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We review an approach for reconstructing oscillatory networks’ undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network’s coupling matrix in the first approximation in the coupling strength.
Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
Taking a new perspective
(2016)
Network analysis has attracted significant attention when researching the phenomenon of transnational terrorism, particularly Al Qaeda. While many scholars have made valuable contributions to mapping Al Qaeda, several problems remain due to a lack of data and the omission of data provided by international organizations such as the UN. Thus, this article applies a social network analysis and subsequent mappings of the data gleaned from the Security Council's consolidated sanctions list, and asks what they can demonstrate about the structure and organizational characteristics of Al Qaeda. The study maps the Al Qaeda network on a large scale using a newly compiled data set. The analysis reveals that the Al Qaeda network consists of several hundred individual and group nodes connecting almost all over the globe. Several major nodes are crucial for the network structure, while simultaneously many other nodes only weakly and foremost regionally connect to the network. The article concludes that the findings tie in well to the latest research pointing to local and simultaneously global elements of Al Qaeda, and that the new data is a valuable source for further analyses, potentially in combination with other data.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.
Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network
(2017)
Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later, of the whole network increased by up to 0.1 cm per iteration depending on the network model. The general increase in height within the network depended on connectedness and on the amount of height information that was exchanged between neighboring districts. If higher amounts of neighborhood height information were exchanged, the general increase in height within the network was large (strong secular trend). The trend in the homogeneous fishnet like network was lowest, the trend in the random network was highest. Yet, some network properties, such as the heteroscedasticity and autocorrelations of the migration simulation models differed greatly from the natural features observed in Swiss military conscript networks. Autocorrelations of district heights for instance, were much higher in the migration models. Conclusion: This study confirmed that secular height trends can be modeled by preferred migration of tall individuals into network hubs. However, basic network properties of the migration simulation models differed greatly from the natural features observed in Swiss military conscripts. Similar network-based data from other countries should be explored to better investigate height trends with Monte Carlo migration approach.
Objective: Hormone secretion from metabolically active tissues, such as pancreatic islets, is governed by specific and highly regulated signaling pathways. Defects in insulin secretion are among the major causes of diabetes. The molecular mechanisms underlying regulated insulin secretion are, however, not yet completely understood. In this work, we studied the role of the GTPase ARFRP1 on insulin secretion from pancreatic 13-cells. <br /> Methods: A 13-cell-specific Arfrp1 knockout mouse was phenotypically characterized. Pulldown experiments and mass spectrometry analysis were employed to screen for new ARFRP1-interacting proteins. Co-immunoprecipitation assays as well as super-resolution microscopy were applied for validation. <br /> Results: The GTPase ARFRP1 interacts with the Golgi-associated PDZ and coiled-coil motif-containing protein (GOPC). Both proteins are co localized at the trans-Golgi network and regulate the first and second phase of insulin secretion by controlling the plasma membrane localization of the SNARE protein SNAP25. Downregulation of both GOPC and ARFRP1 in Min6 cells interferes with the plasma membrane localization of SNAP25 and enhances its degradation, thereby impairing glucose-stimulated insulin release from 13-cells. In turn, overexpression of SNAP25 as well as GOPC restores insulin secretion in islets from 13-cell-specific Arfrp1 knockout mice. <br /> Conclusion: Our results identify a hitherto unrecognized pathway required for insulin secretion at the level of trans-Golgi sorting. (c) 2020 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).