Miracle-dz/UniFAR-FTUs huggingface dataset:
MedTextus: An Ontology-enhanced Medical Portal

In this paper we describe MedTextus, an online medical search portal with dynamic search and browse tools. To search for information, MedTextus lets users request synonyms and related terms specifically tailored to their query. A mapping algorithm dynamically builds the query context based on the UMLS ontology and then selects thesaurus terms that fit this context. Users can add these terms to their query and meta-search five medical databases. To facilitate browsing, the search results can be reviewed as a list of documents per database, as a set of folders into which all the documents are automatically categorized based on their content, and as a map that is built on the fly. We designed a user study to compare these dynamic support tools with the static query support of NLM Gateway and report on initial results for the search task. The users used NLM Gateway more effectively, but used MedTextus more efficiently and preferred its query formation tools.
Balancing Structural and Temporal Constraints in Multitasking Contexts

Recent research has shown that when people multitask, both the subtask structure and the temporal constraints of the component tasks strongly influence people’s task-switching behavior. In this paper, we propose an integrated theoretical account and associated computational model that aims to quantify how people balance structural and temporal constraints in everyday multitasking. We validate the theory using data from an empirical study in which drivers performed a visual-search task while navigating a driving environment. Through examination of illustrative protocols from the model and human drivers as well as the overall fit on the aggregate glance data, we explore the implications of the theory and model for time-critical multitasking domains.
Removal of growth hormone receptor (GHR) in muscle of male mice replicates some of the health benefits seen in global GHR−/− mice

Global disruption of the GH receptor in mice (GHR−/−) produces a large and reproducible extension in lifespan. Since lack of GH action in muscle resulting in improved glucose homeostasis is potentially a mechanism by which GHR−/− mice are long-lived, and since no information on muscle-specific GHR disruption in females is available, we generated and characterized a line of muscle-specific GHR disrupted (MuGHRKO) mice. As expected, male MuGHRKO mice had improved fasting blood glucose, insulin, c-peptide, and glucose tolerance. In contrast, female MuGHRKO mice exhibited normal glucose, insulin, and glucose tolerance. Body weight was mildly but significantly altered in opposite directions in males (decreased) and females (increased) compared to controls. Grip strength and treadmill endurance were unchanged with advanced age in both sexes, suggesting that the direct action of GH on muscle has minimal effect on age-related musculoskeletal frailty. Longevity was unchanged in both sexes at Ohio University and significantly increased for males at University of Michigan. These data suggest that removal of GHR in muscle of male MuGHRKO mice replicates some of the health benefits seen in global GHR−/− mice including improvements to glucose homeostasis and smaller body weight in males, which may explain the trends observed in lifespan.
A new Xist allele driven by a constitutively active promoter is dominated by Xist locus environment and exhibits the parent-of-origin effects

The dosage difference of X-linked genes between the sexes in mammals is compensated for by genetic inactivation of one of the X chromosomes in XX females. A noncoding RNA transcribed from the Xist gene at the onset of X chromosome inactivation coats the X chromosome in cis and induces chromosome-wide heterochromatinization. Here, we report a new Xist allele (XistCAG) driven by a CAG promoter, which is known to be constitutively active in many types of cells. The paternal transmission of XistCAG resulted in the preferential inactivation of the targeted paternal X (Xp) not only in the extra-embryonic but also the embryonic lineage, whereas maternal transmission ended with embryonic lethality at the early postimplantation stage with a phenotype that resembled mutant embryos carrying a maternal deficiency in Tsix, an antisense negative regulator of Xist, in both sexes. Interestingly, we found that the upregulation of XistCAG in preimplantation embryos temporally differed depending on its parental origin: its expression started at the 4- to 8-cell stages when paternally inherited, and XistCAG was upregulated at the blastocyst stage when maternally inherited. This might indicate that the Xist locus on Xp is permissive to transcription, but the Xist locus on the maternal X (Xm) is not. We extrapolated from these findings that the maternal Xist allele might manifest a chromatin structure inaccessible by transcription factors relative to the paternal allele. This might underlie the mechanism for the maternal repression of Xist at the early cleavage stage when Tsix expression has not yet occurred on Xm. Summary: The CAG promoter drives a new Xist allele, XistCAG, upregulation of which in preimplantation embryos temporally differs depending on its parental origin.
Context-sensitive delta inference for identifying workload-dependent performance bottlenecks (invited talk abstract)

Software hangs can be caused by expensive operations in responsive actions (such as time-consuming operations in UI threads). Some of the expensive operations depend on the input workloads, referred to as workload-dependent performance bottlenecks (WDPBs). WDPBs are usually caused by workload-dependent loops (i.e., WDPB loops) that contain relatively expensive operations. Traditional performance testing and single-execution profiling may not reveal WDPBs due to incorrect assumptions of workloads. To address these issues, we propose the Infer approach that predicts WDPB loops under large workloads via inferring iteration counts of WDPB loops using complexity models for the workload size. Infer incorporates a novel concept named context-sensitive delta inference that consists of two parts: temporal inference for inferring the complexity models of different program locations, and spatial inference for identifying WDPB loops as WDPB candidates. We conducted evaluations on two popular open-source GUI applications, and identified impactful WDPBs that caused 10 performance bugs.
Virus-induced diabetes mellitus. XVIII. Inhibition by a nondiabetogenic variant of encephalomyocarditis virus

Plaque purification of the M variant of encephalomyocarditis (EMC) virus resulted in the isolation of two stable variants: one diabetogenic and designated D and the other nondiabetogenic and designated B. When the D variant was inoculated into SJL/J male mice, hypoinsulinemia and hyperglycemia developed in > 90% of the animals. In contrast, none of the mice inoculated with the B variant developed diabetes. Histologic examination of pancreata from mice infected with the D variant revealed insulitis and necrosis of beta cells, whereas islets from mice infected with the B variant showed little, if any, change. When islets were assayed for infectious virus, approximately 10 times more virus was recovered from animals inoculated with the D as compared with the B variant. Moreover, approximately 60% of islet cells from mice infected with the D variant contained viral antigens when stained with fluorescein-labeled anti-EMC virus antibody, whereas < 5% of islet cells from animals infected with the B variant contained viral antigens. Co-infection experiments showed that the induction of diabetes by the D variant was inhibited by the B variant. When the B and D variants were mixed together at B:D ratios of 1, 9, and 99, diabetes developed in 60, 11, and 0% of the mice, respectively. Tissue- culture experiments revealed that the B variant induced considerably more interferon than the D variant, and studies in animals showed that interferon appeared earlier and in greater amounts in the circulation of mice infected with the B as compared with the D variant. These studies suggest that the induction of interferon by the B variant is, at least in part, responsible for the inhibition of diabetes by the D variant.
Emerging features for speaker recognition

Cepstral features and their derivatives have been successfully used in speaker recognitions tasks over the past two decades. Recently new features are emerging that make use of information that is not contained in cepstral features or their derivative, namely the phase spectra and instantaneous frequency. There are numerous approaches to capture instantaneous amplitude and frequency variations in the signal, resulting in an amplitude and frequency modulation (AM-FM) feature set. While the addition of modulation features has been shown to improve speech recognition systems based on cepstral features, their use in speaker recognition is new and has not been fully explored. This paper outlines some of these emerging features and their extraction methods for use in speaker recognition tasks. The paper also talks about the recently proposed Empirical Mode Decomposition (EMD) as an alternative method of extracting instantaneous frequency.
Sumatriptan challenge in bipolar patients with and without migraine: a neuroendocrine study of 5-HT1D receptor function

An association between bipolar disorder and migraine has been lately recognized and an abnormality of central serotonergic function is suggested as the underlying neurophysiological disturbance. To examine the role of serotonin in bipolar disorder and migraine, we used the neuroendocrine challenge paradigm, and we chose sumatriptan, a 5HT1D agonist, as the pharmacological probe. We studied nine bipolar patients with migraine, nine bipolar patients without it, seven migraine patients, and nine matched normal controls. A post-hoc analysis showed subsensitivity of serotonergic function, reflected in a blunted growth hormone response to sumatriptan challenge in bipolar patients who also suffered from migraine.
NESVM: A Fast Gradient Method for Support Vector Machines

Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf \cite{SVM_Perf}\cite{PerfML} (whose convergence rate in solving the dual SVM is upper bounded by $\mathcal O(1/\sqrt{k})$ where $k$ is the number of iterations) and Pegasos \cite{Pegasos} (online SVM that converges at rate $\mathcal O(1/k)$ for the primal SVM), NESVM achieves the optimal convergence rate at $\mathcal O(1/k^{2})$ and a linear time complexity. In particular, NESVM smoothes the non-differentiable hinge loss and $\ell_1$-norm in the primal SVM. Then the optimal gradient method without any line search is adopted to solve the optimization. In each iteration round, the current gradient and historical gradients are combined to determine the descent direction, while the Lipschitz constant determines the step size. Only two matrix-vector multiplications are required in each iteration round. Therefore, NESVM is more efficient than existing SVM solvers. In addition, NESVM is available for both linear and nonlinear kernels. We also propose ``homotopy NESVM'' to accelerate NESVM by dynamically decreasing the smooth parameter and using the continuation method. Our experiments on census income categorization, indoor/outdoor scene classification, event recognition and scene recognition suggest the efficiency and the effectiveness of NESVM. The MATLAB code of NESVM will be available on our website for further assessment.
Mesonia hippocampi sp. nov., isolated from the brood pouch of a diseased Barbour's Seahorse (Hippocampus barbouri).

An orange-pigmented, Gram-staining-negative, rod-shaped bacterium, designated 96_Hippo_TS_3/13(T) was isolated from the brood pouch of a diseased seahorse male of the species Hippocampus barbouri from the animal facility of the University of Giessen, Germany. Phylogenetic analyses based on the nearly full-length 16S rRNA gene sequence placed strain 96_Hippo_TS_3/13(T) into the monophyletic cluster of the genus Mesonia within the family Flavobacteriaceae. However, the strain shared only 92.2-93.8% sequence similarity to type strains of species of the genus Mesonia, with highest sequence similarity to the type strain of Mesonia aquimarina. Cellular fatty acid analysis showed a Mesonia-typical fatty acid profile including several branched and hydroxyl fatty acids with highest amounts of iso-C15 : 0 (40.9%) followed by iso-C17 : 0 3-OH (14.8%). In the polyamine pattern, sym-homospermidine was predominant. The diagnostic diamino acid of the peptidoglycan was meso-diaminopimelic acid. The quinone system contained exclusively menaquinone MK-6. The only identified compound in the polar lipid profile was phosphatidylethanolamine present in major amounts. Additionally, major amounts of an unidentified aminolipid and two unidentified lipids not containing a phosphate group, an amino group or a sugar residue were detected. The genomic G+C content of strain 96_Hippo_TS_3/13(T) was 30 mol%. Based on genotypic, chemotaxonomic and physiological characterizations we propose a novel species of the genus Mesonia, Mesonia hippocampi sp. nov., with strain 96_Hippo_TS_3/13(T) ( = CIP 110839T = LMG 28572(T) = CCM 8557(T)) as the type strain. An emended description of the genus Mesonia is also provided.
Utilization of Chemical Structure Information for Analysis of Spectra Composites

In this paper, we propose the utilization of structural infor- mation of spectral data during the preprocessing to extend the ability of subsequent analysis methods. Specically, we expect a dataset of measured spectra containing mixtures of only a few spectral components. Using the concentration ratios for a small subset of mixtures and the chemical struc- tural knowledge, theoretical spectral components are generated. Then a set, which combines measured and theoretical spectra, is analyzed using a self-organizing map to predict the unknown mixture ratios of the remain- ing subset by an associative learning. At this time, the initial study on simulated data reached very good results.
GenFin: Genetic Algorithm-Based Multiobjective Statistical Logic Circuit Optimization Using Incremental Statistical Analysis

As the semiconductor technology node scales into the deep submicrometer regime, it has become very difficult to obtain high IC yields because the process-voltage-temperature variations induce large spreads in delay and power. In this paper, we propose a new framework, called GenFin, which is, as far as we know, the first to target the multiobjective yield optimization of logic circuits. Since FinFETs are a promising substitute for CMOS at 22-nm technology node and beyond, we evaluate the framework with a 22-nm FinFET logic library. By combining the power of genetic algorithm (GA) and adaptive multiobjective optimization, GenFin produces a set of nondominated logic circuits whose timing, leakage power, and dynamic power yields are simultaneously optimized. This can help designers make tradeoff decisions wisely and avoid suboptimal solutions. We also propose an incremental statistical circuit analyzer, called incremental FinPrin, that speeds up the statistical static timing analysis by up to 9.6× and the statistical power analysis by up to 2235.7×, while incurring errors of only up to 0.031% in mean and 0.74% in standard deviation relative to nonincremental analysis. We use heuristics based on the deterministic timing analysis and gate criticality to reduce the GA search space and also improve the quality of its solutions. We present extensive experimental results to demonstrate the efficacy of GenFin.
Gender differences in externalising problems among preschool children: implications for early childhood educators

Gender differences in student achievement have been widely studied. Considering the long‐term effects of externalising behaviour problems on children’s later academic performance, it can be conjectured that gender disparities in student achievement may be rooted in developmental characteristics in early childhood. As an effort towards contributing insights to the growing body of knowledge on child development, this study investigated whether variability in externalising behaviour problems among preschool children could be attributed to gender differences. Participants were 110 parents who completed a sociodemographic questionnaire, and rated their child’s behaviour on the Achenbach Child Behaviour Checklist. The children (mean age in months = 45.62 (SD = 10.12)) consisted of 52 boys and 58 girls. This study revealed that boys were reported to exhibit higher rates of externalising problems than girls, and that higher rates of internalising problems also predicted higher rates of externalising problems among these preschool children.
The Scalability of the HyperPlay Technique for Imperfect-Information Games

In the field of General Game Playing the imperfectinformationgames present a special challenge for researchers.In general the search space is larger, and thelack of information requires a different decision makingtechnique. A simple Monte Carlo sampling using a particlefilter may serve for the simple games, but this soonfails when more complex games are played. The HyperPlaytechnique was one such ”simple” player, soonenhanced to HyperPlay-II capable of handling the mostcomplex of games. However, this technique is very resourcehungry and may not scale well for larger games.We explore the scalability of HyperPlay-II for a varietyof imperfect-information games and test some perfectinformationpruning techniques to see if they will improveefficiency.
Automatic Localization of Bugs to Faulty Components in Large Scale Software Systems Using Bayesian Classification

We suggest a Bayesian approach to the problem of reducing bug turn-around time in large software development organizations. Our approach is to use classification to predict where bugs are located in components. This classification is a form of automatic fault localization (AFL) at the component level. The approach only relies on historical bug reports and does not require detailed analysis of source code or detailed test runs. Our approach addresses two problems identified in user studies of AFL tools. The first problem concerns the trust in which the user can put in the results of the tool. The second problem concerns understanding how the results were computed. The proposed model quantifies the uncertainty in its predictions and all estimated model parameters. Additionally, the output of the model explains why a result was suggested. We evaluate the approach on more than 50000 bugs.
Lexical frequency and third-graders' stress accuracy in derived English word production

ABSTRACT This study examined the effects of lexical frequency on children's production of accurate primary stress in words derived with nonneutral English suffixes. Forty-four third-grade children participated in an elicited derived word task in which they produced high-frequency, low-frequency, and nonsense-derived words with stress-changing suffixes (i.e., -tion, -ic, -ity). Derived word frequency affected stress production accuracy; however, the individual suffix also played an important role in stress placement, with -tion productions more accurate than either -ic or -ity productions. For the real words, derived word frequency relative to stem frequency was related to performance. Stress was less accurate on derived words that were much lower in frequency than their stems (e.g., tranquil/tranquility) and more accurate on derived words that approximated or exceeded their stem frequency (e.g., motivate/motivation). In addition to derived word and stem frequency, results are discussed with reference to several phonological characteristics that may also influence stress production accuracy.
Computational Models of Emotion and Cognition

In this paper, we seek to review the broad landscape of research in computational emotions and cognition. We begin by classifying and organizing an enumeration of recent models and systems and then discuss some of the landmark models from the literature, such as EMA and WASABI. We then discuss open problems with the current state of research. These issues are standardizing criteria for evaluation of models, the complexity and breadth of the domain, and the need to implement a working system which addresses integration with more of the rich history of AI research. We also provide suggestions for future research, particularly standardization to facilitate community collaboration.
Neurological soft signs in obsessive-compulsive disorder: two empirical studies and meta-analysis

Background Neurological soft signs (NSS) have been inconsistently reported in obsessive-compulsive disorder (OCD) but may make an impact on treatment response. Method The current study examined the presence of NSS in two independent European samples of OCD patients (combined 85 patients and 88 matched healthy controls) using a standardized instrument and conducted a meta-analysis of all published studies identified in the literature with the aim to provide a more definitive answer to the question of whether OCD patients are characterized by increased NSS. Results Both empirical studies found elevated NSS scores in patients compared with matched controls. The results of the meta-analysis, which included 15 studies (combined 498 patients and 520 controls) showed large effect sizes (Hedges' g=1.27, 95% confidence interval 0.80–1.75), indicating that OCD patients have significantly higher rates of NSS than matched controls on both sides of the body and in multiple domains (motor coordination, sensory integration and primitive reflexes). The results were robust and remained largely unchanged in our reliability analyses, which controlled for possible outliers. Meta-regression was employed to examine the role of potential variables of interest including sociodemographic variables, symptom severity, medication effects and the use of different instruments, but none of these variables was clearly associated with NSS. Conclusions As a group, OCD patients are characterized by increased rates of NSS, compared with healthy controls. However, their origins and potential clinical importance remain to be clarified. Future directions for research are discussed.
Chaos and the Abuse of Power

Bullying is a significant workplace problem—a fact highlighted by a growing body of social science literature. Its causes, however, have received little systematic attention beyond analyses of the personality attributes of bullies. This article explores the roles of relational power and organizational chaos in the emergence of workplace bullying. The analysis of content-coded organizational ethnographies integrates quantitative and qualitative techniques and draws heavily from the ethnographies themselves. Results suggest that the interplay of relational powerlessness and organizational chaos gives rise to bullying. In contrast, where there is a disjuncture between organizational and relational factors, the extent of bullying is determined by underlying, context-specific aspects of power. These results suggest a need for organizations not only to protect the weak, but also to eliminate chaos—chaos that creates openings for the abuse of power.