尼罗罗非鱼视顶盖细胞构筑及其诱发电位特性

快Golgi法显示,尼罗罗非鱼Oreochromise niloticus视顶盖的组织结构可描述为6层,由表及里依次为:边缘层(SM),视觉层(SO),表面纤维层(SFGS),中央细胞层(SGC),中央纤维层(SAC)及围脑室层(SPV)。多种神经元类型分布于视顶盖。诱发电位(VETP)的暗适应曲线时程为90min,并出现平台。VETP的暗视Sλ峰值为520nm,当logI_B=-2.0,明视峰值位移至555nm.这表明尼罗罗非鱼可能有辩色能力。     

三种有机紫外吸收剂对菲律宾蛤仔(Ruditapes philippinarum)鳃组织抗氧化能力和细胞凋亡相关基因表达的影响

二苯甲酮-3 (BP-3)、4-甲基苄亚基樟脑(4-MBC)和4-甲氧基肉桂酸-2-乙基己酯(EHMC)是三种常用的有机紫外吸收剂, 在水环境中被频繁检出, 对水生生态系统安全构成潜在威胁。为探究三种有机紫外吸收剂对菲律宾蛤仔(Ruditapes philippinarum)鳃组织抗氧化响应和相关细胞凋亡基因的影响, 将蛤仔分别暴露于环境相关浓度的三种紫外吸收剂溶液中, 检测鳃组织抗氧化酶活性和细胞凋亡相关基因转录水平, 并通过第二代整合生物标志物响应法(IBRv2)对三种紫外吸收剂的生物毒性进行比较分析。结果显示, 三种紫外吸收剂短期暴露会诱导抗氧化响应提高抗氧化能力, 而长期高浓度暴露会导致抗氧化能力的降低。BP-3、4-MBC和EHMC可能通过启动线粒体途径和fas介导的死亡受体途径诱导菲律宾蛤仔鳃组织产生细胞凋亡。通过IBRv2分析发现, 在环境常见浓度1 μg/L的暴露水平下, 短期(1 d, 7 d)暴露时, BP-3对菲律宾蛤仔鳃组织表现出的综合毒性效应最强, 而随着暴露时间的延长(28 d), 三种紫外吸收剂表现出的综合毒性效应相近。研究结果为水环境中有机紫外吸收剂的生态风险评估提供了参考数据。     

罗氏沼虾(Macrobrachium rosenbergii)雌雄虾不同形态阈值性状的遗传变异分析

氏沼虾(Macrobrachium rosenbergii)雄虾有四种不同形态:小雄虾(SM)、橙色螯(OC)、蓝色螯(BC)和老蓝色螯(OBC)雄虾;雌虾分为四种:性腺未发育(IF)、性腺成熟(MOF)、抱卵(BF)和已产幼体(SF)的雌虾。已有研究表明不同形态的雌雄虾的体重存在显著差异,为了探讨是否可以对罗氏沼虾不同形态雌雄虾所占比例开展选择来间接提高产量,首次对上述性状的遗传参数及其与收获体重的遗传相关进行了评估。记录了罗氏沼虾核心育种群体两代共计40 935尾个体的形态和体重信息,使用父母本育种分析模型和阈值统计模型估计不同形态二分类(0和1)阈值性状在世代间和世代内的遗传参数,并使用两性状模型估计其与体重性状的遗传相关。方差组分估计结果表明,雄、雌虾不同形态阈值性状的遗传力估计值分别为0.08~0.12和0.04~0.22。雄虾形态OC、BC阈值性状与收获体重间为低度正相关(0.01~0.43)。雌虾形态IF、BF、SF阈值性状和收获体重性状之间为中等程度相关(–0.61~0.53)。在我国土塘养殖模式下,罗氏沼虾雌雄个体不同形态阈值性状存在可利用的加性遗传变异,具有较大的改良空间;选择OC形态雄虾并增加其在育种群体中比例,并不会降低其生长速度;选择IF形态雌虾并增加其在育种群体中的比例,可能会降低其生长速度。     

南海红树内生海洋真菌#2492代谢产物的研究

研究了分离自中国南海的红树内生海洋真菌Stysanuslikesp.(#2492)菌丝体的代谢产物,从该菌菌丝体分离得到2个化合物,经现代波谱技术分别鉴定为N-(2-羟基二十四碳酰基)-1,3,4-三羟基-2-氨基十八烷(A)和γ-硬脂酸内酯(B)。其中γ-硬脂酸内酯(B)是首次以天然产物的形式从海洋真菌中分离得到。     

南海红树内生海洋真菌#2492代谢产物的研究

研究了分离自中国南海的红树内生海洋真菌Stysanuslikesp.(#2492)菌丝体的代谢产物,从该菌菌丝体分离得到2个化合物,经现代波谱技术分别鉴定为N-(2-羟基二十四碳酰基)-1,3,4-三羟基-2-氨基十八烷(A)和γ-硬脂酸内酯(B)。其中γ-硬脂酸内酯(B)是首次以天然产物的形式从海洋真菌中分离得到。     

求解反应扩散方程的紧致隐积分因子方法

积分因子(IF)方法是近年来提出的求解刚性常微分方程组的一种有效的数值方法。本文应用改进的紧致隐积分因子(cIIF)方法求解二维反应扩散方程。在空间离散上采用二阶中心差分方法。数值模拟得到各种斑图结构与所引文献结果相当一致。     

2种新型18β-甘草次酸修饰物对四氯化碳致小鼠肝损伤的保护作用

本文研究了顺式-3-βO-[4-(R)-(3-氯苯基)-2-氧代-1,3,2-二氧磷杂环己基]-18β-甘草次酸(GA16R)和顺式-3β-O-[4-(S)-(3-氯苯基)-2-氧代-1,3,2-二氧磷杂环己基]-18β-甘草次酸(GA16S)2种修饰物对四氯化碳致小鼠急性肝损伤的保护作用。结果表明,GA16R和GA16S均能减轻CCl4致小鼠急性肝损伤程度,明显降低血清中丙氨酸氨基转移酶(ALT)、天门冬氨酸氨基转移酶(AST)、乳酸脱氢酶(LDH)的升高,明显降低肝组织中丙二醛(MDA)的含量,明显增加肝组织中超氧化物歧化酶(SOD)、谷胱甘肽过氧化物酶(GSH-PX)的活力。证明2种新型甘草次酸化合物对四氯化碳致小鼠急性肝损伤均有明显的保护作用。     

“大陆架科学钻探”首次准确揭示黄海第四系底界

正日前,中国地质调查局青岛海洋地质研究所"大陆架科学钻探"项目组在南黄海第四纪年代地层学研究中取得了新突破,通过对采自南黄海陆架的钻孔(CSDP-1,孔深300.1m)进行高分辨磁性地层学等研究,最终建立了钻孔的磁性地层年代框架。该项成果发表在国际知名地学期刊《Quaternary Science Reviews》上(IF=4.572)。这为该区后续研究提供了有效的年代     

大弹涂鱼不同组织器官的同工酶研究

采用垂直聚丙烯酰胺平板电泳技术,对大弹涂鱼(Boleophthalmus pectinirostris)的眼睛、心脏、肝脏、肌肉、脾脏和鳃等6种组织器官中的13种同工酶进行研究,并分析了其酶谱表型。结果表明,四唑氧化酶(TO)和酸性磷酸酶(ACP)没有检测到酶带;三梨醇脱氢酶(SDH)和葡萄糖-6-磷酸脱氢酶(G-6-PDH)仅在肝脏中表达;超氧化物歧化酶(SOD)在所有组织中均存在,部分组织中存在差异;乳酸脱氢酶(LDH)、苹果酸脱氢酶(MDH)、苹果酸酶(MEP)、酯酶(EST)、醇脱氢酶(ADH)、D-葡萄糖脱氢酶(GcDH)、甲酸脱氢酶(FDH)、谷氨酸脱氢酶(GDH)则具有明显的组织特异性。     

多层感知机分类器行为延拓线性分析及其应用

严谨的线性判别函数与判别面的理论 ,适用于线性阈值 (MP模型 )神经元分类行为分析。本文将此理论扩展到非线性 sigmoid神经元 ,分析了用来解决模式分类问题的、由 sigmoid神经元构成的单隐层 MLP(多层感知机 )的内部行为 ;并通过一系列近似推理与实验验证 ,提出了将隐层权重矢量初始值均匀地分布在权重空间的一个圆 (超球面 )上的方法。针对几个困难的分类问题的实验表明 ,该方法抓住了 MLP分类器内部行为的重要特征 ,它使 MLP分类器跨越了可能存在的学习难点 ,把学习起点放在达到目标较简便的路经上。此方法在理论上简单直接 ,应用上方便有效 ,具有一定的普遍性。     

Seafloor sediment classification from single beam echo sounder data using LVQ network

Seafloor sediment classification based on echo characteristics obtained from single-beam echosounder is very useful in remote and instant sediment classification. Results of different classification techniques using such data provide robust results when the acoustic beam has a normal incidence with the seabottom. This may not always be true and show poor classification, with the data acquired during rough sea periods corresponding to both oblique and normal incidence of the acoustic pulse, due to roll and pitch motion of the ship. In the present study, an attempt is made to exploit the artificial neural network (ANN) techniques for better classification with such data. Learning Vector Quantisation (LVQ) is a supervised learning algorithm of ANN that is found to be an effective tool and show good performance. The input data to the network include the roughness index (E1) and hardness index (E2) derived from echo characteristics. The network utilizes the competitive learning, a distance function in the first layer and a linear function in the second layer. The network was tried with a different size of hidden neurons and training data size to see the influence on classification. It is found that with ten neurons in the first layer and four neurons in the second layer good performance in classification for the data was achieved.     

Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables

Based on Bayesian network (BN) and information flow (IF), a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inference mechanism of BN is applied to achieve the synchronous interpolation of multiple missing time series. With the IFBN, all ocean variables are placed in a causal network visually, making full use of information about related variables to fill missing data. More importantly, the synchronous interpolation of multiple variables can avoid model retraining when interpolative objects change. Interpolation experiments show that IFBN has even better interpolation accuracy, effectiveness and stability than existing methods.     

System identification for nonlinear maneuvering of large tankers using artificial neural network

This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn.     

Blind Estimation of the Ocean Acoustic Channel by Time–Frequency Processing

A blind estimator of the ocean acoustic channel impulse response envelope is presented. The signal model is characterized by a deterministic multipath channel excited by a highly nonstationary deterministic source signal. The time-frequency (TF) representation of the received signal allows for the separation between the channel and the source signal. The proposed estimator proceeds in two steps: First, the unstable initial arrivals allow for the estimation of the source signal instantaneous frequency (IF) by maximization of the radially Gaussian kernel distribution; then, the Wigner-Ville distribution (WV) is sequentially windowed and integrated, where the window is defined by the previously estimated IF. The integral gives the channel impulse response envelope, which turns to be an approximation to the blind conventional matched filter (MF). The blind channel estimator (CE) is applicable upon the following conditions: that the multipath channel contains at least one dominant arrival well separated from the others, and that the IF of the source signal is a one-to-one function. Results obtained on real data from the INternal TIde Measurements with Acoustic Tomography Experiments (INTIMATE'96), where the acoustic channel was driven by an linear frequency modulation signal, show that the channel's envelope detailed structure could be accurately and consistently recovered, with the correlation of the estimates ranging from 0.796 to 0.973, as compared to the MF result     

An intelligent control system for remotely operated vehicles

The application of a neural network controller to remotely operated vehicles (ROVs) is described. Three learning algorithms for online implementation of a neural net controller are discussed with a critic equation. These control schemes do not require any information about the system dynamics except an estimate of the inertia terms. Selection of the number of layers in the neural network, the number of neurons in the hidden layer, initial weights for the network and the critic coefficient were done based on the results of preliminary tests. The performances of the three learning algorithms were compared by computer simulation. The effectiveness of the neural net controller in handling time-varying parameters and random noise is shown by a case study of the ROV system     

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.     

径向基函数网络在云检测中的应用

作者提出一种应用径向基函数网络 (RBF)的云检测方法。此方法要求晴空海域与有云海域均以一定数量的基函数来表征 ,两种基函数可组成一个径向基函数网络。使用欧空局沿轨道扫描辐射计 (ERS- 1/ ATSR)资料对径向基函数网络在云检测中的性能作了验证 ,重点研究网络结构的复杂度对分类结果的影响 ,并与人眼的目视解译作比较 ,结果表明径向基函数网络在云检测中性能良好。     

The olivo-cerebellar circuit as a universal motor control system

The olivo-cerebellar system is one of the central networks organizing movement coordination in vertebrates. This system consists of three main anatomical structures: the inferior olive (IO), the cerebellar nuclei, and the cerebellar cortex. Over the last four decades studies in many laboratories have contributed significantly to our understanding of the electrophysiology of IO and cerebellar neurons. However, addressing the dynamic properties of olivo-cerebellar network requires information beyond the limits attainable using single cell recordings. Research at the neuronal network level is presently being implemented in order to determine the spatiotemporal activity profiles of ensemble neuronal activity using optical imaging of voltage-sensitive dye signals. We summarize here results of such type of study using the in vitro IO slices. The dynamic characteristic of the system is addressed using the imaging results as well as mathematical modeling of the network, as a heuristic tool. A computer-based control system based on such biological findings is outlined.