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OBJECTIVE: The structural alteration that occurs within the salience network (SN) in patients with insular glioma is unclear. Therefore, we aimed to investigate the changes in the topological network and brain structure alterations within the SN in patients with insular glioma. METHODS: We enrolled 46 patients with left insular glioma, 39 patients with right insular glioma, and 21 demographically matched healthy controls (HCs). We compared the topological network, gray matter (GM) volume, and fractional anisotropy (FA) between HCs and patients after controlling for the effects of age and gender. RESULTS: Patients with insular glioma showed topological network decline mainly in the insula, basal ganglia region, and anterior cingulate cortex (ACC). Compared with HCs, patients primarily showed GM volume increased in the ACC, inferior temporal gyrus (ITG), superior temporal gyrus (STG), temporal pole: middle temporal gyrus (TPOmid), insula, middle temporal gyrus (MTG), middle frontal gyrus, and superior occipital gyrus (SOG), but decreased in TPOmid, ITG, temporal pole: superior temporal gyrus, and SOG. FA declined mainly in the STG, MTG, ACC, superior frontal gyrus, and SOG, and also showed an increased cluster in SOG. CONCLUSIONS: FA represents the integrity of the white matter. In patients with insular glioma, decreased FA may lead to the destruction of the topological network within the SN, which in turn may lead to the decrease of network efficiency and brain function, and the increase of GM volume may compensate for these changes. Overall, this pattern of structural changes provides new insight into the compensation model of insular glioma.

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Introduction: Adolescent exposure to neurotoxic metals adversely impacts cognitive, motor, and behavioral development. Few studies have addressed the underlying brain mechanisms of these metal-associated developmental outcomes. Furthermore, metal exposure occurs as a mixture, yet previous studies most often consider impacts of each metal individually. In this cross-sectional study, we investigated the relationship between exposure to neurotoxic metals and topological brain metrics in adolescents. Methods: In 193 participants (53% females, ages: 15-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of four metals (manganese, lead, copper, and chromium) in multiple biological media (blood, urine, hair, and saliva) and acquired resting-state functional magnetic resonance imaging scans. Using graph theory metrics, we computed global and local efficiency (global:GE; local:LE) in 111 brain areas (Harvard Oxford Atlas). We used weighted quantile sum (WQS) regression models to examine association between metal mixtures and each graph metric (GE or LE), adjusted for sex and age. Results: We observed significant negative associations between the metal mixture and GE and LE [ßGE = -0.076, 95% CI (-0.122, -0.031); ßLE= -0.051, 95% CI (-0.095, -0.006)]. Lead and chromium measured in blood contributed most to this association for GE, while chromium measured in hair contributed the most for LE. Discussion: Our results suggest that exposure to this metal mixture during adolescence reduces the efficiency of integrating information in brain networks at both local and global levels, informing potential neural mechanisms underlying the developmental toxicity of metals. Results further suggest these associations are due to combined joint effects to different metals, rather than to a single metal.

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Microplastics (MPs) are emerging contaminants in aquatic environments, yet their impact on sediment microbiota and biogeochemical processes were not well reported. Herein, microcosm experiments were performed to investigate the effects of MPs (Polystyrene, PS and Polyethylene, PE) with three size classes (ranging from 100 nm to 150-200 µm) on sediment bacterial and fungal communities over 60-day incubation from Taihu Lake. High-throughput sequencing revealed the alpha diversities of bacterial and fungal communities were reduced by MPs, dependent on MPs' size and type. Bacterial community structures were significantly altered under all MPs treatments, with clustering for the same size class for PS and PE. Fungal community structures were significantly affected for all MPs, with PS and PE exhibiting different effects. Co-occurrence network analysis suggested MPs changed bacterial and fungal network complexities. Proteobacteria and Ascomycota formed strong associations with other phyla and demonstrated tolerance to MPs exposure. Actinobacteria, Firmicutes, and Chytridiomycota were the main respondents to MPs. The enzyme concentrations were stimulated by MPs, indicating carbon and nitrogen uptakes might be increased. Therefore, PS and PE had similar impacts on the microbial community (particularly bacteria), and sizes of MPs were the main influencing factors. MPs shifted community structure and network with distinct responses from bacteria and fungi, likely leading to the alteration of microbial-involved carbon and nitrogen cycling.

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Aquaculture has significant impacts on freshwater lakes, but plankton communities, as key components of the microbial food web, are rarely considered when assessing the impacts of aquaculture. Revealing the dynamics of plankton communities, including bacterioplankton, phytoplankton and zooplankton, under anthropological disturbances is critical for predicting the freshwater ecosystem functioning in response to future environmental changes. In the present study, we examined the impacts of aquaculture on water quality, plankton diversity and the co-occurrence patterns within plankton metacommunities in a shallow freshwater lake. The study zones are influenced by the 20-year historical intensive aquaculture, but now they are undergoing either ecological aquaculture or ecological restoration. Our results showed that ecological aquaculture was more efficient in nitrogen removal than ecological restoration. Moreover, lower bacterioplankton diversity but higher phytoplankton and zooplankton diversity were found in the ecological aquaculture and ecological restoration zones compared to the control zone. The lower network connectivity of the plankton metacommunities in the ecological aquaculture and ecological restoration zones indicated the decreasing complexity of potential microbial food web, suggesting a possible lower resistance of the plankton metacommunities to future disturbance. Furthermore, plankton communities of different trophic levels were driven under distinct mechanisms. The bacterioplankton community was primarily affected by abiotic factors, whereas the phytoplankton and zooplankton communities were explained more by trophic interactions. These results revealed the impacts of aquaculture on the plankton communities and their potential interactions, thereby providing fundamental information for better understanding the impacts of aquaculture on freshwater ecosystem functioning.

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Type 2 diabetes is associated with a higher risk of dementia. The pathogenesis is complex and partly influenced by genetic factors. The hippocampus is the most vulnerable brain region in individuals with type 2 diabetes. However, whether the genetic risk of type 2 diabetes is associated with the hippocampus and episodic memory remains unclear. This study explored the influence of polygenic risk score (PRS) of type 2 diabetes on the white matter topological properties of the hippocampus among individuals with and without type 2 diabetes and its associations with episodic memory. This study included 103 individuals with type 2 diabetes and 114 well-matched individuals without type 2 diabetes. All the participants were genotyped, and a diffusion tensor imaging-based structural network was constructed. PRS was calculated based on a genome-wide association study of type 2 diabetes. The PRS-by-disease interactions on the bilateral hippocampal topological network properties were evaluated by analysis of covariance (ANCOVA). There were significant PRS-by-disease interaction effects on the nodal topological properties of the right hippocampus node. In the individuals with type 2 diabetes, the PRS was correlated with the right hippocampal nodal properties, and the nodal properties were correlated with the episodic memory. In addition, the right hippocampal nodal properties mediated the effect of PRS on episodic memory in individuals with type 2 diabetes. Our results suggested a gene-brain-cognition biological pathway, which might help understand the neural mechanism of the genetic risk of type 2 diabetes affects episodic memory in type 2 diabetes.

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Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

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In recent years, neuroimaging evidence shows that the brains of Parkinson disease (PD) with impulse control disorders (ICDs) patients have functional disconnection changes. However, so far, it is still unclear whether the topological organization is damaged in PD patients with ICD. In this study, we aimed to explore the functional brain network in 18 patients with PD with ICDs (PD-ICD) and 18 patients with PD without ICDs (PD-nICD) by using functional magnetic resonance imaging and graph theory approach. We found that the PD-ICD patients had increased clustering coefficient and characteristic path length, while decreased small-world index compared with PD-nICD patients. Furthermore, we explored the hypothesis whether the abnormality of the small-world network parameters of PD-ICD patients is accompanied by the change of nodal centrality. As we hypothesized, the nodal centralities of the default mode network, control network, and dorsal attention network were found to be significantly damaged in the PD-ICD group compared with the PD-nICD group. Our study provides more evidence for PD-ICD patients' brain network abnormalities from the perspective of information exchange, which may be the underlying pathophysiological basis of brain abnormalities in PD-ICD patients.

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Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

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Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

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Based on the trophic field overlap of species in the food webs, we propose using the weighted trophic field overlap (WTO) to determine the uniqueness of species in a topological network by considering the food web structure and the proportions of prey in the diets of predators. This proposed method measures uniqueness structurally and mathematically and considers cannibalism and mutual predation between species to overcome the deficiencies of the traditional method (the sum of trophic field overlap, STO), which only relies on the topological structure of the food web. Species with the lowest WTO values have high interaction strengths with other species in the food web weighted by the proportion of prey and play important roles as prey in the initial ecosystem, which are not recognized by the traditional method. The proposed index is sensitive to changes in the diets of predators since slight fluctuations may cause the index to vary considerably. The proposed methodology could be extended to other marine ecosystems to identify unique species from a practical and dynamic perspective and will contribute to the protection of unique species that maintain the trophic diversity of food webs and ecosystem robustness. •A WTO index was proposed for identifying unique species in food webs.•This index considers both the topological network structure and proportion of prey.•Cannibalism and mutual predation between species are also accounted for.

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We explore a network of electronic quantum valley Hall states in the moiré crystal of minimally twisted bilayer graphene. In our transport measurements, we observe Fabry-Pérot and Aharanov-Bohm oscillations that are robust in magnetic fields ranging from 0 to 8 T, which is in strong contrast to more conventional two-dimensional systems where trajectories in the bulk are bent by the Lorentz force. This persistence in magnetic field and the linear spacing in density indicate that charge carriers in the bulk flow in topologically protected, one-dimensional channels. With this work, we demonstrate coherent electronic transport in a lattice of topologically protected states.

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BACKGROUND: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. RESULTS: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. CONCLUSIONS: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

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Topological defects in highly repetitive structural proteins strongly affect their mechanical properties. However, there are no universal rules for structure-property prediction in structural proteins due to high diversity in their repetitive modules. Here, we studied the mechanical properties of tandem-repeat proteins inspired by squid ring teeth proteins using rheology and tensile experiments as well as spectroscopic and X-ray techniques. We also developed a network model based on entropic elasticity to predict structure-property relationships for these proteins. We demonstrated that shear modulus, elastic modulus, and toughness scale inversely with the number of repeats in these proteins. Through optimization of structural repeats, we obtained highly efficient protein network topologies with 42 MJ/m3 ultimate toughness that are capable of withstanding deformations up to 350% when hydrated. Investigation of topological network defects in structural proteins will improve the prediction of mechanical properties for designing novel protein-based materials.