Enteric viruses in molluscan shellfish

One hundred and thirty-seven bivalves were collected for environmental monitoring and the market; all the samples were analysed by RT-PCR test. Bacteriological counts meeting the European Union shellfish criteria were reached by 69.5% of all the samples, whereas the overall positive values for enteric virus presence were: 25.5%, 18.2%, 8.0% and 2.1% for Rotavirus, Astrovirus, Enteroviruses, Norovirus, respectively. Mussels appear to be the most contaminated bivalves, with 64.8% of positive samples, 55.7% and 22.7% respectively for clams and oysters, whereas in the bivalves collected for human consumption 50.7% were enteric virus positive, as compared to 56.4% of the samples collected for growing-area classification. The overall positive sample was 54.0%.     

人工神经原网络处理PCR数据在细菌鉴定中的应用

目的16SrRNA和16S-23SrRNA间区片段是常用细菌分类鉴定靶点,本研究探讨人工神经原网络（ANN）对上述位点PCR扩增产物数据分析在细菌快速鉴定方面的价值。方法2对15SrRNA基因荧光引物和1对16S-23SrRNA区间基因引物用于扩增血液标本中分离出的317株细菌。相关毛细管电泳（CE）限制性片段长度多态性（RFLP）和单链构象多态性（SSCP）数据进行人工神经原网络分析。结果16S-23SrRNA基因的RFLP数据对未知菌鉴定的准确率高于16SrRNA基因的SSCP数据,分别为98．0％和79．6％。结论实验证明了人工神经原网络作为一种模式识别方法对于简化细菌鉴定十分有价值。     

Artificial astrocytes improve neural network performance

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.     

Accumulation of sediment-associated viruses in shellfish

The present study focused on the importance of contaminated sediments in shellfish accumulation of human viruses. Epifaunal (Crassostrea virginica) and infaunal (Mercenaria mercenaria) shellfish, placed on or in cores, were exposed to either resuspended or undisturbed sediments containing bound poliovirus type 1 (LSc 2ab). Consistent bioaccumulation by oysters (four of five trials) was only noted when sediment-bound viruses occurred in the water column. Virus accumulation was observed in a single instance where sediments remained in an undisturbed state. While the incidence of bioaccumulation was higher with resuspended rather than undisturbed contaminated sediment, the actual concentration of accumulated viruses was not significantly different. The accumulation of viruses from oysters residing on uninoculated sediments. When clams were exposed to undisturbed, virus-contaminated sediments, two of five shellfish pools yielded viral isolates. Bioaccumulation of undisturbed sediments by these bivalves was considered marginal when related to the concentration of virus in contaminated sediments; they would only represent a significant threat when suspended in the water column. Arguments were advanced for water-column sampling in the region of the water-sediment interface to provide an accurate determination of the virological quality of shellfish harvesting waters.     

Accumulation of sediment-associated viruses in shellfish.

The present study focused on the importance of contaminated sediments in shellfish accumulation of human viruses. Epifaunal (Crassostrea virginica) and infaunal (Mercenaria mercenaria) shellfish, placed on or in cores, were exposed to either resuspended or undisturbed sediments containing bound poliovirus type 1 (LSc 2ab). Consistent bioaccumulation by oysters (four of five trials) was only noted when sediment-bound viruses occurred in the water column. Virus accumulation was observed in a single instance where sediments remained in an undisturbed state. While the incidence of bioaccumulation was higher with resuspended rather than undisturbed contaminated sediment, the actual concentration of accumulated viruses was not significantly different. The accumulation of viruses from oysters residing on uninoculated sediments. When clams were exposed to undisturbed, virus-contaminated sediments, two of five shellfish pools yielded viral isolates. Bioaccumulation of undisturbed sediments by these bivalves was considered marginal when related to the concentration of virus in contaminated sediments; they would only represent a significant threat when suspended in the water column. Arguments were advanced for water-column sampling in the region of the water-sediment interface to provide an accurate determination of the virological quality of shellfish harvesting waters.     

应用人工神经网络评价湖泊的富营养化

应用人工神经网络方法,以化学需氧量、总氮、总磷和透明度作为评价参数,经反复尝试,构建了具有４层结构用于评价湖泊富营养化的误差逆传播网络．其输入层有４个神经元,２个隐含层也各有４个神经元,输出层有１个神经元．以太湖富营养化评价标准作为样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过３７６８４次学习后,网络达到预先给定的收敛标准．使网络具备了识别湖泊富营养化程度的功能．应用该网络对我国１７个湖泊的富营养化程度进行评价,操作过程简便易行,评价结果切合实际,展示了这种方法的一系列优点．     

Artificial neural network learning of nonstationary behavior in time series

We refine and complement a previously-proposed artificial neural network method for learning hidden signals forcing nonstationary behavior in time series. The method adds an extra input unit to the network and feeds it with the proposed profile for the unknown perturbing signal. The correct time evolution of this new input parameter is learned simultaneously with the intrinsic stationary dynamics underlying the series, which is accomplished by minimizing a suitably-defined error function for the training process. We incorporate here the use of validation data, held out from the training set, to accurately determine the optimal value of a hyperparameter required by the method. Furthermore, we evaluate this algorithm in a controlled situation and show that it outperforms other existing methods in the literature. Finally, we discuss a preliminary application to the real-world sunspot time series and link the obtained hidden perturbing signal to the secular evolution of the solar magnetic field.     

Artificial metaplasticity neural network applied to credit scoring

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.     

Artificial neural network classification of Drosophila courtship song mutants

Courtship songs produced by Drosophila males — wild-type, plus the cacophony and dissonance behavioral mutants — were examined with the aid of newly developed strategies for adaptive acoustic analysis and classification. This system used several techniques involving artificial neural networks (a.k.a. parallel distributed processing), including learned vector quantization of signals and non-linear adaption (back-propagation) of data analysis. Pulse song from several individual wild-type and mutant males were first vector-quantized according to their frequency spectra. The accumulated quantized data of this kind, for a given song, were then used to teach or adapt a multiple-layered feedforward artificial neural network, which classified that song according to its original genotype. Results are presented on the performance of the final adapted system when faced with novel test data and on acoustic features the system decides upon for predicting the song-mutant genotype in question. The potential applications and extensions of this new system are discussed, including how it could be used to screen for courtship mutants, search novel behavior patterns or cause-and-effect relationships associated with reproduction, compress these kinds of data for digital storage, and analyze Drosophila behavior beyond the case of courtship song.     

Curved trajectory prediction using a self-organizing neural network

Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.     

Artificial neural network model for predicting membrane protein types

Membrane proteins can be classified among the following five types: (1) type I membrane protein. (2) type II membrane protein. (3) multipass transmembrane proteins. (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. T. Kohonen's self-organization model which is a typical neural network is applied for predicting the type of a given membrane protein based on its amino acid composition. As a result, the high rates of self-consistency (94.80%) and cross-validation (77.76%), and stronger fault-tolerant ability were obtained.     

Artificial neural nets and abundance prediction of aquatic insects in small streams

Abundance prediction of aquatic insects (Ephemeroptera, Plecoptera, Trichoptera = EPT) based on environmental variables (precipitation, discharge, temperature) and abundance of the parent generation with Artificial Neural Nets (ANN) was carried out successfully. A general model for all species does not exist. Easy to understand models for individual species were restricted to stream sections with a characteristic set of variables. The amount of zero-values in the data did not affect the models. Transfer of one model to other stream sections resulted in a decrease of the determination coefficient B. Sufficient models for populations that have larvae in the stream all the year round required more information than for species with a diapause. All scaling options used decreased prediction quality. Long term mean values of variables and the deviation of actual from long term data were the best predictors, indicating a successful temporal link between seasonal variables and univoltine life cycles of most species tested. Prediction of monthly emergence in individual years was adequate with determination coefficients > 0.8 for five, and < 0.5 for only two out of ten years.     

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut‐off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R²) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte‐Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436–1443, 2016     

NETASA: neural network based prediction of solvent accessibility

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.     

A neural network for breakthrough prediction in packed bed adsorption

A single hidden layer back propagation neural network has been used to predict the occurrence of breakthrough in an ion-exchange adsorption column using signals derived from a thermal monitoring system. After training the neural network was capable of a complete prediction of breakthrough. This is in contrast with the mechanistic models used to date, which all show significant deviations in one or more regions of the breakthrough response.     

Locus minimization in breed prediction using artificial neural network approach

Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible ( http://nabg.iasri.res.in/bisgoat ) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51 850 samples of allelic data of microsatellite‐marker‐based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio‐piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.