Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Individualized Resistance Strength Programs on Knee Muscular Imbalances in Junior Elite Soccer Players

The purpose of this study was to investigate the effects of a resistance training program on the muscular strength of soccer players’ knees that initially presented unilateral and bilateral differences. For this study, a team of 24 male well-trained junior soccer players was divided into two strength program training groups: a Resistance Training Control Group (RTCG) composed of 10 players that did not have muscular imbalances and a Resistance Training Experimental Group (RTEG) composed of 14 players that had muscular imbalances. All players followed a resistance training program for six weeks, two times per week, during the transition period. The program of individualized strength training consisted of two parts. The first part, which was identical in terms of the choice of training loads, was intended for both training groups and contained two series of exercises including upper and lower body exercises. The second part of the program was intended only for RTEG and consisted of two additional series for the groups of muscles that had identified unilateral and bilateral differences. The applied program showed various directions in the isokinetic profile of changes. In the case of RTCG, the adaptations related mainly to the quadriceps muscle (the peak torque (PT) change for the dominant leg was statistically significant (p < 0.05)). There were statistically significant changes in RTEG (p < 0.05) related to PT for the hamstrings in both legs, which in turn resulted in an increase in the conventional hamstring/quadriceps ratio (H/Q). It is interesting that the statistically significant (p < 0.05) changes were noted only for the dominant leg. No statistically significant changes in bilateral differences (BD) were noted in either group. These results indicate that individualized resistance training programs could provide additional benefits to traditional strength training protocols to improve muscular imbalances in post-adolescent soccer players.
Cognitive Processes in Contextual Cueing

A computational model of learning in visual attention is described, and it is shown how it can integrate information over time to form a representation of the visual context. This visual context is subsequently used to guide the focus of attention in a visual search task. Computer simulations shows that the model can learn to use contextual cues when they are available. The performance using contextual cues is compared to a situation where they are not used. The result shows that contextual cueing is only useful when the visual scene is sufﬁciently complex to warrant the extra time needed to establish context identity.
Better cooperative control with limited look-ahead

This paper is an extension of the current planning methods used in UAV tactical decision-making areas. We propose a repetitive online optimization method with limited look-ahead intervals. A theoretical foundation is built and the condition of its applicability is given. Optimal solutions can be approached by iteration. The relationship of this method with other well-known control methods is described. Advantages and disadvantages are discussed with suggestions for possible remediation. Interesting examples are presented to demonstrate the strength of this method. Possible applications are suggested
IRAS 18511+0146: a proto Herbig Ae/Be cluster?

Context. The evolution of a young protocluster depends on the relative spatial distribution and dynamics of both stars and gas. Aims. We study the distribution and properties of the gas and stars surrounding the luminous (10^4 L_☉) protocluster IRAS 18511+0146. Methods. IRAS 18511+0146 and the cluster associated with it has been investigated using the sub-millimetre (JCMT-SCUBA), infrared (Spitzer-MIPSGAL, Spitzer-GLIMPSE, Palomar) and radio (VLA) continuum data. Cluster simulations have been carried out in order to understand the properties of clusters as well as to compare with the observations. Results. The central most obscured part of the protocluster coincident with the compact sub-millimetre source found with SCUBA is responsible for at least 2/3 of the total luminosity. A number of cluster members have been identified which are bright in mid infrared and show rising (near to mid infrared) spectral energy distributions suggesting that these are very young stellar sources. In the mid infrared 8.0 μm image, a number of filamentary structures and clumps are detected in the vicinity of IRAS 18511+0146. Conclusions. Based on the luminosity and cluster size as well as on the evolutionary stages of the cluster members, IRAS 18511+0146 is likely to be protocluster with the most massive object being a precursor to a Herbig type star.
Multi-object Segmentation with Coupled Deformable Models

For biomechanical simulations, the segmentation of multiple adjacent anatomical structures from medical image data is often required. If adjacent structures are barely distinguishable in image data, in general automatic segmentation methods for single structures do not yield sufficiently accurate results. To improve segmentation accuracy in these cases, knowledge about adjacent structures must be exploited. Optimal graph searching (graph cuts) based on deformable surface models allows for a simultaneous segmentation of multiple adjacent objects. However, this method requires a correspondence relation between vertices of adjacent surface meshes. Line segments, each containing two corresponding vertices, may then serve as shared displacement directions in the segmentation process. In this paper we propose a scheme for constructing a correspondence relation in adjacent regions of two arbitrary surfaces. This correspondence relation implies shared displacement directions that we apply for segmentation with deformable surfaces. Here, overlap of the surfaces is guaranteed not to occur. We show correspondence relations for regions on a femoral head and acetabulum and other adjacent structures, as well as an evaluation of segmentation results on 50 ct images of the hip joint.
Evaluating the effects of monetary policy with economic models

Because of limited knowledge about how the actual, complex economy operates, policymakers depend on models for understanding its workings. For models to be usable for evaluating monetary policy effects, modelers must recognize that fluctuations or shocks in the actual economy are often driven by developments beyond the central bank's control. There are no simple rules, and neither is there a single model that represents the exact interactions between monetary policy and the rest of the economy. How good a model is depends on particular criteria. ; This article assesses the usability of a specific economic model for policy evaluation on the basis of certain criteria the economic literature recognizes. The author uses the model as an example to address a set of recurring questions regularly asked by policymakers, questions concerning projecting multiple key macroeconomic variables under alternative policy scenarios at the time when the policy decision has to be made. ; The discussion focuses on the two conceptual issues that are central to answering these questions: the baseline forecast and policy shifts. The author concludes that use of a baseline forecast serves only as a convenient technical tool for computing a menu of policy projections under alternative scenarios. The important message is that a combination, not a separation, of baseline forecast and identified policy shifts provides economically coherent ways of evaluating the effects of monetary policy.
Finding Rectilinear Paths Among Obstacles in a Two-Layer Interconnection Model

Finding the best rectilinear path with respect to the bends and teh lengths of paths connecting two given points in the presence of rectilinear obstacles in a two-layer model is studied in this paper. In this model, rectilinear obstacles on each layer are specified separately, and the orientation of routing in each layer is fixed. An algorithm is presented to transform any problem instance in the two-layer model to one in a one-layer model, so that almost all algorithms for finding rectilinear paths among obstacles in the plane can be applied. The transformation algorithm runs in O(e log e) time where e is the number of edges on obstacles lying on both layers. A direct graph-theoretic approach to finding a shortest path in the two-layer model, which is easier to implement is also presented. The algorithm runs in O(e log2 e) time.
Collision detection for high-resolution deformable object using particle-based approach

Computational time of collision detection can be exceeded when there are too many checking for non-colliding primitives on some particular areas of high-resolution deformable objects. The problem is usually solved with best-fit bounding volume hierarchies (BVHs) which require much more memory and time for updating the bounding volumes when the objects deform. Hence, a particle-based collision detection method is enhanced to reduce the checking for non-colliding primitives by adding movable particles on the object vertices corresponding to each particular area. The distance of corresponding particles are computed for selecting the closest vertices between each pair of objects. The experimental results show that the proposed method has less colliding checking time than using BVHs when using with the deformable objects. Moreover, the proposed primitive-checking method can be parallel processed on GPU increasing speed performance while accuracy is still preserved when the results are compared to the previous BVH method.
Influence of Age on Brain Edema Formation, Secondary Brain Damage and Inflammatory Response after Brain Trauma in Mice

After traumatic brain injury (TBI) elderly patients suffer from higher mortality rate and worse functional outcome compared to young patients. However, experimental TBI research is primarily performed in young animals. Aim of the present study was to clarify whether age affects functional outcome, neuroinflammation and secondary brain damage after brain trauma in mice. Young (2 months) and old (21 months) male C57Bl6N mice were anesthetized and subjected to a controlled cortical impact injury (CCI) on the right parietal cortex. Animals of both ages were randomly assigned to 15 min, 24 h, and 72 h survival. At the end of the observation periods, contusion volume, brain water content, neurologic function, cerebral and systemic inflammation (CD3+ T cell migration, inflammatory cytokine expression in brain and lung, blood differential cell count) were determined. Old animals showed worse neurological function 72 h after CCI and a high mortality rate (19.2%) compared to young (0%). This did not correlate with histopathological damage, as contusion volumes were equal in both age groups. Although a more pronounced brain edema formation was detected in old mice 24 hours after TBI, lack of correlation between brain water content and neurological deficit indicated that brain edema formation is not solely responsible for age-dependent differences in neurological outcome. Brains of old naïve mice were about 8% smaller compared to young naïve brains, suggesting age-related brain atrophy with possible decline in plasticity. Onset of cerebral inflammation started earlier and primarily ipsilateral to damage in old mice, whereas in young mice inflammation was delayed and present in both hemispheres with a characteristic T cell migration pattern. Pulmonary interleukin 1β expression was up-regulated after cerebral injury only in young, not aged mice. The results therefore indicate that old animals are prone to functional deficits and strong ipsilateral cerebral inflammation without major differences in morphological brain damage compared to young.
Molecular Screening of Virulence Genes in Extraintestinal Pathogenic Escherichia coli Isolated from Human Blood Culture in Brazil

Extraintestinal pathogenic Escherichia coli (ExPEC) is one of the main etiological agents of bloodstream infections caused by Gram-negative bacilli. In the present study, 20 E. coli isolates from human hemocultures were characterized to identify genetic features associated with virulence (pathogenicity islands markers, phylogenetic group, virulence genes, plasmid profiles, and conjugative plasmids) and these results were compared with commensal isolates. The most prevalent pathogenicity island, in strains from hemoculture, were PAI IV536, described by many researchers as a stable island in enterobacteria. Among virulence genes, iutA gene was found more frequently and this gene enconding the aerobactin siderophore receptor. According to the phylogenetic classification, group B2 was the most commonly found. Additionally, through plasmid analysis, 14 isolates showed plasmids and 3 of these were shown to be conjugative. Although in stool samples of healthy people the presence of commensal strains is common, human intestinal tract may serve as a reservoir for ExPEC.
Modal Markov Logic for Multiple Agents

Modal Markov Logic for a single agent has previously been proposed as an extension to propositional Markov logic. While the framework allowed reasoning under the principle of maximum entropy for various modal logics, it is not feasible to apply its counting based inference to reason about the beliefs and knowledge of multiple agents due to magnitude of the numbers involved. We propose a modal extension of propositional Markov logic that avoids this problem by coarsening the state space. The problem stems from the fact that in the single-agent setting, the state space is only doubly exponential in the number of propositions in the domain, but the state space can potentially become infinite in the multi-agent setting. In addition, the proposed framework adds only the overhead of deciding satisfiability for the chosen modal logic on the top of the complexity of exact inference in propositional Markov logic. The proposed framework allows one to find a distribution that matches probabilities of formulas obtained from training data (or provided by an expert). Finally, we show how one can compute lower and upper bounds on probabilities of arbitrary formulas.
Classification of knee-joint vibroarthrographic signals using time-domain and time-frequency domain features and least-squares support vector machine

Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant turns detected with the fixed threshold from signal variability analysis were extracted as prominent features for the classification over the data set of 89 VAG signals. Compared with the Fisher linear discriminant analysis, the nonlinear least-squares support vector machine (LS-SVM) is able to achieve higher overall accuracy of 73.03%, and the area of 0.7307 under the receiver operating characteristic curve.
A Comparative Study of Biological Distances for EST Clustering

The paper presents the results of an experimental study in which different string distance measures were compared and evaluated as to their applicability to EST clustering. We implemented two tools, SeqGen (Sequence Generator) and ECLEST (Evaluator for Clusterings of ESTs). These were used to generate simulated ESTs from input human cDNAs; and to run EST clustering on these ESTs and compute a score for the quality of the clustering, respectively. We propagate the use of simulated data for comparative studies of this type, because they allow evaluation w.r. t. a known ideal solution (in this case, the correct clustering), which is not possible in most cases with real-life data. The distance measures we compared include both subword-based and alignment-based measures. We ran a large number of tests and obtained statistically significant results as to the applicability of the distance measures included. For example, we show that certain subword-based measures produce output, in a significant number of cases, that is comparable to alignment-based ones, and that certain (easy-to-compute) measures are well suited for a preprocessing step. Our results have significant applications in studies of gene expression and discovery of products of alternative splicing, where there is a pressing need for fast clustering of increasingly large sets of ESTs.
Efficient Matrix Models for Relational Learning

Abstract : Relational learning deals with the setting where one has multiple sources of data, each describing different properties of the same set of entities. We are concerned primarily with settings where the properties are pairwise relations between entities, and attributes of entities. We want to predict the value of relations and attributes, but relations between entities violate the basic statistical assumption of exchangeable data points, or entities. Furthermore, we desire models that scale gracefully as the number of entities and relations increase. This thesis rests on two claims, that (i) that Collective Matrix Factorization can effectively integrate different sources of data to improve prediction; and, (ii) that training scales well as the number of entities and observations increase. We consider two real-world data sets in experimental support of these claims: augmented collaborative filtering and augmented brain imaging. In augmented collaborative filtering, we show that genre information about movies can be used to increase the predictive accuracy of user's ratings. In augmented brain imaging, we show that word co-occurrence information can be used to increase the predictive accuracy of a model of changes in brain activity to word stimuli, even in regions of the brain that were never included in the training data.
Cultural Variations in Children’s Mirror Self-Recognition

Western children first show signs of mirror self-recognition (MSR) from 18 to 24 months of age, the benchmark index of emerging self-concept. Such signs include self-oriented behaviors while looking at the mirror to touch or remove a mark surreptitiously placed on the child’s face. The authors attempted to replicate this finding across cultures using a simplified version of the classic “mark test. ” In Experiment 1, Kenyan children (N = 82, 18 to 72 months old) display a pronounced absence of spontaneous self-oriented behaviors toward the mark. In Experiment 2, the authors tested children in Fiji, Saint Lucia, Grenada, and Peru (N = 133, 36 to 55 months old), as well as children from urban United States and rural Canada. As expected from existing reports, a majority of the Canadian and American children demonstrate spontaneous self-oriented behaviors toward the mark. However, markedly fewer children from the non-Western rural sites demonstrate such behaviors. These results suggest that there are profound cross-cultural differences in the meaning of the MSR test, questioning the validity of the mark test as a universal index of self-concept in children’s development.
CHALLENGES IN STRUCTURAL HEALTH MONITORING 22 US-JAPAN BRIDGE ENGINEERING WORKSHOP SEATTLE, WASHINGTON

Structural health monitoring through field instrumentation has been carried out successfully on many highway bridges to monitor the short-term and long-term performance of structures. Properly designed and strategically placed instruments can help collect valuable data regarding the condition, the stress and strain, deflection, temperature gradients, and the time-dependent properties, such as creep and shrinkage in a structure. The data may be used to verify the design assumptions, to make modifications, to improve on future designs, to assess the general health of bridges, and to provide valuable information for a bridge management system for making decisions regarding preservation decisions. The challenges in structural health monitoring are in finding cost-effective and reliable sensors to remotely and continuously monitor the conditions of major bridges.
A Real-Time Demonstrator for Video-Based Recognition of Dynamic Head Gestures, Using Discrete Hidden Markov Models

This work describes a powerful demonstrator of a videobased approach for detecting and classifying dynamic head gestures. The head of the user is localized via a combination of colorand shape-based segmentation. For a continuous feature extraction, we use a template matching of the nose bridge in combination with selected features derived from the optical flow. The core classification unit consists of discrete Hidden Markov Models (DHMMs). We extensively tested the system in two different domains (desktop VirtualReality and automotive environment). In the current state of development, six different gestures can be classified with an overall recognition rate of 97.3% in the VR, and 95.5% in the automotive environment, respectively. The approach works absolutely independent from the image background and additional gesture types can easily be integrated.
Real-time captioning of sign language by groups of deaf and hard-of-hearing people

In order to realize smooth communication between people with or without difficulty in hearing, we aim to implement an information support system based on crowdsourcing, a problem-solving model, in which numerous people cooperate to accomplish a job. More specifically, we implement a crowdsourced captioning system that enables groups of deaf and hard-of-hearing people to collaboratively interpret a speech or lecture given by sign language to text in real-time. Deaf and hard-of-hearing people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others. In this paper, we explore the issues that need be resolved for the practical use of the proposed system through several experiments and succeed in verifying its feasibility.
Cannabidiol Induces Programmed Cell Death in Breast Cancer Cells by Coordinating the Cross-talk between Apoptosis and Autophagy

Cannabidiol (CBD), a major nonpsychoactive constituent of cannabis, is considered an antineoplastic agent on the basis of its in vitro and in vivo activity against tumor cells. However, the exact molecular mechanism through which CBD mediates this activity is yet to be elucidated. Here, we have shown CBD-induced cell death of breast cancer cells, independent of cannabinoid and vallinoid receptor activation. Electron microscopy revealed morphologies consistent with the coexistence of autophagy and apoptosis. Western blot analysis confirmed these findings. We showed that CBD induces endoplasmic reticulum stress and, subsequently, inhibits AKT and mTOR signaling as shown by decreased levels of phosphorylated mTOR and 4EBP1, and cyclin D1. Analyzing further the cross-talk between the autophagic and apoptotic signaling pathways, we found that beclin1 plays a central role in the induction of CBD-mediated apoptosis in MDA-MB-231 breast cancer cells. Although CBD enhances the interaction between beclin1 and Vps34, it inhibits the association between beclin1 and Bcl-2. In addition, we showed that CBD reduces mitochondrial membrane potential, triggers the translocation of BID to the mitochondria, the release of cytochrome c to the cytosol, and, ultimately, the activation of the intrinsic apoptotic pathway in breast cancer cells. CBD increased the generation of reactive oxygen species (ROS), and ROS inhibition blocked the induction of apoptosis and autophagy. Our study revealed an intricate interplay between apoptosis and autophagy in CBD-treated breast cancer cells and highlighted the value of continued investigation into the potential use of CBD as an antineoplastic agent. Mol Cancer Ther; 10(7); 1161–72. ©2011 AACR.