Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Projector as an Interactive Teaching Tool in the Classroom: Evaluating Teaching Efficiency and Interactivity.

This study reports on a measurement that is used to investigate interactivity in the classrooms and examines the impact of integrating the interactive projector into middle school science classes on classroom interactivity and students’ biology learning. A total of 126 7th grade Taiwanese students were involved in the study and quasi-experimental research with two-group posttest-only design was employed. Students in the experimental group were taught by using interactive projector (n=61) and their counterparts were taught by general data projector (n=65). The results show that there was no significant difference in students’ learning achievement between teaching through interactive projector and general data projector. More interactions were observed in the experimental group; however, its perceived teaching efficiency was not better than teaching with a general data projector. It is suggested that the integration of interactive technologies in the classrooms might not ensure better learning performance or teaching efficiency, although various types of interactive actions were observed. The possible interpretations and suggestions for future studies are provided.
Sparsity-promoting adaptive sensor selection for non-linear filtering

Sensor selection is an important design task in sensor networks. We consider the problem of adaptive sensor selection for applications in which the observations follow a non-linear model, e.g., target/bearing tracking. In adaptive sensor selection, based on the dynamical state model and the state estimate from the previous time step, the most informative sensors are selected to acquire the measurements for the next time step. This is done via the design of a sparse selection vector. Additionally, we model the evolution of the selection vector over time to ensure a smooth transition between the selected sensors of subsequent time steps. The original non-convex optimization problem is relaxed to a semi-definite programming problem that can be solved efficiently in polynomial time.
Thinking beyond organism energy use: a trait-based bioenergetic mechanistic approach for predictions of life history traits in marine organisms

The functional trait-based bioenergetic approach is emergent in many ecological spectra, from the conservation of natural resources to mitigation and adaptation strategies in a global climate change context. Such an approach relies on being able to exploit mechanistic rules to connect environmental human-induced variability to functional traits (i.e. all those specific traits defining species in terms of their ecological roles) and use these to provide estimates of species life history traits (LH; e.g. body size, fecundity per life span, number of reproductive events). LHs are species-specific and proximate determinants of population characteristics in a certain habitat. They represent the most valuable quantitative information to investigate how broad potential distributional boundaries of a species are, and to feed predictive population models. There is much to be found in the current literature that describes mechanistic functional trait-based bioenergetics models, using them to test ecological hypotheses, but a mathematical framework often renders interpretation and use complicated. Here, we wanted to present a simpler interpretation and description of one of the most important recent mechanistic bioenergetic theories: the dynamic energy budget theory by Kooijman (Dynamic Energy Budget Theory for Metabolic Organisation, 2010, Cambridge University Press, Cambridge). Our main aim was to disentangle those aspects that at first reading may seem too mathematically challenging to many marine biologists, ecologists and environmental scientists, and present them for use in mechanistic applications.
Comparative Analyses of Low, Medium and High-Resolution HLA Typing Technologies for Human Populations

Human Leukocyte Antigen (HLA) encoding genes are part of the major histocompatibility complex (MHC) on human chromosome 6. This region is one of the most polymorphic regions in the human genome. Prior knowledge of HLA allelic polymorphisms is clinically important for matching donor and recipient during organ/tissue transplantation. HLA allelic information is also useful in predicting immune responses to various infectious diseases, genetic disorders and autoimmune conditions. India harbors over a billion people and its population is untapped for HLA allelic diversity. In this study, we explored and compared three HLA typing methods for South Indian population, using Sequence-Specific Primers (SSP), NGS (Roche/454) and single- molecule sequencing (PacBio RS II) platforms. Over 1020 DNA samples were typed at low resolution using SSP method to determine the major HLA alleles within the South Indian population. These studies were followed up with medium resolution HLA typing of 80 samples based on exonic sequences on the Roche/454 sequencing system and high-resolution (6-8 digit) typing of 8 samples for HLA alleles of class I genes (HLA-A, B and C) and class II genes (HLA-DRB1 and DQB1) using PacBio RS II platform. The long reads delivered by SMRT technology, covered the full-length class I and class II genes/alleles in contiguous reads including untranslated regions, exons and introns, which provided phased SNP information. We have identified three novel alleles from PacBio data that were verified by Roche 454 sequencing. This is the first case study of HLA typing using second and third generation NGS technologies for an Indian population. The PacBio platform is a promising platform for large-scale HLA typing for establishing an HLA database for the untapped ethnic populations of India.
Semantic priming in schizophrenia: Systematic review and meta-analysis

Background Increased semantic priming is an influential theory of thought disorder in schizophrenia. However, studies to date have had conflicting findings. Aims To investigate semantic memory in patients with schizophrenia with and without thought disorder. Method Data were pooled from 36 studies comparing patients with schizophrenia and normal controls in semantic priming tasks. Data from 18 studies comparing patients with thought disorder with normal controls, and 13 studies comparing patients with and without thought disorder were also pooled. Results There was no support for altered semantic priming in schizophrenia as a whole. Increased semantic priming in patients with thought disorder was supported, but this was significant only in comparison with normal controls and not in comparison with patients without thought disorder. Stimulus onset asynchrony (SOA) and general slowing of reaction time moderated the effect size for priming in patients with thought disorder. Conclusions Meta-analysis provides qualified support for increased semantic priming as a psychological abnormality underlying thought disorder. However, the possibility that the effect is an artefact of general slowing of reaction time in schizophrenia has not been excluded.
A relation between automorphic representations of GL(2) and GL(3)

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Self-calibration of radio astronomical arrays with non-diagonal noise covariance matrix

The radio astronomy community is currently building a number of phased array telescopes. The calibration of these telescopes is hampered by the fact that covariances of signals from closely spaced antennas are sensitive to noise coupling and to variations in sky brightness on large spatial scales. These effects are difficult and computationally expensive to model. We propose to model them phenomenologically using a non-diagonal noise covariance matrix. The parameters can be estimated using a weighted alternating least squares (WALS) algorithm iterating between the calibration parameters and the additive nuisance parameters. We demonstrate the effectiveness of our method using data from the low frequency array (LOFAR) prototype station.
Stigma: the feelings and experiences of 46 people with mental illness

Background Stigma defines people in terms of some distinguishing characteristic and devalues them as a consequence. Aims To describe the relationship of stigma with mental illness, psychiatric diagnosis, treatment and its consequences of stigma for the individual. Method Narrative interviews were conducted by trained users of the local mental health services; 46 patients were recruited from community and day mental health services in North London. Results Stigma was a pervasive concern to almost all participants. People with psychosis or drug dependence were most likely to report feelings and experiences of stigma and were most affected by them. Those with depression, anxiety and personality disorders were more affected by patronising attitudes and feelings of stigma even if they had not experienced any overt discrimination. However, experiences were not universally negative. Conclusions Stigma may influence how a psychiatric diagnosis is accepted, whether treatment will be adhered to and how people with mental illness function in the world. However, perceptions of mental illness and diagnoses can be helpful and non-stigmatising for some patients.
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning

Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasonic signals are commonly compressed in order to reconstruct B-mode images. Based on raw data acquired from breast lesions, we reconstruct B-mode images using different compression levels. Next, transfer learning is applied for classification. Differently reconstructed images are employed for training and evaluation. We show that the modification of the reconstruction algorithm leads to decrease of classification performance. As a remedy, we propose a method of data augmentation. We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification. Our study suggests that it is important to take into account image reconstruction algorithms implemented in medical scanners during development of computer aided diagnosis systems.
Co-morbid psychiatric disorders among incarcerated ADHD populations: a meta-analysis

Background Rates of psychiatric disorders are highly prevalent among prison inmates, and recent evidence confirms over-representation of youths and adults with attention deficit hyperactivity disorder (ADHD). The risk for psychiatric co-morbidity may be greater among offenders with ADHD. We undertook a systematic review and meta-analysis of reported rates of co-existing psychiatric morbidity with ADHD in prison samples. Method Studies published from 1980 to 2015 were identified using five bibliographic indexes, review articles and reference lists. Included studies had a defined ADHD group and provided additional prevalence on at least one of the following: conduct disorder, substance use disorder, mood disorder, anxiety disorder, or personality disorder. We performed meta-analytical estimates of the prevalence of each co-morbid disorder within ADHD, and estimated the risk for co-existing disorders among prisoners with ADHD by pooling odds ratios (OR) with 95% confidence intervals. Results Eighteen studies with data for 1615 with ADHD and 3128 without ADHD were included. The risk (OR) of all psychiatric morbidity is increased among adult inmates with ADHD. Associations in youths with ADHD were restricted to mood disorder (OR 1.89, 95% confidence interval 1.09–3.28). Conclusions This study quantifies the extent of co-morbidity presented by offenders with ADHD, especially adults. The differences between risk estimates for youths and adults indicate an incremental effect in both frequency and severity for the development of further co-morbid pathology through adulthood. The findings have implications for clinical intervention and for criminal justice policy.
An Information System to Perform Services Remotely from a Web Browser

This paper presents the development of BioMen (Biological Management Executed over Network), an Internet-managed system. By using service ontologies, the user is able to perform services remotely from a web browser. In addition, artificial intelligence techniques have been incorporated so that the necessary information may be obtained for the study of biodiversity. We have built a tool which will be of particular use to botanists and which can by accessed from anywhere in the world thanks to Internet technology. In this paper, we shall present the results and how we developed the tool.
A Metagenomic Approach to Characterization of the Vaginal Microbiome Signature in Pregnancy

While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age.
Decreased risk of congenital cytomegalovirus infection in children born to HIV-1-infected mothers in the era of highly active antiretroviral therapy.

BACKGROUND We evaluated the prevalence of congenital cytomegalovirus (CMV) infection before and after highly active antiretroviral therapy (HAART) availability among neonates born to human immunodeficiency virus type 1 (HIV-1)-infected mothers. We also identified maternal risk factors associated with in utero CMV transmission. METHOD Routine screening for congenital CMV infection was performed from 1993 through 2004 in children born to HIV-1-infected mothers included in the French Perinatal Cohort (Enquête Périnatale Française). Interpretable tests on urine samples collected within the first 10 days of life were available for 4797 of the 7563 live-born infants. Prevalence was estimated for different time periods. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CMV transmission in the HAART era. RESULTS Among live-born children, the overall prevalence of CMV infection was 2.3% (95% confidence interval, 1.9%-2.8%). Prevalence was higher among HIV-1-infected neonates (10.3%; 95% confidence interval, 5.6%-17.0%) than among HIV-1-uninfected neonates (2.2%; 95% confidence interval, 1.8%-2.7%; P < .01). Among HIV-1-uninfected neonates, the prevalence of CMV infection decreased over time, from 3.5% in 1997-1998 to 1.2% in 2003-2004. Delivery period, maternal age, time at antiretroviral treatment initiation, and maternal CD4(+) cell count <200 cells/mm(3) close to delivery were independently associated with CMV infection in logistic regression analysis. The percentage of symptomatic CMV infections was 23.1% among HIV-1-infected newborns and 6.7% among HIV-1-uninfected neonates. CONCLUSIONS The prevalence of congenital CMV infection was high and associated with high morbidity rates among HIV-1-infected neonates. Conversely, the prevalence of CMV infection decreased over time among neonates not infected with HIV-1, reaching levels similar to those observed in the general population, following the introduction and increasing use of HAART for prevention of mother-to-child HIV-1 transmission.
A General Regression Procedure for Method Transformation. Application of Linear Regression Procedures for Method Comparison Studies in Clinical Chemistry, Part III

The biometrical treatment of laboratory data may require the estimation of a regression line for the transformation of one set of measurements to another. The regression procedure introduced in part I (1) of our work does not always yield unbiased results in such situations, since its estimators are not scale invariant. In part III we present the parameter estimation of a general regression equation which is scale invariant and retains all properties of the method comparison procedure, in particular its robustness. Its application is demonstrated by several examples, and the results are compared with other robust biometrical procedures. The mathematical aspects are explained in the appendix.
Application of swarm and reinforcement learning techniques to requirements tracing

OF DISSERTATION APPLICATION OF SWARM AND REINFORCEMENT LEARNING TECHNIQUES TO REQUIREMENTS TRACING Today, software has become deeply woven into the fabric of our lives. The quality of the software we depend on needs to be ensured at every phase of the Software Development Life Cycle (SDLC). An analyst uses the requirements engineering process to gather and analyze system requirements in the early stages of the SDLC. An undetected problem at the beginning of the project can carry all the way through to the deployed product. The Requirements Traceability Matrix (RTM) serves as a tool to demonstrate how requirements are addressed by the design and implementation elements throughout the entire software development lifecycle. Creating an RTM matrix by hand is an arduous task. Manual generation of an RTM can be an error prone process as well. As the size of the requirements and design document collection grows, it becomes more challenging to ensure proper coverage of the requirements by the design elements, i.e., assure that every requirement is addressed by at least one design element. The techniques used by the existing requirements tracing tools take into account only the content of the documents to establish possible links. We expect that if we take into account the relative order of the text around the common terms within the inspected documents, we may discover candidate links with a higher accuracy. The aim of this research is to demonstrate how we can apply machine learning algorithms to software requirements engineering problems. This work addresses the problem of requirements tracing by viewing it in light of the Ant Colony Optimization (ACO) algorithm and a reinforcement learning algorithm. By treating the documents as the starting (nest) and ending points (sugar piles) of a path and the terms used in the documents as connecting nodes, a possible link can be established and strengthened by attracting more agents (ants) onto a path between the two documents by using pheromone deposits. The results of the work show that ACO and RL can successfully establish links between two sets of documents.
Socioeconomic position, population density and site‐specific cancer mortality: A multilevel analysis of Belgian adults, 2001–2011

Our study explores the association between individual and neighborhood socioeconomic position (SEP) and all‐cancer and site‐specific cancer mortality. Data on all Belgian residents are retrieved from a population‐based dataset constructed from the 2001 census linked to register data on emigration and mortality for 2001–2011. The study population contains all men and women aged 40 years or older during follow‐up. Individual SEP is measured using education, employment status and housing conditions. Neighborhood SEP is measured by a deprivation index (in quintiles). Directly age‐standardized mortality rates and multilevel Poisson models are used to estimate the association between individual SEP and neighborhood deprivation and mortality from all‐cancer and cancer of the lung, colon and rectum, pancreas, prostate and female breast. The potential confounding role of population density is assessed using multilevel models as well. Our findings show an increase in mortality from all‐cancer and site‐specific cancer by decreasing level of individual SEP for both men and women. In addition, individuals living in highly deprived neighborhoods experience significantly higher mortality from all‐cancer, lung cancer, pancreatic cancer and female colorectal cancer after controlling for individual SEP. Male colorectal and prostate cancer and female breast cancer are not associated with neighborhood deprivation. Population density acts as a confounder for female lung cancer only. Our study indicates that deprivation at both the individual and neighborhood level is associated with all‐cancer mortality and mortality from several cancer sites. More research into the role of life‐style related and clinical factors is necessary to gain more insight into causal pathway.