Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete Sequence and Gene Organization of the Nosema spodopterae rRNA Gene

Abstract. By sequencing the entire ribosomal RNA (rRNA) gene of Nosema spodopterae, we show here that its gene organization follows a pattern similar to the Nosema type species, Nosema bombycis, i.e. 5′‐large subunit rRNA (2,497 bp)‐internal transcribed spacer (185 bp)‐small subunit rRNA (1,232 bp)‐intergenic spacer (277 bp)‐5S rRNA (114 bp)‐3′. Gene sequences and the secondary structures of large subunit rRNA, small subunit rRNA, and 5S rRNA are compared with the known corresponding sequences and structures of closely related microsporidia. The results suggest that the Nosema genus may be heterogeneous and that the rRNA gene organization may be a useful characteristic for determining which species are closely related to the type species.
Speaker adaptation using combined transformation and Bayesian methods

The performance and robustness of a speech recognition system can be improved by adapting the speech models to the speaker, the channel and the task. In continuous mixture-density hidden Markov models the number of component densities is typically very large, and it may not be feasible to acquire a large amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, we propose a constrained estimation technique for Gaussian mixture densities, and combine it with Bayesian techniques to improve its asymptotic properties. We evaluate our algorithms on the large-vocabulary Wall Street Journal corpus for nonnative speakers of American English. The recognition error rate is comparable to the speaker-independent accuracy achieved for native speakers.
Placental mRNA in maternal plasma: Prospects for fetal screening

Increasingly, over the past 30 years, couples at risk of bearing children with certain types of severe handicaps have had the option of fetal diagnosis, followed by selective termination of pregnancy, with the prospect of trying for a healthy baby later. In a society where financial and other help is seldom available to provide adequate support to the parent caring for a severely disabled infant, it is understandable that there is an increasing demand for obstetric services to provide prenatal screening to determine risks, and safe tests to confirm, or exclude, the diagnosis. Modern obstetric ultrasonography brings the fetus into sight and is one of the main screening tests for developmental malformations. Nuchal translucency is a measurable ultrasound feature associated with Down's syndrome and, when used in conjunction with maternal serum screening, increases the detection rate in the first trimester to >85% for a false-positive rate of 5% (1). The key maternal serum markers are the levels of the β subunit of human chorionic gonadotrophin (βhCG), and pregnancy-associated plasma protein-A (PAPP-A). Ultrasound bears no hazard for the fetus, but the procedures used for definitive diagnosis, namely chorion villus sampling (CVS) and amniocentesis, carry a risk of miscarriage of ≈1%. Almost all current tests for fetal chromosome, genetic, and biochemical disorders depend on either of these two invasive procedures to provide fetal cells for culture or DNA analysis.
Digging for Data Structures

Because writing computer programs is hard, computer programmers are taught to use encapsulation and modularity to hide complexity and reduce the potential for errors. Their programs will have a high-level, hierarchical structure that reflects their choice of internal abstractions. We designed and forged a system, Laika, that detects this structure in memory using Bayesian unsupervised learning. Because almost all programs use data structures, their memory images consist of many copies of a relatively small number of templates. Given a memory image, Laika can find both the data structures and their instantiations. We then used Laika to detect three common polymorphic botnets by comparing their data structures. Because it avoids their code polymorphism entirely, Laika is extremely accurate. Finally, we argue that writing a data structure polymorphic virus is likely to be considerably harder than writing a code polymorphic virus.
DrugBank 4.0: shedding new light on drug metabolism

DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug–target, –enzyme and –transporter associations to provide insight on drug–drug interactions.
Automated vision-based system for parallel contactless micromanipulation

This paper presents the automated parallel noncontact manipulation of glass beads ranging from 30 up to 300 μm in size under water. Non-contact micromanipulation is performed by generating controllable laser-induced thermocapillary flows that are capable of dragging the beads. Automated manipulation process is achieved with visual servoing in order to accurately manipulate the beads in a parallel and high-speed manner. Image correlation allowed to detect the bead positions and to provide these positions to a mirror scanner that addressed the IR laser beam at a certain position from the bead. By scanning the laser beam from one bead to another, automated parallel manipulation of beads at speeds in the range of mm·s-1 was demonstrated.
Protein Structure-structure Alignment with Discrete FrÉchet Distance

Matching two geometric objects in two-dimensional (2D) and three-dimensional (3D) spaces is a central problem in computer vision, pattern recognition, and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of biomolecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in two dimensions to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in three dimensions. We also describe our empirical results on the application of the discrete Fréchet distance to protein structure-structure alignment.
What are Software Engineers asking about Android Testing on Stack Overflow?

Software testing represents an important activity to achieve quality during mobile application development. The constant evolution of mobile applications in previous years relating to size and complexity entails the need to improve testing techniques and tools. In this context, developers/testers often resort to specialized communities or Question & Answer repositories to clarity doubts regarding testing in a practical and efficient way. Thus, these repositories become a popular source of data to understand the current context of software testing practices. In this paper, we present a study using the Stack Overflow repository for analyzing and clustering the main topics on Android testing. We employed the LDA algorithm to summarize the mobile testing related questions. Our findings show that topics such as testing tools, functional testing, and unit testing are often discussed when compared to other topics. We also analyzed the evolution of the interest of Android testing tools. Results show that developers are more interested in Appium, Espresso, Monkey, and Robotium tools.
Using SUMMA for Language Independent Summarization at TAC 2011

The paper describes a language independent multi-document centroid-based summarization system. The system has been evaluated in the 2011 TAC Multilingual Summarization pilot task where summaries were automatically produced for document clusters in Arabic, English, French and Hindi. The system had a reasonable performance in content selection for languages such as Arabic and Hindi and medium performance for English, but poor performance for French. Evaluation results in content selection for French and summary quality in all languages indicate that the system has to be better adapted to the summarization task.
Problematic Social Networking Site Use and Comorbid Psychiatric Disorders: A Systematic Review of Recent Large-Scale Studies

Background and Aims: Research has shown a potential association between problematic social networking site (SNS) use and psychiatric disorders. The primary objective of this systematic review was to identify and evaluate studies examining the association between problematic SNS use and comorbid psychiatric disorders. Sampling and Methods: A literature search was conducted using the following databases: PsychInfo, PsycArticles, Medline, Web of Science, and Google Scholar. Problematic SNS use (PSNSU) and its synonyms were included in the search. Information was extracted based on problematic SNS use and psychiatric disorders, including attention deficit and hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), depression, anxiety, and stress. The inclusion criteria for papers to be reviewed were (i) being published since 2014 onwards, (ii) being published in English, (iii) having population-based studies with sample sizes >500 participants, (iv) having specific criteria for problematic SNS use (typically validated psychometric scales), and (v) containing empirical primary data reporting on the correlation between PSNSU and psychiatric variables. A total of nine studies met the predetermined inclusion and exclusion criteria. Results: The findings of the systematic review demonstrated that most research has been conducted in Europe and all comprised cross-sectional survey designs. In eight (of the nine) studies, problematic SNS use was correlated with psychiatric disorder symptoms. Of the nine studies (some of which examined more than one psychiatric symptom), there was a positive association between PSNSU and depression (seven studies), anxiety (six studies), stress (two studies), ADHD (one study), and OCD (one study). Conclusions: Overall, the studies reviewed showed associations between PSNSU and psychiatric disorder symptoms, particularly in adolescents. Most associations were found between PSNSU, depression, and anxiety.
VectorBase: a home for invertebrate vectors of human pathogens

VectorBase () is a web-accessible data repository for information about invertebrate vectors of human pathogens. VectorBase annotates and maintains vector genomes providing an integrated resource for the research community. Currently, VectorBase contains genome information for two organisms: Anopheles gambiae, a vector for the Plasmodium protozoan agent causing malaria, and Aedes aegypti, a vector for the flaviviral agents causing Yellow fever and Dengue fever.
Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition

This paper views target detection and pattern recognition as a kind of communications problem and applies error-correcting coding to the outputs of a convolutional neural network to improve the accuracy and reliability of detection and recognition of targets. The outputs of the convolutional neural network are designed according to codewords with maximum Hamming distances. The effects of the codewords on the performance of the convolutional neural network in target detection and recognition are then investigated. Images of hand-written digits and printed English letters and symbols are used in the experiments. Results show that error-correcting output coding provides the neural network with more reliable decision rules and enables it to perform more accurate and reliable detection and recognition of targets. Moreover, our error-correcting output coding can reduce the number of neurons required, which is highly desirable in efficient implementations.
Neural network models for intelligent networks : deriving the location from signal patterns

The knowledge of the locationof a mobile terminalcanbe usedto reducethecognitiveburdenon theusersin context–awaresystemsandit is acrucial informationfor routing in ad–hoc networks andfor optimizingthetopology in orderto minimize theenergy consumptionof the nodes.Thestrengthsof the RF signalsarriving from moreaccesspointsarerelatedto theposition(location fingerprinting), but the complexity of the inverseproblemto derive theposition from thesignalsandthelackof completeinformation,motivateto considerflexiblemodelsbasedonanetwork of functions(neural networks), developedthrough a supervisedlearningstrategy. Theadvantageof themethodis thatit doesnotrequiread-hocinfrastructurein addition to thewirelessLAN, while theflexible modellingandlearningcapabiliti es of neuralnetworksachievesmallerrorsin determiningtheposition,areamenable to incrementalimprovements,anddo not requirethe detailedknowledgeof the accesspoint locationsandof the building characteristics. The systemdoesnot participatein anactive mannerto determinetheposition,thereforeguaranteeing a completeprivacy. Experimentalresultsandcomparisonswith alternati ve techniquesarepresented anddiscussed.
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization

The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiting factor when approaching new applications. To help alleviating these limitations, we propose an efficient learning strategy for layer-wise unsupervised training of deep CNNs on conventional hardware in acceptable time. Our proposed strategy consists of randomly convexifying the reconstruction contractive auto-encoding (RCAE) learning objective and solving the resulting large-scale convex minimization problem in the frequency domain via coordinate descent (CD). The main advantages of our proposed learning strategy are: (1) single tunable optimization parameter; (2) fast and guaranteed convergence; (3) possibilities for full parallelization. Numerical experiments show that our proposed learning strategy scales (in the worst case) linearly with image size, number of filters and filter size.
Street-involved youth’s unstructured leisure: activities and their social consequences

Research has shown that leisure can be an effective tool in reducing disaffiliation for those who live on the streets; however, there is little research that incorporates input from street-involved persons themselves, especially from street-involved youth. In the study described herein, we sought to examine the role of unstructured leisure activities in forming social connections between street-involved youth as well as between these youth and members of the mainstream community. We conducted eight interviews with street-involved youth in Ottawa, Ontario. Our findings reveal that the youth used unstructured leisure to form social connections with others. Further, in contrast to popular assumptions, our findings suggest that street-involved youth participate in a range of conventional unstructured leisure activities within the mainstream. Nevertheless, although the youth engaged in conventional leisure within the mainstream, most did not result in the generation of feelings of connections to the broader community. These findings illustrate the complicated nature of using leisure in attempts to help street-involved youth form social ties. They also illustrate that street-involved youth exercise agency in managing constraints in ways that enable them to be resilient in the face of often daunting life circumstances.
Intermediate maturation of Mycobacterium tuberculosis LAM‐activated human dendritic cells

Contrasting observations raise the question of the role of mycobacterial derived products as compared with the whole bacterium Mycobacterium tuberculosis on maturation and function of human dendritic cells (DCs). DC‐SIGN has been identified as the key DC receptor for M. tuberculosis through its interaction with the mannosylated lipoarabinomannan (ManLAM). Although ManLAM is a major mycobacterial component released from infected antigen‐presenting cells, there is no formal evidence yet for an effect of ManLAM per se on DC maturation and function. DCs activated with purified ManLAM displayed an intermediate maturation phenotype as compared with lipopolysaccharide fully matured DCs with reduced expression of MHC class I and class II molecules, CD83 and CD86 and of the chemokine receptor CCR7. They were sensitive to autologous natural killer (NK) lysis, thus behaving like immature DCs. However, ManLAM‐activated DCs lost phagocytic activity and triggered priming of naive T‐cells, confirming their intermediate maturation. Partial maturation of ManLAM‐activated DCs was overcome by triggering the CD40/CD40L pathway as a second signal, which completed maturation phenotypically and abolished autologous NK lysis susceptibility. Altogether, these data provide evidence that ManLAM may induce a partial maturation phenotype on non‐infected bystander DCs during infection suggesting that ManLAM released from infected cells might impair adaptive immune response towards M. tuberculosis.
InterProScan 5: genome-scale protein function classification

Motivation: Robust large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterize many millions of sequences. Here, we describe a new Java-based architecture for the widely used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete reimplementation of the software framework, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the open source code is hosted at Google Code. Availability and implementation: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google. com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or interhelp@ebi.ac.uk or mitchell@ebi.ac.uk
A 40-Gb/s optical transceiver front-end in 45nm SOI CMOS technology

A low-power, 40-Gb/s optical transceiver front-end is demonstrated in a 45 nm silicon on insulator (SOI) CMOS technology. A modulator driver uses floating body devices to realize voltage swing of 2 VPP with a small-signal gain of 7.6 dB over 33 GHz. The optical receiver consists of a transimpedance amplifier (TIA) and post-amplifier with 55-dB·Ω of transimpedance over 30 GHz. The group-delay variation is ±3.9 ps over the 3-dB bandwidth and the average input-referred noise density is 20.47 pA/√Hz. The TIA consumes 9 mW from a 1 V supply for a transimpedance figure of merit of 1874.5 Ω/pJ. To the author's knowledge, this represents the lowest power consumption for a transmitter and receiver operating at 40-Gb/s in a CMOS process.
Online sexual compulsivity: Getting tangled in the net

Abstract The use of adult Web sites for the purpose of sexual expression is becoming increasingly popular and at times problematic. Online sexual behavior is different for each person, ranging from recreational to pathological use. The types of online users proposed in this article include Recreational Users, Sexual Compulsives, and At-Risk Users (consisting of two subtypes: the Depressive Type and the Stress Reactive Type). Assessment techniques and treatment strategies for each type of user are provided and suggestions for future research are proposed.
Predictors of Job Burnout in Reporters and Copy Editors

This exploratory study applied a widely used psychological test to measure job burnout among a sample of reporters and copy editors at five daily newspapers of dissimilar size. Results indicate that the employee most likely to suffer from burnout is a young, entry-level journalist working as a copy editor at a small dairy newspaper. However, other variables were also found to significantly relate to job burnout. These include age, job title, total years of experience, income and job satisfaction.
Left ventricular systolic and diastolic function in normotensive type 1 diabetic patients with or without autonomic neuropathy: a radionuclide ventriculography study.

OBJECTIVE To investigate the relation between diabetic autonomic neuropathy (DAN) and left ventricular (LV) function in type 1 diabetic patients. RESEARCH DESIGN AND METHODS A total of 57 type 1 diabetic patients free of coronary artery disease and arterial hypertension were studied. Diagnosis of DAN was established by autonomic nervous function (ANF) tests, and LV systolic and diastolic functions were assessed by radionuclide ventriculography at rest. RESULTS There were 24 patients who had definite DAN, established by the presence of two or more abnormal ANF tests, and 33 subjects were without DAN. DAN patients had impaired LV filling pattern, obvious by a reduced peak filling rate (3.1 +/- 1.1 vs. 3.7 +/- 0.7 end-diastolic volume [EDV]/s, P = 0.011) and first third filling fraction (35.3 +/- 19.5 vs. 50.8 +/- 16%, P = 0.002) as well as an increased time to peak filling (159.4 +/- 45.1 vs. 134.2 +/- 33.4 ms, P = 0.02) after correction for age and heart rate. There were no differences between the two groups with regard to ejection fraction, cardiac output, and cardiac index, whereas the peak emptying rate was greater in DAN patients (4.1 +/- 0.8 vs. 3.6 +/- 0.8 EDV/s, P = 0.019), suggesting LV hypercontractility. DAN patients had an increased heart rate (83.4 +/- 11.9 vs. 72.7 +/- 9.3 bpm, P = 0.001) and slightly higher systolic blood pressure. As a result, LV working load at rest was higher in DAN patients (11,109 vs. 9,096 bpm x mmHg, P < 0.001). Moreover, a correlation was found between abnormal LV systolic and diastolic indexes and the number of abnormal ANF tests. CONCLUSIONS At rest, DAN patients have impaired LV filling pattern, slightly increased LV systolic function, and a higher LV working load, in comparison to non-DAN type 1 diabetic patients.