Dynamic capability, ambidexterity and social network—empirical evidence from SMEs in China

PurposeThe investigation of organization's ambidextrous innovation is a challenge in the research studies of management sciences. As existent literature showed a positive relation between dynamic capability (DC) and innovation, few empirical studies are conducted to explain how DC impacts on the balanced and combined dimension of ambidexterity and still less on how social network moderates this relation. As a result, this paper aims to investigate and provide empirical evidence on DC’s influence on ambidexterity in the context of China.Design/methodology/approachBy a relational model of DC, ambidextrous innovation and social network, this study has conducted multiple regression analysis on the data collected from 350 small and medium enterprises (SMEs) in mainland China.FindingsThe results show that, DC has positive influence on both the combined and balanced dimension of ambidexterity; and both the relational network and structural network play an inverted U moderating role, where the moderation of relational network is stronger than that of structural network.Originality/valueThis study provides empirical support on DC's influence on ambidexterity together with the moderation of social network.

Network controllability mediates the relationship between rigid structure and flexible dynamics

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.

$${\text{COSNet}}_i$$: ComplexOme-Structural Network Interpreter used to study spatial enrichment in metazoan ribosomes

Background Upon environmental stimuli, ribosomes are surmised to undergo compositional rearrangements due to abundance changes among proteins assembled into the complex, leading to modulated structural and functional characteristics. Here, we present the ComplexOme-Structural Network Interpreter ($${\text{COSNet}}_i$$ COSNet i ), a computational method to allow testing whether ribosomal proteins (rProteins) that exhibit abundance changes under specific conditions are spatially confined to particular regions within the large ribosomal complex. Results $${\text{COSNet}}_i$$ COSNet i translates experimentally determined structures into graphs, with nodes representing proteins and edges the spatial proximity between them. In its first implementation, $${\text{COSNet}}_i$$ COSNet i considers rProteins and ignores rRNA and other objects. Spatial regions are defined using a random walk with restart methodology, followed by a procedure to obtain a minimum set of regions that cover all proteins in the complex. Structural coherence is achieved by applying weights to the edges reflecting the physical proximity between purportedly contacting proteins. The weighting probabilistically guides the random-walk path trajectory. Parameter tuning during region selection provides the option to tailor the method to specific biological questions by yielding regions of different sizes with minimum overlaps. In addition, other graph community detection algorithms may be used for the $${\text{COSNet}}_i$$ COSNet i workflow, considering that they yield different sized, non-overlapping regions. All tested algorithms result in the same node kernels under equivalent regions. Based on the defined regions, available abundance change information of proteins is mapped onto the graph and subsequently tested for enrichment in any of the defined spatial regions. We applied $${\text{COSNet}}_i$$ COSNet i to the cytosolic ribosome structures of Saccharomyces cerevisiae, Oryctolagus cuniculus, and Triticum aestivum using datasets with available quantitative protein abundance change information. We found that in yeast, substoichiometric rProteins depleted from translating polysomes are significantly constrained to a ribosomal region close to the tRNA entry and exit sites. Conclusions $${\text{COSNet}}_i$$ COSNet i offers a computational method to partition multi-protein complexes into structural regions and a statistical approach to test for spatial enrichments of any given subsets of proteins. $${\text{COSNet}}_i$$ COSNet i is applicable to any multi-protein complex given appropriate structural and abundance-change data. $${\text{COSNet}}_i$$ COSNet i is publicly available as a GitHub repository https://github.com/MSeidelFed/COSNet_i and can be installed using the python installer pip.

CNTM-04. Alterations in structural connectomic properties associated with neurocognitive changes following glioma resection

BACKGROUND Patients with brain tumors often experience decline in neurocognitive functioning (NCF) following surgical tumor resection. Connectomic studies have begun to uncover how abnormalities to underlying cerebral networks contribute to NCF deficits; however, few studies have investigated relationships between pre- to postoperative changes in structural connectomics and NCF. METHODS Fifteen right-handed adults with left perisylvian tumors underwent MRI of the brain with diffusion tensor imaging (DTI) and neuropsychological assessment before and after awake tumor resection. Graph theoretical analysis was applied to DTI-derived connectivity matrices to calculate structural network properties. Structural network properties and NCF measures were compared across the pre- to postoperative periods with matched pairs Wilcoxon signed-rank tests. Associations between pre- to postoperative change in network properties and change in NCF were determined with Spearman rank-order correlations (ρ). RESULTS Nearly 90% of the sample showed postoperative decline on 1 or more NCF measures. Significant postoperative NCF decline was found across measures of verbal memory, processing speed, executive functioning, receptive language, and the Clinical Trial Battery Composite (CTB COMP) index. Regarding connectomic properties, significant postoperative changes were observed in global and local efficiency, characteristic path length, clustering coefficient, betweenness centrality, and assortativity, with medium effect sizes. Significant associations (ρ = .59 to .62, all p < .05) were observed between changes in aspects of NCF and connectomic properties. CONCLUSIONS Decline in NCF was common following resection and some postoperative outcomes were associated with changes in structural connectomic properties following surgery.

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.