Burst phase distribution of SGR J1935+2154 based on Insight-HXMT

On April 27, 2020, the soft gamma ray repeater SGR J1935+2154 entered its intense outburst episode again. Insight-HXMT carried out about one month observation of the source. A total number of 75 bursts were detected during this activity episode by Insight-HXMT, and persistent emission data were also accumulated. We report on the spin period search result and the phase distribution of burst start times and burst photon arrival times of the Insight-HXMT high energy detectors and Fermi Gamma-ray Burst Monitor (GBM). We find that the distribution of burst start times is uniform within its spin phase for both Insight-HXMT and Fermi-GBM observations, whereas the phase distribution of burst photons is related to the type of a burst's energy spectrum. The bursts with the same spectrum have different distribution characteristics in the initial and decay episodes for the activity of magnetar SGR J1935+2154.Comment: 12 pages, 9 figure

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component

Associations between computed tomography markers of cerebral small vessel disease and hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke patients

BackgroundHemorrhagic transformation (HT) is common among acute ischemic stroke patients after treatment with intravenous thrombolysis (IVT). We analyzed potential relationships between markers of cerebral small vessel disease (CSVD) and HT in patients after IVT.MethodsThis study retrospectively analyzed computed tomography (CT) data for acute ischemic stroke patients before and after treatment with recombinant tissue plasminogen activator at a large Chinese hospital between July 2014 and June 2021. Total CSVD score were summed by individual CSVD markers including leukoaraiosis, brain atrophy and lacune. Binary regression analysis was used to explore whether CSVD markers were related to HT as the primary outcome or to symptomatic intracranial hemorrhage (sICH) as a secondary outcome.ResultsA total of 397 AIS patients treated with IVT were screened for inclusion in this study. Patients with missing laboratory data (n = 37) and patients treated with endovascular therapy (n = 42) were excluded. Of the 318 patients included, 54 (17.0%) developed HT within 24–36 h of IVT, and 14 (4.3%) developed sICH. HT risk was independently associated with severe brain atrophy (OR 3.14, 95%CI 1.43–6.92, P = 0.004) and severe leukoaraiosis (OR 2.41, 95%CI 1.05–5.50, P = 0.036), but not to severe lacune level (OR 0.58, 95%CI 0.23–1.45, P = 0.250). Patients with a total CSVD burden ≥1 were at higher risk of HT (OR 2.87, 95%CI 1.38–5.94, P = 0.005). However, occurrence of sICH was not predicted by CSVD markers or total CSVD burden.ConclusionIn patients with acute ischemic stroke, severe leukoaraiosis, brain atrophy and total CSVD burden may be risk factors for HT after IVT. These findings may help improve efforts to mitigate or even prevent HT in vulnerable patients

White Matter Injury After Intracerebral Hemorrhage

Spontaneous intracerebral hemorrhage (ICH) accounts for 15% of all stroke cases. ICH is a devastating form of stroke associated with high morbidity, mortality, and disability. Preclinical studies have explored the mechanisms of neuronal death and gray matter damage after ICH. However, few studies have examined the development of white matter injury (WMI) following ICH. Research on WMI indicates that its pathophysiological presentation involves axonal damage, demyelination, and mature oligodendrocyte loss. However, the detailed relationship and mechanism between WMI and ICH remain unclear. Studies of other acute brain insults have indicated that WMI is strongly correlated with cognitive deficits, neurological deficits, and depression. The degree of WMI determines the short- and long-term prognosis of patients with ICH. This review demonstrates the structure and functions of the white matter in the healthy brain and discusses the pathophysiological mechanism of WMI following ICH. Our review reveals that the development of WMI after ICH is complex; therefore, comprehensive treatment is essential. Understanding the relationship between WMI and other brain cells may reveal therapeutic targets for the treatment of ICH

distinguishing between automatic and manual aspects of model driven development

IEEE Comp Soc, Univ Potsdam, Hasso Plattner Inst Software Syst EngnManual portion and automatable aspects are often not explicitly differentiated and defined in most model driven software development (AdDSD). This may hinder the advancement of the automation level of AMSD with problems such as defining the b

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions

Trans-Pacific transport of dust aerosols from East Asia: Insights gained from multiple observations and modeling

East Asia is one of the world's largest sources of dust and anthropogenic pollution. Dust particles originating from East Asia have been recognized to travel across the Pacific to North America and beyond, thereby affecting the radiation incident on the surface as well as clouds aloft in the atmosphere. In this study, integrated analyses are performed focusing on one trans-Pacific dust episode during 12–22 March 2015, based on space-borne, ground-based observations, reanalysis data combined with Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), and the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). From the perspective of synoptic patterns, the location and strength of Aleutian low pressure system largely determined the eastward transport of dust plumes towards western North America. Multi-sensor satellite observations reveal that dust aerosols in this episode originated from the Taklimakan and Gobi Deserts. Moreover, the satellite observations suggest that the dust particles can be transformed to polluted particles over the East Asian regions after encountering high concentration of anthropogenic pollutants. In terms of the vertical distribution of polluted dust particles, at the very beginning, they were mainly located in the altitudes ranging from 1 km to 7 km over the source region, then ascended to 2 km–9 km over the Pacific Ocean. The simulations confirm that these elevated dust particles in the lower free troposphere were largely transported along the prevailing westerly jet stream. Overall, observations and modeling demonstrate how a typical springtime dust episode develops and how the dust particles travel over the North Pacific Ocean all the way to North America

A New Cell Line Derived from the Spleen of the Japanese Flounder (Paralichthys olivaceus) and Its Application in Viral Study

A new cell line Japanese flounder spleen (JFSP) derived from the spleen of Japanese flounder (Paralichthys olivaceus) was established and characterized in this study. The JFSP cells grew rapidly at 29 °C, and the optimum fetal bovine serum concentration in the L-15 medium was 15%. Cells were subcultured for more than 80 passages. The JFSP cells have a diploid chromosome number of 2n = 68, which differs from the chromosome number of normal diploid Japanese flounder. The established cells were susceptible to Bohle virus (BIV), Viral hemorrhagic septicemia virus (VHSV), Hirame rhabdovirus (HIRRV), Infectious hematopoietic necrosis virus (IHNV), and Lymphocystis disease virus (LCDV), as evidenced by varying degrees of cytopathic effects (CPE). Replication of the virus in JFSP cells was confirmed by qRT-PCR and transmission electron microscopy. In addition, the expression of four immune-related genes, TRAF3, IL-1β, TNF-α, and TLR2, was differentially altered following viral infection. The results indicated that the cells underwent an antiviral immune response. JFSP cell line is an ideal tool in vitro for virology. The use of fish cell lines to study the immune genes and immune mechanism of fish and to clarify the immune mechanism of fish has important theoretical significance and practical application value for the fundamental prevention and treatment of fish diseases

Automatic literature screening using the PAJO deep-learning model for clinical practice guidelines

Abstract Background Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images. Currently, there are no effective/efficient AI-based systems for screening literature. Therefore, developing an effective method for automatic literature screening can provide significant advantages. Methods Using advanced AI techniques, we propose the Paper title, Abstract, and Journal (PAJO) model, which treats article screening as a classification problem. For training, articles appearing in the current CPGs are treated as positive samples. The others are treated as negative samples. Then, the features of the texts (e.g., titles and abstracts) and journal characteristics are fully utilized by the PAJO model using the pretrained bidirectional-encoder-representations-from-transformers (BERT) model. The resulting text and journal encoders, along with the attention mechanism, are integrated in the PAJO model to complete the task. Results We collected 89,940 articles from PubMed to construct a dataset related to neck pain. Extensive experiments show that the PAJO model surpasses the state-of-the-art baseline by 1.91% (F1 score) and 2.25% (area under the receiver operating characteristic curve). Its prediction performance was also evaluated with respect to subject-matter experts, proving that PAJO can successfully screen high-quality articles. Conclusions The PAJO model provides an effective solution for automatic literature screening. It can screen high-quality articles on neck pain and significantly improve the efficiency of CPG development. The methodology of PAJO can also be easily extended to other diseases for literature screening

Activation of Nurr1 with Amodiaquine Protected Neuron and Alleviated Neuroinflammation after Subarachnoid Hemorrhage in Rats

Background. Nurr1, a member of the nuclear receptor 4A family (NR4A), played a role in neuron protection, anti-inflammation, and antioxidative stress in multidiseases. We explored the role of Nurr1 on subarachnoid hemorrhage (SAH) progression and investigated the feasibility of its agonist (amodiaquine, AQ) as a treatment for SAH. Methods. SAH rat models were constructed by the endovascular perforation technique. AQ was administered intraperitoneally at 2 hours after SAH induction. SAH grade, mortality, weight loss, neurological performance tests, brain water content, western blot, immunofluorescence, Nissl staining, and qPCR were assessed post-SAH. In vitro, hemin was introduced into HT22 cells to develop a model of SAH. Results. Stimulation of Nurr1 with AQ improved the outcomes and attenuated brain edema. Nurr1 was mainly expressed in neuron, and administration of AQ alleviated neuron injury in vivo and enhanced the neuron viability and inhibited neuron apoptosis and necrosis in vitro. Besides, AQ reduced the amount of IL-1β+Iba-1+ cells and inhibited the mRNA level of proinflammatory cytokines (IL-1β and TNF-α) and the M1-like phenotype markers (CD68 and CD86). AQ inhibited the expression of MMP9 in HT22 cells. Furthermore, AQ reduced the expression of nuclear NF-κB and Nurr1 while increased cytoplasmic Nurr1 in vivo and in vitro. Conclusion. Pharmacological activation of Nurr1 with AQ alleviated the neuron injury and neuroinflammation. The mechanism of antineuroinflammation may be associated with the Nurr1/NF-κB/MMP9 pathway in the neuron. The data supported that AQ might be a promising treatment strategy for SAH