Miracle-dz/UniFAR-FTUs huggingface dataset:
UK nationals who received their medical degrees abroad: selection into, and subsequent performance in postgraduate training: a national data linkage study

Objectives To compare the likelihood of success at selection into specialty training for doctors who were UK nationals but obtained their primary medical qualification (PMQ) from outside the UK (‘UK overseas graduates’) with other graduate groups based on their nationality and where they gained their PMQ. We also compared subsequent educational performance during postgraduate training between the graduate groups. Design Observational study linking UK medical specialty recruitment data with postgraduate educational performance (Annual Review of Competence Progression (ARCP) ratings). Setting Doctors recruited into national programmes of postgraduate specialist training in the UK from 2012 to 2016. Participants 34 755 UK-based trainee doctors recruited into national specialty training programmes with at least one subsequent ARCP outcome reported during the study period, including 1108 UK overseas graduates. Main outcome measures Odds of being deemed appointable at specialty selection and subsequent odds of obtaining a less versus more satisfactory category of ARCP outcome. Results UK overseas graduates were more likely to be deemed appointable compared with non-EU medical graduates who were not UK citizens (OR 1.29, 95% CI 1.16 to 1.42), although less so than UK (OR 0.25, 95% CI 0.23 to 0.27) or European graduates (OR 0.66, 95% CI 0.58 to 0.75). However, UK overseas graduates were subsequently more likely to receive a less satisfactory outcome at ARCP than other graduate groups. Adjusting for age, sex, experience and the economic disparity between country of nationality and place of qualification reduced intergroup differences. Conclusions The failure of recruitment patterns to mirror the ARCP data raises issues regarding consistency in selection and the deaneries’ subsequent annual reviews. Excessive weight is possibly given to interview performance at specialty recruitment. Regulators and selectors should continue to develop robust processes for selection and assessment of doctors in training. Further support could be considered for UK overseas graduates returning to practice in the UK.
Small Fault Detection for a Class of Closed-Loop Systems via Deterministic Learning

In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach.
Characteristics of Autonomic Activity and Reactivity During Rest and Emotional Processing and Their Clinical Correlations in Somatic Symptom Disorder

Objective Altered autonomic nervous system activity is considered to be involved in the pathophysiology of somatic symptom disorder (SSD). This study aimed to investigate whether patients with SSD have disturbed autonomic activity during rest and reactivity to emotional processing and whether altered autonomic nervous system correlates with clinical characteristics and interoceptive accuracy in SSD. Methods We recruited 23 patients with SSD and 20 healthy controls. Heart rate variability (HRV) was assessed during recording at rest and during performance of an emotional face dot-probe task. Alpha-amylase responses were also assessed. Patients with SSD completed a self-assessment survey and heart beat perception task, which reflects interoceptive awareness. Results Patients with SSD had lower low-frequency HRV, high-frequency HRV, standard deviation of normal to normal R-R intervals (SDNN), and proportion of successive R-R intervals greater than 50 milliseconds (pNN50) at rest (p < .05). The reactivity scores (during-task activity minus resting activity) for SDNN and pNN50 were significantly different between patients with SSD and controls (SDNN: p = .013; pNN50: p = .008). In addition, resting HRV parameters (low-frequency, high-frequency, SDNN, pNN50) correlated with heart beat perception error (p < .01) in patients with SSD. No significant differences in &agr;-amylase activity were found. Conclusions Our findings showed that patients with SSD have altered resting-state autonomic activity and reactivity to emotional processing, and the resting-state autonomic activity correlated with their interoceptive awareness. These findings suggest that disturbed interactions between the autonomic nervous, affective, and interoceptive systems may be involved in the pathophysiology of SSD.
Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters

A digital predistorter, modeled by an augmented real-valued time-delay neural network (ARVTDNN), has been proposed and found suitable to mitigate the nonlinear distortions of the power amplifier (PA) along with modulator imperfections for a wideband direct-conversion transmitter. The input signal of the proposed ARVTDNN consists of Cartesian in-phase and quadrature phase ( $I/Q$ ) components, as well as envelope-dependent terms. Theoretical analysis shows that the proposed model is able to produce a richer basis function containing both the desired odd- and even-order terms, resulting in improved modeling capability and distortion mitigation. Its actual performance has been validated through extensive simulations and experiments. The results show that the compensation and hardware impairment mitigation capabilities of the ARVTDNN are superior to the existing state-of-the-art real-valued focused time-delay neural network (RVFTDNN) by 3–4 dB for the adjacent channel power ratio and by 2–3 dB in terms of the normalized mean square error. Other important features of the proposed model are its reduced complexity, in terms of the number of parameters and floating-point operations, and its improved numerical stability compared to the RVFTDNN model.
Development of Amoxicillin-Loaded Electrospun Polyurethane/Chitosan/ $\beta$ -Tricalcium Phosphate Scaffold for Bone Tissue Regeneration

Biocompatible nanocomposite electrospun fibers containing Polyurethane/Chitosan/ $\beta $ -Tri calcium phosphate with diverse concentrations were designed and produced through the electrospinning process for bone tissue engineering applications. After the production process, density measurement, viscosity, electrical conductivity, and tensile strength measurement tests were carried out as physical analyses of blended solutions. The chemical structural characterization was scrutinized using Fourier transform infrared spectrometer (FTIR), and scanning electron microscopy (SEM) was used to observe the morphological details of developed electrospun scaffolds. Cell viability, attachment, and proliferation were performed using a L929 fibroblast cell line. Based on the physical, SEM, FTIR analysis, and cell culture studies, preferable nanofiber composition was selected for further studies. Amoxicillin (AMX) was loaded to that selected nanofiber composition for examination of the drug release. In comparison with other studies on similar AMX controlled products, higher drug loading and encapsulation efficiencies were obtained. It has been clearly found that the developed nanofiber composites have potential for bone tissue engineering applications.
Perinatal exposures to phthalates and phthalate mixtures result in sex-specific effects on body weight, organ weights and intracisternal A-particle (IAP) DNA methylation in weanling mice

Abstract Developmental exposure to phthalates has been implicated as a risk for obesity; however, epidemiological studies have yielded conflicting results and mechanisms are poorly understood. An additional layer of complexity in epidemiological studies is that humans are exposed to mixtures of many different phthalates. Here, we utilize an established mouse model of perinatal exposure to investigate the effects of three phthalates, diethylhexyl phthalate (DEHP), diisononyl phthalate (DINP) and dibutyl phthalate (DBP), on body weight and organ weights in weanling mice. In addition to individual phthalate exposures, we employed two mixture exposures: DEHP+DINP and DEHP+DINP+DBP. Phthalates were administered through phytoestrogen-free chow at the following exposure levels: 25 mg DEHP/kg chow, 25 mg DBP/kg chow and 75 mg DINP/kg chow. The viable yellow agouti (A vy ) mouse strain, along with measurement of tail DNA methylation, was used as a biosensor to examine effects of phthalates and phthalate mixtures on the DNA methylome. We found that female and male mice perinatally exposed to DINP alone had increased body weights at postnatal day 21 (PND21), and that exposure to mixtures did not exaggerate these effects. Females exposed to DINP and DEHP+DINP had increased relative liver weights at PND21, and females exposed to a mixture of DEHP+DINP+DBP had increased relative gonadal fat weight. Phthalate-exposed A vy /a offspring exhibited altered coat color distributions and altered DNA methylation at intracisternal A-particles (IAPs), repetitive elements in the mouse genome. These findings provide evidence that developmental exposures to phthalates influence body weight and organ weight changes in early life, and are associated with altered DNA methylation at IAPs.
Reducing HIV Vulnerability Through a Multilevel Life Skills Intervention for Adolescent Men (The iREACH Project): Protocol for a Randomized Controlled Trial

Background Few HIV interventions have demonstrated efficacy in reducing HIV risk among adolescent men who have sex with men (AMSM), and fewer still have recognized the unique needs of AMSM based on race/ethnicity or geographical setting. Recognizing that youths’ HIV vulnerability is intricately tied to their development and social context, delivering life skills training during adolescence might delay the onset or reduce the consequences of risk factors for HIV acquisition and equip AMSM with the skills to navigate HIV prevention. This protocol describes the development and testing of iREACH, an online multilevel life skills intervention for AMSM. Objective This randomized controlled trial (RCT) aims to test the efficacy of an online-delivered life skills intervention, iREACH, on cognitive and behavioral HIV-related outcomes for AMSM. Methods iREACH is a prospective RCT of approximately 600 cisgender adolescent males aged 13 to 18 years who report same-sex attractions. The intervention will be tested with a racial/ethnically diverse sample (≥50% racial/ethnic minority) of AMSM living in four regions in the United States: (1) Chicago to Detroit, (2) Washington, DC to Atlanta, (3) San Francisco to San Diego, and (4) Memphis to New Orleans. Results This project is currently recruiting participants. Recruitment began in March 2018. Conclusions iREACH represents a significant innovation in the development and testing of a tailored life skills-focused intervention for AMSM, and has the potential to fill a significant gap in HIV prevention intervention programming and research for AMSM. Registered Report Identifier RR1-10.2196/10174
HetRCNA: A Novel Method to Identify Recurrent Copy Number Alternations from Heterogeneous Tumor Samples Based on Matrix Decomposition Framework

A common strategy to discovering cancer associated copy number aberrations (CNAs) from a cohort of cancer samples is to detect recurrent CNAs (RCNAs). Although the previous methods can successfully identify communal RCNAs shared by nearly all tumor samples, detecting subgroup-specific RCNAs and their related subgroup samples from cancer samples with heterogeneity is still invalid for these existing approaches. In this paper, we introduce a novel integrated method called HetRCNA, which can identify statistically significant subgroup-specific RCNAs and their related subgroup samples. Based on matrix decomposition framework with weight constraint, HetRCNA can successfully measure the subgroup samples by coefficients of left vectors with weight constraint and subgroup-specific RCNAs by coefficients of the right vectors and significance test. When we evaluate HetRCNA on simulated dataset, the results show that HetRCNA gives the best performances among the competing methods and is robust to the noise factors of the simulated data. When HetRCNA is applied on a real breast cancer dataset, our approach successfully identifies a bunch of RCNA regions and the result is highly correlated with the results of the other two investigated approaches. Notably, the genomic regions identified by HetRCNA harbor many breast cancer related genes reported by previous researches.
Analysis of a Plasmonic Pole-Absorber Using a Periodic Structure

A plasmonic black pole (PBP) consisting of a series of touching spherical metal surfaces is analyzed using the finite-difference time-domain (FDTD) method with the periodic boundary condition. First, the wavelength characteristics of the PBP are studied under the assumption that the PBP is omnidirectionally illuminated. It is found that partial truncation of each metal sphere reduces the reflectivity over a wide wavelength range. Next, we consider the case where the PBP is illuminated with a cylindrical wave from a specific direction. It is shown that an absorptivity of more than 80% is obtained over a wavelength range of λ = 500 nm to 1000 nm. Calculation regarding the Poynting vector distribution also shows that the incident wave is bent and absorbed towards the center axis of the PBP. key words: plasmonic devices, frequency-dependent FDTD method, trapezoidal recursive convolution
Model assisted sensitivity analyses for hidden bias with binary outcomes

In medical and health sciences, observational studies are a major data source for inferring causal relationships. Unlike randomized experiments, observational studies are vulnerable to the hidden bias introduced by unmeasured confounders. The impact of unmeasured covariates on the causal effect can be assessed by conducting a sensitivity analysis. A comprehensive framework of sensitivity analyses has been developed for matching designs. Sensitivity parameters are introduced to capture the association between the missing covariates and the exposure or the outcome. Fixing sensitivity parameter values, it is possible to compute the bounds of the p‐value of a randomization test on causal effects. We propose a model assisted sensitivity analysis with binary outcomes for the general 1:k matching design, which provides results equivalent to the conventional nonparametric approach in large sample. By introducing a conditional logistic outcome model, we substantially simplify the implementation and interpretation of the sensitivity analysis. More importantly, we are able to provide a closed form representation for the set of sensitivity parameters for which the maximum p‐values are non‐significant. This methodology can be easily extended to matching designs with multilevel treatments. We illustrate our method using a U.S. trauma care database to examine mortality difference between trauma care levels.
Relevance of human fat distribution on lipid and lipoprotein metabolism and cardiovascular disease risk

Purpose of review Upper body abdominal and lower body gluteofemoral fat depot masses display opposing associations with plasma lipid and lipoprotein and cardiovascular disease (CVD) risk profiles. We review developments on adipose tissue fatty acid metabolism in the context of body fat distribution and how that might be related to adverse lipid and lipoprotein profiles and CVD risk. Recent findings Recent data have confirmed the paradoxical relationship of upper abdominal and lower body gluteofemoral adiposity and CVD risk. Mechanistically, this is likely to reflect the different ways fat depots handle lipid storage and release, which impacts directly and indirectly on lipid and lipoprotein metabolism. The upper body enhances immediate fat storage pathway with rapid uptake of dietary-derived fatty acids, whereas the lower body fat depot has a reduced lipid turnover accommodating a slower fat redistribution. Body fat distribution and the fat depots’ ability to undergo appropriate expansion when fat storage is required, rather than overall body fatness, appear as the important determinant of metabolic health. Summary A focus on fat distribution in overweight people, preferably using precise imaging methods, rather than quantifying total body fatness, is likely to provide the medical community with better tools to stratify and treat patients with obesity-related complications.
Blind Denoising Autoencoder

The term “blind denoising” refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on a separate training data and have used the learned model to denoise test samples. Such a methodology fails when the test image (to denoise) is not of the same kind as the models learned with. This will be the first work, where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method performs better than dictionary learning (K-singular value decomposition), transform learning, sparse stacked denoising autoencoder, and the gold standard BM3D algorithm.
A Multi Rate Marginalized Particle Extended Kalman Filter for P and T Wave Segmentation in ECG Signals

The marginalized particle extended Kalman filter (MP-EKF) has been known as an effective model-based nonlinear Bayesian framework in the field of electrocardiogram (ECG) signal denoising. In this paper, we reveal another potential capability of an MP-EKF and propose a multirate MP-EKF based framework for P- and T-wave segmentation in ECG signals. The proposed multirate implementation of MP-EKF leads to better estimation of states and avoids unwanted errors in estimation procedure. The behavior of particles in the multirate MP-EKF is controlled by a novel particle weighting strategy that helps the particles adapt themselves with respect to ECG signal trajectory. After ECG filtering, a novel morphology-based algorithm uses the estimates of a multirate MP-EKF to determine the P- and T-wave fiducial points. This algorithm is a combination of well-known morphological operators such as “opening,” closing, “top-hat,” and “bottom-hat” transforms. The segmentation performance of the proposed algorithm was evaluated on QT database and it showed promising results in comparison to other Bayesian frameworks such as partially collapsed Gibbs sampler and extended Kalman filter.
Online Training of LSTM Networks in Distributed Systems for Variable Length Data Sequences

In this brief, we investigate online training of long short term memory (LSTM) architectures in a distributed network of nodes, where each node employs an LSTM-based structure for online regression. In particular, each node sequentially receives a variable length data sequence with its label and can only exchange information with its neighbors to train the LSTM architecture. We first provide a generic LSTM-based regression structure for each node. In order to train this structure, we put the LSTM equations in a nonlinear state-space form for each node and then introduce a highly effective and efficient distributed particle filtering (DPF)-based training algorithm. We also introduce a distributed extended Kalman filtering-based training algorithm for comparison. Here, our DPF-based training algorithm guarantees convergence to the performance of the optimal LSTM coefficients in the mean square error sense under certain conditions. We achieve this performance with communication and computational complexity in the order of the first-order gradient-based methods. Through both simulated and real-life examples, we illustrate significant performance improvements with respect to the state-of-the-art methods.
Low-Complexity Approximate Convolutional Neural Networks

In this paper, we present an approach for minimizing the computational complexity of the trained convolutional neural networks (ConvNets). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with an efficient approximations capable of extreme reductions in computational complexity. Low-complexity convolution filters are obtained through a binary (zero and one) linear programming scheme based on the Frobenius norm over sets of dyadic rationals. The resulting matrices allow for multiplication-free computations requiring only addition and bit-shifting operations. Such low-complexity structures pave the way for low power, efficient hardware designs. We applied our approach on three use cases of different complexities: 1) a “light” but efficient ConvNet for face detection (with around 1000 parameters); 2) another one for hand-written digit classification (with more than 180 000 parameters); and 3) a significantly larger ConvNet: AlexNet with $\approx 1.2$ million matrices. We evaluated the overall performance on the respective tasks for different levels of approximations. In all considered applications, very low-complexity approximations have been derived maintaining an almost equal classification performance.
Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection

In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed for myocardial infarction detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and fuzzy information granulation is adopted for preprocessing. Then, the beats are input into our multilead-CNN (ML-CNN), a novel model that includes sub two-dimensional (2-D) convolutional layers and lead asymmetric pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 and 26.75 ms, respectively, which indicate that this method has good potential for mobile healthcare applications.