Effect of electric pulse rolling on plastic forming ability of AZ91D magnesium alloy

AZ91D magnesium alloy rolled under four rolling conditions, namely cold rolling, electric pulse cold rolling, hot rolling and electric pulse hot rolling, and the first principles calculation of Mg with or without external electric field carried out. The results show that: The application of pulse current in the rolling process of AZ91D magnesium alloy can effectively improve the edge crack of the sample, optimize the texture of AZ91D magnesium alloy and reduce its texture strength, promote the generation of tensile twins and the transition from small Angle grain boundaries to large Angle grain boundaries, and thus improve the plastic forming ability of AZ91D magnesium alloy. Make it more prone to plastic deformation. Compared with ordinary rolling, the microhardness of α-Mg matrix decreases by 15%. The tensile strength and elongation increased from 137MPa and 3.4% to 169MPa and 4.7%, respectively. The results show that the stiffness of Mg decreases and the Poisson's ratio of Mg increases when the electric field applies. When the B/G value is greater than 1.75, the plasticity of Mg is improved. The fault energy at the base surface of Mg does not change much, while the fault energy at the prismatic surface of Mg decreases obviously, showing the external electric field mainly affects the prismatic surface slip of Mg, which makes the prismatic surface slip easier to start, and thus improves the plastic forming ability of Mg

RT-QuIC Reactivity in the Brain and Other Organs and Tissues in Early, Middle-early, Middle-late and Terminal Stages of Scrapie Agent 263K Intracerebral Infection in Hamsters

This study was aimed at analyzing the distribution and progression of prion seeds across the central nervous system (CNS) and peripheral tissues, by evaluating the real time quaking-induced conversion (RT-QuIC) reactivity of the brain, and other organs and tissues, from hamsters in early, middle-early, middle-late and terminal stages of intracerebral infection with scrapie agent 263K. Brain, skin and other organ specimens were collected at approximately 20, 40, 60 and 80 (terminal stage) days post-inoculation, and homogenates were prepared. Protein misfolding cyclic amplification (PMCA) and RT-QuIC were used for sample detection. The 50% seeding dose (SD 50 ) values for prion seeding were calculated with the Spearman-Karber method, and RT-QuIC reactivity lag times were compared among tissues and time points. RT-QuIC indicated positive reactions in brain tissue samples across all time points (20–80 dpi), and increasing seeding capacity and decreasing lag times were observed during the incubation period. In peripheral tissues, including the heart, liver, spleen, lung, colon and skin, RT-QuIC positivity showed delayed onset, appearing at later stages (≥40 dpi) than observed in the brain. The brain exhibited significantly higher SD 50 values than peripheral tissues, thus reflecting greater seeding capacity. Prion seeds were widely distributed in the CNS and peripheral tissues at the terminal stage, and the highest RT-QuIC positivity and SD 50 values were observed in the brain. Prions were widely distributed in the CNS and peripheral tissues of scrapie-infected hamsters, and CNS tissues exhibited a particularly high level of reactivity and seeding capacity in the RT-QuIC assay

Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy

Background and purposeFutile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.MethodsConsecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature.ResultsA total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance.ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Deep Learning Artificial Neural Network for Pricing Multi-Asset European Options

This paper studies a p-layers deep learning artificial neural network (DLANN) for European multi-asset options. Firstly, a p-layers DLANN is constructed with undetermined weights and bias. Secondly, according to the terminal values of the partial differential equation (PDE) and the points that satisfy the PDE of multi-asset options, some discrete data are fed into the p-layers DLANN. Thirdly, using the least square error as the objective function, the weights and bias of the DLANN are trained well. In order to optimize the objective function, the partial derivatives for the weights and bias of DLANN are carefully derived. Moreover, to improve the computational efficiency, a time-segment DLANN is proposed. Numerical examples are presented to confirm the accuracy, efficiency, and stability of the proposed p-layers DLANN. Computational examples show that the DLANN’s relative error is less than 0.5% for different numbers of assets d=1,2,3,4. In the future, the p-layers DLANN can be extended into American options, Asian options, Lookback options, and so on