Radio Sources Segmentation and Classification with Deep Learning

Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies

Sulfur-doped graphene with iron pyrite (FeS 2 ) as an efficient and stable electrocatalyst for the iodine reduction reaction in dye-sensitized solar cells

As an alternative to platinum (Pt), hybrid electrocatalysts based on sulfur-doped graphene with FeS2 microspheres (SGN-FeS2) were used as a counter electrode (CE) in dye-sensitized solar cells (DSSCs). Benefiting from the high conductivity of SGN and excellent electrocatalytic activity of the FeS2, the bifunctional hybrid electrocatalyst-based device displays a power conversion efficiency (PCE) of 8.1%, which is comparable to that (8.3%) of traditional Pt CE-based DSSC, while also exhibiting excellent stability in ambient conditions. These characteristics, in addition to its low-cost and facile preparation, make the SGN–FeS2 hybrid an ideal CE material for DSSCs

Four novel variants identified in primary hyperoxaluria and genotypic and phenotypic analysis in 21 Chinese patients

Background: Primary hyperoxaluria (PH) is a rare genetic disorder characterized by excessive accumulation of oxalate in plasma and urine, resulting in various phenotypes due to allelic and clinical heterogeneity. This study aimed to analyze the genotype of 21 Chinese patients with primary hyperoxaluria (PH) and explore their correlations between genotype and phenotype.Methods: Combined with clinical phenotypic and genetic analysis, we identified 21 PH patients from highly suspected Chinese patients. The clinical, biochemical, and genetic data of the 21 patients were subsequently reviewed.Results: We reported 21 cases of PH in China, including 12 cases of PH1, 3 cases of PH2 and 6 cases of PH3, and identified 2 novel variants (c.632T > G and c.823_824del) in AGXT gene and 2 novel variants (c.258_272del and c.866-34_866-8del) in GRHPR gene, respectively. A possible PH3 hotspot variant c.769T > G was identified for the first time. In addition, patients with PH1 showed higher levels of creatinine and lower eGFR than those with PH2 and PH3. In PH1, patients with severe variants in both alleles had significantly higher creatinine and lower eGFR than other patients. Delayed diagnosis still existed in some late-onset patients. Of all cases, 6 had reached to end-stage kidney disease (ESKD) at diagnosis with systemic oxalosis. Five patients were on dialysis and three had undergone kidney or liver transplants. Notably, four patients showed a favorable therapeutic response to vitamin B6, and c.823_824dup and c.145A > C may be identified as potentially vitamin B6-sensitive genotypes.Conclusion: In brief, our study identified 4 novel variants and extended the variant spectrum of PH in the Chinese population. The clinical phenotype was characterized by large heterogeneity, which may be determined by genotype and a variety of other factors. We first reported two variants that may be sensitive to vitamin B6 therapy in Chinese population, providing valuable references for clinical treatment. In addition, early screening and prognosis of PH should be given more attention. We propose to establish a large-scale registration system for rare genetic diseases in China and call for more attention on rare kidney genetic diseases

On the Brightening Propagation of Post-Flare Loops Observed by TRACE

Examining flare data observed by TRACE satellite from May 1998 to December 2006, we choose 190 (151 M-class and 39 X-class) flare events which display post-flare loops (PFLs), observed by 171 \AA and 195 \AA wavelengths. 124 of the 190 events exhibit flare ribbons (FRs), observed by 1600 \AA images. We investigate the propagation of the brightening of these PFLs along the neutral lines and the separation of the FRs perpendicular to the neutral lines. In most of the cases, the length of the FRs ranges from 20 Mm to 170 Mm. The propagating duration of the brightening is from 10 to 60 minutes, and from 10 minutes to 70 minutes for the separating duration of the FRs. The velocities of the propagation and the separation range from 3 km/s to 39 km/s and 3 km/s to 15 km/s, respectively. Both of the propagating velocities and the separating velocities are associated with the flare strength and the length of the FRs. It appears that the propagation and the separation are dynamically coupled, that is the greater the propagating velocity is, the faster the separation is. Furthermore, a greater propagating velocity corresponds to a greater deceleration (or acceleration). These PFLs display three types of propagating patterns. Type I propagation, which possesses about half of all the events, is that the brightening begins at the middle part of a set of PFLs, and propagates bi-directionally towards its both ends. Type II, possessing 30%, is that the brightening firstly appears at one end of a set of PFLs, then propagates to the other end. The remnant belongs to Type III propagation which displays that the initial brightening takes place at two (or more than two) positions on two (or more than two) sets of PFLs, and each brightening propagates bi-directionally along the neutral line.Comment: 13 pages, 11 figures, APJ in pres

Three-dimensional reconstruction optimization of tunnel face and intelligent extraction of discontinuity orientation based on binocular stereo vision

In the process of grading and dynamically optimizing the design and construction parameters of the surrounding rock mass of a rock tunnel face, efficiently and accurately acquiring the geometrical parameters of the rock discontinuities is an important basic task. To address the problems of time consuming, low accuracy, and high danger associated with traditional methods of obtaining the structural information of rock mass, this paper proposes a method for three-dimensional reconstruction and intelligent information extraction of tunnel face based on binocular stereo vision (BSV). First, the parallel binocular device with a single camera was improved, calibrated using the checkerboard calibration method. By integrating with the semi-global matching algorithm, the BSV based method for the three-dimensional reconstruction of the rock mass of the tunnel face was optimized. Furthermore, based on the results from on-site engineering applications, this study leveraged two parameters, point cloud density and algorithm runtime, to determine the optimal values for the disparity range and window size parameters within the semi-global stereo matching algorithm. This enhancement improved the performance of the 3D reconstruction method based on binocular stereo vision. Finally, efficient and refined intelligent methods for extracting structural parameters of the rock mass were proposed based on k-nearest neighbor search and kernel density estimation. The research results can provide reliable technical support for the intelligent and efficient acquisition of rock mass structural information in rock tunnel engineering faces

Optimized mixed Markov models for motif identification

BACKGROUND: Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. RESULTS: We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. CONCLUSION: Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance

Drag reduction in turbulent channel flow using bidirectional wavy Lorentz force

Turbulent control and drag reduction in a channel flow via a bidirectional traveling wave induced by spanwise oscillating Lorentz force have been investigated in the paper. The results based on the direct numerical simulation (DNS) indicate that the bidirectional wavy Lorentz force with appropriate control parameters can result in a regular decline of near-wall streaks and vortex structures with respect to the flow direction, leading to the effective suppression of turbulence generation and significant reduction in skin-friction drag. In addition, experiments are carried out in a water tunnel via electro-magnetic (EM) actuators designed to produce the bidirectional traveling wave excitation as described in calculations. As a result, the actual substantial drag reduction is realized successfully in these experiments

Solvability of nonlocal boundary value problem for a class of nonlinear fractional differential coupled system with impulses

Abstract This paper is considered with a class of nonlinear fractional differential coupled system with fractional differential boundary value conditions and impulses. By means of the Banach contraction principle and the Schauder fixed point theorem, some sufficient criteria are established to guarantee the existence of solutions. As applications, some interesting examples are given to illustrate the effectiveness of our main results