Miracle-dz/UniFAR-FTUs huggingface dataset:
Gastrointestinal symptoms in elite athletes: time to recognise the problem?

Gastrointestinal (GI) symptoms can arise from a number of physiological and pathological processes including inflammation, infection and malignancy.1 Such symptoms are a common cause for referral to secondary care. They impair quality of life, even in the absence of organic pathology.2 GI symptoms are common in endurance sports, particularly running. Prevalence of symptoms varies depending on the event, assessment tool used and what is classified as a ‘symptom’ in each study, but between 30% and 90% of individuals have reported GI symptoms.3 However, there has been less research in examining the prevalence of such symptoms in other sports, particularly at elite level. This is surprising given that many of the factors associated with GI damage and symptoms are commonly …
Cogmetrics for Artiicial Agents

The paper proposes a means of quantifying exogenic knowledge (knowledge of external phenomena) in artiicial agents. The basis of the method is the derivation of a ratio between two values relating to the agent's differential capacities. The sensitivity of the agent is deened as the extent to which the agent can diierentiate phenomena. The informedness of the agent is deened as the extent to which phenomena are diierentiated by its sensory surfaces. The degree to which sensitivity exceeds informedness is then used as a means of guaging the level of exogenic knowledge. Examples are presented illustrating how the application of this measure is established in artiicial-agent scenarios.
Presence of Mesenchymal Stem Cells in Human Bone Marrow After Exposure to Chemotherapy: Evidence of Resistance to Apoptosis Induction

For various potential clinical applications, the use of autologous human MSCs (hMSCs) would be favorable. In vitro observations suggested that hMSCs are resistant for chemotherapeutic substances; however, no data exist on the characteristics of hMSCs from bone marrow (BM) of chemotherapeutically treated patients. Here, we analyzed the character of hMSCs derived from chemotherapy‐exposed BM and the in vitro response of hMSCs to chemotherapeutic substances. Colony‐forming units‐fibroblast (CFU‐Fs) were isolated from BM aspirates of patients after high‐dose or standard chemotherapy and of donors with unaffected BM. CFU‐Fs from chemotherapy‐exposed and unaffected BM contained hMSCs with similar phenotype, proliferation capacity, and differentiation potential. No obvious influence of age, sex, or time since chemotherapy exposure on the presence and characteristics of hMSCs was observed. In vitro, hMSCs showed a significant resistance for cisplatin, vincristine, and etoposide compared with sensitive tumor cell lines, particularly at apoptosis‐inducing doses. The phenotype and differentiation potential of hMSCs was not altered by genotoxic treatment under clinically relevant conditions in vitro. hMSCs showed an elevated threshold for cisplatin‐induced apoptosis, which was characterized by a lack of caspase‐9 activity in apoptotic cells. In vitro exposure of hMSCs to cisplatin, vincristine, and etoposide resulted in an increased p53 expression, independent of apoptosis induction. We conclude that hMSCs can be isolated from chemotherapy‐exposed BM in sufficient number and quality for potential clinical applications in chemotherapeutically treated patients. Our data suggest that an elevated apoptotic threshold contributes not only to the persistence of hMSCs in the BM after chemotherapy but also to their lifelong presence in the adult BM.
Affine registration for multidimensional point sets under the framework of Lie group

Abstract. An affine registration algorithm for multidimensional point sets under the framework of Lie group is proposed. This algorithm studies the affine registration between two data sets, and puts the expectation maximization-iterative closest point (EM-ICP) algorithm into the framework of Lie group, since all affine transformations form a Lie transformation group. The registration is carried out via minimizing an energy functional depending on elements of the affine transformation Lie group. The key point for applying the idea of Lie group is that, during the minimization via iteration, we must guarantee the next iteration step of the transformation is still an element in the same group, starting from an element in a Lie group. Our solution is utilizing the element of Lie algebra to represent that of Lie group near the identity via the exponential map, i.e., we use the first canonical coordinate representation of Lie group. Several comparative experiments between the proposed Lie-EM-ICP algorithm and the Lie-ICP algorithm are performed, showing that the proposed algorithm is more accurate and robust, especially in the presence of outliers. This algorithm can also be generalized to other registration problems in general, provided that desired transformations are within certain Lie group.
CLASSIFICATION OF MOVIE REVIEWS BY SUPERVISED MACHINE LEARNING APPROACHES

Large volumes of data are available in the web. The discussion forum, review sites, blogs and news corpora are some of the opinion rich resources. The opinions obtained from those can be classified and used for gathering online customer’s preferences. Techniques are being applied to design a system that identifies and classify opinions spread largely in the internet. Few different problems such as sentiment classification, feature based classification and handling negotiations are predominating this research community. This paper studies online movie reviews using sentiment analysising approaches. In this study, sentiment classification techniques were applied to movie reviews. Specifically, we compared three supervised machine learning approaches SVM, Navie Bayes and kNN for Sentiment Classification of Reviews. Empirical results states that SVM approach outperformed the Navie Bayes and kNN approaches, and the training dataset had a large number of reviews, SVM approach reached accuracies of atleast 80%.
Fecal calprotectin is a surrogate marker for endoscopic lesions in inflammatory bowel disease

Background: Fecal calprotectin is a marker of inflammation in inflammatory bowel disease (IBD). Since mucosal healing has become a goal of treatment in IBD we examined how reliably calprotectin levels reflect mucosal disease activity. Methods: In all, 126 IBD patients and 32 irritable bowel syndrome (IBS) patients needing colonoscopy delivered a sample of feces prior to the start of bowel cleansing. Besides collection of symptom scores and blood tests, experienced endoscopists recorded the Simple Endoscopic Score for Crohn's Disease (SES‐CD) and the Crohn's Disease Endoscopic Index of Severity (CDEIS) in Crohn's disease (CD) patients and the Mayo endoscopic score in ulcerative colitis (UC) patients. Stool samples were shipped for central calprotectin PhiCal Assay (enzyme‐linked immunosorbent assay [ELISA]). Correlation analysis was done with Pearson statistics. Results: The median (interquartile range [IQR]) fecal calprotectin levels were 175 (44–938) &mgr;g/g in CD, 465 (61–1128) &mgr;g/g in UC, and 54 (16–139) &mgr;g/g in IBS. Correlations were significant with endoscopic disease scores in both CD and in UC. Using ROC statistics, a cutoff value of 250 &mgr;g/g indicated the presence of large ulcers with a sensitivity of 60.4% and a specificity of 79.5% (positive predictive value [PPV] 78.4%, negative predictive value [NPV] 62.0%) in CD. Levels ≤250 &mgr;g/g predicted endoscopic remission (CDEIS ≤3) with 94.1% sensitivity and 62.2% specificity (PPV 48.5%, NPV 96.6%). In UC, a fecal calprotectin >250 &mgr;g/g gave a sensitivity of 71.0% and a specificity of 100.0% (PPV 100.0%, NPV 47.1%) for active mucosal disease activity (Mayo >0). Calprotectin levels significantly correlated with symptom scores in UC (r = 0.561, P < 0.001), but not in CD. Conclusions: Fecal calprotectin levels correlate significantly with endoscopic disease activity in IBD. The test appears useful in clinical practice for assessment of endoscopic activity and remission. (Inflamm Bowel Dis 2012;)
A Lattice-based Framework for Joint Chinese Word Segmentation, POS Tagging and Parsing

For the cascaded task of Chinese word segmentation, POS tagging and parsing, the pipeline approach suffers from error propagation while the joint learning approach suffers from inefficient decoding due to the large combined search space. In this paper, we present a novel lattice-based framework in which a Chinese sentence is first segmented into a word lattice, and then a lattice-based POS tagger and a lattice-based parser are used to process the lattice from two different viewpoints: sequential POS tagging and hierarchical tree building. A strategy is designed to exploit the complementary strengths of the tagger and parser, and encourage them to predict agreed structures. Experimental results on Chinese Treebank show that our lattice-based framework significantly improves the accuracy of the three sub-tasks.
Growth Rate Analysis of Electromagnetic Waves using Beam Pre-bunching in Cerenkov Free Electron Laser

The Cerenkov free electron laser (CFEL) model under consideration consists of a pre-bunched relativistic electron beam and a dielectric loaded waveguide. The increase in the growth rate and gain with the change in phase by using a prebunched electron beam in a Cerenkov free electron laser (CFEL) has been studied. The growth rate and gain is calculated at experimentally known CFEL and FEL parameters and it is seen that beam pre-bunching on Cerenkov free electron laser (CFEL) offers considerable enhancement in gain and efficiency when the phase of the pre-bunching electron beam is
The effect of section thickness on MR lesion detection and quantification in multiple sclerosis.

BACKGROUND AND PURPOSE The purpose of our study was to investigate the effect of section thickness on MR detection of brain lesion volume and measurement precision in patients with multiple sclerosis (MS). METHODS Eight subjects with known MS were studied on a 1.5-T MR system. We used a 3D fast fluid-attenuated inversion-recovery sequence to obtain contiguous axial brain images at section thicknesses of 5 mm, 3 mm, and 1 mm. Two sets of images were acquired at each section thickness during two sessions, between which the patient was removed from the scanner. Lesion volumes were measured at each section thickness using a semiautomated local thresholding technique. RESULTS We found that progressive reduction in section thickness led to detection of smaller lesions, resulting in a significant (8%) increase in lesion volume on MR images as section thickness was reduced from 5 mm to 3 mm. However, despite a further increase in lesion detection at a section thickness of 1 mm, this did not result in an increase in total lesion volume. This finding indicates that the relationship between section thickness and lesion volume on MR images is not linear. Scan-rescan reproducibility was improved by reducing section thickness, at the cost of increased analysis time. CONCLUSION This study shows that acquisition of very thin sections increases the sensitivity and precision of MS lesion measurement. Serial studies assessing lesion changes over time are needed to define the impact of this increase on sample size requirements for MS treatment trials.
Compact closed-form micromechanical expressions for the effective uncoupled and coupled linear properties of layered composites

This work aims mainly to derive closed-form expressions for the effective properties of layered composites when linear uncoupled and coupled phenomena, such as conduction, elasticity, thermoelectricity and piezomagnetoelectricity, are concerned. In addition, properties may be graded in the direction normal to the layer plane. The remarkable feature of the obtained results is that they are expressed in a unified and very compact way for all uncoupled and coupled linear mechanical and physical phenomena. Our results include as special ones all the relevant results reported up to now in the literature. The key to obtaining our general compact coordinate-free results resides in extending Hill’s interfacial relations for a perfect elastic interface to the setting of an arbitrary finite number of uncoupled and coupled linear phenomena. The effects of the presence of stiffeners and softeners on the effective properties of layered composites are also investigated. In particular, four limiting cases for piezoelectricity are studied in detail. As illustrations, the general results are applied to several coupled phenomena and to functionally graded layered composites.
Genetic network inference: from co-expression clustering to reverse engineering

MOTIVATION Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.
A generic context service for ubiquitous environments

The increase of mobile and interconnected devices demands for equally ubiquitous and mobile applications. Due to the heterogeneity of devices, applications have to adapt themselves to the environment they are executed in. This requires context management in the environment and context-sensitivity of the application. In this paper, we present a generic context service with a novel generic context model. Our context service has a modular architecture and provides context collection, discovery and monitoring. It offers extension points for integrating future context domains and provides a Web service interface for standard-compliant interaction in heterogeneous environments. We sketch the integration of the prototype into our SAMProc middleware, which provides adaptivity as well as migratability to applications. As a use case, we outline a novel context-aware mobile media player application, which uses context for automatically migrating in order to follow the mobile user and for selecting the best presentation device in the user's surroundings.
Instruction distribution heuristics for quad-cluster, dynamically-scheduled, superscalar processors

We investigate instruction distribution methods for quad-cluster, dynamically-scheduled superscalar processors. We study a variety of methods with different cost, performance and complexity characteristics. We investigate both Pion-adaptive and adaptive methods and their sensitivity both to inter-cluster communication latencies and pipeline depth. Furthermore, we develop a set of models that allow us to identify how well each method attacks issue-bandwidth and inter-cluster communication restrictions. We find that a relatively simple method that changes clusters every other three instructions offers only a 17% performance slowdown compared to a non-clustered configuration operating at the same frequency Moreover; we show that by utilizing adaptive methods it is possible to further reduce this gap down to about 14%. Furthermore, performance appears to be more sensitive to inter-cluster communication latencies rather than to pipeline depth. The best performing method offers a slowdown of about 24% when inter-cluster communication latency is two cycle. This gap is only 20% when two additional stages are introduced in the front-end pipeline.
Recognizing communicative facial expressions for discovering interpersonal emotions in group meetings

This paper proposes a novel facial expression recognizer and describes its application to group meeting analysis. Our goal is to automatically discover the interpersonal emotions that evolve over time in meetings, e.g. how each person feels about the others, or who affectively influences the others the most. As the emotion cue, we focus on facial expression, more specifically smile, and aim to recognize ``who is smiling at whom, when, and how often'', since frequently smiling carries affective messages that are strongly directed to the person being looked at; this point of view is our novelty. To detect such communicative smiles, we propose a new algorithm that jointly estimates facial pose and expression in the framework of the particle filter. The main feature is its automatic selection of interest points that can robustly capture small changes in expression even in the presence of large head rotations. Based on the recognized facial expressions and their directions to others, which are indicated by the estimated head poses, we visualize interpersonal smile events as a graph structure, we call it the interpersonal emotional network; it is intended to indicate the emotional relationships among meeting participants. A four-person meeting captured by an omnidirectional video system is used to confirm the effectiveness of the proposed method and the potential of our approach for deep understanding of human relationships developed through communications.
A Statistical Approach for Image Interpolation

Interpolation is an image processing operation for improve the resolution of a digital image. In this work, following the orthogonality principle, and under the assumption that the actual image is a locally stationary random process, we propose an alternative scheme for image interpolation. In our approach, the computational complexity is similar to the first-order spline algorithm. The algorithm was compared with classical B-spline methods and also with a statistical interpolator previously proposed in the literature. According to the normalized mean square error criteria, the proposed method produced accurate results.
Design and expression of a synthetic mucin gene fragment in Escherichia coli.

Adenocarcinomas of glandular tissues produce a hypoglycosylated form of a normal glycoprotein (mucin) that elicits an immune response. A tumor-specific epitope of mucin occurs in a 20-amino-acid, tandemly repeated domain of human MUC1 mucin. A synthetic gene encoding five tandem repeats of the tumor-specific epitope of human mucin (m5tr) was designed for efficient cloning and expression in Escherichia coli for subsequent use in preparing reagent quantities of the mucin 5 tandem repeat (mtr5) polypeptide. The synthetic gene was cloned in the correct reading frame into the maltose-binding protein (MBP) fusion expression vector pMAL-p2. Bacterial clones containing the mucin synthetic gene (m5tr) were shown to produce the intended recombinant fusion protein, MBP-mtr5. The fusion protein represents a significant fraction of the cell protein, 50% or more of which is secreted into the periplasm. The MBP-mtr5 protein is largely intact and easily prepared in sufficient quantity and purity for preliminary structure-function studies.
The Dorsocross T-box transcription factors promote tissue morphogenesis in the Drosophila wing imaginal disc

The Drosophila wing imaginal disc is subdivided into notum, hinge and blade territories during the third larval instar by formation of several deep apical folds. The molecular mechanisms of these subdivisions and the subsequent initiation of morphogenic processes during metamorphosis are poorly understood. Here, we demonstrate that the Dorsocross (Doc) T-box genes promote the progression of epithelial folds that not only separate the hinge and blade regions of the wing disc but also contribute to metamorphic development by changing cell shapes and bending the wing disc. We found that Doc expression was restricted by two inhibitors, Vestigial and Homothorax, leading to two narrow Doc stripes where the folds separating hinge and blade are forming. Doc mutant clones prevented the lateral extension and deepening of these folds at the larval stage and delayed wing disc bending in the early pupal stage. Ectopic Doc expression was sufficient to generate deep apical folds by causing a basolateral redistribution of the apical microtubule web and a shortening of cells. Cells of both the endogenous blade/hinge folds and of folds elicited by ectopic Doc expression expressed Matrix metalloproteinase 2 (Mmp2). In these folds, integrins and extracellular matrix proteins were depleted. Overexpression of Doc along the blade/hinge folds caused precocious wing disc bending, which could be suppressed by co-expressing MMP2RNAi.
State of the art in MapReduce: issues and approaches

In the last years, new data sources appeared: social networks, mobile, internet of things, open Data, etc., and therefore data are rapidly increasing. These data is voluminous, various, and difficult to measure and analyze, which appears the concept of Big Data. The vast amount of data makes the ETL (Extract-Transform-Load) process heavy in data warehousing, renders the data mining process more complex, and makes the slow loading of data in database management systems. The solution to make these process more efficient is the use of parallelization technologies, many researchers opt for the use of MapReduce paradigm for its flexibility and powerful. In this paper, we provide an overview of state of the art in MapReduce research and we present its various axis.
SAVE: A framework for semantic annotation of visual events

In this paper we propose a framework that performs automatic semantic annotation of visual events (SAVE). This is an enabling technology for content-based video annotation, query and retrieval with applications in Internet video search and video data mining. The method involves identifying objects in the scene, describing their inter-relations, detecting events of interest, and representing them semantically in a human readable and query-able format. The SAVE framework is composed of three main components. The first component is an image parsing engine that performs scene content extraction using bottom-up image analysis and a stochastic attribute image grammar, where we define a visual vocabulary from pixels, primitives, parts, objects and scenes, and specify their spatio-temporal or compositional relations; and a bottom-up top-down strategy is used for inference. The second component is an event inference engine, where the video event markup language (VEML) is adopted for semantic representation, and a grammar-based approach is used for event analysis and detection. The third component is the text generation engine that generates text report using head-driven phrase structure grammar (HPSG). The main contribution of this paper is a framework for an end-to-end system that infers visual events and annotates a large collection of videos. Experiments with maritime and urban scenes indicate the feasibility of the proposed approach.