Miracle-dz/UniFAR-FTUs huggingface dataset:
Family perceptions of facilitators and inhibitors of effective school-based dysphagia management.

PURPOSE This qualitative study focused on the perceptions of family members of children with dysphagia by asking what the family-identified factors are that facilitate or inhibit effective school-based management of pediatric dysphagia. METHOD Semistructured interviews of 7 family members of 6 children with dysphagia, ages 2 through 11 years, were conducted. Collective case study and cross-case analyses were conducted. The findings were confirmed through triangulation, respondent validation, and member checking. RESULTS Interviewees identified 5 facilitating factors that enhance school-based dysphagia programs: (a) setting, (b) therapist and/or program, (c) capitalizing on positive child characteristics, (d) home-school interactions, and (e) external supports. Four factors that inhibit effectiveness also emerged: (a) setting, (b) therapist and/or program, (c) limiting child characteristics, and (d) home-school interactions. CONCLUSION Practitioners may consider addressing the factors that were identified as facilitators of effective programs. More research is needed concerning (a) factors promoting effective parent-professional relationships, (b) methods for increasing professional preparation in pediatric dysphagia, and (c) the efficacy of school-based pediatric dysphagia management programs.
Inferring the mode of origin of polyploid species from next‐generation sequence data

Many eukaryote organisms are polyploid. However, despite their importance, evolutionary inference of polyploid origins and modes of inheritance has been limited by a need for analyses of allele segregation at multiple loci using crosses. The increasing availability of sequence data for nonmodel species now allows the application of established approaches for the analysis of genomic data in polyploids. Here, we ask whether approximate Bayesian computation (ABC), applied to realistic traditional and next‐generation sequence data, allows correct inference of the evolutionary and demographic history of polyploids. Using simulations, we evaluate the robustness of evolutionary inference by ABC for tetraploid species as a function of the number of individuals and loci sampled, and the presence or absence of an outgroup. We find that ABC adequately retrieves the recent evolutionary history of polyploid species on the basis of both old and new sequencing technologies. The application of ABC to sequence data from diploid and polyploid species of the plant genus Capsella confirms its utility. Our analysis strongly supports an allopolyploid origin of C. bursa‐pastoris about 80 000 years ago. This conclusion runs contrary to previous findings based on the same data set but using an alternative approach and is in agreement with recent findings based on whole‐genome sequencing. Our results indicate that ABC is a promising and powerful method for revealing the evolution of polyploid species, without the need to attribute alleles to a homeologous chromosome pair. The approach can readily be extended to more complex scenarios involving higher ploidy levels.
Active selection of training instances for a random forest meta-learner

Several approaches have been applied to the task of algorithm selection. In this context, Meta-Learning provides an efficient solution by adopting a supervised strategy. Despite its promising results, Meta-Learning requires an adequate number of instances to produce a rich set of meta-examples. Recent approaches to generate synthetic or manipulated datasets have been adopted with success in the context of Meta-Learning. These proposals include the datasetoids approach, a simple data manipulation technique that generates new datasets from existing ones. Although such proposals can actually produce relevant datasets, they can eventually produce redundant, or even irrelevant, problem instances. Active Meta-Learning arises in this context to select only the most informative instances for meta-example generation. In this work, we investigate the Active Meta-Learning combined with datasetoids, focusing on using the Random forest algorithm in meta-learning. Our experiments revealed that it is possible to reduce the computational cost of generating meta-examples and obtain a significant gain in Meta-Learning performance.
Design of unified support vector machine circuit for pedestrians and cars detection

This paper describes the design of unified support vector machine circuit for pedestrians and cars detection. By unifying the algorithms and architectures of linear and nonlinear SVM classifications, the proposed circuit can support both linear and non-linear classifications very efficiently in terms of circuit size and performance. The circuit size is minimized by sharing most of the resources required in the computation for both classification types. Parallel architecture with pipeline is adopted to accelerate the processing speed to handle a large amount of operations for real-time processing. 48×96 and 64×64 sliding windows with 6 window strides are used to detect pedestrians and cars, respectively. The synthesized circuit using 65nm standard cell library consists of 848,349 gates and its maximum operating frequency is 435MHz. The circuit can process 91.9 640×480 image frames per second assuming three cameras equipped on front, right and left side positions of the vehicle.
Involuntary Computing: Hacking the Cloud

—In this short wild and crazy paper we argue that cloud computing has a lot to learn from current cybercriminals who plague the internet with botnets, malware, and viruses, with the intent of ﬁnancial gain through extortion or exploitation. We present basic distributed system designs from the perspective of a hacker and argue that the same design features can be and are indeed used in cloud computing: decentralized scheduling, network overlays, heterogneous and intermittent resources, redundancy of task execution, and asynchronous messaging. In botnet architectures, these features are implemented via secure channels, typically using the ubiquitous HTTP protocol. We propose the replication of these nefarious distributed architectures for legitimate applications by means of Kestrel, a job scheduling framework built around XMPP messaging and customized for cloud resources. Our results show that Kestrel scales well over tens of thousands of nodes in heterogeneous environments containing NAT and ﬁrewall boundaries, and we envision a one million agent deployment in the near future. Since a botnet is an inexpensive cloud – at a reported cost of $200 a week for 6,000 cores – we hope to capitalize upon this architecture to lower the cost of cloud resources for scientiﬁc and educational applications.
Who Wants to Work with the Poor and Homeless?

This study examines factors associated with MSW students’ (N = 5,793) desires to work with the poor and homeless over the course of their education. Independent variable classes include (1) sociodemographic characteristics; (2) ideological beliefs as they relate to politics; (3) past social work-related experiences; (4) motivations for pursuing a graduate degree in social work; and, (5) field work experience and school attended. Findings show that sociodemographic variables are the least stable predictors of students’ interest in working with the poor and homeless, whereas ideological beliefs and personal motivations appear as the most noteworthy influences upon students’ desires to work with this population.
Is there a correlation between socioeconomic disparity and functional outcome after acute ischemic stroke?

Background To investigate the impact of low socioeconomic status (SES), indicated by low level of education, occupation and income, on 3 months functional outcome after ischemic stroke. Methods We analyzed data from the China National Stroke Registry (CNSR), a multicenter and prospective registry of consecutive patients with acute cerebrovascular events occurred between September 2007 and August 2008. 11226 patients with ischemic stroke had SES and clinical characteristics data collected at baseline and mRS measured as indicator of functional outcome in 3 months follow up. Multinomial and ordinal logistic regression models were performed to examine associations between SES and the functional outcome. Results At 3 months after stroke, 5.3% of total patients had mRS scored at 5, 11.3% at score 4, 11.1% at score 3, 14.4% at score 2, 34.2% at score 1 and 23.7% at score 0. Compared to patients with educational level of ≥ 6 years and non-manual laboring, those < 6 years and manual laboring tended to have higher mRS score (P<0.001). Multinomial adjusted odds ratios (ORs) of outcome in manual workers were significantly increased (ORs from1.38 to 1.87), but OR in patients with less income was not significant. There were similar patterns of association The impact may be stronger in patients aged <65 years (P = 0.003, P<0.001 respectively) and being male (P = 0.001, P<0.001 respectively). Conclusions Our study provides evidence that people who are relatively more deprived in socioeconomic status suffer poorer outcome after ischemic stroke. The influence of low educational level and manual laboring can be more intensive than low income level on 3-month outcome. Health policy and service should target the deprived populations to reduce the public health burden in the society.
Automatic Identification of Nutritious Contexts for Learning Vocabulary Words

Vocabulary knowledge is crucial to literacy development and academic success. Previous research has shown learning the meaning of a word requires encountering it in diverse informative contexts. In this work, we try to identify “nutritious” contexts for a word – contexts that help students build a rich mental representation of the word’s meaning. Using crowdsourced ratings of vocabulary contexts retrieved from the web, AVER learns models to score unseen contexts for unseen words. We specify the features used in the models, measure their individual informativeness, evaluate AVER’s cross-validated accuracy in scoring contexts for unseen words, and compare its agreement with the human ratings against the humans’ agreement with each other. The automated scores are not good enough to replace human ratings, but should reduce human effort by identifying contexts likely to be worth rating by hand, subject to a tradeoff between the number of contexts inspected by hand, and how many of them a human judge will consider nutritious.
ProFET: Feature engineering captures high-level protein functions

MOTIVATION The amount of sequenced genomes and proteins is growing at an unprecedented pace. Unfortunately, manual curation and functional knowledge lag behind. Homologous inference often fails at labeling proteins with diverse functions and broad classes. Thus, identifying high-level protein functionality remains challenging. We hypothesize that a universal feature engineering approach can yield classification of high-level functions and unified properties when combined with machine learning approaches, without requiring external databases or alignment. RESULTS In this study, we present a novel bioinformatics toolkit called ProFET (Protein Feature Engineering Toolkit). ProFET extracts hundreds of features covering the elementary biophysical and sequence derived attributes. Most features capture statistically informative patterns. In addition, different representations of sequences and the amino acids alphabet provide a compact, compressed set of features. The results from ProFET were incorporated in data analysis pipelines, implemented in python and adapted for multi-genome scale analysis. ProFET was applied on 17 established and novel protein benchmark datasets involving classification for a variety of binary and multi-class tasks. The results show state of the art performance. The extracted features' show excellent biological interpretability. The success of ProFET applies to a wide range of high-level functions such as subcellular localization, structural classes and proteins with unique functional properties (e.g. neuropeptide precursors, thermophilic and nucleic acid binding). ProFET allows easy, universal discovery of new target proteins, as well as understanding the features underlying different high-level protein functions. AVAILABILITY AND IMPLEMENTATION ProFET source code and the datasets used are freely available at https://github.com/ddofer/ProFET. CONTACT michall@cc.huji. ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Common ground for behavioural and neuroimaging research

Abstract The division between cognitive research that employs neuroimaging data and cognitive research that excludes such data has led to extreme views on both sides. As in most conflicts, these extreme views reinforce each other. They are further deepened by some poor science and a cavalier attitude towards the interpretation of neuroimaging data by some practitioners. I discuss two of the most problematic aspects of the interpretation of neuroimaging data: the logical fallacy of affirming the consequent; and the difficulties of linking assumptions which link the data with theoretical constructs. Both of these problems are not unique to neuroimaging research, and errors with them certainly occur in standard behavioural psychology. Nevertheless, when executed carefully, and when interpreted cautiously, neuroimaging has the potential to contribute to the understanding of cognition.
Scientific Summaries for Families With ASD

There is a substantial amount of data providing evidence for, but also against the hypothesis that individuals with autism spectrum disorders (ASD) encounter inhibitory control deficits. ASD is often associated with interference control deficits rather than prepotent response inhibition. Moreover, the developmental trajectory for these inhibitory control processes is hypothesized to differ in ASD as compared to typical development. In efforts to gain a more comprehensive perspective of inhibition in ASD, separate quantitative analysis for prepotent response inhibition studies and interference control studies were conducted. Together, these two meta‐analyses included 41 studies with a combined sample size of 1,091 people with ASD (M age 14.8 years), and 1,306 typically developing (TD) controls (M age 13.8 years). The meta‐analyses indicated that individuals with ASD show increased difficulties in prepotent response inhibition (effect size 0.55) and in interference control (effect size 0.31). In addition, age was a relevant moderator for prepotent response inhibition but not for interference control. Exploratory analyses revealed that when IQ was taken into account, heterogeneity considerably decreased among interference control studies but not among prepotent response inhibition. In contrast to the general belief, both prepotent response inhibition and interference control problems were observed in individuals with ASD. However, a large variation between studies was also found. Therefore, there remain factors beyond inhibition type, age, or IQ that significantly influence inhibitory control performance among individuals with ASD. Autism Res 2014, 7: 407–420. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.
Easing the Excision of Earlobe Keloid

Earlobe keloids are commonly encountered in this era where ear piercing is routine in various traditions. Various medical and surgical methods have been tried for its management. Surgically, fillet flap surgery has better acceptability worldwide. Various difficulties are faced during keloid excision, and we hereby describe few innovative modifications (use of needle, hydrodissection, pressure dressing, bolster button) using cost-effective and simple materials (needles, dynaplast, buttons) to ease the excision of keloids. These have easy adaptability and also reduce the recurrence rate of earlobe keloids.
Elicitation of profile attributes by transparent communication

When people are seeking information they are interested in, they need time, need to know exactly what they are looking for and require attention capacities to check different sources. Recommender systems help to overcome the information overflow and filter out irrelevant sources by comparing different types of information and selecting the best results in consideration of customer preferences. Therefore accurate customer profiles are necessary which nowadays do not exist. In a mobile environment customers are not willing to spend time on disclosing their preferences; maybe they are not aware of them or have difficulties to respond to system requests. The paper in hand follows recommendations by critiquing to improve profiling quality but instead of collecting information, the transparent communication of profile extensions is focused. The customer can add preferences to his profile without explicitly expressing. Furthermore, the connection between proposed preferences and the systems conclusion behind is visible.
The Lumbar Zygapophyseal (Facet) Joints: A Role in the Pathogenesis of Spinal Pain Syndromes and Degenerative Spondylolisthesis

The zygapophyseal joints in the lumbar spine are important structural components contributing to the stability of the lumbar motion segments. Pathology of the zygapophyseal joints in the lumbar spine may be a significant cause of low back pain and segmental instability within the lumbar spine. Management of pathology related to the zygapophyseal joints remains a difficult challenge for the physician caring for patients with spinal disorders. Future investigations with tissue engineering, ligamentous reconstructions, and intervertebral disc replacement or regeneration may have useful applications in the treatment of zygapophyseal joint pathology.
OWL ontology translation for the semantic web

This paper presents an ontology translation Web Service based on theO3F ontology representation framework that translates between ontologies expressed in OWL. In the O3F framework, ontology translation is based on a mapping from the basic concepts of the object ontology to basic or compound concepts of the target ontology. Using publicly available mappings, it is possible to recursively translate compound concepts of the object ontology into the target ontology. We describe the algorithms used by and the interface of the Ontology Translation Web Service. The FIPA Ontology Service specification[4] was used as a starting point for the definition of relationships between ontologies. Finally, the paper presents the definition of two ontologies in OWL, and the source of mappings from the object ontology into the target ontology. In an hypothetical scenario, two programs using different ontologies interact with the ontology translation web service in order to obtain interchangeability between information that is heterogeneously organised. The translation web service was implemented using Tomcat 1, which implements the Servlet 2.3 specification[13, v2.3].
3 D Unsharp Masking for Scene Coherent Enhancement Supplemental Material 1 : Experimental Validation of the Algorithm

To study the meaningful range of settings for the parameters of interest, a standard psychophysical procedure, the “method of adjustment” was used [Gescheider 1997]. This was deemed to be the most appropriate choice for the current study since it allows both for an efficient implementation of the experiment and provides a single means of estimating all thresholds and preference values of interest.
Triggering Memories of Conversations using Multimodal Classifiers

Our personal conversation memory agent is a wearable ‘experience collection’ system, which unobtrusively records the wearer’s conversation, recognizes the face of the dialog partner and remembers his/her voice. When the system sees the same person’s face or hears the same voice it uses a summary of the last conversation with this person to remind the wearer. To correctly identify a person and help remember the earlier conversation, the system must be aware of the current situation, as analyzed from audio and video streams, and classify the situation by combining these modalities. Multimodal classifiers, however, are relatively unstable in the uncontrolled real word environments, and a simple linear interpolation of multiple classification judgments cannot effectively combine multimodal classifiers. We propose a meta-classification strategy using a Support Vector Machine as a new combination strategy. Experimental results show that combining face recognition and speaker identification by meta-classification is dramatically more effective than a linear combination. This meta-classification approach is general enough to be applied to any situation-aware application that needs to combine multiple classifiers.
Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94

This article introduces MMFF94, the initial published version of the Merck molecular force field (MMFF). It describes the objectives set for MMFF, the form it takes, and the range of systems to which it applies. This study also outlines the methodology employed in parameterizing MMFF94 and summarizes its performance in reproducing computational and experimental data. Though similar to MM3 in some respects, MMFF94 differs in ways intended to facilitate application to condensed‐phase processes in molecular‐dynamics simulations. Indeed, MMFF94 seeks to achieve MM3‐like accuracy for small molecules in a combined “organic/protein” force field that is equally applicable to proteins and other systems of biological significance. A second distinguishing feature is that the core portion of MMFF94 has primarily been derived from high‐quality computational data—ca. 500 molecular structures optimized at the HF/6‐31G* level, 475 structures optimized at the MP2/6‐31G* level, 380 MP2/6‐31G* structures evaluated at a defined approximation to the MP4SDQ/TZP level, and 1450 structures partly derived from MP2/6‐31G* geometries and evaluated at the MP2/TZP level. A third distinguishing feature is that MMFF94 has been parameterized for a wide variety of chemical systems of interest to organic and medicial chemists, including many that feature frequently occurring combinations of functional groups for which little, if any, useful experimental data are available. The methodology used in parameterizing MMFF94 represents a fourth distinguishing feature. Rather than using the common “functional group” approach, nearly all MMFF parameters have been determined in a mutually consistent fashion from the full set of available computational data. MMFF94 reproduces the computational data used in its parameterization very well. In addition, MMFF94 reproduces experimental bond lengths (0.014 Å root mean square [rms]), bond angles (1.2° rms), vibrational frequencies (61 cm−1 rms), conformational energies (0.38 kcal/mol/rms), and rotational barriers (0.39 kcal/mol rms) very nearly as well as does MM3 for comparable systems. MMFF94 also describes intermolecular interactions in hydrogen‐bonded systems in a way that closely parallels that given by the highly regarded OPLS force field. © 1996 John Wiley & Sons, Inc.