Network Analysis Identifies Phase Transitions for Tumor With Interacting Cells.

Metastasis is the process by which cancer cells acquire the capability to leave the primary tumor and travel to distant sites. Recent experiments have suggested that the epithelial-mesenchymal transition can regulate invasion and metastasis. Another possible scenario is the collective motion of cells. Recent studies have also proposed a jamming-unjamming transition for epithelial cells based on physical forces. Here, we assume that there exists a short-range chemical attraction between cancer cells and employ the Brownian dynamics to simulate tumor growth. Applying the network analysis, we suggest three possible phases for a given tumor and study the transition between these phases by adjusting the attraction strength.

A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus.

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.

The network structure affects the fixation probability when it couples to the birth-death dynamics in finite population.

The study of evolutionary dynamics on graphs is an interesting topic for researchers in various fields of science and mathematics. In systems with finite population, different model dynamics are distinguished by their effects on two important quantities: fixation probability and fixation time. The isothermal theorem declares that the fixation probability is the same for a wide range of graphs and it only depends on the population size. This has also been proved for more complex graphs that are called complex networks. In this work, we propose a model that couples the population dynamics to the network structure and show that in this case, the isothermal theorem is being violated. In our model the death rate of a mutant depends on its number of neighbors, and neutral drift holds only in the average. We investigate the fixation probability behavior in terms of the complexity parameter, such as the scale-free exponent for the scale-free network and the rewiring probability for the small-world network.

Ultraviolet solar flare signatures in the framework of complex network.

Studying natural phenomena via the complex network approach makes it possible to quantify the time-evolving structures with too many elements and achieve a deeper understanding of interactions among the components of a system. In this sense, solar flare as a complex system with the chaotic behavior could be better characterized by the network parameters. Here, we employed an unsupervised network-based method to recognize the position and occurrence time of the solar flares by using the ultraviolet emission (1600 Å) recorded by the Atmospheric Imaging Assembly on board Solar Dynamics Observatory. Three different regions, the flaring active regions, the non-flaring active regions, and the quiet-Sun regions, were considered to study the variations of the network parameters in the presence and absence of flaring phases in various datasets over time intervals of several hours. The whole parts of the selected datasets were partitioned into sub-windows to construct networks based on computing the Pearson correlation between time series of the region of interest and intensities. Analyzing the network parameters such as the clustering coefficient, degree centrality, characteristic length, and PageRank verified that flare triggering has an influence on the network parameters around the flare occurrence time and close to the location of flaring. It was found that the values of the clustering coefficient and characteristic length approach those obtained for the corresponding random network in the flaring phase. These findings could be used for detecting the occurrence times and locations of the region at ultraviolet images.

PageRank: An alarming index of probable earthquake occurrence.

Here, we introduce PageRank (PR) in a seismic network as an appropriate alarming clue before the occurrence of the event to be worthwhile in hazard probabilistic evaluation of earthquakes. Studying PR changes of two main shocks in Iran and Italy by means of temporal and spatial windows reveals that their PR values increase drastically before the event, while there is no trend for other considered locations and/or other time intervals. Therefore, the PR value seems to be an appropriate index of a place induction by previous events and its susceptibility for having a new earthquake. Moreover, summing over the PRs of areas close to the Italy event location and tracking this newly defined PR behavior show an increasing trend before the main shock implying that the close regions are influenced and become highly connected before the event as well as the earthquake location itself. It is also indicated that PR behavior is not necessarily correlated to the number of occurring earthquakes and is inherently the result of points connectivity and interactions.

Functional brain segregation changes during demanding mathematical task.

Aim of the study: The neural basis of demanding mathematical problem solving is currently indeterminate and unclear. Mathematical problem solving engages higher order cognition and a complex associative activity of functional neural networks occurs during demanding problem solving. Method: Twenty right handed subjects (mean age: 24.6 years; SD = 3.97 years; 50% female) participated in this study. An arithmetic logic puzzle was used as a demanding mathematical task. EEGs were recorded in the eye open rest and eye open task conditions. To clarify functional connectivity of brain networks, clustering coefficient, transitivity, global efficiency, degree and entropy were investigated in two conditions. Results: During problem solving, disrupted brain connectivity and decreased brain segregation were observed in the alpha band. However, in the beta band, increased connectivity, transitivity and clustering associated with higher modularity were observed. Theta exhibited unaltered brain network function. Conclusion: In the demanding problem solving task, decreased local alpha coupling may suggest that default mode network activity is interrupted. Since there is no significant difference within the theta network, the central executive network may not be as strongly involved. Increased segregation of functional brain network (without increasing of integration level) can be discussed in relation of demanding aspects of mathematical problem. We suggest a complex network may involve in the real situation of demanding problem solving.

Centrality in earthquake multiplex networks.

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

The role of community structure on the nature of explosive synchronization.

In this paper, we analyze explosive synchronization in networks with a community structure. The results of our study indicate that the mesoscopic structure of the networks could affect the synchronization of coupled oscillators. With the variation of three parameters, the degree probability distribution exponent, the community size probability distribution exponent, and the mixing parameter, we could have a fast or slow phase transition. Besides, in some cases, we could have communities which are synchronized inside but not with other communities and vice versa. We also show that there is a limit in these mesoscopic structures which suppresses the transition from the second-order phase transition and results in explosive synchronization. This could be considered as a tuning parameter changing the transition of the system from the second order to the first order.

Functional Brain Connectivity Differences Between Different ADHD Presentations: Impaired Functional Segregation in ADHD-Combined Presentation but not in ADHD-Inattentive Presentation.

INTRODUCTION: Contrary to Diagnostic and Statistical Manual of Mental Disorders (DSM-5), fifth edition, some studies indicate that ADHD-inattentive presentation (ADHD-I) is a distinct diagnostic disorder and not an ADHD presentation. METHODS: In this study, 12 ADHD-combined presentation (ADHD-C), 10 ADHD-I, and 13 controls were enrolled and their resting state EEG recorded. Following this, a graph theoretical analysis was performed and functional integration and segregation of brain network was calculated. RESULTS: The results show that clustering coefficient of theta band was significantly different among three groups and significant differences were observed in theta global efficiency between controls and ADHD-C. Regarding the alpha band, a lower clustering coefficient was observed in control subjects. In the beta band, clustering coefficient was significantly different between the control and children with ADHD-C and also between ADHD-I and ADHD-C. The clustering coefficient, in the subjects with ADHD-C, demonstrated a rapid decline and was significantly lower than the subjects with ADHD-I and control. CONCLUSION: Decreased clustering, in high thresholds, may be associated with hyperactivity while increased segregation in low thresholds with inattentiveness. A different functional network occurs in the ADHD-C brain that is consistent with several studies that have reported ADHD-I as a distinct disorder.

Distinguishing Functional DNA Words; A Method for Measuring Clustering Levels.

Functional DNA sub-sequences and genome elements are spatially clustered through the genome just as keywords in literary texts. Therefore, some of the methods for ranking words in texts can also be used to compare different DNA sub-sequences. In analogy with the literary texts, here we claim that the distribution of distances between the successive sub-sequences (words) is q-exponential which is the distribution function in non-extensive statistical mechanics. Thus the q-parameter can be used as a measure of words clustering levels. Here, we analyzed the distribution of distances between consecutive occurrences of 16 possible dinucleotides in human chromosomes to obtain their corresponding q-parameters. We found that CG as a biologically important two-letter word concerning its methylation, has the highest clustering level. This finding shows the predicting ability of the method in biology. We also proposed that chromosome 18 with the largest value of q-parameter for promoters of genes is more sensitive to dietary and lifestyle. We extended our study to compare the genome of some selected organisms and concluded that the clustering level of CGs increases in higher evolutionary organisms compared to lower ones.