Properties Of Networks Research Articles

418 - Changes of topological properties of white matter network in schizophrenia with metabolic syndrome after risperidone and clozapine treatment

418 - Changes of topological properties of white matter network in schizophrenia with metabolic syndrome after risperidone and clozapine treatment

Structure and properties of aluminoborosilicate glasses for OLED display substrate

The effects of Al2O3/SiO2 and B2O3/SiO2 substitution on the network structure and properties of high aluminium and low boron alkali-free aluminoborosilicate glasses used as OLED display glass substrate were investigated. A more polymerized network within 3 mol.% substitution for Al2O3/SiO2 was obtained, represented by the decreasing value of NBO/T from 0.14 to 0.08, as well as the opposite result for B2O3/SiO2 substitution. Q3 and Q4 units held the vast majority of Si groups, and the tetrahedral [AlO4] occupied the majority among Al groups, while [AlO5] and [AlO6] was too low or even too tiny to be detected obviously in the investigated glass system. [BO4] concentration decreased from 59.04 % to 52.74 % with the increase of B2O3 content, and [BO3] increased accordingly, which indicated that [BO4] was not the preferential borate group in this glass system even if (RO-Al2O3)/B2O3 > 1. Higher [BO3] at lower (RO-Al2O3)/B2O3 value (>1) indicated the low conversion efficiency from [BO3] to [BO4] in the investigated glasses. The glass network between the bulk sample and glass melt was also investigated, and some differences in Al groups could be concluded from the test of the glass melt. Finally, samples with satisfied properties for OLED display substrate were acquired.

Early developmental alterations of CA1 pyramidal cells in Dravet syndrome

Dravet Syndrome (DS) is most often caused by heterozygous loss-of-function mutations in the voltage-gated sodium channel gene SCN1A (Nav1.1), resulting in severe epilepsy and neurodevelopmental impairment thought to be cause by reduced interneuron excitability. However, recent studies in mouse models suggest that interneuron dysfunction alone does not completely explain all the cellular and network impairments seen in DS. Here, we investigated the development of the intrinsic, synaptic, and network properties of CA1 pyramidal cells in a DS model prior to the appearance of overt seizures. We report that CA1 pyramidal cell development is altered by heterozygous reduction of Scn1a, and propose that this is explained by a period of reduced intrinsic excitability in early postnatal life, during which Scn1a is normally expressed in hippocampal pyramidal cells. We also use a novel ex vivo model of homeostatic plasticity to show an instability in homeostatic response during DS epileptogenesis. This study provides evidence for the early effects of Scn1a haploinsufficiency in pyramidal cells in contributing to the pathophysiology of DS.

Elucidation of characteristics of networks where every node has its own lifetime

Growth is regarded as an important mechanism for explaining the structures of real networks. However, when the increase in the number of nodes is suppressed owing to their lifetime, the growth property alone is not sufficient to explain even fundamental network properties, such as the scale-free property. In this paper, we propose a network model that considers the lifetime of nodes and the excess addition of local internal links as a mechanism that supports network structures. By investigating the model network, we aimed to elucidate the network characteristics supported by local interactions between nodes via their common neighbors even when the rates of node addition and deletion were balanced. We found that the stationary state of the number of nodes is characterized by a scale-free property with the power-law exponent γ≃1 and localization of the peaks at l=2 in the distance distributions of neighboring nodes (DDN) as the node degree k increases. The specific behavior of the DDN explains the very slow decrease in the clustering strength C(k) with k compared with the normal behavior C(k)∼k−1 and the accelerated growth of the neighborhood graph of each node. Moreover, we showed that some real networks share local structures similar to those of the model network. These findings suggest that the same mechanism as that of the proposed model plays an essential role in supporting the local structures of some real networks.

Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis

Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma. In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0-4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0-12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host-environment correlation network, quantified by a simple descriptor: trajectory function G(t). Wheeze outcomes at age 2-6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants. CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, G(t) distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and G(t) was also able to distinguish between the farming and non-farming exposure groups (p<0·0001). Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host-environment correlation network properties (G(t)) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 Lancet Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se. The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease.

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

Ecological networks reveal important taxa for ecosystem function in two subtropical South African estuaries

Biodiversity is essential for estuarine ecosystems' functioning, resilience, and persistence. While biodiversity assessments provide some insight into the ecosystem state, they cannot quantify every aspect of ecosystem-level function. We assessed aspects of estuarine ecosystem function and pinpointed taxa of interest that fulfil specific roles in estuarine ecosystems, using a series of novel food web model ensembles and Ecological Network Analysis. We investigated emergent network properties of two subtropical South African estuaries, the anthropogenically impacted large temporarily closed uMdloti Estuary and the ‘near natural’ predominantly open uMlalazi Estuary, during a drought period induced by the El Niño Southern Oscillation. In addition, important network components were identified to elucidate the importance of estuarine/marine taxa in maintaining ecosystem function. The key findings included (1) estuaries exhibit system-specific functional responses under drought conditions, and (2) estuarine/marine taxa are critical for overall ecosystem function. Both estuaries showed seasonal differences in ecosystem activity and reliance on trophic level I (microalgae and detritus) to fuel the food web, and persistently low cycling (<9.3 %). However, compared to uMdloti Estuary, uMlalazi Estuary showed less variation in mean seasonal activity (1597–2095 mgC m−2 d−1), and higher reliance on detritus to fuel the food web (D:H > 2.02), indicating greater ecosystem functional resilience to external perturbations. uMlalazi Estuary ecosystem function could be attributed to predominantly open mouth conditions maintaining a salinity gradient, and subsequent large contributions to overall ecosystem function from typically estuarine/marine invertebrate taxa. In contrast, the uMdloti Estuary, dominated by freshwater/brackish taxa under prolonged closed mouth conditions, showed greater seasonal variations in mean system activity (1084–2289 mgC m−2 d−1) and generally high reliance on microalgae (D:H < 1.26), indicative of decreased resilience to external perturbations. During this study, a single mouth breach acted as a ’biological reset’ of uMdloti Estuary, enabling recruitment of estuarine/marine taxa, and a temporary recovery in ecosystem function reflected in increased system cycling (FCI >14.05 %) and decreased reliance on microalgae (D:H > 1. 41). These findings suggest that future predicted drought impacts on estuarine ecosystem function may be partially system-specific due to differing estuarine types, anthropogenic impact, and invertebrate community composition. This study provides a scientific link between ecosystem-level indicators and the role of individual taxa within the system, which is a valuable complement to biodiversity assessments and ecosystem state evaluation.

Engineering consensus in static networks with unknown disruptors

Distributed control can increase system scalability, flexibility, and redundancy. Foundational to such scalability via decentralisation is consensus formation, by which decision-making and coordination are achieved. However, decentralised multi-agent systems are inherently vulnerable to disruption. To develop a resilient consensus approach, inspiration is taken from the study of social dynamics; specifically, the Deffuant Model which evaluates the impact of tolerance in social systems. A dynamic protocol is presented enabling efficient consensus to be reached with an unknown number of disruptors present within a multi-agent system. By inverting typical social tolerance, agents filter out extremist non-standard opinions that would drive them away from consensus. This approach allows distributed systems to deal with unknown disruptions, without knowledge of the network topology or the numbers and behaviours of the disruptors, a general requirement of other resilient consensus algorithms. A disruptor-agnostic algorithm is particularly suitable to real-world applications where information regarding disruptors or network properties is typically unknown. Faster, tighter, and more robust convergence can be achieved across a range of scenarios with the social dynamics inspired algorithm presented herein, when compared with Mean-Subsequence-Reduced-type methods.

Research on brain functional network property analysis and recognition methods targeting brain fatigue

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands’ node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

Influence of TEMPO on preparation of softwood nanofibrils and their hydrogel network properties

From an economic and environmental perspective, the use of less chemicals in the production of cellulose nanofibrils (CNFs) is advantageous. In this study, we investigated the oxidation (TEMPO/NaClO2/NaClO, pH 6.8) of softwood (SW) particles with varying amounts of TEMPO (16, 8 or 0 mg g−1 of wood). Following, TEMPO-oxidized SW nanofibrils (TO-SWNFs) were obtained by nanofibrillation and their size, morphology, and crystallite size were assessed. Hydrogel networks of TO-SWNFs were prepared and mechanical properties were measured in dH2O and phosphate buffered saline (PBS) to compare their performance for possible biomedical applications such as wound dressings. The results reveal that the presence of TEMPO is of importance for TO-SWNF network properties, presenting higher eq. H2O absorption (≈2500 %) and elongation at break (≈10 %) with good wet strength (≈180 kPa). In addition, a decrease in use of TEMPO catalyst from 16 to 8 mg g−1 of wood is possible, without detrimental effects on hydrogel network properties (dH2O absorption ≈ 2000 %, elongation at break ≈ 13 %, wet strength ≈ 190 kPa) related to applications as wound dressings.

Structural characterisation for the synthesis of large‐scale combined electric–gas networks

AbstractIn this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled‐infrastructure analysis with large‐scale realistic test cases that do not contain non‐public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering‐based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.

The goodness-of-fit of models for fracture trace length distribution: Does the power law provide a good fit?

Fracture trace length distributions are often assumed to follow a power law, which implies that the distribution is scale-independent. The present study tests this assumption by evaluating the goodness-of-fit of three statistical models—the power law, piecewise power law, and lognormal distribution—upon a dataset of 57 trace maps that cover a range of fracture modes, host rock types, network scales, and topologies. The goodness-of-fit was assessed through the unbiased Kolmogorov-Smirnov (KS) test, which accounts for the fitting procedure and the degrees of freedom of each model. The results show that the power law provides a poor fit to trace length distributions, being rejected in 24 trace maps at a significance level of 0.05. In contrast, the piecewise power law and lognormal distribution demonstrated better fits across the fracture networks, with the piecewise power law performing the best overall. The poor fit of the power law can be attributed to mechanical and chemical controls on fracture growth, mainly fracture abutment, as well as stress shadowing and cementation, which affect growth rate at different length scales and result in scale-dependent trace length distributions. The consistent poor fit of the power law across various fracture networks suggests that these controls are prevalent in natural systems. While the power law remains a simple and effective model for trace length distribution, it should be recognized that it overlooks such controls that can influence the mechanical and hydraulic properties of fracture networks. Meanwhile, the fit of the piecewise power-law suggests the existence of a characteristic length where a transition in fracture growth occurs.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein’s median copy number and hierarchically appends mRNA–protein and mRNA–mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26–29% of luminal tumors. By adopting a gene-centered perspective of mRNA–protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Seasonal wet–dry transition and denitrifying communities contributed to nitrous oxide emissions in the water-level fluctuating zone of the largest freshwater lake in China

The water-level fluctuating zone (WLFZ) is the alternating dry/wet zone in freshwater lakes, and plays a crucial role in nitrogen (N) biogeochemical cycling and nitrous oxide (N2O) emissions. However, N2O emission fluxes, the underlying microbial mechanisms, and their feedbacks to global warming in the extensive WLFZ of the freshwater lakes with dramatic water-level variations remain unclear. Here, we sampled through both wet and dry seasons and compared the N removal rates, N2O emission fluxes, composition and co-occurrence network properties of the denitrifying microbial communities of the WLFZ, continuously flooded, and non-flooded zones covered with the dominant plant (Carex. cinerascens) in Poyang Lake, the largest freshwater lake of China. We observed higher potential denitrification (1.129–5.657 nmol N g-1h−1) and anammox (0.047–0.756 nmol N g-1h−1) rates in the WLFZ for both wet and dry seasons than those in the other two zones. A large seasonal N2O sink-source transition (−2.297 and 0.297 μg m-2h−1 for wet and dry season, respectively) was observed in WLFZ. Moreover, the composition of nirK and nirS communities significantly varied in WLFZ between the wet and dry seasons. Especially, several keystone taxa (e.g., Mesorhizobium and Rhodanobacter) were enriched in the WLFZ and were responsible for denitrification and N2O emissions under drought stress in the dry season. Variations in denitrification rates and N2O fluxes were driven by pH, C and N substrates, as well as the co-occurrence network properties, including natural connectivity and small-world coefficients of the nirK and nirS communities. Our results suggested that WLFZ in freshwater lakes under future climate change scenarios would be a substantial N2O source and aggravate climate, which would result in the positive feedback.

Examining the Properties of (Co)Polymer Networks Prepared by the Phosphane‐Ene Reaction

Examining the Properties of (Co)Polymer Networks Prepared by the Phosphane‐Ene Reaction

Properties of rhythmogenic currents in spinal Shox2 interneurons across postnatal development.

Locomotor behaviors are performed by organisms throughout life, despite developmental changes in cellular properties, neural connectivity, and biomechanics. The basic rhythmic activity in the central nervous system that underlies locomotion is thought to be generated via a complex balance between network and intrinsic cellular properties. Within mature mammalian spinal locomotor circuitry, we have yet to determine which properties of spinal interneurons (INs) are critical to rhythmogenesis and how they change during development. Here, we combined whole cell patch clamp recordings, immunohistochemistry, and RNAscope targeting lumbar Shox2 INs in mice, which are known to be involved in locomotor rhythm generation. We focused on the properties of putatively rhythmogenic ionic currents and the expression of corresponding ion channels across postnatal time points in mice. We show that subsets of Shox2 INs display voltage-sensitive conductances, in addition to respective ion channels, which may contribute to or shape rhythmic bursting. Persistent inward currents, M-type potassium currents, slow afterhyperpolarization, and T-type calcium currents are enhanced with age. In contrast, the hyperpolarization-activated and A-type potassium currents were either found with low prevalence in subsets of neonatal, juvenile, and adult Shox2 INs or did not developmentally change. We show that Shox2 INs become more electrophysiologically diverse by juvenile and adult ages, when locomotor behavior is weight-bearing. These results suggest a developmental shift in the magnitude of rhythmogenic ionic currents and the expression of corresponding ion channels that may be important for mature, weight-bearing locomotor behavior.

Disrupted white matter structural networks in patients with acute ischemic stroke in the right basal ganglia

Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. Graph theoretical analysis was employed to investigate the topological architecture of whole-brain white matter structural networks in 40 AIS patients in the right BG and 40 healthy controls (HC), and network-based statistics (NBS) were applied to examine structural connectivity alterations. Compared to HC, AIS patients exhibited altered global network properties characterized by increased small-worldness, normalized clustering coefficient, and shortest path length, as well as decreased clustering coefficient, local efficiency, and global efficiency. The nodes with significantly decreased nodal properties in AIS patients were primarily located in the default mode network, limbic system, sensorimotor system, salience network, and central executive network. Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.

Adaptive microbiome responses to anthracnose in sorghum: Enhanced network complexity and disease resistance across plant niches

Anthracnose threatens sorghum productivity globally, with yield losses exceeding 50 % in some cases, making it crucial to understand plant-microbe-pathogen interactions to develop effective, sustainable control strategies. To probe deeper into the complexity of plant-microbe-pathogen relationships, this study used high-throughput sequencing to characterize microbial communities present in association with sorghum leaves and seeds harvested from both anthracnose infected and uninfected control plants. Our analysis shows that the variation in the diversity and composition of the microbiome due to anthracnose is indicative of an adaptive regulation by coexisting microbes with a plant for enhancing resistance against stresses. These changes were not only reflected by community composition, but also had remarkable effects on the topological properties of microbial ecological networks which further influence certain functions as well as key functional guilds related to plant growth. The presence of anthracnose significantly increased network complexity and resistance in the phyllosphere and leaf endosphere meta-microbiome, which may reflect an adaptive microbiome response to pathogen. The seed-associated microbiomes, however, had relatively no change in diversity and network properties indicating a stable background that may repel pathogen invasion. The study also suggests that anthracnose may facilitate its spread through the reorganization of microbial communities and the enrichment of specific beneficial microbes, aiding in plant defense against pathogens. This is achieved through an upregulation of siderophore production, antimicrobial compounds, phosphate solubilization and sulfur metabolism-specifically genes. Our work also uncovers the possibility for microbiome transfer through phyllosphere-and seed associated microbiomes, thereby indicating that pathogen spread between both above and below ground plant niches limit management options. To conclude, the present study reveals fresh insights into how plant-associated microbiomes respond to anthracnose stress and offers insight on their prospective in sustainable agriculture.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations. Additionally, effectively utilizing network structure to fit the interaction properties of regulatory networks and combining specific case biomarker validations remains an unresolved issue in cancer biomarker prediction methods. To overcome these limitations, we propose a multi-view learning framework, CeRVE, based on directed graph neural networks (DGNN) for predicting unknown type cancer biomarkers. CeRVE effectively extracts and integrates subgraph information through multi-view feature learning. Subsequently, CeRVE utilizes DGNN to simulate the entire regulatory network, propagating node attribute features and extracting various interaction relationships between molecules. Furthermore, CeRVE constructed a comparative analysis matrix of three cancers and adjacent normal tissues through The Cancer Genome Atlas and identified multiple types of potential cancer biomarkers through differential expression analysis of mRNA, microRNA, and long noncoding RNA. Computational testing of multiple types of biomarkers for 72 cancers demonstrates that CeRVE exhibits superior performance in cancer biomarker prediction, providing a powerful tool and insightful approach for AI-assisted disease biomarker discovery.

Dynamical predictive coding with reservoir computing performs noise-robust multi-sensory speech recognition.

Multi-sensory integration is a perceptual process through which the brain synthesizes a unified perception by integrating inputs from multiple sensory modalities. A key issue is understanding how the brain performs multi-sensory integrations using a common neural basis in the cortex. A cortical model based on reservoir computing has been proposed to elucidate the role of recurrent connectivity among cortical neurons in this process. Reservoir computing is well-suited for time series processing, such as speech recognition. This inquiry focuses on extending a reservoir computing-based cortical model to encompass multi-sensory integration within the cortex. This research introduces a dynamical model of multi-sensory speech recognition, leveraging predictive coding combined with reservoir computing. Predictive coding offers a framework for the hierarchical structure of the cortex. The model integrates reliability weighting, derived from the computational theory of multi-sensory integration, to adapt to multi-sensory time series processing. The model addresses a multi-sensory speech recognition task, necessitating the management of complex time series. We observed that the reservoir effectively recognizes speech by extracting time-contextual information and weighting sensory inputs according to sensory noise. These findings indicate that the dynamic properties of recurrent networks are applicable to multi-sensory time series processing, positioning reservoir computing as a suitable model for multi-sensory integration.