Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Throughput and delay analysis of an opportunistic tree algorithm

Tree algorithms are a well-known class of random access algorithms with a provable maximum stable throughput under the infinite population model (as opposed to ALOHA or the binary exponential backoff algorithm). In this article, we propose a tree algorithm for opportunistic spectrum usage in cognitive radio networks. A channel in such a network is shared among so-called primary and secondary users, where the secondary users are allowed to use the channel only if there is no primary user activity. The tree algorithm designed in this article can be used by the secondary users to share the channel capacity left by the primary users.

We analyze the maximum stable throughput and mean packet delay of the secondary users by developing a tree structured Quasi-Birth Death Markov chain under the assumption that the primary user activity can be modeled by means of a finite state Markov chain and that packets lengths follow a discrete phase-type distribution.

Numerical experiments provide insight on the effect of various system parameters and indicate that the proposed algorithm is able to make good use of the bandwidth left by the primary users.     

高通量分析中生物传感器阵列研究的进展

高通量分析可同时处理与分析多个样品 极大地提高了分析速度 在药物研究与生产、环境监测、临床诊断、基因组学和蛋白质组学等领域发挥了重要作用。高通量分析对分析测试方法和相应仪器设备都提出了较高要求。生物传感器阵列不仅具有高的选择性和灵敏度,且易于微型化、集成化和自动化,成为高通量分析中极为重要的分析技术。本文综述了电化学、光学、质量、磁致阻抗等生物传感器阵列基础与应用研究的新进展,分析了生物传感器阵列在高通量分析中的潜在发展趋势。     

Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

This is a comparative study of various clustering and classification algorithms as applied to differentiate cancer and non-cancer protein samples using mass spectrometry data. Our study demonstrates the usefulness of a feature selection step prior to applying a machine learning tool. A natural and common choice of a feature selection tool is the collection of marginal p-values obtained from t-tests for testing the intensity differences at each m/z ratio in the cancer versus non-cancer samples. We study the effect of selecting a cutoff in terms of the overall Type 1 error rate control on the performance of the clustering and classification algorithms using the significant features. For the classification problem, we also considered m/z selection using the importance measures computed by the Random Forest algorithm of Breiman. Using a data set of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration and National Cancer Institute Clinical Proteomics Database, we undertake a comparative study of the net effect of the machine learning algorithm–feature selection tool–cutoff criteria combination on the performance as measured by an appropriate error rate measure.     

波分复用/光码分多址网络性能研究

基于波分复用/光码分复用混合网络模型,研究了光码分多址和波分复用/光码分复用网络性能,导出网络误码率公式;针对加性白色高斯噪声信道模型,将信息论应用到网络系统性能的分析上,得到网络的吞吐量公式。仿真结果表明:波分复用/光码分复用混合网络较单纯光码分多址网络具有容量大,网络扩展容易、方便等优点。     

实现千兆防火墙的关键技术及其发展前景

针对目前国内很多"号称"具有千兆防火墙的技术局面,阐述了真正的千兆防火墙所要达到的性能要求,详细地分析了实现千兆防火墙的关键技术,展望了防火墙包过滤技术、体系结构和系统管理3方面的发展趋势.     

基于灰色马尔可夫组合模型的福州港吞吐量预测

灰色马尔可夫组合模型是将灰色预测模型与马尔可夫模型进行组合优化,通过其对福州港2012-2016年货物吞吐量及集装箱吞吐量进行预测,并利用残差模型检验,证明了灰色马尔可夫组合模型能够解决单一模型的局限性、粗糙性和不稳定性问题,预测精度高,符合事物的发展规律,对业界预测港口吞吐量具有实践意义和参考价值。     

基于代谢视角的物质经济代谢分析框架研究

忽视社会经济因素对物质流的影响,是造成"循环经济不经济"现象的重要原因.本文认为,经济系统与生态系统的物质代谢过程具有相同的内涵;经济系统的组织依赖于经济系统的代谢过程.基于上述观点,提出融合社会经济因素的物质经济代谢概念,建立包括物质经济代谢通量分析、物质经济代谢路径分析、物质经济代谢的调控管理的逻辑分析框架,来理解经济系统的物质经济代谢规模和过程.     

随机多址信道冲突分解算法及吞吐量分析

讨论了一种改进的随机多址信道二叉树形、三叉树形冲突分解算法，导出了分解冲突所需平均时隙数的解析表达式。获得了较高的分解效率，并将其结果应用于阻塞接入信道的计算机仿真实验，数值分析表明，可获得较高的系统稳定最大吞吐量。