Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Strategy for the Diagnosis, Management and Prevention of COPD: 2003 update

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) programme was initiated in January 1997 to increase awareness of chronic obstructive pulmonary disease (COPD) and to decrease morbidity and mortality from this chronic lung disorder. One strategy to help achieve the objectives of the GOLD programme is to provide healthcare workers, healthcare authorities and the general public with state-of-the-art information about COPD and specific recommendations on the most appropriate management and prevention strategies. The GOLD Workshop Report, Global Strategy for the Diagnosis, Management and Prevention of COPD 1 was published in April 2001. It was prepared by a panel of experts nominated by the National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health and the World Health Organization with the aim of providing the best validated current concepts of COPD pathogenesis and the best available evidence on the most appropriate management and prevention strategies. In an effort to keep the GOLD Workshop Report as up to date as possible, GOLD assembled a Scientific Committee whose aim was to review clinical research that has an impact on COPD management. The initial review included publications that were published in June 2000 (approximately the time of completion of the 2001 report) through to March 2003. The results of the first 2 yrs of activity were posted on the GOLD website (www.goldcopd.com) in July 2003 2. Each year, a new update report will be posted. The GOLD Scientific Committee will also prepare a revision of the entire GOLD Workshop Report approximately every 5 yrs. The process for the first complete revision (to appear in 2006) will be developed in the autumn of 2003. The process included a PubMed search using search fields established by the GOLD Scientific Committee: 1) COPD OR chronic bronchitis OR emphysema, All Fields, All Adult, 19+ yrs, only items …
Analysis of Facial Expression using LBP and Artificial Neural Network

Facial Expression Recognition is rapidly becoming area of interest in computer science and human computer interaction. The most expressive way of displaying the emotions by human is through the facial expressions. Local Binary Patterns are widely used for texture classification. In this research paper, we have projected a method for facial expression recognition using Local Binary Patterns (LBP) as features and Artificial Neural Network as a classification tool and we developed associated scheme. The six universal expressions i. e. anger, Generalized Feed-forward Neural Network recognizes disgust, fear, happy, sad, and surprise as well as seventh one neutral. The Neural Network trained and tested by using Levenberg Marquart (LM) nonlinear optimization algorithm. We are able to attain 93. 3 % classification rate with testing performance 0. 0573.
Power consumption measurements of virtual machines

The environment sustainability is one of the main directions of intervention for all developed countries. Solutions for alternative energies generation and energy reduction are already discussed. Furthermore, energy efficiency has become an important aspect in data centers and large server systems. Virtualization is one of the main research directions for both large scale data centers and servers. This paper explores how virtualization solutions influence the power consumption of physical systems they are implemented on and which is the most effective way to test and measure the energy efficiency of these solutions. In our tests we used two physical machines (one laptop and one desktop), both running Windows and Linux operating systems and we selected the VMWare virtualization solution.
Selective activation of ectopic grey matter during motor task

The role of ectopic grey matter in neuronal migration disorders is still a matter of debate. Using fMRI we studied the activation pattern of ectopic and regular cortical neurons in a female patient with subcortical bilateral laminar heterotopia (double cortex syndrome) performing a simple motor task. We demonstrate a synchronous activation pattern of the motor cortex and the adjacent subcortical ectopic grey matter layer during finger-tapping. We also observed activation in the cerebellum ipsilaterally and in the basal ganglia contralaterally. The present findings are consistent with the hypothesis that ectopic neurons may be functionally integrated in motor circuits during repetitive voluntary movements.
Effects of oral rehydration and external cooling on physiology, perception, and performance in hot, dry climates

Only limited research evaluates possible benefits of combined drinking and external cooling (by pouring cold water over the body) during exercise. Therefore, this study examined cold water drinking and external cooling on physiological, perceptual, and performance variables in hot, dry environments. Ten male runners completed four trials of walking 90 min at 30% VO2max followed by running a 5‐km time trial in 33 ± 1 °C and 30 ± 4% relative humidity. Trials examined no intervention (CON), oral rehydration (OR), external cooling (EC), and oral rehydration plus external cooling (OR + EC). Investigators measured rectal temperature, skin temperatures, heart rate, thirst, thermal sensation, and ratings of perceived exertion (RPE). Oral rehydration (OR and OR + EC) significantly lowered heart rate (P < 0.001) and thirst (P < 0.001) compared with nondrinking (CON and EC) during low‐intensity exercise. External cooling (EC and OR + EC) significantly reduced chest and thigh temperature (P < 0.001), thermal sensation (P < 0.001), and RPE (P = 0.041) compared with non‐external cooling (CON and OR) during low‐intensity exercise. Performance exhibited no differences (CON = 23.86 ± 4.57 min, OR = 22.74 ± 3.20 min, EC = 22.96 ± 3.11 min, OR + EC = 22.64 ± 3.73 min, P = 0.379). Independent of OR, pouring cold water on the body benefited skin temperature, thermal sensation, and RPE during low‐intensity exercise in hot, dry conditions but failed to influence high‐intensity performance.
Enhanced Real-Time Software Development Management System EPM : Graph using SRGM and Linkage with Source Code Analysis Systems

Understanding the current development status is the essential activities for improvement of the development process. Our EASE project developed a real-time data collection and analysis system, named EPM(Empirical Project Monitor). EPM collects the development history data and make a quantitative data analysis. In this paper, we suggest an enhancing method for EPM. At first, we suggest a graph which predicts the number of latent defects by using several SRGM models. We also discuss about the cooperation method with the existing source code analysis tools, such as code-clone analysis system (CCFinder), and software component search system (SPARS-J), and so on.
Impaired neural response to internal but not external feedback in schizophrenia

Background Accurate monitoring and integration of both internal and external feedback is crucial for guiding current and future behavior. These aspects of performance monitoring are commonly indexed by two event-related potential (ERP) components: error-related negativity (ERN) and feedback negativity (FN). The ERN indexes internal response monitoring and is sensitive to the commission of erroneous versus correct responses, and the FN indexes external feedback monitoring of positive versus negative outcomes. Although individuals with schizophrenia consistently demonstrate a diminished ERN, the integrity of the FN has received minimal consideration. Method The current research sought to clarify the scope of feedback processing impairments in schizophrenia in two studies: study 1 examined the ERN elicited in a flanker task in 16 out-patients and 14 healthy controls; study 2 examined the FN on a simple monetary gambling task in expanded samples of 35 out-patients and 33 healthy controls. Results Study 1 replicated prior reports of an impaired ERN in schizophrenia. By contrast, patients and controls demonstrated comparable FN differentiation between reward and non-reward feedback in study 2. Conclusions The differential pattern across tasks suggests that basic sensitivity to external feedback indicating reward versus non-reward is intact in schizophrenia, at least under the relatively simple task conditions used in this study. Further efforts to specify intact and impaired reward-processing subcomponents in schizophrenia may help to shed light on the diminished motivation and goal-seeking behavior that are commonly seen in this disorder.
Antifatigue properties of dragonfly Pantala flavescens wings

The wing of a dragonfly is thin and light, but can bear high frequent alternating stress and present excellent antifatigue properties. The surface morphology and microstructure of the wings of dragonfly Pantala flavescens were observed using SEM in this study. Based on the biological analysis method, the configuration, morphology, and structure of the vein were studied, and the antifatigue properties of the wings were investigated. The analytical results indicated that the longitudinal veins, cross veins, and membrane of dragonfly wing form a optimized network morphology and spacially truss‐like structure which can restrain the formation and propagation of the fatigue cracks. The veins with multilayer structure present high strength, flexibility, and toughness, which are beneficial to bear alternating load during the flight of dragonfly. Through tensile‐tensile fatigue failure tests, the results were verified and indicate that the wings of dragonfly P. flavescens have excellent antifatigue properties which are the results of the biological coupling and synergistic effect of morphological and structural factors. Microsc. Res. Tech. 77:356–362, 2014. © 2014 Wiley Periodicals, Inc.
Unlocking markets to smallholders

This book assesses the institutional, technical and market constraints as well as opportunities for smallholders, notably, emerging farmers in disadvantaged areas such as the former homelands of South Africa. Emerging farmers are previously disadvantaged black people who started or will start their business with the support of special government programs. Public support programs have been developed as part of the Black Economic Empowerment strategy of the South African government. These programs aim to improve the performance of emerging farmers. This requires, first and foremost, upgrading the emerging farmers skills by providing access to knowledge about agricultural and entrepreneurial practices. To become or to remain good farmers they also need access to suitable agricultural land and sufficient water for irrigation and for feeding their cattle. Finally, for emerging farmers to be engaged in viable farming operations, various factors need to be in place such as marketing and service institutions to give credit for agricultural inputs and investments; input markets for farm machinery, farm implements, fertilizers and quality seeds; and accessible output markets for their end products. This book develops a policy framework and potential institutional responses to unlock the relevant markets for smallholders
Advances in Unit Testing: Theory and Practice

Parameterized unit testing, recent advances in unit testing, is a new methodology extending the previous industry practice based on traditional unit tests without parameters. A parameterized unit test (PUT) is simply a test method that takes parameters, calls the code under test, and states assertions. Parameterized unit testing allows the separation of two testing concerns or tasks: the specification of external, black-box behavior (i.e., assertions or specifications) by developers and the generation and selection of internal, white-box test inputs (i.e., high-code-covering test inputs) by tools. PUTs have been supported by various testing frameworks. Various open source and industrial testing tools also exist to generate test inputs for PUTs. This technical briefing presents latest research on principles and techniques, as well as practical considerations to apply parameterized unit testing on real-world programs, highlighting success stories, research and education achievements, and future research directions in developer testing.
Trainable Greedy Decoding for Neural Machine Translation

Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.
Computer-Aided Diagnostic System based on Wavelet Analysis for Microcalcification Detection in Digital Mammograms

Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three stages: (a) the region of interest (ROI) selection of 32times32 pixels size which identifies clusters of microcalcifications, (b) the feature extraction stage is based on the wavelet decomposition of locally processed image (region of interest) to compute the important features of each cluster and (c) the classification stage, which classify between normal and microcalcifications' patterns and then classify between benign and malignant microcalcifications. In classification stage, four methods were used, the voting K-nearest neighbor classifier (K-NN), support vector machine (SVM) classifier, neural network (NN) classifier, and fuzzy classifier. The proposed method was evaluated using the Mammographic Image Analysis Society (MIAS) mammographic databases. The proposed system was shown to have the large potential for microcalcifications detection in digital mammograms.
Multiscale SAR image segmentation using a double Markov random field model

In this paper, we propose a multiscale Bayesian segmentation algorithm for SAR image. This approach uses a hierarchical two-level Markov random field (MRF) to represent both texture and region label over the wavelet lattice. The high level uses an isotropic multilevel logistic (MLL) random field to characterize the blob-like region formation process at each scale and the interscale dependencies over the corresponding multiresolution region. At lower level a novel causal Gaussian autoregressive (CGAR) process is proposed to describe the fill-in of multiresolution region. Once the multiscale double MRFs model is established, in term of sequential maximum a posterior (SMAP), model parameter estimate and region segmentation are performed alternately from coarse to fine scale. Our segmentation method is tested on both synthetic and ERS-1 SAR images.
Near misses of ACR/EULAR criteria for remission: effects of patient global assessment in Boolean and index-based definitions

Background The American College of Rheumatology/European League Against Rheumatism remission criteria for rheumatoid arthritis (RA) have been published recently. Objective To quantify the proportions of patients fulfilling only three of the four Boolean criteria and the relevance of patient global assessment (PGA) in context of remission. Methods From an observational prospective RA database the first visit of patients, fulfilling just three of the four Boolean criteria was identified. Logistic regression and descriptive analyses were processed, also defining remission by index-based (Simplified Disease Activity Index (SDAI)) definition and comparing outcomes with the evaluator global assessment (EGA). Results 52% had at least one visit, fulfilling just three criteria (not fulfilled were: PGA 61%; swollen joints 20%; tender joints 13%; C-reactive-protein 7%). 67% of patients not fulfilling the PGA criterion had an EGA≤1 cm, 25% of those fulfilled the SDAI definition. Increased pain (OR=1.28), EGA (OR=1.10) and discrepancy towards higher PGA than EGA (OR=1.28) could explain PGA failure to reach remission. Conclusions PGA is often the limiting factor for reaching remission; index-based remission showed balancing effects by adjusting for elevated variables in the summative score.
Loop closure detection using depth images

We investigate the question whether loop closure detection using depth images is feasible using currently available depth features. For this reason, we collected a benchmark dataset consisting of a total number of 15 logfiles with several loops in various environments, implemented a modular and easily extensible loop closure detector and used this to evaluate the adequacy of state-of-the art depth features on our benchmark dataset. To allow for a fair comparison, we determined the best values for the sometimes large number of user-chosen parameters using a large-scale grid search. Since our benchmark dataset contains both depth and RGB images, we can compare the performance relying on depth features with the performance achieved when using intensity image features.
Microfluidic Large-Scale Integration

We developed high-density microfluidic chips that contain plumbing networks with thousands of micromechanical valves and hundreds of individually addressable chambers. These fluidic devices are analogous to electronic integrated circuits fabricated using large-scale integration. A key component of these networks is the fluidic multiplexor, which is a combinatorial array of binary valve patterns that exponentially increases the processing power of a network by allowing complex fluid manipulations with a minimal number of inputs. We used these integrated microfluidic networks to construct the microfluidic analog of a comparator array and a microfluidic memory storage device whose behavior resembles random-access memory.
An electrophysiological validation of stochastic DCM for fMRI

In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data—acquired in epilepsy patients—to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.
Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Significance Chemical diversity has recently risen as key structural feature for the discovery of novel selective drugs of G protein-coupled receptors (GPCRs). However, the traditional drug discovery technique of combinatorial chemistry coupled to high-throughput screening has become less attractive because of its immense financial impact. To address this problem, we implemented a computer-aided drug design approach, using the M2 muscarinic acetylcholine receptor (mAChR) as a GPCR model, and performed computational enhanced sampling simulations to account for the receptor flexibility. Through iterative molecular docking and experimental testing, half of the 38 computationally selected National Cancer Institute compounds were validated as allosteric modulators of the M2 mAChR. Our method successfully identified positive and negative allosteric modulators of M2 mAChR with unprecedented chemical diversity. Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [3H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.
Enhancing Automatic Incident Detection Using Vehicular Communications

One of the fundamental requirements of a traffic management system is the ability to determine when an incident has occurred so that proper responses can be initiated. Most of the existing automatic incident detection techniques suffer from many limitations including their inability to detect incidents under non dense traffic conditions and generation of many false positive alarms. In this paper, we introduce a novel Bayesian-based approach to enhance the performance of existing techniques specially under non-dense traffic flow through vehicle to readside communications. The proposed technique also offers zero false positive alarms under most situations and can be integrated with any of the current techniques.