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.     

深水多波束测深系统脉冲压缩实时算法

线性调频信号是大时宽带宽积信号,通过脉冲压缩可提高探测距离和分辨率,已在深水多波束测深系统中得到广泛应用。针对深水多波束测深系统中脉冲压缩运算量大的问题,提出了一种基于分组的脉冲压缩实时算法。该算法对需要进行脉冲压缩的波束进行分组,每个节拍只计算一组波束,可大幅减少深水多波束测深系统脉冲压缩的运算时间,节省了处理器资源。该算法已成功应用于某深水多波束测深系统,经多次海试证明,工作稳定可靠。     

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.     

Cr(VI) and Cu(II) removal from aqueous solution in fixed bed column using rice bran; experimental,statistical and GA modelling

Rice bran, a green and low-cost adsorbent, is used for Chromium (VI) and Copper (II) remotion from its aqueous solution. The influence of different process parameters in a fixed-bed on the removing efficiency has been investigated. The results show that the removal efficiency is higher at a minimum flow rate, low metal ion concentration, and higher bed height. The adsorption studies show that the rice bran has a better affinity to Chromium (VI) than Copper (II). Different kinetic models are used for the prediction of the column performance. This study shows that rice bran could be a potential and eco-friendly adsorbent for chromium (VI) and copper (II) removal and is suitable for developing countries like India. Multiple linear regression and ANN-based genetic algorithm modelling have been applied successfully to predict both metal ions' percentage removal separately.     

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.     

An improved robust delay-dependent stability criterion for genetic regulatory networks with interval time delays

This paper proposes an improved robust delay-dependent criterion for stability of genetic regulatory networks with delays which vary in an interval. A modified Lyapunov-Krasovskii functional is used to derive a sufficient condition in terms of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. The derived stability criterion is less conservative than ones in the literature. A numerical example and simulation results show the effectiveness of the proposed method.     

Spectroscopic ellipsometry of very thin tantalum pentoxide on Si

Variable angle spectroscopic ellipsometry of very thin T₂O₅ layers on Si and the previously published appropriate algorithm for data interpretation have been successfully applied in terms of accurate characterization of very thin T₂O₅/Si systems. The simulation procedure following a simple three and four layered model was used assuming an existence of inhomogeneous interfacial layers. Quantitative determination of the thicknesses and composition identification were achieved, both for the top T₂O₅ layer and for an interfacial layer. The constituents in the interfacial layer and its depth profiles were recognized.