Miracle-dz/UniFAR-FTUs huggingface dataset:
A two-stage framework for optical coherence tomography angiography image quality improvement

Introduction Optical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis. Methods In this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement. Results and discussion To evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities.
Using machine learning to estimate the calendar age based on autonomic cardiovascular function

Introduction Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants’ age. Methods We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (>30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR). To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample. Results In a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0.81 and a mean absolute error of MAE=5.6 years. In men, the error (MAE=5.4 years) seemed to be lower than that in women (MAE=6.0 years). In comparison to normal-weight subjects, GPR and SVR significantly overestimated the age of obese participants compared with controls. The highest age gap indicated advanced cardiovascular aging by 5.7 years in obese participants. Discussion In conclusion, machine learning can be used to estimate age on cardiovascular function in a healthy population when considering previous models of biological aging. The estimated age might serve as a comprehensive and readily interpretable marker of cardiovascular function. Whether it is a useful risk predictor should be investigated in future studies.
COVID-19 and atrial fibrillation: Intercepting lines

Almost 20% of COVID-19 patients have a history of atrial fibrillation (AF), but also a new-onset AF represents a frequent complication in COVID-19. Clinical evidence demonstrates that COVID-19, by promoting the evolution of a prothrombotic state, increases the susceptibility to arrhythmic events during the infective stages and presumably during post-recovery. AF itself is the most frequent form of arrhythmia and is associated with substantial morbidity and mortality. One of the molecular factors involved in COVID-19-related AF episodes is the angiotensin-converting enzyme (ACE) 2 availability. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses ACE2 to enter and infect multiple cells. Atrial ACE2 internalization after binding to SARS-CoV-2 results in a raise of angiotensin (Ang) II, and in a suppression of cardioprotective Ang(1–7) formation, and thereby promoting cardiac hypertrophy, fibrosis and oxidative stress. Furthermore, several pharmacological agents used in COVID-19 patients may have a higher risk of inducing electrophysiological changes and cardiac dysfunction. Azithromycin, lopinavir/ritonavir, ibrutinib, and remdesivir, used in the treatment of COVID-19, may predispose to an increased risk of cardiac arrhythmia. In this review, putative mechanisms involved in COVID-19-related AF episodes and the cardiovascular safety profile of drugs used for the treatment of COVID-19 are summarized.
The effects of three different low-volume aerobic training protocols on cardiometabolic parameters of type 2 diabetes patients: A randomized clinical trial

Objective To compare the effects of different aerobic training protocols on cardiometabolic variables in patients with type 2 diabetes mellitus (T2DM). Methods This study was a parallel clinical trial. Fifty-two men and women with T2DM (>40 years) were randomly allocated into three groups, and 44 (22 males/22 females) were included in the final analysis. Exercise intensity was based on the speed corresponding to the maximum oxygen consumption (v V ˙ O2max). Moderate intensity continuous training (MICT) involved 14 minutes at 70% of v V ˙ O2max; short interval high-intensity interval training (S-HIIT) consisted of 20 bouts of 30 seconds at 100% of V˙O2max with 30 seconds passive recovery; long interval high-intensity training (L-HIIT) consisted of 5 bouts of 2 minutes at 100% of v V ˙ O2max with 2 minutes passive recovery. Training protocols were performed on a motorized treadmill two times per week for eight weeks. Glycated hemoglobin (Hb1Ac), total cholesterol, triglycerides, resting systolic blood pressure (SBP), resting diastolic blood pressure (DBP), resting heart rate (resting HR) and maximum oxygen consumption (V˙O2max) were measured before and after the exercise intervention. The study was registered on the Brazilian clinical trial records (ID: RBR45 4RJGC3). Results There was a significant difference between groups for changes on V ˙ O2max. Greater increases on V ˙ O2max were achieved for L-HIIT (p = 0.04) and S-HIIT (p = 0.01) in comparison to MICT group, with no significant difference between L-HIIT and S-HIIT (p = 0.9). Regarding comparison within groups, there were significant reductions on HbA1c and triglycerides levels only for L-HIIT (p< 0.05). V ˙ O2max significantly increased for both L-HIIT (MD = 3.2 ± 1.7 ml/kg/min, p< 0.001) and S-HIIT (MD = 3.4 ± 1.7, p< 0.001). There was a significant reduction on resting SBP for L-HIIT group (MD = -12.07 ± 15.3 mmHg, p< 0.01), but not for S-HIIT and MICT. There were no significant changes from pre- to post-training on fasting glycemia, total cholesterol, HDL, LDL, resting HR and resting DBP for any group (p > 0.05). Conclusion Low-volume HIIT promoted greater improvements in cardiorespiratory capacity in comparison with low-volume MICT, independent of the protocols used. There were no other differences between groups. All protocols improved at least one of the variables analyzed; however, the most evident benefits were after the high-intensity protocols, especially L-HIIT.
Micro-CT and deep learning: Modern techniques and applications in insect morphology and neuroscience

Advances in modern imaging and computer technologies have led to a steady rise in the use of micro-computed tomography (µCT) in many biological areas. In zoological research, this fast and non-destructive method for producing high-resolution, two- and three-dimensional images is increasingly being used for the functional analysis of the external and internal anatomy of animals. µCT is hereby no longer limited to the analysis of specific biological tissues in a medical or preclinical context but can be combined with a variety of contrast agents to study form and function of all kinds of tissues and species, from mammals and reptiles to fish and microscopic invertebrates. Concurrently, advances in the field of artificial intelligence, especially in deep learning, have revolutionised computer vision and facilitated the automatic, fast and ever more accurate analysis of two- and three-dimensional image datasets. Here, I want to give a brief overview of both micro-computed tomography and deep learning and present their recent applications, especially within the field of insect science. Furthermore, the combination of both approaches to investigate neural tissues and the resulting potential for the analysis of insect sensory systems, from receptor structures via neuronal pathways to the brain, are discussed.
Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone

Introduction Kawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images. Methods Specifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework. Results The experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5. Conclusions Scaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution.
An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.
Linear Quadratic Guidance Laws for Imposing a Terminal Intercept Angle

Linear quadratic guidance laws that explicitly enable imposing a predetermined intercept angle are presented. Two such guidance laws are derived, using optimal control and differential game theories, for arbitrary-order linear missile dynamics. The obtained guidance laws are dependent on the well-known zero-effort miss distance and on a new variable denoted zero-effort angle. It is shown that imposing the terminal angle constraint raises considerably the gains of the guidance laws. Theoretic conditions for the existence of a saddle-point solution in the differential game are also derived. These conditions show that imposing the terminal angle constraint requires a higher maneuverability advantage from the missile. The performance of the proposed guidance laws is investigated using a nonlinear two-dimensional simulation of the missile's lateral dynamics and relative kinematics, while assuming first-order dynamics for the target's evasive maneuvers. Using a Monte Carlo study, it is shown that, for the investigated problem, a target can be intercepted with a negligible miss distance and intercept angle error even when the target maneuvers and there are large initial heading errors.
Type 4 renal tubular acidosis and uric acid nephrolithiasis: two faces of the same coin?

Purpose of review The present review summarizes findings of recent studies examining the epidemiology, pathophysiology, and treatment of type 4 renal tubular acidosis (RTA) and uric acid nephrolithiasis, two conditions characterized by an abnormally acidic urine. Recent findings Both type 4 RTA and uric acid nephrolithiasis disproportionately occur in patients with type 2 diabetes and/or chronic kidney disease. Biochemically, both conditions are associated with reduced renal ammonium excretion resulting in impaired urinary buffering and low urine pH. Reduced ammoniagenesis is postulated to result from hyperkalemia in type 4 RTA and from insulin resistance and fat accumulation in the renal proximal tubule in uric acid nephrolithiasis. The typical biochemical findings of hyperkalemia and systemic acidosis of type 4 RTA are rarely reported in uric acid stone formers. Additional clinical differences between the two conditions include findings of higher urinary uric acid excretion and consequent urinary uric acid supersaturation in uric acid stone formers but not in type 4 RTA. Summary Type 4 RTA and uric acid nephrolithiasis share several epidemiological, clinical, and biochemical features. Although both conditions may be manifestations of diabetes mellitus and thus have a large at-risk population, the means to the shared biochemical finding of overly acidic urine are different. This difference in pathophysiology may explain the dissimilarity in the prevalence of kidney stone formation.
Physics-Based Learning Models for Ship Hydrodynamics.

We present the concepts of physics-based learning models (PBLM) and their relevance and application to the field of ship hydrodynamics. The utility of physics-based learning is motivated by contrasting generic learning models for regression predictions, which do not presume any knowledge of the system other than the training data provided with methods such as semi-empirical models, which incorporate physical insights along with data-fitting. PBLM provides a framework wherein intermediate models, which capture (some) physical aspects of the problem, are incorporated into modern generic learning tools to substantially improve the predictions of the latter, minimizing the reliance on costly experimental measurements or high-resolution highfidelity numerical solutions. To illustrate the versatility and efficacy of PBLM, we present three wave-ship interaction problems: 1) at speed waterline profiles; 2) ship motions in head seas; and 3) three-dimensional breaking bow waves. PBLM is shown to be robust and produce error rates at or below the uncertainty in the generated data at a small fraction of the expense of high-resolution numerical predictions
Bayes binomial sampling by attributes with a general-beta prior distribution

In binomial sampling the standard beta is frequently used as a prior due to its conjugate property for a sample from a Bernoulli distribution. This article looks into the case when this prior is a general beta. Practical advantages in the elicitation of expert opinion (to obtain the prior) and a convenient expression of the posterior are presented. A computer program (in double precision) on an IBM-PC (or compatible) permits the simple use of the general beta. This program was used to solve the numerical examples, showing the advantages of using the general beta as a prior. >
Goodness of prediction fit

SUMMARY Fitting a parametric model or estimating a parametric density function plays an important role in a number of statistical applications. Two widely-used methods, one replacing the unknown parameter by an efficient estimate and so termed estimative and the other using a mixture of the possible density functions and commonly termed predictive, are compared. On a general criterion of closeness of fit based on a discriminating information measure the predictive method is shown to be preferable. Explicit measures of the relative closeness of predictive and estimative fits are obtained for gamma and multinormal models.
Comptonization of the Lyman edge in active galactic nuclei

Recent X-ray observations indicate the presence of both relativistic particles and cold gas very close to the centre of active galactic nuclei. However, the UV radiation from this cold gas does not exhibit the Lyman edge predicted by many accretion disc models. It is shown that Comptonization of the intrinsic spectrum by gas in the form of a hot corona at the intermediate temperatures (T ∼ 10 8 K) can efficiently smear out the Lyman edge without destroying the observed iron K-fluorescent line
Eddy viscosity of three-dimensional flow

Detailed theoretical and numerical results are presented for the eddy viscosity of three-dimensional forced spatially periodic incompressible flow. As shown by Dubrulle & Frisch (1991), the eddy viscosity, which is in general a fourth-order anisotropic tensor, is expressible in terms of the solution of auxiliary problems. These are, essentially, three-dimensional linearized Navier–Stokes equations which must be solved numerically. The dynamics of weak large-scale perturbations of wavevector k is determined by the eigenvalues – called here ‘eddy viscosities’ – of a two by two matrix, obtained by contracting the eddy viscosity tensor with two k-vectors and projecting onto the plane transverse to k to ensure incompressibility. As a consequence, eddy viscosities in three dimensions, but not in two, can become complex. It is shown that this is ruled out for flow with cubic symmetry, the eddy viscosities of which may, however, become negative. An instance is the equilateral ABC-flow (A = B = C = 1). When the wavevector k is in any of the three coordinate planes, at least one of the eddy viscosities becomes negative for R = 1/v > Rc [bsime ] 1.92. This leads to a large-scale instability occurring for a value of the Reynolds number about seven times smaller than instabilities having the same spatial periodicity as the basic flow.
First report of

In Bangladesh, citrus is an important fruit crop that is becoming more commonly cultivated in commercial plantations. In March 2019, we observed yellowing, vein clearing, and mild mosaic symptoms in citron (Citrus medica). Based on systematic serological and molecular analyses of the symptomatic leaf samples, the Citrus tristeza virus (CTV) was identified. To the best of our knowledge, this is the first report of CTV and any citrus virus in Bangladesh.
Phenomenological Thermodynamics of Metastable States

Abstract The method of the entropy variation in thermodynamics, closely related to the mean-field Landau description and to the theory of Landau, is presented. it allows the formulation of a simple phenomenological approach for the metastable states, which allows one to calculate the explicit dependence of the Gibbs free energy on temperature, to calculate the heat capacity, the thermodynamic barrier, dividing metastable and more stable states, and the thermal expansion coefficient. Thermodynamic stability under mechanical loading is considered. The influence of the heating (cooling) rate on the measured dynamic heat capacity is investigated. A phase shift of the temperature oscillations of an ac heated sample is shown to be determined by the time of the relaxation of the metastable nonequilibrium state back to the metastable equilibrium one. This dependence allows one to calculate the relaxation time. A general description of the metastable phase equilibrium is proposed. Metastable states in AB 3 alloys are considered. Reasons for the change from the diffusional mechanism of the supercritical nucleus growth to the martensitic one as the heating rate increases are discussed. The Ostwald's stage rule is derived.
Large amplitude double-layers in a dusty plasma with a q-nonextensive electron velocity distribution and two-temperature isothermal ions

Arbitrary amplitude dust-acoustic (DA) double-layers in a plasma with nonextensive electrons, two-temperature thermal ions, and warm drifting dust grains are addressed. It is shown that DA double-layer structures, the onset of which depends sensitively on the plasma parameters, can exist. In particular, it may be noted that the electron nonextensivity may affect drastically the existence of these localized structures. In view of recent observation, our results should assist in the interpretation of the nonlinear double-layers observed in the downward current region of the aurora.
Gibbs Sampling for Marginal Posterior Expectations

In earlier work (Gelfand and Smith, 1990 and Gelfand et al, 1990) a sampling based approach using the Gibbs sampler was offered as a means for developing marginal posterior densities for a wide range of Bayesian problems several of which were previously inaccessible. Our purpose here is two-fold. First we flesh out the implementation of this approach for calculation of arbitrary expectations of interest. Secondly we offer comparison with perhaps the most prominent approach for calculating posterior expectations, analytic approximation involving application of the LaPlace method. Several illustrative examples are discussed as well. Clear advantages for the sampling based approach emerge.
Deep spectroscopy under high spatial resolution of the high-redshift radio source 3CR 368 - The monster elucidated

New spectroscopic measurements of 3CR 368 under unmatched spatial resolution are presented. It is found that the brightest optical component of 3CR 368 is a foreground object and is likely to be a Galactic star - possibly an M5 dwarf - rather than a foreground galaxy. Removing the foreground object decreases the 3CR 368 luminosity substantially especially at red and infrared wavelengths. The spectral energy distribution of the 3CR 368 optical counterpart is also substantially modified. It may also alter the radio properties and the previously reported optical polarization. The residual source at z = 1.13 actually shows very strong and somewhat broad emission lines associated with a flat and slightly reddened continuum. 3CR 368 therefore resembles more an AGN associated with an extended emission-line region rather than a distant galaxy dominated by stellar emission. 49 refs.