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.

Graph theory analysis of whole brain functional connectivity to assess disturbances associated with suicide attempts in bipolar disorder

Brain targets to lower the high risk of suicide in Bipolar Disorder (BD) are needed. Neuroimaging studies employing analyses dependent on regional assumptions could miss hubs of dysfunction critical to the pathophysiology of suicide behaviors and their prevention. This study applied intrinsic connectivity distribution (ICD), a whole brain graph‐theoretical approach, to identify hubs of functional connectivity (FC) disturbances associated with suicide attempts in BD. ICD, from functional magnetic resonance imaging data acquired while performing a task involving implicit emotion regulation processes important in BD and suicide behaviors, was compared across 40 adults with BD with prior suicide attempts (SAs), 49 with BD with no prior attempts (NSAs) and 51 healthy volunteers (HVs). Areas of significant group differences were used as seeds to identify regional FC differences and explore associations with suicide risk-related measures. ICD was significantly lower in SAs than in NSAs and HVs in bilateral ventromedial prefrontal cortex (vmPFC) and right anterior insula (RaIns). Seed connectivity revealed altered FC from vmPFC to bilateral anteromedial orbitofrontal cortex, left ventrolateral PFC (vlPFC) and cerebellum, and from RaIns to right vlPFC and temporopolar cortices. VmPFC and RaIns ICD were negatively associated with suicidal ideation severity, and vmPFC ICD with hopelessness and attempt lethality severity. The findings suggest that SAs with BD have vmPFC and RaIns hubs of dysfunction associated with altered FC to other ventral frontal, temporopolar and cerebellar cortices, and with suicidal ideation, hopelessness, and attempt lethality. These hubs may be targets for novel therapeutics to reduce suicide risk in BD.

OPTIMAL NAVAL PATH PLANNING IN ICE-COVERED WATERS

The Northern Sea Route (NSR) links the Atlantic and Pacific oceans through the Arctic and it is critical for global trade as it provides a route between Asia and Europe that is significantly shorter than the alternatives. NSR is soon expected to open for intercontinental shipping due to global warming and thus presents tremendous opportunities for reductions in shipping time, cost, and environmental impacts. On the other hand, facilitating this route requires innovative approaches due to the navigation risks associated with its ice-covered waters. This study presents a graph-theoretical approach for optimal naval navigation in ice-covered sea routes with flexible turn angles based on the idea of large-adjacency grid graphs. Our model allows for asymmetric left and right turn radii as well as turn speeds that vary as a function of the turn angle and it offers natural-looking navigation paths.

Veracity assessment by single and multi-source identification algorithms during the crisis

Social networks have become a popular communication tool for information sharing. Twitter offers access to data and provides a significant opportunity to analyze data. During pandemics, Twitter becomes a big source for the dispersal of unverified information. In social media, it is difficult to find the sources of rumors. To tackle this problem the authors have developed a hybrid rumor centrality algorithm for rumor source detection in social networks. The authors propose an S-RSI algorithm for identifying a single rumor centre and an M-RSI algorithm for identifying the propagations of multiple rumor centres in the thread of conversation. The proposed rumor centrality algorithm efficiently predicts the rumor disseminating possibilities in a conversation tree with the aid of graph theoretical approach. The authors have evaluated the performance of the algorithms on the PHEME dataset containing seven real-time event conversational trees based on the tweet messages. The results show that the proposed is best suitable in finding the rumor source centre with a high probability in social media during a crisis.

Partitioning Hückel–London Currents into Cycle Contributions

Ring-current maps give a direct pictorial representation of molecular aromaticity. They can be computed at levels ranging from empirical to full ab initio and DFT. For benzenoid hydrocarbons, Hückel–London (HL) theory gives a remarkably good qualitative picture of overall current patterns, and a useful basis for their interpretation. This paper describes an implemention of Aihara’s algorithm for computing HL currents for a benzenoid (for example) by partitioning total current into its constituent cycle currents. The Aihara approach can be used as an alternative way of calculating Hückel–London current maps, but more significantly as a tool for analysing other empirical models of induced current based on conjugated circuits. We outline an application where examination of cycle contributions to HL total current led to a simple graph-theoretical approach for cycle currents, which gives a better approximation to the HL currents for Kekulean benzenoids than any of the existing conjugated-circuit models, and unlike these models it also gives predictions of the HL currents in non-Kekulean benzenoids that are of similar quality.

Disrupted fronto-temporal function in panic disorder: a resting-state connectome study

Recent neuroimaging studies have identified altered activations and connectivity among many brain regions as potential biomarkers for panic disorder. However, little was known about how topological properties would change in panic disorder. Therefore, a graph-theoretical approach was applied in this study to construct functional networks of patients and healthy controls to discover topological changes in panic disorder. 31 patients and 33 matched healthy controls underwent resting-state functional magnetic resonance imaging. Brain network of each participant was structured using the Anatomical Automatic Labeling template as nodes and connectivity matrixes as edges. Then, topological organizations of networks were calculated. Network-based statistic analysis was conducted and global and nodal properties were compared between patients and controls. Patients with panic disorder showed small-world attribute, which was lower than that in controls. Patients revealed decreased nodal efficiency in superior and middle frontal gyrus, right superior temporal gyrus and left middle temporal gyrus. Decreased functional connectivity was found in panic disorder between right middle temporal gyrus and extensive temporal regions. Results indicated decreased function of global and regional information transmission in panic disorder, highlighted the disrupted “top-down” processing in fronto-temporal regions and emphasized the role of temporal regions in the pathology of panic disorder.

Brain network dynamics codify heterogeneity in seizure propagation

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm

Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families.