Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of PRRSV Nucleic Acid and Antibody Detection in Pen-Based Oral Fluid and Individual Serum Samples in Three Different Age Categories of Post-Weaning Pigs from Endemically Infected Farms

Background Porcine reproductive and respiratory syndrome virus (PRRSV) is the causative agent of an economically important disease in swine. Since it has been shown that PRRSV and PRRSV specific antibodies can be detected in oral fluid, many different aspects have been studied to show that oral fluid could be a worthy alternative diagnostic sample to serum for monitoring and surveillance of this disease. Thorough field evaluations are however missing to convincingly show its usefulness under representative field conditions. Methodology Pen-based oral fluid samples and serum samples from all individual pigs in the corresponding pens were collected from post-weaning pigs of three different age categories in eight endemically PRRSV infected farms and one PRRSV free farm in Belgium. All samples were tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and ELISA to detect PRRSV RNA and PRRSV specific antibodies, respectively. Results While the relative specificity of PRRSV detection by qRT-PCR in pen-based oral fluid compared to serum collected from individual pigs was high in all age categories (>90%), the relative sensitivity decreased with the age of the pigs (89, 93 and 10% in 8-12w, 16-20w and 24-28w old pigs, respectively). The latter correlated with a lower percentage of PRRSV positive pigs in serum/pen in the different age categories (55, 29 and 6%, respectively). Irrespective of the age category, pen-based oral fluid samples were always found PCR positive when at least 30% of the individual pigs were positive in serum. PRRSV specific antibody detection in oral fluid by ELISA showed a 100% relative sensitivity to detection in serum since oral fluid samples were always positive as soon as one pig in the pen was positive in serum. On the other hand, two false positive oral fluid samples in 11 pens without serum positive pigs were found, resulting in a relative specificity of 82%. Indications are however present that the oral fluid result indicated the correct infection status but the absence of a golden standard test makes it difficult to define definitive test characteristics. Conclusions Overall it can be concluded that oral fluid seems to be a useful matrix for diagnosis of PRRSV under field conditions and that differences in kinetics of PRRSV and PRRSV specific antibody detection in oral fluid and serum of individual pigs can also be reflected in pen-based oral fluid results.
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study.
Improved Image Segmentation With A Modified Bayesian Classifier

A method for improving texture segmentation results by slightly modifying the decision surfaces of a Bayesian classifier is presented. Although a Bayesian classifier provides optimum classification within homogeneous regions, it does not necessarily provide accurate localization of region boundaries. In the proposed method, a modified classifier is formed by using a mixture probability density. This approach has the advantage that it is easily implemented in multidimensional classifiers such as those used in classifying the vector output of a filter bank. Experimental results demonstrate improved texture segmentation using the proposed classifier
Multivariate Maximal Correlation Analysis

Correlation analysis is one of the key elements of statistics, and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces. We propose MAC, a novel multivariate correlation measure designed for discovering multi-dimensional patterns. It belongs to the powerful class of maximal correlation analysis, for which we propose a generalization to multivariate domains. We highlight the limitations of current methods in this class, and address these with MAC. Our experiments show that MAC outperforms existing solutions, is robust to noise, and discovers interesting and useful patterns.
User Perspectives of Characteristics of Improved Cookstoves from a Field Evaluation in Western Kenya

Over half of the world’s population uses biomass fuels; these households cook on open fires indoors, increasing their risk of adverse health effects due to household air pollution (HAP) from biomass combustion. This study evaluated six improved cookstoves (ICS) for effectiveness and acceptability in a rural community in Western Kenya. This paper describes women’s views on each ICS compared to the traditional three-stone fire. Views on stove characteristics, fuel consumption, health effects and acceptability were assessed through structured interviews and focus group discussions. Data were coded and analyzed using a thematic approach. In total, 262 interviews and 11 focus groups were conducted from 43 women. Overall, women preferred the ICS over the traditional three-stone fire for various reasons including ease of use, efficiency, fuel efficiency and perceived reduction in smoke and improved health. However, there were clear preferences for specific ICS with almost half of women preferring a Philips stove. Despite acceptance and use of ICS, women used multiple stoves to meet their daily needs. Qualitative studies are essential to field evaluations to provide insight into user perspectives and acceptability of ICS and to inform research and development of technologies that are both effective in reducing HAP and practical in use.
Dysfunction of the cortico‐basal ganglia‐cortical loop in a rat model of early parkinsonism is reversed by metabotropic glutamate receptor 5 antagonism

This study examined the cellular correlates of the akinetic deficits produced in Wistar rats by discrete bilateral 6‐hydroxydopamine (6‐OHDA) striatal infusions in the dorsolateral striatum, mimicking the preferential denervation of the motor striatal territory in early symptomatic stage of Parkinson's disease (PD). Intraneuronal gene expression of cytochrome oxidase subunit I (COI), a metabolic index of neuronal activity, was increased in the subthalamic nucleus, substantia nigra pars reticulata and decreased in frontal cortical areas, but paradoxically unchanged in the striatum, globus pallidus, entopeduncular nucleus and ventrolateral thalamic nucleus. Neither preproenkephalin A nor preprotachykinin mRNA expression, markers of striatal projection neurons, were modified in the denervated striatal area despite 90% loss of dopamine (DA) terminals. Preproenkephalin A mRNA expression was however, decreased in the nondepleted striatal region, suggesting compensatory increase of dopamine tone from those spared areas. A chronic treatment with the metabotropic glutamate receptor 5 (mGluR5) antagonist 2‐methyl‐6‐(phenylethylnyl)‐pyridine (MPEP), which alleviated the akinetic disorders produced by the lesion, reversed the lesion‐induced variations of COI gene expression, moderately increased this marker in the structures unaffected by the lesion and did not modify the striatal neuropeptides gene expression. These data suggest that the expression of akinetic deficits in early parkinsonism is associated with focused metabolic changes in the cortico‐basal ganglia‐cortical loop downstream of the striatum and pallidal complex.
Application flow control in YouTube video streams

This paper presents the results of an investigation into the application flow control technique utilised by YouTube. We reveal and describe the basic properties of YouTube application flow control, which we term block sending, and show that it is widely used by YouTube servers. We also examine how the block sending algorithm interacts with the flow control provided by TCP and reveal that the block sending approach was responsible for over 40% of packet loss events in YouTube flows in a residential DSL dataset and the retransmission of over 1% of all YouTube data sent after the application flow control began. We conclude by suggesting that changing YouTube block sending to be less bursty would improve the performance and reduce the bandwidth usage of YouTube video streams.
Mining Relevant and Extreme Patterns on Climate Time Series with CLIPSMiner

One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensi cation of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields. In this context we propose a new technique to identify extreme values in climate time series and to correlate climate and remote sensing data in order to improve agricultural monitoring. Accordingly, this paper presents a new unsupervised algorithm, called CLIPSMiner (CLImate PatternS Miner) that works on multiple time series of continuous data, identifying relevant patterns or extreme ones according to a relevance factor, which can be tuned by the user. Results show that CLIPSMiner detects, as expected, patterns that are known in climatology, indicating the correctness and feasibility of the proposed algorithm. Moreover, patterns detected using the highest relevance factor is coincident with extreme phenomena. Furthermore, series correlations detected by the algorithm show a relation between agroclimatic and vegetation indices, which con rms the agrometeorologists' expectations.
Abusive Supervision, Employee Well-Being and Ill-Being: The Moderating Role of Core Self-Evaluations

Abstract Purpose This chapter investigates the relationship between abusive supervision and employee well-being (i.e., job satisfaction, engagement) and ill-being (i.e., burnout, workaholism) and examines whether follower core self-evaluations (CSE) moderate this relationship. Methodology/approach The study uses cross-sectional survey data collected from 111 professional employees across a range of industry sectors. Findings Results show that abusive supervision is negatively related to employee well-being (i.e., engagement and job satisfaction) and positively related to employee ill-being, namely burnout. In addition, employees low in CSE are less engaged and less satisfied than employees high in CSE. Research limitations/implications The study’s cross-sectional design limits the strength of its conclusions. Practical implications This chapter notes the ethical and legal obligations of organizations to provide a safe working environment and identifies the policies and procedures that will signal a commitment to employee well-being. Originality/value The study contributes to the leadership and well-being literatures by exploring the influence of abusive leaders on follower well-being and engagement. It also goes beyond merely identifying correlations between leadership style and follower well-being outcomes to investigate how leader and follower attributes can combine to influence these outcomes.
When the Frame Fits the Social Picture: The Effects of Framed Social Norm Messages on Healthy and Unhealthy Food Consumption

We investigated the influence of framed norm messages about food consumption on motivation to consume, and actual consumption of, healthy and unhealthy foods. We proposed that the effects of positive and negative message frames would vary by the type of underlying norms (i.e., injunctive, descriptive). More specifically, based on information processing theories, it was expected that injunctive norms would be more effective when framed negatively compared with positively, while the opposite was expected for descriptive norms. In both experiments, participants were randomly assigned to one of four framed social norm conditions or a no-norm control condition. In Experiment 1, motivation to consume healthy and unhealthy foods was assessed by means of both indirect and self-report measures. In Experiment 2, actual food consumption was assessed. In both experiments, the predicted interaction was found. Results show that injunctive norms benefit from a negative (vs. positive) frame, while preliminary evidence suggests the opposite for descriptive norms.
Human Monocytes Differentiate into Dendritic Cells Subsets that Induce Anergic and Regulatory T Cells in Sepsis

Background Sepsis is a multifactorial pathology with high susceptibility to secondary infections. Innate and adaptive immunity are affected in sepsis, including monocyte deactivation. Methodology/Principal Findings To better understand the effects of alterations in monocytes on the regulation of immune responses during sepsis, we analyzed their differentiation in dendritic cell (DC). Cells from septic patients differentiated overwhelmingly into CD1a−negative DC, a population that was only a minor subset in controls and that is so far poorly characterized. Analysis of T cell responses induced with purified CD1a−negative and CD1a+ DC indicated that (i) CD1a−negative DC from both healthy individuals and septic patients fail to induce T cell proliferation, (ii) TGFβ and IL-4 were strongly produced in mixed leukocyte reaction (MLR) with control CD1a−negative DC; reduced levels were produced with patients DC together with a slight induction of IFNγ, (iii) compared to controls, CD1a+ DC derived from septic patients induced 3-fold more Foxp3+ T cells. Conclusion/Significance Our results indicate a strong shift in DC populations derived from septic patients’ monocytes with expanded cell subsets that induce either T cell anergy or proliferation of T cells with regulatory potential. Lower regulatory cytokines induction on a per cell basis by CD1a−negative dendritic cells from patients points however to a down regulation of immune suppressive abilities in these cells.
Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k. a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation.
Molecular Epidemiology of Multidrug-Resistant Acinetobacter baumannii in a Single Institution over a 10-Year Period

ABSTRACT Multidrug-resistant Acinetobacter baumannii is a worldwide nosocomial menace. We sought to better understand its behavior through studying the molecular epidemiology of this organism at the Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia, over a 10-year period. Multilocus sequence typing (MLST), semiautomated repetitive sequence-based PCR (rep-PCR), and pulsed-field gel electrophoresis (PFGE) were performed on a selection of 31 A. baumannii isolates collected over the 10-year period to determine their relationships to one another. MLST also allowed us to put this information in a global context. The presence or absence of bla OXA-23 was also established. The presence of bla OXA-23 closely correlated with carbapenem resistance in our collection. Sequence type 92 (ST92) was the dominant sequence type and was present in the hospital for 9 years. There was also evidence of the spread of ST69, ST73, and ST125 (novel) within the hospital, but this was not sustained over long periods. There were only single examples of the novel sequence types ST126 and ST127. The different typing methods clustered the isolates similarly; however, PFGE and rep-PCR were more discriminatory than MLST. Worldwide, ST92 and the associated clonal complex 92 represent the most sampled and widespread sequence type(s) and are also known as European clone 2 and worldwide clonal lineage 2. Antibiotic susceptibility within ST92 is variable, suggesting a role for mechanisms other than antibiotic resistance in its success.
Robust scene cut detection by supervised learning

The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it captures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content analysis, indexing and classification. Although many video shot boundary detection algorithms have been proposed in the literature, in most approaches, several parameters and thresholds have to be set in order to achieve good results. In this paper, we present a robust learning detector of sharp cuts without any threshold to set nor any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classifier method.
Computerized Finger Tapping Task in Adult Unipolar Depressed Patients and Healthy Subjects: Influence of Age, Gender, Education, and Hand Dominance

Introduction: Current diagnostic criteria for depression include psychomotor retardation, being the Finger Tapping Test (FTT) as one of the most utilized instruments to assess fine psychomotor performance. Method: This study aimed to compare the performance of a sample of 51 unipolar depressed patients (30 women and 21 men, with a mean age of 45.12 years old [ SD = 14.09]) with 51 healthy controls (29 women and 22 men, with a mean age of 44.49 years old [ SD = 15.59]) in a computerized version of the Finger Tapping Test (FTT) from the Psychology Experiment Building Language (PEBL). Another objective was to test this version’s validity in comparison to other FTTs. Results: We found significant differences between depressed patients and healthy controls. Significant effects of age and gender were found. Conclusion: Results allowed us to identify differences in performance between the two groups, therefore this version of the FTT revealed adequate reliability values, one instrument accessible to all clinicians.
Correlation Between Intrasac Pressure Measurements of a Pressure Sensor and an Angiographic Catheter During Endovascular Repair of Abdominal Aortic Aneurysm

PURPOSE To establish a correlation between intrasac pressure measurements of a pressure sensor and an angiographic catheter placed in the same aneurysm sac before and after its exclusion by an endoprosthesis. METHODS Patients who underwent endovascular abdominal aortic aneurysm repair and received an EndoSureTM wireless pressure sensor implant between March 19 and December 11, 2004 were enrolled in the study. Simultaneous readings of systolic, diastolic, mean, and pulse pressure within the aneurysm sac were obtained from the catheter and the sensor, both before and after sac exclusion by the endoprosthesis (Readings 1 and 2, respectively). Intrasac pressure measurements were compared using Pearson’s correlation and Student’s t test. Statistical significance was set at p<0.05. RESULTS Twenty-five patients had the pressure sensor implanted, with simultaneous readings (i.e., recorded by both devices) obtained in 19 patients for Reading 1 and in 10 patients for Reading 2. There was a statistically significant correlation for all pressure variables during both readings, with p<0.01 for all except the pulse pressure in Reading 1 (p<0.05). Statistical significance of pressure variations before and after abdominal aortic aneurysm exclusion was coincident between the sensor and catheter for diastolic (p>0.05), mean (p>0.05), and pulse (p<0.01) pressures; the sole disagreement was observed for systolic pressure, which varied, on average, 31.23 mmHg by the catheter (p<0.05) and 22 mmHg (p>0.05) by the sensor. CONCLUSION The excellent agreement between intrasac pressure readings recorded by the catheter and the sensor justifies use of the latter for detection of post-exclusion abdominal aortic aneurysm pressurization.