Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel mutations in SLC6A5 with benign course in hyperekplexia

Infants suffering from life-threatening apnea, stridor, cyanosis, and increased muscle tone may often be misdiagnosed with infantile seizures and inappropriately treated because of lack and delay in genetic diagnosis. Here, we report a patient with increased muscle tone after birth and hypertonic attacks with life-threatening apnea but no epileptiform patterns in EEG recordings. We identified novel compound heterozygous variants in SLC6A5 (NM_004211.4:c.[1429T > C];[1430delC]) by trio whole-exome sequencing, containing a base deletion inherited by the asymptomatic mother leading to a frameshift (c.1430delC, p.Ser477PhefsTer9) and a de novo base exchange leading to an amino acid change (c.1429T > C, p.Ser477Pro). To date, there are four known disease-associated genes for primary hyperekplexia, all of which are involved in the functioning of glycinergic synapses. SLC6A5 encodes the sodium- and chloride-dependent glycine transporter 2 (GlyT2), which recaptures glycine, a major inhibitory transmitter in the brainstem and spinal cord. The diagnosis altered the patient's medical care to his benefit because SLC6A5 mutations with rather benign courses of hyperekplexia may be spared of needless pharmacotherapy. Symptoms eventually decreased in frequency until about once in 2 mo at 2 yr age. We present the first report of halting hyperekplexia episodes by maternal soothing in multiple instances. We highlight the importance of clarifying the genetic diagnosis by rapid next-generation sequencing techniques in this group of infantile apneic attacks with hyperekplexia due to the broad differential diagnoses.
Application of the adverse outcome pathway framework to genotoxic modes of action

In May 2017, the Health and Environmental Sciences Institute's Genetic Toxicology Technical Committee hosted a workshop to discuss whether mode of action (MOA) investigation is enhanced through the application of the adverse outcome pathway (AOP) framework. As AOPs are a relatively new approach in genetic toxicology, this report describes how AOPs could be harnessed to advance MOA analysis of genotoxicity pathways using five example case studies. Each of these genetic toxicology AOPs proposed for further development includes the relevant molecular initiating events, key events, and adverse outcomes (AOs), identification and/or further development of the appropriate assays to link an agent to these events, and discussion regarding the biological plausibility of the proposed AOP. A key difference between these proposed genetic toxicology AOPs versus traditional AOPs is that the AO is a genetic toxicology endpoint of potential significance in risk characterization, in contrast to an adverse state of an organism or a population. The first two detailed case studies describe provisional AOPs for aurora kinase inhibition and tubulin binding, leading to the common AO of aneuploidy. The remaining three case studies highlight provisional AOPs that lead to chromosome breakage or mutation via indirect DNA interaction (inhibition of topoisomerase II, production of cellular reactive oxygen species, and inhibition of DNA synthesis). These case studies serve as starting points for genotoxicity AOPs that could ultimately be published and utilized by the broader toxicology community and illustrate the practical considerations and evidence required to formalize such AOPs so that they may be applied to genetic toxicity evaluation schemes. Environ. Mol. Mutagen. 61:114–134, 2020. © 2019 Wiley Periodicals, Inc.
A Framework for Interpretable Full-Body Kinematic Description Using Geometric and Functional Analysis

Rapid advances in cost-effective and non-invasive depth sensors, and the development of reliable and real-time 3D skeletal data estimation algorithms, have opened up a new application area in computer vision – statistical analysis of human kinematic data for fast, automated assessment of body movements. These assessments can play important roles in sports, medical diagnosis, physical therapy, elderly monitoring and related applications. This paper develops a comprehensive geometric framework for quantification and statistical evaluation of kinematic features. The key idea is to avoid analysis of individual joints, as is the current paradigm, and represent movements as temporal evolutions, or trajectories, on shape space of full body skeletons. This allows metrics with appropriate invariance properties to be imposed on these trajectories and leads to definitions of higher-level features, such as spatial symmetry (sS), temporal symmetry (tS), action’s velocity (Vl) and body’s balance (Bl), during performance of an action. These features exploit skeletal symmetries in space and time, and capture motion cadence to naturally quantify motions of individual subjects. The study of these features as functional data allows us to formulate certain hypothesis tests in feature space. This, in turn, leads to validation of existing assumptions and discoveries of new relationships between kinematics and demographic factors, such as age, gender, and athletic training. We use the clinically validated K3Da kinect dataset to illustrate these ideas, and hope these tools will lead to discovery of new relationships between full-body kinematic features and demographic, health, and wellness factors that are clinically relevant.
Prevalence and Predicting Factors of Chronic Pain among Workers of Petrochemical and Petroleum Refinery Plants

Background: Taking into account the differences in job requirements and conditions, it is expected that workers in some occupations are more susceptible to pain than others. Objective: To examine the prevalence of chronic pain among workers of several petrochemical and petroleum refinery plants. We also examined the predictive role of psycho-familial variables (depression, work-family conflict and job stress) in causing chronic pain when controlling for demographic and occupational factors. Methods: This cross-sectional study was carried out among 674 workers. Those with chronic pain were identified by affirmative answers to screening questions based on the ICD-11 criteria. Results: There were 162 (24.0%; 95% CI 20.8% to 27.3%) workers meeting the ICD11 criteria for chronic pain. Headache was the most frequently reported pain (29.9%). We found a significantly (p=0.03) higher prevalence of pain among the middle age than in other age groups. Chronic pain more frequently affected divorced/widowed workers (p<0.001), and those with more work experience (p=0.04). Workers with chronic pain reported significantly higher levels of depression (p<0.001), job stress (p=0.007), and work-family conflict (p<0.001). After controlling for demographic and occupational factors, depression (p<0.001) and work-family conflict (p=0.003) were found to be independent predictors of chronic pain among studied workers. Conclusion: Workers who experience higher levels of depression, work-family conflict and job stress might be more prone to chronic pain. The majority of these factors are modifiable, and the problem may thus be solved by establishing appropriate screening programs, and availability of proper services and education.
Adaptive Neural Sliding-Mode Controller for Alternative Control Strategies in Lower Limb Rehabilitation

Research on control strategies for rehabilitation robots has gradually shifted from providing therapies with fixed, relatively stiff assistance to compelling alternatives with assistance or challenge strategies to maximize subject participation. These alternative control strategies can promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. In this paper, we propose a control strategy that dynamically switches between assistance and challenge modes based on the user’s performance by amplifying or reducing the deviation between the user and the rehabilitation robot. For a seamless cognitive and physical interaction between the robot and the patient, we propose a multisensor fusion system to provide accurate activity and motor capability recognition, fall detection and physical fitness assessment in the rehabilitation training process. Moreover, an adaptive radial basis function (RBF) neural sliding-mode (ARNNSM) controller that dominates a mobile chaperonage lower limb rehabilitation training robot is proposed. The controller employs asynchronous deviation and functional assessment to determine the subject’s capabilities and computes a corresponding assistance torque with a challenging factor for the desired locomotive function. Some sufficient conditions are derived based on algebraic graph theory and Lyapunov theory to ensure the asymptotic stability of the systems. Simulation examples illustrate the effectiveness of the proposed controllers. The ARNNSM controller and accompanying algorithm are demonstrated experimentally with healthy subjects in a new type of mobile chaperonage lower limb rehabilitation robot.
Exploiting Block-Sparsity for Hyperspectral Kronecker Compressive Sensing: A Tensor-Based Bayesian Method

Bayesian methods are attracting increasing attention in the field of compressive sensing (CS), as they are applicable to recover signals from random measurements. However, these methods have limited use in many tensor-based cases such as hyperspectral Kronecker compressive sensing (HKCS), because they exploit the sparsity in only one dimension. In this paper, we propose a novel Bayesian model for HKCS in an attempt to overcome the above limitation. The model exploits multi-dimensional block-sparsity such that the information redundancies in all dimensions are eliminated. Laplace prior distributions are employed for sparse coefficients in each dimension, and their coupling is consistent with the multi-dimensional block-sparsity model. Based on the proposed model, we develop a tensor-based Bayesian reconstruction algorithm, which decouples the hyperparameters for each dimension via a low-complexity technique. Experimental results demonstrate that the proposed method is able to provide more accurate reconstruction than existing Bayesian methods at a satisfactory speed. Additionally, the proposed method can not only be used for HKCS, it also has the potential to be extended to other multi-dimensional CS applications and to multi-dimensional block-sparse-based data recovery.
Genomic profiling of the residual disease of advanced high‐grade serous ovarian cancer after neoadjuvant chemotherapy

The goal of our study was to demonstrate the spectrum of genomic alterations present in the residual disease of patients with advanced high‐grade serous ovarian cancer (HGSOC) after neoadjuvant chemotherapy (NAC), including matched pretreatment biopsies. During the study period between 2006 and 2017, we collected pre‐NAC and post‐NAC tumor tissue samples from patients with advanced HGSOC. We performed combined next‐generation sequencing and immunohistochemistry to identify actionable targets and pathway activation in post‐NAC residual tumors. We also examined whether post‐NAC profiling of residual HGSOC identified targetable molecular lesions in the chemotherapy‐resistant component of tumors. Among 102 post‐NAC samples, 41 (40%) of patients had mutations in homologous recombination repair (HRR) genes (HRR deficiency). Patients with HRR mutations had higher tumor mutation burdens (p < 0.001) and higher alterations in the PI3K–AKT–mTOR pathway (p = 0.004) than patients without these HRR mutations. Nevertheless, we found no significant differences in progression‐free survival (p = 0.662) and overall survival (OS; p = 0.828) between the two groups. Most patients (91%) had alterations in at least one of the targetable pathways, and those patients with cell cycle (p = 0.004) and PI3K–AKT–mTOR signaling (p = 0.005) pathway alterations had poorer OS (Bonferroni‐corrected threshold = 0.0083, 0.05/6). We showed the genomic landscape of tumor cells remaining in advanced HGSOC after NAC. Once validated, these data can help inform biomarker‐driven adjuvant studies in targeting residual tumors to improve the outcomes of patients with advanced HGSOC after NAC.
Sinusoidal Sampling Enhanced Compressive Camera for High Speed Imaging

Compressive sensing technique allows capturing fast phenomena at a much higher frame rate than the camera sensor, by recovering a frame sequence from their encoded combination. However, most conventional compressive video sensing methods limit the achieved frame rate improvement to tenfold and only support low resolution recovery. Making use of the camera's redundant spatial resolution for further frame rate improve, here we report a novel compressive video acquisition technique termed Sinusoidal Sampling Enhanced Compressive Camera (S2EC2) to encode denser frames within a snapshot. Specifically, we decompose the dense frames into groups and apply combinational coding: random codes within each group for compressive acquisition; group specific sinusoidal codes to multiplex different groups onto the high resolution sensor. The sinusoidal codes designed for these groups would shift their frequency components by different offsets in the Fourier domain and staggered the dominant frequencies of the coded measurements of these groups. Correspondingly, the reconstruction successfully separate coded measurements of different groups and recovers frames within each group. Besides, we also solve the implementation problem of insufficient gray scale spatial light modulation speed, and build a prototype achieving 2000 fps reconstruction with a 15.6 fps camera (the actual compression ratio is 0.009). The extensive experiments validate the proposed approach.
Urinary angiotensinogen antedates the development of stage 3 CKD in patients with type 1 diabetes mellitus

We examined if urinary angiotensinogen (uAOG), a marker of intrarenal renin‐angiotensin system activity, antedates stage 3 chronic kidney disease (CKD) using samples from participants in the Diabetes Control and Complications Trial (DCCT) and later in the Epidemiology of Diabetes Intervention and Complications (EDIC) trial. In a nested case–control design, cases were matched at the outcome visit (eGFR less than 60, 21‐59 mL/min per 1.73 m2) on age, gender, and diabetes duration, with controls: eGFR (95, 75‐119, mL/min per 1.73 m2.) Additionally, in an exploratory analysis progressive renal decline (PRD), defined as eGFR loss >3.5 mL/min per 1.73m2/year, was evaluated using only data from EDIC because no progressions were observed during DCCT. At the EDIC visit, which antedated the GFR outcome visit by 2 years (range 1–7years) the median uAOG/creatinine was markedly higher in cases than in controls (13.9 vs. 3.8 ng/mg P = 0.003) whereas at the DCCT visit, which antedated the GFR outcome by 17 to 20 years it was not (2.75 vs. 3.16 ng/mg, respectively). The Odds Ratio for uAOG and CKD stage 3 development was significant after adjusting for eGFR, HbA1c, and systolic blood pressure 1.82 (1.00–3.29) but no longer significant when Albumin Excretion Ratio (AER) was included 1.21 (0.65–2.24). In the PRD analysis, uAOG/creatinine was sixfold higher in participants who experienced PRD than in those who did not (26 vs. 4.0 ng/mg, P = 0.003). The Odds Ratio for uAOG and PRD was significant after adjusting for eGFR, HbA1c, and systolic blood pressure 2.48 (1.46–4.22) but no longer significant when AER was included 1.32 (0.76–2.30). In people with type1 diabetes, a robust increase in uAOG antedates the development of stage 3 CKD but is not superior to AER in predicting this renal outcome. Increased uAOG moreover is associated with PRD, an index of progression to End Stage Kidney Disease (ESKD).
Identity Recognition Based on Bioacoustics of Human Body

Current biometrics rely on images obtained from the structural information of physiological characteristics, which is inherently a fatal problem of being vulnerable to spoofing. Here, we studied personal identification using the frequency-domain information based on human body vibration. We developed a bioacoustic frequency spectroscopy system and applied it to the fingers to obtain information on the anatomy, biomechanics, and biomaterial properties of the tissues. As a result, modulated microvibrations propagated through our body could capture a unique spectral trait of a person and the biomechanical transfer characteristics persisted for two months and resulted in 97.16% accuracy of identity authentication in 41 subjects. Ultimately, our method not only eliminates the practical means of creating fake copies of the relevant characteristics but also provides reliable features.
The Effects of Different Forms of Caffeine Supplement on 5-km Running Performance.

PURPOSE Caffeine is frequently used by athletes as an ergogenic aid. Various alternate forms of caffeine administration are available, which may produce different effects. This investigation compares the effects of different forms of caffeine supplementation on 5-km running performance, and the relationship between athlete ability and degree of enhancement attained. METHODS Fourteen amateur runners completed a series of self-paced outdoor time trials following unknown ingestion of a placebo (P) or one of 3 alternate forms of caffeine supplement. Trials were randomized in a crossover design with caffeine (approximately 3-4.5 mg·kg-1) administered 15 minutes before each trial via chewing gum (CG), dissolvable mouth strips (CS), or tablet (CT). RESULTS Compared with P, all caffeine supplements led to worthwhile enhancements in running performance with a mean (±95% confidence limit) overall effect across all supplements of 1.4% ± 0.9%. Individual caffeine treatment effects (CG = 0.9% ± 1.4%, CS = 1.2% ± 1.0%, and CT = 2.0% ± 1.1%) were not significantly different (P > .05) from each other; however, CT trials produced the largest gain and was significantly different (P = .02) compared with P. There was no significant difference in heart rate or rate of perceived exertion across the performance trials. The magnitude of caffeine enhancement was also strongly correlated (r = .87) with no-treatment performance time. CONCLUSIONS The findings showed that irrespective of delivery form, moderate dose of caffeine supplementation produces worthwhile gains in 5-km running performance compared with a P. Furthermore, the magnitude of caffeine enhancement is highly individualized, but it appears related to athlete performance ability.
Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edgeand region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.
Research of Image Recognition and Classification Based on NIN Model

Focus on the weak feature expression ability of traditional Convolution Neural Network (CNN) in image recognition and classification, the CNN has been improved and optimized by introduction of Mlpconv layer to form a Network in Network (NIN). The NIN enhances feature extraction and abstract expression of the local patch; get better performance of the image recognition. The experiments of weld image recognition and classification show that the optimized CNN can improve the feature expression ability of the whole network by reducing the number of parameters, obtain higher recognition and classification precision, and avoid the fitting of the network model effectively. The CNN achieve improvement in both performance and efficiency.