Neural networks and REM sleep

The nature of certain forms of memory is discussed in relation to neural networks and REM sleep.     

What is plant behaviour?*

The nature of plant behaviour is discussed, and it is concluded that it is best described as what plants do. The possibility that plant behaviour is simply signal‐induced phenotypic plasticity is outlined, and some limitations of this assumption are considered. Natural environments present many challenges to growing plants, and the consequent signalling that plants perceive is becoming extremely complex. Plant behaviour is active, purposeful and intentional, and examples are discussed. Much plant behaviour, concerned with stress and herbivory, is also based on an assessment of the future likelihood of further damaging episodes and is therefore predictive. Plant behaviour involves the acquisition and processing of information. Informational terminology provides a suitable way of incorporating the concepts of learning, memory and intelligence into plant behaviour, capabilities that plants are rarely credited with. Finally, trade‐offs, cost–benefit assessments and decision making are common plant behavioural attributes. It is suggested that intelligent assessments that involve the whole plant are essential to optimize these adaptive capabilities.     

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

Application of the Percolation Theory to Random Networks of Biochemical Reactions

Percolation Theory is used to get statements about random networks. It can be seen that the probability distribution that vertices belong to a finite cluster in dependence on the mean catalytic activity C = K/N has an analogy to phase transition of second kind.     

L-缬氨酸发酵培养基的神经网络建模与遗传算法优化

运用神经网络对L-缬氨酸发酵培养基组成进行建模,在神经网络模型的基础上采用遗传算法对培养基组成进行优化,得到最佳发酵培养基组成.结果表明,运用神经网络并结合遗传算法是一种行之有效的优化方法.按最佳发酵培养基组成进行发酵实验64h,可在发酵液中积累L-缬氨酸28.5g/L.     

荒漠草原盐沼湿地苦豆子土壤细菌群落构建机制及其影响因素

【背景】荒漠草原盐沼湿地是陆地生态系统的重要组成部分,土壤水分和盐分变化是影响该生态系统土壤细菌群落构建的重要因素。【目的】土壤细菌群落构建是由确定性和随机性主导的连续生态过程,阐明荒漠草原盐沼湿地土壤细菌群落的构建机制对于加深微生物作为关键生态系统因子重要性的理解具有积极意义。【方法】以宁夏中部典型荒漠草原盐沼苦水湖湿地为研究对象,对近湖边（near the lake,NL）和远离湖边（far from the lake,FL）苦豆子群落土壤理化特性进行测定并结合土壤细菌高通量测序分析。【结果】NL和FL样地具有明显的水盐梯度变化,NL样地土壤pH、含水量和电导率均显著高于FL样地;变形菌门、放线菌门、厚壁菌门、拟杆菌门和黏菌门是研究区域土壤细菌群落的优势菌门,变形菌门相对丰度随水盐梯度上升而升高,放线菌门和厚壁菌门相对丰度则随之下降,门下成员大多与水盐变化具有明显的相关性;此外,FL样地土壤细菌网络则具有稳定的网络关系;随着NL样地向FL样地的延伸,土壤细菌群落由随机过程主导,并且受pH、电导率和环境变量的影响。【结论】荒漠草原盐沼湿地水分和盐分的变化改变了土壤细菌群落结构;土壤细菌群落通过生态位占据等策略提高逆境下的生存能力;细菌群落构建是随机过程和确定性过程组成的连续统一体,同样受环境变化的影响。本结果揭示了荒漠草原盐沼湿地细菌群落结构和相互关系对环境变化的响应特征,同时阐明了该区土壤细菌群落的构建机制及影响因素,也为相关科学研究提供了一定理论参考。     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

基于BP神经网络的siRNA活性预测

RNA干扰(RNAinterference,RNAi)是由双链RNA(dsRNA)引起的基因沉默现象,它通过降解具有同源序列的mRNA来起作用,特殊设计的siRNA能使靶基因发生特异性沉默,起到确定基因功能或沉默致病基因从而治疗疾病的目的。在RNAi技术的应用中,通常采用的是长度为19bp,正、反义链3'端各有2个不配对碱基的双链RNA(siRNA)。但针对靶基因不同位点设计的siRNA作用效果差别很大。影响siRNA效果的因素是多方面的,这些因素的作用又是非线性的。本文在研究影响siRNA作用效果的各种因素的基础上,对已经公开发表的实验数据进行特征提取,作为BP神经网络的训练数据,并将训练好的BP神经网络用于siRNA活性预测。     

Toward an individual-level understanding of vigilance: the role of social information

A gregarious lifestyle affords the benefit of collective detectionof predators through the many-eyes effect. Studies of vigilanceare generally concerned with exploring the relationship betweenvigilance rates and group size. However, a mechanistic understandingof the rules individual animals use to achieve this group-levelbehavior is lacking. Building on a previous modeling approach,we suggest that individuals reconcile their own private informationagainst the social information they receive from their groupmates in order to decide whether to feed or be vigilant at anyone time. We present a novel modeling approach utilizing a Markovchain Monte Carlo process to describe the transition betweenvigilant and nonvigilant states. Many of our assumptions arebased qualitatively on recently published experimental observations.We vary the amount of social information and the fidelity withwhich individuals process this information and show that thishas a profound effect on the individual vigilance rate, theindividual vigilant bout length, and the proportion of vigilantindividuals at any one time. A wide range of group-level vigilancepatterns can be obtained by varying simple behavioral characteristicsof individual animals. We find that generally, increasing theamount of, and sensitivity to, social information generatesa more cooperative vigilance behavior. This model potentiallyprovides a theoretical and conceptual framework for examiningspecific real-life systems. We propose analyzing individual-baseddata from real animals by considering their group to be a connectednetwork of individuals, with information transfer between them.     

The complex fluctuations of probabilistic Boolean networks

Probabilistic Boolean networks (PBNs) are extensions of Boolean networks (BNs), and both have been widely used to model biological systems. In this paper, we study the long-range correlations of PBNs based on their corresponding Markov chains. PBN states are quantified by the deviation of their steady-state distributions. The results demonstrate that, compared with BNs, PBNs can exhibit these dynamics over a wider and higher noise range. In addition, the constituent BNs significantly impact the generation of 1/f dynamics of PBNs, and PBNs with homogeneous steady-state distributions tend to sustain the 1/f dynamics over a wider noise range.