C-terminal fragment of tetanus toxin: its use in neuronal network analysis and its potential as non-viral vector

The atoxic C-terminal fragment of tetanus neurotoxin or TTC fragment presents similar retrograde and transsynaptic properties to that of holotoxin. Detection of this fragment is easier when it is associated with a fluorescent marker or with beta-galactosidase activity by genetic fusion or chemical conjugation. Thus, these tracers have been used to study and analyse the synaptic connections of a neural network. In this article, we shortly review the various methods used with this aim including: injection of the fusion protein, adenovirus in vivo expression and transgenesis. Since neural activity is essential for neuronal TTC binding and internalization, the functionality of connections can be also evaluated. Moreover, modifications of the retrograde transport can be detected by using this fragment. Thus, TTC fragment is an excellent tracer to analyse the connectivity and functionality of a neural network. The TTC fragment was also soon proposed as potential therapeutic vector to transport and to deliver a biological activity or gene in a neural network. With this aim, the efficiency of a translocation domain to induce the cytosolic release of the associated activity has been evaluated. The use of the TTC fragment to target specifically a neurotrophic factor to neurons and thus avoid secondary effects has been tested with interesting results.     

Sequential RAM-based neural networks: learnability, generalisation, knowledge extraction, and grammatical inference

A fundamental question in the field of artificial neural networks is what set of problems a given class of networks can perform (computability). Such a problem can be made less general, but no less important, by asking what these networks could learn by using a given training procedure (learnability). The basic purpose of this paper is to address the learnability problem. Specifically, it analyses the learnability of sequential RAM-based neural networks. The analytical tools used are those of Automata Theory. In this context, this paper establishes which class of problems and under what conditions such networks, together with their existing learning rules, can learn and generalize. This analysis also yields techniques for both extracting knowledge from and inserting knowledge into the networks. The results presented here, besides helping in a better understanding of the temporal behaviour of sequential RAM-based networks, could also provide useful insights for the integration of the symbolic/connectionist paradigms.     

Malagasy cichlids differentially limit impacts of body shape evolution on oral jaw functional morphology

Patterns of trait covariation, such as integration and modularity, are vital factors that influence the evolution of vertebrate body plans. In functional systems, decoupling of morphological modules buffers functional change in one trait by reducing correlated variation with another. However, for complex morphologies with many‐to‐one mapping of form to function (MTOM), resistance to functional change may also be achieved by constraining morphological variation within a functionally stable region of morphospace. For this research, we used geometric morphometrics to evaluate the evolution of body shape and its relationship with jaw functional morphology in two independent radiations of endemic Malagasy cichlid (Teleostei: Cichlidae). Our results suggested that the two subfamilies used different strategies to mitigate impacts of body shape variation on a metric of jaw function, maxillary kinematic transmission (MKT): (1) modularity between cranial and postcranial morphologies, and (2) integration of body and jaw evolution, with jaw morphologies varying in a manner that limits change in MKT. This research shows that, unlike modularity, MTOM allows traits to retain strong evolutionary covariation while still reducing impacts on functionality. These results suggest that MTOM, and its influence on the evolution of correlated traits, is likely much more widespread than is currently understood.     

Small universal spiking neural P systems

In search for small universal computing devices of various types, we consider here the case of spiking neural P systems (SN P systems), in two variants: as devices that compute functions and as devices that generate sets of numbers. We start with the first case and we produce a universal spiking neural P system with 84 neurons. If a slight generalization of the used rules is adopted, namely, we allow rules for producing simultaneously several spikes, then a considerable reduction, to 49 neurons, is obtained. For SN P systems used as generators of sets of numbers, we find a universal system with restricted rules having 76 neurons and one with extended rules having 50 neurons.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

The interrelations between malfunctioning DNA damage response (DDR) and the functionality of the neuro-glio-vascular unit

A hallmark of neurodegenerative diseases is impairment of certain aspects of “brain functionality”. Brain functionality is defined as the total input and output of the brain's neural circuits and networks. A given brain degenerative disorder does not deregulate total brain functionality but rather the activity of specific circuits in a given network, affecting their organization and topology, their cell numbers, their cellular functionality, and the interactions between neural circuits. Similarly, our concept of neurodegenerative diseases, which for many years revolved around neural survival or death, has now been extended to emphasize the role of glia. In particular, the role of glial cells in neuro-vascular communication is now known to be central to the effect of insults to the nervous system. In addition, a malfunctioning vascular system likely plays a role in the etiology of certain neurodegenerative diseases. Thus, the symptoms of neurodegenerative or more correctly brain degenerative disease are, to a very large extent, a result of impairment in glial cells that lead to pathological neuro-vascular interactions that, in turn, generate a rather “hostile” environment in which the neurons fail to function. These events lead to systematic neural cell death on a scale that appears to be proportional to the severity of the neurological deficit.     

Discrimination of outer membrane proteins using machine learning algorithms

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees, etc. for discriminating OMPs. We found that most of the machine learning techniques discriminate OMPs with similar accuracy. The neural network-based method could discriminate the OMPs from other proteins [globular/transmembrane helical (TMH)] at the fivefold cross-validation accuracy of 91.0% in a dataset of 1,088 proteins. The accuracy of discriminating globular proteins is 88.8% and that of TMH proteins is 93.7%. Further, the neural network method is tested with globular proteins belonging to 30 different folding types and it could successfully exclude 95% of the considered proteins. The proteins with SAM domain such as knottins, rubredoxin, and thioredoxin folds are eliminated with 100% accuracy. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.     

Knowledge extraction from evolving spiking neural networks with rank order population coding

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.     

Prediction of siRNA knockdown efficiency using artificial neural network models

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.     

Discrimination of mesophilic and thermophilic proteins using machine learning algorithms

Discriminating thermophilic proteins from their mesophilic counterparts is a challenging task and it would help to design stable proteins. In this work, we have systematically analyzed the amino acid compositions of 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively. We found that the charged residues Lys, Arg, and Glu as well as the hydrophobic residues, Val and Ile have higher occurrence in thermophiles than mesophiles. Further, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees and so forth for discriminating mesophilic and thermophilic proteins. We found that most of the machine learning techniques discriminate these classes of proteins with similar accuracy. The neural network-based method could discriminate the thermophiles from mesophiles at the five-fold cross-validation accuracy of 89% in a dataset of 4684 proteins. Moreover, this method is tested with 325 mesophiles in Xylella fastidosa and 382 thermophiles in Aquifex aeolicus and it could successfully discriminate them with the accuracy of 91%. These accuracy levels are better than other methods in the literature and we suggest that this method could be effectively used to discriminate mesophilic and thermophilic proteins.     

Nerve repair and behavioral recovery following brain transplantation in Notoplana acticola, a polyclad flatworm

Although Notoplana acticola, a marine polyclad, cannot regenerate brain tissue, neuronal repair is rapid. Brains were transplanted into decerebrate flatworms to determine the anatomical patterns and functionality of neural connections established between a new brain and the peripheral nerve network of the recipient animal. Sixty-nine transplants were performed. Four brain transplant orientations were used: normal, reversed, inverted, and reversed inverted. The functionality of the transplanted brains was tested and measured using both behavioral and electrophysiological criteria. Within 23 days, 56% of the transplants that survived and retained the transplants recovered the four behaviors tested: righting behavior, avoidance turning, ditaxic locomotion, and feeding. Nerves exiting the brain tended to join with the peripheral nerves closest to them. Anatomical connections were made within 24 hr of surgery. Some normal behavior was seen within the first 36 hrs after surgery. Control decerebrate worms did not recover behavior. Preliminary intracellular recordings from three types of identified brain sensory interneurons, in transplants, revealed normal electrophysiological properties and this implied that appropriate connections with peripheral sensory cells had been reestablished. Intracellular dye-marking of these neurons in reverse-oriented brains revealed that, although individual nerve processes apparently leave the brain and associate with inappropriate nerve cords, some of the processes turn 180 degrees to reinervate nerve cords, which they normally occupy in unoperated animals. Thus, although anatomical and functional neural connections apparently were made rapidly following brain transplantation, the specificity of the reconnections remains to be shown.     

A plausible neural circuit for decision making and its formation based on reinforcement learning

A human’s, or lower insects’, behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit’s constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit’s output layer, which generates a stable spike firing rate to encode flight commands, controls the insect’s angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit’s information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee’s behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit’s generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.     

Rule extraction from minimal neural networks for credit card screening

While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.     

Fate of amnion-derived stem cells transplanted to the fetal rat brain: migration, survival and differentiation

We have recently characterized a stem cell population isolated from the rodent amniotic membrane termed amnion-derived stem cells (ADSCs). In vitro ADSCs differentiate into cell types representing all three embryonic layers, including neural cells. In this study we evaluated the neuroectodermal potential of ADSCs in vivo after in utero transplantation into the developing rat brain. A clonal line of green fluorescent protein-expressing ADSCs were infused into the telencephalic ventricles of the developing embryonic day 15.5 rat brain. At E17.5 donor cells existed primarily as spheres in the ventricles with subsets fused to the ventricular walls, suggesting a mode of entry into the brain parenchyma. By E21.5 green fluorescent protein (GFP) ADSCs migrated to a number of brain regions. Examination at postnatal time points revealed that donor ADSCs expressed vimentin and nestin. Subsets of transplanted ADSCs attained neuronal morphologies, although there was no immunohistochemical evidence of neural or glial differentiation. Some donor cells migrated around blood vessels and differentiated into putative endothelial cells. Donor ADSCs transplanted in utero were present in recipients into adulthood with no evidence of immunological rejection or tumour formation. Long-term survival may suggest utility in the treatment of disorders where differentiation to a neural cell type is not required for clinical benefit.     

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.     

Genetic factors affecting HDL levels, structure, metabolism and function

PURPOSE OF REVIEW: HDL is a recognized negative risk factor for the cardiovascular diseases. Establishing the genetic determinants of HDL concentration and functions would add to the prediction of cardiovascular risk and point to the biochemical mechanisms underlying this risk. The present review focuses on various approaches to establish genetic determinants of the HDL concentration, structure and function. RECENT FINDINGS: While many genes contribute to the HDL concentration and collectively account for half of the variability, polymorphism of individual candidate genes contributes little. There are strong interactions between environmental and genetic influences. Recent findings have confirmed that APOA1 and ABCA1 exert the strongest influence on HDL concentrations and risk of atherosclerosis. CETP and lipases also affect the HDL concentration and functionality, but their connection to the atherosclerosis risk is conditional on the interaction between environmental and genetic factors. SUMMARY: Analysis of genetic determinants of HDL-cholesterol in patients with specific disease states or in response to the environmental condition may be a more accurate way to assess variations in HDL concentration. This may result in defining the rules of interaction between genetic and environmental factors and lead to understanding the mechanisms responsible for the variations in HDL concentration and functionality.     

Anticipatory Functions, Digital-Analog Forms and Biosemiotics: Integrating the Tools to Model Information and Normativity in Autonomous Biological Agents

We argue that living systems process information such that functionality emerges in them on a continuous basis. We then provide a framework that can explain and model the normativity of biological functionality. In addition we offer an explanation of the anticipatory nature of functionality within our overall approach. We adopt a Peircean approach to Biosemiotics, and a dynamical approach to Digital-Analog relations and to the interplay between different levels of functionality in autonomous systems, taking an integrative approach. We then apply the underlying biosemiotic logic to a particular biological system, giving a model of the B-Cell Receptor signaling system, in order to demonstrate how biosemiotic concepts can be used to build an account of biological information and functionality. Next we show how this framework can be used to explain and model more complex aspects of biological normativity, for example, how cross-talk between different signaling pathways can be avoided. Overall, we describe an integrated theoretical framework for the emergence of normative functions and, consequently, for the way information is transduced across several interconnected organizational levels in an autonomous system, and we demonstrate how this can be applied in real biological phenomena. Our aim is to open the way towards realistic tools for the modeling of information and normativity in autonomous biological agents.     

Extensive feature detection of N-terminal protein sorting signals

MOTIVATION: The prediction of localization sites of various proteins is an important and challenging problem in the field of molecular biology. TargetP, by Emanuelsson et al. (J. Mol. Biol., 300, 1005-1016, 2000) is a neural network based system which is currently the best predictor in the literature for N-terminal sorting signals. One drawback of neural networks, however, is that it is generally difficult to understand and interpret how and why they make such predictions. In this paper, we aim to generate simple and interpretable rules as predictors, and still achieve a practical prediction accuracy. We adopt an approach which consists of an extensive search for simple rules and various attributes which is partially guided by human intuition. RESULTS: We have succeeded in finding rules whose prediction accuracies come close to that of TargetP, while still retaining a very simple and interpretable form. We also discuss and interpret the discovered rules.