Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Olfactory Bulb Output in the Zebrafish Telencephalon analyzed by Electrophysiology and 2-Photon Ca2+ - Imaging

To understand how the brain generates representations of the external world it is crucial to analyze the processing of sensory information between early processing centers and higher brain regions. In the first olfactory relay, the olfactory bulb (OB), odors are represented by dynamic patterns of activity across the population of principal neurons, the mitral cells. During an odor response, subsets of mitral cells synchronize their action potentials and convey information that is different from the information contained in non-synchronized firing patterns. It is, however, poorly understood how these combinatorial representations are further processed in higher brain areas. I used a small vertebrate model system, the zebrafish, to examine how neurons in the dorsal posterior telencephalon (Dp), a direct target of OB output that is homologous to olfactory cortex, extract information from OB output activity patterns. Using 2-photon Ca2+ - imaging and whole-cell patch-clamp recordings, I found that individual Dp neurons receive input from diverse sets of mitral cells. Unlike in mitral cells, responses of Dp neurons to binary mixtures of odors could not be predicted from their responses to the components. Electrophysiological and pharmacological results demonstrated that suprathreshold responses are controlled by the convergence of excitatory and inhibitory pathways in single Dp neurons. I next analyzed the temporal integration properties of neurons and neuronal circuits to examine whether neurons in Dp may selectively extract the information contained in synchronized mitral cells spikes. No evidence for coincidence detection mechanisms was found; rather, action potential firing is controlled primarily by a slow membrane depolarization. In conclusion, the readout of information in Dp is determined by a balance of slow excitatory and inhibitory inputs that allows Dp neurons to detect defined patterns of excitation and inhibition across the population of mitral cells in the olfactory bulb. This mechanism does not depend on the synchronization of inputs and mediates the association of information about multiple molecular features of an odor stimulus. Together, these data suggest that neurons in Dp form synthetic representations of olfactory objects.
Robustness analysis of avionics embedded systems

Avionics embedded systems are confronted to sensors errors or inaccuracies. It is essential to be able to estimate the impact and the propagation of these errors on the system computed outputs. This paper comes from achieved work in the context of an industrial project in the avionics area. It presents a formal approach allowing to analyse the reaction of a LUSTRE program to inaccurate input values. It uses program analysis techniques like abstract interpretation and interval analysis. The approach has been implemented in Java and applied to parts of a flight control system of the AIRBUS aircrafts.
Score-line effect on work-rate in English FA Premier League soccer

This paper investigates the effect of score-line on work-rate in English FA Premier League soccer. Player movement data from 110 matches where a goal was scored between 15 minutes and the end of the first half were captured by the ProzoneTM player tracking system. The number of V-cut path changes performed per minute declined more after the first goal in matches that were won by one of the sides than in drawn matches (p < 0.017). V-cut path changes involve players changing direction more than 135° to the left or the right. There was also a significant interaction of match type (won, drawn or lost by the team scoring first), period of the match (before and after the first goal), venue and the relative quality of the teams on the total number of path changes performed (p < 0.05). Players from the scoring and conceding teams spent significantly less time in the middle third of the pitch after the first goal then before (p < 0.017). This suggests that variability in work-rate is influenced by a combination of factors. The results suggest that the first goal has an influence on teams’ tactics and work-rate. However, the study did not find any differences in work-rate between teams achieving different outcomes having scored first.
Subjectivity grouping: learning from users' rating behavior

Considering opinions of other users (called advisors) has become increasingly important for each user in open and dynamic online environments. However, users might be subjectively different or strategically dishonest. Previous approaches on this problem generally suffer from the issue of limited (especially shared) historic experience when tracking each individual advisor's behavior. In this paper, instead, we model each advisor as part of groups by proposing a two-layered clustering approach. Specifically, in the first layer, the agent of each user clusters her advisors into different subjectivity groups and dishonest types, with respect to their rating behavior. In the second layer, each advisor is assigned to groups with respective membership degrees. Finally, each agent adopts an alignment approach to help its user align advisors' ratings to the ones of her own. Experimental results on both simulations and real data verify that our approach can better help users utilize ratings provided by advisors in opinion evaluation and recommender systems.
Sentiment Detection of Web Users Using Probabilistic Latent Semantic Analysis

With the wide application of Internet in almost all fields, it has become the most important way for information publication, providing a large number of channels for spreading public opinion. Public opinions, as the response of Internet users to the information such as social events and government policies, reflect the status of both society and economics, which is highly valuable for the decision-making and public relations of enterprises. At present, the analysis methods for Internet public opinion are mainly based on discriminative approaches, such as Support Vector Machine (SVM) and neural network. However, when these approaches analyze the sentiment of Internet public opinion, they are failed to exploit information hidden in text, e.g. topic. Motivated by the above observation, this paper proposes a detection method for public sentiment based on Probabilistic Latent Semantic Analysis (PLSA) model. PLSA inherits the advantages of LSA, exploiting the semantic topic hidden in data. The procedure of detecting the public sentiment using this algorithm is composed of three main steps: (1) Chinese word segmentation and word refinement, with which each document is represented by a bag of words; (2) modeling the probabilistic distribution of documents using PLSA; (3) using the Z-vector of PLSA as the features of documents and delivering it to SVM for sentiment detection. We collect a set of text data from Weibo, blog, BBS etc. to evaluate our proposed approach. The experimental results shows that the proposed method in this paper can detect the public sentiment with high accuracy, outperforming the state-of-the-art approaches, i.e., word histogram based approach. The results also suggest that, text semantic analysis using PLSA could significantly boost the sentiment detection.
Convergence and Tracking Behavior of the New SPVS-NLMS Algorithm

In this paper, the segment proportionate variable-step-size normalized least mean square (SPVS-NLMS) algorithm is proposed. Using computer simulations, we show that the proposed SPVS-NLMS algorithm performs a faster convergence compared to the segment proportionate normalized least mean square algorithm by Hongyang and Doroslovacki (2005) with a higher tracking ability for speech signals
Where Is the Patient in Diabetes Performance Measures?

OBJECTIVE—Health policies are important determinants of clinician and patient behavior, and an important policy issue is what items are included in healthcare quality and performance measures. There is consensus that patient-centered care and self-management support are essential evidence-based components of good diabetes care. However, most major diabetes performance measures such as the National Committee for Quality Assurance (NCQA)/American Diabetes Association (ADA) Provider Recognition Program indexes have not included self-management or psychosocial items. RESEARCH DESIGN AND METHODS AND RESULTS—We review the case for and propose a set of patient-centered, self-management indicators to be included as a standard part of diabetes quality indicators. The proposed indicators include: patient self-management goal(s), measures of health behaviors (e.g., healthy eating, medication taking, physical activity, and smoking status), quality of life, and patient-centered collaborative care. We discuss the evidence and the concerns about patient-report measures and summarize successful incorporation of such patient-centered measures in other countries and by the American Association of Diabetes Educators (AADE). CONCLUSIONS—The adage that “what gets measured, gets done” applies to diabetes management and many other areas of healthcare. Inclusion of the proposed indicators in national diabetes performance measures would be consistent with Institute of Medicine (IOM), ADA, Centers for Disease Control (CDC), Diabetes Attitudes, Wishes, and Needs (DAWN), AADE, and Society of Behavioral Medicine (SBM) recommendations. Such action would enhance both the priority and delivery of quality, patient-centered care, and diabetes self-management support.
Improving Text Classification with LSI Using Background Knowledge

We present work in progress that uses Latent Semantic Indexing (LSI) in conjunction with background knowledge and unlabeled examples to improve text classification accuracy. The singular value decomposition (SVD) that is performed by LSI is done on an expanded term by document matrix that includes the labeled training examples as well as the unlabeled examples. We report classification accuracy on different data sets both with and without the inclusion of background knowledge and compare it to other known work.
Tensor-Based Morphometry Reveals Volumetric Deficits in Moderate=Severe Pediatric Traumatic Brain Injury

Abstract Traumatic brain injury (TBI) can cause widespread and prolonged brain degeneration. TBI can affect cognitive function and brain integrity for many years after injury, often with lasting effects in children, whose brains are still immature. Although TBI varies in how it affects different individuals, image analysis methods such as tensor-based morphometry (TBM) can reveal common areas of brain atrophy on magnetic resonance imaging (MRI), secondary effects of the initial injury, which will differ between subjects. Here we studied 36 pediatric moderate to severe TBI (msTBI) participants in the post-acute phase (1–6 months post-injury) and 18 msTBI participants who returned for their chronic assessment, along with well-matched controls at both time-points. Participants completed a battery of cognitive tests that we used to create a global cognitive performance score. Using TBM, we created three-dimensional (3D) maps of individual and group differences in regional brain volumes. At both the post-acute and chronic time-points, the greatest group differences were expansion of the lateral ventricles and reduction of the lingual gyrus in the TBI group. We found a number of smaller clusters of volume reduction in the cingulate gyrus, thalamus, and fusiform gyrus, and throughout the frontal, temporal, and parietal cortices. Additionally, we found extensive associations between our cognitive performance measure and regional brain volume. Our results indicate a pattern of atrophy still detectable 1-year post-injury, which may partially underlie the cognitive deficits frequently found in TBI.
Simultaneous Determination of Three Furanocoumarins by UPLC/MS/MS: Application to Pharmacokinetic Study of Angelica dahurica Radix after Oral Administration to Normal and Experimental Colitis-Induced Rats

In traditional oriental medicine, Angelica dahurica Radix (ADR) is used in the treatment of gastrointestinal, respiratory, neuromuscular, and dermal disorders. We evaluated the pharmacokinetic profiles of oxypeucedanin, imperatorin, and isoimperatorin, major active ingredients of ADR, in normal and 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis rats. A rapid, sensitive, and validated UPLC/MS/MS method was established for evaluating the pharmacokinetics of three furanocoumarins. After oral administration of ADR (0.5 and 1.0 g/kg), blood samples were collected periodically from the tail vein. In colitis rats, the time to reach the peak concentration (Tmax) of imperatorin and isoimperatorin was significantly delayed (p < 0.05). Lower peak plasma concentrations (Cmax) and longer mean residence times for all furanocoumarins were also observed (p < 0.05) compared with normal rats. There was no significant difference in the area under the plasma concentration–time curve or elimination half-lives. Thus, the delayed Tmax and decreased Cmax, with no influence on the elimination half-life, could be colitis-related changes in the drug-absorption phase. Therefore, the prescription and use of ADR in colitis patients should receive more attention.
Automatic Selection of Synthesis Units from a Large Speech Database

In this paper, a novel method for the selection of synthesis unit is proposed. The monosyllables are adopted as the basic synthesis units. A set of high-quality synthesis units is selected from a large continuous speech database based on four procedures: pitch period detection and smoothing, speech unit filtering, unit selection, and manual examination. Two cost functions are proposed for obtaining the synthesis units, which minimize the interand intra-syllable distortion. The cost functions estimate the parameters including the prosodic features, the LSP frequencies, and types of syllable concatenation. Experimental results showed that a match rate of 48.9% was achieved. It indicates that about half of the "best" synthesis units can be automatically obtained. Also, a replacement rate of 4.8% was obtained.
Memory deficit in patients with schizophrenia and posttraumatic stress disorder: relational vs item-specific memory

It has been well established that patients with schizophrenia have impairments in cognitive functioning and also that patients who experienced traumatic events suffer from cognitive deficits. Of the cognitive deficits revealed in schizophrenia or posttraumatic stress disorder (PTSD) patients, the current article provides a brief review of deficit in episodic memory, which is highly predictive of patients’ quality of life and global functioning. In particular, we have focused on studies that compared relational and item-specific memory performance in schizophrenia and PTSD, because measures of relational and item-specific memory are considered the most promising constructs for immediate tangible development of clinical trial paradigm. The behavioral findings of schizophrenia are based on the tasks developed by the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative and the Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CNTRACS) Consortium. The findings we reviewed consistently showed that schizophrenia and PTSD are closely associated with more severe impairments in relational memory compared to item-specific memory. Candidate brain regions involved in relational memory impairment in schizophrenia and PTSD are also discussed.
From Scientific Workflow Patterns to 5-star Linked Open Data

Scientific Workflow management systems have been largely adopted by data-intensive science communities. Many efforts have been dedicated to the representation and exploitation of provenance to improve reproducibility in data-intensive sciences. However, few works address the mining of provenance graphs to annotate the produced data with domain-specific context for better interpretation and sharing of results. In this paper, we propose PoeM, a lightweight framework for mining provenance in scientific workflows. PoeM allows to produce linked in silico experiment reports based on workflow runs. PoeM leverages semantic web technologies and reference vocabularies (PROV-O, P-Plan) to generate provenance mining rules and finally assemble linked scientific experiment reports (Micropublications, Experimental Factor Ontology). Preliminary experiments demonstrate that PoeM enables the querying and sharing of Galaxy-processed genomic data as 5-star linked datasets.
Expression of Interleukin-4 Receptor Alpha in Human Pleural Mesothelioma Is Associated with Poor Survival and Promotion of Tumor Inflammation

Purpose: The origin and pathogenesis of malignant pleural mesothelioma (MPM) are closely aligned with inflammation. MPM tumors express interleukin-4 receptor α (IL-4Rα), the principal subunit of the IL-4 receptor. We set out to determine the biologic function and clinical relevance of IL-4Rα in human MPM. Experimental Design: Expression of IL-4Rα by human MPM tumors was determined by quantitative real-time PCR (n = 37) and immunohistochemistry (n = 52). Intracellular cytokine analysis of T-cell–derived IL-4 was carried out on matched tumor and blood samples from eight patients with MPM. Four human MPM cell lines were used to determine the direct effects of IL-4 on MPM tumor cells. Results: High tumor mRNA expression of IL-4Rα was an independent predictor of poor survival in patients with epithelial MPM [HR, 3.13, 95% confidence interval (CI), 1.68–7.15; P = <0.0001]. Ninety-seven percent of epithelial MPM tumors and 95% of nonepithelial MPM tumors expressed IL-4Rα protein by immunohistochemistry, and strong IL-4Rα staining correlated with worse survival in patients with epithelial histology (P = 0.04). A greater percentage of tumor-infiltrating T cells produced IL-4 compared with matched blood T cells (21% ± 7% vs. 4% ± 2%, P = 0.0002). In response to IL-4, human MPM cells showed increased STAT-6 phosphorylation and increased production of IL-6, IL-8, and VEGF, without effect on proliferation or apoptosis. Conclusions: Tumor expression of IL-4Rα is inversely correlated with survival in patients undergoing surgical resection for epithelial MPM. Tumor-infiltrating T cells in MPMs are polarized to produce IL-4 and may provide endogenous activation signals to MPM tumor cells in situ. The IL-4/IL-4 receptor axis is a potential therapeutic target in human MPM. Clin Cancer Res; 18(6); 1568–77. ©2012 AACR.
Using clustering trees for learning phylogenetic trees

This paper presents ongoing work on an application of machine learning in phylogenetic analysis, which is the study of evolutionary relatedness among various groups of organisms. Insights in evolutionary relationships are important because they can help to determine the function of uncharacterized genes and they can be used to predict future variants of fast-growing viruses. More precisely, we focus on the following task: given a set of DNA sequences, and given that they all originate from a single sequence via successive mutations, find the phylogenetic tree that describes the evolutionary process. In Section 2, we explain what phylogenetic trees are and how they are usually built. Section 3 presents clustering trees, which will be used in Section 4 to construct phylogenetic trees. Section 5 summarizes the advantages of our approach.
The influence of local environmental conditions on surface discharge behaviour

The measurement of surface charge is important in determining the electrical performance of high voltage insulation materials. This paper is concerned with surface charge measurements utilising Pockels effect. The method proposed allows on-line measurement of charge and can generate two-dimensional images that represent the charge behaviour on a material surface. The system has been used to investigate the influence of local environment on surface charge distribution.
THE EVOLUTION OF THE HELIUM-IONIZING BACKGROUND AT z ∼ 2–3

Recent observations suggest that helium became fully ionized around redshift z ∼ 3. The He ii optical depth derived from the Lyα forest decreases substantially from this period to z ∼ 2; moreover, it fluctuates strongly near z ∼ 3 and then evolves smoothly at lower redshifts. From these opacities, we compute, using a semi-analytic model, the evolution of the mean photoionization rate and the attenuation length for helium over the redshift range 2.0 ≲ z ≲ 3.2. This model includes an inhomogeneous metagalactic radiation background, which is expected during and after helium reionization. We find that assuming a uniform background underestimates the required photoionization rate by up to a factor of ∼2. When averaged over the (few) available lines of sight, the effective optical depth exhibits a discontinuity near z ≈ 2.8, but the measurement uncertainties are sizable. This feature translates into a jump in the photoionization rate and, provided the quasar emissivity evolves smoothly, in the effective attenuation length, perhaps signaling the helium reionization era. We then compute the evolution of the effective optical depth for a variety of simple helium reionization models, in which the measured quasar luminosity function and the attenuation length, as well as the evolving He iii fraction, are inputs. A model with reionization ending around redshift z ≈ 2.7 is most consistent with the data, although the constraints are not strong thanks to the sparseness of the data.
Hand shape and hand gesture recognition

We present two problems in the article, first concerns persons identification based on the shape of the hand and the second recognizing gestures and signs executed by hands. Hand-based authentication schemes in the literature are mostly based on invariant geometrical features, typically including length and width of the fingers, aspect ratio of the palm or fingers, thickness of the hand, etc. These features are not as discriminating as other biometric characteristics, however they can easily be used for verification purpose. Gesture recognition is the process by which gestures formed by a user interact with the computer or is the element of the special signs language to convey meaning. In this paper are propose a method for the recognition of hand gestures using geometrical and Radon transform (RT) features.
An Extension of the GISSMO Damage Model Based on Lode Angle Dependence

This paper presents the extension and validation of the damage model GISSMO (Generalized Incremental Stress State dependant damage MOdel). The damage model is extended for 3D usage by utilization of Lode angle parameter. The fracture strain is defined in the stress triaxiality and Lode angle parameter space as a surface. The fracture strain definition is introduced as a table definition. The stress triaxiality and Lode angle parameter space is covered with proper specimens and load conditions. The validation is done for dual phase steel DP600. Round specimens, flat-grooved plane strain specimens with different notches and Nakazima biaxial specimens are used to get information for the Lode angle parameter values 1, 0 and -1, respectively. Additionally butterfly and flat tension specimens are used in order to cover intermediate stress states. The focus of the paper is the introduction of a calibration approach using numerical simulation and comparison with experimental results of specimens. The simulations were performed using LS-DYNA and an extended version of the GISSMO model.
A novel fixed-complexity soft-output MIMO detector using parallel bidirectional scheme

This paper presents a novel detection algorithm for soft-output, multiple-input multiple-output (MIMO) systems. The algorithm obtains a list of candidates used to calculate likelihood information in parallel and it includes two stages for tree searching, where each stage corresponds to one direction of the path selection process. The simulation results indicate that the algorithm achieves better performance with lower complexity than a list-based fixed-complexity soft-output sphere decoder. Moreover, due to its parallel nature, it is well suited for hardware implementation.