Using topographic networks to build a representation of consciousness

The subject of consciousness has intrigued both psychologists and neuroscientists for many years. Recently, following many recent advances in the emerging field of cognitive neuroscience, there is the possibility that this fundamental process may soon be explained. In particular, there have been dramatic insights gained into the mechanisms of attention, cognition and perception in recent decades. Here, simple network models are proposed which are used to create a representation of consciousness. The models are inspired by the structure of the thalamus and all incorporate topographic layers in their structure. Operation of the models allows filtering of the information reaching the representation according to (1) modality and/or (2) sub-modality, in addition several of the models allowing filtering at the topographic level. The models presented have different structures and employ different integrative mechanisms to produce gating or amplification at different levels; the resultant representations of consciousness are discussed.     

Using directed phylogenetic networks to retrace species dispersal history

Methods designed for inferring phylogenetic trees have been widely applied to reconstruct biogeographic history. Because traditional phylogenetic methods used in biogeographic reconstruction are based on trees rather than networks, they follow the strict assumption in which dispersal among geographical units have occurred on the basis of single dispersal routes across regions and are, therefore, incapable of modelling multiple alternative dispersal scenarios. The goal of this study is to describe a new method that allows for retracing species dispersal by means of directed phylogenetic networks obtained using a horizontal gene transfer (HGT) detection method as well as to draw parallels between the processes of HGT and biogeographic reconstruction. In our case study, we reconstructed the biogeographic history of the postglacial dispersal of freshwater fishes in the Ontario province of Canada. This case study demonstrated the utility and robustness of the new method, indicating that the most important events were south-to-north dispersal patterns, as one would expect, with secondary faunal interchange among regions. Finally, we showed how our method can be used to explore additional questions regarding the commonalities in dispersal history patterns and phylogenetic similarities among species.     

Encoding and processing biologically relevant temporal information in electrosensory systems

Wave-type weakly electric fish are specialists in time-domain processing: behaviors in these animals are often tightly correlated with the temporal structure of electrosensory signals. Behavioral responses in these fish can be dependent on differences in the temporal structure of electrosensory signals alone. This feature has facilitated the study of temporal codes and processing in central nervous system circuits of these animals. The temporal encoding and mechanisms used to transform temporal codes in the brain have been identified and characterized in several species, including South American gymnotid species and in the African mormyrid genus Gymnarchus. These distantly related groups use similar strategies for neural computations of information on the order of microseconds, milliseconds, and seconds. Here, we describe a suite of mechanisms for behaviorally relevant computations of temporal information that have been elucidated in these systems. These results show the critical role that behavioral experiments continue to have in the study of the neural control of behavior and its evolution.     

Using prior information to build probabilistic invasive species risk assessments

Understanding why some introduced species become naturalized and invasive whereas others do not is a major focus of invasion ecology. Invasive species risk assessments address this same question, but are not typically based on the results from recent ecological studies. Applying results from the ecological literature to risk assessment is difficult, in part because there are no general explanations of invasion likelihood across taxa. Most ecological studies are also specific to a particular region and it is unclear whether outcomes in one region will necessarily apply to another. Here we show how a hierarchical Bayesian statistical framework can make better use of ecological studies for applied risk assessments. We focus on three key opportunities afforded by these models: (1) the ability to leverage information from one region to form prior expectations for other regions about which little is known, (2) the ability to quantify uncertainty of predictions, and (3) flexibility to incorporate within-group heterogeneities in probabilities of naturalization. We illustrate these principles using a case study where we predict the probability of plant taxa naturalizing in New Zealand and Australia, showing how prior information can be particularly valuable when data are limited. As more studies document invasion patterns around the world, a framework that can formally incorporate prior information will help link the accumulating data on species introductions to risk assessments.     

Conservation helps to identify biologically relevant crystal contacts.

Some crystal contacts are biologically relevant, most are not. We assess the utility of combining measures of size and conservation to discriminate between biological and non-biological contacts. Conservation and size information is calculated for crystal contacts in 53 families of homodimers and 65 families of monomers. Biological contacts are shown to be usually conserved and typically the largest contact in the crystal. A range of neural networks accepting different combinations and encodings of this information is used to answer the following questions: (1) is a given crystal contact biological, and (2) given all crystal contacts in a homodimer, which is the biological one? Predictions for (1) are performed on both homodimer and monomer datasets. The best performing neural network combined size and conservation inputs. For the homodimers, it correctly classified 48 out of 53 biological contacts and 364 out of 366 non-biological contacts, giving a combined accuracy of 98.3 %. A more robust performance statistic, the phi-coefficient, which accounts for imbalances in the dataset, gave a value of 0.92. Taking all 535 non-biological contacts from the 65 monomers, this predictor made erroneous classifications only 4.3 % of the time. Predictions for (2) were performed on homodimers only. The best performing network achieved a prediction accuracy of 98.1 % using size information alone. We conclude that in answering question (1) size and conservation combined discriminate biological from non-biological contacts better than either measure alone. For answering question (2), we conclude that in our dataset size is so powerful a discriminant that conservation adds little predictive benefit.     

Synthesis of biologically relevant biflavanoids--a review

Recent investigations show that naturally occurring biflavanoids possess anti-inflammatory, anticancer, antiviral, antimicrobial, vasorelaxant, and anticlotting activities. These activities have been discovered from the small number of biflavanoid structures that have been investigated, although the natural biflavanoid library is likely to be large. Structurally, biflavanoids are polyphenolic molecules comprised of two identical or non-identical flavanoid units conjoined in a symmetrical or unsymmetrical manner through an alkyl or an alkoxy-based linker of varying length. These possibilities introduce significant structural variation in biflavanoids, which is further amplified by the positions of functional groups--hydroxy, methoxy, keto, or double bond--and stereogenic centers on the flavanoid scaffold. In combination, the class of biflavanoids represents a library of structurally diverse molecules, which remains to be fully exploited. Since the time of their discovery, several chemical approaches utilizing coupling and rearrangement strategies have been developed to synthesize biflavanoids. This review compiles these synthetic approaches into nine different methods including Ullmann coupling of halogenated flavones, biphenyl-based construction of biflavanoids, metal-catalyzed cross-coupling of flavones, Wessely-Moser rearrangements, oxidative coupling of flavones, Ullmann condensation with nucleophiles, nucleophilic substitutions for alkoxy biflavanoids, and dehydrogenation-based or hydrogenation-based synthesis. Newer, more robust synthetic approaches are necessary to realize the full potential of the structurally diverse class of biflavanoids.     

Comparative genomics analysis in Prunoideae to identify biologically relevant polymorphisms

Prunus is an economically important genus with a wide range of physiological and biological variability. Using the peach genome as a reference, sequencing reads from four almond accessions and one sweet cherry cultivar were used for comparative analysis of these three Prunus species. Reference mapping enabled the identification of many biological relevant polymorphisms within the individuals. Examining the depth of the polymorphisms and the overall scaffold coverage, we identified many potentially interesting regions including hundreds of small scaffolds with no coverage from any individual. Non‐sense mutations account for about 70 000 of the 13 million identified single nucleotide polymorphisms (SNPs). Blast2GO analyses on these non‐sense SNPs revealed several interesting results. First, non‐sense SNPs were not evenly distributed across all gene ontology terms. Specifically, in comparison with peach, sweet cherry is found to have non‐sense SNPs in two 1‐aminocyclopropane‐1‐carboxylate synthase (ACS) genes and two 1‐aminocyclopropane‐1‐carboxylate oxidase (ACO) genes. These polymorphisms may be at the root of the nonclimacteric ripening of sweet cherry. A set of candidate genes associated with bitterness in almond were identified by comparing sweet and bitter almond sequences. To the best of our knowledge, this is the first report in plants of non‐sense SNP abundance in a genus being linked to specific GO terms.     

Dynamic approach to predict pH profiles of biologically relevant buffers

Recently, dynamic approach has been applied to determine the steady state concentrations of multiple ionic species present in complex buffers at equilibrium. Here, we have used the dynamic approach to explicitly model the pH profiles of biologically relevant phosphate buffer and universal buffer (a mixture of three tri-protic acids such as citric acid, boric acid and phosphoric acid). The results from dynamic approach are identical to that of the conventional algebraic approach, but with an added advantage that the dynamic approach, allow for the modelling of complex buffer systems relatively easy compared to that of algebraic method.     

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.     

Making statistics biologically relevant in fragmented landscapes

The biological impacts of habitat fragmentation are routinely assessed using standard statistical modelling techniques that are used across many ecological disciplines. However, to assess the biological relevance of fragmentation impacts, we must consider an extra, spatial dimension to the standard statistical model: the biological importance of a significant and well supported model with large effect sizes crucially depends on the configuration of habitat within the study area. We argue that mapping the outputs from statistical models across a study area generates biologically meaningful estimates of fragmentation impacts. Integrating traditional statistical approaches with geographic information systems will facilitate rigorous comparisons of fragmentation impacts between taxa, studies and ecosystems.     

Functionalizing electrospun fibers with biologically relevant macromolecules

The development of functionalized polymers that can elicit specific biological responses is of great interest in the biomedical community, as well as the development of methods to fabricate these biologically functionalized polymers. For example, the generation of fibrous matrices with biological properties and fiber diameters commensurate with those of the natural extracellular matrix (ECM) may permit the development of novel materials for use in wound healing or tissue engineering. The goal of this work is, therefore, to create a biologically active functionalized electrospun matrix to permit immobilization and long-term delivery of growth factors. In this work, poly(ethylene glycol) functionalized with low molecular weight heparin (PEG-LMWH) was fabricated into fibers for possible use in drug delivery, tissue engineering, or wound repair applications. Electrospinning was chosen to process the LMWH into fiber form due to the small fiber diameters and high degree of porosity that can be obtained relatively quickly and using small amounts of starting material. Both free LMWH and PEG-LMWH were investigated for their ability to be incorporated into electrospun fibers. Each of the samples were mixed with a carrier polymer consisting of either a 10 wt % poly(ethylene oxide) (PEO) or 45 wt % poly(lactide-co-glycolide) (PLGA). Field emission scanning electron microscopy (FESEM), energy-dispersive X-ray analysis (EDX), UV-vis spectroscopy, and multiphoton microscopy were used to characterize the electrospun matrices. The incorporation of heparin into the electrospun PEO and PLGA fibers did not affect the surface morphology or fiber diameters. The fibers produced had diameters ranging from approximately 100 to 400 nm. Toluidine blue assays of heparin suggest that it can be incorporated into an electrospun matrix at concentrations ranging from 3.5 to 85 mug per milligram of electrospun fibers. Multiphoton microscopy confirmed that incorporation of PEG-LMWH into the matrix permits retention of the heparin for at least 14 days. Improvements in the binding of basic fibroblast growth factor to the electrospun fibers were also observed for fibers functionalized with PEG-LMWH over those functionalized with LMWH alone. The combination of these results suggests the utility for producing electrospun fibers that are appropriately functionalized for use in biomaterials applications.     

The influence of a biologically relevant substratum topography on human aortic and umbilical vein endothelial cells

A topographically patterned substrate with stochastic surface order that closely mimics the topographic features of native basement membranes has been fabricated to investigate the influence of topographic biophysical cueing on human aortic and umbilical vein endothelial cells. The stochastic substrate was fabricated by first generating a highly porous polyelectrolyte multilayer film of poly(acrylic acid) and poly(allylamine hydrochloride) followed by replicate production of this biomimetic topography via soft lithography. These substrates, which are easy to prepare and replicate, possess a number of prominent features associated with in vivo vascular basement membrane (interwoven ridges and grooves, bumps, and pores), which have typically been studied as singular features that frequently possess anisotropic surface order (e.g., alternating ridges and grooves). When compared to a flat surface of identical chemistry, these biomimetic topographies influenced a number of important cellular behaviors associated with the homeostasis and degradation of vascular tissues. These include modulating cell migration rate and directional persistence, proliferation rate, and gene expression associated with regulation and remodeling of vascular tissues as well as inflammation.     

A biologically inspired hierarchical goal directed navigation model

We propose an extended version of our previous goal directed navigation model based on forward planning of trajectories in a network of head direction cells, persistent spiking cells, grid cells, and place cells. In our original work the animat incrementally creates a place cell map by random exploration of a novel environment. After the exploration phase, the animat decides on its next movement direction towards a goal by probing linear look-ahead trajectories in several candidate directions while stationary and picking the one activating place cells representing the goal location. In this work we present several improvements over our previous model. We improve the range of linear look-ahead probes significantly by imposing a hierarchical structure on the place cell map consistent with the experimental findings of differences in the firing field size and spacing of grid cells recorded at different positions along the dorsal to ventral axis of entorhinal cortex. The new model represents the environment at different scales by populations of simulated hippocampal place cells with different firing field sizes. Among other advantages this model allows simultaneous constant duration linear look-ahead probes at different scales while significantly extending each probe range. The extension of the linear look-ahead probe range while keeping its duration constant also limits the degrading effects of noise accumulation in the network. We show the extended model’s performance using an animat in a large open field environment.     

The biologically relevant parameter in nerve impulse trains

Summary An experiment is described, the results of which indicate that impulse rate is the information carrying parameter of impulse trains. The demodulation of such information occurs by low-pass filtering. This conceptual model is compatible with all the features of synaptic transmission. The implications of this model for the transmission of information in integrative neurons is discussed.     

Clustering formal concepts to discover biologically relevant knowledge from gene expression data

The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here an original way to discover knowledge from gene expression data by means of the so-called formal concepts which hold in derived Boolean gene expression datasets. We first encoded the over-expression properties of genes in human cells using human SAGE data. It has given rise to a Boolean matrix from which we extracted the complete collection of formal concepts, i.e., all the largest sets of over-expressed genes associated to a largest set of biological situations in which their over-expression is observed. Complete collections of such patterns tend to be huge. Since their interpretation is a time-consuming task, we propose a new method to rapidly visualize clusters of formal concepts. This designates a reasonable number of Quasi-Synexpression-Groups (QSGs) for further analysis. The interest of our approach is illustrated using human SAGE data and interpreting one of the extracted QSGs. The assessment of its biological relevancy leads to the formulation of both previously proposed and new biological hypotheses.     

Exploring biologically relevant chemical space with metal complexes

Altering biological processes with small synthetic molecules is a general approach for the design of drugs and molecular probes. Medicinal chemistry and chemical biology are focused predominately on the design of organic molecules, whereas inorganic compounds find applications mainly for their reactivity (e.g. cisplatin as a DNA-reactive therapeutic) or imaging properties (e.g. gadolinium complexes as MRI diagnostics). In such inorganic pharmaceuticals or probes, coordination chemistry in the biological environment or at the target site lies at the heart of their modes of action. However, past and very recent results suggest that it is also worth exploring a different aspect of metal complexes: their ability to form structures with unique and defined shapes for the design of 'organic-like' small-molecule probes and drugs. In such metal-organic compounds, the metal has the main purpose to organize the organic ligands in three-dimensional space. It is likely that such an approach will complement the molecular diversity of organic chemistry in the quest for the discovery of compounds with superior biological activities.     

Synthesis and biologically relevant properties of polyphosphazene polyacids

Polyphosphazene polyacids show potential as immunostimulating compounds and materials for microencapsulation. Their synthesis requires multistep chemical transition from a hydrolytically unstable macromolecular precursor, poly(dichlorophosphazene), to a water-soluble polyelectrolyte. Insufficient synthetic control in these reactions can lead to molecular weight variations and formation of macromolecules with "structural defects" resulting in significant variations in polymer performance. Simple and reproducible "one pot-one solvent" method is reported for the preparation of polyphosphazene polyacids-poly[di(carboxylatophenoxy)phosphazene] and its copolymers. Molecular weight characteristics and polymer compositions were studied as a function of reaction parameters. Macromolecular byproducts, incompletely substituted polymers containing hydroxyl groups and partially deprotected polymers containing propyl ester functionalities, were synthesized and characterized. It was demonstrated, that the presence of such groups can affect polymer characteristics, such as hydrolytic degradation profiles, immunostimulating activity, and microsphere forming properties. In vivo studies showed that the immunostimulating activity of polyphosphazene polyacids correlates with the content of acid functionalities in the polymer.     

Process-based species delimitation leads to identification of more biologically relevant species*

Most approaches to species delimitation to date have considered divergence-only models. Although these models are appropriate for allopatric speciation, their failure to incorporate many of the population-level processes that drive speciation, such as gene flow (e.g., in sympatric speciation), places an unnecessary limit on our collective understanding of the processes that produce biodiversity. To consider these processes while inferring species boundaries, we introduce the R-package delimitR and apply it to identify species boundaries in the reticulate taildropper slug (Prophysaon andersoni). Results suggest that secondary contact is an important mechanism driving speciation in this system. By considering process, we both avoid erroneous inferences that can be made when population-level processes such as secondary contact drive speciation but only divergence is considered, and gain insight into the process of speciation in terrestrial slugs. Further, we apply delimitR to three published empirical datasets and find results corroborating previous findings. Finally, we evaluate the performance of delimitR using simulation studies, and find that error rates are near zero when comparing models that include lineage divergence and gene flow for three populations with a modest number of Single Nucleotide Polymorphisms (SNPs; 1500) and moderate divergence times (<100,000 generations). When we apply delimitR to a complex model set (i.e., including divergence, gene flow, and population size changes), error rates are moderate (∼0.15; 10,000 SNPs), and, when present, misclassifications occur among highly similar models.