Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

Image segmentation plays a pivotal role in several medical-imaging applications by assisting the segmentation of the regions of interest. Deep learning-based approaches have been widely adopted for semantic segmentation of medical data. In recent years, in addition to 2D deep learning architectures, 3D architectures have been employed as predictive algorithms for 3D medical image data. In this paper, we propose a 3D stack-based deep learning technique for segmenting manifestations of consolidation and ground-glass opacities in 3D Computed Tomography (CT) scans. We also present a comparison based on the segmentation results, the contextual information retained, and the inference time between this 3D technique and a traditional 2D deep learning technique. We also define the area-plot, which represents the peculiar pattern observed in the slice-wise areas of the pathology regions predicted by these deep learning models. In our exhaustive evaluation, the 3D technique performs better than the 2D technique for the segmentation of CT scans. We get dice scores of 79% and 73% for the 3D and the 2D techniques respectively. The 3D technique results in a 5X reduction in the inference time compared to the 2D technique. Results also show that the area-plots predicted by the 3D model are more similar to the ground truth than those predicted by the 2D model. We also show how increasing the amount of contextual information retained during the training can improve the 3D model's performance.
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can barely feel its presence, especially in its early stage. A non-invasive, automatic, and effective detection method is therefore needed to help early detection so that medical intervention can be implemented in time to prevent its progression. Electrocardiogram (ECG), which records the electrical activities of the heart, has been widely used for detecting the presence of AF. However, due to the subtle patterns of AF, the performance of detection models have largely depended on complicated data pre-processing and expertly engineered features. In our work, we developed DenseECG, an end-to-end model based on 5 layers 1D densely connected convolutional neural network. We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s). Our trained model was able to outperform the other state-of-the-art AF detection models on this dataset without complicated data pre-processing and expert-supervised feature engineering.
Quasinormal modes and Purcell factors of coupled loss and gain resonators near an exceptional point

We present a quasinormal mode (QNM) theory for coupled loss and gain resonators, working in the vicinity of an exceptional point. Assuming linear media, we show how the QNMs yield a quantitatively good model to a full dipole spontaneous emission response at a variety of spatial positions and frequencies, for coupled loss and gain resonators. We also develop a highly accurate and intuitive QNM coupled-mode theory, which can be used to rigorously model such systems using only the QNMs of the bare resonators, where the hybrid QNMs of the complete system are automatically obtained. In the vicinity of a lossy exceptional point, we analytically show how the QNMs yield a Lorentzian-like and a Lorentzian-squared-like response for the spontaneous emission lineshape, consistent with other works. However, using rigorous analytical and numerical solutions for microdisk resonators, we demonstrate that the general lineshapes are far richer than what has been previously predicted. Indeed, the classical picture of spontaneous emission can take on a wide range of positive and negative Purcell factors from the hybrid modes of the coupled loss-gain system. These negative Purcell factors are unphysical and signal a clear breakdown of the classical dipole picture of spontaneous emission in such media. We also show the rich spectral features of the Green function propagators, which can be used to model various physical observables.
Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal Ensembles

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with rotational invariance. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A “direct” approach to the simulation, where one first generates a dense <inline-formula> <tex-math notation="LaTeX">$n \times n$ </tex-math></inline-formula> matrix from the ensemble, requires at least <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> resource in space and time. The HD algorithm overcomes this <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(nT)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(nT^{2})$ </tex-math></inline-formula>, respectively, with <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> being the number of iterations. When <inline-formula> <tex-math notation="LaTeX">$T \ll n$ </tex-math></inline-formula>, which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly

We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs. We develop two separate models to demarcate the heart and chest regions in an X-ray image using bounding boxes and use their outputs to calculate the cardiothoracic ratio. We obtain a mean absolute error of 0.0209 on a held-out test dataset and a mean absolute error of 0.018 on an independent dataset from a different hospital. We also compare three different segmentation model architectures for the proposed method and observe that Attention U-Net yields better results than SE-Resnext U-Net and EfficientNet U-Net. By providing a numeric measurement of the cardiothoracic ratio, instead of just providing presence or absence, we hope to mitigate human subjectivity arising out of visual assessment in the detection of cardiomegaly.
MIMOSA: Reducing Malware Analysis Overhead with Coverings

There is a growing body of malware samples that evade automated analysis and detection tools. Malware may measure fingerprints ("artifacts") of the underlying analysis tool or environment and change their behavior when artifacts are detected. While analysis tools can mitigate artifacts to reduce exposure, such concealment is expensive. However, not every sample checks for every type of artifact-analysis efficiency can be improved by mitigating only those artifacts most likely to be used by a sample. Using that insight, we propose MIMOSA, a system that identifies a small set of"covering"tool configurations that collectively defeat most malware samples with increased efficiency. MIMOSA identifies a set of tool configurations that maximize analysis throughput and detection accuracy while minimizing manual effort, enabling scalable automation to analyze stealthy malware. We evaluate our approach against a benchmark of 1535 labeled stealthy malware samples. Our approach increases analysis throughput over state of the art on over 95% of these samples. We also investigate cost-benefit tradeoffs between the fraction of successfully-analyzed samples and computing resources required. MIMOSA provides a practical, tunable method for efficiently deploying analysis resources.
Quality Assessment of In Situ Plasma‐Etched Diamond Surfaces for Chemical Vapor Deposition Overgrowth

In situ plasma etching is a common method to prepare diamond substrates for epitaxial overgrowth to effectuate higher quality. However, there is no feasible direct qualitative method established so far to assess the performance of the etching pretreatment. An optimization of the pretreatment process on grounds of high‐resolution X‐ray diffraction measurements is proposed to judge the structural quality gain of the diamond substrates and the effectiveness of the polishing‐induced subsurface damage removal. The obtained data shows that parameters such as thickness and misorientation angle of the diamond substrates seem to have no clear‐cut influence on the gain of structural quality. The process duration, however, is an important key factor when the amount of material removal and the arising roughness are discussed. Furthermore, the impact of the oxygen‐to‐hydrogen ratio is examined. With rising oxygen percentage, the structural quality gain remains similar; only the overall as well as local mean roughness increases strongly. Within the utilized reactor setup, the best results are obtained after a 20 min in situ hydrogen plasma‐etching step. The optimal pretreatment process, however, changes for each reactor type and will always embody a trade‐off. Due to the introduced method, a better evaluation and comparison of the achievements is accomplishable.
The Magnitude of Satisfaction and Associated Factors Among Household Heads Who Visited Health Facilities with Community-Based Health Insurance Scheme in Anilemo District, Hadiya Zone, Southern Ethiopia

Background Community-based health insurance (CBHI) schemes are an emerging strategy for providing financial protection against healthcare-related poverty. In Ethiopia, CBHI is being piloted in 13 districts, but community experience and satisfaction with the scheme have yet to be studied. Objective To assess the magnitude of satisfaction and associated factors among household heads who visited health facilities with community-based health insurance schemes in the Anilemo district Hadiya Zone Southern Ethiopia. Methods A community-based cross-sectional study design was conducted for 627 household heads in the Anilemo district, from March 1–30, 2020. Study participants were selected using stratified random sampling for kebeles and systematic sampling for study households. Data were collected by trained data collectors using a pre-tested structured questionnaire. Descriptive statistics, bivariate, and multivariate logistic regression analyses were performed. P values less than 0.05 with 95% confidence intervals were used to determine associations between independent and dependent variables. Results The magnitude of household heads’ satisfaction was 54.1%. Household heads age [AOR=1.70;95% CI 1.09–2.67], households income [AOR=0.19; 95% CI 0.11–0.35], knowledge of CBHI benefit packages [AOR=3.15; 95% CI 1.97–5.03], agreement with laboratory services [AOR=2.25; 95% CI 1.40–3.62], and got and agreed with prescribed drugs [AOR=2.69; 95% CI 1.66–4.37] were significantly associated with the magnitude of household heads satisfaction with community-based health insurance. Conclusion About half of the household heads who visited health facilities with CBHIS were satisfied. Age, household’s income, knowledge of CBHI benefit packages, agreement with laboratory service provision, availability and agreement with prescribed drugs were significant predictors of satisfaction with CBHI. Therefore, much effort could be required to increase the magnitude of the household head’s satisfaction with the scheme.
Safety interlock and remote computer monitoring for a diffusion-pumped vacuum system

A safety interlock system for a diffusion-pumped ultra-high vacuum chamber is presented. It controls and monitors vacuum system valves, diffusion pumps, liquid nitrogen filling, chilled water flow, and pressures in the chamber. The interlock system is interfaced with a phone dialer and a personal computer to provide remote monitoring and diagnostics of the vacuum system. This interlock prevents many operational errors and problems due to the power and liquid nitrogen failure.
Suicidal Behavior and Flood Effects in Bangladesh: A Two-Site Interview Study

Background Bangladeshi flood survivors are reported with such higher mental disorders that are not ever observed in any other cohorts. Although there are a few studies that assessed mental disorders, suicide or suicidal behaviors are not investigated yet. Hence, the present study for the first time investigated suicidal behaviors and its relationship with socio-demographics, flood effects and psychopathology. Methods A cross-sectional interview study was carried out between November and December 2019, after 4/5 months of the flood occurrence. Two completely affected villages from two districts residing in two parts of the country were randomly selected (whereas Manikganj district was less affected by the recent flood compared to Kurigram), and a total of 348 flood survivors were interviewed (45.53 ± 14.85 years). Questions related to basic socio-demographics, flood effects, psychological impacts, and suicidal behaviors were asked in the interviews. Results In the total sample, 57.5% of flood survivors reported having suicidal ideation, whereas 5.7% and 2.0% madea suicide plan and suicide attempt, respectively. Within two study sites, participants belonging to Kurigram reported significantly higher suicidal ideation compared to Manikganj (84.8% vs 33.2%, χ2 = 94.475, p<0.001). Belonging to a lower-class family, having less education, and less earning members in the family, being affected severely by the flood, suffering from depression, anxiety, and PTSD, and experiencing financial threat, and economic hardship were suicidal behavior risk factors in the total sample. Conclusion Considering the present findings (ie, suicidality commensurately increases with flood effects), a multi-sectoral policy and its effective implementation should be adopted for alleviating the flood-related psychological burdens.
A KCa3.1 Channel Opener, ASP0819, Modulates Nociceptive Signal Processing from Peripheral Nerves in Fibromyalgia-Like Pain in Rats

Purpose Although abnormal peripheral and central pain processing has been observed in fibromyalgia (FM) patients, the biomechanics and pathophysiology, surrounding the peripheral mechanism are not well understood. An intermediate conductance channel, KCa3.1, is expressed in peripheral sensory nerve fibers where it maintains the resting membrane potential and controls nerve firing, making it a plausible target for peripheral therapeutic interventions. ASP0819, a KCa3.1 channel opener, is an orally available molecular entity and is used in this investigation to elucidate the role of KCa3.1 in signal processing of pain in FM. Methods Human or rat KCa3.1 channel-expressing cells were used for evaluating the main action of the compound. Effects of the compound on withdrawal behavior by mechanical stimulation were examined in reserpine-induced myalgia (RIM) and vagotomy-induced myalgia (VIM) models of rats. In addition, in vivo electrophysiological analysis was performed to examine the peripheral mechanisms of action of the compound. Other pain models were also examined. Results ASP0819 increased the negative membrane potential in a concentration-dependent manner. Oral administration of ASP0819 significantly recovered the decrease in muscle pressure threshold in rat FM models of RIM and VIM. The in vivo electrophysiological experiments showed that Aδ- and C-fibers innervating the leg muscles in the RIM model demonstrated increased spontaneous and mechanically evoked firing compared with normal rats. Intravenous infusion of ASP0819 significantly reduced both the spontaneous activity and mechanically evoked responses in Aδ-fibers in the rat RIM model. ASP0819 significantly reduced the number of abdominal contractions as an indicator of abdominal pain behaviors in the rat visceral extension model and withdrawal responses in the osteoarthritis model, respectively. Conclusion These findings suggest that ASP0819 may be a promising analgesic agent with the ability to modulate peripheral pain signal transmission. Its use in the treatment of several pain conditions should be explored, chief amongst these being FM pain.
Obesity as an additional factor for autonomic imbalance and poor sleep behavior in chronic obstructive pulmonary disease: a case-control study

OBJECTIVES: We compared the autonomic modulation and sleep behavior of eutrophic and overweight patients with chronic obstructive pulmonary disease (COPD). METHODS: COPD participants were divided into the overweight and eutrophic groups. Pulmonary function, blood pressure, body composition, autonomic modulation, and the Pittsburgh Sleep Quality Index score were assessed. Participants performed the six-minute walk test for functional assessment. RESULTS: Spirometric variables obtained in eutrophic and overweight individuals with COPD showed no statistically different results. We observed that the SDNN index indicated lower overall variability (p=0.003), and root mean square of successive differences between normal heart beats (RMSSD) (p=0.04) indicated lower parasympathetic modulation in the overweight group than observed in the eutrophic group. The indexes of the frequency domain presented lower values of total variability (p<0.01), low frequency bands (p<0.01), and high frequency (p=0.02), suggesting a higher sympathetic modulation and reduced parasympathetic modulation of the overweight group compared to eutrophic group. The overweight group also showed reduced sleep quality than the eutrophic group. CONCLUSION: Overweight COPD patients showed lower autonomic modulation and impaired sleep quality, latency, and efficiency as compared eutrophic subjects. These results reinforce the importance of weight control and the acquisition of healthy habits in this population.
Performance of mutation pathogenicity prediction tools on missense variants associated with 46,XY differences of sex development

OBJECTIVES: Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred. METHODS: We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve. RESULTS: Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). CONCLUSION: Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.
Changes in Menopausal Risk Factors in Early Postmenopausal Osteopenic Women After 13 Months of High-Intensity Exercise: The Randomized Controlled ACTLIFE-RCT

Abstract The menopausal transition is a critical period in women’s lives. Exercise might be the most promising non-pharmaceutic intervention to address the large variety of risk factors related to the pronounced estradiol decline during peri- and early-postmenopause. The aim of this study was to determine the effect of an 18-month multipurpose exercise program on risk factors and symptoms related to the menopausal transition. Fifty-four women 1–5 years postmenopause with osteopenia or osteoporosis were randomly assigned 1) to a high impact weight-bearing/high-intensity/velocity resistance training group (EG: n=27) exercising three times a week or 2) to an attendance control group (CG: n=27) that performed low-intensity exercise once a week. Both groups were supplemented with cholecalciferol and calcium. The primary study endpoint was bone mineral density (BMD) at lumbar spine (LS) and total hip, secondary outcomes were lean body mass (LBM), total and abdominal body percentage, metabolic syndrome Z-Score (MetS-Z), menopausal symptoms and muscle strength and power. Due to COVID-19, the study was stopped after 13 months. We observed significant effects for BMD-LS (EG: 0.002±.018 versus CG: −.009±0.018 mg/cm2, p=0.027) but not for BMD total hip (EG: −0.01±.016 versus CG: −.009±0.020 mg/cm2, p=0.129). LBM improved significantly in the EG and decreased in the CG (0.39±1.08 vs −0.37±1.34 kg, p=0.026). Total and abdominal body fat improved significantly in the EG and was maintained in the CG (−1.44±1.49 vs −0.02±1.55 kg, p=0.002 and -1.50±2.33 vs 0.08±2.07 kg, p=0.011). Significant effects in favor of the EG were also determined for menopausal symptoms (p=0.029), hip/leg extension strength (p<0.001) and power (p<0.001). However, changes of the MetS-Z did not differ significantly (p=0.149) between EG and CG. In summary, with minor exceptions, we demonstrated the effectiveness of a multipurpose exercise protocol dedicated to early-postmenopausal women on various risk factors and complaints related to the menopausal transition.