The online competition between pro- and anti-vaccination views.

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

Shockwavelike Behavior across Social Media.

Online communities featuring "anti-X" hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Generalized Gelation Theory Describes Onset of Online Extremist Support.

We introduce a generalized form of gelation theory that incorporates individual heterogeneity and show that it can explain the asynchronous, sudden appearance and growth of online extremist groups supporting ISIS (so-called Islamic State) that emerged globally post-2014. The theory predicts how heterogeneity impacts their onset times and growth profiles and suggests that online extremist groups present a broad distribution of heterogeneity-dependent aggregation mechanisms centered around homophily. The good agreement between the theory and empirical data suggests that existing strategies aiming to defeat online extremism under the assumption that it is driven by a few "bad apples" are misguided. More generally, this generalized theory should apply to a range of real-world systems featuring aggregation among heterogeneous objects.

Time-resolved molecular measurements reveal changes in astronauts during spaceflight.

From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.

Time-resolved molecular measurements reveal changes in astronauts during spaceflight.

From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n = 27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects.

Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals' multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.

Applying differential network analysis to longitudinal gene expression in response to perturbations.

Differential Network (DN) analysis is a method that has long been used to interpret changes in gene expression data and provide biological insights. The method identifies the rewiring of gene networks in response to external perturbations. Our study applies the DN method to the analysis of RNA-sequencing (RNA-seq) time series datasets. We focus on expression changes: (i) in saliva of a human subject after pneumococcal vaccination (PPSV23) and (ii) in primary B cells treated ex vivo with a monoclonal antibody drug (Rituximab). The DN method enabled us to identify the activation of biological pathways consistent with the mechanisms of action of the PPSV23 vaccine and target pathways of Rituximab. The community detection algorithm on the DN revealed clusters of genes characterized by collective temporal behavior. All saliva and some B cell DN communities showed characteristic time signatures, outlining a chronological order in pathway activation in response to the perturbation. Moreover, we identified early and delayed responses within network modules in the saliva dataset and three temporal patterns in the B cell data.

Visibility graph based temporal community detection with applications in biological time series.

Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

Longitudinal saliva omics responses to immune perturbation: a case study.

Saliva omics has immense potential for non-invasive diagnostics, including monitoring very young or elderly populations, or individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over three periods (100 timepoints) involving: (1) hourly sampling over 24 h without intervention, (2) hourly sampling over 24 h including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (3) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome, and small RNA from salivary extracellular vesicles were profiled, including mRNA, miRNA, piRNA and bacterial RNA. The two 24-h periods were used in a paired analysis to remove daily variation and reveal vaccination responses. Over 18,000 omics longitudinal series had statistically significant temporal trends compared to a healthy baseline. Various immune response and regulation pathways were activated following vaccination, including interferon and cytokine signaling, and MHC antigen presentation. Immune response timeframes were concordant with innate and adaptive immunity development, and coincided with vaccination and reported fever. Overall, mRNA results appeared more specific and sensitive (timewise) to vaccination compared to other omics. The results suggest saliva omics can be consistently assessed for non-invasive personalized monitoring and immune response diagnostics.

The MathIOmica Toolbox: General Analysis Utilities for Dynamic Omics Datasets.

MathIOmica is a package for bioinformatics, written in the Wolfram language, that provides multiple utilities to facilitate the analysis of longitudinal data generated from omics experiments, including transcriptomics, proteomics, and metabolomics data, as well as any generalized time series. MathIOmica uses Mathematica's notebook interface, wherein users can import longitudinal datasets, carry out quality control and normalization, generate time series, and classify temporal trends. MathIOmica provides spectral methods based on periodograms and autocorrelations for automatically detecting classes of temporal behavior and allowing the user to visualize collective temporal behavior, and also assess biological significance through Gene Ontology and pathway enrichment analyses. MathIOmica's time-series classification methods address common issues including missing data and uneven sampling in measurements. As such, the software is ideally suited for the analysis of experimental data from individualized profiling of subjects, can facilitate analysis of data from the emerging field of individualized health monitoring, and can detect temporal trends that may be associated with adverse health events. In this article, we import a transcriptomics (RNA-sequencing) dataset collected over multiple timepoints and generate time series for each transcript represented in the data. We classify the time series to identify classes of significant temporal trends (using autocorrelations). We assess statistical significance cutoffs in the classification by generating null distributions using randomly resampled time series. We then visualize the significant trends in heatmaps and assess biological significance using enrichment analyses. Finally, we visualize pathway results for statistically significant pathways of interest. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Time series analysis of transcriptomics expression dataset.

Unifying casualty distributions within and across conflicts.

The distribution of whole war sizes and the distribution of event sizes within individual wars, can both be well approximated by power laws where size is measured by the number of fatalities. However the power-law exponent value for whole wars has a substantially smaller magnitude - and hence a flatter distribution - than for individual wars. We provide detailed numerical evidence that confirms that these numerically different power-law exponent values are interrelated in a simple way by the effect of aggregating fatalities from individual events within wars to whole wars. We offer intuition for this finding and hence strengthen the case for a unified description and understanding of human conflict across scales.

Continuous measurement of dynamic classroom social interactions.

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children's interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children's location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children's velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.