Miracle-dz/UniFAR-FTUs huggingface dataset:
Catecholamine Response to Exercise in Children with Attention Deficit Hyperactivity Disorder

The objective of this study was to examine differences in catecholamine (CA) response to exercise between children who had received a diagnosis of attention-deficit/hyperactivity disorder (ADHD) and age- and gender-matched controls. On the basis of the notion of a CA dysfunction in ADHD, we reasoned that the normal robust increase in circulating CA seen in response to exercise would be blunted in children with ADHD. To test this, we recruited 10 treatment-naïve children with newly diagnosed ADHD and 8 age-matched controls (all male) and measured CA response to an exercise test in which the work was scaled to each subject's physical capability. After exercise, epinephrine and norepinephrine increased in both control and ADHD subjects (p = 0.006 and p = 0.002, respectively), but the responses were substantially blunted in the ADHD group (p = 0.018) even though the work performed did not differ from controls. Circulating dopamine increased significantly in the control subjects (p < 0.016), but no increase was noted in the subjects with ADHD. Finally, a significant attenuation in the lactate response to exercise was found in ADHD (between groups, p < 0.005). Our data suggest that CA excretion after exercise challenges in children with ADHD is deficient. This deficiency can be detected using a minimally invasive, nonpharmacologic challenge.
GLS Estimation of Dynamic Factor Models

In this article a simple two-step estimation procedure of the dynamic factor model is proposed. The estimator allows for heteroscedastic and serially correlated errors. It turns out that the feasible two-step estimator has the same limiting distribution as the generalized least squares (GLS) estimator assuming that the covariance parameters are known. In a Monte Carlo study of the small sample properties, we find that the GLS estimators may be substantially more efficient than the usual estimator based on principal components. Furthermore, it turns out that the iterated version of the estimator may feature considerably improved properties in sample sizes usually encountered in practice.
Colonic leptin: source of a novel pro‐inflammatory cytokine involved in inflammatory bowel disease

Leptin, a peptide encoded by the obese (ob) gene, is primarily secreted by adipocytes and is a critical hormone that controls body weight due to its central effects. Recently, additional roles for leptin in the gastrointestinal tract have been suggested because gastric lining cells also produce and release leptin in response to meal‐related stimuli. While gastric epithelia might thus directly contribute to circulating leptin following a meal, here we show that inflamed colonic epithelial cells express and release leptin apically into the intestinal lumen. In addition, we demonstrate leptin expression and secretion in vitro in epithelial cells. In response to luminal leptin, model intestinal epithelia critically activate the NF‐κB, a key signaling system to pro‐inflammatory stimuli. The inflammatory effect of luminal leptin was characterized in vivo in mice administered intrarectal leptin. Leptin induced epithelial wall damage and neutrophil infiltration that represent characteristic histological findings in acute intestinal inflammation. These observations provide evidence for an intraluminal biological signaling of leptin and a new pathophysiological role for intraluminal leptin during states of intestinal inflammation such as inflammatory bowel disease.
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm

In global optimization, when the evaluation of the target function is costly, the usual strategy is to learn a surrogate model for the target function and replace the initial optimization by the optimization of the model. Gaussian processes have been widely used since they provide an elegant way to model the fitness and to deal with the exploration-exploitation trade-off in a principled way. Several empirical criteria have been proposed to drive the model optimization, among which is the well-known Expected Improvement criterion. The major computational bottleneck of these algorithms is the exhaustive grid search used to optimize the highly multimodal merit function. In this paper, we propose a competitive "adaptive grid" approach, based on a properly derived cross-entropy optimization algorithm with mixture proposals. Experiments suggest that 1) we outperform the classical single-Gaussian cross-entropy method when the fitness function is highly multimodal, and 2) we improve on standard exhaustive search in GP-based surrogate optimization.
Stabilization for a class of delayed coupled PDE-ODE systems with boundary control

A class of coupled PDE-ODE systems with delays at the input and two-directional interconnection at the interfaces (one single point) is discussed in this paper. Exponential stability in the sense of the corresponding norms for the original closed-loop system with the derived controller is obtained by finally transforming the system into an exponentially stable PDEs-ODE cascade with a boundary feedback backstepping controller, and the result is also rigidly proved.
Postural Control Is Impaired in People with COPD: An Observational Study.

PURPOSE We investigated deficits in postural control and fall risk in people with chronic obstructive pulmonary disease (COPD). METHOD Twenty people with moderate to severe COPD (mean age 72.3 years, standard deviation [SD] 6.7 years) with a mean forced expiratory volume in 1 second (FEV(1)) of 46.7% (SD 13%) and 20 people (mean age 68.2 years, SD 8.1) who served as a comparison group were tested for postural control using the Sensory Organization Test (SOT). A score of zero in any trial of the SOT was registered as a fall. On the basis of the SOT results, participants were categorized as frequent fallers (two or more falls) or as fallers (one fall). To explore the potential influence of muscle weakness on postural control, knee extensors concentric muscle torque was assessed with an isokinetic dynamometer. Physical activity level was assessed with the Physical Activity Scale for the Elderly. RESULTS People with COPD showed a 10.8% lower score on the SOT (p=0.016) and experienced more falls (40) than the comparison group (12). The proportion of frequent fallers and fallers during the SOT was greater (p=0.021) in the COPD group (four of 10) than in the comparison group (two of seven). People with COPD showed deficits in knee extensors muscle strength (p=0.01) and a modest trend toward reduced physical activity level. However, neither of these factors explained the deficits in postural control observed in the COPD group. CONCLUSIONS People with COPD show deficits in postural control and increased risk of falls as measured by the SOT. The deficits in postural control appear to be independent of muscle weakness and level of physical activity. Postural control interventions and fall risk strategies in the pulmonary rehabilitation of COPD are recommended.
ECT AND PLATELET 5HT UPTAKE IN MAJOR DEPRESSION

Several studies have reported decreased platelet 5-HT uptake in patients of major depression. The mechanism of antidepressant action of ECT is not clear. The present work was undertaken with the aim to study the active platelet 5-HT uptake and the effect of ECT on it in patients of major depression. 15 patients of major depression (DSM-lll-R) and equal number of age and sex-matched controls were included in the study. Active platelet 5-HT uptake was determined before ECT, after a course of ECT and 7 days after last ECT. Platelet 5-HT uptake was. significantly lower in der essives than normal controls. After ECT treatment there was significant increase in 5-HT uptake which came down to pretreatment level after 1 week of last ECT. The effect of ECT on serotonergic system is discussed.
Contingent cooperation between wild female baboons

The apparent rarity of contingent cooperation in animals has convinced many investigators that such reciprocity is unimportant, stimulating consideration of alternative explanations for cooperation, such as by-product mutualism and biological markets motivated by the likelihood of immediate reward. Nevertheless, there is also limited evidence that animals do sometimes rely on memory of recent interactions when behaving altruistically toward others. Here we describe a playback experiment conducted on wild female baboons, suggesting that contingent cooperation may occur among unrelated individuals, even when there is a temporal delay between the two cooperative acts. Hearing the recruitment call of an unrelated recent grooming partner caused subjects to move in the direction of the loudspeaker and approach their former partner, particularly when this partner had an infant. When the subject and her partner were close kin no such effect was observed. Subjects’ responses were not influenced by any type of recent interaction, because prior grooming and prior aggression influenced their behavior in qualitatively different ways. Similarly, their responses were not prompted only by the motivation to resume friendly interactions, because prior grooming alone did not elicit approach. Instead, subjects were most likely to approach their former grooming partner when they had also heard her recruitment call. Results suggest that at least some forms of cooperation in animals may be based on memory of specific recent interactions.
OSBIA: Open Source Business Intelligence Analytics System Based on Domestic Platform

Nowadays, online comments and other textual data become more and more significant for business intelligence service. However, there is blank in the area of IS based on domestic platform at present. We designed and implemented OSBIA: an open source business intelligence analytics system based on domestic platform. OSBIA system concentrates on analyzing open source textual intelligence for the business purpose and adopts self-designed distributed crawler system so that a closed circle is formed from intelligence collection to analysis process and push service. For the efficiency of OSBIA, the improved parallel OPTICS algorithm (IPOPTICS) is described in this paper, which effectively solves the problem that OPTICS is limited by its parameterradius of neighborhood. And we also illustrate one typical application for market research: “consumer’s comments discovery” based on Stanford parser and IPOPTICS algorithm and the like. The results of experiences show that the OSBIA system is much faster than the original OPTICS and suitable for large scale textual intelligence analysis.
Functional Relevance of Activin A in the Intestinal Epithelium

Background: Activin A modulates inflammation and repair in various tissues. The aim of this study was to characterize the effects of activin A on intestinal epithelial cell function and to evaluate a potential role in intestinal epithelial wound repair and inflammation. Methods: The expression of activin A and its receptors ActRI and ActRII in intestinal epithelial IEC-6 cells and in tissues from IBD patients and non-inflamed controls was evaluated using RT-PCR and immunohistochemistry. Functional effects of activin A on intestinal epithelial cell migration and proliferation were assessed using an in vitro wounding model and colorimetric MTT assays. Results: Expression of the activin β A subunit and the activin receptors ActRI and ActRII in IEC-6 cells was demonstrated using RT-PCR. Activin A (50 ng/ml) caused a significant, on average 2.8-fold enhancement of epithelial cell migration and a significant on average 4.1-fold inhibition of IEC-6 cell proliferation. Expression of ActRI and ActRII was observed in all intestinal tissues from patients with IBD and in all controls. In contrast, no expression of the activin β A-subunit was observed in controls, while β A expression was found in intestinal tissues from IBD patients. Conclusions: Activin A may play an important role in the modulation of intestinal epithelial cell function, thus providing a new approach to modulate intestinal wound repair.
PIK3CA mutation and amplification in human lung cancer

To explore the significance of phosphatidylinositol‐3‐kinase, catalytic, alpha (PIK3CA) in the carcinogenesis in human lung, mutations and copy number changes were investigated in 148 Japanese patients with primary cancer of the lung. For biological validation, the effects of exogenously expressed wild‐type and mutated PIK3CA were studied in an immortalized human airway epithelial cell line. Mutations in PIK3CA were found in five (3.6%) of the 139 available patients, and copy number gains were found in 21 (18.3%) of 115 patients, respectively. Overall, mutations or copy number gains were detected in 24 of the 106 patients (22.6%) for whom results in both analyses were available. The prevalence of copy number gains was higher in men, smokers, and in patients with squamous cell carcinoma than in the opposite categories. The copy number changes showed a trend toward higher prevalence in the earlier stages (P = 0.038). Interestingly, the presence of mutations and of copy number alterations were mutually exclusive in the present patients, implying that both entail equivalent oncogenic potential. Over‐expressed wild‐type PIK3CA and its two common mutants, K545E and H1047R, significantly enhanced the anchorage‐independent growth activity and migration activity of immortalized airway epithelium 16HBE14o– cells, but the effects of the K545E and H1047R mutants were more remarkable than those of the wild‐type. The present demonstrates an important role of PIK3CA in human lung carcinogenesis.
Analysis and Comparison of Eigenspace-Based Face Recognition Approaches

Different eigenspace-based approaches have been proposed for the recognition of faces. They differ mostly in the kind of projection method being used and in the similarity matching criterion employed. The aim of this paper is to present a comparative study between some of these different approaches. This study considers theoretical aspects as well as experiments performed using a face database with a few number of classes (Yale) and also with a large number of classes (FERET).
Limits of Static Analysis for Malware Detection

Malicious code is an increasingly important problem that threatens the security of computer systems. The traditional line of defense against malware is composed of malware detectors such as virus and spyware scanners. Unfortunately, both researchers and malware authors have demonstrated that these scanners, which use pattern matching to identify malware, can be easily evaded by simple code transformations. To address this shortcoming, more powerful malware detectors have been proposed. These tools rely on semantic signatures and employ static analysis techniques such as model checking and theorem proving to perform detection. While it has been shown that these systems are highly effective in identifying current malware, it is less clear how successful they would be against adversaries that take into account the novel detection mechanisms. The goal of this paper is to explore the limits of static analysis for the detection of malicious code. To this end, we present a binary obfuscation scheme that relies on the idea of opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value. Based on opaque constants, we build obfuscation transformations that obscure program control flow, disguise access to local and global variables, and interrupt tracking of values held in processor registers. Using our proposed obfuscation approach, we were able to show that advanced semantics-based malware detectors can be evaded. Moreover, our opaque constant primitive can be applied in a way such that is provably hard to analyze for any static code analyzer. This demonstrates that static analysis techniques alone might no longer be sufficient to identify malware.
A Methodology for Building XML Data Warehouses

Developing a data warehouse for XML documents involves two major processes: one of creating it, by processing XML raw documents into a specified data warehouse repository; and the other of querying it, by applying techniques to better answer usersâ€™ queries. This paper focuses on the first part; that is identifying a systematic approach for building a data warehouse of XML documents, specifically for transferring data from an underlying XML database into a defined XML data warehouse. The proposed methodology on building XML data warehouses covers processes including data cleaning and integration, summarization, intermediate XML documents, and updating/linking existing documents and creating fact tables. In this paper, we also present a case study on how to put this methodology into practice. We utilise the XQuery technology in all of the above processes.
A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.
PhySIC: a veto supertree method with desirable properties.

This paper focuses on veto supertree methods; i.e., methods that aim at producing a conservative synthesis of the relationships agreed upon by all source trees. We propose desirable properties that a supertree should satisfy in this framework, namely the non-contradiction property (PC) and the induction property (PI). The former requires that the supertree does not contain relationships that contradict one or a combination of the source topologies, whereas the latter requires that all topological information contained in the supertree is present in a source tree or collectively induced by several source trees. We provide simple examples to illustrate their relevance and that allow a comparison with previously advocated properties. We show that these properties can be checked in polynomial time for any given rooted supertree. Moreover, we introduce the PhySIC method (PHYlogenetic Signal with Induction and non-Contradiction). For k input trees spanning a set of n taxa, this method produces a supertree that satisfies the above-mentioned properties in O(kn(3) + n(4)) computing time. The polytomies of the produced supertree are also tagged by labels indicating areas of conflict as well as those with insufficient overlap. As a whole, PhySIC enables the user to quickly summarize consensual information of a set of trees and localize groups of taxa for which the data require consolidation. Lastly, we illustrate the behaviour of PhySIC on primate data sets of various sizes, and propose a supertree covering 95% of all primate extant genera. The PhySIC algorithm is available at http://atgc.lirmm. fr/cgi-bin/PhySIC.
Sampled-data model following output feedback control for discrete-valued input systems

This paper considers a sampled-data model following control for continuous-time discrete-valued input systems. The synthesis problem the authors address is the simultaneous synthesis of the nominal controller and the dynamic quantizers. A synthesis condition is recast as a set of matrix inequalities via the matrix uncertainty approach, while the quantized accuracy and the sampling time of discretizing the continuous-valued signal are considered simultaneously. To obtain the synthesis condition, the improved approach dividing the region of the switching intervals and the change of variable approach reducing the controller order are applied.
Daytime Arctic Cloud Detection Based on Multi-Angle Satellite Data With Case Studies

Global climate models predict that the strongest dependences of surface air temperatures on increasing atmospheric carbon dioxide levels will occur in the Arctic. A systematic study of these dependences requires accurate Arctic-wide measurements, especially of cloud coverage. Thus cloud detection in the Arctic is extremely important, but it is also challenging because of the similar remote sensing characteristics of clouds and ice-and snow-covered surfaces. This article proposes two new operational Arctic cloud detection algorithms using Multiangle Imaging SpectroRadiometer (MISR) imagery. The key idea is to identify cloud-free surface pixels in the imagery instead of cloudy pixels as in the existing MISR operational algorithms. Through extensive exploratory data analysis and using domain knowledge, three physically useful features to differentiate surface pixels from cloudy pixels have been identified. The first algorithm, enhanced linear correlation matching (ELCM), thresholds the features with either fixed or data-adaptive cutoff values. Probability labels are obtained by using ELCM labels as training data for Fisher's quadratic discriminant analysis (QDA), leading to the second (ELCM-QDA) algorithm. Both algorithms are automated and computationally efficient for operational processing of the massive MISR data set. Based on 5 million expert-labeled pixels, ELCM results are significantly in terms of both accuracy (92%%) and coverage (100%%) compared with two MISR operational algorithms, one with an accuracy of 80%% and coverage of 27%% and the other with an accuracy of 83%% and a coverage of 70%%. The ELCM-QDA probability prediction is also consistent with the expert labels and is more informative. In conclusion, ELCM and ELCM-QDA provide the best performance to date among all available operational algorithms using MISR data.
Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain's inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images.