Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints

Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.     

Genetic profile characterization and population study of 21 autosomal STR in Chinese Kazak ethnic minority group

Short tandem repeat loci have been recognized as useful tools in the routine forensic application and in recent decades, more and more new short tandem repeat (STR) loci have been constantly discovered, studied, and applied in forensic caseworks. In this study, we investigated the genetic polymorphisms of 21 STR loci in the Kazak ethnic minority as well as the genetic relationships between the Kazak ethnic minority and other populations. Allelic frequencies of 21 STR loci were obtained from 114 unrelated healthy Kazak individuals in the Ili Kazak Autonomous Prefecture, Xinjiang Uigur Autonomous Region of China. We observed a total of 159 alleles in the group with the allelic diversity values ranging from 0.0044 to 0.5088. The highest polymorphism was found at D19S433 locus and the lowest was found at D1S1627. Statistical analysis of the generated data indicated no deviation from Hardy–Weinberg equilibriums at all 21 STR loci. In order to estimate the population differentiation, allelic frequencies of all STR loci of the Kazak were compared with those of other neighboring populations using analysis of molecular variance method. Statistically significant differences were found between the studied population and other populations at 2–7 STR loci. A neighbor‐joining tree was constructed based on allelic frequencies of the 21 STR loci and phylogenetic analysis indicates that the Kazak has a close genetic relationship with the Uigur ethnic group. The present results may provide useful information for forensic sciences and population genetics studies, and can also increase our understanding of the genetic background of this group. The present findings showed that all the 21 STR loci are highly genetically polymorphic in the Kazak group, which provided valuable population genetic data for the genetic information study, forensic human individual identification, and paternity tests.     

Multivariate optimization of a headspace solid‐phase microextraction method followed by gas chromatography with mass spectrometry for the determination of terpenes in Nicotiana langsdorffii

A simple and sensitive procedure based on headspace solid‐phase microextraction and gas chromatography with mass spectrometry was developed for the determination of five terpenes (α‐pinene, limonene, linalool, α‐terpineol, and geraniol) in the leaves of Nicotiana langsdorffii. The microextraction conditions (extraction temperature, equilibration time, and extraction time) were optimized by means of a Doehlert design. The experimental design showed that, for α‐pinene and limonene, a low temperature and a long extraction time were needed for optimal extraction, while linalool, α‐terpineol, and geraniol required a high temperature and a long extraction time. The chosen compromise conditions were temperature 60°C, equilibration time 15 min and extraction time 50 min. The main analytical figures of the optimized method were evaluated; LODs ranged from 0.07 ng/g (α‐pinene) to 8.0 ng/g (geraniol), while intraday and interday repeatability were in the range 10–17% and 9–13%, respectively. Finally, the procedure was applied to in vitro wild‐type and transgenic specimens of N. langsdorffii subjected to abiotic stresses (chemical and heat stress). With the exception of geraniol (75–374 ng/g), low concentration levels of terpenes were measured (ng/g level or lower); some interesting variations in terpene concentration induced by abiotic stress were observed.     

Optimal design of non-equilibrium experiments for genetic network interrogation

Many experimental systems in biology, especially synthetic gene networks, are amenable to perturbations that are controlled by the experimenter. We developed an optimal design algorithm that calculates optimal observation times in conjunction with optimal experimental perturbations in order to maximize the amount of information gained from longitudinal data derived from such experiments. We applied the algorithm to a validated model of a synthetic Brome Mosaic Virus (BMV) gene network and found that optimizing experimental perturbations may substantially decrease uncertainty in estimating BMV model parameters.     

Analysis of plant genomic DNAs and the genetic relationship among plants by using surface-enhanced Raman spectroscopy

The study aimed to distinguish genomic DNAs from nine species of plants belonging to six families and analyze their genetic relationship by using surface-enhanced Raman scattering (SERS). The silver nano-colloid and excitation wavelength of 785 nm used in this study yielded excellent quality of the SERS spectra. Raman signals were remarkably enhanced. Although the spectra for the nine species of plants appeared very similar, there were significant differences according to the analysis of variance analysis. There were three strong characteristic peaks. The peak at 625 cm⁻¹ was due to the vibration overlap of C3′-endo/anti deoxyribose, cytosine, and guanine; the one at 715 cm⁻¹ was due to the scissoring vibrations of C2N1C6 of adenine; and that at 1011 cm⁻¹ was due to the stretching vibration of the CO bond of deoxyribose and vibrations of cytosine. The SERS data were smoothed and standardized and evaluated using second derivative analysis, principal component analysis, and hierarchical cluster analysis. A model was established using the data from hierarchical cluster analysis and principal components of the second derivative. The clustering result of this model was highly consistent with the traditional classification of plants; all plant species investigated were correctly clustered into classes according to the cluster distance coefficient among them; the accuracy of clustering was 100%. Chinese cabbage (Brassica pekinensis Rupr.) and green cabbage (Brassica chinensis L.) belonging to Cruciferae, maize (Zea mays L.) and bamboo (Sinocalamus affinis McClure) belonging to Gramineae, and magnolia (Magnolia delavayi Franch.) and champaca (Michelia alba DC.) belonging to Magnoliaceae were clustered into three separate classes, and fern (Nephrolepis auriculata L., Nephrolepidaceae), garlic (Allium sativum L., Amaryllidaceae), and ginkgo (Ginkgo biloba L., Ginkgoaceae) were each clustered into separate classes. These findings suggest that the SERS spectra of plant genomic DNAs can be used to classify species and analyze their genetic relationship. It is an effective and perfect supplement to traditional classification and can form the basis for genetic analysis.     

基于上转换纳米粒子的低损伤神经界面技术

Optogenetics is a neuromodulation technology that combines light control technology with genetic technology, thus allowing the selective activation and inhibition of the electrical activity in specific types of neurons with millisecond time resolution. Over the past several years, optogenetics has become a powerful tool for understanding the organization and functions of neural circuits, and it holds great promise to treat neurological disorders. To date, the excitation wavelengths of commonly employed opsins in optogenetics are located in the visible spectrum. This poses a serious limitation for neural activity regulation because the intense absorption and scattering of visible light by tissues lead to the loss of excitation light energy and also cause tissue heating. To regulate the activity of neurons in deep brain regions, it is necessary to implant optical fibers or optoelectronic devices into target brain areas, which however can induce severe tissue damage. Non- or minimally-invasive remote control technologies that can manipulate neural activity have been highly desirable in neuroscience research. Upconversion nanoparticles (UCNPs) can emit light with a short wavelength and high frequency upon excitation by light with a long wavelength and low frequency. Therefore, UCNPs can convert low-frequency near-infrared (NIR) light into high-frequency visible light for the activation of light-sensitive proteins, thus indirectly realizing the NIR optogenetic system. Because NIR light has a large tissue penetration depth, UCNP-mediated optogenetics has attracted significant interest for deep-tissue neuromodulation. However, in UCNP-mediated in vivo optogenetic experiments, as the up-conversion efficiency of UCNPs is low, it is generally necessary to apply high-power NIR light to obtain up-converted fluorescence with energy high enough to activate a photosensitive protein. High-power NIR light can cause thermal damage to tissues, which seriously restricts the applications of UCNPs in optogenetic technology. Therefore, the exploration of strategies to increase the up-conversion efficiency, fluorescence intensity, and biocompatibility of UCNPs is of great significance to their wide applications in optogenetic systems. This review summarizes recent developments and challenges in UCNP-mediated optogenetics for deep-brain neuromodulation. We firstly discuss the correspondence between the parameters of UCNPs and employed opsins in optogenetic experiments, which mainly include excitation wavelengths, emission wavelengths, and luminescent lifetimes. Thereafter, we introduce the methods to enhance the conversion efficiency of UCNPs, including optimizing the structure of UCNPs and modifying the organic dyes in UCNPs. In addition, we also discuss the future opportunities in combining UCNP-mediated optogenetics with flexible microelectrode technology for the long-term detection and regulation of neural activity in the case of minimal injury.     

一种基于遗传算法的温度控制方法研究

提出一种基于遗传算法的温度控制方法,这种方法就是将控制系统的输入值编码成遗传算法的染色体,利用遗传算法对其进行优化并实现对温度系统的控制。模拟实验和真实实验的结果表明此方法是可行的,有效的。     

遗传算法在层次化验证平台中的应用

在当前SOC验证领域中,如何提高验证平台的可靠性和效率是一个迫切需要解决的问题。传统的向量产生方法存在着效率低、覆盖率不高等缺陷。本文介绍了一种基于遗传算法的随机向量产生器。它结合覆盖率驱动技术,能够自动搜索适合于待测模块的测试激励。与传统的向量生成方法相比,基于遗传算法的产生器极大地提高了验证效率,并且减轻了验证工程师的工作,缩短了芯片上市的时间。     

基于遗传算法的波长选择方法在绿茶近红外光谱分析模型中的应用

利用遗传算法(GA)提取茶叶的近红外吸收特征波长的方法,研究建立了绿茶水分和氨基酸的近红外分析模型,并对波长选择前后两种成分的模型进行了比较分析。结果表明,经遗传算法波长选择后,简化了分析模型,同时模型的稳健性增强。氨基酸预测集的均方根误差(SEP)减小82.1%,水分预测集的均方根误差减小(SEP)76.6%,它们在波长选择前后对应的分析波长数之比分别为995∶7和1990∶33。     

基于变形模板的多目标识别与定位

典型目标的识别与定位是无人机应用系统的关键技术之一,但实现完全地自动目标识别和定位存在一定的困难。该文提出一种采用变形模板进行多个目标的识别和定位方法,以机载传感器的融合图像为基础,并引入人在回路的思想,设计了基于变形模板的多目标识别和定位算法,重点研究了多目标变形模板库的构造和相应能量函数的定义,而变形模板的优化采用具有保优策略的遗传算法实现。仿真实验表明该方法具有较高的目标识别与定位能力,可以有效解决无人机图像中多个典型目标的识别与定位问题。