A Novelty Method for Identifying Risk Factors of Sudden Food Safety Event

Food is the basic material basis for human survival. Sudden food safety event risks mainly derive from accidental or natural food safety risks, poor food storage environments, and inefficient government regulation policies. The factor identification of sudden food safety risks is the key to controlling such risks. Therefore, the efficient and scientific identification of risk sources and types will be very important in managing sudden food safety risks. In this study, 16 sudden food safety event risk factors were identified through a literature review, and their interactive relationships were clarified using an interpretive structural model (ISM). Then, the weights of influencing factors were calculated through the analytic hierarchy process (AHP), and the combined weight of indices was determined. Results show that the 16 sudden food safety event risk factors can be divided into four levels. The quality standard for food safety (S5) and food storage (S14) is at the bottom layer of risks of sudden food safety events (the first-layer index weight is 36.899%). The judgment matrices at the four levels passed the consistency check. The influence weight of the factor "whether it contains transgenic raw materials" (S9) ranks second (the total weight is 18.151%). This index system for sudden food safety event risk factors is highly effective, with good operability for managing sudden food safety event risks. The obtained conclusions are important reference values for identifying the factors influencing food safety risk management, determining the emphasis of food safety supervision, realizing food risk prevention and control, and strengthening and guaranteeing the food safety level

Topology optimization of microstructures with perturbation analysis and penalty methods

Topology optimization at the continuum nano/microscale is of wide interest in designing and developing more efficient micro/nano electromechanical systems. This paper presents a new methodology for topology optimization of microstructures that is based on perturbation analysis and the penalty methods. The homogenized material coefficients are numerically computed based on perturbation analysis, and periodic boundary conditions are imposed by the penalty methods. The sensitivity analysis is implemented directly without the adjoint method. The extension of the proposed method to the design of components for multi-field analysis is straightforward. The capability and performance of the presented methodology are demonstrated through several numerical examples

Topology optimization of microstructures with perturbation analysis and penalty methods

Topology optimization at the continuum nano/microscale is of wide interest in designing and developing more efficient micro/nano electromechanical systems. This paper presents a new methodology for topology optimization of microstructures that is based on perturbation analysis and the penalty methods. The homogenized material coefficients are numerically computed based on perturbation analysis, and periodic boundary conditions are imposed by the penalty methods. The sensitivity analysis is implemented directly without the adjoint method. The extension of the proposed method to the design of components for multi-field analysis is straightforward. The capability and performance of the presented methodology are demonstrated through several numerical examples

Correction to: Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

In the original publication of the article, the author wanted to correct the authors group and affiliation as it was wrongly updated. The correct authors group and affiliation should be: Hongwei Guo1,2, Xiaoying Zhuang1,2, Xiaolong Fu3, Yunzheng Zhu4 and Timon Rabczuk5 1 Department of Geotechnical Engineering,Tongji University,Shanghai, 200092, P.R. China. 2 Chair of Computational Science and Simulation Technology, Leibniz Universitat Hannover, Hannover, Germany. 3 Xi’an Modern Chemistry Research Institute, Xi’an, China. 4 Department of Electrical and Computer Engineering, UCLA, 420 Westwood Plaza, Los Angeles, CA 90095, USA. 5 Institute of Structural Mechanics, Bauhaus Universität Weimar, Weimar, Germany Now, the original article has been updated

Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge–Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis

Revisiting Cancer Stem Cells as the Origin of Cancer-Associated Cells in the Tumor Microenvironment: A Hypothetical View from the Potential of iPSCs

The tumor microenvironment (TME) has an essential role in tumor initiation and development. Tumor cells are considered to actively create their microenvironment during tumorigenesis and tumor development. The TME contains multiple types of stromal cells, cancer-associated fibroblasts (CAFs), Tumor endothelial cells (TECs), tumor-associated adipocytes (TAAs), tumor-associated macrophages (TAMs) and others. These cells work together and with the extracellular matrix (ECM) and many other factors to coordinately contribute to tumor growth and maintenance. Although the types and functions of TME cells are well understood, the origin of these cells is still obscure. Many scientists have tried to demonstrate the origin of these cells. Some researchers postulated that TME cells originated from surrounding normal tissues, and others demonstrated that the origin is cancer cells. Recent evidence demonstrates that cancer stem cells (CSCs) have differentiation abilities to generate the original lineage cells for promoting tumor growth and metastasis. The differentiation of CSCs into tumor stromal cells provides a new dimension that explains tumor heterogeneity. Using induced pluripotent stem cells (iPSCs), our group postulates that CSCs could be one of the key sources of CAFs, TECs, TAAs, and TAMs as well as the descendants, which support the self-renewal potential of the cells and exhibit heterogeneity. In this review, we summarize TME components, their interactions within the TME and their insight into cancer therapy. Especially, we focus on the TME cells and their possible origin and also discuss the multi-lineage differentiation potentials of CSCs exploiting iPSCs to create a society of cells in cancer tissues including TME

Graph Out-of-Distribution Generalization with Controllable Data Augmentation

Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., training on small graphs and testing on large graphs, or training on dense graphs and testing on sparse graphs), distribution deviation is widespread. More importantly, we often observe \emph{hybrid structure distribution shift} of both scale and density, despite of one-sided biased data partition. The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets. To alleviate this problem, we propose \texttt{OOD-GMixup} to jointly manipulate the training distribution with \emph{controllable data augmentation} in metric space. Specifically, we first extract the graph rationales to eliminate the spurious correlations due to irrelevant information. Secondly, we generate virtual samples with perturbation on graph rationale representation domain to obtain potential OOD training samples. Finally, we propose OOD calibration to measure the distribution deviation of virtual samples by leveraging Extreme Value Theory, and further actively control the training distribution by emphasizing the impact of virtual OOD samples. Extensive studies on several real-world datasets on graph classification demonstrate the superiority of our proposed method over state-of-the-art baselines.Comment: Under revie

Draft Genome Sequence of the Yeast Pachysolen tannophilus CBS 4044/NRRL Y-2460

A draft genome sequence of the yeast Pachysolen tannophilus CBS 4044/NRRL Y-2460 is presented. The organism has the potential to be developed as a cell factory for biorefineries due to its ability to utilize waste feedstocks. The sequenced genome size was 12,238,196 bp, consisting of 34 scaffolds. A total of 4,463 genes from 5,346 predicted open reading frames were annotated with function

Predictive value of alpha-fetoprotein in the long-term risk of developing hepatocellular carcinoma in patients with hepatitis B virus infection--results from a clinic-based longitudinal cohort.

BACKGROUND: Although serum level of alpha-fetoprotein (AFP) has long been used to complement imaging tests in the screening and diagnosis of hepatocellular carcinoma (HCC), whether it can be used as a predictive marker of long-term risk for developing HCC in patients with hepatitis B virus (HBV) has not been extensively evaluated and thus remains controversial. METHODS: We retrospectively conducted a clinic-based longitudinal cohort study including 617 Korean American patients with HBV who had been followed for up to 22 years (median follow-up time, 6.2 years) to evaluate the association between baseline serum AFP level and the long-term risk of HCC. RESULTS: The median baseline AFP value of these patients was 3.8 ng/ml. Compared to patients with lower-than-median AFP value, those with higher-than-median baseline serum AFP had a significantly increased risk of developing HCC with a hazard ratio (HR) of 2.73 (95% confidence interval [CI] 1.25-5.99), independent of other major HCC risk factors. In addition, we calculated the cumulative incidence of HCC during different years of follow-up time by baseline serum AFP, and found that the cumulative incidence of HCC was significantly higher in HBV patients with high baseline serum AFP compared to those with low baseline serum AFP in each of the five follow-up time periods examined. CONCLUSIONS: Our results indicated that AFP was a strong independent prospective predictor of long-term HCC risk in high-risk HBV patients. More targeted prevention and early detection of HCC may be considered for these patients