Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of augmented feedback on motor function of the affected upper extremity in rehabilitation patients: a systematic review of randomized controlled trials.

OBJECTIVE Assessment of the available evidence regarding the effect of augmented feedback on motor function of the upper extremity in rehabilitation patients. METHODS A systematic literature search was performed to identify randomized controlled trials that evaluated the effect of augmented feedback on motor function. Two reviewers systematically assessed the methodological quality of the trials. The reported effects were examined to evaluate the effect of therapeutic interventions using augmented feedback and to identify a possible relationship with patient characteristics, type of intervention, or methodological quality. RESULTS Twenty-six randomized controlled trials were included, 9 of which reported a positive effect on arm function tests. Follow-up measurements were performed in 8 trials, 1 of which reported a positive effect. Different therapeutic interventions using augmented feedback, i.e. electromyographic biofeedback, kinetic feedback, kinematic feedback, or knowledge of results, show no difference in effectiveness. CONCLUSION No firm evidence was found of effectiveness regarding the use of augmented feedback to improve motor function of the upper extremity in rehabilitation patients. Future studies should focus more on the content, form and timing of augmented feedback concerning the therapeutic intervention. It should be emphasized that motor learning effects can only be determined by re-examining the population after a follow-up period.
An Analysis of a Classroom Discourse: A Case of a Selected English Class in the Eastern Cape of South Africa

Abstract This study reviews the classroom interaction discussions between the teachers and learners in a selected primary school in the Eastern Cape Province of South Africa. The discourse analysis was used to examine verbal interaction as a significant component of teaching techniques for enhancing effective teaching of English language. The main objective of this study was to analyze the verbal communication between the teachers and pupils in the classroom during teaching and learning of materials in English language. Qualitative research design was used in this study. Data were collected through interviews. As the teaching and learning of English language develops between the teachers and pupils in the primary schools, both negative and positive factors were observed when they were communicating. The relationship between the pupils and teachers responses to the factors influencing teaching and learning the subject was critically examined. The interviews conducted confirmed some of the challenges to include the use of local words by the pupils and lack of understanding of some local isiXhosa words by their teachers. The result also shows that pupils faced different hindrances in understanding some English language vocabularies taught in the classroom. The results also revealed that the classroom interaction was successful due to good methods introduced by the teachers in transferring knowledge to the pupils. Recommendations were made on how the teaching and learning of English language could be improved by appropriate mechanism or strategies to obtain successful results.
Distribution and Coexistence of Myoclonus and Dystonia as Clinical Predictors of SGCE Mutation Status: A Pilot Study

Introduction Myoclonus–dystonia (M–D) is a young onset movement disorder typically involving myoclonus and dystonia of the upper body. A proportion of the cases are caused by mutations to the autosomal dominantly inherited, maternally imprinted, epsilon-sarcoglycan gene (SGCE). Despite several sets of diagnostic criteria, identification of patients most likely to have an SGCE mutation remains difficult. Methods Forty consecutive patients meeting pre-existing diagnostic clinical criteria for M–D underwent a standardized clinical examination (20 SGCE mutation positive and 20 negative). Each video was reviewed and systematically scored by two assessors blinded to mutation status. In addition, the presence and coexistence of myoclonus and dystonia was recorded in four body regions (neck, arms, legs, and trunk) at rest and with action. Results Thirty-nine patients were included in the study (one case was excluded owing to insufficient video footage). Based on previously proposed diagnostic criteria, patients were subdivided into 24 “definite,” 5 “probable,” and 10 “possible” M–D. Motor symptom severity was higher in the SGCE mutation-negative group. Myoclonus and dystonia were most commonly observed in the neck and upper limbs of both groups. Truncal dystonia with action was significantly seen more in the mutation-negative group (p < 0.05). Coexistence of myoclonus and dystonia in the same body part with action was more commonly seen in the mutation-negative cohort (p < 0.05). Conclusion Truncal action dystonia and coexistence of myoclonus and dystonia in the same body part with action might suggest the presence of an alternative mutation in patients with M–D.
Validation of lucigenin as a chemiluminescent probe to monitor vascular superoxide as well as basal vascular nitric oxide production.

Lucigenin has been widely used as a chemiluminescent substrate to monitor vascular superoxide (O*-2) formation. The validity of lucigenin for detection of O*-2 has been questioned because O*-2 is generated by lucigenin itself. It has been shown that the concentration of lucigenin is a critical parameter affecting the validity of this assay. In the present studies we evaluated a reduced concentration of lucigenin (5 microM) as a tool to quantify O*-2 production in vascular tissue. Lucigenin-induced effects on endothelial function were assessed by isometric tension recording of isolated aortic rings suspended in organ baths. The effects of lucigenin on O*-2 production were studied using spin trapping and electron spin resonance spectroscopy. Lucigenin at 250 microM but not at 5 microM caused a significant attenuation of endothelium-dependent relaxations to acetylcholine, which was prevented by pretreatment with superoxide dismutase. Spin-trapping studies revealed that lucigenin at 250 microM increased vascular O*-2 production several fold while 5 microM lucigenin did not stimulate O*-2 production. Inhibition of NO synthase by NG-momomethyl-l-arginine as well as the removal of the endothelium almost doubled lucigenin-derived chemiluminescence (LDCL), indicating that basal production of endothelium-derived NO depresses the baseline chemiluminescence signal. Thus, lucigenin at a concentration of 5 microM seems to be a sensitive and valid probe for assessing O*-2 in vascular tissue. It can also be used as an indirect probe to estimate basal vascular NO release.
MAP Inference in Chains using Column Generation

Linear chains and trees are basic building blocks in many applications of graphical models, and they admit simple exact maximum a-posteriori (MAP) inference algorithms based on message passing. However, in many cases this computation is prohibitively expensive, due to quadratic dependence on variables' domain sizes. The standard algorithms are inefficient because they compute scores for hypotheses for which there is strong negative local evidence. For this reason there has been significant previous interest in beam search and its variants; however, these methods provide only approximate results. This paper presents new exact inference algorithms based on the combination of column generation and pre-computed bounds on terms of the model's scoring function. While we do not improve worst-case performance, our method substantially speeds real-world, typical-case inference in chains and trees. Experiments show our method to be twice as fast as exact Viterbi for Wall Street Journal part-of-speech tagging and over thirteen times faster for a joint part-of-speed and named-entity-recognition task. Our algorithm is also extendable to new techniques for approximate inference, to faster 0/1 loss oracles, and new opportunities for connections between inference and learning. We encourage further exploration of high-level reasoning about the optimization problem implicit in dynamic programs.
Discrimination of Enterohemorrhagic Escherichia coli (EHEC) from Non-EHEC Strains Based on Detection of Various Combinations of Type III Effector Genes

ABSTRACT Enterohemorrhagic Escherichia coli (EHEC) strains comprise a subgroup of Shiga-toxin (Stx)-producing E. coli (STEC) and are characterized by a few serotypes. Among these, seven priority STEC serotypes (O26:H11, O45:H2, O103:H2, O111:H8, O121:H19, O145:H28, and O157:H7) are most frequently implicated in severe clinical illness worldwide. Currently, standard methods using stx, eae, and O-serogroup-specific gene sequences for detecting the top 7 EHEC serotypes bear the disadvantage that these genes can be found in non-EHEC strains as well. Here, we explored the suitability of ureD, espV, espK, espN, Z2098, and espM1 genes and combinations thereof as candidates for a more targeted EHEC screening assay. For a very large panel of E. coli strains (n = 1,100), which comprised EHEC (n = 340), enteropathogenic E. coli (EPEC) (n = 392), STEC (n = 193), and apathogenic strains (n = 175), we showed that these genetic markers were more prevalent in EHEC (67.1% to 92.4%) than in EPEC (13.3% to 45.2%), STEC (0.5% to 3.6%), and apathogenic E. coli strains (0 to 2.9%). It is noteworthy that 38.5% of the EPEC strains that tested positive for at least one of these genetic markers belonged to the top 7 EHEC serotypes, suggesting that such isolates might be Stx-negative derivatives of EHEC. The associations of espK with either espV, ureD, or Z2098 were the best combinations for more specific and sensitive detection of the top 7 EHEC strains, allowing detection of 99.3% to 100% of these strains. In addition, detection of 93.7% of the EHEC strains belonging to other serotypes than the top 7 offers a possibility for identifying new emerging EHEC strains.
Stable Spectral Learning Based on Schur Decomposition

Spectral methods are a powerful tool for inferring the parameters of certain classes of probability distributions by means of standard eigenvalue-eigenvector decompositions. Spectral algorithms can be orders of magnitude faster than log-likelihood based and related iterative methods, and, thanks to the uniqueness of the spectral decomposition, they enjoy global optimality guarantees. In practice, however, the applicability of spectral methods is limited due to their sensitivity to model misspecification, which can cause instability issues in the case of non-exact models. We present a new spectral approach that is based on the Schur triangularization of an observable matrix, and we carry out the corresponding theoretical analysis. Our main result is a bound on the estimation error that is shown to depend linearly on the condition number of the ground-truth conditional probability matrix and inversely on the eigengap of an observable matrix. Numerical experiments show that the proposed method is more stable, and performs better in general, than the classical spectral approach using direct matrix diagonalization.
Working capital and liquidity to the profitability of manufacturing companies listed on the IDX in 2019-2022

This study investigates the interaction between modes of employment, profitability, and market liquidity in the manufacturing sector. The goal is to gain insight into how these factors interact and affect the overall value of a company in a particular region and industry. This analysis uses quantitative methods based on financial data and relevant metrics for a sample of 10 manufacturing companies. This study examines the relationship between working model, profitability, and market liquidity through statistical analysis and regression modeling techniques. The results showed that working capital has no effect on profitability, liquidity significantly impacts profitability in the manufacturing industry, and operating capital and liquidity simultaneously affect profitability. The findings contribute to the existing literature on company valuations by providing empirical evidence specific to the manufacturing sector. The research has implications for policymakers, investors, and managers, as it offers valuable insights into the factors that drive corporate value in the industry, helping stakeholders make decisions and strategies to improve performance and competitiveness. Provide benefits in the context of financial management.
Protection Against Ischemia and Improvement of Cerebral Blood Flow in Genetically Hypertensive Rats by Chronic Pretreatment With an Angiotensin II AT1 Antagonist

Background and Purpose— Pretreatment with angiotensin II AT1 receptor antagonists protects against cerebral ischemia. We studied whether modulation of cerebral blood flow (CBF) and morphometric changes in brain arteries participated in this protective mechanism. Methods— We pretreated adult spontaneously hypertensive rats with equally antihypertensive doses of candesartan (0.1 or 0.3 mg/kg per day), nicardipine (0.1 mg/kg per day), or captopril (3.0 mg/kg per day) for 3 or 28 days via subcutaneous osmotic minipumps followed by permanent left middle cerebral artery (MCA) occlusion distal to the origin of the lenticulostriate arteries. We measured CBF by autoradiography with 4-iodo-[N-methyl-14C]antipyrine 3 hours after operation and the areas of infarct and tissue swelling 24 hours after operation. Morphometric changes in the MCA were studied after antihypertensive treatment. Results— Twenty-eight days of candesartan pretreatment decreased the infarct area by 31%; reduced the CBF decrease at the peripheral area of ischemia and the cortical volume of severe ischemic lesion, where CBF was <0.50 mL/g per minute; increased the MCA external diameter by 16%; and reduced the media thickness of the MCA by 23%. Captopril pretreatment for 28 days decreased the infarct area by 25%. Pretreatment with candesartan for 3 days or nicardipine for 28 days was ineffective. Conclusions— Angiotensin II system inhibition protects against neuronal injury more effectively than calcium channel blockade. Protection after AT1 receptor blockade is not directly correlated with blood pressure reduction but with normalization of MCA media thickness, leading to increased arterial compliance and reduced CBF decrease during ischemia at the periphery of the lesion.
The Prediction of Students' Academic Performance Using Classification Data Mining Techniques

Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naive Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester.
Filtering interferometric phase images by anisotropic diffusion

We present an anisotropic diffusion equation designed to restore interferometric images. It has two main purposes, the first is to preserve the structures and discontinuities formed by the fringes. The second is to incorporate the noise modeling which is specific to this kind of images. Besides we show that our model formalizes previous related work in interferometry filtering.
RTCM: a natural language based, automated, and practical test case generation framework

Based on our experience of collaborating with industry, we observed that test case generation usually relies on test case specifications (TCSs), commonly written in natural language, specifying test cases of a System Under Test at a high level of abstraction. In practice, TCSs are commonly used by test engineers as reference documents to perform these activities: 1) Manually executing test cases in TCSs; 2) Manually coding test cases in a test scripting language for automated test case execution. In the latter case, the gap between TCSs and executable test cases has to be filled by test engineers, requiring a significant amount of coding effort and domain knowledge. Motivated by the above observations from the industry, we first propose, in this paper, a TCS language, named as Restricted Test Case Modeling (RTCM), which is based on natural language and composed of an easy-to-use template, a set of restriction rules and keywords. Second, we propose a test case generation tool (aToucan4Test), which takes TCSs in RTCM as input and generates either manual test cases or automatically executable test cases, based on various coverage criteria defined on RTCM. To assess the applicability of RTCM, we manually modeled two industrial case studies and examined 30 automatically generated TCSs. To evaluate aToucan4Test, we modeled three subsystems of a Video Conferencing System developed by Cisco Systems, Norway and automatically generated executable test cases. These test cases were successfully executed on two commercial software versions. In the paper, we also discuss our experience of applying RTCM and aToucan4Test in an industrial context and compare our approach with other model-based testing methodologies.
The Cytosolic DnaJ-like Protein Djp1p Is Involved Specifically in Peroxisomal Protein Import

The Saccharomyces cerevisiae DJP1 gene encodes a cytosolic protein homologous to Escherichia coli DnaJ. DnaJ homologues act in conjunction with molecular chaperones of the Hsp70 protein family in a variety of cellular processes. Cells with a DJP1 gene deletion are viable and exhibit a novel phenotype among cytosolic J-protein mutants in that they have a specific impairment of only one organelle, the peroxisome. The phenotype was also unique among peroxisome assembly mutants: peroxisomal matrix proteins were mislocalized to the cytoplasm to a varying extent, and peroxisomal structures failed to grow to full size and exhibited a broad range of buoyant densities. Import of marker proteins for the endoplasmic reticulum, nucleus, and mitochondria was normal. Furthermore, the metabolic adaptation to a change in carbon source, a complex multistep process, was unaffected in a DJP1 gene deletion mutant. We conclude that Djp1p is specifically required for peroxisomal protein import.
Nonparametric Tail Copula Estimation: An Application to Stock and Volatility Index Returns

In this study, we measure asymmetric negative tail dependence and discuss their statistical properties. In a simulation study, we show the reliability of nonparametric estimators of tail copula to measure not only the common positive lower and upper tail dependence, but also the negative “lower–upper” and “upper–lower” tail dependence. The use of this new framework is illustrated in an application to financial data. We detect the existence of asymmetric negative tail dependence between stock and volatility indices. Many common parametric copula models used in finance fail to capture this characteristic.
Weighted sequence loss based spatial-temporal deep learning framework for human body orientation estimation

Accurate human body orientation estimation (HBOE) can significantly promote the analysis of human behavior. However, conventional methods cannot holistically exploit the complementary nature of spatial and temporal information for H-BOE. Different from existing methods, we propose an end-to-end temporal-spatial deep learning framework to accurately estimate the human body orientation. In this framework, we firstly utilize the convolutional neural network to capture the spatial information for human orientation. Furthermore, the spatial-temporal information are fused in the recurrent neural networks (RNNs), which can automatically memorize a long-term temporal information of human orientation transformation. More important, to effectively adapt different moving speeds and diversity actions of people, we design a weighted sequence loss function, which can capture the significant orientation conversion to guide the RNN training. According to the comprehensive evaluations, the proposed method greatly outperforms the states-of-the-art methods. Although only utilizing the 2D information, it can perform better than the 3-D/RGB-D based approaches.
Structural alignment from sequence alone by integration of predicted secondary structure

Protein sequence alignment is at the core of a variety of fundamental tasks such as homology modeling, fold recognition, evolutionary studies and more. Much attention was dedicated over recent years for alignment of sequences in the twilight zone, where pairwise sequence alignment methods (e.g. BLAST) perform poorly. State of the art methods employ sequence profiles, and sometimes meta-sequence information such as predicted secondary structure assignment (SSA) at various stages of fold recognition methods. However, these methods typically use ad-hoc scoring for SSA substitution and do not use the structural information in the initial stages of profile construction from pairwise alignments (E.g. by PSI-Blast).
Cellware-a multi-algorithmic software for computational systems biology

UNLABELLED The intracellular environment of a cell hosts a wide variety of enzymatic reactions, diffusion events, molecular binding, polymerization and metabolic channeling. To transform these biological events into a computational framework, distinct modeling strategies are required. While currently no tool is capable of capturing all these events, progress is being made to create an integrated environment for the modeling community. To address this niche requirement, Cellware has been developed to offer a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. AVAILABILITY The software is available for free and can be downloaded from http://www.bii. a-star.edu. sg/sbg/cellware
Investigating The Factors Influencing User Acceptance and Attitude towards Technology Usage in Online Learning

The COVID-19 pandemic has accelerated the adoption of technology in Malaysian education, although many students, particularly accounting students, still prefer traditional methods. The sudden shift to online learning poses challenges for accounting students who favor face-to-face classes and have limited IT knowledge. Poor internet coverage in rural areas further complicates online learning. To address these issues, the study emphasizes the importance of infrastructure support and digital skill development. The research at UiTM aims to understand factors influencing accounting students' acceptance and attitudes toward technology use in online learning. A survey involving 402 respondents examined performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). Only two out of five hypotheses were significant: PE and FC. Moderation tests for attitude, gender, and age did not show significant results, indicating no moderation effects. The analysis revealed that only PE and FC had significant effects among the independent variables, while EE and SI showed insignificant relationships with behavioral intention. Moderating effects of attitude, gender, and age were inconsistent, with attitude moderating FC and EE on behavioral intention, and gender and age moderating only FC. PE and SI did not significantly moderate behavioral intention. The study suggests potential impacts on Malaysian institutions and accounting students, encouraging active use of online learning post-COVID-19.