Predicting secondary structures of membrane proteins with neural networks

Back-propagation, feed-forward neural networks are used to predict the secondary structures of membrane proteins whose structures are known to atomic resolution. These networks are trained on globular proteins and can predict globular protein structures having no homology to those of the training set with correlation coefficients (C) of 0.45, 0.32 and 0.43 for a-helix, -strand and random coil structures, respectively. When tested on membrane proteins, neural networks trained on globular proteins do, on average, correctly predict (Q_i) 62%, 38% and 69% of the residues in the -helix, -strand and random coil structures. These scores rank higher than those obtained with the currently used statistical methods and are comparable to those obtained with the joint approaches tested so far on membrane proteins. The lower success score for -strand as compared to the other structures suggests that the sample of -strand patterns contained in the training set is less representative than those of a-helix and random coil. Our analysis, which includes the effects of the network parameters and of the structural composition of the training set on the prediction, shows that regular patterns of secondary structures can be successfully extrapolated from globular to membrane proteins. Correspondence to: R. Casadio     

Minimization of intermediate concentrations as a suggested optimality principle for biochemical networks

The criterion of minimum intermediate concentrations in steady states is suggested to be of essential relevance in the evolution of biochemical reaction networks. This extremum principle is phrased in two different ways, firstly in terms of total osmolarity of intermediates and, secondly, as a multiple criterion problem. The relationships between the two problems are elucidated and a solving method for the latter is then given. It turns out that in each optimal state, the network can be subdivided into a slow and a fast subsystem. The notion of convex conservation relations is introduced and the implications of such relations for the optimization problem are investigated.     

Neural networks with constrained inputs as models for pattern formation in primate visual cortex

We present a neural network model for the formation of ocular dominance stripes on primate visual cortex and examine the generic phase behavior and dynamics of the model. The dynamical equation of ocular dominance development can be identified with a class of Langevin equations with a nonconserved order parameter. We first set up and examine an Ising model with long-range interactions in an external field, which is equivalent to the model described by the Langevin equation. We use both mean-field theory and Monte-Carlo simulations to study the equilibrium phase diagram of this equivalent Ising model. The phase diagram comprises three phases: a striped phase, a hexagonal bubble phase, and a uniform paramagnetic phase. We then examine the dynamics of the striped phase by solving the Langevin equation both numerically and by singular perturbation theory. Finally, we compare the results of the model with physiological data. The typical striped structure of the ocular dominance columns corresponds to the zero-field configurations of the model. Monocular deprivation can be simulated by allowing the system to evolve in the absence of an external field at early times and then continuing the simulation in the presence of an external field. The physical and physiological applications of our model are discussed in the conclusion.     

Electron spin resonance spectra of penta- and hexacoordinated nitrosyl iron protoporphyrin IX complexes

Electron spin resonance (ESR) spectra of frozen aqueous solutions of NO · haem · base complexes and NO · haem intercalated into dodecyl sulfate micelles have been measured at 77 K and analyzed for the hyperfine components of ¹⁵NO,¹⁴N-base, ¹⁴N-pyrroles and ⁵⁷Fe which coincide with the principal directions of the g tensor. The influence of the basicity of the nitrogen base on the spin distribution and geometry of the Fe-N-O grouping has been demonstrated by replacing imidazole for pyridine and by comparing the ESR spectra with those obtained for the monomeric insect haemoglobin CTT IV.The comparison of the hyperfine parameters described for the so-called pentacoordinated nitrosyl complex of CTT IV with those of the NO · haem intercalated into detergent micelles has furnished evidence that the ESR spectrum of this conformation state of haemoglobin has to be definitely assigned to a pentacoordinated nitrosyl complex.The a_zz values increase with the following orders: CTT IV (2.98 mT) < imidazole complex (3.04mT) < pyridine complex (3.15mT) for ¹⁵NO, and pyridine complex (0.59 mT) < imidazole complex (0.67 mT) < CTT IV (0.70 mT) for the ¹⁴N-base. This result is in conformity with an increase of the donor and the acceptor strengths of the nitrogen base in trans-position to ¹⁵NO. The a_yy and a_xx components of ¹⁵NO and the ¹⁴N-base are strongly nonequivalent in the nitrosyl haemoglobin CTT IV, and less nonequivalent in the NO · haem · pyridine complex, indicating bending of the Fe-N-O grouping. The hyperfine components of the axial ligands coinciding with the x and y component of the g tensor are nearly equal for the NO · haem · imidazole complex.     

Invariant properties in coevolutionary networks of plant–animal interactions

Plant–animal mutualistic networks are interaction webs consisting of two sets of entities, plant and animal species, whose evolutionary dynamics are deeply influenced by the outcomes of the interactions, yielding a diverse array of coevolutionary processes. These networks are two‐mode networks sharing many common properties with others such as food webs, social, and abiotic networks. Here we describe generalized patterns in the topology of 29 plant–pollinator and 24 plant–frugivore networks in natural communities. Scale‐free properties have been described for a number of biological, social, and abiotic networks; in contrast, most of the plant–animal mutualistic networks (65.6%) show species connectivity distributions (number of links per species) with a power‐law regime but decaying as a marked cut‐off, i.e. truncated power‐law or broad‐scale networks and few (22.2%) show scale‐invariance. We hypothesize that plant–animal mutualistic networks follow a build‐up process similar to complex abiotic nets, based on the preferential attachment of species. However, constraints in the addition of links such as morphological mismatching or phenological uncoupling between mutualistic partners, restrict the number of interactions established, causing deviations from scale‐invariance. This reveals generalized topological patterns characteristic of self‐organized complex systems. Relative to scale‐invariant networks, such constraints may confer higher robustness to the loss of keystone species that are the backbone of these webs.     

Current topics in signal transduction in bacteria

Among the signal transfer systems in bacteria two types predominate: two-component regulatory systems and quorum sensing systems. Both types of system can mediate signal transfer across the bacterial cell envelope; however, the signalling molecule typically is not taken up into the cells in the former type of system, whereas it usually is in the latter. The Two-component systems include the recently described (eukaryotic) phosphorelay systems; quorum sensing systems can be based upon autoinducers of the N-acylated homoserine lactones, and on autoinducers of a peptidic nature. A single bacterial cell contains many signalling modules that primarily operate in parallel. This may give rise to neural-network behaviour. Recently, however, for both types of basic signal transfer modules, it has been demonstrated that they also can be organised in series (i.e. in a hierarchical order). Besides their hierarchical position in the signal transduction network of the cell, the spatial distribution of individual signalling modules may also be an important factor in their efficiency in signal transfer. Many challenges lie hidden in future work to understand these signal transfer processes in more detail. These are discussed here, with emphasis on the mutual interactions between different signal transfer processes. Successful contributions to this work will require rigorous mathematical modelling of the performance of signal transduction components, and -networks, as well as studies on light-sensing signal transduction systems, because of the unsurpassed time resolution obtainable in those latter systems, the opportunity to apply repeated reproducible stimuli, etc. The increased understanding of bacterial behaviour that already has resulted – and may further result – from these studies, can be used to fine-tune the beneficial activities of bacteria and/or more efficiently inhibit their deleterious ones.     

Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste

Soil-transmitted helminths (STHs) are parasitic intestinal worms that infect almost a fifth of the global population. Sustainable control of STHs requires understanding the complex interaction of factors contributing to transmission. Identifying risk factors has mainly relied on logistic regression models where the underlying assumption of independence between variables is not always satisfied. Previously demonstrated risk factors including water, sanitation and hygiene (WASH) access and behaviours, and socioeconomic status are intrinsically linked. Similarly, environmental factors including climate, soil and land attributes are often strongly correlated. Alternative methods such as recursive partitioning and Bayesian networks can handle correlated variables, but there are no published studies comparing these methods with logistic regression in the context of STH risk factor analysis. Baseline cross-sectional data from school-aged children in the (S)WASH-D for Worms study were used to compare risk factors identified from modelling the same data using three different statistical techniques. Outcomes of interest were infection with Ascaris spp. and any hookworm species (Necator americanus, Ancylostoma duodenale, and Ancylostoma ceylanicum). Mixed-effects logistic regression identified the fewest risk factors. Recursive partitioning identified the most WASH and demographic risk factors, while Bayesian networks identified the most environmental risk factors. Recursive partitioning produced classification trees that visualised potentially at-risk population sub-groups. Bayesian networks helped visualise relationships between variables and enabled interactive modelling of outcomes based on different scenarios for the predictor variables of interest. Model performance was similar across all techniques. Risk factors identified across all techniques were vegetation for Ascaris spp., and cleaning oneself with water after defecating for hookworm. This study adds to the limited body of evidence exploring alternative data modelling approaches in identifying risk factors for STH infections. Our findings suggest these approaches can provide novel insights for more robust interpretation.     

An automatic method for removing empty camera trap images using ensemble learning

Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine‐learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small‐size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.     

丛枝菌根菌丝网络在植物互作中的作用机制研究进展

植物间的相互作用是生态学领域关注的重要方向之一,其对高效利用养分资源、提高生产力以及构建植物群落均具有重要意义.丛枝菌根真菌是重要的植物互惠共生微生物,其菌丝可以将邻近植物的根部连接起来,形成共同的菌丝网络(CMNs),这些网络对转移养分、水分以及调节植物群落具有重要作用.近期的研究表明,CMNs可以充当植物之间传递病...