The mystery of structure and function of sensory processing areas of the neocortex: a resolution

Many different neural models have been proposed to account for major characteristics of the memory phenomenon family in primates. However, in spite of the large body of neurophysiological, anatomical and behavioral data, there is no direct evidence for supporting one model while falsifying the others. And yet, we can discriminate models based on their complexity and/or their predictive power. In this paper we present a computational framework with our basic assumption that neural information processing is performed by generative networks. A complex architecture is 'derived' by using information-theoretic principles. We find that our approach seems to uncover possible relations among the functional memory units (declarative and implicit memory) and the process of information encoding in primates. The architecture can also be related to the entorhinal-hippocampal loop. An effort is made to form a prototype of this computational architecture and to map it onto the functional units of the neocortex. This mapping leads us to claim that one may gain a better understanding by considering that anatomical and functional layers of the cortex differ. Philosophical consequences regarding the homunculus fallacy are also considered.     

A high-order graph generating self-organizing structure

A large class of neural network models have their units organized in a lattice with fixed topology or generate their topology during the learning process. These network models can be used as neighborhood preserving map of the input manifold, but such a structure is difficult to manage since these maps are graphs with a number of nodes that is just one or two orders of magnitude less than the number of input points (i.e., the complexity of the map is comparable with the complexity of the manifold) and some hierarchical algorithms were proposed in order to obtain a high-level abstraction of these structures. In this paper a general structure capable to extract high order information from the graph generated by a large class of self-organizing networks is presented. This algorithm will allow to build a two layers hierarchical structure starting from the results obtained by using the suitable neural network for the distribution of the input data. Moreover the proposed algorithm is also capable to build a topology preserving map if it is trained using a graph that is also a topology preserving map.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Chebanov law and Vakar formula in mathematical models of complex systems

Ecological models written in a mathematical language L(M) or model language, with a given style or methodology can be considered as a text. It is possible to apply statistical linguistic laws and the experimental results demonstrate that the behaviour of a mathematical model is the same of any literary text of any natural language. A text has the following characteristics: (a) the variables, its transformed functions and parameters are the lexic units or LUN of ecological models; (b) the syllables are constituted by a LUN, or a chain of them, separated by operating or ordering LUNs; (c) the flow equations are words; and (d) the distribution of words (LUM and CLUN) according to their lengths is based on a Poisson distribution, the Chebanov's law. It is founded on Vakar's formula, that is calculated likewise the linguistic entropy for L(M). We will apply these ideas over practical examples using MARIOLA model. In this paper it will be studied the problem of the lengths of the simple lexic units composed lexic units and words of text models, expressing these lengths in number of the primitive symbols, and syllables. The use of these linguistic laws renders it possible to indicate the degree of information given by an ecological model.     

Do Chinese Readers Follow the National Standard Rules for Word Segmentation during Reading?

We conducted a preliminary study to examine whether Chinese readers’ spontaneous word segmentation processing is consistent with the national standard rules of word segmentation based on the Contemporary Chinese language word segmentation specification for information processing (CCLWSSIP). Participants were asked to segment Chinese sentences into individual words according to their prior knowledge of words. The results showed that Chinese readers did not follow the segmentation rules of the CCLWSSIP, and their word segmentation processing was influenced by the syntactic categories of consecutive words. In many cases, the participants did not consider the auxiliary words, adverbs, adjectives, nouns, verbs, numerals and quantifiers as single word units. Generally, Chinese readers tended to combine function words with content words to form single word units, indicating they were inclined to chunk single words into large information units during word segmentation. Additionally, the “overextension of monosyllable words” hypothesis was tested and it might need to be corrected to some degree, implying that word length have an implicit influence on Chinese readers’ segmentation processing. Implications of these results for models of word recognition and eye movement control are discussed.     

Informing species conservation at multiple scales using data collected for marine mammal stock assessments

Background

Conservation planning and the design of marine protected areas (MPAs) requires spatially explicit information on the distribution of ecological features. Most species of marine mammals range over large areas and across multiple planning regions. The spatial distributions of marine mammals are difficult to predict using habitat modelling at ecological scales because of insufficient understanding of their habitat needs, however, relevant information may be available from surveys conducted to inform mandatory stock assessments.

Methodology and Results

We use a 20-year time series of systematic aerial surveys of dugong (Dugong dugong) abundance to create spatially-explicit models of dugong distribution and relative density at the scale of the coastal waters of northeast Australia (∼136,000 km²). We interpolated the corrected data at the scale of 2 km * 2 km planning units using geostatistics. Planning units were classified as low, medium, high and very high dugong density on the basis of the relative density of dugongs estimated from the models and a frequency analysis. Torres Strait was identified as the most significant dugong habitat in northeast Australia and the most globally significant habitat known for any member of the Order Sirenia. The models are used by local, State and Federal agencies to inform management decisions related to the Indigenous harvest of dugongs, gill-net fisheries and Australia''s National Representative System of Marine Protected Areas.

Conclusion/Significance

In this paper we demonstrate that spatially-explicit population models add value to data collected for stock assessments, provide a robust alternative to predictive habitat distribution models, and inform species conservation at multiple scales.     

On the Use of Enterprise Models

This paper presents the results of a field study designed to determine how enterprise models are used. The paper begins with an introduction to enterprise models then provides an overview of the survey method used. No known empirical studies have been conducted to determine how enterprise models actually are used by industry. The research is designed to answer this question. A screening sample and complete web-based survey of 72 enterprise modelers was conducted. The primary research question of this survey was on the use of enterprise models, with particular focus on the three dimensions of living models: scope, enactment, and dynamicity. Half the respondents claim that their enterprise models were of their entire division, multiple divisions, and even multiple enterprises. It is encouraging to see that enterprise models are used on such a wide scope. The enactment of the enterprise models was not as large as was expected. Of the respondents, 75% claimed that their models did not receive information from the enterprise more frequently than quarterly. The same was true for how often the models provided information to the enterprise. Seventy-five percent did not update their models more than five times (although, 32% did update the model three to five times). Finally, additional areas of research are proposed.     

Chemical,Target, and Bioactive Properties of Allosteric Modulation

Allosteric modulators are ligands for proteins that exert their effects via a different binding site than the natural (orthosteric) ligand site and hence form a conceptually distinct class of ligands for a target of interest. Here, the physicochemical and structural features of a large set of allosteric and non-allosteric ligands from the ChEMBL database of bioactive molecules are analyzed. In general allosteric modulators are relatively smaller, more lipophilic and more rigid compounds, though large differences exist between different targets and target classes. Furthermore, there are differences in the distribution of targets that bind these allosteric modulators. Allosteric modulators are over-represented in membrane receptors, ligand-gated ion channels and nuclear receptor targets, but are underrepresented in enzymes (primarily proteases and kinases). Moreover, allosteric modulators tend to bind to their targets with a slightly lower potency (5.96 log units versus 6.66 log units, p<0.01). However, this lower absolute affinity is compensated by their lower molecular weight and more lipophilic nature, leading to similar binding efficiency and surface efficiency indices. Subsequently a series of classifier models are trained, initially target class independent models followed by finer-grained target (architecture/functional class) based models using the target hierarchy of the ChEMBL database. Applications of these insights include the selection of likely allosteric modulators from existing compound collections, the design of novel chemical libraries biased towards allosteric regulators and the selection of targets potentially likely to yield allosteric modulators on screening. All data sets used in the paper are available for download.     

Use of NADH fluorescence to determine mitochondrial function in vivo

Normal mitochondrial function is a critical factor in maintaining cellular homeostasis in various organs of the body. Due to the involvement of mitochondrial dysfunction in many pathological states, the real-time in vivo monitoring of the mitochondrial metabolic state is crucially important. This type of monitoring in animal models as well as in patients provides real-time data that can help interpret experimental results or optimize patient treatment. In this paper we are summarizing the following items: (1) presenting the solid scientific ground underlying nicotine amide adenine dinucleotide (NADH) NADH fluorescence measurements based on published materials. (2) Presenting NADH fluorescence monitoring and its physiological significance. (3) Providing the reader with basic information on the methodologies of the fluorometers reflectometers. (4) Clarifying various factors affecting the monitored signals, including artifacts. (5) Presenting the potential use of monitoring mitochondrial function in vivo for the evaluation of drug development. The large numbers of publications by different groups testify to the valuable information gathered in various experimental conditions. The monitoring of NADH levels in the tissue provides the most important information on the metabolic state of the mitochondria in terms of energy production and intracellular oxygen levels. Although NADH signals are not calibrated in absolute units, their trend monitoring is important for the interpretation of physiological or pathological situations. To better understand the tissue function, the multiparametric approach has been developed where NADH serves as the key parameter to be monitored.     

Receptive field organization of electrosensory neurons in the paddlefish (Polyodon spathula)

Paddlefish use their electrosense to locate small water fleas (daphnia), their primary prey, in three-dimensional space. High sensitivity and a representation of object location are essential for this task. High sensitivity can be achieved by convergence of information from a large number of receptors and object location is usually represented in the nervous system by topographic maps. However the first electrosensory center in the brain, the dorsal octavolateral nucleus in the hindbrain, is neither topographically organized nor does it show a higher sensitivity than primary afferent fibers. Here, we investigated the response properties of electrosensory neurons in the dorsal octavolateral nucleus (DON), the lateral mesencephalic nucleus (LMN) and the tectum mesencephali (TM). LMN units are characterized by large receptive fields, which suggest a high degree of convergence. TM units have small receptive fields and are topographically arranged, at least in the rostro-caudal axis, the only dimension we could test. Well-defined receptive fields, however, could only be detected in the TM with a moving DC stimulus. The receptive fields of TM units, as determined by slowly scanning the rostrum and head with a 5 Hz stimulus, were very large and frequently two or more receptive fields were present. The receptive fields for LMN units were located in the anterior half of the rostrum whereas TM units had receptive fields predominantly on the head and at the base of the rostrum. A detailed analysis of the prey catching behavior revealed that it consists of two phases that coincide with the location of the receptive fields in LMN and TM, respectively. This suggests that LMN units are responsible for the initial orienting response that occurs when the prey is alongside the anterior first half of the rostrum. TM units, in contrast, had receptive fields at locations where the prey is located when the fish opens its mouth and attempts the final strike.     

A conceptual model of farmers' informational activity: a tool for improved support of livestock farming management

Farmers have been slow to adopt decision support system (DSS) models and their outputs, mainly owing to (i) the complexity of the data involved, which most potential users are unable to collect and process; and (ii) inability to integrate these models into real representations of their informational environments. This situation raises questions about the way farm management researchers have modelled information and information management, and especially about the quality of the information assessed by the farmers. We consider that to review advisory procedures we need to understand how farmers select and use farm management-related information, rather than focusing on decisions made in particular situations. The aim of this study was to build a conceptual model of the farmer-targeted farm management-related information system. This model was developed using data collected in commercial beef cattle farms. The design structure and operational procedures are based on (i) data categories representing the diversity of the informational activity; and (ii) selected criteria for supporting decisions. The model is composed of two subsystems, each composed of two units. First, an organizational subsystem organizes, finalizes and monitors informational activity. Second, a processing subsystem builds and exploits the informational resources. This conceptual model makes it possible to describe and understand the diverse range of farmers' informational activity by taking into account both the flow of information and the way farmers make sense of that information. This model could serve as a component of biodecisional DSS models for assigning information in the decision-making process. The next task will be to take into account the broad range of farmers' perceptions of the management situations in DSS models.     

Dynamic domain threading

A modeling method is described that avoids the need to consider the domain structure of the template used for modeling, and automatically extracts compact fragments of structure that would be of a suitable size to build the model. This aids automation as the size or nature of the template structure can be ignored and does not have to be broken into domain (or multi-domain) units beforehand. The approach leads to the generation of a large number of models each based on slightly differing domain definitions and this variation was further increased by considering alternative secondary structure predictions. Each model, of which there may be thousands, takes the form of a complete alpha-carbon trace and some methods (including residue burial) were investigated for their power to discriminate good models from bad models using decoys. The method is also compared to an earlier retroviral capsid modeling problem for which the X-ray structure is now known. Some potential extensions of the approach to more distant modeling problems are discussed.     

The application of demographic models to anthropological data

There has recently been great interest in the demography of primitive human populations. This can often be dealt with successfully by the use of demographic theory and model life tables; however, small populations present problems not encountered in large national censuses. This paper discusses some of these problems and illustrates the determination of model life tables in anthropology with a particular population, the Yanomama Indians. The concern is for the methods to be used, and their applicability, and not with the specific population illustrating them. By applying theoretical models, one can derive information that is otherwise unavailable but is necessary in the important biomedical, social, and ecological understanding of primitive societies.     

Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies

Protein folding and protein binding are similar processes. In both, structural units combinatorially associate with each other. In the case of folding, we mostly handle relatively small units, building blocks or domains, that are covalently linked. In the case of multi-molecular binding, the subunits are relatively large and are associated only by non-covalent bonds. Experimentally, the difficulty in the determination of the structures of such large assemblies increases with the complex size and the number of components it contains. Computationally, the prediction of the structures of multi-molecular complexes has largely not been addressed, probably owing to the magnitude of the combinatorial complexity of the problem. Current docking algorithms mostly target prediction of pairwise interactions. Here our goal is to predict the structures of multi-unit associations, whether these are chain-connected as in protein folding, or separate disjoint molecules in the assemblies. We assume that the structures of the single units are known, either through experimental determination or modeling. Our aim is to combinatorially assemble these units to predict their structure. To address this problem we have developed CombDock. CombDock is a combinatorial docking algorithm for the structural units assembly problem. Below, we briefly describe the algorithm and present examples of its various applications to folding and to multi-molecular assemblies. To test the robustness of the algorithm, we use inaccurate models of the structural units, derived either from crystal structures of unbound molecules or from modeling of the target sequences. The algorithm has been able to predict near-native arrangements of the input structural units in almost all of the cases, suggesting that a combinatorial approach can overcome the imperfect shape complementarity caused by the inaccuracy of the models. In addition, we further show that through a combinatorial docking strategy it is possible to enhance the predictions of pairwise interactions involved in a multi-molecular assembly.     

Developments in microbiological risk assessment models for drinking water—a short review

Microbiological risk assessment (MRA) is the emerging method to predict the risks of infection from waterborne pathogens (e.g. rotavirus and Cryptosporidium parvum ) in the drinking water supply. The objectives of this paper are to review the appropriateness of current models, with emphasis on pathogen exposures through drinking water, and to consider the information necessary to further their development. Calculating pathogen exposures in MRA is currently limited by the fact that pathogen density data for drinking water supplies are only available for very large volume samples—much larger than imbibed daily by any consumer. To develop MRA, information is needed on how pathogens are dispersed within those large volumes at the resolution of volumes typically consumed daily by individuals. Available evidence suggests that micro-organisms, including pathogens, are clustered to some degree, even within small volumes, exposing some drinking water consumers to much higher doses than others. By assuming pathogens are randomly dispersed, current models overestimate the risk from the more infectious agents (e.g. rotaviruses) but underestimate the risk from less infectious pathogens (e.g. C. parvum ). Approaches to modelling pathogen densities in drinking water from source water data and treatment removal efficiencies require additional information on the degree to which treatment processes (e.g. filtration and coagulation) increase pathogen clustering. The missing information could be obtained from large-scale pilot plant studies.     

A Software Architecture for Multi-Cellular System Simulations on Graphics Processing Units

The first aim of simulation in virtual environment is to help biologists to have a better understanding of the simulated system. The cost of such simulation is significantly reduced compared to that of in vivo simulation. However, the inherent complexity of biological system makes it hard to simulate these systems on non-parallel architectures: models might be made of sub-models and take several scales into account; the number of simulated entities may be quite large. Today, graphics cards are used for general purpose computing which has been made easier thanks to frameworks like CUDA or OpenCL. Parallelization of models may however not be easy: parallel computer programing skills are often required; several hardware architectures may be used to execute models. In this paper, we present the software architecture we built in order to implement various models able to simulate multi-cellular system. This architecture is modular and it implements data structures adapted for graphics processing units architectures. It allows efficient simulation of biological mechanisms.     

Strategies for crystallization and structure determination of very large macromolecular assemblies

High-resolution structures of macromolecular assemblies are pivotal for our understanding of their biological functions in fundamental cellular processes. In the field of X-ray crystallography, recent methodological and instrumental advances have led to the structure determinations of macromolecular assemblies of increased size and complexity, such as those of ribosomal complexes, RNA polymerases, and large multifunctional enzymes. These advances include the use of robotic screening techniques that maximize the chances of obtaining well-diffracting crystals of large complexes through the fine sampling of crystallization space. Sophisticated crystal optimization and cryoprotection techniques and the use of highly brilliant X-ray beams from third-generation synchrotron light sources now allow data collection from weakly diffracting crystals with large asymmetric units. Combined approaches are used to derive phase information, including phases calculated from electron microscopy (EM) models, heavy atom clusters, and density modification protocols. New crystallographic software tools prove valuable for structure determination and model refinement of large macromolecular complexes.     

Group testing for case identification with correlated responses

This article examines group testing procedures where units within a group (or pool) may be correlated. The expected number of tests per unit (i.e., efficiency) of hierarchical- and matrix-based procedures is derived based on a class of models of exchangeable binary random variables. The effect on efficiency of the arrangement of correlated units within pools is then examined. In general, when correlated units are arranged in the same pool, the expected number of tests per unit decreases, sometimes substantially, relative to arrangements that ignore information about correlation.