Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering a Tool to Detect Automatically Generated Papers

In the last decade, a number of nonsense automatically-generated scientific papers have been published, most of them were produced using probabilis-tic context free grammar generators. Such papers may also appear in scientific social networks or in open archives and thus bias metrics computation. This shows that there is a need for an automatic detection process to discover and remove such nonsense papers. Here, we present and compare different methods aiming at automatically classifying generated papers.
Correcting Sampling Bias in Structural Genomics through Iterative Selection of Underrepresented Targets

In this study we proposed an iterative procedure for correcting sampling bias in labeled datasets for supervised learning applications. Given a much larger and unbiased unlabeled dataset, our approach relies on training contrast classifiers to iteratively select unlabeled examples most highly underrepresented in the labeled dataset. Once labeled, these examples could greatly reduce the sampling bias present in the labeled dataset. Unlike active learning methods, the actual labeling is not necessary in order to determine the most appropriate sampling schedule. The proposed procedure was applied on an important bioinformatics problem of prioritizing protein targets for structural genomics projects. We show that the procedure is capable of identifying protein targets that are underrepresented in current protein structure database, the Protein Data Bank (PDB). We argue that these proteins should be given higher priorities for experimental structural characterization to achieve faster sampling bias reduction in current PDB and make it more representative of the protein space.
COVARIATE SELECTION IN HIERARCHICAL MODELS OF HOSPITAL ADMISSION COUNTS: A BAYES FACTOR APPROACH

The Bayes factor is employed to select covariates for a hierarchical model applied to a collection of hospital admission counts. Integrals representing the Bayes factor numerator and denominator marginal probabilities are intractable for the model used. We examine three approaches to integral approximation: Laplace approximation, Monte Carlo integration, and a Markov chain Monte Carlo (MCMC) approach. Laplace-approximated Bayes factors are found to be nearly unbiased, and can be obtained very quickly. MCMC was used to implement an importance sampling scheme. The resulting approximate Bayes factors exhibited some bias and were obtained at a relatively high cost in computer time. Software to implement the Laplace approximation is available from StatLib.
Evaluating Mapping Repair Systems with Large Biomedical Ontologies

In this paper we provide empirical evidence of the necessity of integrating mapping repair techniques within the ontology matching process, an aspect that is neglected in many ontology matching systems. We also evaluate the feasibility of using state-of-the-art mapping repair techniques in practice, such as those implemented in Alcomo and LogMap. A preliminary evaluation was conducted in the context of the Ontology Alignment Evaluation Initiative (OAEI) 2012. We extend this evaluation and report about the results in detail.
Catecholaminergic Neuromodulation Shapes Intrinsic MRI Functional Connectivity in the Human Brain

The brain commonly exhibits spontaneous (i.e., in the absence of a task) fluctuations in neural activity that are correlated across brain regions. It has been established that the spatial structure, or topography, of these intrinsic correlations is in part determined by the fixed anatomical connectivity between regions. However, it remains unclear which factors dynamically sculpt this topography as a function of brain state. Potential candidate factors are subcortical catecholaminergic neuromodulatory systems, such as the locus ceruleus-norepinephrine system, which send diffuse projections to most parts of the forebrain. Here, we systematically characterized the effects of endogenous central neuromodulation on correlated fluctuations during rest in the human brain. Using a double-blind placebo-controlled crossover design, we pharmacologically increased synaptic catecholamine levels by administering atomoxetine, an NE transporter blocker, and examined the effects on the strength and spatial structure of resting-state MRI functional connectivity. First, atomoxetine reduced the strength of inter-regional correlations across three levels of spatial organization, indicating that catecholamines reduce the strength of functional interactions during rest. Second, this modulatory effect on intrinsic correlations exhibited a substantial degree of spatial specificity: the decrease in functional connectivity showed an anterior–posterior gradient in the cortex, depended on the strength of baseline functional connectivity, and was strongest for connections between regions belonging to distinct resting-state networks. Thus, catecholamines reduce intrinsic correlations in a spatially heterogeneous fashion. We conclude that neuromodulation is an important factor shaping the topography of intrinsic functional connectivity. SIGNIFICANCE STATEMENT The human brain shows spontaneous activity that is strongly correlated across brain regions. The factors that dynamically sculpt these inter-regional correlation patterns are poorly understood. Here, we test the hypothesis that they are shaped by the catecholaminergic neuromodulators norepinephrine and dopamine. We pharmacologically increased synaptic catecholamine levels and measured the resulting changes in intrinsic fMRI functional connectivity. At odds with common understanding of catecholamine function, we found (1) overall reduced inter-regional correlations across several levels of spatial organization; and (2) a remarkable spatial specificity of this modulatory effect. Our results identify norepinephrine and dopamine as important factors shaping intrinsic functional connectivity and advance our understanding of catecholamine function in the central nervous system.
The role of biomaterials in burn treatment

Burns are among the most challenging injuries to manage. Although the great advances in treatment that have been made have arguably been sufficient to address the related medical challenges of the past century, modern society now faces even greater difficulties. These are caused predominantly by increase in population and lifespan, which frequently are not matched by an increased effectiveness in the maintenance of health and quality of life.
Automatic Recognition of Chinese Organization Name Based on Conditional Random Fields

Person, location and organization have been always mentioned as a bottleneck of a named entity recognition (NER) system. Automatic recognition of Chinese organization name is the most difficult problem in NER tasks. This paper presents a new approach of Chinese organization name recognition based on cascaded conditional random fields. In the proposed approach, we first recognize the person name and location name before recognizing organization. The model structure has been designed with the cascade way, the result then is passed to the high model and suppose the decision of high model for recognition of the complicated organization names. And we proposed the new feature to realize this task. We evaluate our approach on large-scale corpus with open test method using People's Daily (January. 1998). Chinese ORG recalling rate achieves 88.78% and the precision rate is 82.35%. The evaluation results show that our approach based on cascaded conditional random fields significantly outperforms previous approaches.
LINE-PROFILE VARIABILITY FROM TIDAL FLOWS IN ALPHA VIRGINIS (SPICA)

We present the results of high precision, high-resolution (R ∼ 68,000) optical observations of the short-period (4 days) eccentric binary system Alpha Virginis (Spica) showing the photospheric line-profile variability that in this system can be attributed to non-radial pulsations driven by tidal effects. Although scant in orbital-phase coverage, the data provide signal-to-noise ratio > 2000 line profiles at full spectral resolution in the wavelength range Δλ4000–8500 Å, allowing a detailed study of the night-to-night variability as well as changes that occur on ∼2 hr timescale. Using an ab initio theoretical calculation, we show that the line-profile variability can arise as a natural consequence of surface flows that are induced by the tidal interaction.
The Meanings of Destination: A Q Method Approach

While researchers increasingly address place meaning and place attachment as the cornerstones for understanding human–place relations, the factors that make a tourism destination meaningful have received less attention. Using Q method, this study investigated the dynamic nature of place meanings from the tourist perspective, and developed a theoretical framework to explain the construction of destination meanings. Data analyses identified four subjective views toward the meanings of destination and four underlying continuums that illustrate how a complex process of co-construction reveals distinct patterns of destination meanings. This study makes theoretical as well as practical contributions to the literature.
Stable Orthogonal Local Discriminant Embedding for Linear Dimensionality Reduction

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.
Spinal Interneurons Facilitate Coactivation of Hand Muscles during a Precision Grip Task in Monkeys

Grasping is a highly complex movement requiring coordination of a number of hand joints and muscles. In contrast to cortical descending systems, the contribution of the subcortical system for coordinating this higher degree of freedom is largely unknown. Here we explore how spinal interneurons (INs) contribute to the coordination of hand muscles by recording their activity from the cervical spinal cord (C5–T1) simultaneously with electromyographic (EMG) activity from hand and arm muscles in three monkeys performing a precision grip task. Spike-triggered averages of the rectified EMGs were compiled for 255 neurons (4821 neuron–muscle pairs). Twenty-six neurons produced 68 significant postspike effects in hand and arm muscles and were identified as premotor interneurons (PreM-INs), which presumably have relatively direct synaptic effects on spinal motoneurons. The majority of the PreM-INs (22/26 neurons) produced postspike effects in finger muscles (intrinsic and extrinsic hand muscles) compared with wrist (9/26 neurons) and elbow muscles (1/26 neurons). The effects in finger muscles were mostly facilitative [postspike facilitations (PSFs), 19/22 neurons], and few had suppressive effects (postspike suppressions, 3/22 neurons). Moreover, PreM-INs produced more divergent PSFs in intrinsic hand muscles (2.5 ± 1.9 muscles/neuron) than in wrist muscles (1.2 ± 0.4 muscles/neurons). We conclude that spinal PreM-INs produce divergent facilitations preferentially in intrinsic hand muscles. These results suggest that spinal interneurons contribute to the control of hand grasping in primates by combining and coordinating multiple finger muscles.
Generation of Novel Traj18-Deficient Mice Lacking Vα14 Natural Killer T Cells with an Undisturbed T Cell Receptor α-Chain Repertoire

Invariant Vα14 natural killer T (NKT) cells, characterized by the expression of a single invariant T cell receptor (TCR) α chain encoded by rearranged Trav11 (Vα14)-Traj18 (Jα18) gene segments in mice, and TRAV10 (Vα24)-TRAJ18 (Jα18) in humans, mediate adjuvant effects to activate various effector cell types in both innate and adaptive immune systems that facilitates the potent antitumor effects. It was recently reported that the Jα18-deficient mouse described by our group in 1997 harbors perturbed TCRα repertoire, which raised concerns regarding the validity of some of the experimental conclusions that have been made using this mouse line. To resolve this concern, we generated a novel Traj18-deficient mouse line by specifically targeting the Traj18 gene segment using Cre-Lox approach. Here we showed the newly generated Traj18-deficient mouse has, apart from the absence of Traj18, an undisturbed TCRα chain repertoire by using next generation sequencing and by detecting normal generation of Vα19Jα33 expressing mucosal associated invariant T cells, whose development was abrogated in the originally described Jα18-KO mice. We also demonstrated here the definitive requirement for NKT cells in the protection against tumors and their potent adjuvant effects on antigen-specific CD8 T cells.
Machine learning used by Personal WebWatcher

This paper describes design of personal browsing assistant Personal WebWatcher that suggests interesting hyperlinks on the requested Web documents. Machine learning is used to generate a model of user's interests. We consider two approaches that di er in the information included in training examples: (1) include information presented to the user, that is a part of the text from the document that contains a hyperlink and (2) include information that was not presented to the user, that is the content of the document pointed to by a hyperlink. We compare two classication algorithms k-Nearest Neighbor and Naive Bayes. Bag of words document representation is used and features are selected using Information gain. Preliminary experiments show that there is no signi cant difference between the used classi ers and that using only a small number of features gives almost the same results as using all features. In all experiments the achieved classi cation accuracy is the same or slightly higher than the default accuracy. Since the default accuracy is higher for approach (1) than for approach (2), the results of approach (1) show higher classi cation accuracy.
Functional Upregulation of Ca2+ -Activated K+ Channels in the Development of Substantia Nigra Dopamine Neurons

Many connections in the basal ganglia are made around birth when animals are exposed to a host of new affective, cognitive, and sensori-motor stimuli. It is thought that dopamine modulates cortico-striatal synapses that result in the strengthening of those connections that lead to desired outcomes. We propose that there must be a time before which stimuli cannot be processed into functional connections, otherwise it would imply an effective link between stimulus, response, and reward in uterus. Consistent with these ideas, we present evidence that early in development dopamine neurons are electrically immature and do not produce high-frequency firing in response to salient stimuli. We ask first, what makes dopamine neurons immature? and second, what are the implications of this immaturity for the basal ganglia? As an answer to the first question, we find that at birth the outward current is small (3nS-V), insensitive to , TEA, BK, and SK blockers. Rapidly after birth, the outward current increases to 15nS-V and becomes sensitive to , TEA, BK, and SK blockers. We make a detailed analysis of the kinetics of the components of the outward currents and produce a model for BK and SK channels that we use to reproduce the outward current, and to infer the geometrical arrangement of BK and channels in clusters. In the first cluster, T-type and BK channels are coupled within distances of 20 nm (200 Å). The second cluster consists of L-type and BK channels that are spread over distances of at least 60 nm. As for the second question, we propose that early in development, the mechanism of action selection is in a “locked-in” state that would prevent dopamine neurons from reinforcing cortico-striatal synapses that do not have a functional experiential-based value.
Non-Canonical EZH2 Transcriptionally Activates RelB in Triple Negative Breast Cancer

Enhancer of zeste homology 2 (EZH2) is the methyltransferase component of the polycomb repressive complex (PRC2) which represses gene transcription via histone H3 trimethylation at lysine 23 (H3K27me3). EZH2 activity has been linked with oncogenesis where it is thought to block expression of certain tumor suppressors. Relative to a role in cancer, EZH2 functions to promote self-renewal and has been shown to be important for the tumor-initiating cell (TIC) phenotype in breast cancer. Recently a non-canonical role for EZH2 has been identified where it promotes transcriptional activation of certain genes. Here we show that EZH2, through a methyltransferase-independent mechanism, promotes the transcriptional activation of the non-canonical NF-κB subunit RelB to drive self-renewal and the TIC phenotype of triple-negative breast cancer cells.
Religion, Gendered Authority, and Identity in American Politics

Abstract Religious identity serves as a central cleavage in American politics. However, little attention has been granted to how gendered views of authority conveyed in religious doctrine shape political identities and attitudes. Using a nation-wide sample of adult Americans, we demonstrate that gendered notions of divine and human authority exert considerable influence on political thinking. In particular, belief in a masculine God and preferences for traditional gender roles strongly relate to political conservatism. Adherence to gendered notions of authority influences political identity and policy preferences, even when controlling for more conventional indicators of religiosity. Accounting for gendered beliefs about authority also partially explains well-documented gender gaps in American politics, providing insight into women's apparently contradictory tendencies toward both political liberalism and religiosity. The relationships uncovered here, coupled with the continued salience of both gender and religion in contemporary political campaigns, underscore the importance of attending to the gendered dimensions of authority.
Implementation of a mentoring component embedded within an early childhood scholarship programme

ABSTRACT This study examined the implementation of a pilot mentoring component as part of a college scholarship programme for child care teachers. The purpose of the mentoring programme was to encourage the application of course material in the student’s classroom. Programme documents, interviews, surveys, and field observations were used to compare the actual mentoring implementation to the design. The pilot programme took place in a Midwestern state, with 3 mentors representing three rural community colleges working with 40 child care providers. Stakeholder perspectives were reported and descriptive output data revealed the variances in the implementation of this mentoring initiative. Lessons learned from this study are shared to inform future child care and educator mentoring initiatives.
Semantics in the Semantic Web: A Critical Evaluation †

In recent years, the term “semantics” has been widely used in various fields of research and particularly in areas related to information technology. One of the motivators of such an appropriation is the vision of the Semantic Web, a set of developments underway, which might allow one to obtain better results when querying on the web. However, it is worth asking what kind of semantics we can find in the Semantic Web, considering that studying the subject is a complex and controversial endeavour. Working within this context, we present an account of semantics relying on the main linguist approaches, in order to then analyze what semantics is within the scope of information technology. We critically evaluate a spectrum, which proposes the ordination of instruments (models, languages, taxonomic structures, to mention but a few) according to a semantic scale. In addition to proposing a new extended spectrum, the article suggests alternative interpretations with the aim of clarifying the use of the term “semantics” in different contexts. Finally, we offer our conclusions regarding the semantic in the Semantic Web and mention future directions and complementary works.
Balancing scalability and uniformity in SAT witness generator

Constrained-random simulation is the predominant approach used in the industry for functional verification of complex digital designs. The effectiveness of this approach depends on two key factors: the quality of constraints used to generate test vectors, and the randomness of solutions generated from a given set of constraints. In this paper, we focus on the second problem, and present an algorithm that significantly improves the state-of-the-art of (almost-)uniform generation of solutions of large Boolean constraints. Our algorithm provides strong theoretical guarantees on the uniformity of generated solutions and scales to problems involving hundreds of thousands of variables.
Pattern-based design recovery of Java software

In this paper we show how to recover design information from Java source code. We take a pattern-based approach and proceed in a step by step manner deriving several layers of increasing abstraction. A compiler collects information about inheritance hierarchies and method call relations. It also looks for particular source text patterns coming from naming conventions or programming guidelines.The result of the compile phase is a graph acting as the starting graph of a graph grammar that describes our design recovery process. We define criteria for the automatic detection of associations and aggregations between classes, as well as for some of the popular design patterns such as composite or strategy.
Intersection of calorie restriction and magnesium in the suppression of genome-destabilizing RNA–DNA hybrids

Dietary calorie restriction is a broadly acting intervention that extends the lifespan of various organisms from yeast to mammals. On another front, magnesium (Mg2+) is an essential biological metal critical to fundamental cellular processes and is commonly used as both a dietary supplement and treatment for some clinical conditions. If connections exist between calorie restriction and Mg2+ is unknown. Here, we show that Mg2+, acting alone or in response to dietary calorie restriction, allows eukaryotic cells to combat genome-destabilizing and lifespan-shortening accumulations of RNA–DNA hybrids, or R-loops. In an R-loop accumulation model of Pbp1-deficient Saccharomyces cerevisiae, magnesium ions guided by cell membrane Mg2+ transporters Alr1/2 act via Mg2+-sensitive R-loop suppressors Rnh1/201 and Pif1 to restore R-loop suppression, ribosomal DNA stability and cellular lifespan. Similarly, human cells deficient in ATXN2, the human ortholog of Pbp1, exhibit nuclear R-loop accumulations repressible by Mg2+ in a process that is dependent on the TRPM7 Mg2+ transporter and the RNaseH1 R-loop suppressor. Thus, we identify Mg2+ as a biochemical signal of beneficial calorie restriction, reveal an R-loop suppressing function for human ATXN2 and propose that practical magnesium supplementation regimens can be used to combat R-loop accumulation linked to the dysfunction of disease-linked human genes.