Modular Network Research Articles

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Motif-based community detection in heterogeneous multilayer networks

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Lorawan-Based RSSI-Trilateration Model for Node Location: A Simulation Integrating Flora and Omnet++

Abstract This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

Organic 6G networks: ultra-flexibility through extensive stateless functional split

With the increase in hardware performance, the 5G mobile network architecture shifted from physical components to software-only micro-services. The very modular network functions can be deployed flexibly on commodity hardware. However, the extensive modularity of these network functions is increasing the number of managed entities, and the core network request latency. Also, it requires extensive procedures to be able to re-select the components for specific devices, a fundamental condition for a potential system scale-down. In this paper, we propose a new organic 6G network architecture that handles these challenges through a new functionality split based on the experience of IT software services. Furthermore, we provide an analysis based on main 5G procedures, showing that the newly proposed architecture is handling the re-selection of functionality significantly better, which is a cornerstone of high-speed scaling (especially scaling-out), as well as migration of functionality and users.

Modular horizontal network within mouse primary visual cortex.

Interactions between feedback connections from higher cortical areas and local horizontal connections within primary visual cortex (V1) were shown to play a role in contextual processing in different behavioral states. Layer 1 (L1) is an important part of the underlying network. This cell-sparse layer is a target of feedback and local inputs, and nexus for contacts onto apical dendrites of projection neurons in the layers below. Importantly, L1 is a site for coupling inputs from the outside world with internal information. To determine whether all of these circuit elements overlap in L1, we labeled the horizontal network within mouse V1 with anterograde and retrograde viral tracers. We found two types of local horizontal connections: short ones that were tangentially limited to the representation of the point image, and long ones which reached beyond the receptive field center, deep into its surround. The long connections were patchy and terminated preferentially in M2 muscarinic acetylcholine receptor-negative (M2-) interpatches. Anterogradely labeled inputs overlapped in M2-interpatches with apical dendrites of retrogradely labeled L2/3 and L5 cells, forming module-selective loops between topographically distant locations. Previous work showed that L1 of M2-interpatches receive inputs from the lateral posterior thalamic nucleus (LP) and from a feedback network from areas of the medial dorsal stream, including the secondary motor cortex. Together, these findings suggest that interactions in M2-interpatches play a role in processing visual inputs produced by object-and self-motion.

Cancer drug sensitivity estimation using modular deep Graph Neural Networks.

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.

Learning site-invariant features of connectomes to harmonize complex network measures.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

Evolution of cooperation on reinforcement-learning driven-adaptive networks.

Complex networks are widespread in real-world environments across diverse domains. Real-world networks tend to form spontaneously through interactions between individual agents. Inspired by this, we design an evolutionary game model in which agents participate in a prisoner's dilemma game (PDG) with their neighboring agents. Agents can autonomously modify their connections with neighbors using reinforcement learning to avoid unfavorable environments. Interestingly, our findings reveal some remarkable results. Exploiting reinforcement learning-based adaptive networks improves cooperation when juxtaposed with existing PDGs performed on homogeneous networks. At the same time, the network's topology evolves from homogeneous to heterogeneous states. This change occurs as players gain experience from past games and become more astute in deciding whether to join PDGs with their current neighbors or disconnect from the least profitable neighbors. Instead, they seek out more favorable environments by establishing connections with second-order neighbors with higher rewards. By calculating the degree distribution and modularity of the adaptive network in a steady state, we confirm that the adaptive network follows a power law and has a clear community structure, indicating that the adaptive network is similar to networks in the real world. Our study reports a new phenomenon in evolutionary game theory on networks. It proposes a new perspective to generate scale-free networks, which is generating scale-free networks by the evolution of homogeneous networks rather than typical ways of network growth and preferential connection. Our results provide new aspects to understanding the network structure, the emergence of cooperation, and the behavior of actors in nature and society.

Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

On inference for modularity statistics in structured networks

This paper revisits the classical concept of network modularity and its spectral relaxations used throughout graph data analysis. We formulate and study several modularity statistic variants for which we establish asymptotic distributional results in the large-network limit for networks exhibiting nodal community structure. Our work facilitates testing for network differences and can be used in conjunction with existing theoretical guarantees for stochastic blockmodel random graphs. Our results are enabled by recent advances in the study of low-rank truncations of large network adjacency matrices. We provide confirmatory simulation studies and real data analysis pertaining to the network neuroscience study of psychosis, specifically schizophrenia. Collectively, this paper contributes to the limited existing literature to date on statistical inference for modularity-based network analysis. Supplemental materials for this article are available online.

How do non-Western authoritarian countries respond to disasters? Structural difference from the pluralistic model

Effective coordination, networking, and voluntary civil engagement are crucial for ensuring effective disaster responses. It should be noted, however, that these recommendations are primarily derived from the experiences of Western pluralistic countries. Thus, there has been insufficient consideration given to the applicability of these recommendations to non-pluralistic countries and the ways in which non-Western authoritarian countries navigate the difficulties of coordinating and establishing networks with civil sector organizations. This study broadens the scope of research by examining non-Western authoritarian countries through an analysis of how China responded to the floods of 2020. The results of the study indicate that the Chinese flood response system was primarily government-driven, centralized, and hierarchical. According to the pluralistic model, this arrangement encounters difficulties in integrating resources and information from partners in different sectors and jurisdictions. Consequently, this poses additional challenges to the Chinese disaster response systems. Nevertheless, the study findings reveal that the Chinese system proficiently resolved problems. This was accomplished by flattening the hierarchical structure via innovative interventions and remedying the lack of voluntary civic sector engagement through mass mobilization. Consequently, it would be fallacious to hold that the pluralist model can be effortlessly transplanted to non-Western authoritarian nations without regard for their distinct political, cultural, economic, and social contexts. Points for practitioners To improve disaster response, practitioners should consider adapting hierarchical networks and embracing flexibility. This can be achieved by reducing bureaucratic layers, which enhances effectiveness and encourages innovative interventions for streamlined decision making. It is important to emphasize provincial–local support and mass mobilization, as seen in China, which underscores the need for engaging civil sectors in non-pluralistic contexts. Context-specific approaches are crucial and should consider political, cultural, and social factors. Transforming hierarchical structures into modular networks may improve response.

Mediterranean pine forest decline: A matter of root-associated microbiota and climate change

Forest ecosystems worldwide currently face worrying episodes of forest decline, which have boosted weakening and mortality of the trees. In the Mediterranean region, especially in the southeast Iberian Peninsula, Pinus sylvestris forests are severely affected by this phenomenon, and it has been commonly attributed to drought events. Remarkably, the role of root microbiota on pine decline has been overlooked and remains unclear. We therefore used metabarcoding to identify the belowground microbial communities of decline-affected and unaffected pine trees. Taxonomic composition of bacterial and fungal rhizosphere communities, and fungal populations dwelling in root endosphere showed different profiles depending on the health status of the trees. The root endosphere of asymptomatic trees was as strongly dominated by ‘Candidatus Phytoplasma pini’ as the root of decline-affected pines, accounting for >99 % of the total bacterial sequences in some samples. Notwithstanding, the titer of this phytopathogen was four-fold higher in symptomatic trees than in symptomless ones. Furthermore, the microbiota inhabiting the root endosphere of decline-affected trees assembled into a less complex and more modularized network. Thus, the observed changes in the microbial communities could be a cause or a consequence of forest decline phenomenon. Moreover, ‘Ca. Phytoplasma pini’ is positively correlated to Pinus sylvestris decline events, either as the primary cause of pine decline or as an opportunistic pathogen exacerbating the process once the tree has been weaken by other factors.

Abstract 7494: Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules

Abstract Background: Patient-derived organoids represent an opportunity into deciphering human cancer biology at the personal-level resolution. Phenotyping living cell properties, such as drug sensitivity, complements deep sequencing to improve the prediction of cancer treatment response and holds promises for functional precision medicine. Modern cancer therapeutics target genetic alterations in modular networks, also known as cancer hallmarks, which drive biological essentialities for cancer progression. However, it remains elusive the relative robustness of each module among the naturally occurring solid tumors. Methods: In order to explore this issue, we analyzed a drug sensitivity database of a collection of circulating tumor cells-derived organoids (CDO) from 532 patients with advanced/metastatic solid tumors to 15 chemicals. In this collection, each CDO was derived from 20 ml peripheral blood on a binary colloid crystal cell culture system over 4 weeks. CDO were tested for survival fraction at the Cmax of the designated chemicals. Cell viability was measured with ATP abundance normalized to vehicle controls. For each chemical tested, the probability density function (PDF) of drug sensitivity was fitted to log normal distribution and goodness of fit was analyzed with Kolmogorov Smirnov test with the significance cut-off set to 0.05. The targeted biological pathway of each chemical was mapped to canonical human pathways in the Gene Ontology and KEGG databases. Strength of the specific evolution conserved module (ECM) was evaluated with the published analysis using clustering by inferred models of evolution (CLIME) algorithm. Results: There were 25 types of tumors in the database. The top 3 tumor types were soft-tissue sarcoma, breast cancers, central nervous system tumor. The average number in each tumor type was 21. The PDF of commonly known chemotherapeutic agents, such as gemcitabine, doxorubicin, docetaxel followed log normal distribution. In contrast, the PDF of commonly known biological targeted agents such as everolimus, cabozantinib or enzalutamide do not. For chemicals whose PDF followed a log normal distribution, the biological pathways that were affected tend to have a higher strength of ECM value. Conclusions: Our early analysis revealed the feasibility to quantitatively estimate the robustness of biological pathways in the cancer hallmark modules with a CDO bank. The biological implication of deviation from classical log normal distribution for biologically targeted agents warrants further investigation. Citation Format: Yihsuan Chen, Shih-Pei Wu, Yin-Ju Chen, Long-Sheng Lu. Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7494.

Network specificity decreases community stability and competition among avian haemosporidian parasites and their hosts

AbstractAimParasites play a fundamental role in shaping ecological communities and influencing trophic interactions. Understanding the factors that drive parasite impacts on community structure and stability (i.e. resilience to disturbances) is crucial for predicting disease dynamics and implementing effective conservation strategies. In this study, using avian malaria and malaria‐like parasites as a model system, we investigated the relationship between specificity, community stability and parasite vulnerability and their association with host diversity and climate.LocationGlobal.Time period2009–2023.Major taxa studiedAvian malaria and malaria‐like parasites.MethodsBy compiling occurrence data from a global avian haemosporidian parasite database (MalAvi), we constructed a comprehensive dataset encompassing 60 communities. We utilized a phylogenetic model approach to predict missing host–parasite interactions, enhancing the accuracy of our analyses. Network analyses based on bipartite interactions provided measures of network specificity, stability, modularity, parasite competition and vulnerability to extinction.ResultsWe found that the high network specificity reduced community stability and decreased competition among parasites. Furthermore, we found that parasite vulnerability decreased with increasing community stability, highlighting the importance of community stability in host–parasite interactions for long‐term parasite persistence. When exploring the influence of local host diversity and climate conditions on host–parasite community stability, we demonstrated that increasing host biodiversity and precipitation reduces parasite competition. Conversely, higher temperature raises competition among parasites.ConclusionThese findings provide valuable insights into the mechanisms underlying parasite impacts on communities and the interplay between specificity, community stability and environmental factors. Further, we reveal the role of climate in shaping host–parasite interactions. By unravelling the complexities of parasite‐mediated interactions, our research substantially improves the current knowledge of the importance of specificity as a modulator of interactions in bipartite networks.

Reduced lactic acid strengthens microbial community stability and function during Jiang-flavour Baijiu fermentation

Unravelling the relationships between microbial community stability and function under environmental stress is critical to understanding microbial ecology but remains understudied in fermentation ecosystems. In this study, the relationship between lactic acid and microbial community was revealed during Jiang-flavour Baijiu fermentation using high-throughput sequencing and nontargeted metabolomics. Lactic acid was reduced by mono- and co-inoculation of lactic acid-degrading strains, Pichia kudriavzevii and Zygosaccharomyces bailii. Furthermore, reduced lactic acid strengthened microbial community stability, mainly characterized by higher network modularity and more positive interactions of species. Lactic acid had a significantly negative relation with network modularity (P = 0.03, Pearson's r = − 0.97). Moreover, the increase in network stability contributed to stronger community function, as indicated by the increase in ethanol, total esters, and total alcohols. Finally, metabolic function analysis showed that amino acid metabolism, starch and sucrose metabolism, pyruvate metabolism, the TCA cycle, nucleotide sugar metabolism, pyrimidine metabolism, and purine metabolism were the main pathways resulting in changes in microbial community stability and function. This study indicated that microbial community stability and function can be enhanced by the regulation of environmental factors and provided a possible strategy for the steady-state regulation of the microbial community in Baijiu fermentation.

Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data

Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high-resolution single-cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to “tipping points” whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single-cell transcriptomics data to infer the stability and gene regulatory networks (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell-state stability along the lineage is inferred based on spectral analysis of the model’s Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in GRNs and their variation along the cell lineage. When applied to epithelial-mesenchymal transition in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling epithelial-mesenchymal transition rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single-cell transcriptomics data.

Afforestation alters soil microbial community composition and reduces microbial network complexity in a karst region of Southwest China

AbstractSoil microbial community plays important roles in altering ecological processes and biogeochemical cycles in ecosystems. However, little is known about how afforestation influences the diversity, composition, and ecological network of soil microbial community in karst regions. In this study, soil samples were collected from one farmland (FL) and three afforestation lands including one bamboo forest (BA), one landscape tree planting forest (LAT), and one orange orchard (ORO) in a karst region of Southwest China. The bacterial and fungal communities were characterized using the high‐throughput sequencing approach, and soil properties including soil organic carbon, pH, soil water content, and soil total and available nutrient contents were measured under different land use treatments. Results showed that conversion from FL to BA and LAT significantly reduced the Shannon diversity of bacterial community. At the phylum level (top 10), genus level (top 30), and operational taxonomic units (OTUs) level, afforestation from FL resulted in significant changes in nine phyla, 24 genera, and 31.32% OTUs of bacterial community, and in three phyla, 13 genera, and 11.62% OTUs of fungal community. The number of nodes, number of negative edges, connectivity, average degree, and relative modularity of the microbial network under afforestation lands decreased by 9.33%–18.66%, 47.98%–72.75%, 0.45%–5.93%, 14.73%–22.29%, and 6.46%–23.50%, respectively, compared with FL. The soil organic carbon, total potassium and total phosphorus were identified as the key soil properties affecting the microbial community. Compared with LAT and ORO, the changes in bacterial and fungal communities under BA were more obvious because of the higher contents in soil organic carbon (13.48%) and total potassium (27.18%). In conclusion, afforestation significantly changed compositions and ecological network complexity of soil microbial community in karst regions, and conversion from FL to BA had stronger influences on the changes of soil microbial community than other afforestation lands in Southwest China. These findings provide the context necessary to evaluate the responses of soil microbial community to land use changes in karst regions.

Learning ambidexterity and technology innovation: The moderating effect of knowledge network modularity

The aim of this paper is to investigate the impact of learning ambidexterity on firms’ technological innovation, especially in the context of varying degrees of modularity in firms’ knowledge networks. By combining temporal and cognitive aspects, this study focuses on the micro-level utilization of knowledge and redefines exploratory and exploitative learning as learning unfamiliar and familiar knowledge, respectively. Building upon previous research, we classify learning ambidexterity into combined and balanced forms, further investigate their influence on innovation performance. Utilizing a panel data set spanning 10 years of 63 semiconductor firms listed in the US market, our findings reveal that combined learning ambidexterity effectively harnesses the complementary effects of both learning patterns, resulting in a positive influence on technological innovation. Conversely, balanced learning ambidexterity hampers knowledge transferability between these two patterns leading to a negative impact on technological innovation. Additionally, we employ patent data to measure the degree of modularity within firms’ knowledge networks. Our results indicate that higher levels of modularity enhance the positive effect of combined learning ambidexterity on innovation while mitigating its negative impact for balanced learning ambidexterity. These findings suggest the importance for firms to strategically manage and dynamically orchestrate learning ambidexterity alongside double-loop learning practices, while continuously structuring and facilitating reuse within organizational knowledge networks to create favorable circumstances for the effective implementation of learning ambidexterity.

Long-term Functional Connectivity Alterations in the Mismatch Negativity, P300 and N400 Language Potentials in Adults with a Childhood Acquired Brain Injury

ABSTRACT Objective To investigate whether alterations in the functional connectivity of networks underlying phoneme perception and semantic-categorical processing persist in adulthood following childhood acquired brain injury (cABI). Methods EEG was recorded in seven adults with a cABI and seven matched controls during the administration of an inattentive and attentive phoneme-level oddball task and a categorical priming task. Functional connections underlying the mismatch negativity (MMN), P300 and N400 were compared between participant groups in five frequency bands by calculating the weighted phase lag index between 32 × 32 electrode pairs. Network modularity and path length were compared between the cABI and control group. Results We observed a positive network for MMN deviant processing in the low gamma band and N400 related processing in the alpha band, but a negative network for P300 standard processing in the theta band in cABI compared to control adults. No differences in graph measures of functional networks were observed between participant groups. Conclusions Alterations in the functional connectivity of speech perception and semantic-categorical processing networks persist in adulthood following cABI. Nevertheless, normal-like network properties in terms of local segregation and global integration are observed.