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opencc-zeroDec 2013View details →

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

Data from: The topological differences between visitation and pollen transport networks: a comparison in species rich communities of the Himalaya-Hengduan Mountains

Pollination networks are usually constructed and assessed by direct field observations which commonly assume that all flower visitors are true pollinators. However, this assumption is often invalid and the use of data based on mere visitors to flowers may lead to a misunderstanding of intrinsic pollination networks. Here, using a large dataset by both sampling floral visitors and analyzing their pollen loads, we constructed 32 networks pairs (visitation vs. pollen transport) across one flowering season at four elevation sites in the Himalaya-Hengduan Mountains region. Pollen analysis was conducted to determine which flower visitors acted as potential pollinators (pollen vectors) or as cheaters (those not carrying pollen of the visited plants). We tested whether there were topological differences between visitation and pollen transport networks and whether different taxonomic groups of insect visitors differed in their ability to carry pollen of the visited plants. Our results indicated that there was a significantly higher degree of specialization at both the network and species levels in the pollen transport networks in contrast to the visitation networks. Modularity was lower but nestedness was higher in the visitation networks compared to the pollen transport networks. All the cheaters were identified as peripheral species and most of them contributed positively to the nested structure. This may explain in part the differences in modularity and nestedness between the two network types. Bees carried the highest proportion of pollen of the visited plants. This was followed by Coleoptera, other Hymenoptera and Diptera. Lepidoptera carried the lowest proportion of pollen of the visited plants. Our study shows that the construction of pollen transport networks could provide a more in-depth understanding of plant-pollinator interactions. Moreover, it suggests that detecting and removing cheater interactions when studying the topology of other mutualistic networks might be also important.

opencc-zeroDec 2018View details →

Data from: Biogeographical network analysis of cretaceous terrestrial tetrapods: a phylogeny-based approach

Network methods are widely used to represent and analyse biogeography. It is difficult, however, to convert occurrence data of fossil vertebrates to a biogeographical network, as most species were known from a single locality. A new method for creating a biogeographical network that can incorporate phylogenetic information is proposed in this study, which increases the number of edges in the network of fossil vertebrates and enables the application of various network methods. Using ancestral state reconstruction via maximum parsimony, the method first estimates the biogeographical regions of all internal nodes of a given phylogeny using biogeographical information on the terminal taxa. Then, each internal node in the phylogenetic tree is converted to an edge in the biogeographical network that connects the region(s), if unambiguously estimated, of its two descendants. The new method was applied to phylogenetic trees generated by a birth-death model. Under all conditions tested, an average of >70% of the internal nodes in phylogenetic trees were converted into edges. Three network indices—link density, average link weight, and endemism index—were evaluated for their usefulness in comparing different biogeographical networks. The endemism index reflects the rate of dispersal; the other indices reflect nonbiogeographical parameters, the number of taxa and regions, which highlights the importance of evaluating network indices before applying them to biogeographical studies. Multiple Cretaceous biogeographical networks were constructed from the phylogenies of five tetrapod taxa: terrestrial crocodyliforms, terrestrial turtles, non-avian dinosaurs, avians, and pterosaurs. The networks of avians and pterosaurs showed similar topologies and a strong correlation, and unexpectedly high endemism indices. These similarities were probably a result of shared taphonomic biases (i.e., the Lagerstätten effect) for volant taxa with fragile skeletons. The crocodyliform network was partitioned into the Gondwanan and Laurasian continents. The dinosaur network was partitioned into three groups of continents: (1) North America, Asia, and Australia; (2) Europe and Africa; (3) India, Madagascar, and South America. When Early and Late Cretaceous dinosaurs were analysed separately, the dinosaur networks were divided into (1) North America, Asia, and Australia; and (2) Europe, Africa, India, and South America for the Early Cretaceous and (1) North America, Asia, and Europe; (2) India, Madagascar, and South America for the Late Cretaceous. This partitioning of dinosaur and crocodyliform networks corroborates the results of previous biogeographical studies and indicates that the method introduced here can retrieve biogeographical signals from a source phylogeny when sufficient data are available for most targeted biogeographical regions.

Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.

opencc-zeroDec 2013View details →

Data from: Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced "superacceptor" activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.

Data from: Trait-based modeling of multi-host pathogen transmission: plant-pollinator networks

Epidemiological models for multi-host pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities, that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution, but have not accounted for within-species trait variation or between-species trait overlap. Here, we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop SIS models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks, and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and co-specialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions, in any community.

Data from: Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders.

The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási-Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (1) the proposed selection strategy significantly can improve spreading efficiency in the short-term spreading and (2) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.

Data from: Social rank, color morph, and social network metrics predict oxidative stress in a cichlid fish

Dominance hierarchies are a fundamental part of social systems in many species and social rank can influence access to resources and impact health and physiology. While social subordination is a profound stressor, few studies consider the social stress experienced by dominant males due to constantly needing to defend their dominance status through costly aggressive displays. Recent studies suggest that in species that use body coloration to signal status, these costs may also be color morph-specific. Our study examines the link between the social rank, intensity of territorial defense, body coloration, and oxidative stress in males of the color polymorphic cichlid fish Astatotilapia burtoni where males are either blue or yellow. We studied behavior in naturalistic communities and examined circulating reactive oxygen metabolites and antioxidant defenses. We found that dominant males experience higher concentrations of circulating reactive oxygen metabolites without notably increasing their antioxidant defenses, but this effect was not related to color morph. Aggression and social network ties predicted oxidative stress in a morph-specific manner, with yellow but not blue males showing signs of increased oxidative damage with increasing agonistic effort. In contrast to expectation, oxidative stress was not influenced by cortisol or testosterone levels. We conclude that oxidative stress is instrumental to understanding the costs and benefits of high social rank.

opencc-zeroDec 2014View details →

Data from: Face patch resting state networks link face processing to social cognition

Faces transmit a wealth of social information. How this information is exchanged between face-processing centers and brain areas supporting social cognition remains largely unclear. Here we identify these routes using resting state functional magnetic resonance imaging in macaque monkeys. We find that face areas functionally connect to specific regions within frontal, temporal, and parietal cortices, as well as subcortical structures supporting emotive, mnemonic, and cognitive functions. This establishes the existence of an extended face-recognition system in the macaque. Furthermore, the face patch resting state networks and the default mode network in monkeys show a pattern of overlap akin to that between the social brain and the default mode network in humans: this overlap specifically includes the posterior superior temporal sulcus, medial parietal, and dorsomedial prefrontal cortex, areas supporting high-level social cognition in humans. Together, these results reveal the embedding of face areas into larger brain networks and suggest that the resting state networks of the face patch system offer a new, easily accessible venue into the functional organization of the social brain and into the evolution of possibly uniquely human social skills.

opencc-zeroDec 2012View details →

Data from: Accurate measurements of dynamics and reproducibility in small genetic networks

Quantification of gene expression has become a central tool for understanding genetic networks. In many systems the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a two-fold increase in profile reproducibility due to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.

Data from: Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows

The effects of sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplemented to diets contrasting in the proportion of forage and concentrate (FC) (65:35 vs 35:65), were evaluated for their influence on rumen microbiome. Four multiparous Nordic Red dairy cows fitted with rumen cannulae were used in a 4 × 4 Latin square with a 2 × 2 factorial arrangement of treatments and four 35-d periods. Ruminal digesta samples were collected on d 22 and d 24 of each experimental period and DNA was extracted from a combined sample. Diet effect on rumen microbial community was explored by qPCR, T-RFLP and metabarcoding sequencing. QPCR analysis showed that the total amounts of bacteria, archaea or ciliate protozoa were not significantly altered either by FC ratio or addition of SO. Only fungi were reduced by half in high concentrate (H) compared to high forage (L) diets (P=0.03). Further significant reduction of fungi was observed due to SO in both HSO and LSO diets but the effect was stronger in HSO (H vs HSO by 10.5x, P=0.03; L vs LSO by 1.9x, P=0.04). Metabarcoding sequencing analysis showed that SO affected bacterial, archaeal, ciliate protozoa and fungal community structure and diversity and the effect was FC ratio dependent. As expected, Simpson's index of diversity was higher in diets containing higher proportion of forage. These diets were dominated by Firmicutes, while Bacteroidetes and Proteobacteria were more abundant in H diets. Methanobrevibacter ruminantium and Methanobrevibacter gottschalkii dominated archaea community but they were in negative relation to each other. Methanobrevibacter gottschalkii was more abundant in L, while Methanobrevibacter ruminantium in H diet. The strongest diet effect was observed on fungal community, represented by both well classified and novel fungal groups. Both, increase in concentrate and supplementation of SO significantly reduced fungal diversity. We explored microbial interactions by building taxa co-occurrence networks. Our results suggest that studying entire rumen microbiome simultaneously is needed aiming to better understand how diet induced changes within microbial community are associated with microbial function, subsequently leading to better understanding of rumen fermentation and methanogenesis.

Data from: The fossil Osmundales (Royal Ferns)—a phylogenetic network analysis, revised taxonomy, and evolutionary classification of anatomically preserved trunks and rhizomes

The Osmundales (Royal Fern order) originated in the late Paleozoic and is the most ancient surviving lineage of leptosporangiate ferns. In contrast to its low diversity today (less than 20 species in six genera), it has the richest fossil record of any extant group of ferns. The structurally preserved trunks and rhizomes alone are referable to more than 100 fossil species that are classified in up to 20 genera, four subfamilies, and two families. This diverse fossil record constitutes an exceptional source of information on the evolutionary history of the group from the Permian to the present. However, inconsistent terminology, varying formats of description, and the general lack of a uniform taxonomic concept renders this wealth of information poorly accessible. To this end, we provide a comprehensive review of the diversity of structural features of osmundalean axes under a standardized, descriptive terminology. A novel morphological character matrix with 45 anatomical characters scored for 15 extant species and for 114 fossil operational units (species or specimens) is analysed using networks in order to establish systematic relationships among fossil and extant Osmundales rooted in axis anatomy. The results lead us to propose an evolutionary classification for fossil Osmundales and a revised, standardized taxonomy for all taxa down to the rank of (sub)genus. We introduce several nomenclatural novelties: (1) a new subfamily Itopsidemoideae (Guaireaceae) is established to contain Itopsidema, Donwelliacaulis, and Tiania; (2) the thamnopteroid genera Zalesskya, Iegosigopteris, and Petcheropteris are all considered synonymous with Thamnopteris; (3) 12 species of Millerocaulis and Ashicaulis are assigned to modern genera (tribe Osmundeae); (4) the hitherto enigmatic Aurealcaulis is identified as an extinct subgenus of Plenasium; and (5) the poorly known Osmundites tuhajkulensis is assigned to Millerocaulis. In addition, we consider Millerocaulis stipabonettiorum a possible member of Palaeosmunda and Millerocaulis estipularis as probably constituting the earliest representative of the (Todea-)Leptopteris lineage (subtribe Todeinae) of modern Osmundoideae.

Data from: Geographical variation in the heterogeneity of mutualistic networks

Plant–animal mutualistic networks are characterized by highly heterogeneous degree distributions. The majority of species interact with few partner species, while a small number are highly connected to form network hubs that are proposed to play an important role in community stability. It has not been investigated, however, if or how the degree distributions vary among types of mutualisms or communities, or between plants and animals in the same network. Here, we evaluate the degree distributions of pollination and seed-dispersal networks, which are two major types of mutualistic networks that have often been discussed in parallel, using an index based on Pielou's evenness. Among 56 pollination networks we found strong negative correlation of the heterogeneity between plants and animals, and geographical shifts of network hubs from plants in temperate regions to animals in the tropics. For 28 seed-dispersal networks, by contrast, the correlation was positive, and there is no comparable geographical pattern. These results may be explained by evolution towards specialization in the presence of context-dependent costs that occur if plants share the animal species as interaction partner. How the identity of network hubs affects the stability and resilience of the community is an important question for future studies.

Data from: The response of migratory populations to phenological change: a Migratory Flow Network modelling approach

1. Declines in migratory species have been linked to anthropogenic climate change through phenological mismatch, which arises due to asynchronies between the timing of life-history events (such as migration) and the phenology of available resources. Long-distance migratory species may be particularly vulnerable to phenological change in their breeding ranges, since the timing of migration departure is based on environmental cues at distant non-breeding sites. 2. Migrants may, however, be able to adjust migration speed en route to the breeding grounds and thus ability of migrants to update their timing of migration may depend critically on stopover frequency during migration; however, understanding how migratory strategy influences population dynamics is hindered by a lack of predictive models explicitly linking habitat quality to demography and movement patterns throughout the migratory cycle. 3. Here, we present a novel modelling framework, the Migratory Flow Network (MFN), in which the seasonally varying attractiveness of breeding, winter, and stopover regions drives the direction and timing of migration based on a simple general flux law. 4. We use the MFN to investigate how populations respond to shifts in breeding site phenology based on their frequency of stopover and ability to detect and adapt to these changes. 5. With perfect knowledge of advancing phenology, 'jump' migrants (low frequency stopover) require more adaptation for populations to recover than 'hop' and 'skip' (high or medium frequency stopover) migrants. If adaptation depends on proximity, hop and skip migrants' populations can recover but jump migrants cannot adjust and decline severely. 6. These results highlight the importance of understanding migratory strategies and maintaining high-quality stopover habitat to buffer migratory populations from climate-induced mismatch. 7. We discuss how MFNs could be applied to diverse migratory taxa, and highlight the potential of MFNs as a tool for exploring how migrants respond to other environmental changes such as habitat loss.

Data from: The architecture of river networks can drive the evolutionary dynamics of aquatic populations

It is widely recognized that physical landscapes can shape genetic variation within and between populations. However, it is not well understood how riverscapes, with their complex architectures, affect patterns of neutral genetic diversity. Using a spatially explicit agent-based modeling (ABM) approach, we evaluate the genetic consequences of dendritic river shapes on local population structure. We disentangle the relative contribution of specific river properties to observed patterns of genetic variation by evaluating how different branching architectures and downstream flow regimes affect the genetic structure of populations situated within river networks. Irrespective of the river length, our results illustrate that the extent of river branching, confluence position, and levels of asymmetric downstream migration dictate patterns of genetic variation in riverine populations. Indeed, comparisons between simple and highly branched rivers show a 20-fold increase in the overall genetic diversity and a 7-fold increase in the genetic differentiation between local populations. Given that most rivers have complex architectures, these results highlight the importance of incorporating riverscape information into evolutionary models of aquatic species and could help explain why riverine fishes represent a disproportionately large amount of global vertebrate diversity per unit of habitable area.

Data from: Bayesian inference of reticulate phylogenies under the multispecies network coalescent

The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We report on a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devise a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under MSNC. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation.

Data from: Metformin reverses early cortical network dysfunction and behavior changes in Huntington's disease

Catching primal functional changes in early, "very far from disease onset" (VFDO) stages of Huntington's disease is likely to be the key to a successful therapy. Focusing on VFDO stages, we assessed neuronal microcircuits in premanifest Hdh150 knock-in mice. Employing in vivo two-photon Ca2+ imaging, we revealed an early pattern of circuit dysregulation in the visual cortex- one of the first regions affected in premanifest Huntington's disease - characterized by an increase in activity, an enhanced synchronicity and hyperactive neurons. These findings are accompanied by aberrations in animal behavior. We furthermore show that the anti-diabetic drug metformin diminishes aberrant Huntingtin protein load and fully restores both, early network activity patterns and behavioral aberrations. This network-centered approach reveals a critical window of vulnerability far before clinical manifestation and establishes metformin as a promising candidate for a chronic therapy starting early in premanifest Huntington's disease pathogenesis long before the onset of clinical symptoms.

opencc-zeroDec 2018View details →

Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.

Data from: Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils

A central aim of current microbial ecology research is to investigate the mechanisms shaping the assembly of soil microbial communities. Despite the importance of bacterial and fungal mediation of carbon cycling in forest ecosystems, knowledge concerning their distribution patterns and underlying mechanisms remains insufficient. Here, soils were sampled from six bamboo forests across the main planting area of Moso bamboo in southern China. The bacterial and fungal diversities were assessed by sequencing 16S rRNA and ITS gene amplicons, respectively, with an Illumina MiSeq. Based on structural equation modeling, dispersal limitation had strongest impact on bacterial beta diversity, while the mean annual precipitation had a smaller impact by directly or indirectly mediating the soil organic carbon density. However, only the mean annual temperature and precipitation played direct roles in fungal beta diversity. Moreover, the co-occurrence network analyses revealed a possibly much higher network connectivity in the fungal network than in the bacteria. With less dispersal limitation, stronger environmental selection, and a potentially more connected network, the fungal community had more important roles in the soil carbon metabolisms in bamboo forests. Fungal beta diversity and the clustering coefficient explained approximately 14.4% and 6.1% of the variation in the carbon metabolic profiles among sites, respectively, but that of bacteria only explained approximately 1.7% and 1.8%, respectively. This study explored soil microbial spatial patterns along with the underlying mechanisms of dispersal limitation, selection, and connectivity of ecological networks, thus providing novel insights into the study of the distinct functional traits of different microbial taxa.

Data from: Division of labor as a bipartite network

Bipartite ecological networks are increasingly used to described and model relationships between interacting species (e.g. plant-pollinator or host parasite). Here, we apply network methods developed in community ecology to quantify division of labor in insect societies. We consider two quantitative indices (H2' and d') derived from information theory that inform on how much the actual patterns of task performance deviates from the null expectation that workers perform tasks randomly. In addition, we computed network modularity to identify clusters of specialized individuals that are preferentially engaged in the completion of subset of available tasks. We analyzed both simple synthetic networks, varying in size and degree of specialization, and published datasets to introduce the metrics and to show that a bipartite approach provides useful insights into task allocation. Considering division of labor as a bipartite network offers a conceptual framework that could substantially increase our understanding of division of labor in animal societies.