A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the comprehensive information of them benefits more robust perceptions. In this paper, we present a unified multi-modal LiDAR segmentation network, termed UniSeg, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously. Specifically, we first design the Learnable cross-Modal Association (LMA) module to automatically fuse voxel-view and range-view features with image features, which fully utilize the rich semantic information of images and are robust to calibration errors. Then, the enhanced voxel-view and range-view features are transformed to the point space,where three views of point cloud features are further fused adaptively by the Learnable cross-View Association module (LVA). Notably, UniSeg achieves promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes, and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks, including the LiDAR semantic segmentation challenge of nuScenes and panoptic segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase. It contains most of the popular outdoor LiDAR segmentation algorithms and provides reproducible implementations. The OpenPCSeg codebase will be made publicly available at https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at https://github.com/PJLab-ADG/PCSe

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE

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Measurements of D0-$\bar {D}^0$ mixing and CP violation in D0 decays are interesting as any difference with respect to the Standard Model prediction would be a signature of new physics. We report on recent searches and measurements of D0-$\bar {D}^0$ mixing and CP violation in charm meson decays at BaBar and Belle experiments. In particular, the D0-$\bar {D}^0$ mixing parameters in D0 → π+ π − π0 are measured by the BaBar experiment for the first time with x = (1.5±1.2±0.6)% and y = (0.2±0.9±0.5)%. The first T-odd asymmetry measurement in D0 → K0Sπ + π − π0 is performed at the Belle experiment with aCP = (−0.28 ±1.38+0.23-0.76)×10−3, which is consistent with no CP violation. The measurements of CP asymmetry in D0 → KS0K0Swith ACP = (−2.9±5.2±2.2)% and D+ → π+π0 with ACP = (+2.31 ± 1.24 ± 0.23)% are also reported, which shows no CP violation

D0-D¯0Dˉ0 \bar {D}^0 mixing and CPV measurements at the B-factories

Measurements of D0-D¯0$\bar {D}^0$ mixing and CP violation in D0 decays are interesting as any difference with respect to the Standard Model prediction would be a signature of new physics. We report on recent searches and measurements of D0-D¯0$\bar {D}^0$ mixing and CP violation in charm meson decays at BaBar and Belle experiments. In particular, the D0-D¯0$\bar {D}^0$ mixing parameters in D0 → π+ π − π0 are measured by the BaBar experiment for the first time with x = (1.5±1.2±0.6)% and y = (0.2±0.9±0.5)%. The first T-odd asymmetry measurement in D0 → K0Sπ + π − π0 is performed at the Belle experiment with aCP = (−0.28 ±1.38+0.23-0.76)×10−3, which is consistent with no CP violation. The measurements of CP asymmetry in D0 → KS0K0Swith ACP = (−2.9±5.2±2.2)% and D+ → π+π0 with ACP = (+2.31 ± 1.24 ± 0.23)% are also reported, which shows no CP violation

Dual Opposing Roles of Metallothionein Overexpression in C57BL/6J Mouse Pancreatic β-Cells

<div><p>Background</p><p>Growing evidence indicates that oxidative stress (OS), a persistent state of excess amounts of reactive oxygen species (ROS) along with reactive nitrogen species (RNS), plays an important role in insulin resistance, diabetic complications, and dysfunction of pancreatic β-cells. Pancreatic β-cells contain exceptionally low levels of antioxidant enzymes, rendering them susceptible to ROS-induced damage. Induction of antioxidants has been proposed to be a way for protecting β-cells against oxidative stress. Compared to other antioxidants that act against particular β-cell damages, metallothionein (MT) is the most effective in protecting β-cells from several oxidative stressors including nitric oxide, peroxynitrite, hydrogen peroxide, superoxide and streptozotocin (STZ). We hypothesized that MT overexpression in pancreatic β-cells would preserve β-cell function in C57BL/6J mice, an animal model susceptible to high fat diet-induced obesity and type 2 diabetes.</p><p>Research Design and Methods</p><p>The pancreatic β-cell specific MT overexpression was transferred to C57BL/6J background by backcrossing. We studied transgenic MT (MT-tg) mice and wild-type (WT) littermates at 8 weeks and 18 weeks of age. Several tests were performed to evaluate the function of islets, including STZ <i>in vivo</i> treatment, intraperitoneal glucose tolerance tests (IPGTT) and plasma insulin levels during IPGTT, pancreatic and islet insulin content measurement, insulin secretion, and islet morphology assessment. Gene expression in islets was performed by quantitative real-time PCR and PCR array analysis. Protein levels in pancreatic sections were evaluated by using immunohistochemistry.</p><p>Results</p><p>The transgenic MT protein was highly expressed in pancreatic islets. MT-tg overexpression significantly protected mice from acute STZ-induced ROS at 8 weeks of age; unexpectedly, however, MT-tg impaired glucose stimulated insulin secretion (GSIS) and promoted the development of diabetes. Pancreatic β-cell function was significantly impaired, and islet morphology was also abnormal in MT-tg mice, and more severe damage was detected in males. The unique gene expression pattern and abnormal protein levels were observed in MT-tg islets.</p><p>Conclusions</p><p>MT overexpression protected β-cells from acute STZ-induced ROS damages at young age, whereas it impaired GSIS and promoted the development of diabetes in adult C57BL/6J mice, and more severe damage was found in males.</p></div

Characterization and phylogenetic analysis of the complete mitochondrial genome in Xiaoxiang chicken (Gallus gallus domesticus)

Xiaoxiang chicken (Gallus gallus domesticus) is one of the native breeds in the Southeastern of Guizhou province, China. The complete mitochondrial genome sequence of Xiaoxiang chicken (small-sized breed chicken) was obtained for the first time. The mitogenome is 16,784 bp in length, and it contained a D-loop region, two rRNA genes, 13 protein-coding genes, and 22 tRNA genes. A neighbour-joining phylogenetic tree was structured based on the D-loop, which indicated that the Red junglefowl was the direct ancestor of Xiaoxiang chicken, and both were closed to the Silky chicken and Dongan black chicken

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized