Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of major bacterial groups to bacterial biomass production along a salinity gradient in the South China Sea

Bacterial abundance and assimilation of 3 H-leucine were examined using a combination of microautoradiography and fluorescent in situ hybridization (Micro-FISH) to determine the bacterioplankton community structure and estimate the contribution of major bacterial groups to total bacterial biomass production (leucine incorporation) from the Pearl River mouth to the open water area in the South China Sea. Alpha-, beta-, gamma-proteobacteria and the Cytophaga-Flavo- bacterium cluster differed substantially in their relative abundance and contribution to leucine assim- ilation along the salinity gradient. The contribution of major bacterial groups to leucine assimilation was closely associated with their relative abundance in the communities. Alpha-proteobacteria were the dominant group in the high-salinity water in terms of abundance and 3 H-leucine assimilation, whereas beta-proteobacteria were more important in fresh water. At all stations, gamma-proteobac- teria were a minor component, but the Cytophaga-Flavobacterium cluster was always a significant component in both marine and freshwater systems. The relative abundance of bacterial groups explained the variation in 3 H-leucine assimilation by the major bacterial groups to a great extent. At the singlebacterial group level, a statistically significant correlation between abundance and leucine- uptake activity was observed for beta-proteobacteria, suggesting that the relative abundance of beta- proteobacteria in bacterioplankton communities was controlled by growth-related processes. In general, the numerical dominance of the major phylogenetic groups and their contribution to total bacterial biomass production varied consistently along the salinity gradient in the South China Sea.
Fully automated QuEChERS clean-up and LC / MS-QQQ analysis of pesticides in fruits and vegetables

Two different DSPE methods exist, the EN and AOAC, which differ in the following ways. Firstly, the buffered extraction system in the EN method uses sodium chloride, sodium citrate and disodium citrate sesquihydrate instead of sodium acetate in the AOAC extraction system. Secondly, in the DSPE step, the EN method uses 25 mg PSA per ml of extract rather than 50 mg PSA per ml of extract as stated in the AOAC method. For the purposes of the following study, samples were treated according to EN protocols. The automation of this methodology can be applied to either DSPE method, as long as suitable quantities of reagent are present in sample vials.
Character strengths in Spanish music students: An exploratory study / Fortaleses de caràcter en els estudiants de música espanyols: un estudi exploratori

Summary. The factors in the choice of musical instruments and how it influences and modifies the personality of the musicians are a common theme in research. However, less attention has been given to the formative effect of music on the character of music students, whose long-term commitment to acquiring professional competences as performers, teachers, conductors or composers can be examined using the research on Positive Psychology, which is mainly concerned with furthering people’s psychological wellbeing. This paper profiles the virtues and character strengths of a sample of eighty Spanish students completing bachelor’s degrees in music during the academic year 2016-2017, using the VIA Inventory of Strengths –free version (VIA-IS) (Peterson & Seligman, 2004) to analyse the respondents’ key strengths. The survey showed high scores in judgment and lower scores in self-regulation. The results were contrasted with samples of students in bachelor’s degrees in the social sciences.Resum. Els factors que contribueixen a l’eleccio dels instruments musicals i la seva influencia i modificacio en la personalitat dels musics son un tema comu en la recerca. Tanmateix, s’ha posat menys atencio a en aspectes com l’efecte formatiu de la musica en el caracter dels estudiants de musica; el compromis d’aquestes persones amb l’adquisicio de competencies professionals a llarg termina com a interprets, professors, directors o compositors pot ser examinat aplicant la Psicologia Positiva, que s’ocupa principalment del benestar psicologic de les persones. Aquest article estudia les virtuts i les fortaleses del caracter d’una mostra de vuitanta estudiants espanyols cursant el grau en musica durant el curs 2016-2017 amb el VIA Inventory of Strengths – versio lliure (VIA-IS; Peterson & Seligman, 2004) per analitzar les fortaleses clau dels enquestats. Els resultats van mostrar puntuacions altes en judici i menors en auto-regulacio. Els resultats es contrasten amb mostres d’estudiants de grau en ciencies socials.
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task

This paper describes our submission for the International Workshop on Semantic Evaluation (SemEval-2018) shared task 11– Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b). We use a deep neural network model to choose the correct answer from the candidate answers pair when the document and question are given. The interactions between document, question and answers are modeled by attention mechanism and a variety of manual features are used to improve model performance. We also use CoVe (McCann et al., 2017) as an external source of knowledge which is not mentioned in the document. As a result, our system achieves 80.91% accuracy on the test data, which is on the third place of the leaderboard.
Safe learning-based optimal motion planning for automated driving

This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal solution was still guaranteed since dynamic obstacles can only increase the cost. However, significant variations in the search efficiency are observed depending whether dynamic obstacles are present or not. This paper introduces machine learning (ML) based heuristic that takes into account dynamic obstacles, thus adding to the performance consistency for achieving real-time implementation.
Collision Avoidance Behavior between Walkers: Global and Local Motion Cues

Daily activities require agents to interact with each other, such as during collision avoidance. The nature of visual information that is used for a collision free interaction requires further understanding. We aim to manipulate the nature of visual information in two forms, global and local information appearances. Sixteen healthy participants navigated towards a target in an immersive computer-assisted virtual environment (CAVE) using a joystick. A moving passive obstacle crossed the participant's trajectory perpendicularly at various pre-defined risks of collision distances. The obstacle was presented with one of five virtual appearances, associated to global motion cues (i.e., a cylinder or a sphere), or local motion cues (i.e., only the legs or the trunk). A full body virtual walker, showing both local and global motion cues, used as a reference condition. The final crossing distance was affected by the global motion appearances, however, appearance had no qualitative effect on motion adaptations. These findings contribute towards further understanding what information people use when interacting with others.
Method to optimize NVH-behaviour of a brushless electrically excited synchronous machine

As part of the ongoing NGC-project (Next Generation Car) related to the research of new technologies for future road vehicles at the DLR, this paper is dedicated to the substitution of an optimization method of a brushless electrically excited synchronous machine for use in electrical vehicles. In this application high torque densities and small vibrations are needed, so an optimization of torque and torque ripple is done. As optimization parameters torque ripple reduction techniques as skewing, cosine formed tooth and width of tooth are chosen. The combined multi-parameter optimization is necessary because of interactions between the optimization parameter effects. An analysis for a machine of 25 kW, used in urban electric vehicles, is done with different excitations to build up the working points. The results show a large effect of increasing skewing to decrease torque and increase ripples independent of excitation. The effects of the other techniques are smaller. The cosine form has in the example optimization a strong dependency on excitation.
Evaluation of experimental activities

Importance of evaluating the effectiveness of experimental activities in engineering education is discussed. A set of online experimental activities that are used in undergraduate engineering programs are described. A strategy for evaluating student's reaction to the experimental activities, some implemented with these online resources, is proposed. The main purposes are to evaluate the impact of experimentation in conceptual knowledge gain and in students' motivation. Two different case studies are presented to illustrate the methodology.
Article Impact Value for Nearby Citation Network Analysis

In today's large and rapidly increasing amount of scientific publications, exploring recent studies in a given research area has become more difficult than any time before. As a result, authors and publishers rely on different factors to measure the relationships and the impact of their produced research work using parameters such as the impact factor, number of citations, co-citations, and others. In this paper, we propose an Article Impact Value (AIV) that enables us to identify the actual impact of each cited articles in a citation relationship. We utilize the article metadata to calculate a weighted content-based similarity between articles. We also utilize the proceedings and journals impact index into the computation of the AIV. We demonstrate, experimentally, an applicable algorithm that employs the publications impact factor and the citation counts to estimate the impact value of each cited article in a citation network.
Abnormalities in Skeletal Muscle Myogenesis, Growth, and Regeneration in Myotonic Dystrophy

Myotonic dystrophy type 1 (DM1) and 2 (DM2) are autosomal dominant degenerative neuromuscular disorders characterized by progressive skeletal muscle weakness, atrophy, and myotonia with progeroid features. Although both DM1 and DM2 are characterized by skeletal muscle dysfunction and also share other clinical features, the diseases differ in the muscle groups that are affected. In DM1, distal muscles are mainly affected, whereas in DM2 problems are mostly found in proximal muscles. In addition, manifestation in DM1 is generally more severe, with possible congenital or childhood-onset of disease and prominent CNS involvement. DM1 and DM2 are caused by expansion of (CTG•CAG)n and (CCTG•CAGG)n repeats in the 3′ non-coding region of DMPK and in intron 1 of CNBP, respectively, and in overlapping antisense genes. This critical review will focus on the pleiotropic problems that occur during development, growth, regeneration, and aging of skeletal muscle in patients who inherited these expansions. The current best-accepted idea is that most muscle symptoms can be explained by pathomechanistic effects of repeat expansion on RNA-mediated pathways. However, aberrations in DNA replication and transcription of the DM loci or in protein translation and proteome homeostasis could also affect the control of proliferation and differentiation of muscle progenitor cells or the maintenance and physiological integrity of muscle fibers during a patient’s lifetime. Here, we will discuss these molecular and cellular processes and summarize current knowledge about the role of embryonic and adult muscle-resident stem cells in growth, homeostasis, regeneration, and premature aging of healthy and diseased muscle tissue. Of particular interest is that also progenitor cells from extramuscular sources, such as pericytes and mesoangioblasts, can participate in myogenic differentiation. We will examine the potential of all these types of cells in the application of regenerative medicine for muscular dystrophies and evaluate new possibilities for their use in future therapy of DM.
A 1-Gb/s 6–10-GHz, Filterless, Pulsed UWB Transmitter With Symmetrical Waveform Analysis and Generation

This paper presents a fully integrated, regulation-aware pulsed ultra wideband (UWB) transmitter (TX) for low-cost, high-data-rate applications. Waveform nonidealities have been systematically analyzed for the first time, demonstrating that envelope asymmetry may cause spectrum regulation violations. In order to generate regulation-compliant UWB pulses without costly filters, the proposed high-data-rate TX employs an intrinsically symmetrical carrier-based pulse generation scheme. The prototype was fabricated using a 130-nm CMOS process, occupying 0.37 mm2. This biphase-modulated TX can support a measured maximum data rate of 1 Gb/s with a pulsewidth of 0.5 ns and a power efficiency of 14.4 pJ/b. Without filters, the output spectrums are not only compliant with the regulations but also consistent with the theoretical results.
The effect of illumination on gray color

The present study explored the perceptual process of integration of luminance information in the production of the gray color of an object placed in an environment viewed from a window. The mean luminance of the object was varied for each mean luminance of the environment. Participants matched the gray color of the object with that of Munsell chips in a viewing box. The results show that the Munsell values so obtained are linear measures of gray color. The results support the possibility that the gray color of the object derives from an additive integration of the information about mean luminance of the object and about mean luminance of the environment, with the weights of this information varying with the mean luminances.
Value Propagation Networks

We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.
MCS: Memory Constraint Strategy for Unified Memory Manager in Spark

Apache Spark is an increasingly popular distributed computation framework based on in-memory computations, which enables iterative or interactive applications to run faster. In Spark, memory management is the key to performance enhancement to avoid memory bloat problems. Compared with previous static memory manager, in Spark 1.6 and later versions, unified memory manager is implemented as the default memory management model, targeting to achieve optimal memory utilization by borrowing between storage and execution memory. However, the storage memory borrowed from execution memory may frequently be evicted when memory pressure arises. It is because of frequently re- computation during cache evicted and frequent garbage collection during shuffle in iterative applications. This situation will produce runtime overhead caused by garbage collection including cache eviction and cache re-computation. We propose a memory constraint strategy for unified memory manager in Spark to reduce runtime overhead caused by garbage collection by reducing the cache eviction size. We implement the strategy in Spark 1.6. 1 using SparkPageRank, WordCount and GroupByTest to compare three different memory managers. Experimental results reveal that compared unified memory manager, memory constraint strategy can achieve better performance improvement with lower job runtime and garbage collection time when the dataset sizes or the iterations are increasing.
Fibernet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

The brain’s white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimer’s disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brain’s neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.
Gene expression DEGseq : an R package for identifying differentially expressed genes from RNA-seq data

Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au. tsinghua.edu. cn/software/degseq Contact: xwwang@tsinghua.edu. cn; zhangxg@tsinghua.edu. cn Supplementary information: Supplementary data are available at Bioinformatics online.
Polarized x‐ray excitation for scatter reduction in x‐ray fluorescence computed tomography

PURPOSE X-ray fluorescence computer tomography (XFCT) is a new molecular imaging modality which uses x-ray excitation to stimulate the emission of fluorescent photons in high atomic number contrast agents. Scatter contamination is one of the main challenges in XFCT imaging which limits the molecular sensitivity. When polarized x rays are used, it is possible to reduce the scatter contamination significantly by placing detectors perpendicular to the polarization direction. This study quantifies scatter contamination for polarized and unpolarized x-ray excitation and determines the advantages of scatter reduction. METHODS The amount of scatter in preclinical XFCT is quantified in Monte Carlo simulations. The fluorescent x rays are emitted isotropically, while scattered x rays propagate in polarization direction. The magnitude of scatter contamination is studied in XFCT simulations of a mouse phantom. In this study, the contrast agent gold is examined as an example, but a scatter reduction from polarized excitation is also expected for other elements. The scatter reduction capability is examined for different polarization intensities with a monoenergetic x-ray excitation energy of 82 keV. The study evaluates two different geometrical shapes of CZT detectors which are modeled with an energy resolution of 1 keV FWHM at an x-ray energy of 80 keV. Benefits of a detector placement perpendicular to the polarization direction are shown in iterative and analytic image reconstruction including scatter correction. The contrast to noise ratio (CNR) and the normalized mean square error (NMSE) are analyzed and compared for the reconstructed images. RESULTS A substantial scatter reduction for common detector sizes was achieved for 100% and 80% linear polarization while lower polarization intensities provide a decreased scatter reduction. By placing the detector perpendicular to the polarization direction, a scatter reduction by factor up to 5.5 can be achieved for common detector sizes. The image reconstruction showed that for a scatter magnitude decrease by a factor of 2.4, the molecular sensitivity could almost be doubled. CONCLUSION Scatter reduction lowers the amount of noise in the projection datasets and reconstructed images which enhance molecular sensitivity at equal dose. The results support the use of linear polarized x rays to reduce scatter in XFCT imaging.
Trip Planning by an Integrated Search Paradigm

In this paper, we build a trip planning system called TISP, which enables user's interactive exploration of POIs and trajectories in their incremental trip planning. At the back end, TISP is able to support seven types of common queries over spatial-only, spatial-textual and textual-only data, based on our proposed unified indexing and search paradigm [7]. At the front end, we propose novel visualisation designs to present the result of different types of queries; our user-friendly interaction designs allow users to construct further queries without inputting any text.
Convolutional Neural Network with SDP-Based Attention for Relation Classification

Relation classification plays an important role in the field of natural language processing (NLP). The state-of-theart methods for this task use prior knowledge as features such as WordNet, Part-of-Speech(POS), shortest dependency path (SDP), which is helpful but brings error propagation. In this paper, we propose a convolutional neural network architecture, which builds word-level attention mechanism based on SDP to capture task-oriented patterns in sentences. We explore the way of combining prior knowledge and deep models properly to ease errors in prior knowledge. Additionally, a new objective function is designed to reduce the impact of artificial class, which is seldom touched in previous works. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model outperforms some of the state-of-the-art methods.
Assessing the Physical Impact of Cyberattacks on Industrial Cyber-Physical Systems

Industrial cyber-physical systems (ICPSs) are widely applied in critical infrastructures such as chemical plants, water distribution networks, and power grids. However, they face various cyberattacks, which may cause physical damage to these industrial facilities. Therefore, ensuring the security of ICPSs is of paramount importance. For this purpose, a new risk assessment method is presented in this paper to quantify the impact of cyberattacks on the physical system of ICPSs. This method helps carry out appropriate attack mitigation measures. The method uses a Bayesian network to model the attack propagation process and infers the probabilities of sensors and actuators to be compromised. These probabilities are fed into a stochastic hybrid system (SHS) model to predict the evolution of the physical process being controlled. Then, the security risk is quantified by evaluating the system availability with the SHS model. The effectiveness of the proposed method is demonstrated with a case study on a hardware-in-the-loop simulation test bed.