Miracle-dz/UniFAR-FTUs huggingface dataset:
Resveratrol ameliorates Serratia marcescens‐induced acute pneumonia in rats

Serratia marcescens is an important nosocomial pathogen, which has been especially problematic as a cause of hospital‐acquired pneumonia in the past two decades. Treatment of S. marcescens‐related infections has been limited by emergence of multiple drug‐resistant strains. Thus, the development of alternative agents for the prevention and treatment of Serratia infection is urgently needed. Resveratrol (RSV) is a compound with diverse biological effects including anti‐cancer, anti‐inflammation, anti‐diabetes, and cancer chemoprevention. Whether RSV has in vivo prophylactic or therapeutic potential against infection remains uncharacterized. In the present study, we used a murine acute pneumonia model initiated by intratracheal application of S. marcescens to evaluate whether RSV possesses anti‐infection properties. We showed that pretreatment with RSV for 3 days markedly increased alveolar macrophage infiltration, elevated NK cell activity, and decreased bacterial burden in the infected lung with a subsequent decrease in mortality. These effects were associated with significantly less‐severe inflammatory phenotypes in lung tissue and bronchoalveolar lavage fluid, including reduced neutrophil infiltration of the lungs, reduced phagocytosis activity, and reduced secretion of cytokines such as TNF‐α, IL‐1β, and IL‐6. To further characterize the underlying mechanism responsible for these effects of RSV, LPS derived from S. marcescens was used to induce acute pneumonia in rats, with or without RSV pretreatment. RSV was shown to ameliorate acute pneumonia via inhibition of the NF‐κB signaling pathway, including inhibition of IκBα phosphorylation and subsequent NF‐κB activation. These findings suggest that RSV might be beneficial as a prophylactic treatment in patients at risk of an episode of S. marcescens‐induced acute pneumonia.
Tracking hierarchical structure of web video groups based on salient keyword matching including semantic broadness estimation

This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously. Specifically, the proposed method first extracts the hierarchical structure of Web video groups and salient keywords of them on the basis of an improved scheme of our previously reported method. Moreover, to calculate similarities between Web video groups obtained in different time stamps, salient keyword matching is newly developed by considering both co-occurrences of the salient keywords and semantic broadness of each Web video group. Consequently, tracking of the hierarchical structure over time becomes feasible to easily understand popularity trends of many Web videos for realizing effective retrieval.
Accelerating MapReduce with Distributed Memory Cache

MapReduce is a partition-based parallel programming model and framework enabling easy development of scalable parallel programs on clusters of commodity machines. In order to make time-intensive applications benefit from MapReduce on small scale clusters, this paper proposes a new method to improve the performance of MapReduce by using distributed memory cache as a high speed access between map tasks and reduce tasks. Map outputs sent to the distributed memory cache can be gotten by reduce tasks as soon as possible. Experiment results show that our prototype’s performance is much better than that of the original on small scale clusters. To our knowledge, this is the first effort to accelerate MapReduce with the help of distributed memory cache.
Stochastic Computations in Cortical Microcircuit Models

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
Personalised phone placement recognition in daily life using RFID tagging

We investigate the recognition of phone placement in the vicinity of their user using different pattern classification strategies and phone-integrated sensors. The novel approach in our work is to use RFID tags for annotating phone placement. We use the NFC function of smartphones to continuously read tags during training data acquisition at different phone sites. We show that the RFID-based annotation approach requires minimal effort from users to acquire annotated data, enabling users to personalise phone placement recognition. In an evaluation study with 39 hours of phone placement recordings from six participants and four frequently used phone sites, we compare the annotation accuracy of our RFID-based approach to expert-verified annotations. Our results show that RFID-based annotations perform approx. 2 % below the expert-verified variant. Personalised classification models outperform models trained from all participants, suggesting that our RFID-based approach is viable for a personalised phone placement recognition. Sub-sequently, we analyse the amount of training data, sensors, and features required to achieve an average recognition rate of 80 %.
Reaching Agreement on Processor Group Membership in Synchronous Distributed Systems Key Words: Communication Network { Distributed System { Failure Detection { Fault Tolerance { Real Time System { Replicated Data

Reaching agreement on the identity of correctly functioning processors of a distributed system in the presence of random communication delays, failures and processor joins is a fundamental problem in fault-tolerant distributed systems. Assuming a synchronous communication network that is not subject to partition occurrences, we specify the processor-group membership problem and we propose three simple protocols for solving it. The protocols provide all correct processors with consistent views of the processor-group membership and guarantee bounded processor failure detection and join delays.
A Database for Person Re-Identification in Multi-Camera Surveillance Networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.
Punchscan: Introduction and System Definition of a High-Integrity Election System

Punchscan is a unique hybrid paper/electronic voting system concept. As a receipt-based system, Punchscan provides high voter privacy and election integrity, yet it does not rely on the complex and fragile electronic voting machines found in many current implementations. In this paper, we define the Punchscan system and voting protocol, including the people, objects and events involved and the ways they interact. We also trace the flow of data throughout the election process. This definition will aid those implementing the Punchscan system, but also lays a foundation for critical analysis and discussion within the voting research community.
Edge Detection for Hardwood Seedlings Leaves Based on Intuitionistic Fuzzy Set

The task of reliably segmenting leaves is significant for plants recognition and reconstruction. Leaves image segmentation, registration and identification are based on edge detection. In this paper, a novel method to detect hardwood leaves edges is proposed, which clusters, thresholds, and then detects edges of hardwood seedlings leaves using intuitionistic fuzzy set (IFS) theory. Clustering segments image into several clusters and histogram threshold eliminates unwanted clusters that are not related to leaves region. Finally, image edge is detected, where a clear boundary is obtained. Proposed method performs better than classical edge detection methods. Experiments for kinds of hardwood seedlings are carried out and the results indicate that the proposed method is effectiveness to detect the leaves edges.
Probabilistic models of indexing and searching

for example, Robertson and Sparck Jones, 1976; van Rijsbergen, 1977; Harper and van Rijsbergen, 1978), and the work done in the USA on automatic indexing using within-document frequencies of terms (notably by Bookstein and Swanson, 1974, 1975; Harter, 1975a, b; Bookstein and Kraft, 1977). (There is a considerable body of related work by Salton, Yu and associates
C2D Spitzer-IRS spectra of disks around T Tauri stars. II. PAH emission features

Aims. We search for Polycyclic Aromatic Hydrocarbon (PAH) features towards young low-mass (T Tauri) stars and compare them with surveys of intermediate mass (Herbig Ae/Be) stars. The presence and strength of the PAH features are interpreted with disk radiative transfer models exploring the PAH feature dependence on the incident UV radiation, PAH abundance and disk parameters. Methods. Spitzer Space Telescope 5-35 mu m spectra of 54 pre-main sequence stars with disks were obtained, consisting of 38 T Tauri, 7 Herbig Ae/Be and 9 stars with unknown spectral type. Results. Compact PAH emission is detected towards at least 8 sources of which 5 are Herbig Ae/Be stars. The 11.2 mu m PAH feature is detected in all of these sources, as is the 6.2 mu m PAH feature for the 4 sources for which short wavelength data are available. However, the 7.7 and 8.6 mu m features appear strongly in only 1 of these 4 sources. Based on the 11.2 mu m feature, PAH emission is observed towards at least 3 T Tauri stars, with 14 tentative detections, resulting in a lower limit to the PAH detection rate of 8%. The lowest mass source with PAH emission in our sample is T Cha with a spectral type G8. All 4 sources in our sample with evidence for dust holes in their inner disk show PAH emission, increasing the feature/continuum ratio. Typical 11.2 mu m line intensities are an order of magnitude lower than those observed for the more massive Herbig Ae/Be stars. Measured line fluxes indicate PAH abundances that are factors of 10-100 lower than standard interstellar values. Conversely, PAH features from disks exposed to stars with T(eff)
Image Parsing via Stochastic Scene Grammar

This paper proposes a parsing algorithm for scene understanding which includes four aspects: computing 3D scene layout, detecting 3D objects (e.g. furniture), detecting 2D faces (windows, doors etc.), and segmenting background. In contrast to previous scene labeling work that applied discriminative classifiers to pixels (or super-pixels), we use a generative Stochastic Scene Grammar (SSG). This grammar represents the compositional structures of visual entities from scene categories, 3D foreground/background, 2D faces, to 1D lines. The grammar includes three types of production rules and two types of contextual relations. Production rules: (i) AND rules represent the decomposition of an entity into sub-parts; (ii) OR rules represent the switching among sub-types of an entity; (iii) SET rules represent an ensemble of visual entities. Contextual relations: (i) Cooperative “+” relations represent positive links between binding entities, such as hinged faces of a object or aligned boxes; (ii) Competitive “-” relations represents negative links between competing entities, such as mutually exclusive boxes. We design an efficient MCMC inference algorithm, namely Hierarchical cluster sampling, to search in the large solution space of scene configurations. The algorithm has two stages: (i) Clustering: It forms all possible higher-level structures (clusters) from lower-level entities by production rules and contextual relations. (ii) Sampling: It jumps between alternative structures (clusters) in each layer of the hierarchy to find the most probable configuration (represented by a parse tree). In our experiment, we demonstrate the superiority of our algorithm over existing methods on public dataset. In addition, our approach achieves richer structures in the parse tree.
The Effect of Mycotoxin Deoxynivalenol on Haematological and Biochemical Indicators and Histopathological Changes in Rainbow Trout (Oncorhynchus mykiss)

Deoxynivalenol (DON), produced by the Fusarium genus, is a major contaminant of cereal grains used in the production of fish feed. The effect of mycotoxin deoxynivalenol on rainbow trout (Oncorhynchus mykiss) was studied using a commercial feed with the addition of DON in a dose of 2 mg/kg feed. The fish (n = 40) were exposed to the mycotoxin for 23 days. The trout were divided into two groups, control and experimental groups. Control groups were fed a commercial feed naturally contaminated with a low concentration of DON (225 μg/kg feed); experimental groups were fed a commercial feed with the addition of DON (1964 μg/kg feed). Plasma biochemical and haematological indices, biometric parameters, and histopathological changes were assessed at the end of the experiment. The experimental groups showed significantly lower values in MCH (P < 0.05). In biochemical indices, after 23-day exposure, a significant decrease in glucose, cholesterol (P < 0.05), and ammonia (P < 0.01) was recorded in the experimental group compared to the control group. Our assessment showed no significant changes in biometric parameters. The histopathological examination revealed disorders in the caudal kidney of the exposed fish. The obtained data show the sensitivity of rainbow trout (O. mykiss) to deoxynivalenol.
Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction.
Decreased Expression of BNC1 and BNC2 Is Associated with Genetic or Epigenetic Regulation in Hepatocellular Carcinoma

The aberrant expression of transcription factor Basonuclin (BNC) had been reported in different kinds of tumors. Here, we investigated the expression and methylation status of two Basonuclin homologs, BNC1 and BNC2 in hepatocellular carcinoma (HCC). We found that the expression levels of both BNC1 and BNC2 were down-regulated in HCC cell lines and primary HCC tissues. The frequency and intensity of BNC1 promoter hypermethylation in tumor tissues was significantly higher than that in adjacent non-tumor tissues. 5-Aza-2’-Deoxycytidine treatment could significantly increase the BNC1 expression in the methylated HCC cell lines, which implicated that epigenetic modification contributed to the down-regulation of BNC1. In addition, BNC1 hypermethylation in tumor tissues was more likely to happen in female patients. No methylation of the BNC2 promoter was found in HCC tumor tissues. However, a frequent deletion of the BNC2 gene was observed, which indicated that the chromosomal loss of the BNC2 gene might be one important reason for its lower expression level in HCC. Our results suggested that BNC1 and BNC2 were down-regulated in HCC which may provide new insight into the tumorigenesis of HCC.
Effect of downregulation of germline transcripts on immunoglobulin A isotype differentiation

In this study we determined the role of immunoglobulin (Ig) germline transcripts in the isotype switch differentiation of the cloned lymphoma B cell line CH12.LX. In initial studies, we showed that addition of transforming growth factor beta (TGF-beta) and interleukin 4 (IL-4), either alone or in combination, augment switching from membrane (m)IgM+ to mIgA+ cells, and that increased switching is preceded and paralleled by an increase in the steady-state level of alpha germline transcripts (alpha GLT). Interestingly, TGF-beta and IL- 4 affect switching in different ways, as shown by the fact that IL-4 increases and TGF-beta decreases the number of dual-positive (mIgM+/mIgA+) cells; in addition, TGF-beta and IL-4 have different effects on the time course of induction of alpha GLT. In subsequent studies, we established that we could downregulate alpha GLT levels in CH12.LX B cells by transfecting an expression vector that can be induced to produce transcripts antisense to the I alpha exon. Using this approach we downregulated alpha GLT in CH12.LX B cells undergoing switching in the presence of TGF-beta and IL-4 and showed that such downregulation led to decreased switching, as evidenced by decreased appearance of dual-positive B cells as well as decreased IgA synthesis relative to IgM synthesis. This result was corroborated by the fact that incubation of CH12.LX cells with phosphorothio-oligo antisense DNA to I alpha sequence also led to a decrease in the number of dual- positive cells and in the IgA/IgM secretion ratio. In summary, IgA isotype differentiation in CH12.LX B cell, particularly the steps necessary for the elaboration of mIgM+/mIgA+ switch intermediate cells, is inhibited by downregulation of alpha GLT; it is therefore apparent that alpha GLT plays a key role in the initial stage of isotype switch differentiation.
“Is it his Language?” A Neuroeducation Approach to Exploring the Connection Between Levels of Language Function and Prosocial Concepts for Elementary Students Identified with Emotional and Behavioral Disorders

Preparing students to acquire prosocial behaviors is of growing concern for educators. Although a connection between language structures and students struggling to acquire prosocial behaviors has been established, students identified with emotional and behavioral disorders (EBD) are not being consistently identified with language impairment (LI). Viewing language differently, the acquisition of language functions is theorized to play a role in the attainment of prosocial concepts resulting in prosocial behaviors. Currently, limited research exists that explores the connection between language functions and students struggling to acquire prosocial behaviors. The purpose of this study was to: (a) triangulate literature in the areas of cognitive psychology, neuroscience, and language to support a theoretical framework in neuroeducation to address the acquisition of prosocial behaviors; and, (b) apply this framework by exploring the connection between levels of language function and the acquisition of the underlying prosocial concepts through language function sampling analyses with elementary students identified with EBD and/or LI. Four methods of language sampling were used to address two main research questions: (a) What similarities and differences in language function levels and characteristics exist, if any, among varying educational levels of students with EBD, LI, and/or both? (b) Will students identified with EBD, LI and/or both make prosocial or antisocial relationships among the agents, their actions, and the context? This study of language sampling included nine participants identified with EBD and two identified with LI. Language samples were analyzed through deductive content analysis based off predefined codes from existing literature in language function. Key findings include: (a) Commensurate deficits of
Exact isotropic cosmologies with local fractal number counts

We construct an exact relativistic cosmology in which an inhomogeneous but isotropic local region has fractal number counts and matches to a homogeneous background at a scale of the order of . We show that Einstein's equations and the matching conditions imply either a nonlinear Hubble law or a very low large-scale density.
Performance analysis of cooperative ARQ systems for wireless industrial networks

The proliferation of wireless communications has lead to a high interest to establish this technology in industrial settings. The main arguments in favor of wireless are reduced costs in deployment and maintenance, as well as increased flexibility. In contrast to home and office environments, industrial settings include mission-critical machine-to-machine applications, demanding stringent requirements for reliability and latency in the area of 1-10-9 PDR and 1ms, respectively. One way to achieve both is cooperative Automatic Repeat reQuest (ARQ), which leverages spatial diversity. This paper presents a wireless multi-user Time Division Multiple Access system with cooperative ARQ for mission-critical communication. We evaluate two design options analytically, using an outage-capacity model, to investigate whether the relaying of messages should be performed centrally at a multi-antenna AP with perfect Channel State Information (CSI) or decentrally at simultaneously transmitting stations with average CSI. Results indicate that both options are able to achieve the targeted communication guarantees when a certain degree of diversity is implemented, showing a stable system performance even with an increasing number of stations.