Probabilistic Computational Methods Research Articles

Understanding RT’s Audiences: Exposure Not Endorsement for Twitter Followers of Russian State-Sponsored Media

The Russian state-funded international broadcaster RT (formerly Russia Today) has attracted much attention as a purveyor of Russian propaganda. To date, most studies of RT have focused on its broadcast, website, and social media content, with little research on its audiences. Through a data-driven application of network science and other computational methods, we address this gap to provide insight into the demographics and interests of RT’s Twitter followers, as well as how they engage with RT. Building upon recent studies of Russian state-sponsored media, we report three main results. First, we find that most of RT’s Twitter followers only very rarely engage with its content and tend to be exposed to RT’s content alongside other mainstream news channels. This indicates that RT is not a central part of their online news media environment. Second, using probabilistic computational methods, we show that followers of RT are slightly more likely to be older and male than average Twitter users, and they are far more likely to be bots. Third, we identify thirty-five distinct audience segments, which vary in terms of their nationality, languages, and interests. This audience segmentation reveals the considerable heterogeneity of RT’s Twitter followers. Accordingly, we conclude that generalizations about RT’s audience based on analyses of RT’s media content, or on vocal minorities among its wider audiences, are unhelpful and limit our understanding of RT and its appeal to international audiences.

Structural reliability software and calculation tools: a review

Structural reliability is now the leading method in the field of design optimization and assessment of civil engineering structures since it offers a rational treatment of the uncertainties in this kind of studies. However, structural reliability problems become more complex with time due to the high number of random variables leading to very complex numerical problems; moreover, they have to deal with complex finite element structural models. So, many reliability calculation tools and software packages have been developed for this purpose. This paper is an important reference for researchers and engineers to help them select the most suitable reliability calculation tool or software package; they will also know their most common area of use through the application examples mentioned for each tool. Several criteria are taken into account in the investigation such as the type of the structure, the probabilistic computational methods, and the budget assigned to the calculation tool.

The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation.

Depolarization of presynaptic terminals stimulates calcium influx, which evokes neurotransmitter release and activates phosphorylation-based signalling. Here, we present the first global temporal profile of presynaptic activity-dependent phospho-signalling, which includes two KCl stimulation levels and analysis of the poststimulus period. We profiled 1,917 regulated phosphopeptides and bioinformatically identified six temporal patterns of co-regulated proteins. The presynaptic proteins with large changes in phospho-status were again prominently regulated in the analysis of 7,070 activity-dependent phosphopeptides from KCl-stimulated cultured hippocampal neurons. Active zone scaffold proteins showed a high level of activity-dependent phospho-regulation that far exceeded the response from postsynaptic density scaffold proteins. Accordingly, bassoon was identified as the major target of neuronal phospho-signalling. We developed a probabilistic computational method, KinSwing, which matched protein kinase substrate motifs to regulated phosphorylation sites to reveal underlying protein kinase activity. This approach allowed us to link protein kinases to profiles of co-regulated presynaptic protein networks. Ca2+- and calmodulin-dependent protein kinase IIα (CaMKIIα) responded rapidly, scaled with stimulus strength, and had long-lasting activity. Mitogen-activated protein kinase (MAPK)/extracellular signal–regulated kinase (ERK) was the main protein kinase predicted to control a distinct and significant pattern of poststimulus up-regulation of phosphorylation. This work provides a unique resource of activity-dependent phosphorylation sites of synaptosomes and neurons, the vast majority of which have not been investigated with regard to their functional impact. This resource will enable detailed characterization of the phospho-regulated mechanisms impacting the plasticity of neurotransmitter release.

Spatially adaptive probabilistic computation of a sub-kilometre resolution lightning climatology for New Zealand

Lightning is a key component of the Earth's atmosphere and climate systems, and there is a potential positive feedback between a warming climate and increased lightning activity. In the biosphere, lightning is important as the main natural ignition source for wildfires and because of its contribution to the nitrogen cycle. Therefore, it is important to develop lightning climatologies to characterise and monitor lightning activity. While traditional methods for constructing lightning climatologies are suitable for examining lightning's influence on atmospheric processes, they are less well suited for examining questions about biosphere-lightning interactions. For example, examining the interaction between lightning and wildfires requires linking atmospheric processes to finer scale terrestrial processes and patterns. Most wildfires ignited by lightning are less than one hectare in size, and so require lightning climatologies at a comparable spatial resolution. However, such high resolution lightning climatologies cannot be derived using the traditional cell-count methodology. Here we present a novel geocomputational approach for analysing lightning data at high spatial resolutions. Our approach is based on probabilistic computational methods and is capable of producing a sub-kilometre lightning climatology that honours the spatial accuracy of the strike locations and is adaptive to underlying spatial patterns. We demonstrate our methods by applying them to the mid-latitude oceanic landmass of New Zealand, an area with geographic conditions that are under-represented in existing lightning climatologies. Our resulting lightning climatology has unparalleled spatial resolution, and the spatial and temporal patterns we observe in it are consistent with other continental and tropical lightning climatologies. To encourage further use and development of our probabilistic approach, we provide Python scripts that demonstrate the method alongside our resulting New Zealand lightning climatology.

Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Global network modeling of distal regulatory interactions is essential in understanding the overall architecture of gene expression programs. Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model. Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects. Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular. Transcriptional cancer drivers and risk genes were discovered based on the network analysis of somatic and genetic cancer-related DNA variants. Notably, we observed that the risk genes were functionally downstream of the cancer drivers and were selectively susceptible to network perturbation by tumorigenic changes in their upstream drivers. Furthermore, cancer risk alleles tended to increase the susceptibility of the transcription of their associated genes. These findings suggest that transcriptional cancer drivers selectively induce a combinatorial misregulation of downstream risk genes, and that genetic risk factors, mostly residing in distal regulatory regions, increase transcriptional susceptibility to upstream cancer-driving somatic changes.

Modeling Synchronized Neuronal Activities by Quantum Constrained Probabilistic Computational Neuro-Genetic Method: Insight into the Coupling among Brain's Sub-systems from a Systems View

Modeling Synchronized Neuronal Activities by Quantum Constrained Probabilistic Computational Neuro-Genetic Method: Insight into the Coupling among Brain's Sub-systems from a Systems View

Sensitivity and uncertainty analyses of the tritium production in the HCPB breeder blanket mock-up experiment

A neutronics experiment performed at the Frascati Neutron Generator (FNG) on a mock-up of the Helium-Cooled Pebble Bed (HCPB) breeder test blanket module (TBM) has been analysed on the basis of neutron transport, sensitivity and uncertainty calculations using both deterministic and probabilistic computational methods. The calculations revealed a slight but systematic underestimation of the measured tritium activities by 5 to 10% on average, independent of the nuclear data used that were taken from the EFF-3 and FENDL-2.0, -2.1 data files. The uncertainty assessments showed data related uncertainties of the calculated tritium production rates at a level of 4% (2 σ confidence level). The beryllium cross-section data were shown to be the main source of these uncertainties. The total uncertainties of the tritium production prediction including the data uncertainties, the statistical uncertainties of the Monte Carlo calculation and the experimental uncertainties are in the order of 8 to 10% (2 σ). The observed underestimation of the measured tritium production is at the lower bound of the assessed uncertainty margin. The obtained results indicate that design calculations for the tritium breeding ratio (TBR) of fusion power reactors employing a HCPB type breeder blanket are conservative. Thus, an additional TBR margin is provided which allows compensation for potential other uncertainties.

A probabilistic method for field solutions in two‐phase flows

AbstractSolid‐particle motion and related transport phenomena in two‐phase flow are fluctuating processes in space and time. A deterministic method can describe only partially the intrinsic physics of these processes. In this paper, the fluctuations of the flow parameters are modelled by considering the spatial correlations, and a probabilistic computational method for two‐phase flow is presented. The probabilistic governing equations have been discretized in space using a finite volume method, and then solved by applying the Neumann expansion method. This last method is time efficient, and its convergence can be guaranteed even for large fluctuations. A liquid‐solid particle mixture flow in a circular pipe is taken as an example. Computational results illustrate the merit of the probabilistic approach for the prediction of two‐phase flow phenomena.