Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Kinase Cδ Stimulates Proteasome-Dependent Degradation of C/EBPα during Apoptosis Induction of Leukemic Cells

Background The precise regulation and maintenance of balance between cell proliferation, differentiation and death in metazoan are critical for tissue homeostasis. CCAAT/enhancer-binding protein alpha (C/EBPα) has been implicated as a key regulator of differentiation and proliferation in various cell types. Here we investigated the potential dynamic change and role of C/EBPα protein during apoptosis induction. Methodology/Principal Findings Upon onset of apoptosis induced by various kinds of inducers such as NSC606985, etoposide and others, C/EBPα expression presented a profound down-regulation in leukemic cell lines and primary cells via induction of protein degradation and inhibition of transcription, as assessed respectively by cycloheximide inhibition test, real-time quantitative RT-PCR and luciferase reporter assay. Applying chemical inhibition, forced expression of dominant negative mutant and catalytic fragment (CF) of protein kinase Cdelta (PKCδ), which was proteolytically activated during apoptosis induction tested, we showed that the active PKCδ protein contributed to the increased degradation of C/EBPα protein. Three specific proteasome inhibitors antagonized C/EBPα degradation during apoptosis induction. More importantly, ectopic expression of PKCδ-CF stimulated the ubiquitination of C/EBPα protein, while the chemical inhibition of PKCδ action significantly inhibited the enhanced ubiquitination of C/EBPα protein under NSC606985 treatment. Additionally, silencing of C/EBPα expression by small interfering RNAs enhanced, while inducible expression of C/EBPα inhibited NSC606985/etoposide-induced apoptosis in leukemic cells. Conclusions/Significance These observations indicate that the activation of PKCδ upon apoptosis results in the increased proteasome-dependent degradation of C/EBPα, which partially contributes to PKCδ-mediated apoptosis.
Estimación del nivel de competencia entre centros educativos en la región de Madrid y análisis de su efecto sobre el rendimiento académico = Estimation of the school competition level in the region of Madrid and its effects on academic performance

El objetivo del presente trabajo es doble. Por un lado, se obtendra el nivel de competencia actual de los mercados educativos de centros de educacion secundaria que conforman la region de Madrid a traves de una serie de indicadores a nivel municipio o distrito (para el caso de la ciudad de Madrid). Por otro lado, se estudiara el efecto de esta competencia sobre una variable que se entiende objetivo fundamental de todo sistema educativo como es el rendimiento academico, medido a traves de los resultados obtenidos en lengua y literatura y matematicas en las pruebas de Conocimientos y Destrezas Indispensables (CDI), y considerando en todo momento que la eleccion del centro vendra determinada por factores de titularidad y de ubicacion de estos. Dado que los datos se encuentran agrupados en dos niveles (escuela y alumnos) se ha optado por realizar un analisis de regresion multinivel. Los resultados muestran un efecto positivo de la competencia entre centros educativos sobre el rendimiento academico en ambas pruebas, si bien penaliza al alumnado inmigrante, que tienden a estar escolarizados en centros publicos, los cuales tienen un rendimiento por debajo de la media, y la probabilidad de que sus progenitores sean universitarios es bastante inferior a la del conjunto del alumnado. El resto de variables propias del alumno se comportan de acuerdo a la literatura del campo de estudio. Teniendo en cuenta los aspectos que inciden sobre la eficiencia y la equidad, resultaria conveniente fomentar politicas educativas que a la vez que agreguen los mercados educativos y traten de aumentar las opciones disponibles para los demandantes de la educacion, ofrezcan asesoramiento a las familias de menor nivel socioeconomico, asi como incentiven que los centros tengan un porcentaje minimo de alumnos procedentes de familias con menor nivel socioeconomico. Este asesoramiento podria hacerse extensible al alumnado inmigrante.
Different inhibition of Gβγ-stimulated class IB phosphoinositide 3-kinase (PI3K) variants by a monoclonal antibody. Specific function of p101 as a Gβγ-dependent regulator of PI3Kγ enzymatic activity.

Class IB phosphoinositide 3-kinases γ (PI3Kγ) are second-messenger-generating enzymes downstream of signalling cascades triggered by G-protein-coupled receptors (GPCRs). PI3Kγ variants have one catalytic p110γ subunit that can form two different heterodimers by binding to one of a pair of non-catalytic subunits, p87 or p101. Growing experimental data argue for a different regulation of p87-p110γ and p101-p110γ allowing integration into distinct signalling pathways. Pharmacological tools enabling distinct modulation of the two variants are missing. The ability of an anti-p110γ monoclonal antibody [mAb(A)p110γ] to block PI3Kγ enzymatic activity attracted us to characterize this tool in detail using purified proteins. In order to get insight into the antibody-p110γ interface, hydrogen-deuterium exchange coupled to MS (HDX-MS) measurements were performed demonstrating binding of the monoclonal antibody to the C2 domain in p110γ, which was accompanied by conformational changes in the helical domain harbouring the Gβγ-binding site. We then studied the modulation of phospholipid vesicles association of PI3Kγ by the antibody. p87-p110γ showed a significantly reduced Gβγ-mediated phospholipid recruitment as compared with p101-p110γ. Concomitantly, in the presence of mAb(A)p110γ, Gβγ did not bind to p87-p110γ. These data correlated with the ability of the antibody to block Gβγ-stimulated lipid kinase activity of p87-p110γ 30-fold more potently than p101-p110γ. Our data argue for differential regulatory functions of the non-catalytic subunits and a specific Gβγ-dependent regulation of p101 in PI3Kγ activation. In this scenario, we consider the antibody as a valuable tool to dissect the distinct roles of the two PI3Kγ variants downstream of GPCRs.
Multiple features-based image retrieval

Color and texture information are two important visual features of an image. In this paper, an efficient content-based image retrieval system is proposed based on color and texture feature. The color feature is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by color histogram. Texture feature is obtained by local binary pattern (LBP). When computing the similarity between query image and target images in the database, Gaussian normalization is exploited on the feature space and distant space. And then the linear combination of normalized distances for color and texture is performed to obtain the similarity as the index of image. The exhaustive search scheme is used for retrieval, and the evaluation criterion is precision and recall about the number of returned images. The results of experiments demonstrate the efficiency of the proposed system.
On Cyclic Codes of Composite Length and the Minimum Distance

In an interesting paper, Prof. C. Ding provided three constructions of cyclic codes of length being a product of two primes. Numerical data shows that many codes from these constructions are best cyclic codes of the same length and dimension over the same finite field. However, not much is known about these codes. In this paper, we explain some of the numerical data by developing a general method on cyclic codes of composite length and on estimating the minimum distance. We also provide a general construction of cyclic codes of composite length which are related to Ding’s constructions. Numerical data shows that it produces many best cyclic codes as well. Finally, we point out how these cyclic codes can be used to construct convolutional codes with large free distance.
High Dependability Computing Program: Evolving a Dependability Requirements Elicitation and Modeling Framework Based on Use

Correctly identifying and expressing dependability requirements for software systems has wide-ranging consequences for planning and conducting software development as well as for the final system success. Yet crucial difficulties exist, many stemming from the fact that definitions of “dependable” will vary not only from system to system, but will be perceived differently by different stakeholders of the same system. UMD is a requirements engineering framework for eliciting and modeling dependability requirements that has been devised, to mitigate such difficulties. In this report, we introduce UMD and describe an empirical study designed to shed some light on the feasibility of the ideas behind UMD and to identify which aspects of the framework could be improved, in the perspective that software technology transfer from research to industrial use should proceed iteratively and empirically. Subjects in the study consisted of 7 students in a graduate-level class. Empirical qualitative and quantitative results show that the UMD approach is feasible but also allowed us to identify important missing aspects, confirming our assumption that it was not yet mature enough for a rigorous industrial study. The contributions of this study have been twofold: Demonstrating the usefulness of the tech transfer approach which we have followed as well as the feasibility of the UMD approach.
Query-focused multi-document summarization based on query-sensitive feature space

Query-oriented relevance, information richness and novelty are important requirements in query-focused summarization, which, to a considerable extent, determine the summary quality. Previous work either rarely took into account all above demands simultaneously or dealt with part of them in the dynamic process of choosing sentences to generate a summary. In this paper, we propose a novel approach that integrates all these requirements skillfully by treating them as sentence features, making that the finally generated summary could fully reflect the combinational effect of these properties. Experimental results on the DUC2005 and DUC2006 datasets demonstrate the effectiveness of our approach.
Research on Intrusion Recognition and Tracing under Attack and Defense Confront Environment

A new method for intrusion recognition and tracing under attack-defense confront environment is proposed in this paper. In order to do that antagonizing status and state transforming is deeply studied between attacker and defender, and all kinds of security information and knowledge are analyzed and formally described. Then based on obtained information, a safety model for attack-defense confronting is presented, from the model a safety state transition graph can be produced. The model is given a formalized description based on Expanded Finite-State Automata (EFSA), which visually describes both intruding process and defending process. The model is used to thoroughly analyze attack and defense activities and predict the subsequent safety state transitions, and also intuitively illustrates all possible routes and states during attacker's reaching specific target. So the model can be used to trace intruding process and deduce attack intention and target, further to predict follow-up attack, which can provide a useful evidence and guidance for attack response and security decision. Finally this method is demonstrated and validated in an example network environment.
Pronunciation variations of Spanish-accented English spoken by young children

When learning to speak English, non-native speakers may pronounce some English phonemes differently from native speakers. These pronunciation variations can degrade an automatic speech recognition system’s performance on accented English. This paper is a first attempt to find common pronunciation variations in Spanishaccented English as spoken by young children. The analysis of pronunciation variation is performed using dynamic programming-based transcription alignment on 4500 words spoken by children 5-7 years old whose first language is Spanish. The findings are then compared with linguistic hypotheses.
Selection of antibiotic resistance at very low antibiotic concentrations

Abstract Human use of antibiotics has driven the selective enrichment of pathogenic bacteria resistant to clinically used drugs. Traditionally, the selection of resistance has been considered to occur mainly at high, therapeutic levels of antibiotics, but we are now beginning to understand better the importance of selection of resistance at low levels of antibiotics. The concentration of an antibiotic varies in different body compartments during treatment, and low concentrations of antibiotics are found in sewage water, soils, and many water environments due to natural production and contamination from human activities. Selection of resistance at non-lethal antibiotic concentrations (below the wild-type minimum inhibitory concentration) occurs due to differences in growth rate at the particular antibiotic concentration between cells with different tolerance levels to the antibiotic. The minimum selective concentration for a particular antibiotic is reached when its reducing effect on growth of the susceptible strain balances the reducing effect (fitness cost) of the resistance determinant in the resistant strain. Recent studies have shown that resistant bacteria can be selected at concentrations several hundred-fold below the lethal concentrations for susceptible cells. Resistant mutants selected at low antibiotic concentrations are generally more fit than those selected at high concentrations but can still be highly resistant. The characteristics of selection at low antibiotic concentrations, the potential clinical problems of this mode of selection, and potential solutions will be discussed.
Is Women’s Problematic Pornography Viewing Related to Body Image or Relationship Satisfaction?

Abstract Pornography viewing is a growing practice that has been understudied in samples of women. As an answer to calls within the literature, we explored the role of problematic pornography viewing constructs, body image, and relationship satisfaction in a sample of women (n = 949). Structural equation modeling indicated pornography viewing frequency, perceptions of excessive use, and control difficulties were unrelated to body image or relationship satisfaction. However, problematic pornography use to escape negative emotions significantly predicted participants’ body image and relationship dissatisfaction. Future directions, limitations, and clinical implications are discussed.
A variational approach to Bayesian logistic regression problems and their extensions

We consider a logistic regression model with a Gaussian prior distribution over the parame ters. We show that accurate variational techniques can be used to obtain a closed form posterior distribution over the parameters given the data thereby yielding a posterior predictive model. The results are readily extended to (binary) belief networks. For belief networks we also derive closed form posteriors in the presence of missing values. Finally, we show that the dual of the regression problem gives a latent variable density model, the variational formulation of which leads to exactly solvable EM updates.
Establishing Requirements for Information Gathering Tasks

In a disk loading device, an upper tray is moved by pinions among a loading and unloading position for loading and unloading a disk outside a housing, a storage position for storing the disk inside the housing, and a reproduction position for reproducing the disk at the back of the housing. A lower tray is held by a lock lever onto the upper tray between the loading and unloading position and the storage position. As the upper tray is moved from the storage position to the reproduction position, the lower tray is released from the lock lever by a hold releasing rack provided to the housing. Outside the housing, since the lower tray is held onto the upper tray, the strength of the upper tray is increased. Inside the housing, since the lower tray is released from the upper tray, the upper tray is moved alone. This allows the drive mechanism to drive the upper tray alone, making the configuration of the drive mechanism simple. Therefore, it is possible to simplify the configuration of the drive mechanism without a larger housing.
Building XML-driven application generators with compiler construction tools

This paper describes how to use conventional compiler construction tools, and parser generators in particular, to build XML driven application generators. In our approach, the document interface is provided by a standard stream-oriented XML processing framework (e.g., SAX or StAX). This framework is used to program a generic, customizable XML scanner that transforms documents into streams of suitable tokens (opening and closing tags, character data, etc.). The next step is to characterize the syntactic structure of these streams in terms of generation-specific context-free grammars. By adding suitable semantic attributes and semantic actions to these grammars, developers obtain generation-oriented translation schemes: high-level specifications of the generation tasks. These specifications are then turned into working application generators by using standard parser generation technology. We illustrate the approach with , an XML-driven generator of shortest-route search applications in subway networks.
Multiple indicators, multiple causes measurement error models

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood‐based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by‐product of our work, we also obtain a data‐driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure. Copyright © 2014 John Wiley & Sons, Ltd.
Developing the Leuven Embedded Figures Test (L-EFT): testing the stimulus features that influence embedding

Background The Embedded Figures Test (EFT, developed by Witkin and colleagues (1971)) has been used extensively in research on individual differences, particularly in the study of autism spectrum disorder. The EFT was originally conceptualized as a measure of field (in)dependence, but in recent years performance on the EFT has been interpreted as a measure of local versus global perceptual style. Although many have used the EFT to measure perceptual style, relatively few have focused on understanding the stimulus features that cause a shape to become embedded. The primary aim of this work was to investigate the relation between the strength of embedding and perceptual grouping on a group level. Method New embedded figure stimuli (both targets and contexts) were developed in which stimulus features that may influence perceptual grouping were explicitly manipulated. The symmetry, closure and complexity of the target shape were manipulated as well as its good continuation by varying the number of lines from the target that continued into the context. We evaluated the effect of these four stimulus features on target detection in a new embedded figures task (Leuven Embedded Figures Test, L-EFT) in a group of undergraduate psychology students. The results were then replicated in a second experiment using a slightly different version of the task. Results Stimulus features that influence perceptual grouping, especially good continuation and symmetry, clearly affected performance (lower accuracy, slower response times) on the L-EFT. Closure did not yield results in line with our predictions. Discussion These results show that some stimulus features, which are known to affect perceptual grouping, also influence how effectively a stimulus becomes embedded in different contexts. Whether these results imply that the EFT measures individual differences in perceptual grouping ability must be further investigated.
Single-molecule correlated chemical probing of RNA

Significance RNA molecules function as the central conduit of information transfer in biology. To do this, they encode information both in their sequences and in their higher-order structures. Understanding the higher-order structure of RNA remains challenging. In this work we devise a simple, experimentally concise, and accurate approach for examining higher-order RNA structure by converting widely used massively parallel sequencing into an easily implemented single-molecule experiment for detecting through-space interactions and multiple conformations. We then use this experiment to analyze higher-order RNA structure, detect biologically important hidden states, and refine accurate three-dimensional structure models. Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify through-space interactions, create 3D models for RNAs spanning 80–265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA.