Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Calcitonin Gene-Related Peptide in Light-Aversive Behavior: Implications for Migraine

Migraine is a chronic neurological disorder characterized by recurrent episodes of severe unilateral throbbing head pain and associated symptoms, such as photophobia. Our current understanding of the mechanisms underlying migraine has been hampered by limitations in ascertaining migraine symptoms in animal models. Clinical studies have established the neuropeptide calcitonin gene-related peptide (CGRP) as a key player in migraine. Here, we establish a genetic model of photophobia by engineering increased sensitivity to CGRP in mice. These transgenic mice (nestin/hRAMP1) display light-aversive behavior that is greatly enhanced by intracerebroventricular injection of CGRP and blocked by coadministration of the CGRP receptor antagonist olcegepant. This behavior appears to be an indicator of photophobia and cannot be fully explained by gross abnormality of ocular anatomy or differences in general anxiety or motor activity. Our findings demonstrate that a single gene, receptor activity-modifying protein 1 (RAMP1), can be a modifier of photophobia and, by extension, suggest that genetic or epigenetic modulation of RAMP1 levels may contribute to migraine susceptibility. Moreover, they validate CGRP hypersensitive mice as a tool for exploring the neurobiology and novel therapies for migraine and other disorders involving photophobia.
Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

Objective. To date, the majority of Brain–Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.
Fighting Arbitrariness in WordNet-like Lexical Databases - A Natural Language Motivated Remedy

Motivated by doubts on how faithfully and accurately a lexical database models the complicated relations that exist naturally between real-world concepts, we have studied concept organisation in WordNet 1.5 and EuroWordNet 2. Based on the arbitrariness in concept classification observed in these wordnets, we argue that con- cept formation in natural languages is a plausible means to improve concept related- ness in lexical databases. We also illustrate that word formation in Chinese exhibits natural semantic relatedness amongst Chinese concepts which can be exploited to aid word sense disambiguation. Research has shown that lexical databases are good sources of lexical knowledge for various Natural Language Processing (NLP) tasks. Over the years, several lexical databases have been developed, e.g. HowNet (1), WordNet (2), EuroWordNet (3) and CCD (4). These knowledge bases differ in their detailed organisation of real-world concepts and how the knowledge base is structured. However, they all share one common feature - they all aim to specify a hierarchy of language-independent concepts which, in the developers' view, characterises important semantic distinctions between the concepts. These concepts are inter- related through a set of relations. Wong & Fung (5) observed that many of these concepts and relations are in common. While such formalised knowledge bases are known to be well-defined hierarchical systems, there remains doubt as to how faithfully and accurately such artificial constructs model the complicated relations that exist naturally between real-world concepts. Based on the observation done on WordNet 1.5 and EuroWordNet 2, we discuss some common weaknesses in WordNet-like lexical databases. Motivated by Wong & Pala's studies (6,7), we propose a means to alleviate these weaknesses. We have carried out an experiment on the potential applicability of the proposed means of alleviation has been carried out. This paper gives a brief account of the results.
Handwritten and Printed Text Separation in Real Document

The aim of the paper is to separate handwritten and printed text from a real document embedded with noise, graphics including annotations. Relying on run-length smoothing algorithm (RLSA), the extracted pseudo-lines and pseudo-words are used as basic blocks for classification. To handle this, a multi-class support vector machine (SVM) with Gaussian kernel performs a first labelling of each pseudo-word including the study of local neighbourhood. It then propagates the context between neighbours so that we can correct possible labelling errors. Considering running time complexity issue, we propose linear complexity methods where we use k-NN with constraint. When using a kd-tree, it is almost linearly proportional to the number of pseudo-words. The performance of our system is close to 90%, even when very small learning dataset are used, where samples are basically composed of complex administrative documents.
Filarial antigens mediate apoptosis of human monocytes through Toll-like receptor 4.

BACKGROUND Apoptosis of several host cells induced by parasites/parasite products has been investigated in human filariasis to understand immune hyporesponsiveness. However, apoptosis of monocytes-one of the major antigen presenting cells in peripheral circulation, which are chronically exposed to filarial antigens in infected subjects-is yet to be understood. METHODS Apoptosis of human monocytes with Brugia pahangi antigen (BpA) was demonstrated by scoring several apoptotic markers using flow cytometry. Ability of BpA and plasma of infected subjects to suppress lymphocyte proliferation was demonstrated by (3)H thymidine incorporation assay and carboxyfluorescein succinimidyl ester dilution assay. RESULTS BpA induced significant apoptosis of normal human monocytes, primarily through Toll-like receptor 4 (TLR4), and suppressed phytohemagglutinin (PHA)-mediated proliferation of normal human T lymphocytes. However, monocytes of Wuchereria bancrofti-infected subjects were resistant to BpA-induced apoptosis. Plasma of infected subjects also mediated apoptosis of normal monocytes, presumably due to circulating filarial antigens, and resulted in inhibition of PHA-induced proliferation. CONCLUSION Normal human monocytes were found to be qualitatively different from those of filariasis-infected subjects; whereas filarial antigens mediate apoptosis of normal human monocytes through TLR4, those of infected subjects were found to be resistant.
Effects of Dexamethasone Concentration and Timing of Exposure on Chondrogenesis of Equine Bone Marrow–Derived Mesenchymal Stem Cells

Objective Dexamethasone is known to support mesenchymal stem cell (MSC) chondrogenesis, although the effects of dose and timing of exposure are not well understood. The objective of this study was to investigate these variables using a laboratory model of MSC chondrogenesis. Design Equine MSCs were encapsulated in agarose and cultured in chondrogenic medium with 1 or 100 nM dexamethasone, or without dexamethasone, for 15 days. Samples were analyzed for extracellular matrix (ECM) accumulation, prostaglandin E2 and alkaline phosphatase secretion, and gene expression of selected collagens and catabolic enzymes. Timing of exposure was evaluated by ECM accumulation after dexamethasone was withdrawn over the first 6 days, or withheld for up to 3 or 6 days of culture. Results ECM accumulation was not significantly different between 1 and 100 nM dexamethasone, but was suppressed ~40% in dexamethasone-free cultures. Prostaglandin E2 secretion, and expression of catabolic enzymes, including matrix metalloproteinase 13, and type X collagen was generally lowest in 100 nM dexamethasone and not significantly different between 1 nM and dexamethasone-free cultures. Dexamethasone could be withheld for at least 2 days without affecting ECM accumulation, while withdrawal studies suggested that dexamethasone supports ECM accumulation beyond day 6. Conclusion One nanomolar dexamethasone supported robust cartilage-like ECM accumulation despite not having an effect on markers of inflammation, although higher concentrations of dexamethasone may be necessary to suppress undesirable hypertrophic differentiation. While early exposure to dexamethasone was not critical, sustained exposure of at least a week appears to be necessary to maximize ECM accumulation.
Localized Algorithms for Channels Assignment in Ad-Hoc Wireless Sensor Networks

We have developed the first systematic approach for the design of localized algorithms in wireless embedded ad-hoc sensor networks. The approach has three modular phases: information gathering, system structuring, and optimization mechanisms. The phases can be organized in a number of ways, depending on the underlying operating system and media access control mechanisms. As a driver example for testing the approach, we addressed the channel assignment edge-coloring problem. We first developed centralized maximally constrained minimally constraining algorithms. After that we used this algorithm as the optimization engine in TDMA and Aloha media access protocol scenarios to develop a family of localized algorithms. Comprehensive evaluation of the algorithms on variety of benchmarks clearly indicates the effectiveness of the proposed approach and algorithms.
The Psychology of Meat Consumption

This chapter starts with a brief outline of the historic development of the interspecies relationships and discusses the role of norms in changing behavior. Norms play a role in maintaining the ideology of carnism which enables people to eat the flesh of certain animals due to the invisibility of meat production and to the mechanisms of objectification, deindividualization and dichotomization of livestock. According to the meat paradox theory, people alleviate the unpleasant feelings about eating animal flesh by diminishing the minds of the eaten animals. The 4N (normal, natural, necessary and nice) rationalizations which justify eating meat in current society are also pointed out. Furthermore, the role of values, attitudes and different type of motivations are discussed. In conclusion, possible ways of employing community-based social marketing are offered.
A Bayesian classification method for treatments using microarray gene expression data

An important application of microarray gene expression data is classification of treatments. One pioneering work in this area is by Golub et al. (1999) who classified samples into three types of leukemia using oligonucleotide microarrays. Recently, with more applications appearing in the literature, researchers began to realize the importance of constructing accurate and efficient classification methods in order to obtain desirable classification results. Dudoit et al. (2002) provided comprehensive comparisons of several discriminant methods, and Lee and Lee (2002) developed multicategory support vector machines and applied them to classification of cancer data. In this article, we propose an empirical Bayesian classification method that accommodates data structures emerging from microarray experiments such as correlations among gene expressions and high dimensionality with more genes (as predictor variables) than treatments (as data points). Specifically, we build a latent cluster structure for genes into a Bayesian classification process to achieve two goals: to account for the correlation among gene expressions and to reduce the number of nuisance parameters in the Bayesian model. Our simulation studies show that the proposed method generally yields fewer classification errors than the naive Bayesian method which ignores the latent cluster structure of genes by assuming homogeneity among them. We apply our method to two data sets from the literature and demonstrate that desirable results are achieved.
A flexible specification model based on XML for parallel applications

This article introduces a new workflow specification model for parallel applications. Many parallel systems allow only the following basic flow control: (1) a coordinator task distributes the data among the worker tasks, (2) the worker tasks process the data in parallel and (3) the coordinator task gather all the results. In some systems the application execution flow is based on a dependence relationship among tasks, representing a directed acyclic graph (DAG). Even with this model, it is not possible to execute some important parallel applications and, therefore, there is a need for a new specification model with more sophisticated flow controls that allow, for example, conditional branching and iterations at the level of task management. The purpose of this article is to present a proposal for a new parallel application specification model that provides new types of control structures and allows the implementation of a broader range of applications.
On K-LUT based FPGA optimum delay and optimal area mapping

We developed, using structures from MV-SIS, an application dedicated to K-LUT based FPGA synthesis, named Xsynth. Main component of it, levelMap the mapping program, was implemented using the minDepth algorithm. The mapping program was instrumented in order to study and evaluate different heuristics involved in establishing best approach to find optimum delay and optimal area mapping. We did run our mapping application on many circuits from the MCNC and IWLS 2005 benchmark circuits and we obtained good results. We present our main model, procedures, measurement results and brief comparison with previous published relevant similar mapping algorithms.
Factors associated with suicide attempts in patients with bipolar disorder type I.

BACKGROUND The aim of this study is to identify the risk factors that are associated with suicide attempts in patients with bipolar disorder type I. SUBJECTS AND METHODS This cross-sectional study was conducted with inpatients and outpatients with BD type I. Patients who met the study inclusion criteria (n=91) were evaluated in terms of sociodemographic variables, history of childhood trauma, comorbidity of adult attention deficit hyperactivity disorder and posttraumatic stress disorder, and the course of the disease. The patients were divided into two groups: those with a history of suicide attempts and those without a history of suicide attempts. The parameters of the study groups were compared with t and chi-square tests as appropriate. Logistic regression was used to identify the predictors of suicide attempt. RESULTS Logistic regression analysis of the study parameters suggested that the number of major depressive episodes (odds ratio: 7.18; 95% confidence interval: 1.84-28) and history of emotional neglect (odds ratio: 1.83; 95% confidence interval: 1.15-2.90) were significant predictors of suicide attempt in patients with BD. CONCLUSION In BD type I patients with a history of suicide attempts, the number of depressive episodes and emotional neglect, a subtype of childhood traumas, were the most remarkable risk factors. Considering the frequency of depressive episodes during the course of the disease and assessing traumas including those in childhood may help predict future suicide attempts in patients with BD.
Reverse Engineering of Tree Kernel Feature Spaces

We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernel-based machine, e.g. Support Vector Machines (SVMs). In particular, our mining algorithm selects the most relevant features based on SVM estimated weights and uses this information to automatically infer an explicit representation of the input data. The explicit features (a) improve our knowledge on the target problem domain and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with traditional TK classifiers. Experiments on semantic role labeling and question classification illustrate the above claims.
Robust Precoder Design in MISO Downlink Based on Quadratic Channel Estimation

In (Dong and Ding, 2006), it has been proposed that channel estimates in quadratic form can be obtained at the base station by sending training sequences to the mobiles where the received signals are forwarded back to the base for channel estimation. In this paper, we first examine the optimal training sequence design for such quadratic channel estimation and then analyze the error bound and statistics of the channel estimates in quadratic form. With the analytical results, two problems for a multiple-input single-output (MISO) antenna system in the downlink are constructed and optimally solved: Power minimization with individual users' 1) worst-case signal-to-interference plus noise ratio (SINR) and 2) average mean-square-error (MSE) constraints, through optimal multiuser MISO beamforming and power allocation.
An electrophysiological megastudy of spoken word recognition

ABSTRACT This study used electrophysiological recordings to a large sample of spoken words to track the time-course of word frequency, phonological neighbourhood density, concreteness and stimulus duration effects in two experiments. Fifty subjects were presented more than a thousand spoken words during either a go/no go lexical decision task (Experiment 1) or a go/no go semantic categorisation task (Experiment 2) while EEG was collected. Linear mixed effects modelling was used to analyze the data. Effects of word frequency were found on the N400 and also as early as 100 ms in Experiment 1 but not Experiment 2. Phonological neighbourhood density produced an early effect around 250 ms and the typical N400 effect. Concreteness elicited effects in later epochs on the N400. Stimulus duration affected all epochs and its influence reflected changes in the timing of the ERP components. Overall the results support cascaded interactive models of spoken word recognition.
Aptamers: A New Technological Platform in Cancer Immunotherapy

The renaissance of cancer immunotherapy is, nowadays, a reality. In the near future, it will be very likely among the first-line treatments for cancer patients. There are several different approaches to modulate the immune system to fight against tumor maladies but, so far, monoclonal antibodies may currently be the most successful immuno-tools used to that end. The number of ongoing clinical trials with monoclonal antibodies has been increasing exponentially over the last few years upon the Food and Drug Administration (FDA) approval of the first immune-checkpoint blockade antibodies. In spite of the proved antitumor effect of these reagents, the unleashing of the immune system to fight cancer cells has a cost, namely auto-inflammatory toxicity. Additionally, only a small fraction of all patients treated with immune-checkpoint antibodies have a clinical benefit. Taking into account all this, it is urgent new therapeutic reagents are developed with a contained toxicity that could facilitate the combination of different immune-modulating pathways to broaden the antitumor effect in most cancer patients. Based on preclinical data, oligonucleotide aptamers could fulfill this need. Aptamers have not only been successfully used as antagonists of immune-checkpoint receptors, but also as agonists of immunostimulatory receptors in cancer immunotherapy. The simplicity of aptamers to be engineered for the specific delivery of different types of cargos to tumor cells and immune cells so as to harvest an efficient antitumor immune response gives aptamers a significant advantage over antibodies. In this review all of the recent applications of aptamers in cancer immunotherapy will be described.
A shape-match based algorithm for pseudo-3D conversion of 2D videos

This paper presents a shape-match based method of converting conventional 2D videos to their pseudo-3D versions to achieve stereo effect via stereopsis. While conventional 2D videos do not contain true 3D information or difficult to extract such true 3D information, the proposed algorithm is to exploit the tolerance of the human visual perception to estimate pseudo-3D parameters for 3D conversion. The proposed algorithm has the features of: (i) the original 2D video frame is regarded as the reference frame for the pseudo-stereo image pair; (ii) appropriate disparity information is extracted by shape-based matching inside a small library of true stereo image pairs; and (iii) the extracted disparity is then used to construct a right video frame to complete the pseudo-stereo conversion. Our experiments show that a certain level of stereo effect has been achieved for our test video set and some samples of such experiments are illustrated for visual inspection.
CARACTERÍSTICAS COMPORTAMENTALES DE LA AGRESIÓN Y DE LA VIOLENCIA. IMPLICACIONES PARA LA PREVENCIÓN

Se analizan diversas secuencias de conductas violentas, a lo largo de 28 dias, pertenecientes a cinco patrones caracteristicos de comportamientos agresivos, obtenidos en una muestra de 97 participantes. Los cinco patrones permitieron clasificar al conjunto de la muestra, aunque existieron diferencias en la secuencia temporal de las conductas de distintos sujetos pertenecientes a un mismo patron. En general, los individuos muestran ciertas regularidades en la presentacion del comportamiento violento, con oscilaciones en las secuencias de conductas; las diferencias entre los participantes se establecen en el numero de rachas de conductas agrupadas que presentan, en el numero de conductas aisladas, y en los intervalos entre las conductas. Ademas, se senalan algunas de las implicaciones que tendria la elaboracion de un programa de prevencion que se implementara en poblaciones de individuos con diferentes secuencias temporales de conductas violentas.