Large-scale Interaction Research Articles

Study of rainfall variabilities in Southeast Asia using long-term gridded rainfall and its substantiation through global climate indices

This study utilized a long-term (1951–2014) moderate-resolution observed gridded (0.5°×0.5°) rainfall dataset to analyze long-term and short-term rainfall variability and the seasonality in Southeast Asia (SEA). The previous studies for this region revealed significant enhancement in extreme events and seasonal variabilities in rainfall. In this study, the frequency and intensity-based rainfall extremity analysis was carried out by utilizing a number of rainfall extreme indices (e.g. Dry Days, Wet Days, 90P, 95P, and 99P). A seasonal non-parametric Mann-Kendall test was applied to detect significant increase and decrease of rainfall. The possible causes for the acceleration and variabilities of rainfall were investigated by analyzing the impact of large-scale ocean-atmospheric climate interactions through global climate indices such as El Nino (3.4 and 4.0), Southern Oscillation Index (SOI), Madden-Julian Oscillation (MJO), global mean land and ocean temperature index, QBO and NOI. Our results displayed a significant increase of rainfall amount in most of SEA regions, while a minimal decrease was also found over some regions. The rainfall indices displayed substantial intra-decadal variabilities over the SEA region. The intra-decadal percent of change analysis based on rainfall extreme indices revealed significant changes in rainfall extremes over SEA, which also evidenced the computed acceleration in rainfall over the region. The correlation analysis based findings exhibited that a strong correlation existed between the SEA rainfall and the large-scale global climate indices generated based on the ocean-atmosphere climate interactions. After regionalization of the SEA, the global climate indices based results described the spatiotemporal variabilities in rainfall, which were computed over SEA regions. The global climate indices such as El Nino (3.4 & 4.0), SOI, NOI and MJO suggested significant correlations with the seasonal rainfall and the rainfall extreme indices and highlighted that their collinearities vary within regions as per variations in the monsoon.

Data-driven prediction of genetic interactions in Candida glabrata

The rapid acquisition of drug resistance by Candida glabrata has placed an increased burden on health systems. Combination therapies, which apply multiple known compounds, can thwart the compensatory behaviours that lead to drug resistance and can open therapeutic avenues that were not previously available. Genetic interactions, in which the loss of a pair of genes has a stronger effect than the loss of either of the genes alone, can be useful in identifying targets for combination therapy. However, the number of possible interactions is immense while experimentally establishing a genetic interaction is non-trivial. We are developing a method for predicting genetic interactions in C. glabrata as a means to prioritize the experimental validation of interactions and combination therapies. The method applies machine-learning techniques to infer genetic interactions from diverse data in C. glabrata and from other yeast species, such as coexpression, protein-protein interactions and coevolution. We are establishing the method by building a model for Saccharomyces cerevisiae to validate against the large-scale genetic interaction data available for that species. This model will then integrated with C. glabrata-specific data, validated against known genetic interactions, and used to produce novel predictions for further characterisation in the lab.

Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity.

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.

Earth as an exoplanet mission concept for a lunar orbiting cubesat

There is a growing interest in Lunar exploration fed by the perception that the Moon can be made accessible to low-cost missions in the next decade. The on-going projects to set a communications relay in Lunar orbit and a deep space Gateway, as well as the spreading of commercial-of-the shelf (COTS) technology for small space platforms such as the cubesats contribute to this perception. Small, cubesat size satellites orbiting the Moon offer ample opportunities to study the Moon and enjoy an advantage point to monitor the Solar System and the large scale interaction between the Earth and the solar wind. In this article, we describe the technical characteristics of a 12U cubesat to be set in polar Lunar orbit for this purpose and the science behind it. The mission is named EarthASAP (Earth AS An exoPlanet) and was submitted to the Lunar Cubesats for Exploration (LUCE) call in 2016. EarthASAP was designed to monitor hydrated rock reservoirs in the Lunar poles and to study the interaction between the large Earth's exosphere and the solar wind in preparation for future exoplanetary missions.

Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues.

Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system’s development—such as synchronization techniques, system loads, electrodes, and applications—are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain–computer interface, among others.

Discussion of “Implementation of Soil Pressure Sensors in Large-Scale Soil-Structure Interaction Studies” by L. Keykhosropour, A. Lemnitzer, L. Star, A. Marinucci, and S. Keowen, This article was published in Geotechnical Testing Journal, Vol. 41, No. 4, 2018. [DOI: 10.1520/GTJ20170163

Discussion of “Implementation of Soil Pressure Sensors in Large-Scale Soil-Structure Interaction Studies” by L. Keykhosropour, A. Lemnitzer, L. Star, A. Marinucci, and S. Keowen, This article was published in <i>Geotechnical Testing Journal</i>, Vol. 41, No. 4, 2018. [DOI: 10.1520/GTJ20170163

Closure to Discussion of “Implementation of Soil Pressure Sensors in Large-Scale Soil-Structure Interaction Studies”

Closure to Discussion of “Implementation of Soil Pressure Sensors in Large-Scale Soil-Structure Interaction Studies”

FlexTouch

In this paper, we present FlexTouch, a technique that enables large-scale interaction sensing beyond the spatial constraints of capacitive touchscreens using passive low-cost conductive materials. This is achieved by customizing 2D circuit-like patterns with an array of conductive strips that can be easily attached to the sensing nodes on the edge of the touchscreen. FlexTouch requires no hardware modification, and is compatible with various conductive materials (copper foil tape, silver nanoparticle ink, ITO frames, and carbon paint), as well as fabrication methods (cutting, coating, and ink-jet printing). Through a series of studies and illustrative examples, we demonstrate that FlexTouch can support long-range touch sensing for up to 4 meters and everyday object presence detection for up to 2 meters. Finally, we show the versatility and feasibility of FlexTouch through applications such as body posture recognition, human-object interaction as well as enhanced fitness training experiences.

Integrated network pharmacology and targeted metabolomics to reveal the mechanism of nephrotoxicity of triptolide.

Triptolide (TP) is one of the important active components in Tripterygium wilfordii Hook. F., which shows strong anti-inflammatory and immunomodulatory effects. However, a large number of literature studies have reported that TP is the main component causing nephrotoxicity, and the mechanism of nephrotoxicity has not yet been revealed. Therefore, it is of great practical significance to clarify the toxicity mechanism of TP. This study integrated network pharmacology and targeted metabolomics to reveal the nephrotoxicity mechanism of TP. Firstly, network pharmacology screening of 61 action targets related to TP induced nephrotoxicity, with 39 direct targets and 22 indirect targets, was performed. Subsequently, based on a large-scale protein-protein interaction (PPI) and molecular docking validation, the core targets were identified. Based on the above targets and enrichment analysis, the purine metabolism, Toll-like receptor signaling pathway and NF-κB signaling pathway were found play a pivotal role in TP-induced nephrotoxicity. Literature investigation showed that purine and pyrimidine metabolism pathways were closely related to kidney diseases. Therefore, by using the quantitative method of determining endogenous purine and pyrimidine previously established in the laboratory, a targeted metabolomic analysis of TP was carried out. Finally, six nephrotoxicity biomarkers, dihydroorotate, thymidine, 2-deoxyinosine, uric acid, adenosine and xanthine, were found. Combining the above results, the mechanisms underlying the nephrotoxicity of TP were speculated to be due to the over-consumption of xanthine and uric acid, which would result in enormous ROS being released in response to oxidative stress in the body. Furthermore, activation of the Toll-like receptor signalling pathway can promotes the phosphorylation of the downstream protein NF-κB and causes an inflammatory response that ultimately leads to nephrotoxicity.

Enhanced large-scale atmospheric flow interaction with ice sheets at high model resolution

Abstract The development in supercomputing power allows running full-complexity Earth System Models (ESM) at increasingly higher spatial resolutions on a global scale. We show here a recent example where increased model resolution leads to a fundamentally different large-scale fluid dynamical adjustment of the mean wind pattern to the presence of an ice sheet over Europe compared to a coarse resolution simulation. While the higher resolution allows for a more realistic representation of atmospheric flow interaction with complex topographic features, the interpretation and prediction of the model results with a stronger bottom-up mechanical and thermal forcing on the atmosphere becomes increasingly difficult to be studied within a fully coupled model. We emphasize that interdisciplinary approaches should be pursued where the experience from engineering approaches of studying flow around objects and the influence of boundary-layer processes can help to disentangle the complexity within ESM. Ultimately, such engineering approaches will add a more fundamental theoretical understanding and prediction of expected flow interactions and will help to design full-complexity atmospheric model experiments accordingly.

Landscape of the Plasmodium Interactome Reveals Both Conserved and Species-Specific Functionality

SummaryMalaria represents a major global health issue, and the identification of new intervention targets remains an urgent priority. This search is hampered by more than one-third of the genes of malaria-causing Plasmodium parasites being uncharacterized. We report a large-scale protein interaction network in Plasmodium schizonts, generated by combining blue native-polyacrylamide electrophoresis with quantitative mass spectrometry and machine learning. This integrative approach, spanning 3 species, identifies >20,000 putative protein interactions, organized into 600 protein clusters. We validate selected interactions, assigning functions in chromatin regulation to previously unannotated proteins and suggesting a role for an EELM2 domain-containing protein and a putative microrchidia protein as mechanistic links between AP2-domain transcription factors and epigenetic regulation. Our interactome represents a high-confidence map of the native organization of core cellular processes in Plasmodium parasites. The network reveals putative functions for uncharacterized proteins, provides mechanistic and structural insight, and uncovers potential alternative therapeutic targets.

\u03b1-Rank: Multi-Agent Evaluation by Evolution

We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs). The approach leverages continuous-time and discrete-time evolutionary dynamical systems applied to empirical games, and scales tractably in the number of agents, in the type of interactions (beyond dyadic), and the type of empirical games (symmetric and asymmetric). Current models are fundamentally limited in one or more of these dimensions, and are not guaranteed to converge to the desired game-theoretic solution concept (typically the Nash equilibrium). α-Rank automatically provides a ranking over the set of agents under evaluation and provides insights into their strengths, weaknesses, and long-term dynamics in terms of basins of attraction and sink components. This is a direct consequence of the correspondence we establish to the dynamical MCC solution concept when the underlying evolutionary model’s ranking-intensity parameter, α, is chosen to be large, which exactly forms the basis of α-Rank. In contrast to the Nash equilibrium, which is a static solution concept based solely on fixed points, MCCs are a dynamical solution concept based on the Markov chain formalism, Conley’s Fundamental Theorem of Dynamical Systems, and the core ingredients of dynamical systems: fixed points, recurrent sets, periodic orbits, and limit cycles. Our α-Rank method runs in polynomial time with respect to the total number of pure strategy profiles, whereas computing a Nash equilibrium for a general-sum game is known to be intractable. We introduce mathematical proofs that not only provide an overarching and unifying perspective of existing continuous- and discrete-time evolutionary evaluation models, but also reveal the formal underpinnings of the α-Rank methodology. We illustrate the method in canonical games and empirically validate it in several domains, including AlphaGo, AlphaZero, MuJoCo Soccer, and Poker.

PRINCIPAL CLIMATE INDICES FOR THE NORTH PACIFIC: NATURE AND HISTORY (A REVIEW)

Climatic indices reflecting the environmental conditions and patterns of their variability in the entire northern Pacific and in its local regions are overviewed. Their physical nature and mechanisms of the processes, their geography and methods of calculation are presented, with citing of the first descriptions. Among a variety of global and regional climatic indices concern ing the North Pacific, the following ones are described: the indices of Arctic Oscillation (AO), El Niño — Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Aleutian Low Pressure Index (ALPI), Siberian High Index (SHI), North Pacific Index (NPI), Pacific/North American (PNA) Index, and West Pacific Index (WP). AO is a large-scale index of atmospheric circulation reflecting the processes both in the troposphere and stratosphere, where «pumping» of air masses between the high and moderate latitudes occurs continuously. ENSO is also a large-scale index that reflects large-scale interactions in the fields of temperature, atmospheric pressure, wind, and cloudiness over the whole Pacific. Other indices are rather regional, since their influence does not extend far beyond the limits of the domains of their definition. Nevertheless, their role in environmental fluctuations in certain areas could be significant and their influence could be traced throughout the Northern Hemisphere.

A Summary of Research on Wide-area Distributed Electric Vehicle and Grid Interaction Technology

Electric vehicle charging behavior and grid interaction technology, including the impact assessment of electric vehicles for distribution networks and the planning and operation optimization of electric vehicles connected to the grid.Specifically, the main contents include: researching the agglomeration characteristics of large-scale electric vehicle charging behavior based on statistical data and charging history data, and evaluating the regulatory potential of large-scale electric vehicles; wide-area coordination strategy and station-level and regional-level orderly charging.The interaction mode and architecture, the hierarchical control strategy of large-scale electric vehicle charging behavior and grid interaction.

An iteration method for identifying yeast essential proteins from heterogeneous network

BackgroundEssential proteins are distinctly important for an organism’s survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods.ResultsIn this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods.ConclusionsWe demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.

The Effect of Minor and Major Mergers on the Evolution of Low-excitation Radio Galaxies

Abstract We use deep, μ r ≲ 28 mag arcsec−2, r-band imaging from the Dark Energy Camera Legacy Survey to search for past, or ongoing, merger activity in a sample of 282 low-excitation radio galaxies (LERGs) at z &lt; 0.07. Our principal aim is to assess the the role of mergers in the evolution of LERGs. Exploiting the imaging depth, we classify tidal remnants around galaxies as both minor and major morphological disturbances for our LERG sample and 1622 control galaxies matched in redshift, stellar mass, and environment. In groups and in the field, the LERG minor merger fraction is consistent with the control population. In galaxy clusters, 8.8 ± 2.9% of LERGs show evidence of recent minor mergers in contrast to 23.0 ± 2.0% of controls. This ∼4σ deficit of minor mergers in cluster LERGs suggests these events may inhibit this type of nuclear activity for galaxies within the cluster environment. We observe a &gt;4σ excess of major mergers in the LERGs with M * ≲ 1011 M⊙, with 10 ± 1.5% of these active galactic nuclei involved in such large-scale interactions compared to 3.2 ± 0.4% of control galaxies. This excess of major mergers in LERGs decreases with increasing stellar mass, vanishing by M * &gt; 1011.3 M⊙. These observations show that minor mergers do not fuel LERGs, and are consistent with typical LERGs being powered by accretion of matter from their halo. Where LERGs are associated with major mergers, these objects may evolve into more efficiently accreting active galactic nuclei as the merger progresses and more gas falls on to the central engine.

A new computational approach to estimate whole-brain effective connectivity from functional and structural MRI, applied to language development

Recently introduced effective connectivity methods allow for the in-vivo investigation of large-scale functional interactions between brain regions. However, dynamic causal modeling, the most widely used technique to date, typically captures only a few predefined regions of interest. In this study, we present an alternative computational approach to infer effective connectivity within the entire connectome and show its performance on a developmental cohort with emerging language capacities. The novel approach provides new opportunities to quantify effective connectivity changes in the human brain.

Mesoscale Convective Systems: A Case Scenario of the ‘Heavy Rainfall’ Event of 15–20 January 2013 over Southern Africa

Southern east Africa is prone to some extreme weather events and interannual variability of the hydrological cycle, including tropical cyclones and heavy rainfall events. Most of these events occur during austral summer and are linked to shifts in the intertropical convergence zone, changes in El Niño Southern Oscillation signatures, sea surface temperature and sea level pressure. A typical example include mesoscale convective systems (MCSs) that occur between October and March along the eastern part, adjacent to the warm waters of Mozambique Channel and Agulhas Current. In this study we discuss a heavy rainfall event over southern Africa, focusing particularly on the period 15–20 January 2013, the period during which MCSs were significant over the subcontinent. This event recorded one of the historic rainfalls due to extreme flooding and overflows, loss of lives and destruction of economic and social infrastructure. An active South Indian Convergence Zone was associated with the rainfall event sustained by a low-level trough linked to a Southern Hemisphere planetary wave pattern and an upper-level ridge over land. In addition, also noteworthy is a seemingly strong connection to the strength of the African Easterly Jet stream. Using rainfall data, satellite imagery and re-analysis (model processed data combined with observations) data, our analysis indicates that there was a substantial relation between rainfall totals recorded/observed and the presence of MCSs. The low-level trough and upper-level ridge contributed to moisture convergence, particularly from tropical South East Atlantic Ocean, which in turn contributed to the prolonged life span of the rainfall event. Positive temperature anomalies favored the substantial contribution of moisture fluxes from the Atlantic Ocean. This study provides a contextual assessment of rainfall processes and insight into the physical control mechanisms and feedback of large-scale convective interactions over tropical southern Africa.

Optimized Cross-Linking Mass Spectrometry for in Situ Interaction Proteomics.

Recent development of mass spectrometer cleavable protein cross-linkers and algorithms for their spectral identification now permits large-scale cross-linking mass spectrometry (XL-MS). Here, we optimized the use of cleavable disuccinimidyl sulfoxide (DSSO) cross-linker for labeling native protein complexes in live human cells. We applied a generalized linear mixture model to calibrate cross-link peptide-spectra matching (CSM) scores to control the sensitivity and specificity of large-scale XL-MS. Using specific CSM score thresholds to control the false discovery rate, we found that higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD) can both be effective for large-scale XL-MS protein interaction mapping. We found that the coverage of protein-protein interaction maps is significantly improved through the use of multiple proteases. In addition, the use of focused sample-specific search databases can be used to improve the specificity of cross-linked peptide spectral matching. Application of this approach to human chromatin labeled in live cells recapitulated known and revealed new protein interactions of nucleosomes and other chromatin-associated complexes in situ. This optimized approach for mapping native protein interactions should be useful for a wide range of biological problems.

Physical constraints for effective magma-water interaction along volcanic conduits during silicic explosive eruptions: COMMENT

Aravena et al. (2018) breathed fresh air into understanding the role of external water in silicic phreatomagmatic eruptions with their hypothesis that significant involvement of external water is only feasible above the level of primary fragmentation of the magma. We discuss here strong supporting evidence that primary fragmentation does not involve external water from the microtextures of the pyroclasts erupted from the two ‘type’ examples of large-scale interaction of silicic magma with external water.