A Class of Pairwise Models for Epidemic Dynamics on Weighted Networks

In this paper, we study the SIS (susceptible–infected–susceptible) and SIR (susceptible–infected–removed) epidemic models on undirected, weighted networks by deriving pairwise-type approximate models coupled with individual-based network simulation. Two different types of theoretical/synthetic weighted network models are considered. Both start from non-weighted networks with fixed topology followed by the allocation of link weights in either (i) random or (ii) fixed/deterministic way. The pairwise models are formulated for a general discrete distribution of weights, and these models are then used in conjunction with stochastic network simulations to evaluate the impact of different weight distributions on epidemic thresholds and dynamics in general. For the SIR model, the basic reproductive ratio R ₀ is computed, and we show that (i) for both network models R ₀ is maximised if all weights are equal, and (ii) when the two models are ‘equally-matched’, the networks with a random weight distribution give rise to a higher R ₀ value. The models with different weight distributions are also used to explore the agreement between the pairwise and simulation models for different parameter combinations.     

A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures

The present study deals with ANN based prediction of culture parameters in terms of inoculum density, pH and volume of growth medium per culture vessel and sucrose content of the growth medium for Glycyrrhiza hairy root cultures. This kind of study could be a model system in exploitation of hairy root cultures for commercial production of pharmaceutical compounds using large bioreactors. The study is aimed to evaluate the efficiency of regression neural network and back propagation neural network for the prediction of optimal culture conditions for maximum hairy root biomass yield. The training data for regression and back propagation networks were primed on the basis of function approximation, where final biomass fresh weight (f_wt) was considered as a function of culture parameters. On this basis the variables in culture conditions were described in the form of equations which are for inoculum density: y=0.02x+0.04, for pH of growth medium: y=x+2.8, for sucrose content in medium: y=9.9464x+(−9.7143) and for culture medium per culture vessel: y=10x. The fresh weight values obtained from training data were considered as target values and further compared with predicted fresh weight values. The empirical data were used as testing data and further compared with values predicted from trained networks. Standard MATLAB inbuilt generalized regression network with radial basis function radbas as transfer function in layer one and purelin in layer two and back propagation having purelin as transfer function in output layer and logsig in hidden layer were used. Although in comparative assessment both the networks were found efficient for prediction of optimal culture conditions for high biomass production, more accuracy in results was seen with regression network.     

Effects of Edge Directions on the Structural Controllability of Complex Networks

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.     

Engineering of In Vitro 3D Capillary Beds by Self-Directed Angiogenic Sprouting

In recent years, microfluidic systems have been used to study fundamental aspects of angiogenesis through the patterning of single-layered, linear or geometric vascular channels. In vivo, however, capillaries exist in complex, three-dimensional (3D) networks, and angiogenic sprouting occurs with a degree of unpredictability in all x,y,z planes. The ability to generate capillary beds in vitro that can support thick, biological tissues remains a key challenge to the regeneration of vital organs. Here, we report the engineering of 3D capillary beds in an in vitro microfluidic platform that is comprised of a biocompatible collagen I gel supported by a mechanical framework of alginate beads. The engineered vessels have patent lumens, form robust ∼1.5 mm capillary networks across the devices, and support the perfusion of 1 µm fluorescent beads through them. In addition, the alginate beads offer a modular method to encapsulate and co-culture cells that either promote angiogenesis or require perfusion for cell viability in engineered tissue constructs. This laboratory-constructed vascular supply may be clinically significant for the engineering of capillary beds and higher order biological tissues in a scalable and modular manner.     

A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.     

Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.     

Radioligand assays — Methods and applications. IV. Uniform regression of hyperbolic and linear radioimmunoassay calibration curves

In order to handle all types of radioimmunoassay (RIA) calibration curves obtained in our laboratory in the same way, we tried to find a non-linear expression for their regression which allows calibration curves with different degrees of curvature to be fitted. Considering the two boundary cases of the incubation protocol we derived a hyperbolic inverse regression function: x = a₁ya₀ + a_?1y^?1, where x is the total concentration of antigen, a_i constants, and y is the specifically bound radioactivity. An RIA evaluation procedure based on this function is described providing a fitted inverse RIA calibration curve and some statistical quality parameters. The latter are on an order which is normal for RIA systems. There is an excellent agreement between fitted and experimentally obtained calibration curves having a different degree of curvature.     

Low-temperature optical properties and pigment organization of the B875 light-harvesting bacteriochlorophyll-protein complex of purple photosynthetic bacteria

Optical and structural properties of the B875 light-harvesting complex of purple bacteria were examined by measurements of low-temperature circular dichroism (CD) and excitation spectra of fluorescence polarization. In the B875 complex isolated from wild-type Rhodopseudomonas sphaeroides, fluorescence polarization increased steeply across the long-wavelength Q_y bacteriochlorophyll a (BChl) absorption band at both 4 and approx. 300 K. With the native complex in the photosynthetic membranes of Rhodospirillum rubrum and Rps. sphaeroides wild-type and R26-carotenoidless strains, this significant increase in polarization from 0.12 to 0.40 was only observed at low temperature. A polarization of ?0.2 was observed upon excitation in the Q_x BChl band. The results indicate that about 15% of the BChl molecules in the complex absorb at wavelengths about 12 nm longer than the other BChls. All BChls have approximately the same orientation with their Q_y transition dipoles essentially parallel and their Q_x transitions perpendicular to the plane of the membrane. At low temperature, energy transfer to the long-wavelength BChls is irreversible, yielding a high degree of polarization upon direct excitation, whereas at room temperature a partial depolarization of fluorescence by energy transfer between different subunits occurs in the membrane, but not in the isolated complex. CD spectra appear to reflect the two spectral forms of B875 BChl in Rps. sphaeroides membranes. They also reveal structural differences between the complexes of Rps. sphaeroides and Rhs. rubrum, in both BChl and carotenoid regions. The CD spectrum of isolated B875 indicates that the interactions between the BChls but not the carotenoids are altered upon isolation.     

Butterfly species identification by branch length similarity entropy

We previously developed a shape recognition methodology that uses “branch length similarity” (BLS) entropy, which is defined as a simple branching network consisting of a single node and branches. The simple network is referred to as a “unit branching network” (UBN). Our approach involves obtaining BLS entropy profiles from UBNs created by joining each pixel in the outline of a shape with every other pixel in the shape's border. The profiles successfully characterize the shapes by comparing their BLS entropy profiles. Presently, we modified this approach to facilitate its application to butterfly species identification by partitioning and weighting the entropy profile. As a test, we identified the butterfly species Colias erate, Parnassius bremeri, Eurema hecabe, Gonepteryx rhamni, and Papilio maackii. Each species group consisted of 10 specimens. We used wing shape to identify a species. We extracted evenly spaced x–y coordinates of boundary pixels for the wing shapes in a counter counterclockwise direction. The number of the pixels was 749. We then sequentially partitioned 749 x–y pairs into 15 groups, calculated entropy profiles for the groups, and weighted the profiles. The profiles were combined in order, resulting in a single weighted BLS entropy profile for a wing's shape. Subsequently, we statistically compared the correlation coefficient among the weighted BLS profiles. Our experimental results showed that this method was statistically successful for butterfly species identification. The advantage of the partitioning and weighting process in shape recognition is also discussed.     

Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network

A planar 17 muscle model of the monkey's arm based on realistic biomechanical measurements was simulated on a Symbolics Lisp Machine. The simulator implements the equilibrium point hypothesis for the control of arm movements. Given initial and final desired positions, it generates a minimum-jerk desired trajectory of the hand and uses the backdriving algorithm to determine an appropriate sequence of motor commands to the muscles (Flash 1987; Mussa-Ivaldi et al. 1991; Dornay 1991b). These motor commands specify a temporal sequence of stable (attractive) equilibrium positions which lead to the desired hand movement. A strong disadvantage of the simulator is that it has no memory of previous computations. Determining the desired trajectory using the minimum-jerk model is instantaneous, but the laborious backdriving algorithm is slow, and can take up to one hour for some trajectories. The complexity of the required computations makes it a poor model for biological motor control. We propose a computationally simpler and more biologically plausible method for control which achieves the benefits of the backdriving algorithm. A fast learning, tree-structured network (Sanger 1991c) was trained to remember the knowledge obtained by the backdriving algorithm. The neural network learned the nonlinear mapping from a 2-dimensional cartesian planar hand position {x, y} to a 17-dimensional motor command space {u ₁, ..., u ₁₇}. Learning 20 training trajectories, each composed of 26 sample points {{x y{,{u ₁, ..., u ₁₇} took only 20 min on a Sun-4 Spare workstation. After the learning stage, new, untrained test trajectories as well as the original trajectories of the hand were given to the neural network as input. The network calculated the required motor commands for these movements. The resulting movements were close to the desired ones for both the training and test cases.     

Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces

Robustness and evolvability are highly intertwined properties of biological systems. The relationship between these properties determines how biological systems are able to withstand mutations and show variation in response to them. Computational studies have explored the relationship between these two properties using neutral networks of RNA sequences (genotype) and their secondary structures (phenotype) as a model system. However, these studies have assumed every mutation to a sequence to be equally likely; the differences in the likelihood of the occurrence of various mutations, and the consequence of probabilistic nature of the mutations in such a system have previously been ignored. Associating probabilities to mutations essentially results in the weighting of genotype space. We here perform a comparative analysis of weighted and unweighted neutral networks of RNA sequences, and subsequently explore the relationship between robustness and evolvability. We show that assuming an equal likelihood for all mutations (as in an unweighted network), underestimates robustness and overestimates evolvability of a system. In spite of discarding this assumption, we observe that a negative correlation between sequence (genotype) robustness and sequence evolvability persists, and also that structure (phenotype) robustness promotes structure evolvability, as observed in earlier studies using unweighted networks. We also study the effects of base composition bias on robustness and evolvability. Particularly, we explore the association between robustness and evolvability in a sequence space that is AU-rich – sequences with an AU content of 80% or higher, compared to a normal (unbiased) sequence space. We find that evolvability of both sequences and structures in an AU-rich space is lesser compared to the normal space, and robustness higher. We also observe that AU-rich populations evolving on neutral networks of phenotypes, can access less phenotypic variation compared to normal populations evolving on neutral networks.     

Coevolutionary dynamics of adaptive radiation for food-web development

To investigate how complex food-webs can develop through repeated evolutionary diversification, a predator–prey model was analyzed. In the model, each individual has two traits: trait x as a predator and trait y as a prey. These traits constitute a two-dimensional phenotype space, in which the whole group of individuals are represented as a phenotype distribution. Predator–prey interactions among the phenotypes are determined by their relative positions in the phenotype space. Each phenotypic cluster was treated as a species. Each species evolves in y to escape from predation, while it evolves in x to chase their prey. Analytical investigation provided two predictions. First, coupled evolutionary diversifications of y and x may occur when the x of predators have caught up with their prey’s y, which may be repeated. Second, complex food-webs may develop when species’ competitive strengths are kept similar within the communities. If the functional response is close to the ratio-dependent response, the competitive strengths of all species are similar when the relationship between predators and prey corresponds to the ideal free distribution (IFD). These predictions were confirmed by numerical simulations. Electronic supplementary material  The online version of this article doi:() contains supplementary material, which is available to authorized users.     

Multidimensional profiling of components in complex mixtures of natural products for metabolic analysis,proof of concept: Application to Quillaja saponins

A method for separation and detection of major and minor components in complex mixtures has been developed, utilising two-dimensional high-performance liquid chromatography (2D-HPLC) combined with electrospray ionisation ion-trap multiple-stage mass spectrometry (ESI-ITMSⁿ). Chromatographic conditions were matched with mass spectrometric detection to maximise the number of components that could be separated. The described procedure has proven useful to discern several hundreds of saponin components when applied to Quillaja saponaria Molina bark extracts. The discrimination of each saponin component relies on the fact that three coordinates (x, y, z) for each component can be derived from the retention time of the two chromatographic steps (x, y) and the m/z-values from the multiple-stage mass spectrometry (z_n, n = 1, 2, …). Thus an improved graphical representation was obtained by combining retention times from the two-stage separation with +MS¹ (z₁) and the additional structural information from the second mass stage +MS² (z₂, z₃) corresponding to the main fragment ions. By this approach three-dimensional plots can be made that reveal both the chromatographic and structural properties of a specific mixture which can be useful in fingerprinting of complex mixtures.     

Some observations on the minor products of the reaction of 2,2′-dilithiobiphenyl with mercuric chloride

Reaction of 2,2′-dilithiobiphenyl (formed from 2,2′-diiodobiphenyl and lithium in diethyl ether) with mercuric chloride gives the ortho-biphenylenemercury trimer (I) with 2,2′-bis(iodomercury)biphenyl (II) as an isolatable intermediate. The mass spectrum of impure 2,2′-bis(iodomercury)biphenyl at high sensitivity shows ion clusters which are interpreted as the ions of a polyphenyl iodomercury complex [Hg₃(C₆H₄)₄I₂] (III) which is identified as a further intermediate in the production of ortho-biphenylenemercury trimer and several iodomercury cations of general formula [Hg_xI_y]⁺, where x, y = 1, 2, 3. A fragmentation scheme is presented to account for these unusual iodomercury cations. Reaction mechanisms are presented to account for the production of II and III.     

Towards Physarum binary adders

Plasmodium of Physarum polycephalum is a single cell visible by unaided eye. The plasmodium's foraging behaviour is interpreted in terms of computation. Input data is a configuration of nutrients, result of computation is a network of plasmodium's cytoplasmic tubes spanning sources of nutrients. Tsuda et al. (2004) experimentally demonstrated that basic logical gates can be implemented in foraging behaviour of the plasmodium. We simplify the original designs of the gates and show - in computer models - that the plasmodium is capable for computation of two-input two-output gate 〈x, y〉 → 〈xy, x + y〉 and three-input two-output . We assemble the gates in a binary one-bit adder and demonstrate validity of the design using computer simulation.     

Fluorescence properties of the light-harvesting bacteriochlorophyll protein from Rhodopseudomonas sphaeroides R-26

The light-harvesting bacteriochlorophyll-protein (BChl-protein) from Rhodopseudomonas sphaeroides, R-26 mutant, exhibits a strong optical absorption peak near 850 nm (Q_y band) and a weaker peak at 590 nm (Q_x band). This pigment-protein appears to contain two BChl molecules per subunit, and previous circular dichroism studies indicated the presence of excitonic interactions between the BChl molecules. The complex exhibits a fluorescence maximum near 870 nm at room temperature. Excitation in the Q_y region results in polarization p values that vary only from +0.12 at 820 nm to +0.14 near 900 nm. These values are appreciably smaller than that for monomeric BChl in viscous solvents (p > 0.4). By contrast, using Q_x excitation the p value is ?0.25 for the BChl-protein complex, which is close to that observed for the BChl monomer. For the BChl-protein these polarization values do not change greatly at a temperature of 90 K; however, the Stokes' shift of the fluorescence emission increases significantly over that at room temperature.     

Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis

We investigate the general problem of signal classification and, in particular, that of assigning stimulus labels to neural spike trains recorded from single cortical neurons. Finding efficient ways of classifying neural responses is especially important in experiments involving rapid presentation of stimuli. We introduce a fast, exact alternative to Bayesian classification. Instead of estimating the class-conditional densities p(x|y) (where x is a scalar function of the feature[s], y the class label) and converting them to P(y|x) via Bayes’ theorem, this probability is evaluated directly and without the need for approximations. This is achieved by integrating over all possible binnings of x with an upper limit on the number of bins. Computational time is quadratic in both the number of observed data points and the number of bins. The algorithm also allows for the computation of feedback signals, which can be used as input to subsequent stages of inference, e.g. neural network training. Responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analysed. Information latency and response duration increase nonlinearly with presentation duration, suggesting that neural processing speeds adapt to presentation speeds. Action Editor: Alexander Borst     

Comparison of the diversity,composition, and host recurrence of xylariaceous endophytes in subtropical,cool temperate,and subboreal regions in Japan

The diversity, composition, and host recurrence of endophytic fungi in the Xylariaceae were compared in subtropical (ST), cool temperate (CT), and subboreal forests (SB) in Japan based on the 28S ribosomal DNA sequences from fungal isolates. A total of 610 isolates were obtained from the leaves of 167 tree species in three sites, which were classified into 42 operational taxonomic units (OTUs) at the 99 % similarity level of the 28S rDNA sequence. ST, CT, and SB yielded 31, 13, and three OTUs, respectively. The OTU richness, diversity, and evenness of fungal communities were in the order: ST > CT > SB. The 42 OTUs were assigned to nine genera in the Xylariaceae: Xylaria, Annulohypoxylon, Anthostomella, Biscogniauxia, Nemania, Hypoxylon, Muscodor, Daldinia, and Rosellinia. Xylarioid isolates in the subfamily Xylarioideae outnumbered Hypoxyloid isolates in the subfamily Hypoxyloideae in ST and CT, whereas the opposite was found in SB. Sørensen’s quotient of similarity was generally low between the three sites. Host recurrence of fungal OTUs was evaluated with the degree of specialization of interaction network between xylariaceous endophytes and plant species and compared between the three sites. We found that the networks in the three sites showed a significantly higher degree of specialization than simulated networks, where partners were associated randomly. Permutational multivariate analyses of variance indicated that plant family and leaf trait significantly affected the OTU composition in ST, which can account for the specialization of interaction network and host recurrence of xylariaceous endophytes.     

A neural network study on the dynamic identification of a fermentation system

The objective of this paper is to propose neural networks for the study of dynamic identification and prediction of a fermentation system which produces mainly 2,3-butanediol (2,3-BDL). The metabolic products of the fermentation, acetic acid, acetoin, ethanol, and 2,3-BDL were measured on-line via a mass spectrometer modified by the insertion of a dimethylvinylsilicone membrane probe. The measured data at different sampling times were included as the input and output nodes, at different learning batches, of the network. A fermentation system is usually nonlinear and dynamic in nature. Measured fermentation data obtained from the complex metabolic pathways are often difficult to be entirely included in a static process model, therefore, a dynamic model was suggested instead. In this work, neural networks were provided by a dynamic learning and prediction process that moved along the time sequence batchwise. In other words, a scheme of two-dimensional moving window (number of input nodes by the number of training data) was proposed for reading in new data while forgetting part of the old data. Proper size of the network including proper number of input/output nodes were determined by trained with the real-time fermentation data. Different number of hidden nodes under the consideration of both learning performance and computation efficiency were tested. The data size for each learning batch was determined. The performance of the learning factors such as the learning coefficient η and the momentum term coefficient α were also discussed. The effect of different dynamic learning intervals, with different starting points and the same ending point, both on the learning and prediction performance were studied. On the other hand, the effect of different dynamic learning intervals, with the same starting point and different ending points, was also investigated. The size of data sampling interval was also discussed. The performance from four different types of transfer functions, x/(1+|x|), sgn(x)·x ²/(1+x ²), 2/(1+e ^{? x} )?1, and 1/(1+e ^{? x} ) was compared. A scaling factor b was added to the transfer function and the effect of this factor on the learning was also evaluated. The prediction results from the time-delayed neural networks were also studied.