Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurological, physical and sociodemographic correlates of employment in multiple sclerosis: A meta-analysis

Illness severity and sociodemographic characteristics of 7,053 employees with multiple sclerosis and 11,043 peers not in the workforce were compared (Hedges’ g with 95% confidence interval and p values). Pooled findings from 25 studies confirmed the main role of a relapsing–remitting disease course and higher education to employment. To a lesser extent, disease duration, fatigue and pain symptoms and age also differentiated the two groups. Vocational interventions for persons with multiple sclerosis should focus on job retention, including mechanisms to accommodate and facilitate functional independence. Longitudinal data are needed to distinguish the characteristics of those who achieve and maintain competitive employment.
Research on Flower Image Classification Using Sum-Product Networks

Traditional stretch-based preprocessing method in image classification is unable to meet practical needs due to image distortion and information destruction of original structures. This paper presents a novel approach based on Sum-Product Networks (SPNs) for flower image classification. With SPNs, an image preprocessing method maintaining aspect ratio is used and an efficient feature extraction method is then applied. We performed experiments on 17 classes of flower images to examine the efficiency of the method we proposed.
Multi-Touch in the Air: Concurrent Micromovement Recognition Using RF Signals

The human–computer interactions have moved from the conventional approaches of entering inputs into the keyboards/touchpads to the brand-new approaches of performing interactions in the air. In this paper, we propose RF-glove, a system that recognizes concurrent multiple finger micromovement using RF signals, so as to realize the vision of “multi-touch in the air. ” It uses a commercial-off-the-shelf (COTS) RFID reader with three antennas and five COTS tags attached to the five fingers of a glove, one tag per finger. During the process of a user performing finger micromovements, we let the RFID reader continuously interrogate these tags and obtain the backscattered RF signals from each tag. For each antenna–tag pair, the reader obtains a sequence of RF phase values called a phase profile from the tag’s responses over time. To tradeoff between accuracy and robustness in terms of matching resolution, we propose a two phase approach, including coarse-grained filtering and fine-grained matching. To tackle the variation of template phase profiles at different positions, we propose a phase-model-based solution to reconstruct the template phase profiles based on the exact locations. Experiment results show that we achieve an average accuracy of 92.1% under various moving speeds, orientation deviations, and so on.
Real-Time Visual Feedback: A Study in Coding Analytics

Higher dropout and failure rates among computer science students in introductory programming courses tend to be a norm for many institutions. Years of evidence indicate that dropouts and failures persist in spite of advancements in pedagogy, technology, and teacher training. Most advancements have relied on summative assessments and of late formative assessments. This research explores assessments computed from real-time measures, based on observational data collected during student engagement with study and remedial activities. An experiment was conducted to measure the impact of real-time code assessment and dashboard-based feedback in the domain of Programming. Results indicate better course grades for a small percentage of students, and the need for task-level and meta-level interactions to guarantee significant and persistent academic performance and programming mastery.
Energy Management Considering Load Operations and Forecast Errors With Application to HVAC Systems

This paper studies an energy management problem taking into account the relationship between the operating states and the energy consumption of the loads under forecast errors. The energy management problem is formulated as a nonlinear optimization problem, including the operation-to-energy functions and the probability of exceeding the energy supply in the constraints. An energy management algorithm is developed to seek the optimal operating states and pricing strategies for the consumers and the energy provider, respectively. Then, the globally asymptotic stability is proved for the energy management algorithm. Furthermore, the algorithm is applied to achieve energy management of distributed heating ventilation air conditioning systems. Simulation results demonstrate that the energy management algorithm converges to the optimal solution and the demand is approximately equal to the energy supply with margin for reliability.
Liquid delivery MOCVD of PLT for pyroelectric detectors

Pb/sub 1-x/La/sub x/Ti/sub 1-x/4/O/sub 3/ (PLT) films have shown promise for uncooled infrared detector applications. A metalorganic chemical vapor deposition (MOCVD) process has been developed to deposit PLT at 535/spl deg/C using the liquid delivery technique on two inch Pt/ZrO/sub 2//Si substrates. Metalorganic /spl beta/-diketonate and mixed /spl beta/-diketonate-alkoxide precursors were flash vaporized and delivered into a large area MOCVD chamber and high quality crystalline perovskite films were deposited with La contents of 0.02/spl les//spl times//spl les/0.28. Room temperature dielectric constant varied between 400 940 for films /spl sim/1200 /spl Aring/ thick and increased with increasing La content. Pyroelectric measurements were carried out using the Byer-Roundy technique. The pyroelectric coefficients were found to depend on film orientation which varied between [100] and [111] with the Pb/Ti ratio; values between 6-13 nC/cm/sup 2/K were observed.
Random Harmonic Detection and Compensation Based on Synchronous Reference Frame

Algorithms for harmonic detection and compensation are important guarantees for an active power filter (APF) to achieve the harmonic control function and directly determine the overall performance. Existing algorithms usually need a large amount of computation, and the compensation effect of specified order harmonic is also limited. DC side capacitor voltage at sudden change of load is affected by the algorithm as well. This paper proposes a new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF), in which a band-pass filter with center frequency of th harmonic is designed in fundamental frequency SRF to extract random harmonic current with two different frequencies of ( )th harmonic in stationary reference frame. This new algorithm can rapidly detect any specified harmonic, and it can adjust the power factor to compensate reactive power. Meanwhile, it has few impacts on DC side capacitor voltage under complicated operating conditions such as sudden change of load. The correctness and effectiveness of this new algorithm are verified by simulation and experiment.
HLX is a candidate gene for a pattern of anomalies associated with congenital diaphragmatic hernia, short bowel, and asplenia

Isolated congenital diaphragmatic hernia is often a sporadic event with a low recurrence risk. However, underlying genetic etiologies, such as chromosome anomalies or single gene disorders, are identified in a small number of individuals. We describe two fetuses with a unique pattern of multiple congenital anomalies, including diaphragmatic hernia, short bowel and asplenia, born to first‐cousin parents. Whole exome sequencing showed that both were homozygous for a missense variant, c.950A>C, predicting p.Asp317Ala, in the H.20‐Like Homeobox 1 (HLX1) gene. HLX is a homeobox transcription factor gene which is relatively conserved across species. Hlx homozygous null mice have a short bowel and reduced muscle cells in the diaphragm, closely resembling the anomalies in the two fetuses and we therefore suggest that the HLX mutation in this family could explain the fetal findings.
Emo-soundscapes: A dataset for soundscape emotion recognition

Soundscape emotion recognition (SER) aims at the automatic recognition of emotions perceived in soundscape recordings. To benchmark SER, we propose a dataset of audio samples called Emo-Soundscapes and two evaluation protocols for machine learning models. We curated 600 soundscape recordings from Freesound.org and mixed 613 audio clips from a combination of these. The Emo-Soundscapes dataset contains 1213 6-second Creative Commons licensed audio clips. We collected the ground truth annotations of perceived emotion in these 1213 soundscape recordings using a crowdsourcing listening experiment, where 1182 annotators from 74 different countries rank the audio clips according to the perceived valence and arousal. This dataset allows studying SER and how the mixing of various soundscape recordings influences their perceived emotion. The dataset is at http://metacreation.net/emo-soundscapes/.
Modeling information diffusion and community membership using stochastic optimization

Communities are vehicles for efficiently disseminating news, rumors, and opinions in human social networks. Modeling information diffusion through a network can enable us to reach a superior functional understanding of the effect of network structures such as communities on information propagation. The intrinsic assumption is that form follows function-rational actors exercise social choice mechanisms to join communities that best serve their information needs. Particle Swarm Optimization (PSO) was originally designed to simulate aggregate social behavior; our proposed diffusion model, PSODM (Particle Swarm Optimization Diffusion Model) models information flow in a network by creating particle swarms for local network neighborhoods that optimize a continuous version of Holland's hyperplane-defined objective functions. In this paper, we show how our approach differs from prior modeling work in the area and demonstrate that it outperforms existing model-based community detection methods on several social network datasets.
Diamond composites with nanoceramic boride bonding phases

Purpose: Basic mechanical properties of the studied tool composites and microstructure of diamondtitanium diboride composite, and diamond-titanium diboride-titanium nitride composite with participation of nanopowders have been presented. Design/methodology/approach: Composites were prepared on the basis diamond powders of 3-6 μm (MDA36, Element Six) and the TiB2/TiN nanopowders of below 45 nm (Neomat Co. Lithuania firm) and nanopowder of TiB2 with size of crystallite below of 100 nm (American Elements firm). Different amount of a bonding phase changing in range from 10 to 30 wt% was used. Compacts in the shape of disc with dimension O15x5 mm were sintered at pressure 8.0±0.2 GPa and temperature of 2235 K using the Bridgman type apparatus. Microstructure studies using scanning microscope, X-ray and electron diffraction phase analysis were used. Findings: The influence of the bonding phase amount on the tested properties was observed. Vicker’s hardness HV1 was changed in the range from 20.0 to 50.0 GPa, Young’s modulus (E) from 360 to 600 GPa and density (ρ) from 3.30 to 3.63 g/cm3. The highest values of Vickers hardness and Young’s modulus were obtained for diamond composites sintered with 10 wt% TiB2 of bonding phase. Practical implications: : In this work the effect of reduction powder size from submicron scale to nano scale of two ceramic bonding phases: titanium diboride and titanium diboride-nitride in diamond composites on selected mechanical properties has been reported. The results show that using of the TiB2 powders in nano scale size increase the Vicker’s hardness about 30 wt% in comparison to using of the TiB2/TiN phase. Originality/value: These investigations allow enhance possibility of using this materials as burnishing tools and rational use of existing ceramic tools.
Adaptive working memory strategy training in early Alzheimer's disease: randomised controlled trial

Background Interventions that improve cognitive function in Alzheimer's disease are urgently required. Aims To assess whether a novel cognitive training paradigm based on ‘chunking’ improves working memory and general cognitive function, and is associated with reorganisation of functional activity in prefrontal and parietal cortices (trial registration: ISRCTN43007027). Method Thirty patients with mild Alzheimer's disease were randomly allocated to receive 18 sessions of 30 min of either adaptive chunking training or an active control intervention over approximately 8 weeks. Pre- and post-intervention functional magnetic resonance imaging (fMRI) scans were also conducted. Results Adaptive chunking training led to significant improvements in verbal working memory and untrained clinical measures of general cognitive function. Further, fMRI revealed a bilateral reduction in task-related lateral prefrontal and parietal cortex activation in the training group compared with controls. Conclusions Chunking-based cognitive training is a simple and potentially scalable intervention to improve cognitive function in early Alzheimer's disease.
DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets

Purpose: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response (“DiSCoVER”), to identify novel therapeutics that target this specific disease subtype. Experimental Design: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo. Results: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts. Conclusions: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903–14. ©2016 AACR.
Bioassay-guided isolation, identification and molecular ligand-target insight of lipoxygenase inhibitors from leaves of Anisomeles malabarica R.Br.

Background: Anisomeles malabarica R. Br. (Lamiaceae) is extensively used in traditional medicine in major parts of India for several medicinal purposes, including their use in rheumatism. Materials and Methods: The air-dried leaves of A. malabarica were extracted with ethanol, defatted with n-hexane and then successively partitioned into chloroform and n-butanol fractions. Bioassay-guided fractionation and purification of chloroform fraction from A. malabarica lead to the isolation of lipoxygenase (LOX) inhibitors. The structures of isolated compounds were elucidated by ultraviolet, infrared, 1H nuclear magnetic resonance (NMR), 13C NMR and mass spectrometry spectroscopic techniques and assessed further by in vitro soybean lipoxygenase (sLOX) assay. In addition, the enzyme type inhibition was evaluated through molecular docking technique as a part of computational study. Results: The bioactive compounds 3, 4 dihydroxy benzoic acid (1) and 4’, 5, 7-trihydroxyflavone (2) were isolated from chloroform fraction of A. malabarica, whose bioactivity was observed to be dose-dependent compared to n-butanol fraction. Among the compounds, 3, 4 dihydroxy benzoic acid showed significant sLOX inhibitory activity with 74.04% ±2.6% followed by 4’, 5, 7-trihydroxyflavone (34.68% ±1.9%). The computational analysis of compounds showed their molecular interaction with important amino acid residues and nonheme iron atom in the catalytic site of LOX by enlightening their potential binding mode at molecular level. Conclusions: The LOX inhibitory constituents were identified from A. malabarica by means of bioassay-guided fractionation process. The results derived from in vitro and computational experiments confirm the potential of the isolated compounds and provide additional evidence for its traditional use in inflammatory disorders.
nicter: a large-scale network incident analysis system: case studies for understanding threat landscape

We have been developing the Network Incident analysis Center for Tactical Emergency Response (nicter), whose objective is to detect and identify propagating malwares. The nicter mainly monitors darknet, a set of unused IP addresses, to observe global trends of network threats, while it captures and analyzes malware executables. By correlating the network threats with analysis results of malware, the nicter identifies the root causes (malwares) of the detected network threats. Through a long-term operation of the nicter for more than five years, we have achieved some key findings that would help us to understand the intentions of attackers and the comprehensive threat landscape of the Internet. With a focus on a well-knwon malware, i. e. , W32.Downadup, this paper provides some practical case studies with considerations and consequently we could obtain a threat landscape that more than 60% of attacking hosts observed in our dark-net could be infected by W32.Downadup. As an evaluation, we confirmed that the result of the correlation analysis was correct in a rate of 86.18%.
Thermal and Structural Behaviors of Polypropylene Nanocomposites Reinforced with Single-Walled Carbon Nanotubes by Melt Processing Method

In this work, polypropylene (PP) matrix reinforced with several single-walled carbon nanotubes (SWNTs) concentrations were prepared by a melt-mixing method. The effect of SWNTs on the thermal degradation behavior of polypropylene was studied by thermal gravimetric analysis. The results revealed that adding the SWNTs into the PP can increase the decomposition temperature. The results obtained from differential scanning calorimetry showed that incorporating SWNTs reduced the crystallinity but increased the crystallization temperature of the PP. The mechanical measurements showed that the tensile modulus of the nanocomposite was greatly enhanced to 882 MPa, compared to 485 MPa for pristine PP. For wide-angle X-ray diffraction tests, two cooling methods were used. The addition of SWNTs to the polymer in slow-cooled samples resulted in partial crystallization in the γ -form, while SWNTs had no effect in water-cooled samples, the sample crystallizing in the α -form. Scanning electron microscopy observations on the fracture surface of the nanocomposites showed the dispersion of the SWNTs in the nanocomposites.
Visualisation of PCNA Monoubiquitination In Vivo by Single Pass Spectral Imaging FRET Microscopy

Monoubiquitination of the DNA sliding clamp, PCNA, plays a central role in the control of damage bypass during replication. By combining a widely-spaced FRET donor/acceptor pair (CFP and mRFP) with spectral imaging, we have developed a simple method for the visualisation of PCNA monoubiquitination in both fixed and live cells with a single imaging pass. We validate the method with genetic controls in the avian cell line DT40 and use it to examine the intracellular dynamics of PCNA ubiquitination following subnuclear UV irradiation. This general approach is likely to be of utility for live imaging of post-translational modifications of a wide range of substrates in vivo.
Factors affecting participation in physical activities in Saudi children with Down syndrome: mothers’ perspectives

Abstract Purpose: Physical activity provides a wide range of benefits. Several studies conducted in various countries have examined factors influencing participation in physical activities in children with Down syndrome. This study aimed to explore factors affecting participation in physical activities in Saudi children with DS, from their mothers’ perspectives. Materials and methods: In-depth interviews were conducted with 36 mothers of children with Down syndrome to explore facilitators of and barriers to their children’s participation in physical activities. All audio recordings were transcribed, validated, reviewed by the authors independently, and organized into themes. Results: Transcript analysis resulted in four facilitators (mother’s support and siblings’ involvement, involvement of peers, type of physical activity experience, and child’s physical ability, behavioral, and psychological status), and six barriers (conditions associated with Down syndrome, family responsibility, social barriers, environmental constraints, electronic devices, and mother’s personal psychological barriers). Conclusions: The results regarding perceived barriers indicated a need to develop and implement programs involving family consultation. Environmental modifications should be made to accommodate the needs of children with Down syndrome. A well-organized partnership should be established and activated between private investors and the Ministries of Education, Social Affairs, and Health, to support the promotion of physical activity. Implications for rehabilitation The efficacy of home- and Internet-based programs for Saudi children with Down syndrome (DS) should be evaluated. Cultural and climactic conditions in Saudi Arabia suggest the need for environmental modifications to help children with DS engage in physical activity. Well-organized partnerships between private investors and the Ministries of Education, Social Affairs, and Health may be required to achieve higher participation of children with DS. Planning and designing of strategies, policies, facilities, and programs are required to increase engagement in physical activity and maximize the benefits of participation of children with DS.
Bayesian Spatial Nonparametric Models for Confounding Manifest Variables with an Application to China Earthquake Data

We consider a Bayesian nonparametric models for spatial data of mixed category. Moreover, we adopt joint modeling strategy by assuming that responses and confounding variables are corresponding to continuous latent variables with multivariate Gaussian distribution. The model is built on a class of Gaussian Conditional Autoregressive (CAR) models, in combination with dependent sampling models (SSM) as well as probit stick-breaking process prior for accounting for complex interactions and high correlations of data. The key idea is to introducing spatial dependence by modeling the weights via probit transformation of Gaussian Markov random fields or discrete random probability measures of SSM. We illustrate the usefulness and effectiveness of the methodology through a real example from a China earthquake data set.