Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Replication of Loci Involved in Camptothecin-Induced Cytotoxicity Using CEPH Pedigrees

To date, the Centre d'Etude Polymorphism Humain (CEPH) cell line model has only been used as a pharmacogenomic tool to evaluate which genes are responsible for the disparity in response to a single drug. The purpose of this study was demonstrate the model's ability to establish a specific pattern of quantitative trait loci (QTL) related to a shared mechanism for multiple structurally related drugs, the camptothecins, which are Topoisomerase 1 inhibitors. A simultaneous screen of six camptothecin analogues for in vitro sensitivity in the CEPH cell lines resulted in cytotoxicity profiles and orders of potency which were in agreement with the literature. For all camptothecins studied, heritability estimates for cytotoxic response averaged 23.1±2.6%. Nonparametric linkage analysis was used to identify a relationship between genetic markers and response to the camptothecins. Ten QTLs on chromosomes 1, 3, 5, 6, 11, 12, 16 and 20 were identified as shared by all six camptothecin analogues. In a separate validation experiment, nine of the ten QTLs were replicated at the significant and suggestive levels using three additional camptothecin analogues. To further refine this list of QTLs, another validation study was undertaken and seven of the nine QTLs were independently replicated for all nine camptothecin analogues. This is the first study using the CEPH cell lines that demonstrates that a specific pattern of QTLs could be established for a class of drugs which share a mechanism of action. Moreover, it is the first study to report replication of linkage results for drug-induced cytotoxicity using this model. The QTLs, which have been identified as shared by all camptothecins and replicated across multiple datasets, are of considerable interest; they harbor genes related to the shared mechanism of action for the camptothecins, which are responsible for variation in response.
Desarrollo de un sistema de análisis de sentimiento sobre Twitter

Twitter has become one of the most popular online platforms used to express opinions and ideas. For this reason, Twitter is a great source of information which can be used to produce social statistics. This project is focused on one specific aspect of the analysis of this information: polarity. The goal of Sentiment analysis on Twitter is finding the subjectivity within the opinions expressed in this platform. The aim of the project is developing a Django based web application. It should gather different classification tools in order to obtain polarity statistics out of a tweet set. Also, the application will store the outcome in order to show evolution of the results. Moreover, an automatic summarization tool has been also developed. It’s based on the extraction of the most relevant tweets for a specific query. In order to do so it has been implemented a system based on Latent Semantic Analysis. This system also considers the tweets’ popularity when producing the summary. The system was tested by manually scoring a tweet corpus by relevance and studying the different Twitter features that makes a tweet popular.
QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method

Abstract Silent information regulator 2 homologue one (SIRT1) modulators have therapeutic potential for a number of diseases like cardiovascular, metabolic, inflammatory and age related disorders. Here, we have studied both activators and inhibitors of SIRT1 and constructed differential quantitative structure activity relationship (QSAR) models using CORAL software by Monte Carlo optimization method and SMILES notation. 3 splits divided into 3 subsets: sub-training, calibration and test sets, were examined and validated with a prediction set. All the described models were statistically significant models. The values of sensitivity, specificity, accuracy and Matthews’ correlation coefficient for the validation set of best model were 1.0000, 0.8889, 0.9524 and 0.9058, respectively. In mechanistic interpretation, structural features important for SIRT1 activation and inhibition have been defined.
Structural event detection for rich transcription of speech

Although speech recognition technology has significantly improved during the past few decades, current speech recognition systems output only a stream of words without providing other useful structural information that could aid a human reader and downstream language processing modules. This thesis research focuses on the automatic detection of several helpful structural events in speech, including sentence boundaries, type of utterance, filled pauses, discourse markers, and edit disfluencies. The systems evaluated combine prosodic cues and textual information sources in a variety of ways to support automatic detection of these structural events. Experiments were conducted across corpora (conversational speech and broadcast news speech) and with different transcription quality (human transcriptions versus recognition output). The imbalanced data problem is investigated for training the decision tree prosody model component of our system because structural events are much less frequent than non-events. A variety of sampling approaches and bagging are used to address this imbalance. Significant performance improvements are obtained via bagging. Some of the sampling methods are useful depending on the performance metrics used. Sentence boundary detection and disfluency detection tasks are impacted differently by sampling, bagging, and boosting, suggesting the inherent differences between the two tasks. A variety of methods for combining knowledge sources are examined: a hidden Markov model (HMM), the maximum entropy (Maxent) model, and the conditional random field (CRF). The Maxent and CRF approaches are discriminatively trained to model the posterior probabilities and thus correlate with the performance measures. They also support the use of more correlated features and so enable the combination of a variety of textual information sources. The HMM and CRF both model sequence information, unlike the Maxent which explicitly models local information. A model that combines these three approaches is superior to any method alone. Interactions with other research efforts suggest that the methods developed in this thesis generalize well to other corpora (e.g., a multimodal corpus, a multiparty meeting corpus) and to similar tasks (e.g., a gestural model, dialog act segmentation and classification).
Loss-of-Function Mutation in Toll-Like Receptor 4 Prevents Diet-Induced Obesity and Insulin Resistance

Obesity is associated with insulin resistance and a state of abnormal inflammatory response. The Toll-like receptor (TLR)4 has an important role in inflammation and immunity, and its expression has been reported in most tissues of the body, including the insulin-sensitive ones. Because it is activated by lipopolysaccharide and saturated fatty acids, which are inducers of insulin resistance, TLR4 may be a candidate for participation in the cross-talk between inflammatory and metabolic signals. Here, we show that C3H/HeJ mice, which have a loss-of-function mutation in TLR4, are protected against the development of diet-induced obesity. In addition, these mice demonstrate decreased adiposity, increased oxygen consumption, a decreased respiratory exchange ratio, improved insulin sensitivity, and enhanced insulin-signaling capacity in adipose tissue, muscle, and liver compared with control mice during high-fat feeding. Moreover, in these tissues, control mice fed a high-fat diet show an increase in IκB kinase complex and c-Jun NH2-terminal kinase activity, which is prevented in C3H/HeJ mice. In isolated muscles from C3H/HeJ mice, protection from saturated fatty acid–induced insulin resistance is observed. Thus, TLR4 appears to be an important mediator of obesity and insulin resistance and a potential target for the therapy of these highly prevalent medical conditions.
Bisimulation Congruences for Homer a calculus of Higher-order mobile embedded resources

We extend Howe’s method for proving that late labelled transition bisimulations are congruences to a core process passing calculus with local names, extended with non-linear active process mobility and nested locations, as found in the Seal calculus, M-calculus, and Kell-calculus. The calculus we consider, called Homer for Higher-order Mobile Embedded Resources, has a very simple syntax and semantics, which conservatively extend the standard syntax and semantics for process passing calculi. The extension of Howe’s method gives a sound characterisation of barbed bisimulation congruence in terms of a late contextual bisimulation. We show that early contextual bisimulation is complete with respect to barbed bisimulation congruence, but that the late bisimulation is in fact strictly included in the early bisimulation. We discuss the issue of scope extension through location boundaries in detail, in particular the difference between fresh name generation and static local names. We propose free name extension as a primitive construct in calculi with non-linear process mobility, explicit locations and local names.
Inter-list equivalency and reliability of the Persian randomized dichotic digits test

Background and Aim: Dichotic listening evaluation is one of the most common behavioral procedures for assessing the cerebral dominance of language and is considered as the main component of the auditory processing test battery in children and adults. The randomized dichotic digits test (RDDT) has two lists of randomly distributed one-, two-, and three-digit items. This research was performed for studying the inter-list equivalency and test-retest reliability of Persian RDDT. Methods: Persian RDDT was administered at 50 dBHL on 62 right-handed individuals (equal sex ratio) with normal hearing sensitivity in the age range of 12-45 years. The interval between the test sessions was 30 days and each session had two test rounds with an interval of 20 minutes. In each session, list 1 of the Persian RDDT was performed for the first round and list 2 performed for the second round. Results: Results demonstrated that mean right and left ear score and ear advantage of Persian RDDT list 1 and list 2 had no statistically significant difference (right ear p=0.25, left ear p=0.56, ear advantage: p=0.6). Intra-class correlation coefficient of scores in the first and the third test rounds (list 1) for the right and left ears were 0.71 and 0.68, and for the second and the fourth test rounds (list 2) were 0.69 and 0.80, respectively. Conclusion: Based on the results of this study, it would seem that the two lists of Persian RDDT are equivalent and the mean scores have good to excellent test-retest reliability.
Noise propagation for iterative penalized-likelihood image reconstruction based on Fisher information

Iterative reconstruction algorithms have been widely used in PET and SPECT emission tomography. Accurate modeling of photon noise propagation is crucial for quantitative tomography applications. Iteration-based noise propagation methods have been developed for only a few algorithms that have explicit multiplicative update equations. And there are discrepancies between the iteration-based methods and Fessler's fixed-point method because of improper approximations. In this paper, we present a unified theoretical prediction of noise propagation for any penalized expectation maximization (EM) algorithm where the EM approach incorporates a penalty term. The proposed method does not require an explicit update equation. The update equation is assumed to be implicitly defined by a differential equation of a surrogate function. We derive the expressions using the implicit function theorem, Taylor series and the chain rule from vector calculus. We also derive the fixed-point expressions when iterative algorithms converge and show the consistency between the proposed method and the fixed-point method. These expressions are solely defined in terms of the partial derivatives of the surrogate function and the Fisher information matrices. We also apply the theoretical noise predictions for iterative reconstruction algorithms in emission tomography. Finally, we validate the theoretical predictions for MAP-EM and OSEM algorithms using Monte Carlo simulations with Jaszczak-like and XCAT phantoms, respectively.
An Overview of Empirical Natural Language Processing

In recent years, there has been a resurgence in research on empirical methods in natural language processing. These methods employ learning techniques to automatically extract linguistic knowledge from natural language corpora rather than require the system developer to manually encode the requisite knowledge. The current special issue reviews recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction, and machine translation. This article presents an introduction to the series of specialized articles on these topics and attempts to describe and explain the growing interest in using learning methods to aid the development of natural language processing systems.
Organização aprendente: o caso de uma instituição financeira

This dissertation aims to explore the ability of organizations to effectively learn and to change faster than their competitors, which is determinant as a sustainable competitive advantage, particularly in the current dynamic global marketplace. These organizations able to transform themselves, with effective processes and integrated systems for organizational learning are defined, in the literature, as learning organizations. In fact, this learning ability is the key factor for good organizational performances that can last over time. Thus, the emergence of learning organizations is crucial, as it can lead the current organizations to more competitiveness and consequently to success. Although many theoretical models have addressed this subject, we have adopted the model of Integrated Learning Organization proposed by Watkins and Marsick (1993, 1996), as it provides a global perspective of this subject, encompassing four types of characteristics of a learning organization presented by Ortenblad (2002). This has been assessed through the Dimensions of the Learning Organization Questionnaire (DLOQ). Hence, we have enquired a sample of 51 employees of Banco Espirito Santo (a financial institution) in order to assess its learning ability, according to the model mentioned above. For this purpose, an exploratory quantitative approach, combining descriptive and inferential statistical methods has been applied. The results have shown that BES is not yet a learning organization, hence not presenting the characteristics required for such addressed in the proposed model. This evidence can be explained by its organisational structure, i.e., a divisionalised form, still relying in bureaucracy and formal processes.
N=1 Worldsheet Boundary Couplings and Covariance of non-Abelian Worldvolume Theory

A systematic construction is given for N=1 open string boundary coupling to Abelian and non-Abelian Dp-brane worldvolume fields, in general curved backgrounds. The basic ingredient is a set of four ``boundary vectors'' that provide a unified description of boundary conditions and boundary couplings. We then turn to the problem of apparent inconsistency of non-Abelian worldvolume scalar couplings (obtained by T-duality), with general covariance. It means that the couplings cannot be obtained from a covariant action by gauge fixing ordinary general coordinate transformations (GCT). It is shown that the corresponding worldsheet theory has the same problem, but is also invariant under certain matrix-valued coordinate transformations (MCT) that can be used to restore its covariance. The same transformations act on the worldvolume, leading to a covariant action. Then the non-Abelian Dp-brane action obtained by T-duality corresponds to gauge fixing the MCT and not GCT, hence the apparent incompatibility with general covariance.
Measurement of high-order polarization mode dispersion

We demonstrate a new method to measure high-order polarization mode dispersion (PMD) using the Jones matrix exponential expansion. High-order PMD is characterized by measuring a series of characteristic matrices, which are convenient quantities for analyzing PMD effects in the time-domain. An experimental method is developed to estimate the validity range of the exponential expansion.
Highly accelerated dynamic contrast enhanced imaging

Dynamic contrast‐enhanced imaging provides unique physiological information, notably the endothelial permeability (Ktrans), and may improve the diagnosis and management of multiple pathologies. Current acquisition methods provide limited spatial‐temporal resolution and field‐of‐view, often preventing characterization of the entire pathology and precluding measurement of the arterial input function. We present a method for highly accelerated dynamic imaging and demonstrate its utility for dynamic contrast‐enhanced modeling.
Unions of Perfect Matchings in Cubic Graphs and Implications of the Berge-Fulkerson Conjecture

The Berge-Fulkerson Conjecture states that every cubic bridgeless graph has six perfect matchings such that every edge of the graph is in exactly two of the perfect matchings. If the Berge-Fulkerson Conjecture is true, then what can we say about the proportion of edges of a cubic bridgeless graph that can be covered by k of its perfect matchings? This is the question we address in this paper. We then give a possible method for proving, independently of the Berge-Fulkerson Conjecture, the bounds obtained.
Lifestyle-related disease in Crohn's disease: relapse prevention by a semi-vegetarian diet.

AIM To investigate whether semi-vegetarian diet (SVD) has a preventive effect against relapse of Crohn's disease (CD) in patients who have achieved remission, who are a high-risk group for relapse. METHODS A prospective, single center, 2-year clinical trial was conducted. Twenty-two adult CD patients who achieved clinical remission either medically (n = 17) or surgically (n = 5) and consumed an SVD during hospitalization were advised to continue with an SVD and avoid known high-risk foods for inflammatory bowel disease. The primary endpoint was clinical relapse defined as the appearance of active symptoms of CD. Kaplan-Meier survival analysis was used to calculate the cumulative proportion of patients who had a relapse. A 2-year analysis of relapse rates of patients who followed an SVD and those who did not (an omnivorous diet group) was undertaken. RESULTS SVD was continued by 16 patients (compliance 73%). Remission was maintained in 15 of 16 patients (94%) in the SVD group vs two of six (33%) in the omnivorous group. Remission rate with SVD was 100% at 1 year and 92% at 2 years. SVD showed significant prevention in the time to relapse compared to that in the omnivorous group (P = 0.0003, log rank test). The concentration of C-reactive protein was normal at the final visit in more than half of the patients in remission who were taking an SVD, who maintained remission during the study (9/15; 60%), who terminated follow-up (8/12; 67%), and who completed 2 years follow-up (7/10; 70%). There was no untoward effect of SVD. CONCLUSION SVD was highly effective in preventing relapse in CD.
The stream polygon-a technique for 3D vector field visualization

A method is presented for the visualization of 3D vector fields. The stream polygon, which is a regular, n-sided polygon, oriented normal to the local vector, can present local deformations due to rigid body rotation and both normal and shear strain. The effect of translation and scalar functions can be represented by sweeping the stream polygon along the streamline, and by appropriately varying the radius and shading the surface of the resulting streamtube. A mathematical foundation for the stream is developed, and examples with application to velocity field visualization are provided. <<ETX>>
Research on bag of audio words algorithm in the violent videos classification

In this paper, a new method to identify the violent videos by the bag of audio words is introduced. The MPEG-7 audio descriptors are firstly extracted, including the low level features such as AudioSpectrumCentroid and AudioSpectrum-Spread, etc. The audio words are then built according to the MPEG-7 high level descriptor, the AudioSighnature, which is considered as the “fingerprint” of the audio stream. The support vector machine is used to classify the feature vectors into two classes, i.e. the violent and non-violent videos. The experiment results demonstrate that our method can achieve good recall accuracy.
A Hierarchical Representation for Eeciently Learning of Three-dimensional Object Recognition and Description

Giving human-like visual capabilities to computers is an important goal in computer vision research. Recognition and description of 3-dimensional objects is a largely unsolved problem in this area despite the fact that many proposals have been put forth by a number of We propose a framework for representation of complex three-dimensional objects which is motivated by the need for: parsimony (eeciency) in model construction; amenabil-ity to learning (incremental acquisition, assimilation, adaptation, and reenement of models of objects in the environment); and seamless integration as part of an intelligent agent architecture (by keeping the representation and learning structures multipurpose and exi-ble). This paper argues for a hierarchical representational scheme motivated by considerations such as the ones enumerated above; presents such a representational framework; and outlines an approach for the design of learning algorithms for task-driven, parsimonious, in-cremental acquisition and reenement of the necessary representations within the proposed framework. 1 Description of the Represen-tational Framework 1.1 Background Assumptions We will proceed under the assumption that complex 3-dimensional objects are made of 3-dimensional primitive components each of which can be identiied in the Partially supported by Iowa State University College of Liberal Arts and Sciences 2-dimensional image(s) of the scene using known techniques. A number of such techniques have been devel-It is also assumed that the model base stores the representation of objects from a standard view (that is, we will not store diierent views of the object resulting from rotation). Our aim here is to focus on the design of the hierarchical representation scheme and the accompanying learning algorithms. The limitations imposed by this assumption can be revoked later exploiting advances in viewpoint-invariant representations such as those based on aspect graphs We will also assume that the scene contains only a single object or the system has some attentional mechanisms (Tsotsos, 1990) that can limit its eld of active visual analysis to a single object in the scene. Our aim is to design a representational framework that facilitates the descriptions of complex 3-dimensionalob-jects to be incrementally constructed and reened as necessary for the task of object recognition, description , (and eventually, manipulation by eeectors). This is accomplished using an ordered sequence of extraction , abstraction, diierentiation, and generalization operators that operate on instances of 3-dimensional objects and the existing model base. The design of the representational framework and the operators that effect changes in representation is motivated by the need for building …