Characterization and application of an optical sensor for quantification of dissolved O2 in shake-flasks

On-line measurement of dissolved O₂ in shake-flasks was realized via immobilized sensor spots containing a fluorophore with an O₂-dependent luminescent decay time. An unaffected sensor signal during 80 autoclaving cycles suggests multi-usage of sensor equipped shake-flasks. The sensor had a response time of 6 s. Quantification of gas-liquid mass transfer revealed maximum k_La values of 150 h^–1, from which maximum O₂ transfer capacity of 33 mM h^–1 was calculated. Liquid volume and shaking frequency have a strong influence on k_La. Exemplified by cultivations of Corynebacterium glutamicum the importance of shaking rate for O₂ supply of bacterial cultures is shown. Sampling of microbial cultures with intermittent shaking of a few minutes can cause O₂ limitation. Based on the results of this work a simple and straightforward tool is now available for accurate O₂ sensing in shake-flasks, which are widely used in microbial cultivations.     

A neural network dynamic model reproducing the output signal of the ganglion cell

A functional model of a neural network reproducing the output signal of the ganglion cell is proposed. The model assumes that receptive fields with antagonistic center and periphery are formed.     

A density functional theory parameterised neural network model of zirconia

We have developed a Behler–Parrinello Neural Network (BPNN) that can be employed to calculate energies and forces of zirconia bulk structures with oxygen vacancies with similar accuracy as that of the density functional theory (DFT) calculations that were used to train the BPNN. In this work, we have trained the BPNN potential with a reference set of 2178 DFT calculations and validated it against a dataset of untrained data. We have shown that the bulk structural parameters, equation of states, oxygen vacancy formation energies and diffusion barriers predicted by the BPNN potential are in good agreement with the reference DFT data. The transferability of the BPNN potential has also been benchmarked with the prediction of structures that were not included in the reference set. The evaluation time of the BPNN is orders of magnitude less than corresponding DFT calculations, although the training process of the BPNN potential requires non-negligible amount of computational resources to prepare the dataset. The computational efficiency of the NN enabled it to be used in molecular dynamics simulations of the temperature-dependent diffusion of an oxygen vacancy and the corresponding diffusion activation energy.     

A chemotactic model of trunk neural crest cell migration

Trunk neural crest cells follow a common ventral migratory pathway but are distributed into two distinct locations to form discrete sympathetic and dorsal root ganglia along the vertebrate axis. Although fluorescent cell labeling and time‐lapse studies have recorded complex trunk neural crest cell migratory behaviors, the signals that underlie this dynamic patterning remain unclear. The absence of molecular information has led to a number of mechanistic hypotheses for trunk neural crest cell migration. Here, we review recent data in support of three distinct mechanisms of trunk neural crest cell migration and develop and simulate a computational model based on chemotactic signaling. We show that by integrating the timing and spatial location of multiple chemotactic signals, trunk neural crest cells may be accurately positioned into two distinct targets that correspond to the sympathetic and dorsal root ganglia. In doing so, we honor the contributions of Wilhelm His to his identification of the neural crest and extend the observations of His and others to better understand a complex question in neural crest cell biology.     

人工神经网络与相容性生物量模型预测单木地上生物量的比较

森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络.本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差.然后,基于最优的变量组合建立人工神经网络(ANN)...     

A neural network model of attention-modulated neurodynamics

Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.     

基于小波神经网络的麦蚜发生程度预测模型

【目的】建立基于小波神经网络病虫害预测预报模型,对提前采取防病防虫措施、减少农作物病虫害损失、提高农作物产量与质量具有重要意义。【方法】本研究以山西省运城市芮城县1980-2014年麦蚜发生程度和气象因子数据为基础,采用主成分分析法从40个基础气象因子中整合形成9个新的自变量输入模型,采用试凑法筛选隐含层节点数,用1980-2009年的数据进行网络训练,对2010-2014年麦蚜发生程度进行回测,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较。【结果】小波和BP神经网络两种模型对训练样本的平均拟合精度均有10年以上超过80%,两者MAPE 值分别为 89.83% 和83.07%,MSE 值分别为0.0578和0.6192。【结论】两个模型都能较好地描述麦蚜发生程度;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络。     

A continuous-time neural model for sequential action

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.     

生态系统响应气候变化脆弱性的人工神经网络模型评价

生态系统的脆弱性评价对于生态系统的管理具有重要作用。在分析生态系统脆弱性特征和影响因素的基础上 ,构建了针对森林和草地生态系统的脆弱性评价指标体系 ,涵盖了生态系统的结构、功能和生境 3个方面 ,评价指标分别是物种多样性、群落覆盖度、NPP、建群种年生长量、地表干燥度以及土壤有机碳等。评价系统将生态系统的脆弱性划分为轻微脆弱、中度脆弱、重度脆弱以及系统崩溃 4级。作为案例研究 ,构建了结构和性能优化的多层感知器 ,评价了温带落叶阔叶林生态系统的脆弱性。结果表明 ,通过人工神经网络模型评价生态系统的脆弱性是一条可行的途径     

A prototype neural network supervised control system for Bacillus thuringiensis fermentations

This article discusses the development of a prototype neural network-based supervisory control system for Bacillus thuringiensis fermentations. The input pattern to the neural network included the type of inoculum, operation temperature, pH value, accumulated process time, optical density in fermentation medium, and change in optical density. The output from the neural network was the predicted optical density for the next sampling time. The control system has been implemented in both a computer simulation and a laboratory fermentation experiment with promising results. (c) 1994 John Wiley & Sons, Inc.     

基于人工神经网络的刚竹毒蛾发生面积的预测模型

根据神经网络的基本原理,结合福建省尤溪县气象因子及刚竹毒蛾发生面积的实测数据,建立神经网络模型。结果表明:所建立的BP神经网络模型,具有满意的拟合精度和预测精度。2个预留调查点的平均预测精度达96.55%,预测准确率为100%。     

半干旱区春小麦生长系统的人工神经网络模型与产量预测

以半干旱区春小麦生长系统为研究对象。探讨了作物生长系统中水分、土壤养分等生态因子的时空变化特征及春小麦产量形成机制,应用人工神经网络方法建立了半干旱区春小麦生长系统的产量随环境因子变化的神经网络模型,并与传统的CTM模型进行了比较。模拟结果表明,人工神经网络模型可适用于半干旱区春小麦生长系统产量随环境因子变化规律描述,且优于传统模型,从而为春小麦产量预测提供了新的途径,也为作物生态系统的人工调控提供了新的模式与定量依据。     

基于人工神经网络的区域水环境承载力评价模型及其应用

在分析水环境承载力概念及人工神经网络技术基础之上,从阈值角度出发,建立了基于人工神经网络的区域水环境承载力评价模型,并将其应用于辽宁省水环境承载力评价,通过模型计算得到该省水环境承载能力指数。结果表明,2000—2004年,辽宁省水环境承载能力指数分别为0·29、0·36、0·32、0·37和0·43,整体上呈上升趋势,但承载力依然较弱。本文提出的水环境承载力评价模型具有结构简单、建模方便的特点,评价结果可以直观地反映区域水环境承载状态。     

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.     

Optimization for high-density cultivation of heterotrophic Chlorella based on a hybrid neural network model

AIMS: The purpose of this study was to develop a reliable hybrid neural network (HNN) model for heterotrophic growth of Chlorella, based on which optimization for fed-batch (FB) cultivation of Chlorella may be successfully realized. METHODS AND RESULTS: Deterministic kinetic model was preliminarily developed for the optimization of FB cultivation of Chlorella. The highest biomass concentration and the maximum productivity were obtained as: 104.9 g l(-1) dry cell weight and 0.613 g l(-1) h(-1), respectively. After several cultivations had been performed, an HNN model was developed. The efficiency of biomass production was further increased by the optimization using this model. The highest biomass concentration and the maximum productivity attained was: 116.2 g l(-1) dry cell weight and 1.020 g l(-1) h(-1), respectively. CONCLUSION: The HNN model agreed well with experimental results in different cultivations. Comparison between the HNN model and the deterministic model showed that the former had better generalization ability, which made it a reliable tool in modelling and optimization. SIGNIFICANCE AND IMPACT OF THE STUDY: The high cell density and productivity of biomass obtained in this study is of significance for the commercial cultivation of Chlorella. The simple and efficient optimization strategy proposed in this paper may be employed in heterotrophic mass culture of Chlorella as well as other similar organisms.     

A multilayer neural network model for perception of rotational motion

A multilayer neural nerwork model for the perception of rotational motion has been developed usingReichardt's motion detector array of correlation type, Kohonen's self-organized feature map and Schuster-Wagner's oscillating neural network. It is shown that the unsupervised learning could make the neurons on the second layer of the network tend to be self-organized in a form resembling columnar organization of selective directions in area MT of the primate's visual cortex. The output layer can interpret rotation information and give the directions and velocities of rotational motion. The computer simulation results are in agreement with some psychophysical observations of rotation-al perception. It is demonstrated that the temporal correlation between the oscillating neurons would be powerful for solving the "binding problem" of shear components of rotational motion.     

The study of neural network-based controller for controlling dissolved oxygen concentration in a sequencing batch reactor

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.     

基于BP神经网络模型的平潭岛植被景观模拟

以GIS为技术平台,利用Matlab7.0,选用2km×2km网格图对平潭岛植被景观进行切割,得到50个样方数据,其中,44个用于模型训练,6个用于模型检验,在此基础上,选取分维数、Shannon多样性指数、蔓延度指数作为模型输出数据,选取居民点个数、风速和距海边距离作为影响因素,建立平潭岛植被景观的BP神经网络模型,并进行误差检验.结果表明:影响平潭岛植被景观空间格局和植被多样性状况的主要因素为风速、距海岸距离,人为因素对研究区植被景观的空间连接程度造成较大影响.BP神经网络模型对研究区植被景观与环境及人为影响因子之间关系的拟合与实际情况基本吻合,平均误差为7.4%,最小误差仅0.2%,模型模拟误差较小,拟合度较高,可用于对研究区植被景观的定量预测模拟.     

BP神经网络在沙棘生态功能评价中的应用

BP人工神经网络技术在环境评价领域中已经得到越来越广泛的运用,将该法引入到陕蒙砒砂岩区沙棘生态功能综合评价的研究中,以沙棘生态功能评价指标标准值作为样本输入,综合评价级别作为网络输出,建立了一个含有4个输入神经元节点、6个隐含神经元节点和1个输出神经元节点的BP人工神经网络等级模型。将目标年(2008年)各评价指标实际数据作为输入,得到输出值是0.44,大于Ⅱ级标准,研究结果表明:砒砂岩区种植十年沙棘后,其生态效益很好,对砒砂岩地区的生态环境改善作用显著。BP神经网络的评价结果与较成熟的AHP-模糊综合评价结果一致,证明将BP人工神经网络模型用于沙棘生态功能评价是可行的,且评价结论客观。     

A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy

PurposeThis study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.MethodsSixty-six patients were treated for prostate cancer with VMAT. We created the treatment plans using the Acuros XB algorithm with 2 mm grid size, followed by the dose calculated using the anisotropic analytical algorithm with 5 mm grid with the same plan parameters. U-net model was used to predict 2 mm grid dose from 5 mm grid dose. We investigated the two models differing for the training data used as input, one used just the low resolution dose (D model) and the other combined the low resolution dose with CT data (DC model). Dice similarity coefficient (DSC) was calculated to ascertain how well the shape of the dose-volume is matched. We conducted gamma analysis for the following: DVH from the two models and the reference DVH for all prostate structures.ResultsThe DSC values in the DC model were significantly higher than those in the D model (p < 0.01). For the CTV, PTV, and bladder, the gamma passing rates in the DC model were significantly higher than those in the D model (p < 0.002–0.02). The mean doses in the CTV and PTV for the DC model were significantly better matched to those in the reference dose (p < 0.0001).ConclusionsThe proposed U-net model with dose and CT image used as input predicted more accurate dose.