Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiotensin-(1–7) Acts as a Vasodepressor Agent Via Angiotensin II Type 2 Receptors in Conscious Rats

Given that angiotensin-(1–7) (Ang-[1–7]) has been frequently reported to exert direct in vitro vascular effects but less often in vivo, we investigated whether a vasodepressor effect of Ang-(1–7) could be unmasked acutely in conscious spontaneously hypertensive rats (SHR) against a background of angiotensin II type 1 (AT1) receptor blockade. Mean arterial pressure (MAP) and heart rate were measured over a 5-day protocol in various groups of rats randomized to receive the following drug combinations: saline, AT1 receptor (AT1R) antagonist candesartan (0.01 or 0.1 mg/kg IV) alone, Ang-(1–7) (5 pmol/min) alone, candesartan plus Ang-(1–7), and candesartan plus Ang-(1–7) and angiotensin II type 2 (AT2) receptor (AT2R) antagonist PD123319 (50 &mgr;g/kg per minute). In Wistar-Kyoto (WKY) rats, saline, Ang-(1–7), or candesartan alone caused no significant alteration in MAP, whereas Ang-(1–7) coadministered with candesartan caused a marked, sustained reduction in MAP. A similar unmasking of a vasodepressor response to Ang-(1–7) during AT1R blockade was observed in SHR. Moreover, the AT2R antagonist PD123319 markedly attenuated the enhanced depressor response evoked by the Ang-(1–7)/candesartan combination in SHR and WKY rats, whereas in other experiments, the putative Ang-(1–7) antagonist A-779 (5 and 50 pmol/min) did not attenuate this vasodepressor effect. In separate experiments, the bradykinin type 2 receptor antagonist HOE 140 (100 &mgr;g/kg IV) or the NO synthase inhibitor N&ohgr;-nitro-l-arginine methyl ester (1 mg/kg IV) abolished the depressor effect of Ang-(1–7) in the presence of candesartan. Collectively, these results suggest that Ang-(1–7) evoked a depressor response during AT1R blockade via activation of AT2R, which involves the bradykinin–NO cascade.
Semantic Web Mining: Review

— This paper describes about Semantic Web Mining. The Purpose of this paper is to focus on how semantic web technologies can be used to mine the web , for relevant information extraction. Semantic Web Mining is about combining the two emerging research areas Semantic Web and Web Mining. Researchers work on improving the result off web mining by using semantic structure in the web and make use of Web Mining techniques for building the Semantic Web. In this manner both technologies are playing vital role to each other. Semantic Web adds structure to the meaningful content of Web Pages ; hence information is given a well defined meaning; which is both human readable as well as machine-processable. This paper gives an overview of where the two areas meet today , and sketches ways of how a closer integration could be profitable.
Evaluating music emotion recognition: Lessons from music genre recognition?

A fundamental problem with nearly all work in music genre recognition (MGR) is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations.
Endurance-aware cache line management for non-volatile caches

Nonvolatile memories (NVMs) have the potential to replace low-level SRAM or eDRAM on-chip caches because NVMs save standby power and provide large cache capacity. However, limited write endurance is a common problem for NVM technologies, and today's cache management might result in unbalanced cache write traffic, causing heavily written cache blocks to fail much earlier than others. Although wear-leveling techniques for NVM-based main memories exist, we cannot simply apply them to NVM-based caches. This is because cache writes have intraset variations as well as interset variations, while writes to main memories only have interset variations. To solve this problem, we propose i2WAP, a new cache management policy that can reduce both inter- and intraset write variations. i2WAP has two features: Swap-Shift, an enhancement based on existing main memory wear leveling to reduce cache interset write variations, and Probabilistic Set Line Flush, a novel technique to reduce cache intraset write variations. Implementing i2WAP only needs two global counters and two global registers. In one of our studies, i2WAP can improve the NVM cache lifetime by 75% on average and up to 224%. We also validate that i2WAP is effective in systems with different cache configurations and workloads.
GOALS: Modeling Clinical Guidelines Based on TimeML Concepts

Clinical practice guidelines aim at raising the quality of healthcare. They are written in a narrative style and have to be translated into a computer-interpretable guideline (CIG) to be usable in a clinical software application. In this project we present the GOALS methodology which defines a stepwise approach to support this modeling process. The methodology is specified independently from the target CIG language and uses a guideline's text annotated with temporal concepts provided by TimeML as a starting point. It describes step-by-step how parts of the guideline's model can be generated and finally assessed by means of an evaluation scheme. By means of a scenario-based evaluation we show the applicability of GOALS by translating temporally-related sentences of a clinical protocol into its semi-formal model. Thus, we conclude that this methodology indeed supports the translation process.
Chapter 5 Eukaryotic Transcription : A Molecular Ratchet Prone to Bursting

Eukaryotic transcription regulation depends on a multitude of protein complexes that assemble on chromatin. Their activities are modulated by transcription factors and the local chromatin state, which is dynamic during the course of transcription initiation. We present a stochastic molecular-ratchet model of eu-karyotic transcription initiation based on the current knowledge. The ratchet is composed out of a sequence of transitions, each composed out of reversible protein complex formation followed by irreversible histone modifications sensitiz-ing chromatin for the formation of the next protein complex. A coarse-grained description of this model describes a gene as switching between an on and off state, both having non-exponential durations. Transcription activity proves robust to the precise design of protein complex formation and histone modification. Transcription stochasticity (bursts and mRNA noise) is expressed in terms of the design and kinetics of transcription initiation. We discuss which experimental approaches can give most information about the transcription mechanism.
2D Beamforming on Sparse Arrays with Sparse Bayesian Learning

Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with sparse Bayesian learning (SBL) for 2D beamforming in azimuth and elevation. SBL can directly process the sparse array data and significantly outperform conventional beam-forming and MUSIC as seen from simulations.
The value of CAGE, CUGE, and AUDIT in screening for alcohol abuse and dependence among college freshmen.

BACKGROUND This study attempted to (1) determine the prevalence of alcohol problems in college freshmen, (2) assess the performance of both the CAGE and the Alcohol Use Disorders Identification Test (AUDIT) questionnaires in this population, and (3) assess the possibility of improving the CAGE and/or AUDIT. METHODS A sample of 3564 consecutive college freshmen, with a mean age of 18 years, at the Catholic University of Leuven, (Belgium) completed, during a cross-sectional study, a questionnaire assessing drinking behavior and identifying students at risk as defined by DSM-IV criteria. The questionnaire also included the CAGE questionnaire and the AUDIT. Calculations of sensitivity, specificity, negative predictive value, positive predictive value, likelihood ratios, and receiver operating characteristic curves for different scores of the CAGE and the AUDIT were performed, using DSM-IV criteria as the reference standard. RESULTS The area under the receiver operating characteristic curve of the CAGE and the AUDIT was 0.76 and 0.85, respectively. The cutoff score of 1 for the CAGE was associated with a sensitivity of 42%, a specificity of 87%, a positive predictive value of 36%, and a negative predictive value of 90%. A score of 6 or more for the AUDIT gave a sensitivity of 80%, a specificity of 78%, a positive predictive value of 37%, and a negative predictive value of 77%. These results were related with a prevalence of 14.1% of alcohol problems. Replacing one question of the CAGE by "often driving under the influence" resulted in the CUGE (acronym for "cut down, under influence, guilty feelings, and eye opener"), with an area under the curve of 0.96, a positive likelihood ratio of 8.7, and a negative likelihood ratio of 0.04. CONCLUSIONS Prevalence of alcohol problems in college students is confirmed to be high. When screening for alcohol problems in a college freshmen population, one question seems extremely important. The newly constructed CUGE questionnaire may improve screening efforts in students, compared with existing questionnaires.
Detecting low usability web pages using quantitative data of users' behavior

The purpose of this research is to detect low usability web pages from the behavior of users, such as browsing time, mouse movement and eye movement. We experimented to investigate the relation between the quantitative data viewing behavior of users and web usability evaluation by subjects. We analyzed the data to detect low usability web pages using discriminant analysis. Low usability web pages, 94.4% (17pages / 18pages = detectable pages / low usability pages) were detectable from the moving speed of gazing points and the amount of wheel rolling of a mouse. Moreover, this detection reduced the number of web pages which should be evaluated by half (46% = 89 pages / 192 pages = detected pages / all pages).
Underachievement Syndrome: Causes and Cures

A variable impact jarring tool comprising a handled shaft with a massive impact sleeve slideable thereon. A self-tapping screw on the end of the shaft is driven to grip the workpiece and a first collar adjacent the handle serves as an anvil against which the sleeve impacts. A second collar may be fixed on the shaft in either of two positions, one adjacent the end for a full impact stroke and the other midway thereof for a limited stroke, but providing more clearance at the end for placement of the screw in close places.
French presidential elections: what are the most efficient measures for tweets?

Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we address the problem of extracting relevant topics through tweets coming from different communities. More precisely we are interested to address the following question: which are the most relevant terms given a community. To answer this question we define and evaluate new variants of the traditional TF-IDF. Furthermore we also show that our measures are well suited to recommend a community affiliation to a new user. Experiments have been conducted on tweets collected during French Presidential and Legislative elections in 2012. The results underline the quality and the usefulness of our proposal.
Satellite‐based assessment of marine low cloud variability associated with aerosol, atmospheric stability, and the diurnal cycle

[1] This study examines variability in marine low cloud properties derived from semiglobal observations by the Tropical Rainfall Measuring Mission (TRMM) satellite, as linked to the aerosol index (AI) and lower-tropospheric stability (LTS). AI is derived from the Moderate Resolution Imaging Spectroradiometer (Terra MODIS) sensor and the Goddard Chemistry Aerosol Radiation and Transportation (GOCART) model and is used to represent column-integrated aerosol concentrations. LTS is derived from the NCEP/NCAR reanalysis and represents the background thermodynamic environment in which the clouds form. Global statistics reveal that cloud droplet size tends to be smallest in polluted (high-AI) and strong inversion (high-LTS) environments. Statistical quantification shows that cloud droplet size is better correlated with AI than it is with LTS. Simultaneously, the cloud liquid water path (CLWP) tends to decrease as AI increases. This correlation does not support the hypothesis or assumption that constant or increased CLWP is associated with high aerosol concentrations. Global variability in corrected cloud albedo (CCA), the product of cloud optical depth and cloud fraction, is very well explained by LTS, while both AI and LTS are needed to explain local variability in CCA. Most of the local correlations between AI and cloud properties are similar to the results from the global statistics, while weak anomalous aerosol-cloud correlations appear locally in the regions where simultaneous high (low) AI and low (high) LTS compensate each other. Daytime diurnal cycles explain additional variability in cloud properties. CCA has the largest diurnal cycle in high-LTS regions. Cloud droplet size and CLWP have weak diurnal cycles that differ between clean and polluted environments. The combined results suggest that investigations of marine low cloud radiative forcing and its relationship to hypothesized aerosol indirect effects must consider the combined effects of aerosols, thermodynamics, and the diurnal cycle.
DNN-Based Automatic Speech Recognition as a Model for Human Phoneme Perception

In this paper, we test the applicability of state-of-the-art automatic speech recognition (ASR) to predict phoneme confusions in human listeners. Phoneme-specific response rates are obtained from ASR based on deep neural networks (DNNs) and from listening tests with six normal-hearing subjects. The measure for model quality is the correlation of phoneme recognition accuracies obtained in ASR and in human speech recognition (HSR). Various feature representations are used as input to the DNNs to explore their relation to overall ASR performance and model prediction power. Standard filterbank output and perceptual linear prediction (PLP) features result in best predictions, with correlation coefficients reaching r = 0.9.
Histone ADP-Ribosylation Facilitates Gene Transcription by Directly Remodeling Nucleosomes

ABSTRACT The packaging of DNA into nucleosomes imposes obstacles on gene transcription, and histone-modifying and nucleosome-remodeling complexes work in concert to alleviate these obstacles so as to facilitate transcription. Emerging evidence shows that chromatin-associated poly(ADP-ribose) polymerase 1 (PARP-1) and its enzymatic activity facilitate inflammatory gene transcription and modulate the inflammatory response in animal models. However, the molecular mechanisms by which PARP-1 enzymatic activity facilitates transcription are not well understood. Here we show that through an intracellular signaling pathway, lipopolysaccharide (LPS) stimulation induces PARP-1 enzymatic activity and the ADP-ribosylation of histones at transcriptionally active and accessible chromatin regions in macrophages. In vitro DNase I footprinting and restriction endonuclease accessibility assays reveal that histone ADP-ribosylation directly destabilizes histone-DNA interactions in the nucleosome and increases the site accessibility of the nucleosomal DNA to nucleases. Consistent with this, LPS stimulation-induced ADP-ribosylation at the nucleosome-occupied promoters of il-1β, mip-2, and csf2 facilitates NF-κB recruitment and the transcription of these genes in macrophages. Therefore, our data suggest that PARP-1 enzymatic activity facilitates gene transcription through increasing promoter accessibility by histone ADP-ribosylation.
Maximizing Classification Accuracy in Native Language Identification

This paper reports our contribution to the 2013 NLI Shared Task. The purpose of the task was to train a machine-learning system to identify the native-language affiliations of 1,100 texts written in English by nonnative speakers as part of a high-stakes test of general academic English proficiency. We trained our system on the new TOEFL11 corpus, which includes 11,000 essays written by nonnative speakers from 11 native-language backgrounds. Our final system used an SVM classifier with over 400,000 unique features consisting of lexical and POS n-grams occurring in at least two texts in the training set. Our system identified the correct nativelanguage affiliations of 83.6% of the texts in the test set. This was the highest classification accuracy achieved in the 2013 NLI Shared Task.
Incorporation of [3H]Arachidonic and [14C]Eicosapentaenoic Acids into Glycerophospholipids and Their Metabolism via Lipoxygenases in Isolated Brain Cells from Rainbow Trout Oncorhaynchus mykaiss

The incorporation of [3H]arachidonate ([3H]AA) and [14C]eicosapentaenoate ([14C]EPA) into glycerophos‐pholipids was studied in isolated brain cells from rainbow trout, a teleost fish whose lipids are rich in (n‐3) polyunsaturated fatty acids (PUF As), EPA was incorporated into total lipid to a greater extent than AA, but the incorporation of both PUF As into total glycerophospholipids was almost identical. The incorporation of both AA and EPA was greatest into phosphatidylethanolamine (PE). However, when expressed per milligram of individual phosphoglycerides, both AA and EPA were preferentially incorporated into phospha‐tidylinositol (PI), the preference being significantly greater with AA. On the same basis, significantly more EPA than AA was incorporated into phosphatidylcholine (PC). When double‐labelled cells were challenged with calcium ionophore A23187, the 3H and 14C released from the cells closely paralleled each other, peaking at 10 min after addition of ionophore.
Sergan: Speech Enhancement Using Relativistic Generative Adversarial Networks with Gradient Penalty

Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals. Recently, conditional generative adversarial networks (cGANs) have shown promise in addressing the phase mismatch problem by directly mapping the raw noisy speech waveform to the underlying clean speech signal. However, stabilizing and training cGAN systems is difficult and they still fall short of the performance achieved by spectral enhancement approaches. This paper introduces relativistic GANs with a relativistic cost function at its discriminator and gradient penalty to improve time-domain speech enhancement. Simulation results show that relativistic discriminators provide a more stable training of cGANs and yield a better generator network for improved speech enhancement performance.
Position-specific performance indicators that discriminate between successful and unsuccessful teams in elite women’s indoor field hockey: implications for coaching

Abstract The aim of this investigation was to establish median performance profiles for the six playing positions in elite women’s indoor hockey and then identify whether these position-specific profiles could discriminate between qualifying (top four), mid-table and relegated teams in the 2011–2012 England Hockey premier league. Successful passing in relegated teams was significantly lower (P < 0.008) than in mid-table and qualifying teams in four of the five outfield positions. Furthermore, the right backs of qualifying teams demonstrated significantly fewer (P < 0.008) unsuccessful passes (x̃ = 15.5 ± CLs 15.0 and 10.0, respectively) and interceptions (x̃ = 4.0 ± CLs 4.0 and 3.0, respectively) than relegated teams (x̃ = 19.5 ± CLs 21.0 and 17.0; x̃ = 7.5 ± CLs 8.0 and 6.0, respectively). Finally, the right forwards of relegated teams demonstrated significantly fewer (P < 0.008) successful interceptions (x̃ = 4.0 ± CLs 5.0 and 4.0, respectively) than qualifying teams (x̃ = 5.0 ± CLs 6.0 and 3.0, respectively) and significantly more (P < 0.008) unsuccessful interceptions (x̃ = 5.5 ± CLs 6.0 and 4.0, respectively) than mid-table teams (x̃ = 3.0 ± CLs 3.0 and 2.0, respectively). Based on these findings, coaches should adapt tactical strategies and personnel deployment accordingly to enhance the likelihood of preparing a qualifying team. Research should build from these data to examine dribbling, pressing and patterns of play when outletting.
Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization

The effective integration of configuration system development with industrial software development is crucial for a successful implementation of a mass customization strategy. On the one hand, configuration knowledge bases must be easy to develop and maintain due to continuously changing product assortments. On the other hand, flexible integrations into existing enterprise applications, e-marketplaces and different facets of supply chain settings must be supported. This paper shows how the model-driven architecture (MDA) as an industrial framework for model development and interchange can serve as a foundation for standardized configuration knowledge representation, thus enabling knowledge sharing in heterogeneous environments. Using UML/Object Constraint Language as standard configuration knowledge representation languages, the representation of configuration domain-specific modeling concepts within MDA is shown and a formal semantics for these concepts is provided which allows a common understanding and interpretation of configuration task descriptions
Variations in the accuracy of the ECG based detection of obstructive sleep apnoea (OSA) for different numbers of ECG leads and categories of OSA events

This study investigates the finding that there is a more pronounced change to ECG physiological predictors during apnoea events compared to hypopnoea events and therefore accurate detection of hypopnoea events is likely to be more challenging than detection of apnoea events. The relevant statistical analysis was conducted by generating logistic regression models from the two data sets: the first one containing only the apnoea events and controls and the second data set containing only the hypopnoea events and controls. The discriminating ability of the model from the apnoea data set (AUC = 0.903, CI = 0.888 – 0.920) was significantly superior compared to the model from the hypopnoea data set (AUC = 0.842, CI = 0.817–0.866). The second study objective investigated whether regression models comprising the OSA predictors derived from the two ECG signals performed better than models that involved parameters of a single ECG. The optimised two signal ECG model (AUC = 0.878 and CI = 0.864 – 0.893) outperformed the best single ECG model (AUC = 0.843, CI = 0.826 – 0.860), suggesting that improved results can be achieved using an additional ECG lead.