Miracle-dz/UniFAR-FTUs huggingface dataset:
PARP Inhibition Sensitizes to Low Dose-Rate Radiation TMPRSS2-ERG Fusion Gene-Expressing and PTEN-Deficient Prostate Cancer Cells

Exposure to genotoxic agents, such as irradiation produces DNA damage, the toxicity of which is augmented when the DNA repair is impaired. Poly (ADP-ribose) polymerase (PARP) inhibitors were found to be “synthetic lethal” in cells deficient in BRCA1 and BRCA2 that impair homologous recombination. However, since many tumors, including prostate cancer (PCa) rarely have on such mutations, there is considerable interest in finding alternative determinants of PARP inhibitor sensitivity. We evaluated the effectiveness of radiation in combination with the PARP inhibitor, rucaparib in PCa cells. The combination index for clonogenic survival following radiation and rucaparib treatments revealed synergistic interactions in a panel of PCa cell lines, being strongest for LNCaP and VCaP cells that express ETS gene fusion proteins. These findings correlated with synergistic interactions for senescence activation, as indicated by β--galactosidase staining. Absence of PTEN and presence of ETS gene fusion thus facilitated activation of senescence, which contributed to decreased clonogenic survival. Increased radiosensitivity in the presence of rucaparib was associated with persistent DNA breaks, as determined by χ-H2AX, p53BP1, and Rad51 foci. VCaP cells, which harbor the TMPRSS2-ERG gene fusion and PC3 cells that stably express a similar construct (fusion III) showed enhanced sensitivity towards rucaparib, which, in turn, increased the radiation response to a similar extent as the DNA-PKcs inhibitor NU7441. Rucaparib radiosensitized PCa cells, with a clear benefit of low dose-rate radiation (LDR) administered over a longer period of time that caused enhanced DNA damage. LDR mimicking brachytherapy, which is used successfully in the clinic, was most effective when combined with rucaparib by inducing persistent DNA damage and senescence, leading to decreased clonogenic survival. This combination was most effective in the presence of the TMPRSS2-ERG and in the absence of PTEN, indicating clinical potential for brachytherapy in patients with intermediate and high risk PCa.
Self learning machines using Deep Networks

Self learning machines as defined in this paper are those learning by observation under limited supervision, and continuously adapt by observing the surrounding environment. The aim is to mimic the behavior of human brain learning from surroundings with limited supervision, and adapting its learning according to input sensory observations. Recently, Deep Belief Nets (DBNs) [1] have made good use of unsupervised learning as pre-training stage, which is equivalent to the observation stage in humans. However, they still need supervised training set to adjust the network parameters, as well as being nonadaptive to real world examples. In this paper, Self Learning Machine (SLM) is proposed based on deep belief networks and deep auto encoders
AN APPROACH TO EQUIVALENT CIRCUIT MODELING OF RECTANGULAR MICROSTRIP ANTENNAS

Computation of the broadband matching potential of a microstrip antenna requires the wideband lumped equivalent circuit of the antenna. The general topology of the equivalent circuit of rectangular microstrip patch antennas has been used to model the feed- point impedance of microstrip antennas over a wide frequency band and equivalent circuit parameters are determined using optimization techniques. The proposed procedure overcomes the problems of physical realizability of the equivalent circuit and estimation of the starting values of the optimization. Applying this technique, wideband lumped equivalent circuits of a rectangular and E-shaped microstrip antenna have been computed which are in good agreement with measurement data from 0.1 to 6 GHz.
Protein kinase C-mediated sodium glucose transporter 1 activation in precondition-induced cardioprotection

The concept of cardioprotection through preconditioning against ischemia–reperfusion (I/R) injury is well known and established. However, among different proposed mechanisms regarding the concept of ischemic preconditioning, protein kinase C (PKC)-mediated cardioprotection through ischemic preconditioning plays a key role in myocardial I/R injury. Thus, this study was designed to find the relationship between PKC and sodium glucose transporter 1 (SGLT1) in preconditioning-induced cardioprotection, which is ill reported till now. By applying a multifaceted approach, we demonstrated that PKC activates SGLT1, which curbed oxidative stress and apoptosis against I/R injury. PKC activation enhances cardiac glucose uptake through SGLT1 and seems essential in preventing I/R-induced cardiac injury, indicating a possible cross-talk between PKC and SGLT1.
Cybersecurity: Effect of information availability in security games

Cyber-attacks, i.e., disruption of normal functioning of computers and loss of information, are becoming widespread. Cyber security may be studied as a non-cooperative game as described by behavioral game theory. However, current game-theoretic approaches have based their conclusions on Nash equilibriums, while disregarding the role of information availability among hackers and analysts. In this study, we investigated how information availability affected behavior of analysts and hackers in 2×2 security games. In an experiment involving security games, interdependence information available to hackers and analysts was analyzed in two between-subjects conditions: “Info” and “No-Info”. In “Info” condition, both players had complete information about each other's actions and payoffs, while this information was missing in “No-Info” condition. Results showed that presence of information caused analysts and hackers to increase their proportion of defend and attack actions, respectively. We highlight the relevance of our results to cyber-attacks in the real world.
Scrime Studio Report

The SCRIME project is the result of a cooperation convention between the Conservatoire National de Region of Bordeaux, ENSEIRB (school of electronic and computer scientist engineers) and the University of Sciences of Bordeaux. It is composed of electroacoustic music composers and scientific researchers. It is managed by the LaBRI (laboratory of computer science of Bordeaux). Its main missions are research and creation. The SCRIME project is funded by the DMDTS of the French Culture Ministry, the Aquitaine Regional Council, the General Council of the Gironde Department and IDDAC of the Gironde Department. Christian Eloy is the artistic director of the SCRIME. Myriam Desainte-Catherine is the scientific and administrative director. Researchers are professors and associate professors from the LaBRI (MyriamDesainte-Catherine, Pierre Hanna, Sylvain Marchand, and Robert Strandh), associate researchers (Mathieu Lagrange, Matthias Robine and Martin Raspaud), and PhD students (Antoine Allombert, Joan Mouba and Guillaume Meurisse). Furthermore, three composers are associate researchers (Jean-Louis Di Santo, Christophe Havel, Gyorgy Kurtag, and Egdar Nicouleau). The main objective of our research is to build sounds and scores modeling that are well-suited for various kinds of interactions. To reach this objective, several subjects of fundamental research are under study. In addition, several concrete artistic projects are under development in order to develop new ideas of interaction for creation.
From ranked words to dependency trees: two-stage unsupervised non-projective dependency parsing

Usually unsupervised dependency parsing tries to optimize the probability of a corpus by modifying the dependency model that was presumably used to generate the corpus. In this article we explore a different view in which a dependency structure is among other things a partial order on the nodes in terms of centrality or saliency. Under this assumption we model the partial order directly and derive dependency trees from this order. The result is an approach to unsupervised dependency parsing that is very different from standard ones in that it requires no training data. Each sentence induces a model from which the parse is read off. Our approach is evaluated on data from 12 different languages. Two scenarios are considered: a scenario in which information about part-of-speech is available, and a scenario in which parsing relies only on word forms and distributional clusters. Our approach is competitive to state-of-the-art in both scenarios.
The rising prospects of cloud robotic applications

Cloud Robotics is an emerging field within robotics, currently covering various application domains and robot network paradigms. This paper provides a structured, systematic overview of the numerous definitions, concepts and technologies linked to Cloud Robotics and cloud technologies in a broader sense. It also presents a roadmap for the near future, describing development trends and emerging application areas. Cloud Robotics may have a significant role in the future as an explicitly human-centered technology, capable of addressing the dire needs of our society.
Combining Clustering Approaches for Semi-Supervised Parsing : the BASQUE TEAM system in the SPRML ’ 2014 Shared Task

This paper presents a dependency parsing system, presented as BASQUE TEAM at the SPMRL’2014 Shared Task, based on the combination of different clustering approaches. We create new features applying clustering methods to automatically annotated large corpora. Once these new features are calculated, we add them to the base features in order to create a series of analyzers using two freely available and state-of-the-art dependency parsers, MaltParser and Mate. Finally, we will combine previously obtained results using a voting approach.
Control Combinations in Independent Marketing Channels

Abstract Continuing the emergent research on plural governance in interfirm relationships, we hypothesized and verified four control combinations, and empirically investigated antecedent factors resultant in the use of each combination. Different from other reported plural governance studies, we focused on control combinations, rather than each composing control mechanism, as the unit of analysis. Our results indicate that control mechanisms employed simultaneously could play roles of unequal importance: some plays the role of primary control, while others secondary control. This finding challenges the implicit assumption of the extant research that all control mechanisms employed are equal in importance. Additionally, questioning the implicit assumption of the extant research that all governance mechanisms are employed to achieve “active” controls, our results suggest that “passive” controls could be warranted under certain theoretical and practical circumstances. Our study also points to the importance of distinguishing “intended” versus “realized” control mechanisms in interfirm control research.
Illustrating the Gap: Collective Bargaining and Income Distribution in Chile

This book examines the ways in which collective bargaining addresses a variety of workplace concerns in the context of today’s global economy. Globalization can contribute to growth and development, but as the recent financial crisis demonstrated, it also puts employment, earnings and labour standards at risk. This book examines the role that collective bargaining plays in ensuring that workers are able to obtain a fair share of the benefits arising from participation in the global economy and in providing a measure of security against the risk to employment and wages. It focuses on a commonly neglected side of the story and demonstrates the positive contribution that collective bargaining can make to both economic and social goals. The various contributions examine how this fundamental principle and right at work is realized in different countries and how its practice can be reinforced across borders. They highlight the numerous resulting challenges and the critically important role that governments play in rebalancing bargaining power in a global economy. The chapters are written in an accessible style and deal with practical subjects, including employment security, workplace change and productivity, and working time.
DNA-COMPACT: DNA COMpression Based on a Pattern-Aware Contextual Modeling Technique

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.
HEURISTIC SEQUENTIAL CLASSIFIERS BASED ON MULTILAYER PERCEPTRON AND PROBABILISTIC NEURAL NETWORK

This paper presents a method of sequential classification. We assumed that we have a classifier which gives classification results for a single feature vector. We are analyzing five different algorithms of integrating these single classification outputs to give a final classification decision after a given sequence of feature vectors. As a base classification rule (for single feature vector) we used the multilayer perceptron and the probability neural network (PNN). The performance of the proposed integration of sequences of classification outputs from neural network classifiers was tested in an automatic, text independent, speaker identification task. Achieved results are presented.
Conceptual feature generation for textual information using a conceptual network constructed from Wikipedia

A proper semantic representation of textual information underlies many natural language processing tasks. In this paper, a novel semantic annotator is presented to generate conceptual features for text documents. A comprehensive conceptual network is automatically constructed with the aid of Wikipedia that has been represented as a Markov chain. Furthermore, semantic annotator gets a fragment of natural language text and initiates a random walk to generate conceptual features that represent topical semantic of the input text. The generated conceptual features are applicable to many natural language processing tasks where the input is textual information and the output is a decision based on its context. Consequently, the effectiveness of the generated features is evaluated in the task of document clustering and classification. Empirical results demonstrate that representing text using conceptual features and considering the relations between concepts can significantly improve not only the bag of words representation but also other state‐of‐the‐art approaches.
Multimodal vs. unimodal biofeedback in videogames: An empirical player study using a First-Person Shooter

The graphical quality of modern videogames are the result of a steep evolution of hardware over the last two decades, but the game controls that are part of mainstream gaming did not change much in the same time period. To change this, biofeedback techniques using physiological sensors are being studied as possible replacements for traditional videogame interaction devices. In this paper, we continue on-going research by introducing unimodal and, for the first time, multimodal biofeedback game mechanics aiming at enhanced depth and expanded game design possibilities. We developed a First-Person Shooter to test these concepts against traditional unimodal mechanisms, and conducted an empirical study with 32 players. Both unimodal and multimodal variants provided high levels of fun to players, with subtle differences suggesting that these types are best leveraged depending on the interaction context on which they are applied.
Alignment and Characterization of Carbon Nanotubes on Photolithographically Patterned Electrodes by Hyungbin Son

The goal of this work is to make an (n,m) assignment for individual suspended single wall carbon nanotubes (SWNTs) based on the measurements of their Raman Radial Breathing Modes and electron transition energies Eii based on Raman spectroscopy. The suspended SWNTs are grown on a photolithographically defined electrode pattern, which is designed so that suspended SWNTs are grown at known locations with known directions. The suspended SWNTs are then characterized by atomic force microscopy (AFM), scanning electron microscopy (SEM), and Raman spectroscopy. Finally, the information on the diameter distribution and the energy of the electronic transitions of the resonant suspended SWNTs obtained from Raman spectroscopy is compared to other published works to make (n,m) assignments of a number of suspended SWNTs. Thesis Supervisor: Mildred S. Dresselhaus Title: Institute Professor of Physics and Electrical Engineering
Time Series Econometrics

Course Descriptions This is an introductory course on time series econometrics for first-year or second-year Ph.D. students in economics. Through lectures, problem sets and readings, you will learn econometric methods for estimating structural vector autoregressive models and dynamic economic models, such as the method of maximum likelihood, Bayesian methods, and generalized method of moments. Course Goals At the end of this semester you should be able to:
Protein Kinase A Represses Skeletal Myogenesis by Targeting Myocyte Enhancer Factor 2D

ABSTRACT Activation of protein kinase A (PKA) by elevation of the intracellular cyclic AMP (cAMP) level inhibits skeletal myogenesis. Previously, an indirect modulation of the myogenic regulatory factors (MRFs) was implicated as the mechanism. Because myocyte enhancer factor 2 (MEF2) proteins are key regulators of myogenesis and obligatory partners for the MRFs, here we assessed whether these proteins could be involved in PKA-mediated myogenic repression. Initially, in silico analysis revealed several consensus PKA phosphoacceptor sites on MEF2, and subsequent analysis by in vitro kinase assays indicated that PKA directly and efficiently phosphorylates MEF2D. Using mass spectrometric determination of phosphorylated residues, we document that MEF2D serine 121 and serine 190 are targeted by PKA. Transcriptional reporter gene assays to assess MEF2D function revealed that PKA potently represses the transactivation properties of MEF2D. Furthermore, engineered mutation of MEF2D PKA phosphoacceptor sites (serines 121 and 190 to alanine) rendered a PKA-resistant MEF2D protein, which efficiently rescues myogenesis from PKA-mediated repression. Concomitantly, increased intracellular cAMP-mediated PKA activation also resulted in an enhanced nuclear accumulation of histone deacetylase 4 (HDAC4) and a subsequent increase in the MEF2D-HDAC4 repressor complex. Collectively, these data identify MEF2D as a primary target of PKA signaling in myoblasts that leads to inhibition of the skeletal muscle differentiation program.
Aging Impairs the Ability of Conventional Dendritic Cells to Cross-Prime CD8+ T Cells upon Stimulation with a TLR7 Ligand

The aging process is accompanied by altered immune system functioning and an increased risk of infection. Dendritic cells (DCs) are antigen-presenting cells that play a key role in both adaptive and innate immunity, but how aging affects DCs and their influence on immunity has not been thoroughly established. Here we examined the function of conventional DCs (cDCs) in old mice after TLR7 stimulation, focusing on their ability to cross-prime CD8+ T cells. Using polyU, a synthetic ssRNA analog, as TLR7 ligand and OVA as an antigen (Ag) model, we found that cDCs from old mice have a poor ability to stimulate a CD8+ T cell-mediated cytotoxic response. cDCs from old mice exhibit alterations in Ag-processing machinery and TLR7 activation. Remarkably, CD8α+ cDCs from old mice have an impaired ability to activate naïve CD8+ T cells and, moreover, a lower capacity to mature and to process exogenous Ag. Taken together, our results suggest that immunosenescence impacts cDC function, affecting the activation of naïve CD8+ T cells and the generation of effector cytotoxic T cells.
Structure of Rpn10 and Its Interactions with Polyubiquitin Chains and the Proteasome Subunit Rpn12*

Schizosaccharomyces pombe Rpn10 (SpRpn10) is a proteasomal ubiquitin (Ub) receptor located within the 19 S regulatory particle where it binds to subunits of both the base and lid subparticles. We have solved the structure of full-length SpRpn10 by determining the crystal structure of the von Willebrand factor type A domain and characterizing the full-length protein by NMR. We demonstrate that the single Ub-interacting motif (UIM) of SpRpn10 forms a 1:1 complex with Lys48-linked diUb, which it binds selectively over monoUb and Lys63-linked diUb. We further show that the SpRpn10 UIM binds to SpRpn12, a subunit of the lid subparticle, with an affinity comparable with Lys48-linked diUb. This is the first observation of a UIM binding other than a Ub fold and suggests that SpRpn12 could modulate the activity of SpRpn10 as a proteasomal Ub receptor.
Cache-based Statistical Language Models of English and Highly Inflected Lithuanian

This paper investigates a variety of statistical cache-based language models built upon three corpora: English, Lithuanian, and Lithuanian base forms. The impact of the cache size, type of the decay function, including custom corpus derived functions, and interpolation technique (static vs. dynamic) on the perplexity of a language model is studied. The best results are achieved by models consisting of 3 components: standard 3-gram, decaying cache 1-gram and decaying cache 2-gram that are joined together by means of linear interpolation using the technique of dynamic weight update. Such a model led up to 36% and 43% perplexity improvement with respect to the 3-gram baseline for Lithuanian words and Lithuanian word base forms respectively. The best language model of English led up to a 16% perplexity improvement. This suggests that cache-based modeling is of greater utility for the free word order highly inflected languages.
A SURVEY OF MODELS FOR COMPUTER NETWORKS MANAGEMENT

The virtualization concept along with its underlying technologies has been warmly adopted in many fields of computer science. In this direction, network virtualization research has presented considerable results. In a parallel development, the convergence of two distinct worlds, communications and computing, has increased the use of computing server resources (virtual machines and hypervisors acting as active network elements) in network implementations. As a result, the level of detail and complexity in such architectures has increased and new challenges need to be taken into account for effective network management. Information and data models facilitate infrastructure representation and management and have been used extensively in that direction. In this paper we survey available modelling approaches and discuss how these can be used in the virtual machine (host) based computer network landscape; we present a qualitative analysis of the current state-of-the-art and offer a set of recommendations on adopting any particular method.