Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and risk factors for carriage of methicillin-resistant Staphylococcus aureus at admission to the intensive care unit: results of a multicenter study.

BACKGROUND Detection of methicillin-resistant Staphylococcus aureus (MRSA) carriers on admission to the intensive care unit (ICU) is an important component of strategies for controlling the spread of MRSA. METHODS A prospective multicenter study was conducted in 14 French ICUs for 6 months. All patients were screened within 24 hours after admission, using nasal and cutaneous swabs In addition, clinical samples were obtained. Patient data collected on ICU admission included presence of immunosuppression; history of hospital stay, surgery, antimicrobial treatments, or previous colonization with MRSA; chronic health evaluation and McCabe scores; reason for admission; whether the patient was transferred from another ward; severity of illness; presence of skin lesions; and invasive procedures. Risk factors for MRSA carriage at ICU admission were estimated, and significantly associated variables were used to develop a predictive score for MRSA carriage. A cost-benefit analysis was then performed. RESULTS Of the 2347 admissions with MRSA screening, 162 (6.9%; range, 3.7%-20.0% among ICUs) were positive for MRSA, of whom 54.3% were detected through screening specimens only. Of the 2310 first admissions (vs repeat admissions) to the ICU, 96 were newly identified MRSA carriers. Factors associated with MRSA carriage in the multivariate analysis were age older than 60 years, prolonged hospital stay in transferred patients, history of hospitalization or surgery, and presence of open skin lesions in directly admitted patients. Only universal screening detected MRSA carriage with acceptable sensitivity. A cost-benefit analysis confirmed that universal screening and preventive isolation were beneficial. CONCLUSIONS The prevalence of MRSA carriage on admission to the ICU is high in this endemic setting. Screening for MRSA on admission is useful to identify the imported cases and should be performed in all ICU-admitted patients.
Education and Participatory Inequalities in Real Existing Democracies: Probing the Effect of Labor Markets on the Qualities of Democracies

1 ces papers open forum #2, 2010 Does the type of capitalist system affect the qualities of democratic systems? We approach this big question by narrowing down the definition of the qualities of democracies (QoD) to political equality and by operationalizing the latter in terms of equal participation of politically relevant groups in elections. The concept of market economies we narrow down to labor markets and we rank countries on a scale that reflects the degree of regulation and protection of their labor markets. Using rare event logistic regression on micro-level data for 16 countries from multiple waves of the International Social Survey Programme (ISSP), we show that more strongly regulated and protected labor markets do reduce the distorting effect of education on political participation: lower educated citizens in regulated market economies turn out more than the same type of citizen in less regulated market economies; and, at the same time, the over-representation among the politically active citizens of highly educated citizens is less pronounced in regulated than in not regulated labor markets. We interpret these findings such that the type of market economy does matter for QoD and that, more specifically, more regulated labor markets help mitigate the effect of one important source of political inequality. Kristin Makszin is a Doctoral Candidate in the Department of International Relations and European Studies at Central European University in Budapest, and a member of the Center for the Study of Imperfections in Democracies (DISC). She can be reached at: nickel_kristin@ceu-budapest.edu. Carsten Q. Schneider is Associate Professor in the Department of Political Science at the Central European University in Budapest, and Director of the Center for the Study of Imperfections in Democracies (DISC). He can be reached at: schneiderc@ceu.hu.
Investigation of a clinical PET detector module design that employs large-area avalanche photodetectors

We investigated the feasibility of designing an Anger-logic PET detector module using large-area high-gain avalanche photodiodes (APDs) for a brain-dedicated PET/MRI system. Using Monte Carlo simulations, we systematically optimized the detector design with regard to the scintillation crystal, optical diffuser, surface treatment, layout of large-area APDs, and signal-to-noise ratio (SNR, defined as the 511 keV photopeak position divided by the standard deviation of noise floor in an energy spectrum) of the APD devices. A detector prototype was built comprising an 8 × 8 array of 2.75 × 3.00 × 20.0 mm3 LYSO (lutetium-yttrium-oxyorthosilicate) crystals and a 22.0 × 24.0 × 9.0 mm3 optical diffuser. From the four designs of the optical diffuser tested, two designs employing a slotted diffuser are able to resolve all 64 crystals within the block with good uniformity and peak-to-valley ratio. Good agreement was found between the simulation and experimental results. For the detector employing a slotted optical diffuser, the energy resolution of the global energy spectrum after normalization is 13.4 ± 0.4%. The energy resolution of individual crystals varies between 11.3 ± 0.3% and 17.3 ± 0.4%. The time resolution varies between 4.85 ± 0.04 (center crystal), 5.17 ± 0.06 (edge crystal), and 5.18 ± 0.07 ns (corner crystal). The generalized framework proposed in this work helps to guide the design of detector modules for selected PET system configurations, including scaling the design down to a preclinical PET system, scaling up to a whole-body clinical scanner, as well as replacing APDs with other novel photodetectors that have higher gain or SNR such as silicon photomultipliers.
Fully Automatic Cephalometric X-Ray Landmark Detection Using Random Forest Regression and Sparse shape composition

Landmark detection from 2D cephalometric X-ray images is a basic and important function for cephalometric analysis which widely used in oral surgery planning and evaluation. We present a fully automatic landmark detection method for cephalometric analysis. Our method works by combining random forest (RF) regression based landmark detection with sparse shape composition model based landmark correction. Validation on 100 cephalometric X-ray images show that 77.79% landmarks can be detected by our method with an error less than 4.0 mm.
Behavioral and developmental effects of preventing iron-deficiency anemia in healthy full-term infants.

OBJECTIVE To determine the behavioral and developmental effects of preventing iron-deficiency anemia in infancy. METHODS Healthy full-term Chilean infants who were free of iron-deficiency anemia at 6 months were assigned to high- or low-iron groups or to high- or no-added-iron groups. Behavioral/developmental outcomes at 12 months of age included overall mental and motor test scores and specific measures of motor functioning, cognitive processing, and behavior. There were no differences between high- and low-iron groups in the prevalence of iron-deficiency anemia or behavioral/developmental outcome, and they were combined to form an iron-supplemented group (n = 1123) for comparison with the no-added-iron group (n = 534). RESULTS At 12 months, iron-deficiency anemia was present in 3.1% and 22.6% of the supplemented and unsupplemented groups, respectively. The groups differed in specific behavioral/developmental outcomes but not on global test scores. Infants who did not receive supplemental iron processed information slower. They were less likely to show positive affect, interact socially, or check their caregivers' reactions. A smaller proportion of them resisted giving up toys and test materials, and more could not be soothed by words or objects when upset. They crawled somewhat later and were more likely to be tremulous. CONCLUSIONS The results suggest that unsupplemented infants responded less positively to the physical and social environment. The observed differences seem to be congruent with current understanding of the effects of iron deficiency on the developing brain. The study shows that healthy full-term infants may receive developmental and behavioral benefits from iron supplementation in the first year of life.
A Proposed Framework for Detecting and Predicting Diseases through Business Intelligence Applications

---------------------------------------------------------------------- ABSTRACT ------------------------------------------------------------ The Demand for healthcare IT and its analytics increases in the last few years. To improve quality of care (e.g., ensuring that patients receive the correct medication) which will help to improve the efficiency of clinical quality and safety, operations. The Nature of the medical field is rich with information where there ’ s a variety and abundance of data but untapped in a correct and effective manner to get the right knowledge. and therefore, the most serious challenge facing this area is the quality of service provided which means to make the diagnose in a proper manner at a timely manner and provide appropriate medications to patients because Poor diagnosing can lead to serious consequences which are unacceptable. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence ‘ BI ’ which is a data-driven Decision Support System. Which Was developed from 1990s to now, and gradually become one of the most important information systems applied in any sector. BI enables to deal with huge amount of data and extract useful knowledge to support decision making. Data mining ‘ DM ’ is a kind of data processing technology which can be regarded as a part of the BI system, but it can be also considered as an independent and integrated technology which can treat mass data and extract hidden relationships from it. This introduction highlights the main importance of how to apply the business intelligence applications using data mining techniques to help medical professionals in healthcare sector rapidly diagnosing and predicting diseases of any patients not only this but also detecting the disease complications on the patient which will decrease the overall cost of expenditure that the country paid, briefly this is the central research idea which address the motivation for doing this research.
Statistical Applications in Genetics and Molecular Biology Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains

Recent experimental and computational work confirms that CpGs can be unmethylated inside coding exons, thereby showing that codons may be subjected to both genomic and epigenomic constraint. It is therefore of interest to identify coding CpG islands (CCGIs) that are regions inside exons enriched for CpGs. The difficulty in identifying such islands is that coding exons exhibit sequence biases determined by codon usage and constraints that must be taken into account. We present a method for finding CCGIs that showcases a novel approach we have developed for identifying regions of interest that are significant (with respect to a Markov chain) for the counts of any pattern. Our method begins with the exact computation of tail probabilities for the number of CpGs in all regions contained in coding exons, and then applies a greedy algorithm for selecting islands from among the regions. We show that the greedy algorithm provably optimizes a biologically motivated criterion for selecting islands while controlling the false discovery rate. We applied this approach to the human genome (hg18) and annotated CpG islands in coding exons. The statistical criterion we apply to evaluating islands reduces the number of false positives in existing annotations, while our approach to defining islands reveals significant numbers of undiscovered CCGIs in coding exons. Many of these appear to be examples of functional epigenetic specialization in coding exons.
Continuous force decoding from deep brain local field potentials for Brain Computer Interfacing

Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson's disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features. The results suggest that high gamma band (300–500Hz) activity, in addition to the beta (13–30Hz) and gamma band (55–90Hz) activity is the most informative for force prediction but combining all features led to better decoding performance. Furthermore, LFP signals preceding the force output by up to 1256 milliseconds were found to be predictive of the force output.
Motor unit number estimates based on the automated analysis of F‐responses

An automated technique for estimating the number of motor units based on single motor unit action potentials in the F‐response is described. The average surface detected motor unit action potential (SMUAP) was calculated from the datapoint‐by‐datapoint average of a sample of S‐MUAPs automatically selected from a population of F‐responses. The technique was applied to the thenar muscles of young (n = 18, aged 31 ± 11 years) and older (n = 15, aged 68 ± 3) subjects, Motor unit number estimates based on the automated selection of S‐MUAPs from the F‐responses compared well with those derived using a computer‐assisted manual method for selecting S‐MUAPs from the F‐response (automated 245 ± 105 vs. manual 241 ± 100, r = 0.93) and were similar to estimates obtained using multiple point stimulation (219 ± 77). The advantages of the automated technique for collecting S‐MUAPs from the F‐response include the ready tolerance of the technique by subjects, the minimal amount of operator interaction required, and the additional information relating to the conduction velocities and latencies of single motor axons. © 1994 John Wiley & Sons, Inc.
BUE ACE 1 Sustainable Vital Technologies in 8-10 Nov 2016 Engineering & Informatics Data Mining Techniques for Credit Card Fraud Detection : Empirical Study

Fraud detection is a crucial problem that has been facing the e-commerce industry for decades. Financial institutions throughout the world lose billions due to credit card fraud, which necessitate the use of credit card fraud prevention. Several models have been proposed in the literature, however, the accuracy of the model is crucial. In this paper four fraud detection models based on data mining techniques (Support vector machine, K-nearest neighbours, Decision Trees, Naïve Bayes) were developed and their performances were compared when applied on a real life anonymised data set of transactions (“UCSD-FICO Data Mining Contest 2009”). Four relevant metrics were used in evaluating the performance of the classifiers which are True positive rate (TPR), False Positive Rate (FPR), Balanced Classification Rate (BCR) and Matthews Correlation Coefficient (MCC). © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd
Cell Metabolism Resource Dynamic Adipocyte Phosphoproteome Reveals that Akt Directly Regulates mTORC 2

Sean J. Humphrey,1,2 Guang Yang,1 Pengyi Yang,1,3 Daniel J. Fazakerley,1,2 Jacqueline Stöckli,1 Jean Y. Yang,3 and David E. James1,2,* 1Diabetes and Obesity Program 2Biological Mass Spectrometry Unit Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia 3School of Mathematics and Statistics, University of Sydney, Camperdown, New South Wales 2006, Australia *Correspondence: d.james@garvan.org. au http://dx.doi. org/10.1016/j.cmet. 2013.04. 010 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
Application of Free Choice Profiling for the Evaluation of Emotions Elicited by Music

Music evokes and carries emotions. Despite many studies having investigated the relation between music and emotion, current research lacks a systematic and empirically derived taxonomy of musically induced emotions [1]. This work contributes to the question which musical features in particular are able to induce emotions while listening. Problems of defining and measuring emotions are explained. A method to measure affective states induced by music with the help of Free Choice Profiling (FCP) is outlined. Two FCP experiments, assessing the usefulness of the method for emotional research and the selection of test stimuli are described. The shown results are in line with psychological theories of emotions, i.e., the valence/arousal model.
Critical Graphs for the Positive Definite Completion Problem

Among various matrix completion problems that have been considered in recent years, the positive definite completion problem seems to have received the most attention. Indeed, in addition to being a problem of great interest, it is related to various applications as well as other completion problems. It may also be viewed as a fundamental problem in Euclidean geometry. A partial positive definite matrix A is "critical" if A has no positive definite completion, though every proper principal submatrix does. The graph G is called critical for the positive definite completion problem if there is a critical partial positive definite matrix A, the graph of whose specified entries is G. Complete analytical understanding of the general positive definite completion problem reduces to understanding the problem for critical graphs. Thus, it is important to try to characterize such graphs. The first, crucial step toward that understanding is taken here. A novel and restrictive topological graph theoretic condition necessary for criticality is identified. The condition, which may also be of interest on pure graph theoretic grounds, is also shown to be sufficient for criticality of graphs on fewer than 7 vertices, and the authors suspect it to be sufficient in general. In any event, the condition, which may be efficiently verified, dramatically narrows the class of graphs for which completability conditions on the specified data are needed. The concept of criticality and the graph theoretic condition extend to other completion problems, such as that for Euclidean distance matrices.
Generative Models for Graph-Based Protein Design

Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. We develop relational language models for protein sequences that directly condition on a graph specification of the target structure. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.
Stream-Based ORB Feature Extractor with Dynamic Power Optimization

The Oriented Fast and Rotated BRIEF (ORB) feature extractor, which consists of key-point detection and descriptor computation, is a key module in many computer vision systems. Existing hardware implementations of ORB feature extractor only focus on increasing performance with power optimization as a post consideration. In this paper, we present a stream-based ORB feature extractor that incorporates mechanisms to lower the dynamic power consumption. These mechanisms exploit the fact that the number of detected keypoints is typically small. The proposed solution significantly lowers the switching activity of the key-point detection and descriptor computation stages by early pruning of non-likely key-points and gating the descriptor computation stages. Further power reduction and resource minimization are achieved by employing a threshold-guided bit-width optimization strategy to truncate the redundant bits in the key-point detection stage. Finally, we propose an approximation method to achieve rotation invariance of the descriptors. FPGA implementation targeting the Altera Aria V device shows that the proposed strategies lead to over 25% reduction in dynamic power and lower resource utilization, with only marginal loss in accuracy.
Merriam’s Kangaroo Rats (Dipodomys merriami) Voluntarily Select Temperatures That Conserve Energy Rather than Water

Desert endotherms such as Merriam’s kangaroo rat (Dipodomys merriami) use both behavioral and physiological means to conserve energy and water. The energy and water needs of kangaroo rats are affected by their thermal environment. Animals that choose temperatures within their thermoneutral zone (TNZ) minimize energy expenditure but may impair water balance because the ratio of water loss to water gain is high. At temperatures below the TNZ, water balance may be improved because animals generate more oxidative water and reduce evaporative water loss; however, they must also increase energy expenditure to maintain a normal body temperature. Hence, it is not possible for kangaroo rats to choose thermal environments that simultaneously minimize energy expenditure and increase water conservation. I used a thermal gradient to test whether water stress, energy stress, simultaneous water and energy stress, or no water/energy stress affected the thermal environment selected by D. merriami. During the night (i.e., active phase), animals in all four treatments chose temperatures near the bottom of their TNZ. During the day (i.e., inactive phase), animals in all four treatments settled at temperatures near the top of their TNZ. Thus, kangaroo rats chose thermal environments that minimized energy requirements, not water requirements. Because kangaroo rats have evolved high water use efficiency, energy conservation may be more important than water conservation to the fitness of extant kangaroo rats.