The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

A Critical Review of Organizing Knowledge Management for Innovation

This study critically reviews the literature that demonstrates the relevance of knowledge management process and business intelligence, as well as the challenges arising when it comes to organising for innovation in today’s business organisations. Hence, the to attain desired innovation it is important to integrate business intelligence (BI) and knowledge management (KM) for the diffusion of innovation. Hence, importance of integrating business intelligence (BI) and knowledge management (KM) for the diffusion of innovation. Organisations’ innovation dynamics and knowledge processes that lead competitive advantage of organisations are examined. Literature points that many organisations rely on individual employees’ knowledge and skills. As a result, information systems that enable knowledge management (KM) as a critical tool for gaining a competitive advantage (Campbell, 2012). The seminal argument in this study is that knowledge diffusion and knowledge externalities are the main drive of increase in economy. As a result, this is expected to be a win-win value proposition for such organisations integrating business intelligence and knowledge management. However, owing to changing business conditions and the rapidity of technological development, as well as the rising expenses involved with carrying out R&D operations in many of these organisations, maintaining competitive advantage through internal R&D alone is becoming increasingly challenging. The importance of innovation processes and network dynamics in the context of Integrated Knowledge Networks is explored, which provide feasible possibilities for utilising innovation as an interactive process as well as knowledge processes for creating business intelligence in organisations. Due to the challenges of organising for innovation, the organisations figured to rely on “Open innovation” approach to intentionally seek out unique knowledge and information outside of their organisational bounds. This study also discusses the challenges that organisations hurdle on in managing inter-organizational cooperation because of external knowledge sourcing techniques (Campbell, 2009). This is due, in part, to the fact that they span a wide range of organisations, people, and resources, as well as the interactions that exist between them. The creative processes and network dynamics are facilitated by an architecture that blends organisational and technical aspects in Integrated Knowledge Networks. Hence, the study focuses on twofold to sourcing external knowledge in particular: learning from international business environments and corporate venturing strategy for corporate incubators.

Conditions of Network Engagement

There is growing research interest in innovation network dynamics. Based on an explorative case study of a regional innovation network for the tourism industry, this chapter contributes to a better understanding of network engagement as a dynamic and social construct. By following the microfoundational trend, the chapter anchors the concept of engagement at a lower level in order to increase the depth of understanding of the conditions of network engagement. As there are many different notions of engagement, the chapters start by providing an overview of the different notions of engagement in innovation and network literature. Then, inspired by the critical incident technique, a narrative presents the findings, showing the dynamic and social aspect of network engagement, followed by a discussion of the conditions of network engagement and theoretical contributions.

Targeting Galectin 3 to Counteract Spike-Phase Uncoupling of Fast-Spiking Interneurons to Gamma Oscillations in Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is a progressive multifaceted neurodegenerative disorder for which no disease-modifying treatment exists. Neuroinflammation is central to the pathology progression, with evidence suggesting that microglia-released galectin 3 (gal3) plays a pivotal role by amplifying neuroinflammation in AD. However, possible involvement of gal3 in the disruption of cognition-relevant neuronal network oscillations typical of AD remains unknown. Methods: Here, we investigate the functional implications of gal3 signaling on cognition-relevant gamma oscillations (30-80 Hz) by performing electrophysiological recordings in hippocampal area CA3 of wild-type (WT) and 5xFAD mice in vitro. Results: Gal3 application decreases gamma oscillation power and rhythmicity in an activity-dependent manner and is accompanied by impairment of cellular dynamics in fast-spiking interneurons (FSN) and pyramidal cells (PCs). We found that gal3-induced disruption is mediated by the gal3-carbohydrate-recognition domain and prevented by the gal3 inhibitor TD139, which also prevents Aβ42-induced degradation of gamma oscillations. Furthermore, we demonstrate that 5xFAD mice lacking gal3 (5xFAD-Gal3KO) exhibit WT-like gamma network dynamics.Conclusions: We report for the first time that gal3 impairs cognition-relevant neuronal network dynamics by spike-phase uncoupling of FSN inducing a network performance collapse. Moreover, our findings suggest gal3 inhibition as a potential therapeutic target to counteract the neuronal network instability typical of AD and other neurological disorders encompassing neuroinflammation and cognitive decline.

Existence of physical measures in some excitation–inhibition networks*

In this paper we present a rigorous analysis of a class of coupled dynamical systems in which two distinct types of components, one excitatory and the other inhibitory, interact with one another. These network models are finite in size but can be arbitrarily large. They are inspired by real biological networks, and possess features that are idealizations of those in biological systems. Individual components of the network are represented by simple, much studied dynamical systems. Complex dynamical patterns on the network level emerge as a result of the coupling among its constituent subsystems. Appealing to existing techniques in (nonuniform) hyperbolic theory, we study their Lyapunov exponents and entropy, and prove that large time network dynamics are governed by physical measures with the SRB property.

Modularity and Dynamics on Complex Networks

Complex networks are typically not homogeneous, as they tend to display an array of structures at different scales. A feature that has attracted a lot of research is their modular organisation, i.e., networks may often be considered as being composed of certain building blocks, or modules. In this Element, the authors discuss a number of ways in which this idea of modularity can be conceptualised, focusing specifically on the interplay between modular network structure and dynamics taking place on a network. They discuss, in particular, how modular structure and symmetries may impact on network dynamics and, vice versa, how observations of such dynamics may be used to infer the modular structure. They also revisit several other notions of modularity that have been proposed for complex networks and show how these can be related to and interpreted from the point of view of dynamical processes on networks.

Why do hippocampal mossy cells matter? It depends on the frequency and context

The discrimination of similar episodes and places, and their representation as distinct memories, depend on a process called pattern separation that relies on the circuitry of the hippocampal dentate gyrus (DG). Mossy cells (MCs) are key neurons in the circuitry, but how they influence DG network dynamics, function, and seizure risk has not been fully elucidated. We found the net impact of MCs was inhibitory at physiological frequencies connected with learning and behaviour, and their absence associated with deficits in pattern separation and spatial memory; at higher frequencies, their net impact was excitatory, and their absence protected against seizures. Thus, MCs influence DG outputs in a highly dynamic manner that varies with frequency and context.One-Sentence SummaryHippocampal mossy cells are required for learning and memory; but their absence protects against seizures.

Age-related differences in the neural network dynamics underlying the predictability gain

Speech comprehension is often challenged by increased background noise, but can be facilitated via the semantic context of a sentence. This predictability gain relies on an interplay of language-specific semantic and domain-general brain regions. However, age-related differences in the interactions within and between semantic and domain-general networks remain poorly understood. Here we investigated commonalities and differences in degraded speech processing in healthy young and old participants. Participants performed a sentence repetition task while listening to sentences with high and low predictable endings and varying intelligibility. Stimulus intelligibility was adjusted to individual hearing abilities. Older adults showed an undiminished behavioural predictability gain. Likewise, both groups recruited a similar set of semantic and cingulo-opercular brain regions. However, we observed age-related differences in effective connectivity for high predictable speech of increasing intelligibility. Young adults exhibited stronger coupling within the cingulo-opercular network and between a cingulo-opercular and a posterior temporal semantic node. Moreover, these interactions were excitatory in young adults but inhibitory in old adults. Finally, the degree of the inhibitory influence between cingulo-opercular regions was predictive of the behavioural sensitivity towards changes in intelligibility for high predictable sentences in older adults only. Our results demonstrate that the predictability gain is relatively preserved in older adults when stimulus intelligibility is individually adjusted. While young and old participants recruit similar brain regions, differences manifest in network dynamics. Together, these results suggest that ageing affects the network configuration rather than regional activity during successful speech comprehension under challenging listening conditions.

Entropic model of network dynamics of clocking network synchronization

The main task of the clocking network synchronization (CNS) network subsystem is the formation, transmission, distribution and delivery of synchronization signals to the telecommunication system (TCS) digital equipment for the purpose of its coordinated interaction. Indicators of the telecommunication services quality are inextricably linked with the indicators of the CNS network functioning quality, in this regard, the process of monitoring and managing the CNS network comes to the fore for the purpose of prompt detection of failures and their subsequent elimination. The article provides an overview of the main classes of CNS network equipment and their diagnostic parameters, and also indicates the significant influence of the CNS network functioning process on the entire TCS functioning. To assess the technical condition of the CNS network an approach using the entropy analysis of the diagnostic parameters of the CNS network elements is proposed. The entropy model of the network dynamics is obtained in CNS work, which can later be used to develop a methodology for monitoring the technical condition of the CNS network. Using this model, it is possible to estimate not only the differential entropy of each CNS network element, but also to estimate the differential entropy of the entire CNS network or a separate fragment of the CNS network. Differential entropy parameters reflect the technical state of the CNS network.