Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of a Novel Movement Strategy in Decreasing ACL Risk Factors in Female Adolescent Soccer Players: A Randomized Controlled Trial

Objective:To determine the effect of a novel movement strategy incorporated within a soccer warm-up on biomechanical risk factors for anterior cruciate ligament injury during 3 sport-specific movement tasks. Design:Single-blind, randomized controlled clinical trial. Setting:Laboratory setting. Participants:Twenty top-tier female teenage soccer players. Interventions:Subjects were randomized to the Core Position and Control movement strategy (Core-PAC) warm-up or standard warm-up, which took place before their regular soccer practice over a 6-week period. The Core-PAC focuses on getting the centre of mass closer to the plant foot or base of support. Main Outcome Measures:Peak knee flexion angle and abduction moments during a side-hop (SH), side-cut, and unanticipated side-cut task after the 6 weeks with (intervention group only) and without a reminder to use the Core-PAC strategy. Results:The Core-PAC group increased peak flexion angles during the SH task [mean difference = 6.2 degrees; 95% confidence interval (CI), 1.9-10.5 degrees; effect size = 1.01; P = 0.034] after the 6-week warm-up program without a reminder. In addition, the Core-PAC group demonstrated increased knee flexion angles for the side-cut (mean difference = 8.5 degrees; 95% CI, 4.8-12.2 degrees; ES = 2.02; P = 0.001) and SH (mean difference = 10.0 degrees; 95% CI, 5.7-14.3 degrees; ES = 1.66; P = 0.001) task after a reminder. No changes in abduction moments were found. Conclusions:The results of this study suggest that the Core-PAC may be one method of modifying high-risk soccer-specific movements and can be implemented within a practical, team-based soccer warm-up. The results should be interpreted with caution because of the small sample size.
YFa, a chimeric opioid peptide, induces kappa‐specific antinociception with no tolerance development during 6 days of chronic treatment

Our previous study showed that YGGFMKKKFMRFamide (YFa), a chimeric peptide of Met‐enkephalin, and Phe‐Met‐Arg‐Phe‐NH2 induced naloxone‐reversible antinociception and attenuated the development of tolerance to morphine analgesia. In continuation, the present study investigated which specific opioid receptors—μ, δ or κ—mediate the observed YFa antinociception pharmacologically using specific antagonists and whether chronic administration of YFa at 26.01 μmol/kg per day induces tolerance and its effect on the expression of μ and κ opioid receptors from day 4 to day 6, with endomorphine‐1 (EM‐1) and saline taken as positive and negative controls, respectively. Quantitative differential expression analysis was carried out by real‐time reverse‐transcriptase polymerase chain reaction, and the corresponding changes in protein levels were assessed by Western blot. A pharmacological investigation revealed that nor‐binaltorphimine, a specific κ opioid receptor–1 (KOR1) antagonist, completely antagonized the antinociception induced by 39.01 μmol/kg of YFa. Importantly, its chronic intraperitoneal administration did not result in significant tolerance over 6 days, whereas EM‐1 induced significant tolerance after day 4. Differential expression analysis revealed that EM‐1 caused up‐regulation of μ opioid receptor–1 on day 4, followed by down‐regulation on later days. Interestingly, YFa treatment caused a decrease on day 4, followed by an increase in the expression of KOR1 from day 5 onward. In conclusion, YFa induces kappa‐specific antinociception, with no development of tolerance during 6 days of chronic treatment, which further articulates new directions for improved designing of peptide‐based analgesics that may be devoid of adverse effects like tolerance. © 2008 Wiley‐Liss, Inc.
Circuit breaker coil modeling and operation monitoring using feature extraction

This paper presents a novel model of a circuit breaker coil based on electromechanical energy conversion principles which can be employed in circuit breaker condition monitoring. The proposed model takes into account the following: the coil core, springs, magnetic arm and coil. Based on this model, different parameters such as: Friction, the circuit breaker control circuit, inductance of the core and tolerances of the components can affect on coil current are considered. In the evaluation phase the effects of these parameters on coil current are studied and four functions are provided to detect the coil's faults.
MMT: New Open Source MT for the Translation Industry

MMT is a new open source machine translation software speciﬁcally addressing the needs of the translation industry. In this paper we describe its overall architecture and provide details about its major components. We report performance results on a multi-domain benchmark based on public data, on two translation directions, by comparing MMT against state-of-the-art commercial and research phrase-based and neural MT systems
Loop Closure Detection Based On Semantic Information In Dynamic Scenes

This paper proposes an approach to optimize visual loop closure detection (LCD) algorithms for dynamic scenes. Although loop closure detection has been widely used in visual SLAM systems, there are still some deficiencies when running in real scenes, such as the vulnerability in scenes with dynamic targets, which severely interferes with the similarity calculation between images and greatly degrades the performance of loop closure detection. In addition, existing loop closure detection algorithms rely on traditional image features, making them more sensitive to lighting, rotation, etc., resulting in high sensitivity. Our approach aims to enhance the correlation between images by utilizing semantic information, improving the original feature matching algorithm based on the Bag-of-Words (BoW), and constructing a semantic matching algorithm based on the results of semantic data association, thereby improving the accuracy of loop closure detection in dynamic scenes. Extensive experiments on public datasets have demonstrated the reliability and effective-ness of our method, achieving better global localization accuracy compared to other loop closure detection algorithms.
Comparative Pathology of Environmental Lung Disease: An Overview

Environmental factors play a major role in a majority of lung diseases. Asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and many interstitial lung diseases are influenced or caused by environmental factors. Animals and humans may respond differently to the same agent, and a study of the comparative pathology between the two is useful for optimizing animal models of environmental lung disease and for evaluating their predictive value in carcinogenicity studies. This overview describes the most common nonneoplastic pathologic pulmonary responses to inhaled environmental agents in the human and contrasts them with the responses observed in rats exposed to the same agents. We show both similarities and difference in response to the same agents; furthermore, both species have unique responses to some agents (for example, progressive massive fibrosis in the human and proliferative squamous lesions in the rat). Quantitative analysis of the grades of response to three environmental particulate dusts revealed differences between the 2 species at the cellular level. Specifically, acute intra-alveolar inflammation, alveolar epithelial hyperplasia, and alveolar lipoproteinosis were all greater in rats than in humans exposed to the same agents. These differences may account for differences between the 2 species in carcinogenic response to nonfibrous particulates.
A Model for the Evaluation of Instructional Methods

A model was developed for the purpose of evaluating and comparing instructional methods in relation to a broad spectrum of variables associated with their effectiveness. To this end, some forms of evaluation used by educators were incorporated and two learning theories were added, which served as a frame of reference to explain the results obtained in the evaluation. The model is of a descriptive nature in that it takes into account a general picture of the main elements and products which should be borne in mind when evaluating an instructional method. The model is presented in detail, and a description is given of its first application in the evaluation of the personalized system of instruction, as implemented for the teaching of basic physics at Simdn Bolivar University, Caracas, Venezuela. Finally, certain comments are made on the utility of the model, together with recommendations for its use.
Early response to family-based treatment for adolescent anorexia nervosa.

OBJECTIVE The purpose of this study was to determine if early weight gain predicted remission at the end of treatment in a clinic sample of adolescents with anorexia nervosa (AN). METHOD Sixty five adolescents with AN (mean age = 14.9 years, SD = 2.1), from two sites (Chicago n = 45; Columbia n = 20) received a course of manualized family-based treatment (FBT). Response to treatment was assessed using percent ideal body weight (IBW) with remission defined as having achieved ≥ 95% IBW at end of treatment (Session 20). RESULTS Receiver operating characteristic analyses showed that a gain of at least 2.88% in ideal body weight by Session 4 best predicted remission at end of treatment (AUC = 0.674; p = 0.024). DISCUSSION Results suggest that adolescents with AN, receiving FBT, who do not show early weight gain are unlikely to remit at end of treatment.
AutoEval-A Generic Tool for Automatic Evaluation of Natural Language Applications

Natural Language Processing Tools are used as important components in many applications. For example, a word processor uses components like the spell and grammar checker. It is important to regularly evaluate the performance of these components and try to localize the errors that they cause, doing so assists further development and enhancements of the tools. The evaluation is normally performed manually and it is a tedious and time-consuming task. Finding a way to automate the evaluation process would be beneficial. This Master’s thesis will present one solution to the problem: How can one automate evaluation of Natural Language Applications? The solution is given by the development of a versatile computer program that can evaluate Natural Language Processing Applications or parts of them. The focus of this project was evaluation of part of speech taggers (POS-taggers), which are important components of many Natural Language Processing systems. The report starts by laying out the project goals and setting the restrictions. A short introductory presentation follows, focusing on the background of the subject. Different technologies will be described and the chosen implementation will be presented. The software development phase will just briefly be touched upon, while explaining the structure and features of the developed evaluation tool more in detail. Problems that were present at start and those that arose during the project’s course will be brought forward and their solution presented. Concluding the report, a brief summary will be given, describing whether the project objectives were reached or not. Finally, suggestions to future enhancements and implementation ideas will be given. The developed program prototype is a scriptable evaluation tool, which can handle various input formats and tries to be as efficient as possible when handling data. It performs well on simple evaluation tasks. A basic set of functions is provided with the program, these functions serve as the basic building blocks for scripting purposes. Due to the flexible nature of the program, it is easily extendable and thus allows future development to be continued without much trouble. Examples of real life applications for this tool are: evaluation of POS taggers (word class taggers), grammar checkers and correction suggestions from spelling checkers. AutoEval ett generiskt verktyg för automatisk utvärdering av språkverktyg
2D CNN vs 3D CNN: An Empirical Study on Deep Learning-based Facial Emotion Recognition

Human emotion detection is a significant challenge in computer-based, automatic emotion detection. Deep learning techniques have been employed to extract facial features from videos, utilizing both visual and auditory input. However, the effectiveness of deep learning methods, specifically using a lightweight model, to exclusively extract facial features from videos has not been extensively explored. Consequently, this study aimed to compare the performance of two distinct 3-dimensional convolutional neural networks (CNNs) and 2-dimensional CNNs in extracting emotion from visual input in videos. Two benchmark databases, namely the Acted Facial Expressions in the Wild database and the eNTERFACE’05 Audio-Visual Emotion Dataset, were employed for evaluation. Multiple experiments conducted during this study concluded that the 2D CNN exhibited superior performance in accurately detecting facial emotion while also utilizing fewer computational resources in comparison to the 3D CNN.
An Inhibitor of Mutant IDH1 Delays Growth and Promotes Differentiation of Glioma Cells

IDHology Among the most exciting drug targets to emerge from cancer genome sequencing projects are two related metabolic enzymes, isocitrate dehydrogenases 1 and 2 (IDH1, IDH2). Mutations in the IDH1 and IDH2 genes are common in certain types of human cancer. Whether inhibition of mutant IDH activity might offer therapeutic benefits is unclear (see the Perspective by Kim and DeBerardinis). F. Wang et al. (p. 622, published online 4 April) isolated a small molecule that selectively inhibits mutant IDH2, describe the structural details of its binding to the mutant enzyme, and show that this compound suppresses the growth of patient-derived leukemia cells harboring the IDH2 mutation. Rohle et al. (p. 626, published online 4 April) show that a small molecule inhibitor of IDH1 selectively slows the growth of patient-derived brain tumor cells with the IDH1 mutation. A small molecule that inhibits a mutant enzyme in tumors slows malignant growth by inducing cancer cell differentiation. [Also see Perspective by Kim and DeBerardinis] The recent discovery of mutations in metabolic enzymes has rekindled interest in harnessing the altered metabolism of cancer cells for cancer therapy. One potential drug target is isocitrate dehydrogenase 1 (IDH1), which is mutated in multiple human cancers. Here, we examine the role of mutant IDH1 in fully transformed cells with endogenous IDH1 mutations. A selective R132H-IDH1 inhibitor (AGI-5198) identified through a high-throughput screen blocked, in a dose-dependent manner, the ability of the mutant enzyme (mIDH1) to produce R-2-hydroxyglutarate (R-2HG). Under conditions of near-complete R-2HG inhibition, the mIDH1 inhibitor induced demethylation of histone H3K9me3 and expression of genes associated with gliogenic differentiation. Blockade of mIDH1 impaired the growth of IDH1-mutant—but not IDH1–wild-type—glioma cells without appreciable changes in genome-wide DNA methylation. These data suggest that mIDH1 may promote glioma growth through mechanisms beyond its well-characterized epigenetic effects.
A generic respiratory motion model for motion correction in PET/CT

Respiratory motion causes different magnitude qualitative and quantitative artefacts in medical imaging and especially in emission tomography. Solutions presented to date include respiratory synchronized PET and CT acquisitions. In order to increase the signal to noise ratio of the synchronized images the use of non-rigid transformations during the reconstruction process accounting for the respiratory motion have been proposed. In the majority of this work the 4D CT images have been used for the derivation of the necessary deformation maps. The objective of this work is to use a global respiratory model to avoid unnecessary 4D CT acquisitions and associated patient dose as well as to improve the temporal resolution of the deformation matrices used in the PET image correction process. The global model is based on principal component analysis (PCA) and can be adapted to a given patient anatomy needing only two static CT images in combination with respiratory synchronised images of the patient surface. The global model was then used to generate n CT images corresponding to a 4D CT temporal resolution (n = 5, 10, 20, 30 and 50) and corresponding deformation matrices. These deformation matrices were then used to correct for the respiratory motion using an elastic transformation correction method during the reconstruction process. NCAT phantom data for two simulated patients and clinical data for six patients (four patients were used for the model creation and two patients for the method validation) were used to determine the optimal number of deformation matrices needed in the reconstruction process.
An Automata-Theoretic Approach to Minimalism

Vijay-Shanker et al. (1987) note that many interesting linguistic formalisms can be thought of as having essentially context-free structure, but operating over objects richer than simple strings (sequences of strings, trees, or graphs). They introduce linear context-free rewriting systems (LCFRS’s, see also Weir (1988)) as a unifying framework for superficially different such formalisms (like (multi component) tree adjoining grammars, head grammars, and categorial grammars). Later work (Michaelis, 1998) has added minimalist grammars (MGs, see (Stabler, 1997)) to this list. Recently, Fulop et al. (2004) have introduced multiple bottom-up tree transducers (mbutt), which can be thought of as offering a transductive perspective on LCFRSs. The transductive perspective allows us to view a grammar in one of these grammar formalisms as defining both a set of well-formed derivations, and functions which interpret these derivations as the derived structures (trees, strings, or meanings) they are derivations of. Being explicit about the structure of the derivation, and divorcing it from the construction of the object so derived has two main advantages. First, we may entertain and study the effects of modifications to the structure of the derivational process, such as insisting that a particular operation apply only in case there is an isomorphic subderivation somewhere in the same derivation (for example, in deletion under identity with an antecedent), or other non-local filters on wellformed derivations, without worrying about the kinds of data structures that would be required to support such operations in real-time (as in parsers, for example). Secondly, viewing derivational grammar formalisms in this way makes particularly salient two loci of language theoretic complexity:
Virtual reality to improve lower extremity function, kinematic parameters, and walking speed post-stroke: preliminary results

Introduction: Virtual reality (VR) is a tool that can enrich physiotherapy treatment in individuals with stroke. The increased use of feedback provides them with useful additional information to improve walking speed, kinematics, and functionality of the lower extremity (LE). Our aim is to evaluate these changes and describe the intervention in two individuals with stroke. Case description: A 58-year-old man (4.5 months post-stroke) and a 49-year-old man (3 months post-stroke) followed a VR training to improve kinematics, functionality, and gait speed. Intervention: Each participant underwent 15 sessions (VR treatment one hour daily in addition to the one-hour CP program). Outcomes: The LE Fugl-Meyer scale (FM) improved in both participants; in motor evaluation, participant 1 increased 4 points and patient 2 increased 6 points. Participant 1 was highly functional but had difficulty in the race at baseline, while participant 2 improved on the Ambulatory Functional Scale (FAC) from 3/5 to 4/5 and the Berg Balance Scale (BBS) from 50 to 53, with a constant permanent score of 122/126 on the Functional Independence Measure scale (FIM). Both participants improved the kinematic parameters in leg stance on plegic LE (showed a decrease in the spatial error and in submovements) and walking speed > Minimally Clinically Important Difference (MCID) (participant 1: improvement of 0.16m/s, participant 2: 0.34m/s). Discussion: Results of the combined treatment of CP and VR treatment are positive in improving the performance of motor tasks and stability in leg stance on the plegic side, with improvement of functionality during walking. Controlled studies are needed to determine the role of VR in these improvements.
Identifying User Behavior by Analyzing Web Server Access Log File

Summary Web usage mining is application of data mining techniques to discover usage patterns from web data, in order to better serve the needs of web based applications. The user access log files present very significant information about a web server. This paper is concerned with the in-depth analysis of Web Log Data of NASA website to find information about a web site, top errors, potential visitors of the site etc. which help system administrator and Web designer to improve their system by determining occurred systems errors, corrupted and broken links by using web using mining. The obtained results of the study will be used in the further development of the web site in order to increase its effectiveness.
Deep Brain Stimulation of the Subthalamic Nucleus Alters the Cortical Profile of Response Inhibition in the Beta Frequency Band: A Scalp EEG Study in Parkinson's Disease

Stopping an initiated response could be implemented by a fronto-basal-ganglia circuit, including the right inferior frontal cortex (rIFC) and the subthalamic nucleus (STN). Intracranial recording studies in humans reveal an increase in beta-band power (∼16–20 Hz) within the rIFC and STN when a response is stopped. This suggests that the beta-band could be important for communication in this network. If this is the case, then altering one region should affect the electrophysiological response at the other. We addressed this hypothesis by recording scalp EEG during a stop task while modulating STN activity with deep brain stimulation. We studied 15 human patients with Parkinson's disease and 15 matched healthy control subjects. Behaviorally, patients OFF stimulation were slower than controls to stop their response. Moreover, stopping speed was improved for ON compared to OFF stimulation. For scalp EEG, there was greater beta power, around the time of stopping, for patients ON compared to OFF stimulation. This effect was stronger over the right compared to left frontal cortex, consistent with the putative right lateralization of the stopping network. Thus, deep brain stimulation of the STN improved behavioral stopping performance and increased the beta-band response over the right frontal cortex. These results complement other evidence for a structurally connected functional circuit between right frontal cortex and the basal ganglia. The results also suggest that deep brain stimulation of the STN may improve task performance by increasing the fidelity of information transfer within a fronto-basal-ganglia circuit.