Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cloud-Based Workflow Approach for Optimizing Molecular Docking Simulations of Fully-Flexible Receptor Models and Multiple Ligands

The use of conformations achieved from MolecularDynamics (MD) simulations in docking experiments is the mostaccurate approach to simulate the natural interactions betweenreceptor and ligands at molecular environments. However, suchsimulations are computational costly and their overall executionmay become unfeasible due to the large quantities of structuralinformation needed to represent a Fully-Flexible Receptor (FFR) model. The problem is even more challenging when FFR modelsare used to perform docking-based virtual screening in a largedatabase of ligands. This study aims at developing a cloud-basedworkflow to efficiently optimize docking experiments betweena FFR model and multiple ligands in two strategic ways: bydiscarding groups of unpromising MD conformations for specificligands at docking execution time, and by exploiting on-demandresources from the Microsoft Azure cloud platform. The proposedenvironment is built on e-Science Central, which is a powerfulcloud-based workflow enactment system designed to handlescientific high-throughput tasks. As a result, we expect to reducethe number of docking experiments per ligand without affectingthe quality of the produced models and, therefore, considerablydecreasing the time consumed by docking experiments.
DO PROXIMAL AND DISTAL GASTRIC TUMOURS BEHAVE DIFFERENTLY?

ABSTRACT Background: Although the incidence of gastric (adenocarcinoma) cancer has been decreasing over time, it is still one of the most common malignancies worldwide, and proximal tumours tend to have a worse prognosis. Aim: To compare surgical outcomes and prognosis between proximal - excluding tumours of the cardia - and distal gastric cancer. Methods: Out of 293 cases reviewed - 209 with distal and 69 with proximal gastric cancer - were compared for clinical and pathological features, stage, surgical outcome, mortality and survival. Results: Statistically, there was no significant difference between patients in both groups regarding mortality (p=0.661), adjuvant chemotherapy (p 0.661), and radiation (p=1.000). However, there was significant difference in the degree of lymph node dissection employed (p=0.002) and the number of positive lymph nodes resected (p=0.038) between the two groups. The odds of death at five years for patients who had a D0 dissection was three times greater (odds ratio 2.78; (95%CI 1.33-5.82) than that for patients who had a D2 dissection, while for patients who had a D1 dissection the odds ratio was only 1.41 (95%CI 0.71-2.83) compared to D2-dissected patients. Conclusion: Although no significant differences were found between proximal and distal gastric cancer, the increased risk of death in D0- and D1-dissected patients clearly suggests an important role of radical D2 lymph node dissection in survival.
Desired compensation neural network adaptive robust control of an industrial linear motor motion system with comparative experimental investigation

To meet the challenge of excellent tracking performance even under complicated nonlinear disturbance, a desired compensation neural network adaptive robust control (DCN-NARC) strategy is proposed for an industrial linear motor motion system. The proposed control scheme seriously concentrates on attenuating the effects of various model uncertainties and unknown nonlinearities, such as inertia load variation, force ripple, measurement noise, and external disturbances. Particularly, a three-layer radial basis function neural network (RBFNN) is employed to compensate the unknown nonlinearities. Online parameter adaptation is then utilized to reduce the effect of parametric uncertainties and to update the neural network weights. In addition, to facilitate the controller gain tuning process and attenuate the effect of measurement noise, the desired trajectory states are used in the adaptive model compensation following the desired compensation concept. The residual modeling error and unknown uncertainty are handled through certain robust control term. Theoretically, the proposed DCNNARC can achieve not only prescribed transient performance but also excellent tracking accuracy in general. Comparative experiments are conducted on an industrial linear motor system, and the results consistently demonstrate that the proposed scheme can achieve excellent tracking performance with remarkable disturbance rejection ability even under complicated disturbances. The proposed strategy also could be extended to other industrial control applications.
Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia

We examined the status of the neural network mediating the default mode of brain function, which typically exhibits greater activation during rest than during task, in patients in the early phase of schizophrenia and in young first-degree relatives of persons with schizophrenia. During functional MRI, patients, relatives, and controls alternated between rest and performance of working memory (WM) tasks. As expected, controls exhibited task-related suppression of activation in the default network, including medial prefrontal cortex (MPFC) and posterior cingulate cortex/precuneus. Patients and relatives exhibited significantly reduced task-related suppression in MPFC, and these reductions remained after controlling for performance. Increased task-related MPFC suppression correlated with better WM performance in patients and relatives and with less psychopathology in all 3 groups. For WM task performance, patients and relatives had greater activation in right dorsolateral prefrontal cortex (DLPFC) than controls. During rest and task, patients and relatives exhibited abnormally high functional connectivity within the default network. The magnitudes of default network connectivity during rest and task correlated with psychopathology in the patients. Further, during both rest and task, patients exhibited reduced anticorrelations between MPFC and DLPFC, a region that was hyperactivated by patients and relatives during WM performance. Among patients, the magnitude of MPFC task suppression negatively correlated with default connectivity, suggesting an association between the hyperactivation and hyperconnectivity in schizophrenia. Hyperactivation (reduced task-related suppression) of default regions and hyperconnectivity of the default network may contribute to disturbances of thought in schizophrenia and risk for the illness.
Video shot boundary detection by dominant sets approach

In this study, a video shot boundary detection algorithm based on the dominant sets concept is proposed. Dominant sets method is a graph theoretic clustering algorithm. Proposed method is based on a weighted undirected graph. Candidate shot boundaries are determined and graphs are constructed by taking 2 frames from the right of the candidate position and 4 frames from the left of the candidate position. Edge weights among the vertices are evaluated by using pairwise similarities of frames. By using the complete information of the graph, a set of the vertices mostly similar to each other and dissimilar to the others is detected. True cut positions are determined if the dominant set includes the 4 frames before the candidate position. The simulation results indicate that the proposed algorithm can be used for abrupt shot boundary detection.
Iterative T 2 Estimation from Highly Undersampled Radial Fast Spin-Echo Data

C. Graff, Z. Li, A. Bilgin, M. Altbach, A. F. Gmitro, E. W. Clarkson Program in Applied Mathematics, University of Arizona, Tucson, AZ, United States, Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, Radiology, University of Arizona, Tucson, AZ, United States, Optical Sciences Center, University of Arizona, Tucson, AZ, United States Introduction: Recently radial methods have been investigated as a means to obtain MRI-dependent tissue parameters such as T2 and T1 from a single k-space data set. These methods permit the fast measurement of tissue parameters. So far the approach to derive tissue parameter maps from a single radial k-space data set are based on an echo-sharing (or key hole) algorithm where data in the center of k-space is weighted to a specific TE or TI with the rest of the data added in the outer part of k-space. Depending on the number of radial views collected, the echo sharing approach can lead to significant errors for objects with high spatial frequency content. In this work we present an iterative algorithm based on Projection onto Convex Sets (POCS) to estimate T2 maps from a single radial fast spin-echo k-space data set. The method yields accurate T2 estimates for small structures even for highly under sampled radial data.
BEMD Criterion Based on the Characteristic Points' Number and Positions

To establish a reasonable Bidimensional Empirical mode decomposition (BEMD) criterion for stopping the sifting process, the primary idea considering the stability of the position information was brought out. The special requirements for stopping the sifting process in bi-dimensional signal EMD course were analyzed. The BEMD criterion was constituted based on the diversification of the local surface characteristic pointspsila number and distribution. Experiments prove that this novel criterion has obvious advantages comparing with the current criterion.
Research and Development of Monitoring System for Network Servers

In view of the present network management situation, in order to guarantying the network which can provide normal services, it is necessary to monitor the running state of network servers for its core status in the network. In this paper, a framework has been given, in which network-server management system was designed as Web-based proxy manager three-layer construction. By analyzing the simple network management protocol, the management information base (MIB-II), and the host resources MIB, the monitoring parameters of network servers were identified. The implementation of monitoring system for network servers has been presented.
Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation

This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.
Consumer Perception of Food Safety Related Risk

Abstract The purpose of this study is to examine what factors cause consumer risk perception during periods of food safety concern. A survey of 195 respondents was carried out to explore the nature of consumer perception of food safety related risk using multi-dimensional analysis and how this perception was affected. Multiple regression analysis was adopted to assess the effect of risk characteristics on consumer risk perception in terms of perceived health, psychological, financial, time and taste risks. All five regression models were highly significant. Characteristics of risk, such as severity of consequences, concern about safety, involuntary exposure to risk, harmful to the environment, information, exaggerated report and adequate regulations were found to be important for predicting consumer perception of microbiological risk in chicken. Implications for future research are discussed.
The Protectionist Bias of Duty Drawbacks and the New Regionalism

In a political-economy setting where tariffs and duty drawbacks are endogenously chosen through industry lobbying, it is shown that full duty-drawbacks are granted to exporters who use imported intermediates in their production. This in turn decreases their incentives to counter-lobby against high tariff on their inputs. In equilibrium, higher tariffs will be observed on these goods. The creation of a regional block will change the political equilibrium. Duty-drawbacks will be eliminated on intra-regional exports, which in turn will lead to lower tariffs for goods used as inputs by intra-regional exporters. Evidence from Mercosur suggests that the elimination of duty-drawbacks for intra-regional exports, led to increased counter-lobbying by users of intermediate products. In its absence the common external tariff would have been on average 3.5 percentage points (25 percent) higher.
Integrating machine-to-machine measurement framework into oneM2M architecture

Recent challenges in Internet of Things (IoT) include - (i) providing interoperability among IoT data, (ii) interpreting data generated by IoT devices, and (iii) assisting developers in accomplishing these tasks. Current standard development organizations such as oneM2M are designing semantic-based IoT architecture to address such challenges. However, the recent release of oneM2M standards does not provide any concrete tool for the semantic treatment of the IoT data. Previously, we proposed the Machine-to-Machine Measurement (M3) Framework which mitigates the challenges and assist developers in building semantic based IoT applications easily. Considering the usefulness and novel aspects of the M3 framework, the paper proposes to integrate it seamlessly into oneM2M architecture. The semantic requirements from oneM2M are pointed out and the M3 components addressing them are identified. A common service function (CSF) dedicated to semantic engine is introduced to the oneM2M architecture which extends the capabilities of common services entity (CSE) in terms of semantic treatment of IoT data. The new CSF also includes a catalog of semantic based IoT templates which are exploited by Application Entities to easily utilize the semantic engine. Two deployment scenarios of the upgraded oneM2M architecture have been discussed. Finally these extensions have been communicated to the oneM2M Management, Abstraction and Semantics (MAS) Group (Working Group 5).
Ecological genomics in full colour

Colour patterns in animals have long offered an opportunity to observe adaptive traits in natural populations. Colour plays myriad roles in interactions within and among species, from reproductive signalling to predator avoidance, leading to multiple targets of natural and sexual selection and opportunities for diversification. Understanding the genetic and developmental underpinnings of variation in colour promises a fuller understanding of these evolutionary processes, but the path to unravelling these connections can be arduous. The advent of genomic techniques suitable for nonmodel organisms is now beginning to light the way. Two new studies in this issue of Molecular Ecology use genomic sequencing of laboratory crosses to map colour traits in cichlid fishes, a remarkably diverse group in which coloration has played a major role in diversification. They illustrate how genomic approaches, specifically RAD sequencing, can rapidly identify both simple and more complex genetic variation underlying ecologically important traits. In the first, Henning et al. ( ) detect a single locus that appears to control in a Mendelian fashion the presence of horizontal stripes, a trait that has evolved in numerous cichlid lineages. In the second, Albertson et al. ( ) identify several genes and epistatic interactions affecting multiple colour traits, as well as a novel metric describing integration across colour traits. Albertson et al. ( ) go further, by quantifying differential expression of parental alleles at a candidate locus and by relating differentiation among natural populations at mapped loci to trait divergence. Herein lies the promise of ecological genomics – efficiently integrating genetic mapping of phenotypes with population genomic data to both identify functional genes and unravel their evolutionary history. These studies offer guidance on how genomic techniques can be tailored to a research question or study system, and they also add to the growing body of empirical examples addressing basic questions about how ecologically important traits evolve in natural populations.
Optimal basis for banded channel equalizers in OFDM system over doubly selective channels

We prove that discrete Fourier basis channel model is optimal for least squares estimation of channel coefficients used for low-complexity equalization of OFDM transmission over doubly selective channels. We show that regardless of channel statistics, the channel model order is determined by the number of coefficients used in the equalization. Our theoretical findings are numerically validated by using linear zero forcing equalizers and non-linear maximum likelihood equalizer with pilot-aided channel estimation.
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks

We analyze a word embedding method in supervised tasks. It maps words on a sphere such that words co-occurring in similar contexts lie closely. The similarity of contexts is measured by the distribution of substitutes that can fill them. We compared word embeddings, including more recent representations, in Named Entity Recognition (NER), Chunking, and Dependency Parsing. We examine our framework in multilingual dependency parsing as well. The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate. It achieves state-of-the-art results in multilingual dependency parsing. Word embeddings in 7 languages are available for public use.
Pulsing response of the cardiac transcriptome

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Gait analysis for challenged users based on a rollator equipped with force sensors

Gait analysis provides insightful information about people condition, progress of rehabilitation treatments and fall risk. This analysis is frequently performed in very specific conditions, because it often implies the use of treadmills, cameras and/or wearable sensors. Alternatively, gait can be analyzed using a smart rollator, equipped with a basic set of sensors. This work proposes a methodology to obtain relevant gait parameters using a simple, cheap source of information: wheel odometry and handlebar force sensors. The main advantages of this approach are that users are not bothered with extra equipment. Besides, monitoring can be performed anywhere, even during everyday conditions and for extended periods of time. We have tested our system with a set of rehabilitation patients with different disabilities. Preliminary results have successfully proven that extracted parameters are coherent with reported effects of their specific condition.
School of Computing and Information Technology A New Context-Based Information Retrieval System

We present a new context-based information retrieval (IR) system where user could indicate the context in which he/she is seeking information. We use a novel way of representing context based on the type of information. The context defined in this manner is most intuitive to the user and originates from his/her information need in a problem-solving situation. This new context-based information retrieval system overcomes major limitations of the keyword based IR systems currently in use and provides improved performance. Key-Words: Information retrieval, context, retrieval effectiveness, information type, information need, user interface.
Assessment of Child and Adolescent Overweight and Obesity

Accurate appropriate assessment of overweight and obesity in children and adolescents is a critical aspect of contemporary medical care. However, physicians and other health care professionals may find this a somewhat thorny field to enter. The BMI has become the standard as a reliable indicator of overweight and obesity. The BMI is incomplete, however, without consideration of the complex behavioral factors that influence obesity.Because of limited time and resources, clinicians need to have quick, evidence-based interventions that can help patients and their families recognize the importance of reducing overweight and obesity and take action. In an era of fast food, computers, and DVDs, it is not easy to persuade patients to modify their diets and to become more physically active. Because research concerning effective assessment of childhood obesity contains many gaps, this report is intended to provide a comprehensive approach to assessment and to present the evidence available to support key aspects of assessment. The discussion and recommendations are based on >300 studies published since 1995, which examined an array of assessment tools. With this information, clinicians should find themselves better equipped to face the challenges of assessing childhood overweight and obesity accurately.
Addressing Diverse User Preferences: A Framework for Query Results Navigation

Database queries are often exploratory and users often find their queries return too many answers, many of them irrelevant. Existing approaches include categorization, ranking, and query refinement. The success of all these approaches depends on the utilization of user preferences. However, most existing work assumes that all users have the same user preferences, but in real life different users often have different preferences. In this paper, we propose a framework that addresses diverse user preferences in query results navigation.
FINANCIAL STABILITY AND PRICE STABILITY: AN EMPIRICAL ANALYSIS IN EURO AREA

In this paper we study the relationship between price stability and financial stability. We try to determine whether asset prices are useful indicators for determining future inflation rates and we analyze the tensions in the interbank market during the last five years by means of a GARCH (1,1) model. The results show that the interest rate leads to a decrease in the inflation rate, while oil and real estate prices give a positive impulse. Before the crisis, Euribor-Eoniaswap spread had a low volatility. But the intervention of central banks by injecting liquidity into the banking system led to a considerable increase in its volatility. Another explanation for the evolution of this phenomenon is due to the loss of control by the ECB on the MBR and Eoniaswap spread.