Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA binding protein Caprin-2 is a pivotal regulator of the central osmotic defense response

In response to an osmotic challenge, the synthesis of the antidiuretic hormone arginine vasopressin (AVP) increases in the hypothalamus, and this is accompanied by extension of the 3′ poly(A) tail of the AVP mRNA, and the up-regulation of the expression of RNA binding protein Caprin-2. Here we show that Caprin-2 binds to AVP mRNAs, and that lentiviral mediated shRNA knockdown of Caprin-2 in the osmotically stimulated hypothalamus shortens the AVP mRNA poly(A) tail at the same time as reducing transcript abundance. In a recapitulated in vitro system, we confirm that Caprin-2 over-expression enhances AVP mRNA abundance and poly(A) tail length. Importantly, we show that Caprin-2 knockdown in the hypothalamus decreases urine output and fluid intake, and increases urine osmolality, urine sodium concentration, and plasma AVP levels. Thus Caprin-2 controls physiological mechanisms that are essential for the body's response to osmotic stress. DOI: http://dx.doi. org/10.7554/eLife.09656. 001
Improving Resource Efficiency and Minimize Environmental Footprint ¿ a case study preliminary results

This work of the project Remine was funded with public national funds from FCT under the programme for International Cooperation ERA-NET, supported by ERA-MIN (2011-2015) funded under the EU 7th Framework Programme FP7-NMP having also for partners the Technical University of Lulea (Sweden) and the National Institute for Metals and Radioactive Resources (INCDMRR), Romania.
Aspect-based Opinion Mining: A Survey

Opinion mining has been an emerging research field in Computational Linguistics, Text Analysis and Natural Language Processing (NLP) in recent years. It is the computational study of people‟s opinions towards entities and their aspects. Entities usually refer to individuals, events, topics, products and organizations. Aspects are attributes or components of entities. In the last few years, social media has become an excellent source to express and share people‟s opinion on entities and their aspects. With the availability of vast opinionated web contents in the form of comments, reviews, blogs, tweets, status updates, etc. it is harder for people to analyze all opinions at a time to make good decisions. So, there is a need for effective automated systems to evaluate opinions and generate accurate results. Sentiment Analysis, Emotion Analysis, Subjectivity Detection has also become an active research area in recent years along with opinion mining. This article presents a brief overview of opinion mining and its classifications and specifically focuses on the sub topic aspect-based opinion mining, its approaches, metrics used for evaluation and latest research challenges. General Terms Opinion Mining, Text Mining.
Reduced Mobility of the Alternate Splicing Factor (Asf) through the Nucleoplasm and Steady State Speckle Compartments

Compartmentalization of the nucleus is now recognized as an important level of regulation influencing specific nuclear processes. The mechanism of factor organization and the movement of factors in nuclear space have not been fully determined. Splicing factors, for example, have been shown to move in a directed manner as large intact structures from sites of concentration to sites of active transcription, but splicing factors are also thought to exist in a freely diffusible state. In this study, we examined the movement of a splicing factor, ASF, green fluorescent fusion protein (ASF–GFP) using time-lapse microscopy and the technique fluorescence recovery after photobleaching (FRAP). We find that ASF–GFP moves at rates up to 100 times slower than free diffusion when it is associated with speckles and, surprisingly, also when it is dispersed in the nucleoplasm. The mobility of ASF is consistent with frequent but transient interactions with relatively immobile nuclear binding sites. This mobility is slightly increased in the presence of an RNA polymerase II transcription inhibitor and the ASF molecules further enrich in speckles. We propose that the nonrandom organization of splicing factors reflects spatial differences in the concentration of relatively immobile binding sites.
MULTI OBJECTIVE TEST CASE MINIMIZATION COLLABORATED WITH CLUSTERING AND MINIMAL HITTING SET

Software testing aspires to explore and validate the attribute and potentiality of a program to authenticate and cross-verify the requisite results obtained. The broader bifurcation of testing is Precision Testing and Reliability Testing. Regression testing is part of reliability testing as it testifies the changes or modifications pursued to the software have not curtailed the functionality of the software by introducing any bugs. It is a kind of quality assurance to the modifications carried out. The pivotal role of regression testing is comprehended whenever modification towards development of software takes place. Reexecution of large test suites is perhaps an enigma many a times due to the paucity of resources. Here arises the need for a novel technique to minimize the test suite in order to remove the redundant test cases. With this focus to provide an innovate and time-effective strategy to remove the redundant test cases, this paper presents a multi-objective test suite minimization by considering maximum statement coverage and minimum execution time. This article also concentrates on incorporating a multi objective minimization technique using clustering approach and minimal hitting set. Here, the identification of appropriate clusters is achieved, through the weighted distance function for mixed variable type and the minimal hitting set is obtained using HS_DAG (Hitting Set Directed Acyclic Graph) algorithm. The results of this experiment exhibit that the algorithm proposed works with adequate efficacy in minimizing the test cases.
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called AC-BLSTM for modeling sentences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results demonstrate that our model achieves state-of-the-art results on five tasks, including sentiment analysis, question type classification, and subjectivity classification. In order to further improve the performance of AC-BLSTM, we propose a semi-supervised learning framework called G-AC-BLSTM for text classification by combining the generative model with AC-BLSTM.
Program simplification as a means of approximating undecidable propositions

We describe an approach which mixes testing, slicing, transformation and formal verification to investigate speculative hypotheses concerning a program, formulated during program comprehension activity. Our philosophy is that such hypotheses (which are typically undecidable) can, in some sense, be 'answered' by a partly automated system which returns neither 'true' nor 'false' but a program (the 'test program') which computes the answer. The motivation for this philosophy is the way in which, as we demonstrate, static analysis and manipulation technology can be applied to ensure that the resulting test program is significantly simpler than the original program, thereby simplifying the process of investigating the original hypothesis.
Approaches to supporting software visual notation exchange

A wide range of software tools provide software engineers with different views (static and dynamic) of software systems. Much work has focused on software information model exchange. However, most software tools lack support for exchange of information about visualisation notations (both definitions of notations and instances of them). Some basic converters have been developed to support the exchange of notation information between software tools but almost all are custom-built to support specific notations and difficult to maintain. We describe the development of several notation exchange converters for tools supporting software architecture notations. This leads to a unified converter generator framework for notation exchange.
Deregulated expression of HDAC9 in B cells promotes development of lymphoproliferative disease and lymphoma in mice

ABSTRACT Histone deacetylase 9 (HDAC9) is expressed in B cells, and its overexpression has been observed in B-lymphoproliferative disorders, including B-cell non-Hodgkin lymphoma (B-NHL). We examined HDAC9 protein expression and copy number alterations in primary B-NHL samples, identifying high HDAC9 expression among various lymphoma entities and HDAC9 copy number gains in 50% of diffuse large B-cell lymphoma (DLBCL). To study the role of HDAC9 in lymphomagenesis, we generated a genetically engineered mouse (GEM) model that constitutively expressed an HDAC9 transgene throughout B-cell development under the control of the immunoglobulin heavy chain (IgH) enhancer (Eμ). Here, we report that the Eμ-HDAC9 GEM model develops splenic marginal zone lymphoma and lymphoproliferative disease (LPD) with progression towards aggressive DLBCL, with gene expression profiling supporting a germinal center cell origin, as is also seen in human B-NHL tumors. Analysis of Eμ-HDAC9 tumors suggested that HDAC9 might contribute to lymphomagenesis by altering pathways involved in growth and survival, as well as modulating BCL6 activity and p53 tumor suppressor function. Epigenetic modifications play an important role in the germinal center response, and deregulation of the B-cell epigenome as a consequence of mutations and other genomic aberrations are being increasingly recognized as important steps in the pathogenesis of a variety of B-cell lymphomas. A thorough mechanistic understanding of these alterations will inform the use of targeted therapies for these malignancies. These findings strongly suggest a role for HDAC9 in B-NHL and establish a novel GEM model for the study of lymphomagenesis and, potentially, preclinical testing of therapeutic approaches based on histone deacetylase inhibitors. Summary: This study demonstrates that aberrant expression of HDAC9 in B cells promotes development of lymphoproliferative disease and lymphoma through altering expression of genes involved in the cell cycle and survival, and modulating the activity of key B-lineage factors such as BCL6 and p53.
Beaming into the Rat World: Enabling Real-Time Interaction between Rat and Human Each at Their Own Scale

Immersive virtual reality (IVR) typically generates the illusion in participants that they are in the displayed virtual scene where they can experience and interact in events as if they were really happening. Teleoperator (TO) systems place people at a remote physical destination embodied as a robotic device, and where typically participants have the sensation of being at the destination, with the ability to interact with entities there. In this paper, we show how to combine IVR and TO to allow a new class of application. The participant in the IVR is represented in the destination by a physical robot (TO) and simultaneously the remote place and entities within it are represented to the participant in the IVR. Hence, the IVR participant has a normal virtual reality experience, but where his or her actions and behaviour control the remote robot and can therefore have physical consequences. Here, we show how such a system can be deployed to allow a human and a rat to operate together, but the human interacting with the rat on a human scale, and the rat interacting with the human on the rat scale. The human is represented in a rat arena by a small robot that is slaved to the human’s movements, whereas the tracked rat is represented to the human in the virtual reality by a humanoid avatar. We describe the system and also a study that was designed to test whether humans can successfully play a game with the rat. The results show that the system functioned well and that the humans were able to interact with the rat to fulfil the tasks of the game. This system opens up the possibility of new applications in the life sciences involving participant observation of and interaction with animals but at human scale.
A Hop-by-Hop Transport Architecture for Multi-Hop Wireless Networks

TCP can perform poorly in multi-hop wireless networks due to problems that arise with contention and mobi l ty. End-to-end protocols are at an inherent disadvantage in trying to solve these problems because their feedback loop operate s over multiple wireless hops, which makes it difficult to diagnose problems that occur several hops away and hinders their abil ity to adapt to changing network conditions. In this paper, we ex plore a hop-by-hop transport architecture that can solve many of t he problems observed with TCP. By allowing intermediate nodes to actively participate, the protocol can respond more quickly to changing network conditions and can exploit the unique characteristics of wireless networks to reduce overhead. We use a simulation study to show that hop-by-hop transport can achi eve throughput rates that are double those of TCP and ATP when using high-speed wireless links.
How developers use the dynamic features of programming languages: the case of smalltalk

The dynamic and reflective features of programming languages are powerful constructs that programmers often mention as extremely useful. However, the ability to modify a program at runtime can be both a boon-in terms of flexibility-, and a curse-in terms of tool support. For instance, usage of these features hampers the design of type systems, the accuracy of static analysis techniques, or the introduction of optimizations by compilers. In this paper, we perform an empirical study of a large Smalltalk codebase- often regarded as the poster-child in terms of availability of these features-, in order to assess how much these features are actually used in practice, whether some are used more than others, and in which kinds of projects. These results are useful to make informed decisions about which features to consider when designing language extensions or tool support.
Clima Organizacional como antecedente del Engagement en una muestra de empresas colombianas

This study shows a structural equation model with the purpose of identifying the level of prediction of the strategic variables of Organizational Climate (Supervisor Support, Organizational Clarity, Interpersonal Relationships and Availability of Resources) on work Engagement in a sample of 6,301 respondents from 13 Colombian companies, collected between 2014 and 2015. The results of the statistical analysis showed a good fit to the model, all the measured effects were significant. It highlights that the Organizational Clarity predicts Work Engagement at a higher amount compared to the other independent variables of the model. These findings point out the relevance of implementing human management practices to improve the role understanding and its utility at the company, which are related to the internal communication processes. This could favor the healthy and positive affectional bond of the employees to their jobs and their contribution. Received: 23/11/2016 Accepted: 04/12/2016 How to reference this article: Lopez Ospina, J. & Meneghel, I. (2017). Clima Organizacional como antecedente del Engagement en una muestra de empresas colombianas. Modelo de Ecuaciones Estructurales. Revista Interamericana de Psicologia Ocupacional, 35(1), 21-34. Doi: 10.21772/ripo.v35n1a02
Spatio-temporal clustering of probabilistic region trajectories

We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences.
Multiple testing on the directed acyclic graph of gene ontology

MOTIVATION Current methods for multiplicity adjustment do not make use of the graph structure of Gene Ontology (GO) when testing for association of expression profiles of GO terms with a response variable. RESULTS We propose a multiple testing method, called the focus level procedure, that preserves the graph structure of Gene Ontology (GO). The procedure is constructed as a combination of a Closed Testing procedure with Holm's method. It requires a user to choose a 'focus level' in the GO graph, which reflects the level of specificity of terms in which the user is most interested. This choice also determines the level in the GO graph at which the procedure has most power. We prove that the procedure strongly controls the family-wise error rate without any additional assumptions on the joint distribution of the test statistics used. We also present an algorithm to calculate multiplicity-adjusted P-values. Because the focus level procedure preserves the structure of the GO graph, it does not generally preserve the ordering of the raw P-values in the adjusted P-values. AVAILABILITY The focus level procedure has been implemented in the globaltest and GlobalAncova packages, both of which are available on www.bioconductor. org.
An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers’ Adaptations

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations.
Representing classification problems in genetic programming

Five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: (1) binary decomposition, in which the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; (2) static range selection, where the set of real values returned by a genetic program is divided into class boundaries using arbitrarily-chosen division points; (3) dynamic range selection, in which a subset of training examples are used to determine where, over the set of reals, class boundaries lie; (4) class enumeration, which constructs programs similar in syntactic structure to a decision tree; and (5) evidence accumulation, which allows separate branches of the program to add to the certainty of any given class. The results show that the dynamic range selection method is well-suited to the task of multi-class classification and is capable of producing classifiers that are more accurate than the other methods tried when comparable training times are allowed. The accuracy of the generated classifiers was comparable to alternative approaches over several data sets.
Feasibility of Establishing HIV Case-Based Surveillance to Measure Progress Along the Health Sector Cascade: Situational Assessments in Tanzania, South Africa, and Kenya

Background To track the HIV epidemic and responses to it, the World Health Organization recommends 10 global indicators to collect information along the HIV care cascade. Patient diagnosis and medical record data, harnessed through case-based surveillance (CBS), can be used to measure 8 of these. While many high burden countries have well-established systems for monitoring patients on HIV treatment, few have formally adopted CBS. Objective In response to the need for improved strategic HIV information and to facilitate the development of CBS in resource-limited countries, we aimed to conduct situational assessments of existing data collection systems in Tanzania, South Africa, and Kenya. Methods We developed a standardized protocol and a modularized data collection tool to be adapted for the particular focus of the assessments within each country. The three countries were selected based on their stage of readiness for CBS. The assessment included three parts: a desk review of relevant materials on HIV surveillance and program monitoring, stakeholder meetings, and site visits. Results In all three countries, routine HIV program monitoring is conducted, and information on new HIV diagnoses and persons accessing HIV care and treatment services is collected. Key findings from the assessments included substantial stakeholder support for the development of CBS, significant challenges in linking data within and between systems, data quality, the ability to obtain data from multiple sources, and information technology infrastructure. Viral load testing capacity varied by country, and vital registry data were not routinely linked to health systems to update medical records. Conclusions Our findings support the development of CBS systems to systematically capture routinely collected health data to measure and monitor HIV epidemics and guide responses. Although there were wide variations in the systems examined, some of the current program and patient monitoring systems can be adapted to function effectively for CBS, especially if supported by an improved patient registration system with shared unique health identifiers.