Development, potentials, and challenges of the International Long-Term Ecological Research (ILTER) Network

In order to review the development, the potentials, and the challenges of the International Long-Term Ecological Research (ILTER) Network, the necessity for the development of the ILTER Network, the development of ILTER activities in the world, and the body structures of ILTER Network are described. As an example for showing the potentials of ILTER activities, exemplary results of tree growth studies carried out by the author utilizing long-term daily weather data at the Hubbard Brook Experimental Forest, New Hampshire, USA are presented and future potentials are discussed. At this time, as the ILTER Network is situated at a critical point, challenges for the ILTER Network are discussed with regard to its further development. In this review, the author tries to maintain that as the long-term ecological research, whose results can secure comparability in the dimension of time and space, can provide us with a fundamental basis for sound understanding and management of ecosystems, we can publicize the activities and findings from the ILTER Network to the public, on from local to international levels, to continue further efforts around the world for our sustainable future.     

Zebrafish information network,the knowledgebase for Danio rerio research

The Zebrafish Information Network (zfin.org) is the central repository for Danio rerio genetic and genomic data. The Zebrafish Information Network has served the zebrafish research community since 1994, expertly curating, integrating, and displaying zebrafish data. Key data types available at the Zebrafish Information Network include, but are not limited to, genes, alleles, human disease models, gene expression, phenotype, and gene function. The Zebrafish Information Network makes zebrafish research data Findable, Accessible, Interoperable, and Reusable through nomenclature, curatorial and annotation activities, web interfaces, and data downloads. Recently, the Zebrafish Information Network and 6 other model organism knowledgebases have collaborated to form the Alliance of Genome Resources, aiming to develop sustainable genome information resources that enable the use of model organisms to understand the genetic and genomic basis of human biology and disease. Here, we provide an overview of the data available at the Zebrafish Information Network including recent updates to the gene page to provide access to single-cell RNA sequencing data, links to Alliance web pages, ribbon diagrams to summarize the biological systems and Gene Ontology terms that have annotations, and data integration with the Alliance of Genome Resources.     

A strategic plan for an Australian Long‐Term Environmental Monitoring Network

Because of the urgent need for robust, long‐term information on biodiversity loss and environmental change, we have proposed a Long‐Term (>10 years) Environmental Monitoring (LTEM) Network for Australia. The LTEM Network would comprise 25 Nodes distributed throughout Australia, be focused on terrestrial, inland aquatic and coastal estuarine ecosystems, and be established to monitor long‐term biodiversity loss and ecological change (patterns and trends). The LTEM Network would be question‐problem‐process‐driven and not infrastructure‐driven. Thus, the different Nodes in the LTEM Network would explicitly recognize different biota, different environmental problems, different environmental threats and different kinds of management interventions in different ecosystems. We provide a governance structure for the proposed LTEM Network and envisage that it would be characterized by being: (i) lean, yet powerful, agile and adaptive; (ii) both centralized (through a coordinating role by a Federal Government agency), yet decentralized in terms of where the Nodes are located and how they are operated and managed; and (iii) not overly bureaucratic. We also argue that it would be highly cost‐effective; our estimated cost for the establishment of an LTEM Network is $28 million per year or less than 0.25% of the total annual governmental expenditure of $12 billion on environmental management in 2003/2004. The LTEM Network would be a critical part of Australia's environmental infrastructure and provide the Nation, for the first time, with a formal, coordinated, long‐term Network to gauge status and change of biodiversity and environmental condition. We argue that this would allow the Nation to begin to address seriously many of the major data‐related deficiencies that currently exist in the environment and biodiversity conservation sectors in Australia.     

面向异步多时延基因调控网络建模的高阶动态贝叶斯网络模型及其结构 学习算法

目的：由基因芯片数据精确学习建模具有异步多时延表达调控关系的基因调控网络。方法：提出了一种高阶动态贝叶斯网 络模型,并给出了网络结构学习算法,该模型假定基因的调控过程为多阶马尔科夫过程,从而能够建模基因调控网络中的异步多 时延特性。结果：由酵母基因调控网络一个子网络人工生成了加入10%含噪声的表达数据用于调控网络结构学习。在75%的后验 概率下,本文提出的高阶动态贝叶斯网络模型能够正确建模实际网络中全部的异步多时延调控关系,而经典动态贝叶斯网络仅 能够正确建模实际网络中1/3的调控关系;ROC曲线对比表明在各个后验概率水平上高阶动态贝叶斯网络模型的效果均优于经 典动态贝叶斯网络。结论：本文提出的高阶动态贝叶斯网络模型能够精确学习建模具有异步多时延表达调控关系的基因调控网 络。     

面向异步多时延基因调控网络建模的高阶动态贝叶斯网络模型及其结构学习算法

目的：由基因芯片数据精确学习建模具有异步多时延表达调控关系的基因调控网络。方法：提出了一种高阶动态贝叶斯网络模型,并给出了网络结构学习算法,该模型假定基因的调控过程为多阶马尔科夫过程,从而能够建模基因调控网络中的异步多时延特性。结果：由酵母基因调控网络一个子网络人工生成了加入10%含噪声的表达数据用于调控网络结构学习。在75%的后验概率下,本文提出的高阶动态贝叶斯网络模型能够正确建模实际网络中全部的异步多时延调控关系,而经典动态贝叶斯网络仅能够正确建模实际网络中1/3的调控关系;ROC曲线对比表明在各个后验概率水平上高阶动态贝叶斯网络模型的效果均优于经典动态贝叶斯网络。结论：本文提出的高阶动态贝叶斯网络模型能够精确学习建模具有异步多时延表达调控关系的基因调控网络。     

Predicting protein solubility with a hybrid approach by pseudo amino acid composition

Protein solubility plays a major role for understanding the crystal growth and crystallization process of protein. How to predict the propensity of a protein to be soluble or to form inclusion body is a long but not fairly resolved problem. After choosing almost 10,000 protein sequences from NCBI database and eliminating the sequences with 90% homologous similarity by CD-HIT, 5692 sequences remained. By using Chou's pseudo amino acid composition features, we predict the soluble protein with the three methods: support vector machine (SVM), back propagation neural network (BP Neural Network) and hybrid method based on SVM and BP Neural Network, respectively. Each method is evaluated by re-substitution test and 10-fold cross-validation test. In the re-substitution test, the BP Neural Network performs with the best results, in which the accuracy achieves 0.9288 and Matthews Correlation Coefficient (MCC) achieves 0.8513. Meanwhile, the other two methods are better than BP Neural Network in 10-fold cross-validation test. The hybrid method based on SVM and BP Neural Network is the best. The average accuracy is 0.8678 and average MCC is 0.7233. Although all of the three methods achieve considerable evaluations, the hybrid method is deemed to be the best, according to the performance comparison.     

Brain Networks Supporting Execution of Mathematical Skills versus Acquisition of New Mathematical Competence

This fMRI study examines how students extend their mathematical competence. Students solved a set of algebra-like problems. These problems included Regular Problems that have a known solution technique and Exception Problems that but did not have a known technique. Two distinct networks of activity were uncovered. There was a Cognitive Network that was mainly active during the solution of problems and showed little difference between Regular Problems and Exception Problems. There was also a Metacognitive Network that was more engaged during a reflection period after the solution and was much more engaged for Exception Problems than Regular Problems. The Cognitive Network overlaps with prefrontal and parietal regions identified in the ACT-R theory of algebra problem solving and regions identified in the triple-code theory as involved in basic mathematical cognition. The Metacognitive Network included angular gyrus, middle temporal gyrus, and anterior prefrontal regions. This network is mainly engaged by the need to modify the solution procedure and not by the difficulty of the problem. Only the Metacognitive Network decreased with practice on the Exception Problems. Activity in the Cognitive Network during the solution of an Exception Problem predicted both success on that problem and future mastery. Activity in the angular gyrus and middle temporal gyrus during feedback on errors predicted future mastery.     

A Relevance Index for the habitat areas of Natura 2000 Network based on their Rarity and Representativeness

The 231 habitat types included in the European Environmental Agency (EEA) database of the Natura 2000 Network are distributed throughout more than 22 thousand Sites of Community Importance (SCI) covering around 128 million hectares in Europe. However, individual habitat areas have different conservation relevance according to the Rarity of the habitat type and their Representativeness in relation to the whole type.We propose a Relevance Index and evaluate it for each area of each habitat type, taking into account the Rarity of the habitat type, assessed by the extent of its occurrence in the Natura 2000 Network, and the Representativeness of the area, as the contribution of that area to the total extent of that habitat type in the Network.Results allow for an objective analysis of the Natura 2000 Network indicating, for example, that rarity of a habitat type in Natura 2000 Network does not necessarily correlate with its priority status, suggesting the need for reevaluation of the situation.The Relevance Index proposed for a given habitat area is the product of the Rarity Index of its habitat type and the Representativeness of the area. We demonstrate the application of the Index for the areas of three different Natura 2000 Sites. The Relevance Index of one Site (SRI) will be the sum of the Relevance Indices of its corresponding habitat areas (HRI). The same approach is applicable to regions, countries or for any habitat cluster allowing for the quantification of their contribution to the whole Natura 2000 Network.It is suggested that the Relevance index can also be applied in monitoring the components of the Natura 2000 Network, and is useful to support management decisions for conservation in Europe.     

Essential elements of online information networks on invasive alien species

In order to be effective, information must be placed in the proper context and organized in a manner that is logical and (preferably) standardized. Recently, invasive alien species (IAS) scientists have begun to create online networks to share their information concerning IAS prevention and control. At a special networking session at the Beijing International Symposium on Biological Invasions, an online Eastern Asia–North American IAS Information Network (EA–NA Network) was proposed. To prepare for the development of this network, and to provide models for other regional collaborations, we compare four examples of global, regional, and national online IAS information networks: the Global Invasive Species Information Network, the Invasives Information Network of the Inter-American Biodiversity Information Network, the Chinese Species Information System, and the Invasive Species Information Node of the US National Biological Information Infrastructure. We conclude that IAS networks require a common goal, dedicated leaders, effective communication, and broad endorsement, in order to obtain sustainable, long-term funding and long-term stability. They need to start small, use the experience of other networks, partner with others, and showcase benefits. Global integration and synergy among invasive species networks will succeed with contributions from both the top-down and the bottom-up.     

Activities of the life cycle assessment society of Japan (JLCA)

This issue starts a regular update of JLCA, and a preview and profile of Int.J.LCA can be seen in ECP Network News from Japan (ECP - Environmentally Conscious Products). ECP, the Environmental Network Newsletter, is sponsored by JEMAI, Japan (JEMAI -Japan Environmental Management Association for Industry).     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省玉米估产为例

在遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法.     

The Italian Network for the allergenic pollen monitoring

Summary The organization of the Italian Network for the Allergenic Pollen Monitoring is introduced. This Network is made up of 83 survey stations, 70 of which have operated regularly during 1988. They all carry out their activities utilizing the same methods, which are standardized by the Associazione Italiana di Aerobiologia (AIA). The spreading of the sampling and analysis methods worked out by AIA has been supported by preparatory and specialization courses held all over Italy and having as their goal the technical training of all station operators. The data transfer is primarily based on the utilization of Personal Computers with the aim of speeding up the building of the National Data Bank. Some specific stations, which better represent the Italian situation have been selected to join the European Aeroallergen Network.     

遗传算法优化真菌深层培养过程神经网络模型的研究

提出一种简易的真菌深层培养过程网络模型。输入变量为可在线测量的排气中的二氧化碳浓度,网络权数采用遗传算法进行优化训练。所获神经网络模型能准确预测培养过程的状态变量(生物量浓度,产物浓度等)。研究表明遗传算法训练此类神经网络系统是可行的。     

The Network RamDisk: Using remote memory on heterogeneous NOWs

Efficient data storage, a major concern in the modern computer industry, is mostly provided today by traditional magnetic disks. However, the cost of a disk transfer (measured in processor cycles) continues to increase with time, making disk accesses increasingly expensive. In this paper we describe the design, implementation and evaluation of a Network RamDisk device that uses main memory of remote workstations as a fasterthandisk storage device. In our study we propose various reliability policies, making the device tolerant to single workstation crashes. We show that the Network RamDisk is portable, flexible, and can operate under any of the existing Unix file systems. The Network RamDisk has been implemented both on the Linux and the Digital Unix operating systems, as a block device driver without any modifications to the kernel code. Using several real applications and benchmarks, we measure the performance of the Network RamDisk over an Ethernet and an ATM network, and find it to be usually four to eight times better than the magnetic disk. In one benchmark, our system was two orders of magnitude faster than the disk. We believe that a Network RamDisk can be efficiently used to provide reliable lowlatency access to files that would otherwise be stored on magnetic disks.     

Coarse Grained Normal Mode Analysis vs. Refined Gaussian Network Model for Protein Residue-Level Structural Fluctuations

We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms C _α , C, N, and C _β are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.     

Template learning of cellular neural network using genetic programming

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.     

Time-dependent network analysis reveals molecular targets underlying the development of diet-induced obesity and non-alcoholic steatohepatitis

Prolonged high-fat diet leads to the development of obesity and multiple comorbidities including non-alcoholic steatohepatitis (NASH), but the underlying molecular basis is not fully understood. We combine molecular networks and time course gene expression profiles to reveal the dynamic changes in molecular networks underlying diet-induced obesity and NASH. We also identify hub genes associated with the development of NASH. Core diet-induced obesity networks were constructed using Ingenuity pathway analysis (IPA) based on 332 high-fat diet responsive genes identified in liver by time course microarray analysis (8 time points over 24 weeks) of high-fat diet-fed mice compared to normal diet-fed mice. IPA identified five core diet-induced obesity networks with time-dependent gene expression changes in liver. These networks were associated with cell-to-cell signaling and interaction (Network 1), lipid metabolism (Network 2), hepatic system disease (Network 3 and 5), and inflammatory response (Network 4). When we merged these core diet-induced obesity networks, Tlr2, Cd14, and Ccnd1 emerged as hub genes associated with both liver steatosis and inflammation and were altered in a time-dependent manner. Further, protein–protein interaction network analysis revealed Tlr2, Cd14, and Ccnd1 were interrelated through the ErbB/insulin signaling pathway. Dynamic changes occur in molecular networks underlying diet-induced obesity. Tlr2, Cd14, and Ccnd1 appear to be hub genes integrating molecular interactions associated with the development of NASH. Therapeutics targeting hub genes and core diet-induced obesity networks may help ameliorate diet-induced obesity and NASH.     

探讨我国森林野生动物红外相机监测规范

野生动物多样性是生物多样性监测与保护管理评价的关键指标, 因此对野生动物进行长期监测是中国森林生物多样性监测网络(CForBio)等大尺度生物多样性监测研究计划的一个重要组成部分。2011年以来, CForBio网络陆续在多个森林动态监测样地开展以红外相机来监测野生动物多样性。随着我国野生动物红外相机监测网络的初步形成, 亟待建立和执行基于红外相机技术的统一监测规范。基于3年来在我国森林动态监测样地红外相机监测的进展情况, 以及热带生态评价与监测网络针对陆生脊椎动物(兽类和鸟类)所提出的红外相机监测规范, 本文从监测规范和监测注意事项等方面探讨了我国森林野生动物红外相机监测的现状和未来。     

Body-size distributions and size-spectra: universal indicators of ecological status?

The sizes of individual organisms, rather than their taxonomy, are used to inform management and conservation in some aquatic ecosystems. The European Science Foundation Research Network, SIZEMIC, facilitates integration of such approaches with the more taxonomic approaches used in terrestrial ecology. During its 4-year tenure, the Network is bringing together researchers from disciplines including theorists, empiricists, government employees, and practitioners, via a series of meetings, working groups and research visits. The research conducted suggests that organismal size, with a generous helping of taxonomy, provides the most probable route to universal indicators of ecological status.