Miracle-dz/UniFAR-FTUs huggingface dataset:
What Do Parents Need to Know About Concussion? Developing Consensus Using the Delphi Method.

OBJECTIVE Many schools and sports leagues provide preseason concussion education to parents/guardians, and in some jurisdictions, it is required for interscholastic sports participation. However, directives about content are absent or vague. The purpose of this study was to obtain expert consensus about what parents/guardians need to know to about concussion. DESIGN A modified Delphi process with nationally recognized expert panel members was used to obtain consensus about parent/guardian behaviors relevant to concussion prevention, identification, and management and knowledge domains relevant to performance of those behaviors. RESULTS Sixteen parent/guardian behaviors and 24 knowledge domains were identified. However, consensus was not achieved regarding whether it is realistic to expect parents/guardians to perform approximately one-third of the behaviors that experts agreed mattered. This variability may reflect underlying uncertainty about the capacities of some parents/guardians to put knowledge into action or the belief that there are other barriers to action. Furthermore, for most knowledge domains, there was a lack of agreement about whether or not there could be a "correct" answer on the basis of current scientific knowledge. CONCLUSIONS These findings raise practical and ethical questions: how can we expect parents/guardians to help prevent, identify, or manage concussion if they cannot have all the information required to engage in these behaviors due to lack of scientific consensus? This issue is not unique to concussion. Best practices for risk communication and shared decision-making can inform how we think about educating parents/guardians about concussion inside and outside of the health care setting.
Texture classification of aerial image based on Bayesian networks

Classification is a basic topic in data mining and pattern recognition. Following advances in computer science, a lot of new methods have been proposed in recent years, such as artificial neural networks, decision trees, fuzzy set and Bayesian Networks, etc. As a probabilistic network, Bayesian Networks is a powerful tool for handling uncertainty in data mining and many other domains. Naïve Bayes Classifier (NBC) is a simple and effective classification method, which is built on the assumption of conditional independence between the class attributes. This topology structure can not describe the inherent relation among the features. In this paper, we apply Bayesian Networks Augmented Naïve Bayes (BAN) for the texture classification of aerial images, which relaxes the independent assumption in NBC. A new method for learning the networks topology structure based on training samples is adopted in this paper. Comparison experiments show higher accuracy of BAN classifier than NBC. The results also show the potential applicability of the proposed method.
Feedback, value added and teachers attitudes : models, theories and experiments

A review of the literature shows a long history of research on the effects of feedback on performance. Feedback is found to enhance performance when it focuses attention on, or increases the saliency of, desired outcomes, or when the information it conveys helps to diagnose shortcomings in performance. A critical review of school effectiveness research draws attention to the lack of existing evidence about the effects of attempts to improve students' academic performance. The lack of a sound theoretical understanding of the mechanisms by which schools and teachers may influence achievement is also discussed. Two experiments were performed. In the first, 44 teachers of A level in volunteer institutions completed questionnaires designed to elicit their uses of and attitudes towards feedback and their self-perceptions. Teachers were randomly allocated to feedback or control groups, the former receiving information about their own students' value added performance and attitudes in previous years. The same questionnaire was used before and after distributing the feedback. Qualitative data were also collected and analysed. In the second experiment, a random sample of 192 institutions was drawn from the membership of ALIS and departments were allocated to one of three feedback 'treatments' or a control group. In each experiment, student examination performance before and after the intervention was compared. In the first experiment, some attitude changes for the teachers were found between preand post-test, but the validity of the constructs measured by the questionnaire was somewhat challenged by the evidence from the interviews. Student A level performance for those whose teachers received the feedback was about a third of a grade better than for those in the control group, after statistical adjustment (effect sizes from 0.2 to 0.3). No significant differences were found between any of the treatments in the second experiment.
Practical path planning among movable obstacles

Path planning among movable obstacles is a practical problem that is in need of a solution. An efficient heuristic algorithm is presented that uses a generate-and-test paradigm: a good candidate path is hypothesized by a global planner and subsequently verified by a local planner. In the process of formalizing the problem, a technique for modeling object interactions through contact is presented. The algorithm has been tested on a variety of examples, and was able to generate solutions within 10 s on a 17-MIPS Sun Sparc workstation. <<ETX>>
External Validity of the Multicomponent Group Treatment KiSS for School-Aged Children With Insomnia

ABSTRACT Background: Insomnia is common in school-aged children and often associated with other mental disorders. Here, we investigated the effectiveness and acceptance of the KiSS program in an “all-comer” pediatric insomnia clinic. Participants/Methods: Forty-five families (children: 5–10 years, 57.8% female) were randomly assigned to the multicomponent six-session KiSS treatment (CBT-I, hypnotherapy, and imaginations) or a wait-list control group. A sleep diary was recorded before intervention or waiting time, immediately after, as well as 3, 6, and 12 months postintervention (primary outcome: sleep efficiency, SE; sleep-onset latency, SOL). Results: Sleep improved significantly after the KiSS intervention. Three to 12 months after the intervention, only 1.75% of treated children still met the diagnostic criteria for insomnia. SE improved to 96%, and both SOL (reduced to 23 min) and total sleep time were normalized. All improvements were stable for at least one year after the intervention. KiSS was well accepted (0% dropout during intervention) and well tolerated. Conclusions: Treatment with the multimodal sleep training KiSS leads to positive changes in the sleep of children with insomnia, including a subset of children that presented with comorbid mental disorders. With only three sessions for parents and three sessions for children, the treatment can be readily implemented as a stand-alone treatment or be combined with other interventions. Dismantling studies and studies with an active control group are ongoing.
Determining optimal scales for edge detection using regularization

A regularization method for determining the scales of an edge detector adaptively for each part of the image is proposed. The optimal-filter concept of T. Poggio et al. (1984) and the scale space concept of A. Witkin (1983) are extended to an adaptive scale parameter. An energy function defined over continuous scale space is first introduced. Natural constraints for edge detection are incorporated into the energy function. To obtain a set of optimal scales which can minimize the energy function, a parallel relaxation algorithm is introduced. Experiments for synthetic and natural scenes show the advantages of the new algorithm over the edge detectors by B. Marr and E. Hildreth (1980) or J.F. Canny (1986). <<ETX>>
Students’ Reflective Journals: National Standards for School Leaders and University Preparation Programs

This study was conducted to determine the extent to which student’s reflective writings during the educational leadership internship indicated if students were able to understand and relate the ELCC standards to their schools practices. A second purpose was to use these reflective journals to evaluate how well the program prepared the students for their role as future school leaders by examining how well their experiences aligned with the ELCC standards.
Identifying TF Binding Motifs from a Partial Set of Target Genes and its Application to Regulatory Network Inference

Motif identification has been one of the most widely studied problems in bioinformatics. Many methods have been developed to discover binding motifs from a large set of genes. But when the given genes are only a partial set of target genes, the statistical significance usually contains a bias towards the input. If we can identify the TF binding motif from a partial set of target genes, we can save the labor costs and resources for doing many experiments. In this paper, we propose a method MISA (Motif Identification through Segments Assembly) to identify binding motifs from a subset of target genes. By ranking and assembling the segments, MISA discovers a set of binding motifs with the best length to fit our proposed objective function. We also predict the additional target genes as an application of regulatory network inference. We compare our approach with two widely used methods MEME and AlignACE by analyzing both the quality of the binding motif and network inference. Using two model organisms S. cerevisiae and E. coli, we show that with 20 percent of the target genes (minimum sample size of 20), we can achieve a motif similarity of 82 percent with the known motifs. Our results also show that 73 percent of target genes on average can be correctly predicted without introducing many false target genes.
Islanding Detection and Enhancement of Microgrid Performance

Nowadays, renewable energy sources (RES) are widely used in the distribution system. Despite the advantages of RES in the system, they also introduce some problems such as unintentional islanding, protection concerns, reverse power flow, etc. Due to the unpredictable faults or scheduled maintenance plans, a distribution system can be sectionalized into several islands provided that enough dispersed generation units are available. Thus, distribution systems should be capable of detecting islanding condition for smooth transition to an islanded mode. As a first step, an islanding detection method (IDM) is proposed in this paper to detect the islanding phenomenon in a distribution system. The proposed method is a hybrid IDM which consists of a remote detection method and a passive method. In the next step, an adaptive control strategy is proposed to ensure stable operation of islanded subsections. The proposed method utilizes error rates of system parameter such as voltage and power to readjust generator controllers and maintain the system stability. The proposed IDM and adaptive controller are implemented on a generic distribution network using EMTDC/PSCAD software. Results have shown that the hybrid detection method is capable of detecting the islanding in the presence multiple distributed generation units. Moreover, the proposed IDM is not affected by load/generation changes. Results also showed a smooth transition from grid-connected mode to autonomous operation mode. Furthermore, the proposed adaptive control strategy maintains stable operation of the island when fault occurs in the island.
Evolutionary Stochastic Search for Bayesian model exploration

Implementing Bayesian variable selection for linear Gaussian regression models for analysing high dimensional data sets is of current interest in many fields. In order to make such analysis operational, we propose a new sampling algorithm based upon Evolutionary Monte Carlo and designed to work under the "large p, small n" paradigm, thus making fully Bayesian multivariate analysis feasible, for example, in genetics/genomics experiments. Two real data examples in genomics are presented, demonstrating the performance of the algorithm in a space of up to 10,000 covariates. Finally the methodology is compared with a recently proposed search algorithms in an extensive simulation study.
Hemagglutinins (Lectins) Extracted from Maclura Pomifera*

Saline extracts of seeds of Maclura pomqera were prepared by grinding 2.0 g of seeds with a mortar and pestle and extracting with 10 ml of a 0.9% saline solution for 11/, h at room temperature on a serological shaker. Specificity of extracts was determined in tests for agglutination against 2% saline or protein (whole human serum and 22% bovine albumin) suspensions of washed human erythrocytes of various types. The lectin was also tested against bromelin-treated cells. These cells were prepared by mixing equal volumes of packed cells and 0.5yo saline solution of bromelin for 30 min at 37°C. The cells were washed 4 times with saline and used as a 2% suspension. The extracts were also tested against 2% saline suspensions of erythrocytes from sheep, rabbits, horses, dogs and bullfrogs. Tests of homogeneity were made by agar-gel electrophoresis at pH 8.6 [2] and by Sephadex gel filtration (Pharmacia). Separated fractions prepared from eluates of protein zones of unfixed electrophoretogram [3] and fractions from Sephadex were tested for hemagglutinin activity. A thermal inactivation test was performed on 1 ml of extract by heating for 10 min a t 5°C increments from 30°C to 100°C. Tests for hemagglutinin activity were made after each heat step.
Amygdala response to mother Nim Tottenham

In altricial species, like the human, the caregiver, very often the mother, is one of the most potent stimuli during development. The distinction between mothers and other adults is learned early in life and results in numerous behaviors in the child, most notably mother-approach and stranger wariness. The current study examined the influence of the maternal stimulus on amygdala activity and related circuitry in 25 developing children (n = 13) and adolescents (n = 12), and how this circuitry was associated with attachment-related behaviors. Results indicated that maternal stimuli were especially effective in recruiting activity in the left dorsal amygdala, and activity in this amygdala region showed increased functional connectivity with evaluative and motor regions during viewing of maternal stimuli. Increases in this left dorsal amygdala activity and related amygdala-cortical functional connectivity were associated with increased mother-approach behaviors as measured by in-scanner behavioral responding and out-of-scanner child-report. Moreover, age-related changes in amygdala activity to strangers statistically mediated the developmentally typical decline in stranger wariness seen across this period. These results suggest that motherinduced behaviors are enacted by maternal influence on amygdala-cortical circuitry during childhood and adolescence.
How to improve the interpretability of kernel learning

In recent years, machine learning researchers have focused on methods to construct flexible and interpretable prediction models. However, the interpretability evaluation, the relationship between the generalization performance and the interpretability of the model and the method for improving the interpretability are very important factors to consider. In this paper, the quantitative index of the interpretability is proposed and its rationality is given, and the relationship between the interpretability and the generalization performance is analyzed. For traditional supervised kernel machine learning problem, a universal learning framework is put forward to solve the equilibrium problem between the two performances. The uniqueness of solution of the problem is proved and condition of unique solution is obtained. Probability upper bound of the sum of the two performances is analyzed.
Flexible design of computationaly efficient nearly perfect QMF filter banks

This paper introduces a flexible design method of computationally efficient uniform filter bank where each filter channel is obtained by frequency translation of a prototype base-band filter. It is shown that the original signal decomposed by the analysis filter bank into N adjacent uniform subbands subsampled by N, can be reconstructed with negligible distortion. We give a practical implementation which allows an important reduction in computational complexity. It can be therefore applied to such signal processing tasks as speech coding or spectral parametrization of signals.
Criteria for Classification of Systemic Lupus Erythematosus

In 1971, the American Rheumatism Association (ARA) Committee on Therapeutic Criteria proposed "Preliminary Criteria for the Classification of Systemic Lupus Erythematosus [SLE]". 1 Any four of the 14 items listed below, when present, indicate SLE. 1 Facial erythema (butterfly rash) Discoid lupus erythematosus Raynaud phenomenon Alopecia Photosensitivity Oral or nasopharyngeal ulceration Arthritis without deformity Lupus erythematosus cells Chronic false-positive serologic test for syphilis Profuse proteinuria Cellular casts Pleuritis or pericarditis Psychosis or convulsions Hemolytic anemia or leukopenia or thrombocytopenia These criteria were formulated by computer analysis of 57 symptoms, signs, and laboratory findings in 245 patients (with "unequivocal SLE") under the care of 52 different rheumatologists in the United States and Canada. Insufficient data at the time of evaluating these records prevented further analyses of such commonly used tests as antinuclear antibody, Coombs test, anti-DNA antibody, tissue biopsies, and direct immunofluorescence of skin biopsy specimen. The first six criteria relate
Neural Machine Translation based Word Transduction Mechanisms for Low-Resource Languages

Out-Of-Vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for Low-Resource Languages (LRLs). This paper adapts variants of seq2seq models to perform transduction of such words from Hindi to Bhojpuri (an LRL instance), learning from a set of cognate pairs built upon a bilingual dictionary of Hindi-Bhojpuri words. We demonstrate that our models can effectively be used for languages that have a limited amount of parallel corpora, by working at the character-level to grasp phonetic and orthographic similarities across multiple types of word adaptions, whether synchronic or diachronic, loan words or cognates. We provide a comprehensive overview over the training aspects of character-level NMT systems adapted to this task, combined with a detailed analysis of their respective error cases. Using our method, we achieve an improvement by over 6 BLEU on the Hindi-to-Bhojpuri translation task. Further, we show that such transductions generalize well to other languages by applying it successfully to Hindi-Bangla cognate pairs. Our work can be seen as an important step in the process of: (i) resolving the OOV words problem arising in MT tasks, (ii) creating effective parallel corpora for resource-constrained languages, and (iii) leveraging the enhanced semantic knowledge captured by word-level embeddings onto character-level tasks.
Intramedullary nailing of diaphyseal fractures with self‐reinforced polylactide implants

In fracture treatment, several adverse effects of metal, especially its excessive rigidity, often require the removal of the implants after a fracture has healed. Recent research with biodegradable polymers has contributed to the development of totally biodegradable polylactic acid implants with very high initial bending and shear strengths, but a modulus of elasticity comparable to that of bone. These implants were originally intended for use in fractures involving cancellous bone. Encouraging results from treating experimentally produced diaphyseal fractures led the authors to use these implants in a clinical trial on shaft fractures in dogs and cats. In this study, a total of 11 dogs and 14 cats had 28 diaphyseal fractures treated with one or several intramedullary pins of self-reinforced left isomeric polylactic acid. Of the 25 treated cases, 23 healed. Four of these had an unsatisfactory end result attributed to the implants used and one developed an osteomyelitis. Thus, out of 25 fractures, 23 united (92 per cent] and 18 [72 per cent) had a good or excellent outcome.
A congestion control method of SDN data center based on reinforcement learning

With the development of cloud computing and big data, the internal communication business in data center has increased dramatically, and then the traffic in data center has also significantly increased. The bandwidth of data center is difficult to meet the bandwidth requirements of those intensive applications, and data center is facing a risk of network congestion. Under the background of the development of network intelligence, software‐defined network (SDN) should demonstrate its intelligence as a future network architecture. In this paper, we introduce reinforcement learning into the SDN data center to implement congestion control based on flow. We improve the Q‐learning and Sarsa algorithms and propose two methods of congestion control based on the algorithms. Test results show that these two congestion control methods can control congestion effectively. And Sarsa method has a better performance of link utilization. The average link utilization of the Sarsa method is 2.4% higher than the Q‐learning method and is 4.48% higher than the on‐demand method.
Nuclear competence and genetic expression of growth differentiation factor‐9 (GDF‐9) of canine oocytes in 3D culture

To evaluate the ability of a 3D culture system in improving the nuclear and molecular competence of canine oocytes, barium alginate microcapsules were used for in vitro maturation (IVM) and the expression profile of one selected oocyte-secreted factor, the growth differentiation factor-9 (GDF-9) was analysed. In Experiment I, canine grade I cumulus-oocyte complexes (COCs) were in vitro matured in 3D microcapsules in a controlled atmosphere for 72 hr, and meiosis resumption rates were compared to those of oocytes cultured in traditional 2D microdrops of medium. In Experiment II, a primer pair specific for canine GDF-9 was designed, and preliminary tested in conventional PCR on genomic DNA. Total RNA content was isolated from oocytes at different time intervals (T0-T24-T48-T72) during in vitro 3D culture, and a reverse transcription to cDNA was performed. The expression of target gene was assessed by quantitative Reverse Transcription Real-Time PCR (qRT-PCR), and the obtained amplicons were sequenced to check the specificity of the analysis. Canine COCs resumed meiosis at higher rates in 3D microcapsules than in 2D microdrops (p < 0.05), even though no significant differences in the proportions of oocytes achieving full maturational stages were obtained. A significant dynamic decrease in GDF-9 expression was recorded during culture: after 72 hr of IVM, the GDF-9 transcription significantly dropped (p = 0.018) compared to 24 and 48 hr. In conclusion, in vitro 3D culture represents an efficient system for IVM of canine oocytes, and the expression profile of GDF-9 well reflects temporal dynamics for the acquisition of developmental competence in this species.