On the design of a cost-efficient resource management framework for low latency applications

The ability to offer low latency communications is one of the critical design requirements for the upcoming 5G era. The current practice for achieving low latency is to overprovision network resources (e.g., bandwidth and computing resources). However, this approach is not cost-efficient, and cannot be applied in large-scale. To solve this, more cost-efficient resource management is required to dynamically and efficiently exploit network resources to guarantee low latencies. The advent of network virtualization provides novel opportunities in achieving cost-efficient low latency communications. It decouples network resources from physical machines through virtualization, and groups resources in the form of virtual machines (VMs). By doing so, network resources can be flexibly increased at any network locations through VM auto-scaling to alleviate network delays due to lack of resources. At the same time, the operational cost can be largely reduced by shutting down low-utilized VMs (e.g., energy saving). Also, network virtualization enables the emerging concept of mobile edge-computing, whereby VMs can be utilized to host low latency applications at the network edge to shorten communication latency. Despite these advantages provided by virtualization, a key challenge is the optimal resource management of different physical and virtual resources for low latency communications. This thesis addresses the challenge by deploying a novel cost-efficient resource management framework that aims to solve the cost-efficient design of 1) low latency communication infrastructures; 2) dynamic resource management for low latency applications; and 3) fault-tolerant resource management. Compared to the current practices, the proposed framework achieves 80% of deployment cost reduction for the design of low latency communication infrastructures; continuously saves up to 33% of operational cost through dynamic resource management while always achieving low latencies; and succeeds in providing fault tolerance to low latency communications with a guaranteed operational cost

Targeted therapy of RET fusion-positive non-small cell lung cancer

Lung cancer has very high morbidity and mortality worldwide, and the prognosis is not optimistic. Previous treatments for non-small cell lung cancer (NSCLC) have limited efficacy, and targeted drugs for some gene mutations have been used in NSCLC with considerable efficacy. The RET proto-oncogene is located on the long arm of chromosome 10 with a length of 60,000 bp, and the expression of RET gene affects cell survival, proliferation, growth and differentiation. This review will describe the basic characteristics and common fusion methods of RET genes; analyze the advantages and disadvantages of different RET fusion detection methods; summarize and discuss the recent application of non-selective and selective RET fusion-positive inhibitors, such as Vandetanib, Selpercatinib, Pralsetinib and Alectinib; discuss the mechanism and coping strategies of resistance to RET fusion-positive inhibitors

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

BackgroundDistant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.MethodThe study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.ResultThe study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.ConclusionThe study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validatio

Floor Field Model Based on Cellular Automata for Simulating Indoor Pedestrian Evacuation

A new static floor field method for simulations of evacuation processes based on cellular automaton was presented in this paper. This model applies an inertia static floor field approach to describe the interaction between the pedestrians and the cell. Here we study a rather simple situation and a complex scenario. We simulate and reproduce Seyfried’s field experiments at the Research Centre Jülich and use its empirical data to validate our model. The concept of scenario-familiarity of the crowd has been proposed to explain the model. It is shown that the variation of the model parameters deeply impacts the evacuation efficiency. The relation between minimal evacuation times and the knowledge of the exit that the pedestrian acknowledges is discussed

Identification of key phenolic compounds responsible for antioxidant activities of free and bound fractions of blackberry varieties' extracts by boosted regression trees

Free fractions of different blackberry varieties extracts are high in phenolic compounds with antioxidant activities. However, the phenolic profiles and antioxidant activities against peroxyl radicals of bound fractions of different blackberry varieties extracts have not been previously reported. In addition, what are the key antioxidant phenolic compounds in free and bound fractions of blackberry extracts remain unknown. The present study aimed to investigate the phenolic profiles and antioxidant activities of free and bound fractions of eight blackberry varieties extracts and reveal the key antioxidant phenolic compounds by boosted regression trees.Results: Fifteen phenolics (three anthocyanins, four flavonols, three phenolic acids, two proanthocyanidins, and three ellagitannins) were identified in blackberry by UPLC-Q-TOF-MS. Ferulic acid, ellagic acid, procyanidin C1, kaempferol-O-hexoside, ellagitannins hex, and gallic acid were major bound phenolics. Bound fractions of eight blackberry varieties extracts were high in phenolics and showed great antioxidant activity. Boosted regression trees analysis showed that cyanidin-3-O-glucoside and chlorogenic acid were the most significant compounds, contributing 48.4 % and 15.9 % to the antioxidant activity of free fraction, respectively. Ferulic acid was the mostsignificant antioxidant compound in bound fraction with a contribution of 61.5%. Principal component analysis showed that Kiowa was the best among the eight varieties due to its phenolic profile and antioxidant activity.Conclusion: It was concluded that blackberry varieties contained high amounts of bound phenolics, which confer health benefits through reducing oxidative stress. Ferulic acid was key compound to explain the antioxidant activities of bound fractions.Fil: Gong, Er Sheng. Gannan Medical University; ChinaFil: Li, Bin. Shenyang Agricultural University; ChinaFil: Li, Binxu. Shenyang Agricultural University; ChinaFil: Podio, Natalia Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Chen, Hongyu. Shanghai Academy Of Agricultural Science; ChinaFil: Li, Tong. Cornell University; Estados UnidosFil: Sun, Xiyun. Shenyang Agricultural University; ChinaFil: Gao, Ningxuan. Shenyang Agricultural University; ChinaFil: Wu, Wenlong. Jiangsu Province And Chinese Academy Of Sciences; ChinaFil: Yang, Tianran. South China University Of Technology; ChinaFil: Xin, Guang. Shenyang Agricultural University; ChinaFil: Tian, Jinlong. Shenyang Agricultural University; ChinaFil: Si, Xu. Shenyang Agricultural University; ChinaFil: Liu, Changjiang. Shenyang Agricultural University; ChinaFil: Zhang, Jiyue. Shenyang Agricultural University; ChinaFil: Liu, Rui Hai. Cornell University. Department of Food Science & Technology; Estados Unido