Miracle-dz/UniFAR-FTUs huggingface dataset:
A Technique to Avoid Atomic Operations on Large Shared Memory Parallel Systems

—Updating a shared data structure in a parallel program is usually done with some sort of high-level synchronization operation to ensure correctness and consistency. The realization of such high-level synchronization operations is done with appropriate low-level atomic synchronization instructions that the target processor architecture provides. These instructions are costly and often limited in their scalability on larger multi-core / multi-processor systems. In this paper, a technique is discussed that replaces atomic updates of a shared data structure with ordinary and cheaper read/write operations. The necessary conditions are speciﬁed that must be fulﬁlled to ensure overall correctness of the program despite missing synchronization. The advantage of this technique is the reduction of access costs as well as more scalability due to elided atomic operations. But on the other side, possibly more work has to be done caused by missing synchronization. Therefore, additional work is traded against costly atomic operations. A practical application is shown with level-synchronous parallel Breadth-First Search on an undirected graph where two vertex frontiers are accessed in parallel. This application scenario is also used for an evaluation of the technique. Tests were done on four different large parallel systems with up to 64-way parallelism. It will be shown that for the graph application examined the amount of additional work caused by missing synchronization is neglectible and the performance is almost always better than the approach with atomic operations.
SEMG-based multifeatures and predictive model for knee-joint-angle estimation

Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation training as they reflect effectively the motor intentions of users. This study proposed a new sEMG-based multifeature extraction and predictive model to predict knee-joint angle from multichannel sEMG. Six channels of sEMG from relevant muscles were recorded, and knee-joint angles were sampled simultaneously for six kinds of knee-joint movement models. The root–mean–square (RMS), wavelet coefficients (WC), and permutation entropy (PE) as features of sEMG were extracted. The back propagation neural network, generalized regression neural network, and least-square support vector regression machine (LS-SVR) were used as predictive models. To validate the effectiveness of the sEMG features and predictive models, twelve subjects without neural or musculoskeletal deficits participated in the experiment. Six kinds of knee-joint movement models at different speeds and different loads were respectively conducted by the subjects. Results revealed that the combination of the three features (RMS, WC, and PE) and LS-SVR performed well for the knee-joint-angle of all kinds of leg motions. The RMS error for all kinds of leg motions was <7.7°. The estimation results of joint motion state would be used to rehabilitation robot or functional electrical stimulation for active rehabilitation of spinal cord injury patients or stroke patients.Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation training as they reflect effectively the motor intentions of users. This study proposed a new sEMG-based multifeature extraction and predictive model to predict knee-joint angle from multichannel sEMG. Six channels of sEMG from relevant muscles were recorded, and knee-joint angles were sampled simultaneously for six kinds of knee-joint movement models. The root–mean–square (RMS), wavelet coefficients (WC), and permutation entropy (PE) as features of sEMG were extracted. The back propagation neural network, generalized regression neural network, and least-square support vector regression machine (LS-SVR) were used as predictive models. To validate the effectiveness of the sEMG features and predictive models, twelve subjects without neural or musculoskeletal deficits participated in the experiment. Six kinds of knee-joint movement models at different speeds and different loads were respectively conducted by t.
The Role of FUN in Sexual Differentiation and Leaf Development in Zea mays

Author(s): Vajk, Angus Hugo | Advisor(s): Hake, Sarah | Abstract: While most angiosperms bear hermaphroditic flowers, Zea mays bears male and female flowers on separate inflorescences. Grasses have leaves that possess a sheath, ligule, auricle and blade. This Thesis describes a pleiotropic mutant named fun in Zea mays that bears female flowers where male flowers are expected; and deletes the auricle in adult leaves. Analysis of the interactions between fun and other leaf mutants allow the tentative construction of a developmental pathway including FUN. Double mutant analysis with other sex determination and leaf mutants is presented, demonstrating that the mutation is likely to be involved in multiple hormone pathways, displaying synergistic double mutant interactions with both Jasmoinc Acid and Brassinosteroid mutants. Steps towards creating an antibody to the causative gene are described, and the protein is found to be nuclear localised by YFP fusion and bioinformatic analysis. The FUN gene is found to be conserved across plants, with high conservation in the grasses.
Same Data - Different Software - Different Results? Analytic Variability of Group fMRI Results

A plethora of tools and techniques are now available to process and model fMRI data. However, this ‘methodological plurality’ has come with a drawback. Application of different analysis pipelines (Carp, 2012), alterations in software version (Glatard, 2015), and even changes in operating system (Gronenschild, 2012) have all been shown to cause variation in the results of a neuroimaging study. This high analytic flexibility has been pinpointed as a key factor that can lead to increased false-positives (Ioannidis, 2005), and compounded with a lack of data sharing, irreproducible research findings (Poldrack, 2017). In this work, we seek to understand how choice of software package impacts analysis results. We reproduce the results of three published neuroimaging studies (Schonberg, 2012; Moran, 2012; Padmanabhan, 2011) with publicly available data within the three main neuroimaging software packages: AFNI, FSL and SPM, using parametric and nonparametric inference. All information for how to process, analyze, and model each dataset we obtain from the publication. We make a variety of comparisons to assess the similarity of our results across both software packages and choice of inference method.
Le lithium et le divalproex pour le traitement du trouble bipolaire de la personne âgée : étude à répartition aléatoire contrôlée à double insu

Pour toute correspondance : Marie Loranger, Centre hospitalier affilié universitaire régional du Centre intégré universitaire de santé et de services sociaux de la Mauricieet-du-Centre-du-Québec, 991, boulevard du Carmel, Trois-Rivières (Québec) G8Z 3R9, CANADA; Téléphone : 819 697-3333 poste 63364; Télécopieur : 819 372-3590; Courriel : marie.loranger2@ssss.gouv. qc.ca Le lithium et le divalproex pour le traitement du trouble bipolaire de la personne âgée : étude à répartition aléatoire contrôlée à double insu
A Motion Scene Analysis Model of Fuzzy Optimized Watershed Algorithm

For the existing analysis model cannot analyze the scenes in motion video effectively; this paper proposes a motion analysis model of fuzzy optimized watershed algorithm. Firstly, the watershed algorithm is used to extract the scene of motion video, and the optimization of the immersion field of the watershed algorithm is carried out. Then the fuzzy C clustering algorithm is used to extract the motion scene for image clustering and the kernel function is used to optimize the weight of the traditional fuzzy C clustering algorithm, improving the accuracy of image separation. The simulation results show that the proposed method of fuzzy optimization watershed algorithm is better than traditional watershed algorithm, and it is well applied in scene color analysis. Furthermore, it has high performance.
Temperature Dependent EIS Studies Separating Charge Transfer Impedance from Contact Impedance in Lithium-Ion Symmetric Cells

Measuring electrochemical impedance spectra of lithium-ion symmetric cells at low temperatures allows for unambiguous separation of charge transfer impedance contributions from other cell impedance features. Electrodes from dry Li[Ni0.5Mn0. 3Co0.2]O2 (NMC 532)/artificial graphite (AG) pouch cells were used to make blocking electrode configuration symmetric cells and blocking electrode full coin cells. NMC532/AG cells with 1.2M LiPF6 in EC:DMC 3:7 (w:w) electrolyte also underwent the formation process before the electrodes were used to make non-blocking configuration symmetric and full coin cells. Through the use of temperature dependent measurements on blocking and non-blocking cells, the impedance contribution due to faradaic, charge transfer interactions can be distinguished from non-faradaic, high frequency impedance contributions. Fitting the EIS spectra allowed both the charge transfer resistances and the related capacitances to be determined for the solid electrolyte interphase layers on the negative and positive electrodes. The charge transfer resistances were strongly temperature dependent while the capacitances were not. The temperature dependence of the charge transfer resistance was used to determine the activation energies for lithium transport through the solid electrolyte interphase layers on the negative and positive electrodes. Surprisingly, the activation energies were approximately 0.6 eV for both positive and negative electrodes extracted from cells after formation.
Suitability and limitations of mesenchymal stem cells to elucidate human bone illness

Functional impairment of mesenchymal stem cells (MSCs), osteoblast progenitor cells, has been proposed to be a pathological mechanism contributing to bone disorders, such as osteoporosis (the most common bone disease) and other rare inherited skeletal dysplasias. Pathological bone loss can be caused not only by an enhanced bone resorption activity but also by hampered osteogenic differentiation of MSCs. The majority of the current treatment options counteract bone loss, and therefore bone fragility by blocking bone resorption. These so-called antiresorptive treatments, in spite of being effective at reducing fracture risk, cannot be administered for extended periods due to security concerns. Therefore, there is a real need to develop osteoanabolic therapies to promote bone formation. Human MSCs emerge as a suitable tool to study the etiology of bone disorders at the cellular level as well as to be used for cell therapy purposes for bone diseases. This review will focus on the most relevant findings using human MSCs as an in vitro cell model to unravel pathological bone mechanisms and the application and outcomes of human MSCs in cell therapy clinical trials for bone disease.
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection

Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. However, most of the works in binary code vulnerability detection has relied on handcrafted features which are manually chosen by a select few domain experts. In this paper, we attempt to alleviate this severe binary vulnerability detection bottleneck by leveraging recent advances in deep learning representations and propose the Maximal Divergence Sequential Auto-Encoder. In particular, latent codes representing vulnerable and non-vulnerable binaries are encouraged to be maximally divergent, while still being able to maintain crucial information from the original binaries. We conducted extensive experiments to compare and contrast our proposed methods with the baselines, and the results indicate that our proposed methods outperform the baselines in all performance measures of interest.
Microbiomes in the insectivorous bat species Mops condylurus rapidly converge in captivity

Bats are well known reservoir hosts for RNA and DNA viruses. The use of captive bats in research has intensified over the past decade as researchers aim to examine the virus-reservoir host interface. In this study, we investigated the effects of captivity on the fecal bacterial microbiome of an insectivorous microbat, Mops condylurus, a bat species that roosts in close proximity to humans and has likely transmitted viral infections to humans. Using amplicon 16S rRNA gene sequencing, we characterized changes in fecal bacterial community composition for individual bats directly at the time of capture and again after six weeks in captivity. We found that microbial community richness by measure of the number of observed operational taxonomic units (OTUs) in bat feces does increase significantly in captivity. Importantly, we found the similarity of microbial community structures of fecal microbiomes between different bats to converge during captivity. We propose a six week-acclimatization period prior to carrying out infection studies or other research influenced by the microbiome composition, which may be advantageous to reduce variation in microbiome composition and minimize biological variation inherent to in vivo experimental studies.
Investigating Differences and Similarities between Betaxolol Polymorphs

Betaxolol belongs to the class of β1-adrenergic blocking agent. Several polymorphs of racemic betaxolol have been reported in the literature, but only one of them (BE_I) had the crystal structure determined from single-crystal X-ray diffraction. Here, we present a new crystalline phase of betaxolol (BE_IV). Its solid-state structure has been obtained from single-crystal X-ray diffraction data. The molecular and crystal arrangements of betaxolol in BE_IV have been further investigated by molecular modelling, by Cambridge Structural Database (CSD) surveys and by Hirshfeld surface analysis. A comparison with the solid-state structure of BE_I have been carried out. In the two polymorphs the 2-hydroxy-3-(isopropylamino)-propoxy chain, which is common to other β-blocker drugs, adopts a different conformation. In addition, the rotational isomer found in BE_IV is different with respect to the four already observed in the solid-state structure of analogous compounds. In both the polymorphs, the most significant interaction is due to the H-bonds involving the OH group as donor and the NH as acceptor, while the interaction where OH works as acceptor (NH acts as donor) is definitely less important. The resulting H-bond patterns are however different: Alternate R2,2(10) a > a (OH donors) and R2,2(10) b > b (OH acceptors) in BE_I vs. alternate R4,4 (8) a > b > a > b (OH donors) and R2,2 (10) b > b (OH acceptor) in BE_IV.
A Combined Morphometric and Statistical Approach to Assess Nonmonotonicity in the Developing Mammary Gland of Rats in the CLARITY-BPA Study

Background CLARITY-BPA is a rare collaboration of guideline-compliant (core) studies and academic hypothesis-based studies to assess the effects of bisphenol A (BPA). Objectives 1) determine BPA’s effects on the developing rat mammary gland using new quantitative and established semi-quantitative methods in two labs, 2) develop a software tool for semi-automatic evaluation of quantifiable aspects of the mammary ductal tree, and 3) compare those methods. Methods Sprague Dawley rats were exposed to BPA, vehicle, or positive control (ethinyl estradiol, EE2) by oral gavage beginning on gestational day 6 and continuing with direct dosing of the pups after birth. There were two studies; sub-chronic and chronic. The latter used two exposure regimes, one stopping at PND21 the other continuing until tissue harvest. Glands were harvested at multiple time points; whole mounts and histological specimens were analyzed blinded to treatment. Results The subchronic study’s semiquantitative analysis revealed no significant differences between control and BPA dose groups at PND21; whereas at PND90 there were significant differences between control and the lowest BPA dose and between control and the lowest EE2 dose in animals in estrus. Quantitative, automatized analysis of the chronic PND21 specimens displayed non-monotonic BPA effects with a breaking point between the 25 and 250μg/kg/day doses. This breaking point was confirmed by a global statistical analysis of chronic study animals at PND90 and 6 months analyzed by the quantitative method. The BPA response was different from the EE2 effect for many features. Conclusions Both the semiquantitative and the quantitative methods revealed non-monotonic effects of BPA. The quantitative unsupervised analysis used 91 measurements and produced the most striking non-monotonic dose-response curves. At all-time points, lower doses resulted in larger effects, consistent with the core study which revealed a significant increase of mammary adenocarcinoma incidence in the stop-dose animals at the lowest BPA dose tested.
Random Number Generation: a potential target of electromagnetic emanation analysis?

Although the random number generation in a cryptographic system is a key element of the security chain, bibliography dealing with attacks on this part is not really wide. However, among the large number of attacks targeting cyphers, the ones exploiting electromagnetic side channel (in near field) could be very effective against random number generators. This presentation gives an insight on the first results of electromagnetic analysis performed on random number generators. The main idea is to evaluate if the tools elaborated to perform electromagnetic analysis on cyphers could be used on random number generators, too.
Catechins Green Tea GMB4 Clone Increases mRNA of ABCA1 through LXR Signaling in Cultured Macrophage Exposed OX-LDL

Inhibition of atherogenesis through inhibition of lipid metabolism has not been explored while other inhibitions through inflammation, endothelial dysfunction and free radicals have done. Inhibition of atherogenesis via inhibition of lipid metabolism can be done through the mechanism of Reverse Cholesterol Transport (RCT). Signaling pathways that play a role in this mechanism is LXR signaling. LXR activation by an LXR agonist led increasing cholesterol efflux. Catechins based on bioinformatics study showed as a potent candidate LXR agonists that can be used as an inhibitor of atherogenesis. This study aims to prove that the administration Catechins green tea Clone GMB4 can prevent atherosclerosis through increasing mechanism cholesterol efflux from macrophage by taking effect of ABCA1, ABCG1, SRB1 gene expression in cultured macrophages were exposed ox-LDL. Long-term goals of the outcome of the research are the use of Catechins Green Tea Clones GMB4 as an inhibitor of atherogenesis so that it can be used as a complementary therapy for the treatment of atherosclerosis and cardiovascular diseases. The research is divided into 5 groups, namely the culture of macrophages without exposed ox-LDL, culture exposed ox-LDL and groups of Catechins dose I, II, III. In vitro study showed that administration of Catechins increases mRNA of ABCA1, whereas mRNA ABCG1 and SRB1 decreased at all three doses given. The result of protein profilling was identified a protein with a molecular weight of 70 kDa by SDS-PGE with silver staining.
Seleção genômica ampla no melhoramento de Coffea canephora.

O cafe e globalmente uma das commodities mais importante e sua producao e baseada em duas principais especies, Coffea arabica e Coffea canephora, sendo o Brasil o maior produtor mundial. Acredita-se que toda essa producao possa ser comprometida, devido aos efeitos das mudancas climaticas, afetando a viabilidade das flores, o desenvolvimento dos frutos, a producao e a qualidade da bebida. Uma alternativa para auxiliar na obtencao de cafeeiros que sejam mais adaptados as condicoes climaticas futuras e, ao mesmo tempo, sem reducao da producao e a qualidade da bebida pode ser a selecao genomica ampla (SGA). A implementacao desse programa requer muitos marcadores geneticos, que agora sao mais facilmente descobertos com a publicacao do genoma de referencia de C. canephora . A maioria das caracteristicas que sao economicamente importantes sao quantitativas e complexas, requerindo uma cobertura genomica completa de marcadores, e, desta forma, a SGA, atraves do calculo dos valores geneticos genomicos (VGG), se apresenta como uma ferramenta importante para o melhoramento de plantas. Na SGA, esses marcadores sao usados para construir um modelo preditivo usando individuos com informacoes genotipicas e fenotipicas conhecidas. Com este modelo, os VGGs para as caracteristicas desejadas podem ser calculados e usados para classificar os individuos com fenotipo desconhecido para posterior selecao. A SGA e uma nova abordagem no melhoramento genetico de plantas que permite a selecao precoce de materiais de elite, maximizando ganhos geneticos ao longo de geracoes, principalmente no caso de plantas perenes como o cafe. Para este proposito, neste trabalho, um grande numero de individuos e marcadores foram selecionados, para se avaliar a aplicabilidade da SGA em termos de resposta de selecao das caracteristicas alvo e precisao da predicao em uma populacao de C. canephora . Aproximadamente 1.300 individuos de C. canephora de uma populacao localizada na Embrapa Cerrados (Planaltina, DF, Brasil) foram selecionados e avaliados quanto as caracteristicas como producao, precocidade dos frutos, tamanho de grao, tamanho da cereja e peso de 100 graos. O DNA dos individuos foi extraido e os dados genotipicos foram obtidos a partir do chip desenvolvido para C. canephora com aproximadamente 26K SNPs. A populacao amostrada foi utilizada para estimacao e validacao no modelo preditivo. Equacoes de predicao de VGGs foram construidas para cada caracteristica de interesse, previsto pelo G-BLUP. Embora os estudos de SGA ainda nao tenham sido capazes de explicar a variacao fenotipica total, eles identificaram regioes genomicas essenciais, e para especies perenes como cafe, o uso de SGA pode aumentar muito a eficiencia de selecao, reduzindo o tempo de introducao de caracteristicas de interesse.
EBMT Sindhi to Hindi Sentence Translation System

Example Based Machine Translation (EBMT) is the system which translates sentences from Sindhi to Hindi. It uses the parallel corpus for translating sentences. To handle large volume of data the computational model is required. The requirement for a computational resource in EBMT is significantly less as compared to MT (Machine Translator). This makes development of EBMT systems for Sindhi to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The software helps to get a sentential translation according to semantics and syntactic of the language. The frequency of word occurrence is important for translation in EBMT.
Redes neurais para filtragem inversa com aplicação em calorímetros operando a alta taxa de eventos

This paper aims to implement an inverse system to remove a impulse response from a calorimeter operating at a high event rate. The Tilecal calorimeter has a higher impulse response than the acquired event rate, generating signal pile-up in its reading electronics. FIR filters have been proposed that approximate the inverse system, however, nonlinear characteristics intrinsic to the calorimeter such as phase shift and pulse deformation, make linear filters not suitable for this scenario. Due to these characteristics, an inverse filter based on neural networks, implemented in dedicated hardware (FPGA), is proposed. This method proved to be more efficient when compared to filtering method based on linear filter. Keywords— Neural network, FPGA, calorimeter, signal pile-up.