Miracle-dz/UniFAR-FTUs huggingface dataset:
The Salmonella Deubiquitinase SseL Inhibits Selective Autophagy of Cytosolic Aggregates

Cell stress and infection promote the formation of ubiquitinated aggregates in both non-immune and immune cells. These structures are recognised by the autophagy receptor p62/sequestosome 1 and are substrates for selective autophagy. The intracellular growth of Salmonella enterica occurs in a membranous compartment, the Salmonella-containing vacuole (SCV), and is dependent on effectors translocated to the host cytoplasm by the Salmonella pathogenicity island-2 (SPI-2) encoded type III secretion system (T3SS). Here, we show that bacterial replication is accompanied by the formation of ubiquitinated structures in infected cells. Analysis of bacterial strains carrying mutations in genes encoding SPI-2 T3SS effectors revealed that in epithelial cells, formation of these ubiquitinated structures is dependent on SPI-2 T3SS effector translocation, but is counteracted by the SPI-2 T3SS deubiquitinase SseL. In macrophages, both SPI-2 T3SS-dependent aggregates and aggresome-like induced structures (ALIS) are deubiquitinated by SseL. In the absence of SseL activity, ubiquitinated structures are recognized by the autophagy receptor p62, which recruits LC3 and targets them for autophagic degradation. We found that SseL activity lowers autophagic flux and favours intracellular Salmonella replication. Our data therefore show that there is a host selective autophagy response to intracellular Salmonella infection, which is counteracted by the deubiquitinase SseL.
Measuring the Impact of a Community Extension Program in the Philippines

Purpose – This study was designed to create platforms to train students in higher education to be caring and responsible citizens. This was an extension mission that aimed to tap into the expertise of academe and its knowledge to help the community.Based on the framework of academic of community empowerment, the researchers measured the long-term impact of a 44-month community extension program in the Philippines.The extension program described in this study was implemented between March 2009 and December 2015 to address the capacity-building needs of a low-income community.This paper highlights some key development activities which includes partnership with local government, training needs assessment through grassroots-level participation and design of practical education-training programs. Methodology – The study followed a descriptive research design. The Community Outcome Scale (COS) was developed to measure perceived knowledge, attitude, and lifestyle of the beneficiaries. Fifty-four community residents were purposively selected based on their attendance to the community-based education and training programs. The ANOVA method with post hoc analysis was employed to determine the differences between perceived knowledge, attitude, and lifestyle among residents, stratified according to the degree of completion of the training programs.Findings – There was a moderate improvement in community knowledge, attitude, and lifestyle based on the survey which was performed one and a half years after the completion of the extension programs. Based on the results, improved long-term outcomes were noted only amongst the regular and occasional participants.Significance – There is an increasing appreciation of the impact of higher education extension in the teaching and learning process as students apply their disciplinary knowledge to help address real-world problems.This work may be of interest to higher education institutions (HEIs) which are designing community extension programs with optimized societal outcomes.
Future networks and technologies supporting Innovative communications

Within a fully interconnected world, the distinct relationship between end users, consumers and providers rapidly changes towards a scenario of collaboration and competition of multiple parties within one system. `Convergence', `ubiquitous' and `smart' are key words describing future networks and applications. This paper focuses on the technological aspects of ubiquitous networking and communication technologies, including challenges related to green communications, and security, privacy and trust. The paper proposes a novel concept for a Wireless Innovative System for Dynamically Operating Mega-communications (WISDOM) that combines the aspects of personal- and cognitive radio- networks to let seamlessly bridge the virtual and physical worlds offering a constant level of all-senses, context-based, rich communication experience over fixed and wireless networks for the end users while realizing a new generation of ubiquitous communications with a speed of more than lTbps.
Parallel Computing Based Bitcoin Currency System Analysis Approach

With the rise and extensive usage of Bit coin, a peer-to-peer electronic cash system beginning at 2008, the number of transactions is growing. In order to analyze the activity in this currency system, we present a parallel analysis approach for meeting the need of building the transaction graph of this financial system. In order to test the performance and the realistic possibility of our approach, we implemented our approach and conducted some comparing to test the performance of our system. Through the experiment, we confirmed that this method is highly efficient and reliable compared with the traditional method.
Texture Measures for Segmentation

Texture is an important visual cue in both human and computer vision. Segmenting images into regions of constant texture is used in many applications. This work surveys a wide range of texture descriptors and segmentation methods to determine the state of the art in texture segmentation. Two types of texture descriptors are investigated: filter bank based methods and local descriptors. Filter banks deconstruct an image into several bands, each of which emphasises areas of the image with different properties. Textons are an adaptive histogram method which describes the distribution of typical feature vectors. Local descriptors calculate features from smaller neighbourhoods than filter banks. Some local descriptors calculate a scale for their local neighbourhood to achieve scale invariance. Both local and global segmentation methods are investigated. Local segmentation methods consider each pixel in isolation. Global segmentation methods penalise jagged borders or fragmented regions in the segmentation. Pixel labelling and border detection methods are investigated. Methods for measuring the accuracy of segmentation are discussed. Two data sets are used to test the texture segmentation algorithms. The Brodatz Album mosaics are composed of grayscale texture images from the Brodatz Album. The Berkeley Natural Images data set has 300 colour images of natural scenes. The tests show that, of the descriptors tested, filter bank based textons are the best texture descriptors for grayscale images. Local image patch textons are best for colour images. Graph cut segmentation is best for pixel labelling problems and edge detection with regular borders. Non-maxima suppression is best for edge detection with irregular borders. Factors affecting the performance of the algorithms are investigated.
Timing is important: delaying action execution in Plastic Neural Networks

Plastic Neural Networks (PNNs) are known for their ability to adapt to environmental changes. It is generally believed that PNNs cannot solve timing tasks which require a predefined delay before execution of an action. In this study we investigate the ability of PNNs to solve timing tasks. Our experiments evolve PNNs to perform successfully on a task requiring the delayed execution of an action. The results of our experiments show that PNNs are capable of solving the timing task. We analyse the underlying mechanism and find it is based on slow neural activation dynamics. The mechanism is discussed in relation to mechanisms found in other neural models. We conclude that any neural model that can accommodate slow activation dynamics can solve the timing task.
Emotional labor directed at leaders: the differential effects of surface and deep acting on LMX

Abstract We extend leader–member exchange (LMX) theory by examining subordinate emotional labor directed at leaders as a means of influencing LMX. Specifically, we propose that surface and deep acting have differential effects on supervisor support, which, in turn, influences subordinate perceptions of LMX. We develop a model in which supervisor support mediates the opposing indirect effects of surface and deep acting on LMX. We test this model with data collected in two waves from a sample of 590 employees and find empirical support for our hypotheses. Specifically, a series of hierarchical linear modeling analyses was conducted with Monte Carlo simulation. The test results showed that subordinate surface acting directed at supervisors has a detrimental indirect effect on subordinate perceptions of LMX, whereas deep acting directed at supervisors has the opposite (beneficial) effect. These findings indicate that subordinates can improve their relationships with their supervisors by engaging in deep acting; however, surface acting harms this relationship.
Sparse representation via multi-feature based Fisher Discrimination Dictionary Learning

In this paper, we propose a multi-feature based sparse representation method named multi-feature Fisher Discrimination Dictionary Learning (MFDDL) and apply it to face recognition. In the new proposed method, firstly, to extract the texture information, multi-scales and multi-orientations Gabor Wavelet Transform is proposed for feature representation. Then the local characteristics of the face Gabor feature is future enhanced by multi-block rotation invariant LBP, which extracts statistically-significant histogram feature and meanwhile, reduces the dimension of the extracted Gabor features. Finally, the Fisher Discrimination Dictionary Learning is utilized to achieve face recognition. Experimental results on the AR face database show that the proposed method can effectively overcome the effect of light variation and occlusion, and can improve the face image recognition performance.
Test prioritization using system models

During regression testing, a modified system is retested using the existing test suite. Because the size of the test suite may be very large, testers are interested in detecting faults in the system as early as possible during the retesting process. Test prioritization tries to order test cases for execution so the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the code of the system. System modeling is a widely used technique to model state-based systems. In this paper, we present methods of test prioritization based on state-based models after changes to the model and the system. The model is executed for the test suite and information about model execution is used to prioritize tests. Execution of the model is inexpensive as compared to execution of the system; therefore the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared to the existing evaluation framework that is based on experimentation (observation). We have performed an experimental study in which we compared different test prioritization methods. The results of the experimental study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection.
Improving Comprehensibility of Source Code via Traceability Information: a Controlled Experiment

The presence of traceability links between software artefacts is very important to achieve high comprehensibility and maintainability. This is confirmed by several researches and tools aiming at support traceability link maintenance and recovery. We propose to use traceability information combined with Information Retrieval techniques within an Eclipse plug-in to show the software engineer the similarity between source code components being developed and the high level artefacts they should be traced on. Such a similarity suggests actions aiming at improving the correct usage of identifiers and comments in source code and, as a consequence, the traceability and the comprehensibility level. The approach and tool have been assessed with a controlled experiment performed with master students
Why Dempster ’ s F usion R ule is not a G eneralization of Bayes F usion R ule

In this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster’s rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point out some of its properties. A deep analysis of the compatibility of Dempster’s fusion rule with Bayes fusion rule is done. We show that Dempster’s rule is compatible with Bayes fusion rule only in the very particular case where the basic belief assignments (bba’s) to combine are Bayesian, and when the prior information is modeled either by a uniform probability measure, or by a vacuous bba. We show clearly that Dempster’s rule becomes incompatible with Bayes rule in the more general case where the prior is truly informative (not uniform, nor vacuous). Consequently, this paper proves that Dempster’s rule is not a generalization of Bayes fusion rule. Keywords—Information fusion, Probability theory, Bayes fusion rule, Dempster’s fusion rule.
The boundary S-matrix and the AdS to CFT dictionary

An S-matrix analog is deﬁned for anti-de Sitter space by constructing “in” and “out” states that asymptote to the timelike boundary. A derivation parallel to that of the LSZ formula shows that this “boundary S-matrix” is given directly by correlation functions in the boundary conformal theory. This provides a key entry in the AdS to CFT dictionary.
Find Answers from Web Search Results

In a variety of domains, the amount of information grows rapidly; new sources and types of information are proliferating. In recent years, the world-wide web information has been growing at a dramatic pace and might have become the most important information source for most people. In daily life, when people encounter some problems, they tend to retrieve the information from the Web search engines. Many business search engines are efficient at identifying the best web sites for any given keyword query. Unfortunately, the information on the web is not always correct. Moreover, different web sites often provide different information on a subject. One headache problem we have to face is how to distinguish between what is true and what is wrong. In this paper, we propose a computational method to find answers from web search results. Intuitively, if a value is provided by many accurate web sites, it is very likely that the value is correct. We adopt an iterative method to compute the accuracy of web sites and the confidence of values. Finally we find the most likely correct answers and return to the users for further choice.
An Integrated Linear Programming Receiver for LDPC Coded MIMO-OFDM Signals

This work investigates the joint detection and decoding of MIMO-OFDM signals. Traditional receivers either utilize disjoint serial detector and decoder or require turbo message passing between the two functional blocks of detection and decoding. We present a novel approach that can jointly achieve detection and decoding of low density parity check (LDPC) coded multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) signals as a unified optimization algorithm. Our receiver integrates the MIMO-OFDM signal detection and the decoding of LDPC coded data by formulating a linear programming problem regardless of affine or nonaffine quadrature amplitude modulation (QAM) mapping. The proposed joint MIMO-OFDM detector and decoder achieves substantial performance gain over existing joint detection receivers of comparable computational complexity. Furthermore, the proposed receiver also substantially outperforms the more traditional turbo receiver with only modest cost in complexity.
Blind unmixing of remote sensing data with some pure pixels: Extension and comparison of spatial methods exploiting sparsity and nonnegativity properties

Multispectral and hyperspectral imaging systems are among the most powerful tools in the field of remote sensing. In remote sensing imagery, pixel values are often linear mixtures of contributions from pure materials contained in the observed scene. In this paper, we extend our recently developed spatial methods for blindly unmixing each pixel of remote sensing data with some pure pixels and we compare their performance, both for multispectral and hyperspectral images. These extended methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA) and nonnegativity constraints. Spatial correlation-based or variance-based SCA algorithms (which detect a few pure-pixel zones) are firstly used to identify the mixing matrix by means of two different approaches for selecting the columns of this matrix. Nonnegative least squares (NLS) or nonnegative matrix factorization (NMF) methods are then used to extract spatial sources. Experiments based on realistic synthetic data are performed to compare the accuracies and the computational costs of these extended methods. We show that the tested methods yield high accuracy with low computational cost for the variance-based methods as compared to those based on correlation.
Integrated analysis of DNA methylation profiles and gene expression profiles to identify genes associated with pilocytic astrocytomas

The present study performed an integral analysis of the gene expression and DNA methylation profile of pilocytic astrocytomas (PAs). Weighted gene co-expression network analysis (WGCNA) was also performed to examine and identify the genes correlated to PAs, to identify candidate therapeutic targets for the treatment of PAs. The DNA methylation profile and gene expression profile were downloaded from the Gene Expression Omnibus database. Following screening of the differentially expressed genes (DEGs) and differentially methylated regions (DMRs), respectively, integrated analysis of the DEGs and DMRs was performed to detect their correlation. Subsequently, the WGCNA algorithm was applied to identify the significant modules and construct the co-expression network associated with PAs. Furthermore, Gene Ontology enrichment analysis of the associated genes was performed using the Database for Annotation, Visualization and Integrated Discovery. A total number of 2,259 DEGs and 235 DMRs were screened out. Integrated analysis revealed that 30 DEGs were DMRs with prominent negative correlation (cor=−0.82; P=0.02). Based on the DEGs, the gene co-expression network was constructed, and nine network modules associated with PAs were identified. The functional analysis results showed that genes relevant to PAs were closely associated with cell differentiation modulation. The screened PA-associated genes were significantly different at the expression and methylation levels. These genes may be used as reliable candidate target genes for the treatment of PAs.
Suppression of Histone H1 Genes in Arabidopsis Results in Heritable Developmental Defects and Stochastic Changes in DNA Methylation

Histone H1 is an abundant component of eukaryotic chromatin that is thought to stabilize higher-order chromatin structures. However, the complete knock-out of H1 genes in several lower eukaryotes has no discernible effect on their appearance or viability. In higher eukaryotes, the presence of many mutually compensating isoforms of this protein has made assessment of the global function of H1 more difficult. We have used double-stranded RNA (dsRNA) silencing to suppress all the H1 genes of Arabidopsis thaliana. Plants with a >90% reduction in H1 expression exhibited a spectrum of aberrant developmental phenotypes, some of them resembling those observed in DNA hypomethylation mutants. In subsequent generations these defects segregated independently of the anti-H1 dsRNA construct. Downregulation of H1 genes did not cause substantial genome-wide DNA hypo- or hypermethylation. However, it was correlated with minor but statistically significant changes in the methylation patterns of repetitive and single-copy sequences, occurring in a stochastic manner. These findings reveal an important and previously unrecognized link between linker histones and specific patterns of DNA methylation.
Providing Privacy Protection in Personalized Web Search and Efficient Browsing

Personalized web search (PWS) has illustrated its effectiveness by improving the quality of search services on the Internet. But, evidence shows that users' hesitation to disclose their private information during search has become a major barrier for PWS. Privacy protection in PWS applications can be adopted that model user preferences as hierarchical user profiles by studying a PWS framework called UPS that adaptively generalizes profiles by queries while keeping in mind user-specified privacy requirements. The method of query clustering that uses historical preferences of the user. This helps to achieve relevance criterion. Also, analysing the user's decision process on a given query with implicit feedback.
Inhibition of Parainfluenza Virus Type 3 and Newcastle Disease Virus Hemagglutinin-Neuraminidase Receptor Binding: Effect of Receptor Avidity and Steric Hindrance at the Inhibitor Binding Sites

ABSTRACT Zanamivir (4-guanidino-Neu5Ac2en [4-GU-DANA]) inhibits not only the neuraminidase activity but also the receptor interaction of the human parainfluenza virus type 3 (HPIV3) hemagglutinin-neuraminidase (HN), blocking receptor binding and subsequent fusion promotion. All activities of the HPIV3 variant ZM1 HN (T193I/I567V) are less sensitive to 4-GU-DANA's effects. The T193I mutation in HN confers both increased receptor binding and increased neuraminidase activity, as well as reduced sensitivities of both activities to 4-GU-DANA inhibition, consistent with a single site on the HN molecule carrying out both catalysis and binding. We now provide evidence that the HPIV3 variant's resistance to receptor-binding inhibition by 4-GU-DANA is related to a reduced affinity of the HN receptor-binding site for this compound as well as to an increase in the avidity of HN for the receptor. Newcastle disease virus (NDV) HN and HPIV3 HN respond differently to inhibition in ways that suggest a fundamental distinction between them. NDV HN-receptor binding is less sensitive than HPIV3 HN-receptor binding to 4-GU-DANA, while its neuraminidase activity is highly sensitive. Both HPIV3 and NDV HNs are sensitive to receptor-binding inhibition by the smaller molecule DANA. However, for NDV HN, some receptor binding cannot be inhibited. These data are consistent with the presence in NDV HN of a second receptor-binding site that is devoid of enzyme activity and has a negligible, if any, affinity for 4-GU-DANA. Avidity for the receptor contributes to resistance by allowing the receptor to compete effectively with inhibitors for interaction with HN, while the further determinant of resistance is the reduced binding of the inhibitor molecule to the binding pocket on HN. Based upon our data and recent three-dimensional structural information on the HPIV3 and NDV HNs, we propose mechanisms for the observed sensitivity and resistance of HN to receptor-binding inhibition and discuss the implications of these mechanisms for the distribution of HN functions.