Computer aided diagnosis of cerebrovascular disease based on DSA image

In recent years, the incidence of cerebrovascular diseases in China has shown a significant upward trend, and it has become a common disease threatening people's lives. Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of clinical cerebrovascular disease, and it is the most direct method to check the brain lesion. At present, there are the following two problems in the clinical research of DSA images: DSA is a real-time image with numerous frames, containing much useless information in frames; thus, human interpretation and annotation are time-consuming and labor-intensive. The blood vessel structure in DSA images is so complicated that high practical skills are required for clinicians. In the computer-aided diagnosis of DSA sequence images, there is currently a lack of automatic and effective computer-aided diagnosis algorithms for cerebrovascular diseases. Based on the above issues, the main work of this paper is as follows: 1.A multi-target detection algorithm based on Faster-RCNN is designed and applied to the analysis of brain DSA images. The algorithm divides DSA images into arterial phase, capillary phase, pre-venous phase and sinus phase by identifying the main blood vessel structure in each frame. And on this basis, we analyze the time relationship between the time phases. 2.On the basis of DSA phase detection, a key frame location algorithm based on single blood vessel structure detection is designed for moyamoya disease. First, the target detection model is applied to locate the internal carotid artery and the Willis circle. Then, five frames of images are extracted from the arterial period as keyframes. Finally, the nidus' ROI is determined according to the position of the internal carotid artery. 3.A diagnostic method for cerebral arteriovenous malformation (AVM) is designed, which combines temporal features and radiomics features. First, on the basis of DSA time phase detection, we propose a deep learning network to extract vascular time features from the DSA video; then, the time feature is combined with the radiomics features of the static keyframe to establish an AVM diagnosis model. While assisting diagnosis, this method does not require any human intervention, and reduces the workload of clinicians. The diagnostic model that combines time features and radiomics features is applied to the study of AVM staging. The experimental results prove that the classification model trained by fusion features has better diagnostic performance than the model trained by either time features or radiomics features. Based on the above three parts, this paper establishes a cerebrovascular disease analysis framework based on radiomics method and deep learning. We introduce corresponding solutions for DSA automatic image reading, rapid diagnosis of moyamoya disease, and precise diagnosis of AVM. The method proposed in this paper has practical significance for assisting the diagnosis of cerebrovascular disease and reducing the burden of medical staff.Digital Subtraction Angiography(DSA), Radiomics analysis, Arteriovenous malformations, Moyamoya, Faster-RCNN, Temporal features, Fusion feature

Asymptotic-Preserving Neural Networks for Multiscale Kinetic Equations

In this paper, we present two novel Asymptotic-Preserving Neural Networks (APNNs) for tackling multiscale time-dependent kinetic problems, encompassing the linear transport equation and Bhatnagar-Gross-Krook (BGK) equation with diffusive scaling. Our primary objective is to devise efficient and accurate APNN approaches for resolving multiscale kinetic equations. We have established a neural network based on even-odd decomposition and concluded that enforcing the initial condition for the linear transport equation with inflow boundary conditions is crucial. This APNN method based on even-odd parity relaxes the stringent conservation prerequisites while concurrently introducing an auxiliary deep neural network. Additionally, we have incorporated the conservation laws of mass, momentum, and energy for the Boltzmann-BGK equation into the APNN framework by enforcing exact boundary conditions. This is our second contribution. The most notable finding of this study is that approximating the zeroth, first and second moments of the particle density distribution is simpler than the distribution itself. Furthermore, a compelling phenomenon in the training process is that the convergence of density is swifter than that of momentum and energy. Finally, we investigate several benchmark problems to demonstrate the efficacy of our proposed APNN methods

Asymptotic-Preserving Convolutional DeepONets Capture the Diffusive Behavior of the Multiscale Linear Transport Equations

In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transport problem. We observe that the vanilla physics-informed DeepONets with modified MLP may exhibit instability in maintaining the desired limiting macroscopic behavior. Therefore, this necessitates the utilization of an asymptotic-preserving loss function. Drawing inspiration from the heat kernel in the diffusion equation, we propose a new architecture called Convolutional Deep Operator Networks, which employ multiple local convolution operations instead of a global heat kernel, along with pooling and activation operations in each filter layer. Our APCON methods possess a parameter count that is independent of the grid size and are capable of capturing the diffusive behavior of the linear transport problem. Finally, we validate the effectiveness of our methods through several numerical examples

Parallel finite volume simulation of the spherical shell dynamo with pseudo-vacuum magnetic boundary conditions

In this paper, we study the parallel simulation of the magnetohydrodynamic (MHD) dynamo in a rapidly rotating spherical shell with pseudo-vacuum magnetic boundary conditions. A second-order finite volume scheme based on a collocated quasi-uniform cubed-sphere grid is applied to the spatial discretization of the MHD dynamo equations. To ensure the solenoidal condition of the magnetic field, we adopt a widely-used approach whereby a pseudo-pressure is introduced into the induction equation. The temporal integration is split by a second-order approximate factorization approach, resulting in two linear algebraic systems both solved by a preconditioned Krylov subspace iterative method. A multi-level restricted additive Schwarz preconditioner based on domain decomposition and multigrid method is then designed to improve the efficiency and scalability. Accurate numerical solutions of two benchmark cases are obtained with our code, comparable to the existing local method results. Several large-scale tests performed on the Sunway TaihuLight supercomputer show good strong and weak scalabilities and a noticeable improvement from the multi-level preconditioner with up to 10368 processor cores

Thiol-based chemical probes exhibit antiviral activity against SARS-CoV-2 via allosteric disulfide disruption in the spike glycoprotein

The development of small-molecules targeting different components of SARS-CoV-2 is a key strategy to complement antibody-based treatments and vaccination campaigns in managing the COVID-19 pandemic. Here, we show that two thiol-based chemical probes that act as reducing agents, P2119 and P2165, inhibit infection by human coronaviruses, including SARS-CoV-2, and decrease the binding of spike glycoprotein to its receptor, the angiotensin-converting enzyme 2 (ACE2). Proteomics and reactive cysteine profiling link the antiviral activity to the reduction of key disulfides, specifically by disruption of the Cys379–Cys432 and Cys391–Cys525 pairs distal to the receptor binding motif in the receptor binding domain (RBD) of the spike glycoprotein. Computational analyses provide insight into conformation changes that occur when these disulfides break or form, consistent with an allosteric role, and indicate that P2119/P2165 target a conserved hydrophobic binding pocket in the RBD with the benzyl thiol-reducing moiety pointed directly toward Cys432. These collective findings establish the vulnerability of human coronaviruses to thiol-based chemical probes and lay the groundwork for developing compounds of this class, as a strategy to inhibit the SARS-CoV-2 infection by shifting the spike glycoprotein redox scaffold

A review of population-based prevalence studies of physical activity in adults in the Asia-Pacific region

Background: Physical activity (PA) surveillance is an important component of non-communicable disease risk factor monitoring, and occurs through national and international surveillance systems. This review identifies population PA estimates for adults in the Asia-Pacific region, and examines variation in trends and prevalence rates obtained using different PA measures.Methods: Data were obtained from a MEDLINE search; World Health Organization&rsquo;s Global Health Infobase; Government websites and reference lists of relevant papers. Inclusion criteria included: national studies or those reporting large scale population-level data; data published from 2000 to 2010 and trend data prior; sample sizes over n = 1000, or fewer subjects in small nations.Results: In total, 56 population surveys from 29 Asia-Pacific countries were identified. Data on &lsquo;sufficient physical activity&rsquo; amongst adults were available from 45 studies (80%), with estimates ranging from 7% to 93% (median 62%, inter-quartile range 40%-85%). For 14 countries, estimates of &lsquo;sufficient activity&rsquo; were documented in multiple surveys using different methods, with the largest variation from 18% to 92% in Nepal. Median or mean METminutes/ day, reported in 20 studies, ranged from 6 to 1356. Serial trend data were available for 11 countries (22%), for periods spanning 2-10 years. Of these, five countries demonstrated increases in physical activity over time, four demonstrated decreases and three showed no changes.Conclusions: Many countries in the Asia-Pacific region collect population-level PA data. This review highlights differences in estimates within and between countries. Some differences may be real, others due to variation in the PA questions asked and survey methods used. Use of standardized protocols and measures, and combined reporting of data are essential goals of improved international PA surveillance.<br /

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Analysis of the relationship between oipA, babA2, babB genotypes and helicobacter pylori-related gastric precancerous lesions

Objective: To investigate the distribution of helicobacter pylori-related genotypes of oipA, babA2, and babB in patients with gastrointestinal diseases. Method: The retrospective study was conducted at the Jiamusi College, Heilongjiang University of Traditional Chinese Medicine, Harbin, China, and comprised data from February 2017 to May 2020 of patients of either gender 20-80 years who underwent gastroscopy. An instrument based on polymerase chain reaction was used to amplify oipA, babA2 and babB genes, and their distribution in terms of gender, age and pathological types was analysed. Results: Among the 116 patients, 52(44.8%) had oipA genotype, 48(41.2%) babA2, and 72 (62.1%) babB, respectively, and the size of amplified products of 486bp, 219bp and 362bp, respectively. The infection rate of oipA and babB genotypes was highest [26(50.0%) and 31(43.1%)]in those aged 61-80 years, and lowest [9(17.3%) and 15(20.8%)]in those aged 20-40 years. The infection rate of babA2 genotype was highest [23(47.9%)] in those aged 41-60 years, and lowest [12(25.0%)] in those aged 61-80 years. Male patients were under a higher [28(53.9%) and 26(54.2%)] rate of infection with oipA and babA2, and female patients has a higher [40(55.6%)] rate of infection with babB. Among Hp-infected patients with digestive diseases, babB genotype was mainly found in patients with chronic superficial gastritis[17(58.6%)], duodenal ulcer[17(85.0%)], chronic atrophic gastritis[19(59.4%)] and gastric ulcer[16(72.7%)], while oipA genotype was mainly found in patients with gastric cancer[8(61.5%)]. Conclusion: Chronic superficial gastritis, duodenal ulcer, chronic atrophic gastritis, and gastric ulcer may have a close bearing on babB genotype infection, while oipA genotype infection may be associated with gastric cancer. Key Words: Helicobacter pylori, oipA, babA2, babB, Chronic superficial gastritis, Chronic atrophic gastritis, Duodenal ulcer, Gastric ulcer, Gastric cancer