Treatments against Polymorphosal discrepancies in Glioblastoma Multiforme.

Glioblastoma (GB) are aggressive tumors that obstruct normal brain function. While the skull cannot expand in response to cancer growth, the growing pressure in the brain is generally the first sign. It can produce more frequent headaches, unexplained nausea or vomiting, blurred peripheral vision, double vision, a loss of feeling or movement in an arm or leg, and difficulty speaking and concentrating; all depend on the tumor's location. GB can also cause vascular thrombi, damaging endothelial cells and leading to red blood cell leakage. Latest studies have revealed the role of single nucleotide polymorphisms (SNPs) in developing and spreading cancers such as GB and breast cancer. Many discovered SNPs are associated with GB, particularly in great abundance in the promoter region, creating polygenetic vulnerability to glioma. This study aims to compile a list of some of the most frequent and significant SNPs implicated with GB formation and proliferation.

A General Metric for the Similarity of Both Stochastic and Deterministic System Dynamics.

Many problems in the study of dynamical systems-including identification of effective order, detection of nonlinearity or chaos, and change detection-can be reframed in terms of assessing the similarity between dynamical systems or between a given dynamical system and a reference. We introduce a general metric of dynamical similarity that is well posed for both stochastic and deterministic systems and is informative of the aforementioned dynamical features even when only partial information about the system is available. We describe methods for estimating this metric in a range of scenarios that differ in respect to contol over the systems under study, the deterministic or stochastic nature of the underlying dynamics, and whether or not a fully informative set of variables is available. Through numerical simulation, we demonstrate the sensitivity of the proposed metric to a range of dynamical properties, its utility in mapping the dynamical properties of parameter space for a given model, and its power for detecting structural changes through time series data.

Quantifying interactions among car drivers using information theory.

Information-theoretic quantities have found wide applications in understanding interactions in complex systems primarily due to their non-parametric nature and ability to capture non-linear relationships. Increasingly popular among these tools is conditional transfer entropy, also known as causation entropy. In the present work, we leverage this tool to study the interaction among car drivers for the first time. Specifically, we investigate whether a driver responds to its immediate front and its immediate rear car to the same extent and whether we can separately quantify these responses. Using empirical data, we learn about the important features related to human driving behavior. Results demonstrate the evidence that drivers respond to both front and rear cars, and the response to their immediate front car increases in the presence of jammed traffic. Our approach provides a data-driven perspective to study interactions and is expected to aid in analyzing traffic dynamics.

Effect of visual and auditory sensing cues on collective behavior in Vicsek models.

In the present study, we consider two independent sensing modes (auditory and visual) in Vicsek-like models and compare the emergent group-level behaviors in terms of polarization, cohesion, and cluster size. The auditory and visual modes differ in the determination of particle neighbors, which at the level of groups results in higher polarization, lower cohesion, and larger cluster size for the auditory mode relative to the visual. With the increase in average density of the particles, these differences are more pronounced. These differences are due to the fact that these sense modalities robustly generate distinct spatial distributions of the particles. We demonstrate the use of a data-driven approach, called transfer entropy, to distinguish the sensing modalities by considering only a pair of particle trajectories. Such an approach could be applicable to real-world systems, where it may be a challenge to measure the position and velocity of every particle within a swarm.

Extracting Interactions between Flying Bat Pairs Using Model-Free Methods.

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats' use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats' turning behavior when they fly in the absence of obstacles.

Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data.

OBJECTIVE: The objective of this paper is to didactically compare resting state connectivity networks computed using two different methods called phase locking value (PLV) and convergent cross-mapping (CCM). PLV is a ubiquitous measure of connectivity in electrophysiological research but is less often applied to fMRI BOLD timeseries since this model-based metric assumes that oscillatory coupling is a sufficient condition for connectivity. Alternatively, CCM is a model-free method, which detects potentially nonlinear causal influences based on the ability to estimate one timeseries with another and does not assume an oscillatory structure. APPROACH: We use a toy dataset to test the PLV and CCM algorithms under different known synchronization conditions. Additionally, experimental resting state EEG and fMRI datasets are used for comparison. MAIN RESULTS: The results show that the resting state brain networks computed using both algorithms produce similar results for both resting state EEG and fMRI datasets. For both neuroimaging datasets, the network characteristics follow the same trends and the similarity between the computed networks, for both algorithms, is highly significant. SIGNIFICANCE: CCM is able to identify low or one-way connection strengths better than PLV but takes exponentially longer to compute. Based on these results, PLV provides a good metric for on-line network identification because it is both computationally fast and an excellent approximation of the network computed with CCM.

Detecting causality using symmetry transformations.

Detecting causality between variables in a time series is a challenge, particularly when the relationship is nonlinear and the dataset is noisy. Here, we present a novel tool for detecting causality that leverages the properties of symmetry transformations. The aim is to develop an algorithm with the potential to detect both unidirectional and bidirectional coupling for nonlinear systems in the presence of significant sampling noise. Most of the existing tools for detecting causality can make determinations of directionality, but those determinations are relatively fragile in the presence of noise. The novel algorithm developed in the present study is robust and very conservative in that it reliably detects causal structure with a very low rate of error even in the presence of high sampling noise. We demonstrate the performance of our algorithm and compare it with two popular model-free methods, namely transfer entropy and convergent cross map. This first implementation of the method of symmetry transformations is limited in that it applies only to first-order autonomous systems.

Interactional dynamics of same-sex marriage legislation in the United States.

Understanding how people form opinions and make decisions is a complex phenomenon that depends on both personal practices and interactions. Recent availability of real-world data has enabled quantitative analysis of opinion formation, which illuminates phenomena that impact physical and social sciences. Public policies exemplify complex opinion formation spanning individual and population scales, and a timely example is the legalization of same-sex marriage in the United States. Here, we seek to understand how this issue captures the relationship between state-laws and Senate representatives subject to geographical and ideological factors. Using distance-based correlations, we study how physical proximity and state-government ideology may be used to extract patterns in state-law adoption and senatorial support of same-sex marriage. Results demonstrate that proximal states have similar opinion dynamics in both state-laws and senators' opinions, and states with similar state-government ideology have analogous senators' opinions. Moreover, senators' opinions drive state-laws with a time lag. Thus, change in opinion not only results from negotiations among individuals, but also reflects inherent spatial and political similarities and temporal delays. We build a social impact model of state-law adoption in light of these results, which predicts the evolution of state-laws legalizing same-sex marriage over the last three decades.

Leader-follower consensus and synchronization in numerosity-constrained networks with dynamic leadership.

In this work, we study leader-follower consensus and synchronization protocols over a stochastically switching network. The agents representing the followers can communicate with any other agent, whereas the agents serving as leaders are restricted to interact only with the other leaders. The model incorporates the phenomenon of numerosity, which limits the perceptual capacity of the agents while allowing for shuffling with whom each individual interacts at each time step. We derive closed form expressions for necessary and sufficient conditions for consensus, the rate of convergence to consensus, and conditions for stochastic synchronization in terms of the asymptotic convergence factor. We provide simulation results to validate the theoretical findings and to illustrate the dependence of this factor on system parameters. The closed form results enable us to study the factors affecting the feasibility of consensus. We show that agents' traits can be chosen for an engineered system to maximize the convergence speed and that protocol speed is enhanced as the proportion of the leaders increases in certain cases. These results may find application in the design and control of an engineered leader-follower system, where consensus or synchronization at the fastest possible rate is desired.