Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of anxiety disorders in childhood: an integrative review

We present an integrative review of the development of child anxiety, drawing on a number of strands of research. Family aggregation and genetic studies indicate raised vulnerability to anxiety in offspring of adults with the disorder (e.g. the temperamental style of behavioural inhibition, or information processing biases). Environmental factors are also important; these include adverse life events and exposure to negative information or modelling. Parents are likely to be key, although not unique, sources of such influences, particularly if they are anxious themselves. Some parenting behaviours associated with child anxiety, such as overprotection, may be elicited by child characteristics, especially in the context of parental anxiety, and these may serve to maintain child disorder. Emerging evidence emphasizes the importance of taking the nature of child and parental anxiety into account, of constructing assessments and interventions that are both disorder specific, and of considering bidirectional influences.
The Optical Afterglow of the Gamma-Ray Burst GRB 011211

We present early-time optical photometry and spectroscopy of the optical afterglow of the gamma-ray burst GRB 011211. The spectrum of the optical afterglow contains several narrow metal lines that are consistent with the burst's occurring at a redshift of 2.140 ± 0.001. The optical afterglow decays as a power law with a slope of α = 0.83 ± 0.04 for the first ≈2 days after the burst, at which time there is evidence of a break. The slope after the break is ≥1.4. There is evidence of rapid variations in the R-band light approximately 0.5 days after the burst. These variations suggest that there are density fluctuations near the gamma-ray burst on spatial scales of approximately 40–125 AU. The magnitude of the break in the light curve, the spectral slope, and the rate of decay in the optical suggest that the burst expanded into an ambient medium that is homogeneous on large scales. We estimate that the local particle density is between approximately 0.1 and 10 cm-3 and that the total gamma-ray energy in the burst was (1.2–1.9) × 1050 ergs. This energy is smaller than, but consistent with, the "standard" value of (5 ± 2) × 1050 ergs. Comparing the observed color of the optical afterglow with predictions of the standard beaming model suggests that the rest-frame V-band extinction in the host galaxy is ≲0.03 mag.
Revised5 Report on the Algorithmic Language Scheme

The report gives a defining description of the programming language Scheme. Scheme is a statically scoped and properly tail-recursive dialect of the Lisp programming language invented by Guy Lewis Steele, Jr. and Gerald Jay Sussman. It was designed to have an exceptionally clear and simple semantics and few different ways to form expressions. A wide variety of programming paradigms, including imperative, functional, and message passing styles, find convenient expression in Scheme.The introduction offers a brief history of the language and of the report.The first three chapters present the fundamental ideas of the language and describe the notational conventions used for describing the language and for writing programs in the language.Sections 5 and 6 describe the syntax and semantics of expressions, programs, and definitions.Section 7 describes Scheme's built-in procedures, which include all of the language's data manipulation and input/output primitives.Section 8 provides a formal syntax for Scheme written in extended BNF, along with a formal denotational semantics. An example of the use of the language follows the formal syntax and semantics.The report concludes with a list of references and an alphabetic index and is followed by a short list of clarifications and corrections.
Fundamental space radiobiology.

The unique feature of the space radiation environment is the dominance of high-energy charged particles (HZE or high LET radiation) emitted by the Sun and galactic sources, or trapped in the Van Allen radiation belts. These charged particles present a significant hazard to space flight crews, and accelerator-based experiments are underway to quantify the health risks due to unavoidable radiation exposure. There are three principal properties of charged particles that distinguish them from conventional radiation, i.e. gamma rays and x-rays. First, they have a defined range in matter rather than an exponential absorption profile. Second, they undergo nuclear reactions to produce secondary particles. Third, and most important, they deposit their energy along well-defined linear paths or tracks rather than diffuse fields. The structured energy deposition pattern interacts on multiple scales with the biological structures of DNA, cells and tissues to produce correlated patterns of damage that evade repair systems. Traditional concepts of dose and its associated normalization parameter, RBE (relative biological effectiveness), break down under experimental scrutiny, and probabilistic models of risk based on the number of particle traversals per cell may be more appropriate. Unique patterns of DNA damage, gene expression, mobilization of repair proteins, activation of cytokines and remodeling of cellular microenvironment are observed following exposure to high LET radiation. At low levels of exposure the communication of bioactive substances from irradiated to unirradiated "bystander" cells can amplify the damage and cause a significant deviation from linearity in dose vs. response relations. Under some circumstances, there is even a multigenerational delay in the expression of radiation-induced genetic damage (genomic instability) which is not strictly dose dependent. These issues and the experimental evidence derived from ground based experiments at particle accelerators are presented along with speculation about how modified inertial conditions might perturb homeostatic responses to radiation to further complicate risk assessment for space flight.
Attacks recognition using recurrent neural network

The influence of computer technology on the human activities has greatly increased during the last three decades, due to major developments in the VLSI technology. However this widespread use of computer equipments has generated computer a considerable increase of computer crimes. To reduce this problem it is necessary to carried out a network analysis using the computer network traffic. However the increase of network traffic is huge, doing the analysis of traffic data complicated. Thus it is required to develop an effective and automatic algorithm to carry out the traffic network analysis, facilitating I such way the expert forensic work. This paper proposes a network analysis algorithm using recurrent neural network that can analyze computer network attacks facilitating the evidence extraction. Proposed algorithm can reduce time and cost of forensic analysis.
A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality

This study illustrates an overarching psychometric approach of broad relevance to investigations of 2 sources of construct-relevant psychometric multidimensionality present in many complex multidimensional instruments routinely used in psychological and educational research. These 2 sources of construct-relevant psychometric multidimensionality are related to (a) the fallible nature of indicators as perfect indicators of a single construct, and (b) the hierarchical nature of the constructs being assessed. The first source is identified by comparing confirmatory factor analytic (CFA) and exploratory structural equation modeling (ESEM) solutions. The second source is identified by comparing first-order, hierarchical, and bifactor measurement models. To provide an applied illustration of the substantive relevance of this framework, we first apply these models to a sample of German children (N = 1,957) who completed the Self-Description Questionnaire (SDQ–I). Then, in a second study using a simulated data set, we provide a more pedagogical illustration of the proposed framework and the broad range of possible applications of bifactor ESEM models.
Development of a Face Recognition System on an Image Processing LSI Chip

We present a real-time and high-precision face recognition system using an image processing LSI chip:Visconti[Visconti: Multi-VLIW Image Recognition Processor]. The system is compact and operates at low power, making it suitable for many purposes, including home security and robot vision. The LSI includes three media processing modules and peripherals which are suitable for machine vision. Face recognition is based on the constrained mutual sub-space method (CMSM), implemented on the LSI and optimized to make the best use of the hardware features. The optimization consists of four different levels: instruction, data, task and algorithm. It shows possible implementation of face recognition with multi-core CPUs or other LSI chips. Experimental results show the system operates at 20 frames/sec and a recognition rate is 99.59% when a threshold value is set to 0.55; performance comparable to that of a state-of-the-art system.
Lean health care: what can hospitals learn from a world-class automaker?

BACKGROUND With health care costs continuing to rise, a variety of process improvement methodologies have been proposed to address the reported inefficiencies in health care delivery. Lean production is one such method. The management philosophy and tools of lean production come from the manufacturing industry, where they were pioneered by Toyota Motor Corporation, which is viewed as the leader in utilizing these performance improvement methods. Lean has already enjoyed tremendous success in improving quality and efficiency in both the manufacturing and the service sector industries. RESULTS Health care systems have just begun to utilize lean methods, with reports of improvements just beginning to appear in the literature. We describe some of the basic philosophy and principles of lean production methods and how these concepts can be applied in the health care environment. We describe some of the early success stories and ongoing endeavors of lean production in various health care organizations. We believe the hospital is an ideal setting for use of the lean production method, which could significantly affect how health care is delivered to patients. CONCLUSIONS We conclude by discussing some of the potential challenges in introducing and implementing lean production methods in the health care environment. Lean production is a novel approach to delivering high-quality and efficient care to patients, and we believe that the health care sector can anticipate the same high level of success that the manufacturing and service industries have achieved using this approach. Hospitalists are primed to take action in delivering care of greater quality with more efficiency by applying these new principles in the hospital setting.
Nonaxisymmetric Magnetorotational Instability in Proto-Neutron Stars

We investigate the stability of differentially rotating proto-neutron stars (PNSs) with a toroidal magnetic field. Stability criteria for nonaxisymmetric MHD instabilities are derived using a local linear analysis. PNSs are expected to have much stronger radial shear in the rotation velocity compared to normal stars. We find that nonaxisymmetric magnetorotational instability (NMRI) with a large azimuthal wavenumber m is dominant over the kink mode (m = 1) in differentially rotating PNSs. The growth rate of the NMRI is of order the angular velocity Ω, which is faster than that of the kink-type instability by several orders of magnitude. The stability criteria are analogous to those of the axisymmetric magnetorotational instability with a poloidal field, although the effects of leptonic gradients are considered in our analysis. The NMRI can grow even in convectively stable layers if the wavevectors of unstable modes are parallel to the restoring force by the Brunt-Väisälä oscillation. The nonlinear evolution of NMRI could amplify the magnetic fields and drive MHD turbulence in PNSs, which may lead to enhancement of the neutrino luminosity.
Bioinformatics Applications Note Systems Biology Chromaligner: a Web Server for Chromatogram Alignment

Chromaligner is a tool for chromatogram alignment to align retention time for chromatographic methods coupled to spectrophotometers such as high performance liquid chromatography and capillary electrophoresis for metabolomics works. Chromaligner resolves peak shifts by a constrained chromatogram alignment. For a collection of chromatograms and a set of defined peaks, Chromaligner aligns the chromatograms on defined peaks using correlation warping (COW). Chromaligner is faster than the original COW algorithm by k 2 times, where k is the number of defined peaks in a chromatogram. It also provides alignments based on known component peaks to reach the best results for further chemometric analysis.
Characterization of Variations in PB2, NS1, M, Neuraminidase and Hemagglutinin of Influenza A (H3N2) Viruses in Iran

Background: In the influenza A viruses, neuraminidase (NA), hemagglutinin (HA), PB2, NS1 and M are responsible for the disease pathogenicity. The mechanism of pathogenicity differs among these viruses. Binding of host proteases by the viral NA, sequence of HA in the cleavage and receptor-binding sites, number of oligosaccharide side chains of HA, shortening of NA, and substitutions in PB2, NS1 and M genes, all have been suggested as molecular correlates of pathogenicity of influenza viruses. Objectives: The goal of this study was to find the alterations in genes, which might be responsible in the virus pathogenesis. Materials and Methods: Reverse transcription-polymerase chain reaction (RT-PCR) and sequencing of HA, NA, PB2, NS and M genes were performed. Results: In the receptor binding site of HA, Ile-226, Pro-227, Ser-228, and Asp-190 were found. Arg was in the cleavage site of all viruses and 11-12 N-linked glycosylation sites were found. In NS1, Asp-92 and Ala-149 were detected and Lys-627 was found in PB2 of all viruses in this study. Val-15, Thr-139 and Ala-218 of M1 and Val-28, Leu-54 and His-57 were found in M2 gene. At residue 146 of NA, there was N-linked glycosylation, and Ile-222 was found in the enzyme active site. Conclusions: The changes found in these five genes, compared to other studies, suggest that viruses studied in this research had the ability to bind to Neu Acα2,6 Gal linkage and had low pathogenicity. It is important to mention that these changes were at the amino acid level and studies need to be performed on animals to investigate the significance of these findings.
Modulation of cell spreading and cell-substrate adhesion dynamics by dystroglycan

Dystroglycan is a ubiquitously expressed cell adhesion protein. Its principal role has been determined as a component of the dystrophin-glycoprotein complex of muscle, where it constitutes a key component of the costameric cell adhesion system. To investigate more fundamental aspects of dystroglycan function in cell adhesion, we examined the role of dystroglycan in the dynamics and assembly of cellular adhesions in myoblasts. We show that β-dystroglycan is recruited to adhesion structures and, based on staining for vinculin, that overexpression or depletion of dystroglycan affects both size and number of fibrillar adhesions. Knockdown of dystroglycan increases the size and number of adhesions, whereas overexpression decreases the number of adhesions. Dystroglycan knockdown or overexpression affects the ability of cells to adhere to different substrates, and has effects on cell migration that are consistent with effects on the formation of fibrillar adhesions. Using an SH3 domain proteomic screen, we identified vinexin as a binding partner for dystroglycan. Furthermore, we show that dystroglycan can interact indirectly with vinculin by binding to the vinculin-binding protein vinexin, and that this interaction has a role in dystroglycan-mediated cell adhesion and spreading. For the first time, we also demonstrate unequivocally that β-dystroglycan is a resident of focal adhesions.
Influence of Number of Features on Texture Based Residential Area Extraction from Remotely Sensed Imagery

In this study, we implement and compare two texture analytical methods, 2-D Gabor and GLCM, for residential area extraction from high spatial resolution remotely sensed imagery. We elaborately investigate the influence of different settings to the outputs of both methods. Experiments show that both methods have the potential for texture based residential area segmentation, while 2-D Gabor presents an overall higher precision and consistency. Increasing the number of features, however, seems to contribute little to the accuracy of both methods.
Image Denoising Based on MORF and Minimization Total Variation

A new tight frame called monoscale orthonormal ridgelet frame (MORF) is presented in this paper. The localization principle and the orthonormal ridgelet constructed by Donoho are applied to construct the MORF. And then, It is deployed for image denoising, where a thresholding process for MORF coefficients of the noisy image is carried out firstly. To remove the artifact such as pseudo-Gibbs and to restore sharp discontinuities, while the other structures are preserved, a minimization total variation approach is applied to restore the denoised results. The experiments show that the method has more improvement both in terms of PSNR and visual effect than traditional thresholding method for image denoising.
Auxiliary Linear Problem, Difference Fay Identities and Dispersionless Limit of Pfaff-Toda Hierarchy

Recently the study of Fay-type identities revealed some new features of the DKP hierarchy (also known as "the coupled KP hierarchy" and "the Pfaff lattice"). Those results are now extended to a Toda version of the DKP hierarchy (tentatively called "the Pfaff-Toda hierarchy"). Firstly, an auxiliary linear problem of this hierarchy is constructed. Unlike the case of the DKP hierarchy, building blocks of the auxiliary linear problem are difference operators. A set of evolution equations for dressing operators of the wave functions are also obtained. Secondly, a system of Fay-like identities (difference Fay identities) are derived. They give a generating functional expression of auxiliary linear equations. Thirdly, these difference Fay identities have well defined dispersionless limit (dispersionless Hirota equa- tions). As in the case of the DKP hierarchy, an elliptic curve is hidden in these dispersionless Hirota equations. This curve is a kind of spectral curve, whose defining equation is iden- tified with the characteristic equation of a subset of all auxiliary linear equations. The other auxiliary linear equations are related to quasi-classical deformations of this elliptic spectral curve.
Fault-Tolerant Distributed Systems: a Modular Approach to the Non-Blocking Atomic Commitment Problem

Agreement problems allow a set of processes to agree on a common output value. These problems are of primary importance in distributed systems and difficult to solve in presence of failures. This paper considers one of these problems whose practical interest is well known, namely the Non-Blocking Atomic Commitment Problem. First, a generic protocol solving this problem is given and then instantiations of its generic statements are provided for both synchronous and asynchronous distributed systems. These instantiations use timeout mechanism, reliable multicast primitives and unreliable failure detectors as basic components. Incidentally, this paper can also be considered as an introduction to state-of-the-art concepts and mechanisms of distributed fault tolerance.
Comparability of Test-Taker Proficiency Scores on Computer Adaptive Tests Using Real and Simulated Data

The Law School Admission Council (LSAC) is a nonprofit corporation that provides unique, state-of-the-art admission products and services to ease the admission process for law schools and their applicants worldwide. More than 200 law schools in the United States, Canada, and Australia are members of the Council and benefit from LSAC's services. may change without notice at any time. Up-to-date LSAC policies and procedures are available at LSAC.org. Executive Summary The simulation of correct/incorrect responses to test questions (items) is a commonly used analytical technique with many potential benefits, but it is unclear to what extent these artificially generated datasets compare to real-world test-taker item responses. This study compares real, large-scale test-taker item response data to simulated data to determine the extent to which simulated data are an accurate representation of real-world testing outcomes. Using an original method, this study matched item response data from multiple administrations of the Law School Admission Test (LSAT) to create a single dataset of 534 items and 5,000 synthetic test takers. Numerous IRT-based comparisons between real and simulated data were made by three test-length conditions. Results indicate that simulated data are largely similar to real test data, with minor caveats for shorter-length tests.
Aspirin, nonaspirin nonsteroidal anti-inflammatory drug, and acetaminophen use and risk of invasive epithelial ovarian cancer: a pooled analysis in the Ovarian Cancer Association Consortium.

BACKGROUND Regular aspirin use is associated with reduced risk of several malignancies. Epidemiologic studies analyzing aspirin, nonaspirin nonsteroidal anti-inflammatory drug (NSAID), and acetaminophen use and ovarian cancer risk have been inconclusive. METHODS We analyzed pooled data from 12 population-based case-control studies of ovarian cancer, including 7776 case patients and 11843 control subjects accrued between 1992 and 2007. Odds ratios (ORs) for associations of medication use with invasive epithelial ovarian cancer were estimated in individual studies using logistic regression and combined using random effects meta-analysis. Associations between frequency, dose, and duration of analgesic use and risk of ovarian cancer were also assessed. All statistical tests were two-sided. RESULTS Aspirin use was associated with a reduced risk of ovarian cancer (OR = 0.91; 95% confidence interval [CI] = 0.84 to 0.99). Results were similar but not statistically significant for nonaspirin NSAIDs, and there was no association with acetaminophen. In seven studies with frequency data, the reduced risk was strongest among daily aspirin users (OR = 0.80; 95% CI = 0.67 to 0.96). In three studies with dose information, the reduced risk was strongest among users of low dose (<100 mg) aspirin (OR = 0.66; 95% CI = 0.53 to 0.83), whereas for nonaspirin NSAIDs, the reduced risk was strongest for high dose (≥500 mg) usage (OR = 0.76; 95% CI = 0.64 to 0.91). CONCLUSIONS Aspirin use was associated with a reduced risk of ovarian cancer, especially among daily users of low-dose aspirin. These findings suggest that the same aspirin regimen proven to protect against cardiovascular events and several cancers could reduce the risk of ovarian cancer 20% to 34% depending on frequency and dose of use.
Neural Networks For Structure-Activity Relationship Problems

The problem of finding relations between structure of large molecules and their chemical and biological activity is known as the structure-activity relation problem (SAR). Two neural networks developed in our group were applied to this problem: the Feature Space Mapping neurofuzzy system and the constrained MLP network used to extract logical rules. Two SAR data sets were analyzed: antibiotic activity of pyrimidine compounds, and carcinogenicity data from the PredictiveToxicology Evaluation project of the US National Institute of Environmental Health Science (NIEHS).