After differentially expressed lncRNAs in tumour and normal groups were identified, cox regression analyses were performed to construct a prognostic signature which was then assessed through independent prognostic analysis and functional enrichment analysis. Moreover, based on the mRNAs co-expressed with the ARlncRNAs, several potential small molecule drugs were explored in the Connectivity map (Cmap). Results A signature consisting of seven ARlncRNAs (FAM83A−AS1, LINC01116, ILF3-DT, EBLN3P, AL161785.1, AC092279.1 and AC026355.2) was constructed to predict overall survival (OS) for LUAD. The signature was identified to be independent by the cox regression analysis and obtained the largest area under the curve (AUC =0.721) in the receiver operating characteristic (ROC). Six small molecule drugs (MS-275, methotrexate, desipramine, benzbromarone, rifampicin and doxazosin) were selected from Cmap. Conclusions A novel ARlncRNA signature for LUAD prognostic prediction was constructed, which had better efficacy than the TNM stage and used to propose potential therapeutic regimens for LUAD patients.
Topology Optimization of Silicon Anode Structures for Lithium-Ion Battery Applications

This thesis presents a topology optimization methodology for the systematic design of optimal multifunctional silicon anode structures in lithium-ion batteries. In order to develop next generation high performance lithium-ion batteries, key design challenges relating to the silicon anode structure must be addressed, namely the lithiation-induced mechanical degradation and the low intrinsic electrical conductivity of silicon. As such, this work considers two design objectives of minimum compliance under design dependent volume expansion, and maximum electrical conduction through the structure, both of which are subject to a constraint on material volume. Density-based topology optimization methods are employed in conjunction with regularization techniques, a continuation scheme, and mathematical programming methods. The objectives are first considered individually, during which the iteration history, mesh independence, and influence of prescribed volume fraction and minimum length scale are investigated. The methodology is subsequently extended to a bi-objective formulation to simultaneously address both the compliance and conduction design criteria. A weighting method is used to derive the Pareto fronts, which demonstrate a clear trade-off between the competing design objectives. Furthermore, a systematic parameter study is undertaken to determine the influence of the prescribed volume fraction and minimum length scale on the optimal combined topologies. The developments presented in this work provide a foundation for the informed design and development of silicon anode structures for high performance lithium-ion batteries.
Effectiveness, Acceptability and Safety of Pharmaceutical Management for Combat-Related PTSD in Adults Based on Systematic Review of Twenty-Two Randomized Controlled Trials

Objective: This study assessed the efficacy, acceptability, and safety of pharmaceutical management for combat-related post-traumatic stress disorder (PTSD) to provide a clinical decision-making basis for clinicians. Method: A comprehensive search was conducted using Ovid MEDLINE, Ovid EMBASE, Cochrane Library, Scopus, ScienceDirect, and Web of Science for randomized controlled trails (RCTs), which reported pharmaceutical management and placobo for adults with combat-related PTSD, that were published until April 21, 2021. The effectiveness, acceptability, and adverse events (AEs), were designed as interested outcomes. The change in total symptoms of combat-related PTSD according to the clinician rating scale was defined as primary outcome, and the others were defined as secondary outcomes. Results: Twenty-two RCTs with 1,221 patients were involved. Compared with placebo, overall active comparators had statistical differences for all outcomes, including the change in total symptoms of combat-related PTSD [SMD = −0.36, 95%CI (−0.62,−0.09)], depression [SMD = −0.28, 95%CI (−0.45,−0.10)], anxiety [SMD = −0.44, 95%CI (−0.64,−0.23)], re-experience [SMD = −0.33, 95%CI (−0.52,−0.13)], avoidance [SMD = −0.24, 95%CI (−0.43,−0.05)], and hyper-arousal [SMD = −0.26, 95%CI (−0.48,−0.03)]. Compared with the placebo, in terms of acceptability, overall active comparators did not significantly decrease all-cause discontinuance rates [RR = 0.97, 95%CI (0.78,1.20)], and the significance decreased due to AEs [RR = 2.42, 95%CI (1.41,4.13)]. Nevertheless, overall there was no statistically significant difference for overall AEs, including somnolence, sedation, dizziness, paresthesia, anxiety, blurred vision, generalized anxiety disorder, and sleep disturbance. All funnel plots were symmetrical and no publication bias was found. Conclusion: Active drugs, especially amitriptyline, imipramine, and quetiapine, had a positive effect on the improvement of combat-related PTSD symptoms. Despite there being no significant increase in the AEs of the active drugs, the fact that the discontinuation rates of these drugs, including risperidone, imipramine, and topiramate, were increased deserves attention. Furthermore, as active drugs were effective across ethnic groups and battlefields, active drug regimens were revealed to be more appropriate for treating people with symptoms of extreme severe PTSD (≥80) or PTSD that is at least 8 weeks old. In addition, current evidence was from adults under 60 years of age and male combat-related PTSD. Whether this evidence can be extended to other populations of combat-related PTSD needs to be confirmed by subsequent high-quality, large-sample studies.
Phenotypes of atopic dermatitis up to 36 months of age by latent class analysis and associated factors in Japan

Background Atopic dermatitis (AD) in early childhood is the first allergic manifestation in the atopic march. Recently, latent class analysis (LCA) has revealed the presence of AD subgroups in childhood. Objective This study aimed to elucidate different AD phenotypes up to 36 months of age and identify factors associated with a particular AD phenotype in early childhood. Methods Pediatric allergists or dermatologists examined children who visited local public health centers in Chiba or Yokohama city at 4, 18, and 36 months of age for regular health checkups between 2003 and 2005. LCA was used to identify AD subtypes on the basis of the course of skin symptoms and comorbidity of other allergic diseases. After LCA, the association between genetic and environmental factors and AD phenotypes was assessed. Results A total of 1,378 children who underwent the 3 checkups were included. Complete data were available for 515 children up to 36 months of age. Of 515 children, 183 were diagnosed with AD at least at one out of the 3 time points. The LCA model of these children with AD separated 4 AD phenotypes: early-persistent (EP), early-transient (ET), late-onset (LO), and variable (V). Antibiotic use by 4 months of age was significantly higher in EP group than in other 3 groups. Mother’s allergy was significantly higher in EP and LO groups than in other 2 groups. Passive smoking at 18 months of age was higher in LO group than in other groups. Furthermore, >80% of V group was born in spring–summer. Conclusion We identified 4 AD phenotypes using LCA on the basis of the onset/course of AD and comorbidity of other allergic diseases and also identified several factors related to the particular phenotypes, which may be useful markers for the prediction of prognosis of AD in early childhood.
Can the diligent governance increase subjective wellbeing? New evidence from environmental regulations in China

With the appearance of “wellbeing stagnation”, the Chinese government has gradually realized the negative impact of increasingly severe environmental problem on people’s wellbeing, and has then has formulated a series of environmental policies. Based on the balanced panel data from 2014 to 2018 from China Family Panel Studies (CFPS) and by means of the fixed effects model, we analyze the relationships between heterogeneous environmental regulations (ERs) and subjective wellbeing (SWB) from the perspective of diligent governance. Our results show that command-control environmental regulation (CER) and voluntary environmental regulation (VER) have positive effects on SWB, but there exist the heterogeneity effects in the links between ERs and SWB. Vulnerable populations, including those with rural hukou, less educated, have paid more attention to VER, whereas the view of other groups is the opposite. Similarly, the people with low incomes or living in economically underdeveloped areas or western region, are sensitive to VER, while the others only pay attention to CER. The SWB of those with better health can be enhanced by CER, and the SWB of those with poor health are unaffected by CER and VER. Further channel analysis illustrates that CER can improve SWB by increasing people’s evaluation of the government, while VER cannot. Our results imply that the people would place more weight on environmental governance as their income rises, and can help the government institute more flexible environmental policies to improve people’s wellbeing.
Sub-Nyquist Sampling of ECG Signals Based on the Extension of Variable Pulsewidth Model

To prevent lifestyle diseases, portable heartbeat detection systems by electrocardiograms (ECGs) for daily life monitoring have attracted attentions. For portable systems, power consumption is critical and it is thus beneficial to reduce the sampling frequency at the point of acquisition. Recent developed variable pulsewidth finite rate of innovation (VPW-FRI) framework provides an effective way for low-rate sampling and compression of ECG signals. Unfortunately, noise and model mismatch will induce inaccuracies to such scheme in the reconstruction process. In this article, a sub-Nyquist sampling scheme based on the extension of VPW model is proposed for ECG signals, which reduce the model dismatch error, and improve the reconstruction quality under model mismatch and noise scenario. In the proposed scheme, ECG signal is modeled as the differentiated VPW model to reduce the model mismatch error. Then a FRI sampling framework based on the differentiated VPW model is proposed to sample the ECG signal at a sub-Nyquist rate. After the ECG signal is sampled, some consecutive Fourier series coefficients are calculated according to the samples. Then a modified annihilating filter algorithm is proposed to estimate parameters of the ECG signal and reconstruct the ECG signal. Finally, simulation experiments with real ECG signals from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database have shown that the proposed scheme outperformed the traditional methods for model mismatch. The signal-to-residual ratio (SRR) of the reconstructed signal of the proposed scheme is higher than that of the VPW-FRI scheme.
Metodologia baseada em técnicas de mineração de dados para suporte à certificação de raças de ovinos

O objetivo deste trabalho foi desenvolver uma metodologia baseada em tecnicas de mineracao de dados para selecionar os principais marcadores SNP (Single Nucleotide Polymorphism) para as racas de ovinos: Crioula, Morada Nova e Santa Ines. Os dados utilizados foram obtidos do Consorcio Internacional de Ovinos e sao compostos por 72 animais das racas citadas, e cada animal possui 49.034 marcadores SNP. Considerando que o numero de atributos (marcadores) e muito maior que o de observacoes (animais), foram aplicadas as tecnicas de predicao LASSO (Least Absolute Shrinkage and Selection Operator), Random Forest e Boosting para a geracao de modelos preditivos que incorporam metodos de selecao de atributos. Os resultados revelaram que os modelos preditivos selecionaram os principais marcadores SNP para identificacao das racas estudadas. O modelo LASSO selecionou um total de 29 marcadores relevantes. A partir dos modelos Random Forest e Boosting, foram obtidos 27 e 20 marcadores importantes, respectivamente. Por meio da interseccao dos modelos gerados, identificou-se um subconjunto de 18 marcadores com maior potencial de identificacao das racas.
Experimental Model of Cardiotoxicity

The occurrence of heart electrophysiology dysfunction or/and muscle damage is referred to as cardiotoxicity. The heart weakens and becomes less efficient at pumping and hence circulating blood. Cardiomyopathy can be caused by a variety of factors, including viral infections, diseases such as diabetes, ischemia, hypertension, obesity, radiation therapy, antipsychotic drugs, cytotoxic drugs, most notably chemotherapeutic agents; antitumor antibiotics, monoclonal antibodies, tyrosine kinase inhibitors, platinum-based compounds, microtubule inhibitors, vinca alkaloids, antimetabolites, proteasome inhibitors, topoisomerase inhibitors, alkylating agents, corticosteroids. This chapter focuses on the mechanisms of cardiotoxicity, animal models and transgenic methods used in studies, and the effects of therapeutic agents on cardiotoxicity.
ProSGPV: an R package for variable selection with second-generation p-values

We introduce the ProSGPV R package, which implements a variable selection algorithm based on second-generation p-values (SGPV) instead of traditional p-values. Most variable selection algorithms shrink point estimates to arrive at a sparse solution. In contrast, the ProSGPV algorithm accounts for the estimation uncertainty – via confidence intervals – in the selection process. This additional information leads to better inference and prediction performance in finite sample sizes. ProSGPV maintains good performance even in the high dimensional case where $p>n$, or when explanatory variables are highly correlated. Moreover, ProSGPV is a unifying algorithm that works with continuous, binary, count, and time-to-event outcomes. No cross-validation or iterative processes are needed and thus ProSGPV is very fast to compute. Visualization tools are available in this package for assessing the variable selection process. Here we present simulation studies and a real-world example to demonstrate ProSGPV’s inference and prediction performance in relation to the current standards in variable selection procedures.
Simulation and Modeling of Diesel Engine Urea-SCR System

The models of diesel engine,which was based on GT-Power software,and one-dimensional SCR were built and verified on the test bench.For the ESC,the maximum errors of exhaust flow and exhaust temperature between simulation results and measuring results were respectively 4.13% and 8.3%. For the SCR model,the simulation results had a good coincidence with the measuring results.Coupling the two models,the SCR system of diesel engine was simulated and analyzed.The simulation results show that the temperature distribution of SCR-catalyst is linear in the direction of exhaust flow.The SCR reactions mainly occurred in the inlet of catalyst.The converting ability has close relationship with the ammonia coverage of catalyst surface.Along the exhaust flow,the adhesive ability of ammonia can improve the converting performance significantly.The simulation results can be used to make the control strategy of SCR system and the design of SCR converter.
Ultra Low Side Lobe Level Synthesis with Particle Swarm Optimization for Symmetrical Non-uniform Linear Array Antennas

Ultra low side lobe level (ULSLL) array antennas have important applications in radar and communication systems, ULSLLs are usually realized with uniform linear array (ULA) or planar array antennas by excitation amplitude weighting, such as the current distributions resulted from Dolph-Chebyshev or Taylor synthesis. Lower SLL, however, implies lower aperture e-ciency and larger excitation amplitude ratio (EAR), which makes the feed network more complicated and more di-cult to fabricate; lower SLL is also very sensitive to the excitation amplitude/phase errors. In order to mitigate this di-culty, both the excitation amplitudes and the element positions are used for ULSLL synthesis, where the element positions act as extra freedoms for further SLL suppression. A symmetrical non-uniform linear array (SNULA) is proposed, the full information particle swarm optimization (FIPSO) based on the Von Neumann local neighborhood topology is adopted for ULSLL pattern synthesis, because it is efiective for multi-dimensional, multi-local-minimum, non-linear complex problems and robust convergence. In the algorithm, the inertia weight is improved via the sigmoid limiting function; the boundary conditions are dealt with a randomized damp re∞ection. Two numerical examples are given, the flrst one is an 8-element i40dB SLL array antenna, for comparison, a ULA with the same aperture size, element number and peak SLL is synthesized by Dolph-Chebyshev method, the results obtained show that they have nearly identical main beam and equal side lobes, but the SNULA has lower EAR and higher aperture e-ciency than those of the ULA. The second example is a 12-element i45dB SLL array, similar rules are obtained.
Realist review

ABSTRACT In the field of sport and exercise psychology, the systematic review is still considered by many, to be the gold standard literature review. However, reviews like these, which judge whether an intervention works or not, can leave many unanswered questions. When it comes to complex social interventions, failing to suggest reasons for an intervention’s efficacy, often means practitioners struggle to successfully implement research findings in the real world. This lack of detail has led many to question the utility of systematic reviews when examining behaviour change interventions in complex social environments. Realist reviews are increasing in popularity because they go beyond asking if an intervention works, to look for theories as to why a programme worked, who it worked for and in what context it worked. However, despite the growing popularity of realist reviews, there is still limited application of this approach across the sport and exercise sciences. This paper aims to increase awareness regarding the utility of realist research by offering an introduction to realist review and an explanation of the steps involved.
Role of patient-specific blood properties in computational fluid dynamics simulation of flow diverter deployed cerebral aneurysms

BACKGROUND: Hemodynamics and their clinical outcome of cerebral aneurysms treated with flow diverter (FD) stents have thus far been investigated using computational fluid dynamics (CFD) simulations. Although human blood is characterized as a non-Newtonian patientspecific fluid, non-patient-specific blood properties (PSBP) were applied in most extant studies. OBJECTIVE: To investigate the hemodynamic effects caused by PSBPs in aneurysms treated with FD stents. METHODS: We measured blood properties (density and viscosity) for 12 patients who underwent FD stent deployment. We conducted CFD simulations with the measured PSBPs and non-PSBPs quoted from previous studies. The average blood flow velocity and wall shear stress within the aneurysms were calculated and two simulation patterns were compared. RESULTS: The velocity and wall shear stress changed by 2.93% and 3.16% on average, respectively, without an FD stent deployed. Conversely, with the FD stents deployed, the change rates increased to 11.1% and 9.06% on average, respectively. CONCLUSIONS: The change in hemodynamic parameters if PSBPs are considered, may not be negligible when conducting CFD simulations of FD stent deployed aneurysms To obtain an adequate hemodynamic environment for cerebral aneurysms with FD stents deployed, it is recommended to use PSBPs for CFD simulations.