Intelligent calibration of static FEA computations based on terrestrial laser scanning reference

The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

A Review on Nanocomposites. Part 1: Mechanical Properties

Micromachining of nanocomposites is deemed to be a complicated process due to the anisotropic, heterogeneous structure, and advanced mechanical properties of these materials associated with the size effects in micromachining. It leads to poorer machinability in terms of high cutting force, low surface quality, and high rate of tool wear. In part 1 of this two-part review paper, a comprehensive review on mechanical properties of various nanocomposites will be presented while the second part of the paper will focus on the micro-machinability of these nanocomposite materials

A Review on Nanocomposites. Part 2: Micromachining

Micromachining of nanocomposites is deemed to be a complicated process due to the anisotropic, heterogeneous structure and advanced mechanical properties of these materials associated with the size effects in micromachining. It leads to poorer machinability in terms of high cutting force, low surface quality, and high rate of tool wear. A comprehensive review on mechanical properties of nanocomposites aiming to pointout their effects on micro-machinability has been addressed in part 1. In part 2, the subsequent micro-machining processes are critically discussed based on relevant studies from both experimental and modeling approaches. The main findings and limitations of these micro-machining methods in processing nanocomposites have been highlighted together with future prospects

Multi-head attention-based masked sequence model for mapping functional brain networks

The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings. Furthermore, they overlook prior knowledge, such as task designs, during training. This study aims to overcome these limitations and develop a more efficient model by drawing inspiration from techniques in the field of natural language processing (NLP). The proposed model, called the Multi-head Attention-based Masked Sequence Model (MAMSM), uses a multi-headed attention mechanism and mask training approach to learn different states corresponding to the same voxel values. Additionally, it combines cosine similarity and task design curves to construct a novel loss function. The MAMSM was applied to seven task state datasets from the Human Connectome Project (HCP) tfMRI dataset. Experimental results showed that the features acquired by the MAMSM model exhibit a Pearson correlation coefficient with the task design curves above 0.95 on average. Moreover, the model can extract more meaningful networks beyond the known task-related brain networks. The experimental results demonstrated that MAMSM has great potential in advancing the understanding of functional brain networks

Sulforaphane Inhibits Foam Cell Formation and Atherosclerosis via Mechanisms Involving the Modulation of Macrophage Cholesterol Transport and the Related Phenotype

Sulforaphane (SFN), an isothiocyanate, is one of the major dietary phytochemicals found in cruciferous vegetables. Many studies suggest that SFN can protect against cancer and cardiometabolic diseases. Despite the proposed systemic and local vascular protective mecha-nisms, SFN’s potential to inhibit atherogenesis by targeting macrophages remains unknown. In this study, in high-fat-diet-fed ApoE-deficient (ApoE-/-) mice, oral SFN treatment improved dyslipidemia and inhibited atherosclerotic plaque formation and the unstable phenotype, as demonstrated by reductions in the lesion areas in both the aortic sinus and whole aorta, per-centages of necrotic cores, vascular macrophage infiltration and reactive oxygen species (ROS) generation. In THP-1-derived macrophages, SFN pre-administration alleviated oxidized low-density lipoprotein (ox-LDL)-induced lipid accumulation, oxidative stress and mitochondrial injury. Moreover, a functional study revealed that peritoneal macrophages isolated from SFN-treated mice exhibited attenuated cholesterol influx and enhanced apolipoprotein A-I (apoA-I)- and high-density lipoprotein (HDL)-mediated cholesterol efflux. Mechanistic analysis revealed that SFN supplementation induced both intralesional and intraperitoneal macrophage phenotypic switching toward high expression of nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1) and ATP binding cassette subfamily A/G member 1 (ABCA1/G1) and low expression of peroxisome proliferator-activated receptor γ (PPARγ) and cluster of differen-tiation 36 (CD36), which was further validated by the aortic protein expression. These results suggest that the regulation of macrophages cholesterol transport and accumulation may be mainly responsible for SFN's potential atheroprotective properties, and the regulatory mecha-nisms might involve upregulating ABCA1/G1 and downregulating CD36 via the modulation of PPARγ and Nrf2

Novel GLP-1 Analog Supaglutide Reduces HFD-Induced Obesity Associated with Increased Ucp-1 in White Adipose Tissue in Mice

GLP-1, an important incretin hormone plays an important role in the regulation of glucose homeostasis. However, the therapeutic use of native GLP-1 is limited due to its short half-life. We recently developed a novel GLP-1 mimetics (supaglutide) by genetically engineering recombinant fusion protein production techniques. We demonstrated that this formulation possessed long-lasting GLP-1 actions and was effective in glycemic control in both type 1 and type 2 diabetes rodent models. Here, we investigated the effects of supaglutide in regulating energy homeostasis in obese mice. Mice were fed with high-fat diet (HFD) for 6 months to induce obesity and then subjected to supaglutide treatment (300 μg/kg, bi-weekly for 4 weeks), and placebo as control. Metabolic conditions were monitored and energy expenditure was assessed by indirect calorimetry (CLAMS). Cold tolerance test was performed to evaluate brown-adipose tissue (BAT) activities in response to cold challenge. Glucose tolerance and insulin resistance were evaluated by intraperitoneal glucose tolerance test and insulin tolerance tests. Liver and adipose tissues were collected for histology analysis. Expression of uncoupling protein 1(Ucp1) in adipose tissues was evaluated by Western blotting. We found that supaglutide treatment reduced body weight, which was associated with reduced food intake. Compared to the placebo control, supaglutide treatment improved lipid profile, i.e., significantly decreased circulating total cholesterol levels, declined serum triglyceride, and free fatty acid levels. Importantly, the intervention significantly reduced fatty liver, decreased liver triglyceride content, and concomitantly ameliorated liver injury exemplified by declined hepatic alanine aminotransferase (ALT) and aspartic transaminase (AST) content. Remarkably, supaglutide reduced hepatic lipid accumulation and altered morphometry in favor of small adipocytes in fat. This is consistent with the observation that supaglutide increased tolerance of the mice to cold environment associated with up-regulation of Ucp1 in the inguinal fat. Furthermore, supaglutide improved glucose tolerance, and insulin sensitivity in the obese mice suggesting improved glucose and energy homeostasis. Our findings suggest that supaglutide exerts beneficial effect on established obesity through reducing energy intake and is associated with brown remodeling of white adipose tissue

Liver fibrosis and MAFLD: the exploration of multi-drug combination therapy strategies

In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies

White Box Sampling in Uncertain Data Processing Enabled by Program Analysis

Sampling is a very important and low-cost approach to uncertain data processing, in which output variations caused by input errors are sampled. Traditional methods tend to treat a program as a blackbox. In this paper, we show that through program analysis, we can expose the internals of sample executions so that the process can become more selective and focused. In particular, we develop a sampling runtime that can selectively sample in input error bounds to expose discontinuity in output functions. It identifies all the program factors that can potentially lead to discontinuity and hash the values of such factors during execution in a cost-effective way. The hash values are used to guide the sampling process. Our results show that the technique is very effective for real-world programs. It can achieve the precision of a high sampling rate with the cost of a lower sampling rate