Miracle-dz/UniFAR-FTUs huggingface dataset:
Parents’ perceptions of power wheelchair prescription for children with a neuromuscular disorder: a scoping review

Abstract Purpose: To conduct a scoping review of the published evidence on parents’ perceptions of power wheelchair prescription for children with a neuromuscular disorder and clinicians’ influence on timely wheelchair implementation. Method: Nine electronic databases and reference lists of all retrieved full-text articles were searched up to March 2017. Eligibility criteria included (1) at least one child participant with a neuromuscular disorder, (2) power wheelchair as an intervention, and (3) qualitative, quantitative or mixed methods parent-reported outcomes related to power wheelchair equipment. Results: None of the 67 eligible studies examined parental perceptions of wheelchair prescription as a primary aim, and only 10 studies included children with a neuromuscular disorder. Parents reported their own emotional responses including grief and loss, emphasis on their child’s walking and lack of accessibility as key barriers to power wheelchair prescription. Clinicians’ perspectives on walking and powered mobility influenced parental decision-making regarding power wheelchair use for their child. Conclusion: Parents’ experiences of initial wheelchair prescription have not been explored in existing literature. Clinicians’ understanding of the benefits of power wheelchair equipment, particularly in the context of progression of neuromuscular disorders, is critical to facilitating timely wheelchair prescription with children. Condition-specific evidence is urgently needed to inform and support multidisciplinary management of children and their families. Implications for Rehabilitation It is important that rehabilitation professionals recognize parental barriers to initial power wheelchair prescription, such as strong emotional responses, an emphasis on their child’s walking and lack of access. Clinicians’ perspectives on walking and powered mobility may influence parental decision-making regarding engagement in power wheelchair prescription and rehabilitation. It is critical that clinicians are aware of and actively educate families regarding the benefits of powered mobility to facilitate timely prescription and support physical and psychological adjustment.
Effectiveness of Auditory Measures for Detecting Hidden Hearing Loss and/or Cochlear Synaptopathy: A Systematic Review

Abstract Standard audiometric evaluations are not sensitive enough to identify hidden hearing loss (HHL) and/or cochlear synaptopathy (CS). Patients with either of these conditions frequently present with difficulty understanding speech in noise or other complaints such as tinnitus. The purpose of this systematic review is to identify articles in peer-reviewed journals that assessed the sensitivity of audiologic measures for detecting HHL and/or CS, and which showed potential for use in a clinical test battery for these disorders. A reference librarian submitted specific boolean terminology to MEDLINE, Embase, and Web of Science. The authors used a consensus approach with specially designed score sheets for the selection of titles, abstracts, and then articles for inclusion in the systematic review and for quality assessment. Fifteen articles were included in the systematic review. Seven articles involved humans; seven involved animals, and one study used both humans and animals. Results showed that pure-tone audiometry to 20 kHz, otoacoustic emissions, electrocochleography, auditory brainstem response (ABR), electrophysiological tests, speech recognition in noise with and without temporal distortion, interviews, and self-report measures have been used to assess HHL and/or CS. For HHL, ultra-high-frequency audiometry may help identify persons with sensory hair cell loss that does not show up on standard audiograms. Promising nonbehavioral measures for CS included ABR wave I amplitude, the summating potential-to-action potential ratio, and speech recognition in noise with and without temporal distortion. Self-report questionnaires also may help identify auditory dysfunction in persons with normal hearing.
UvA-DARE (Digital Academic Repository) Incremental sparse Bayesian ordinal regression

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that map features to a high-dimensional non-linear space. However, most of the basis function-based algorithms are time consuming. We propose an incremental sparse Bayesian approach to OR tasks and introduce an algorithm to sequentially learn the relevant basis functions in the ordinal scenario. Our method, called Incremental Sparse Bayesian Ordinal Regression (ISBOR), automatically optimizes the hyper-parameters via the type-II maximum likelihood method. By exploiting fast marginal likelihood optimization, ISBOR can avoid big matrix inverses, which is the main bottleneck in applying basis function-based algorithms to OR tasks on large-scale datasets. We show that ISBOR can make accurate predictions with parsimonious basis functions while offering automatic estimates of the prediction uncertainty. Extensive experiments on synthetic and real word datasets demonstrate the efficiency and effectiveness of ISBOR compared to other basis function-based OR approaches. © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
Pneumococcal Meningitis in Adults after Introduction of PCV7 and PCV13, Israel, July 2009–June 2015

The indirect effect of pneumococcal conjugate vaccine on adult pneumococcal meningitis has not been thoroughly investigated. We present data from active surveillance on pneumococcal meningitis in adults in Israel occurring during July 2009–June 2015. Pneumococcal meningitis was diagnosed for 221 patients, 9.4% of all invasive pneumococcal disease (IPD) cases. Although overall IPD incidence decreased during the study period, meningitis increased nonsignificantly from 0.66 to 0.85 cases/100,000 population. Incidence of vaccine type (VT) pneumococcal meningitis (VT13) decreased by 70%, but non-VT13 pneumococcal meningitis increased from 0.32 to 0.75 cases/100,000 population (incident rate ratio 2.35, 95% CI 1.27–4.35). Pneumococcal meningitis patients were younger and healthier than nonmeningitis IPD patients, and 20.2% had a history of previous head surgery or cerebrospinal fluid leak compared with <2.0% of nonmeningitis patients (p<0.0001). Non-VT13 types that rarely cause IPD (15B/C, 6C, 23A, 23B, 24F) seem to be emerging as common causes of meningitis.
How Could Polyhedral Theory Harness Deep Learning?

The holy grail of deep learning is to come up with an automatic method to design optimal architectures for different applications. In other words, how can we effectively dimension and organize neurons along the network layers based on the computational resources, input size, and amount of training data? We outline promising research directions based on polyhedral theory and mixed-integer representability that may offer an analytical approach to this question, in contrast to the empirical techniques often employed.
Eesti koolinoorte tervis ja selle sõltuvus perekonna sotsiaal-majanduslikust olukorrast

Koolilaste hinnangutest oma tervisele ilmnes seadusparasus, et vanuse suurenedes vahenes oma tervist vaga heaks hinnanud poiste ja tutarlaste arv, sealjuures kasvas tutarlastehulk, kes ei hinnanud oma tervist heaks. Lapsevanemad hindasid uldiselt poegade tervist halvemaks kui poisid ise, tutarde tervist aga paremaks kui tudrukud ise. Ilmnes kindel seos laste tervisehinnangu ja perekonna majandusliku olukorra vahel: mida paremaks hinnati pere majanduslikku olukorda, seda parem oli hinnang oma tervisele. Voib oletada, et taiskasvanuea halva tervise juured voivad vahemalt osaliselt alguse saada lapseea kehvast majanduslikust olukorrast.
A Sensitive Electrochemical Immunosensor Based on PAMAM Dendrimer-Encapsulated Au for Detection of Norfloxacin in Animal-Derived Foods

In this work, a sensitive electrochemical immunosensor has been reported for the determination of norfloxacin in animal-derived foods. The poly (amidoamine) dendrimer encapsulated gold nanoparticles (PAMAM-Au) played dual roles in the proposed sensing platform which not only accelerated the electron transfer process of sensing, but also increased the efficiency of the immobilized antibody. The HRP-labeled antigen, as the signal labels in the immunosensor, was introduced to catalyze the following reaction of the substrate hydroquinone with the aid of H2O2 in the competitive reaction. On the basis of the signal amplification of PAMAM-Au, the signal intensity was linearly related to the concentration of norfloxacin in the range of 1 μg·L−1–10 mg·L−1. All the results showed that the proposed strategy with low LOD (0.3837 μg·L−1) and favorable recovery (91.6–106.1%) in the practical sample, and it could provide a suitable protocol for norfloxacin detection in animal-derived foods with high sensitivity, good accuracy, and stability.
Information spreading by a combination of MEG source estimation and multivariate pattern classification

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.
Janus-Faced Nature of Light in the Cold Acclimation Processes of Maize

Exposure of plants to low temperature in the light may induce photoinhibitory stress symptoms, including oxidative damage. However, it is also known that light is a critical factor for the development of frost hardiness in cold tolerant plants. In the present work the effects of light during the cold acclimation period were studied in chilling-sensitive maize plants. Before exposure to chilling temperature at 5°C, plants were cold acclimated at non-lethal temperature (15°C) under different light conditions. Although exposure to relatively high light intensities during cold acclimation caused various stress symptoms, it also enhanced the effectiveness of acclimation processes to a subsequent severe cold stress. It seems that the photoinhibition induced by low temperature is a necessary evil for cold acclimation processes in plants. Greater accumulations of soluble sugars were also detected during hardening at relatively high light intensity. Certain stress responses were light-dependent not only in the leaves, but also in the roots. The comparison of the gene expression profiles based on a microarray study demonstrated that the light intensity is at least as important a factor as the temperature during the cold acclimation period. Differentially expressed genes were mainly involved in most of assimilation and metabolic pathways, namely photosynthetic light capture via the modification of chlorophyll biosynthesis and the dark reactions, carboxylic acid metabolism, cellular amino acid, porphyrin or glutathione metabolic processes, ribosome biogenesis and translation. Results revealed complex regulation mechanisms and interactions between cold and light signalling processes.
The Movement-Image Compatibility Effect: Embodiment Theory Interpretations of Motor Resonance With Digitized Photographs, Drawings, and Paintings

To evoke the impression of movement in the “immobile” image is one of the central motivations of the visual art, and the activating effect of images has been discussed in art psychology already some 100 years ago. However, this topic has up to now been largely neglected by the researchers in cognitive psychology and neuroscience. This study investigates – from an interdisciplinary perspective – the formation of lateralized instances of motion when an observer perceives movement in an image. A first step was to identify images that evoke a perception of movement in a certain direction and to give this a rating. Reaction times leading to the engagement of a joystick following the presentation of images are used to evidence the postulated movement occasioned by the perception of movement in an image. Where the required direction of joystick moves matched the expected perception of movement direction in the image, significantly shorter reaction times were recorded. The experiment was able to prove a “movement-image compatibility effect” in observers of images. Based on this, the paper revisits and brings up to date the theses on motor sensory response to images which were developed in art psychology at the beginning of the 20th century. It furthermore contributes an embodiment theory interpretation to the prevalent representational explanation of compatibility effects.
Biomarkers for Early Diagnosis and Prognosis of Malignant Pleural Mesothelioma: The Quest Goes on

Malignant pleural mesothelioma (MM) is a highly aggressive tumor characterized by a poor prognosis. Although its carcinogenesis mechanism has not been strictly understood, about 80% of MM can be attributed to occupational and/or environmental exposure to asbestos fibers. The identification of non-invasive molecular markers for an early diagnosis of MM has been the subject of several studies aimed at diagnosing the disease at an early stage. The most studied biomarker is mesothelin, characterized by a good specificity, but it has low sensitivity, especially for non-epithelioid MM. Other protein markers are Fibulin-3 and osteopontin which have not, however, showed a superior diagnostic performance. Recently, interesting results have been reported for the HMGB1 protein in a small but limited series. An increase in channel proteins involved in water transport, aquaporins, have been identified as positive prognostic factors in MM, high levels of expression of aquaporins in tumor cells predict an increase in survival. MicroRNAs and protein panels are among the new indicators of interest. None of the markers available today are sufficiently reliable to be used in the surveillance of subjects exposed to asbestos or in the early detection of MM. Our aim is to give a detailed account of biomarkers available for MM.
A Novel Process for Cadaverine Bio-Production Using a Consortium of Two Engineered Escherichia coli

Bio-production of cadaverine from cheap carbon sources for synthesizing bio-based polyamides is becoming more common. Here, a novel fermentation process for cadaverine bio-production from glucose was implemented by using a microbial consortium of two engineered Escherichia coli strains to relieve the toxic effect of cadaverine on fermentation efficiency. To achieve controllable growth of strains in the microbial consortium, two engineered E. coli strains grown separately on different carbon sources were first constructed. The strains were, an L-lysine-producing E. coli NT1004 with glucose as carbon source, and a cadaverine-producing E. coli CAD03 with glucose metabolism deficiency generated by modifying the PTSGlc system with CRISPR-Cas9 technology and inactivating cadaverine degradation pathways. Co-culturing these two engineered E. coli strains with a mixture of glucose and glycerol led to successful production of cadaverine. After optimizing cultivation conditions, a cadaverine titer of 28.5 g/L was achieved with a multi-stage constant-speed feeding strategy.
A nonequilibrium force can stabilize 2D active nematics

Significance Living systems differ from dead matter in one crucial aspect: They are driven by internal engines. In anisotropic fluids, the widely accepted framework of active matter theory predicts that this necessarily leads to an instability from quiescence to large-scale and eventually incoherent motion. Here we challenge this common wisdom by reexamining the symmetries of the most ubiquitous experimental geometry for active systems. We uncover an additional coupling between activity and motion that can make an active system even more stable than its passive counterpart, while preserving other hallmarks of nonequilibrium physics. Our results challenge common views on active matter and clarify the interpretation of multiple experiments. Suspensions of actively driven anisotropic objects exhibit distinctively nonequilibrium behaviors, and current theories predict that they are incapable of sustaining orientational order at high activity. By contrast, here we show that nematic suspensions on a substrate can display order at arbitrarily high activity due to a previously unreported, potentially stabilizing active force. This force moreover emerges inevitably in theories of active orientable fluids under geometric confinement. The resulting nonequilibrium ordered phase displays robust giant number fluctuations that cannot be suppressed even by an incompressible solvent. Our results apply to virtually all experimental assays used to investigate the active nematic ordering of self-propelled colloids, bacterial suspensions, and the cytoskeleton and have testable implications in interpreting their nonequilibrium behaviors.
Psychological treatments for adults and children with epilepsy: Evidence‐based recommendations by the International League Against Epilepsy Psychology Task Force

Given the significant impact that psychosocial factors and epilepsy treatments can have on the health‐related quality of life (HRQOL) of individuals with epilepsy and their families, there is great clinical interest in the role of psychological evaluation and treatments to improve HRQOL and comorbidities. Therefore, the International League Against Epilepsy (ILAE) charged the Psychology Task Force with the development of recommendations for clinical care based on evaluation of the evidence from their recent Cochrane review of psychological treatments in individuals with epilepsy. The literature search for a recent Cochrane review of randomized controlled trials investigating psychological treatments for individuals with epilepsy constitutes the key source of evidence for this article. To provide practical guidance to service providers, we provide ratings on study research designs based on (1) the American Academy of Neurology's Level of Evidence system and (2) the Grading of Recommendations, Assessment, Development, and Evaluation system. This paper is the culmination of an international collaboration process involving pediatric and adult psychologists, neurologists, psychiatrists, and neuropsychiatrists. The process and conclusions were reviewed and approved by the ILAE Executive Committee. The strongest evidence for psychological interventions was identified for the most common mental health problems, including depression, neurocognitive disturbances, and medication adherence. Psychological interventions targeting the enhancement of HRQOL and adherence and a decrease in comorbidity symptoms (anxiety, depression) should be incorporated into comprehensive epilepsy care. There is a range of psychological strategies (ie, cognitive behavioral therapy and mindfulness‐based therapies) that show promise for improving the lives of persons with epilepsy, and clinical recommendations are provided to assist epilepsy health care providers in treating the comorbidities and challenges associated with epilepsy and its treatments.
Stable Prediction across Unknown Environments

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting training data with the ratio of the density between test and training data. However, in many applications training takes place without prior knowledge of the testing distribution. Recently, methods have been proposed to address the shift by learning the underlying causal structure, but those methods rely on diversity arising from multiple training data sets, and they further have complexity limitations in high dimensions. In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments. The global balancing model constructs balancing weights that facilitate estimation of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes. The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier. We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments. Our experiments on both synthetic and real datasets demonstrate that our algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments.
SNrram: An Efficient Sparse Neural Network Computation Architecture Based on Resistive Random-Access Memory

The sparsity in the deep neural networks can be leveraged by methods such as pruning and compression to help the efficient deployment of large-scale deep neural networks onto hardware platforms, such as GPU or FPGA, for better performance and power efficiency. However, for RRAM crossbar-based architectures, the study of efficient methods to consider the network sparsity is still in the early stage. In this study, we propose SNrram, an efficient sparse neural network computation architecture using RRAM, by exploiting the sparsity in both weights and activation. SNrram stores nontrivial weights and organizes them to eliminate zero-value multiplications for better resource utilization. Experimental results show that SNrram can save RRAM resources by 69.8%, reduce the power consumption by 35.9%, and speed up by 2.49×on popular deep learning benchmarks, compared to a state-of-the-art RRAM-based neural network accelerator.