Miracle-dz/UniFAR-FTUs huggingface dataset:
Transmural Dispersion of Repolarization in Failing and Nonfailing Human Ventricle

Rationale: Transmural dispersion of repolarization has been shown to play a role in the genesis of ventricular tachycardia and fibrillation in different animal models of heart failure (HF). Heterogeneous changes of repolarization within the midmyocardial population of ventricular cells have been considered an important contributor to the HF phenotype. However, there is limited electrophysiological data from the human heart. Objective: To study electrophysiological remodeling of transmural repolarization in the failing and nonfailing human hearts. Methods and Results: We optically mapped the action potential duration (APD) in the coronary-perfused scar-free posterior-lateral left ventricular free wall wedge preparations from failing (n 5) and nonfailing (n 5) human hearts. During slow pacing (S1S1 2000 ms), in the nonfailing hearts we observed significant transmural APD gradient: subepicardial, midmyocardial, and subendocardial APD80 were 383 21, 455 20, and 494 22 ms, respectively. In 60% of nonfailing hearts (3 of 5), we found midmyocardial islands of cells that presented a distinctly long APD (537 40 ms) and a steep local APD gradient (27 7 ms/mm) compared with the neighboring myocardium. HF resulted in prolongation of APD80: 477 22 ms, 495 29 ms, and 506 35 ms for the subepi-, mid-, and subendocardium, respectively, while reducing transmural APD80 difference from 111 13 to 29 6 ms (P<0.005) and presence of any prominent local APD gradient. In HF, immunostaining revealed a significant reduction of connexin43 expression on the subepicardium. Conclusions: We present for the first time direct experimental evidence of a transmural APD gradient in the human heart. HF results in the heterogeneous prolongation of APD, which significantly reduces the transmural and local APD gradients. (Circ Res. 2010;106:981-991.)
European Journal of Foreign Language Teaching - ISSN 2501-1754

The importance of learning multiple languages is accepted universally. Improving language skills of the entire population is a priority for Turkey to increase the international competitiveness and also for the perspective integration to EU. Despite the fact that a long history and extensive efforts devoted to foreign language teaching in Turkey, the success has been noted to be limited. In this study, the potential problems and challenges during foreign language education in the higher education institutions were evaluated based on interviews with undergraduate students who were learning English. The study was conducted with a total of 32 students enrolled to various departments at Kafkas University. The results revealed that the students identified a number of challenges that they face while gaining the language skills along with personal strategies to overcome such challenges. Heavy grammar based teaching strategy and evaluation along with low level of native exposure and inadequate student participation were the main problems suggested by students.
How does Working from Home Affect Developer Productivity? - A Case Study of Baidu During COVID-19 Pandemic

Nowadays, working from home (WFH) has become a popular work arrangement due to its many potential benefits for both companies and employees (e.g., increasing job satisfaction and retention of employees). Many previous studies have investigated the impact of working from home on the productivity of employees. However, most of these studies usually use a qualitative analysis method such as survey and interview, and the studied participants do not work from home for a long continuing time. Due to the outbreak of coronavirus disease 2019 (COVID-19), a large number of companies asked their employees to work from home, which provides us an opportunity to investigate whether working from home affects their productivity. In this study, to investigate the difference of developer productivity between working from home and working onsite, we conduct a quantitative analysis based on a dataset of developers' daily activities from Baidu Inc, one of the largest IT companies in China. In total, we collected approximately four thousand records of 139 developers' activities of 138 working days. Out of these records, 1,103 records are submitted when developers work from home due to COVID-19 pandemic. We find that WFH has both positive and negative impacts on developer productivity in terms of different metrics, e.g., the number of builds/commits/code reviews. We also notice that working from home has different impacts on projects with different characteristics including programming language, project type/age/size. For example, working from home has a negative impact on developer productivity for large projects. Additionally, we find that productivity varies for different developers. Based on these findings, we get some feedbacks from developers of Baidu and understand some reasons why WFH has different impacts on developer productivity.
Morphological Typology: From Word to Paradigm

1. Principal parts 2. Plats 3. A typology of principal-part systems 4. Inflection-class transparency 5. Grammatically enhanced plats 6. Impostors and heteroclites 7. Stems as principal parts 8. The marginal detraction hypothesis 9. Inflection classes, implicative relations and morphological theory 10. Entropy, predictability and predictiveness 11. The complexity of inflection-class systems 12. Sensitivity to plat presentation 13. The Principal-Parts Analyzer.
Multi-omics approaches for better understanding of the downstream effects of genetic risk factors

Many disease-associated variants affect gene expression levels (expression quantitative trait loci, eQTLs) and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3’-untranslated regions (3’-UTRs). We detected previously unknown cis-eQTL effects for GWAS hits in diseaseand physiology-associated traits. Apart from cis-eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis-eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis-eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants.
Noncommutative smooth projective curves: local and global skewness

Let H be a noncommutative smooth projective curve over a perfect field k. The square root s(H) of the dimension of the function (skew-) field over its centre we call the skewness of H, since it is a global measure of noncommutativity. For each point x we consider a triple of natural numbers, each of which measures skewness locally. Using local properties of the Auslander-Reiten translation, we prove a local-global principle: for each point x the product of these three numbers coincides with s(H). We show links to the Hasse principle. We apply our results to Klein surfaces with an even number of segmentation points on its ovals. These can naturally be regarded as real noncommutative smooth projective curves, which we call Witt curves. Many examples of genus zero and one are discussed. In particular we present an elliptic Witt curve which is a noncommutative Fourier-Mukai partner of the Klein bottle.
Improving the prediction of differential item functioning: a comparison of the use of an effect size for logistic regression DIF and Mantel-Haenszel DIF methods

Improving the Prediction of Differential Item Functioning: A Comparison of the Use of an Effect Size for Logistic Regression DIF and Mantel-Haenszel DIF Methods. (May 2006) Susan Cromwell Duncan, B.S., Sam Houston State University; M.S., Abilene Christian University Chair of Advisory Committee: Dr. Victor L. Willson Psychometricians and test developers use DIF analysis to determine if there is possible bias in a given test item. This study examines the conditions under which two predominant methods for determining differential item function compare with each other in item bias detection using an effect size statistic as the basis for comparison. The main focus of the present research was to test whether or not incorporating an effect size for LR DIF will more accurately detect DIF and to compare the utility of an effect size index across MH DIF and LR DIF methods. A simulation study was used to compare the accuracy of MH DIF and LR DIF methods using a p value or supplemented with an effect size. Effect sizes were found to increase the accuracy of DIF and the possibility of the detection of DIF across varying ability distributions, population distributions, and sample size combinations. Varying ability distributions and sample size combinations affected the detection of DIF, while population distributions did not seem to affect the detection of DIF.
A Novel Decomposable Pixel Component Analysis Algorithm for Automating Multispectral Satellite Image Denoising

In comparison with the standard RGB or gray-scale images the usual multispectral images (MSI) is intended to convey high definition and an authentic representation for real world scenes to significantly enhance the performance measures of several other tasks involving with computer vision, segmentation of image, object extraction and object tagging operations. However, in practices a MSI image is always prone to corruption by various sources of noises while procuring the images. In this paper we propose a methodology which effectively denoised the MSI images through a novel Decomposable Pixel Component Analysis Algorithm. The proposed algorithm uses two essential properties of a MSI image to its advantage, i.e., the dissimilarity of nonlocal space and the global illumination of the correlated regions across wide spectrum; to consider the correlation between non-variant local sub-spaces within the given noise influenced MSI image. The spatial regularity of neighboring pixels within the image is maintained while the block's homogeneity is checked to extract the decomposable pixels which latter is replaced by neighboring full band patches. Keywords: Multi spectral image denoising, spatial regularity, satellite imagery, noise removal
Dark matter searches with the LUX and LZ experiments

The fundamental nature of our universe is still mostly unknown. Dark matter, known to make up 84% of the mass in the universe, is widely recognised as one of the biggest mysteries in modern science. A promising candidate is the Weakly Interacting Massive Particle, or WIMP; these enigmatic particles have so far evaded detection by anything other than their gravitational influence, but they are an elegant solution that may still be hiding in unexplored phase space. WIMPs may be detected through elastic nuclear scattering in low-background experiments located in deep underground laboratories. The LUX dark matter experiment operates a time projection chamber with a xenon target, and has been world-leading for 3 years. LUX pioneered calibration techniques that have allowed great improvements in the sensitivity to WIMPs, developed novel signal identification algorithms towards rare-event selection, and demonstrated low energy nuclear recoil efficiency for WIMPs to unprecedented levels for noble gas targets. LUX has set the most-stringent constraints on WIMPs to-date with a minimum sensitivity of 2.2E10-46 cm2 at a WIMP mass of 50 GeV/c2. The LUX experiment has been completed, but the search will be taken up by its successor LUXZEPLIN (LZ), now under construction. Building on the technologies and techniques developed for LUX, LZ will probe theoretically well-motivated regions of unexplored electroweak parameter space to reach a sensitivity at 2E10-48 at 50 GeV/c2, 100 times greater than LUX. Detailed Monte Carlo simulations have been conducted for LZ to inform and finalise design, and develop the background model, against which any potential signal will be evaluated for a first definitive discovery.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity (Extended Abstract)

Developing accurate algorithms for learning structures of probabilistic graphical models is an important problem within modern AI research. Here we focus on score-based structure learning for Bayesian networks as arguably the most central class of graphical models. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. In this paper, we provide theoretical contributions towards understanding fundamental aspects of cutting planes and the related separation problem in this context, ranging from NP-hardness re-sults to analysis of polytopes and the related facets in connection to BNSL.
Impact of ion diamagnetic drift on MHD stability at edge pedestal in JT-60U rotating plasmas

The effect of ion diamagnetic drift on the stability of peeling–ballooning modes in rotating tokamak plasmas has been analyzed numerically. The results show that plasma toroidal rotation can not only destabilize the peeling–ballooning mode but also can reduce the ion diamagnetic drift effect on the mode stability, even though the ion diamagnetic drift effect stabilizes the mode in a static plasma. Plasma poloidal rotation can also destabilize the mode and cancel the ion diamagnetic drift effect, even when the rotation frequency is much smaller than the toroidal one. These impacts of the rotation on the stability can resolve the discrepancy between the result of the numerical stability analysis and the experimental result in type-I ELMy H-mode plasmas in JT-60U. The reduction of the ion diamagnetic drift effect on ELM stability due to plasma rotation is shown to depend on the direction of the rotation, so that the ion diamagnetic drift effect becomes negligible only when the JT-60U plasma rotates in the direction counter to the plasma current.
Genetic and epigenetic catalysts in early-life programming of adult cardiometabolic disorders

: Evidence has emerged across the past few decades that the lifetime risk of developing morbidities like type 2 diabetes, obesity, and cardiovascular disease may be influenced by exposures that occur in utero and in childhood. Developmental abnormalities are known to occur at various stages in fetal growth. Epidemiological and mechanistic studies have sought to delineate developmental processes and plausible risk factors influencing pregnancy outcomes and later health. Whether these observations reflect causal processes or are confounded by genetic and social factors remains unclear, although animal (and some human) studies suggest that epigenetic programming events may be involved. Regardless of the causal basis to observations of early-life risk factors and later disease risk, the fact that such associations exist and that they are of a fairly large magnitude justifies further research around this topic. Furthermore, additional information is needed to substantiate public health guidelines on lifestyle behaviors during pregnancy to improve infant health outcomes. Indeed, lifestyle intervention clinical trials in pregnancy are now coming online, where materials and data are being collected that should facilitate understanding of the causal nature of intrauterine exposures related with gestational weight gain, such as elevated maternal blood glucose concentrations. In this review, we provide an overview of these concepts.
Masked Face Recognition Dataset and Application

During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.
The Interplay Between Natural and Accidental Supersymmetry

In this thesis, we will explore the subject of the little hierarchy problem which plagues solutions to the big hierarchy problem of the Standard Model of particle physics. In the first half of this thesis, we study the theoretical framework for a supersymmetric resolution of the little hierarchy problem, known as natural supersymmetry, and argue that regions of the parameter space of this model have been missed by search strategies employed at the large hadron collider, but could be searched for with new search strategies. In the second half of this thesis, we explore the possibility of embedding natural supersymmetry in models of warped extra dimensions in order to UV-complete them by utilizing a mechanism known as accidental supersymmetry. We study the mechanism of accidental supersymmetry in the Randall-Sundrum framework by focusing on a toy model, and argue that accidental supersymmetry is capable solving the little hierarchy problem in that toy model. Finally, as models in the Randall-Sundrum framework themselves require UV completions, we demonstrate that it is possible to ii
The human Transmembrane Protease Serine 2 is necessary for the production of Group 2 influenza A virus pseudotypes

The monomer of influenza haemagglutinin is synthesized as a single polypeptide precursor that during maturation is cleaved by proteases into two active subunits. Other studies have demonstrated that the human Transmembrane Protease Serine 2 (TMPRSS2) can cleave the HA of human seasonal influenza viruses. Consequently, we have investigated the use of human Transmembrane Protease Serine 2 to produce high titre influenza haemmagglutinin (HA) lentiviral pseudotypes from Group 2 influenza viruses. Such pseudotypes represent powerful and safe tools to study viral entry and immune responses. Influenza pseudotype particles are obtained by co-transfecting human embryonic kidney HEK293T/17 cells using plasmids coding for the influenza HA, HIV gag-pol and a lentiviral vector incorporating firefly luciferase. However, in order to produce Group 2 pseudotypes, it was necessary to co-transfect a plasmid expressing the TMPRSS2 endoprotease, to achieve the necessary HA cleavage for infective particle generation. These lentiviral pseudotypes were shown to transduce HEK293T/17 cells with high efficiency. This demonstrates that TMPRSS2 is necessary for the functional activation, in vitro, of both the HA of human seasonal influenza and other Group 2 HA influenza strains. Additionally, we show that the Group 2 influenza pseudotype particles can be used as surrogate antigens in neutralization assays and are efficiently neutralized by corresponding influenza virus reference sera. These data demonstrate that the viral pseudotype system is a powerful method for serological surveillance of a wide range of influenza viruses.
This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks

This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks Kameron Decker Harris Chair of the Supervisory Committee: Professor Eric Shea-Brown Applied Mathematics At first glance, the neuronal network seems like a tangled web in many areas throughout the nervous system. Often, our best guess is that such “messy” connections are close to random, while obeying certain statistical constraints, e.g. the number of connections per neuron. However, neuronal wiring is coordinated across larger mesoscopic distances in a way that differentiates between brain layers, areas, and groups of cells. We work across spatial scales in order to understand this hierarchy of order and disorder in brain networks. Ultimately, the goal is to understand how network structure is important for brain function. This leads to: 1. An inference technique which reconstructs mesoscopic brain networks from tracing experiments targeting spatially contiguous groups of neurons. 2. Models of networks which are random, while also having constrained average connectivity and group structure. 3. Comparing simulated and real respiratory rhythms, highlighting the role of inhibitory neurons and connectivity on rhythmogenesis, in particular synchrony and irregularity.
Emerging Countries Sovereign Rating Adjustment using Market Information : Impact on Financial Institutions Investment Decisions March 27 , 2013

Assets, debts and other financial products issued by emerging countries are usually considered more speculative than those issued by developed economies. Therefore, relying on traditional rating agencies to invest in these countries is problematic as the information used to assess the economic and market condition in these economies is quickly outdated. Consequently, both the investment opportunities and the necessity to clear particular positions may be missed, respectively resulting in potential significant costs of opportunity or losses. Therefore, an approach taking into account the latest information displayed by financial markets may enable us bypassing the traditional limits. As a result, this chapter proposes a creditworthiness evaluation methodology based on adjusting ratings obtained from macroeconomic fundamentals (GDP growth rate, inflation, external debts, etc.) and financial market movements (bonds, equity volume, volatility, etc.). In the first step, a general panel model is applied on country-specific information to generate fundamental ratings. In the second step, applying a multi-factor model to market indicators and breaking down long-term sovereign bond yields into different risk premia, market implied ratings are obtained. Finally, the rating to be considered (denoted “δ-Rating”) for investment purposes is a combination of fundamental ratings and market implied ratings carrying out an adapted Bühlmann-Straub method (Credibility Theory). Then, emerging countries “δ-Rating” will be compared to those obtained from developed countries and discussed against financial institutions risk appetite.