Miracle-dz/UniFAR-FTUs huggingface dataset:
A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms

Objective: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our aim is to increase the accuracy of heart rhythm estimation by the use of extreme gradient boosting trees and the development of a deep convolutional neural network for ECG segmentation. Approach: We trained a convolutional neural network with waveforms from PhysioNet databases to annotate QRS complexes, P waves, T waves, noise and interbeat ECG segments that characterize the essences of normal and irregular heart beats. We evaluated true positive rates, positive predictive values and mean absolute differences of our annotation based on reference annotations of the QT and MIT-BIH P-wave database. Moreover, we compared the results with standard QRS detectors and Ecgpuwave. Extreme gradient boosting trees were used to determine the heart rhythm based on hand-crafted features. More precisely, a noise estimation function was used in combination with heart rate and interval data. Furthermore we defined particular features based on ECG morphology, appearance of P waves and detection of irregular beats. We examined the feature importance and identified key features for normal sinus rhythm, atrial fibrillation, alternative rhythm and noisy recordings. The classification performance was evaluated externally using F1 scores by applying the algorithm to the hidden test set provided by the PhysioNet/CinC Challenge 2017. Main results: The true positive rate of the convolutional neural network in detection of manually revised R peaks in the QT database was and the positive predictive value was . The detection of P and T waves reached a true positive rate of and respectively, given a 50 ms tolerance when comparing the reference to the test annotation set. The rhythm classification performance reached an overall F1 score of 0.82 when applying the algorithm to the hidden test set. Significance: We achieved a shared rank #9 in the post-challenge phase of the PhysioNet/CinC Challenge 2017.
Theory of Mind Development in School-Aged Left-Behind Children in Rural China

The current study aimed to investigate differences in theory of mind between left-behind children and non-left-behind children in rural China and to examine the potential protective role of general reasoning ability in left-behind children’s theory of mind. Participants included 213 children aged 7.10–13.67 years (111 boys and 102 girls, M = 10.51 years, SD = 1.33), 101 of whom were left behind in rural areas by one or both migrating parents for at least 6 months. The Strange Stories task, a second-order false belief task, and a faux pas task were used to measure children’s theory of mind, and Sessions B and C in Raven’s Standard Progressive Matrices were used to test children’s general reasoning ability. The results showed that left-behind children scored lower on both the faux pas task and Strange Stories task. Additionally, on second-order false belief understanding, left-behind boys performed worse than non-left-behind boys, while left-behind girls scored higher than non-left behind girls. Moreover, children’s general reasoning ability moderated the relationship between parental migrant status and children’s faux pas understanding: For children with high levels of general reasoning ability, left-behind children performed similarly to non-left-behind children, while for children with low levels of general reasoning ability, left-behind children scored lower than non-left-behind children, indicating that general reasoning ability buffered the negative effect of being left behind on children’s theory of mind development. The implications of these findings for training directed at left-behind children are discussed.
De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) defined by impairments in social communication and social interactions, accompanied by repetitive behavior and restricted interests. ASD is characterized by its clinical and etiological heterogeneity, which makes it difficult to elucidate the neurobiological mechanisms underlying its pathogenesis. Recently, de novo mutations (DNMs) have been recognized as strong source of genetic causality. Here, we review different aspects of the DNMs associated with ASD, including their functional annotation and classification. In addition, we also focus on the most recent advances in this area, such as the detection of PZMs (post-zygotic mutations), and we outline the main bioinformatics tools commonly employed to study these. Some of these approaches available allow DNMs to be analyzed in the context of gene networks and pathways, helping to shed light on the biological processes underlying ASD. To end this review, a brief insight into the future perspectives for genetic studies into ASD will be provided.
Joint Multilingual Supervision for Cross-lingual Entity Linking

Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.
Development and evaluation of a multiplex reverse-transcription real-time PCR assay for detection of equine respiratory disease viruses

We developed a multiplex reverse-transcription real-time PCR (RT-rtPCR) assay for the simultaneous detection of the main equine respiratory viruses: equid alphaherpesviruses 1 and 4 (EHV-1, -4) and equine influenza virus (EIV; species Influenza A virus). The primers and probes amplified only the targeted viruses, and there were no inter-assay cross-amplifications or nonspecific interactions. The multiplex assay efficiencies were 92.5%, 97%, and 90% for EHV-1, EHV-4, and EIV, respectively. The R2 values of the monoplex and multiplex assays were ⩾0.990, and the slopes were −3.37 to −3.59. The performance of the assay was evaluated by analyzing 152 samples from clinically infected horses. EHV-1 DNA was detected in 12 samples, EHV-4 DNA in 9 samples, and both EHV-1 and EHV-4 in 4 samples. The accuracy of the assay was confirmed by comparing these results using commercial rtPCR and RT-rtPCR kits. Our multiplex RT-rtPCR was a sensitive, specific, accurate, and cost-effective method for the detection of the target viruses whether they occur alone or as part of coinfections.
Cochlear implantation in children with congenital long QT syndrome: Introduction of an evidence-based pathway of care

Congenital long QT syndrome (cLQTS) is an inherited cardiac ion channelopathy characterized by a long corrected-QT interval on the ECG, associated with a risk of syncope and sudden death as a result of arrhythmias. The archetypal arrhythmia associated with cLQTS is torsade de pointes which may degenerate into ventricular fibrillation. Children with Jervell and Lange-Neilsen syndrome have the combination of cLQTS and congenital sensorineural deafness and may present for cochlear implantation (CI). Sympathetic stimulation and administration of QT-prolonging medications may trigger arrhythmias in children with cLQTS and thus the perioperative period is a time of increased risk of adverse events, with deaths reported in the CI literature. Our Paediatric Cochlear Implant Programme had previously elected to discontinue offering CI to children with cLQTS following a perioperative death. However, subsequent demand for this service by parents led us to develop and introduce a multidisciplinary, evidence-based pathway of care. This pathway modifies the perioperative management of these children to reduce the associated risk. We present the cases of four children with cLQTS who underwent CI in our specialist children’s hospital.
The Use of Bioimpedance in Pediatric Health, Nutrition, and Disease - Know Your Limitations.

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A Practical Guide to Data Monitoring Committees in Adaptive Trials

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.
Clinical Perspective on Passive Audio Vocal Measurement in the Evaluation of Selective Mutism

Selective Mutism (SM) is an anxiety disorder often diagnosed in early childhood and characterized by persistent failure to speak in certain social situations but not others. Diagnosing SM and monitoring treatment response can be quite complex, due in part to changing definitions of and scarcity of research about the disorder. Subjective self-reports and parent/teacher interviews can complicate SM diagnosis and therapy, given that similar speech problems of etiologically heterogeneous origin can be attributed to SM. The present perspective discusses the potential for passive audio capture to help overcome psychiatry's current lack of objective and quantifiable assessments in the context of SM. We present supportive evidence from two pilot studies indicating the feasibility of using a digital wearable device to quantify child vocalization features affected by SM. We also highlight comparative analyses of passive audio capture and its potential to enhance diagnostic characterizations for SM, as well as possible limitations of such technologies.
Adjustment for Propensity Score in Nonrandomized Clinical Studies: Comparison of Sivelestat Versus Conventional Therapy for Acute Lung Injury in Acute Respiratory Distress Syndrome

Background To confirm the effectiveness of sivelestat, a clinical trial was conducted comparing sivelestat with conventional treatment in an open, nonrandomized, multicenter study of patients with systemic inflammatory response syndrome (SIRS)–associated acute lung injury. The primary endpoint was ventilator-free days (VFD). Methods This study adopted a “cluster entry” method to control for patient selection bias arising from the unblinded and nonrandomized clinical trial. Thus, all patients in the same hospital during the same entry period entered the same treatment arm, and entry periods did not overlap. In the primary analysis of VFD, adjusted mean VFD values were compared between groups using the inverse probability of treatment weighted (IPTW) method, based on propensity score, for control of confounding factors. Results There were 374 patients in the sivelestat group and 168 in the conventional therapy group. The primary analysis confirmed that sivelestat was effective (between-group difference of adjusted mean was 3.5 [2-sided 95% confidence interval, 1.3-5.8]; P =.0022). Conclusions In general, a study where all patients in the same cluster enter the same treatment arm has within-cluster correlations, which need to be considered in the study analysis. However, in analysis using the IPTW method, it is usual to use a robust variance estimator, the sandwich variance estimator, which is consistent regardless of whether the specification of within-cluster correlation structure is correct. Thus, in the analysis using the IPTW method, it was found that it was not necessary to adopt any other adjustment method for within-cluster correlation.
Faster RER-CNN: application to the detection of vehicles in aerial images

Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large images comprising complicated backgrounds render the work of photo-interpreters tedious and wearisome. Unfortunately even the best classical detection pipelines like Faster R-CNN cannot be used off-the-shelf with good results because they were built to process object centric images from day-to-day life with multi-scale vertical objects. In this work we build on the Faster R-CNN approach to turn it into a detection framework that deals appropriately with the rotation equivariance inherent to any aerial image task. This new pipeline (Faster Rotation Equivariant Regions CNN) gives, without any bells and whistles, state-of-the-art results on one of the most challenging aerial imagery datasets: VeDAI and give good results w.r. t. the baseline Faster R-CNN on two others: Munich and GoogleEarth .
Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.
Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption

Many important computational problems require utilization of high performance computing (HPC) systems that consist of multi-level structures combining higher and higher numbers of devices with various characteristics. Utilizing full power of such systems requires programming parallel applications that are hybrid in two meanings: they can utilize parallelism on multiple levels at the same time and combine together programming interfaces specific for various types of computing devices. The main goal of parallel processing is increasing the processing performance, and therefore decreasing the application execution time. The international HPC community is targeting development of "Exascale" supercomputers (able to sustain $10^{18}$ floating point operations per second) by the year 2020. One of the main obstacles to achieving this goal is power consumption of the computing systems that exceeds the energy supply limits. New programming models and algorithms that consider this criterion are one of the key areas where significant progress is necessary in order to achieve the goal. The goal of the dissertation is to extract a general model of hybrid parallel application execution in heterogeneous HPC systems that is a synthesis of existing specific approaches and developing an optimization methodology for such execution aiming for minimization of the contradicting objectives of application execution time and power consumption of the utilized computing hardware. Both meanings of the application hybridity result in multiplicity of execution parameters of nontrivial interdependences and influence on the considered optimization criteria. Mapping of the application processes on computing devices has also a significant impact on these criteria.
Novel de novo pathogenic variant in the ODC1 gene in a girl with developmental delay, alopecia, and dysmorphic features

The ornithine decarboxylase 1 (ODC1) gene plays an important role in physiological and cell developmental processes including embryogenesis, organogenesis, and neoplastic cell growth. Here, we report an 32‐month‐old Caucasian female with a heterozygous de novo nonsense mutation in the ODC1 gene that leads to a premature abrogation of 14‐aa residues at the ODC protein c‐terminus. This is the first human case confirming similar symptoms observed in a transgenic ODC1 mouse model first described over 20 years ago. Phenotypic manifestations include macrosomia, macrocephaly, developmental delay, alopecia, spasticity, hypotonia, cutaneous vascular malformation, delayed visual maturation, and sensorineural hearing loss. We here describe for the first time a new pediatric disorder that is directly linked to a de novo pathogenic variant in the ODC1 gene. The ODC1 gene mutation (c.1342 A>T) was identified by whole‐exome sequencing and confirmed by Sanger sequencing. Red blood cells obtained from our patient showed elevated ODC protein and polyamine levels compared to healthy controls. Our autosomal dominant patient who carries this gain‐of‐function ODC1 mutation may benefit from treatment with α‐difluoromethylornithine, a well‐tolerated, U.S. Food and Drug Administration (FDA). FDA‐approved drug.
Pathogenicity Induced by Invasive Infection of Streptococcus dysgalactiae subsp. equisimilis in a Mouse Model of Diabetes

Streptococcus dysgalactiae subsp. equisimilis (SDSE) causes severe invasive diseases such as streptococcal toxic shock syndrome, similar to that caused by S. pyogenes (GAS). Invasive SDSE infections are increasing, particularly among patients with diabetes mellitus. Here we investigate the association between the pathogenicity of SDSE and diabetes mellitus in a mouse model, using GAS infection for comparison. Intraperitoneal injection of highly hemolytic SDSE-167 into C57BL6/J mice induced a rapid rise in blood glucose concentrations within 4 h, which was otherwise seen only in mice injected with high doses of hypervirulent GAS mutants. The survival rates of mice injected with SDSE-167 were significantly lower in mice (db/db) with type 2 diabetes than in nondiabetic mice. Injection of db/db mice with SDSE-167 increased the concentrations of cytokines and chemokines, particularly those of interleukin 6 and monocyte chemotactic protein-1. Microarray data indicate that multiple pathways are involved in the pathogenicity of SDSE-167 in db/db mice. These data reveal that the mechanisms underlying streptococcal infection differ between SDSE and GAS.
Teeth‐derived stem cells: A source for cell therapy

Cell therapy is one of the important therapeutic approaches in the treatment of many diseases such as cancer, degenerative diseases, and cardiovascular diseases. Among various cell types, which could be used as cell therapies, stem cell therapy has emerged as powerful tools in the treatment of several diseases. Multipotent stem cells are one of the main classes of stem cells that could originate from different parts of the body such as bone marrow, adipose, placenta, and tooth. Among several types of multipotent stem cells, tooth‐derived stem cells (TDSCs) are associated with special properties such as accessible, easy isolation, and low invasive, which have introduced them as a good source for using in the treatment of several diseases such as neural injuries, liver fibrosis, and Cohrn’s disease. Here, we provided an overview of TDSCs particular stem cells from human exfoliated deciduous teeth and clinical application of them. Moreover, we highlighted molecular mechanisms involved in the regulation of dental stem cells fate.