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8 results for “Network adjustment”
Evaluation of Compatibility among Network Adjustment Software - Results from the JAG3D software package
<p>Together with colleagues from Conservatoire National des Arts et Métiers (CNAM), European Organization for Nuclear Research (CERN), and School of Management and Engineering Vaud (HEIG-VD), we evaluated the compatibility among network adjustment software packages. A detailed description of the procedure and the results was presented at the 5th Joint International Symposium on Deformation Monitoring JISDM 2022.</p> <p>We compared the results of several geodetic networks using software packages developed by the authors' institutions, namely Compensation de Mesures Topographiques (CoMeT), Logiciel Général de Compensation (LGC), Trinet+ as well as JAG3D. Moreover, we included further commercial software packages such as Columbus, Geolab, Move3 and Star*Net. The networks differ mainly in their extent, i.e. the side length and the height. Whereas the smallest network is about 30 m, the largest network under consideration is about 40 km. The height component varies in a range from 30 m to 2.5 km. The raw data and the obtained results can be found on the official CNAM website <a href="https://comet.esgt.cnam.fr/comparisons">comet.esgt.cnam.fr/comparisons</a>. This data set contains the raw data, the JAG3D database as well as the adjustment results.</p>
Asthma Clinical Research Network (ACRN) Trial - Best Adjustment Strategy for Asthma in Long Term (BASALT)
ClinicalTrials.gov study NCT00495157. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Networks of physiological adjustments and defenses, and their synergy with sodium (Na+) homeostasis explain the hidden variation for salinity tolerance across the cultivated Gossypium hirsutum germplasm
<p>The abilities to mobilize and/or sequester excess ions within and outside the plant cell are important components of salt-tolerance mechanisms. Mobilization and sequestration of Na<sup>+</sup> involves three transport systems facilitated by the plasma membrane H<sup>+</sup>/Na<sup>+</sup> antiporter (SOS1), vacuolar<i> </i>H<sup>+</sup>/Na<sup>+</sup> antiporter (NHX1), and Na<sup>+</sup>/K<sup>+</sup> transporter<i> </i>in vascular tissues (HKT1). Many of these mechanisms are conserved across the plant kingdom. While <i>Gossypium hirsutum</i> (upland cotton) is significantly more salt-tolerant relative to other crops, the critical factors contributing to the phenotypic variation hidden across the germplasm have not been fully unraveled. In this study, the spatio-temporal patterns of Na<sup>+</sup> accumulation along with other physiological and biochemical interactions were investigated at different severities of salinity across a meaningful genetic diversity panel across cultivated upland <i>Gossypium. </i>The aim was to define the importance of holistic or integrated effects relative to the direct effects of Na<sup>+</sup> homeostasis mechanisms mediated by <i>GhHKT1, GhSOS1,</i> and <i>GhNHX1. </i>Multi-dimensional physio-morphometric attributes were investigated in a systems-level context using univariate and multivariate statistics, <i>randomForest,</i> and path analysis. Results showed that mobilized or sequestered Na<sup>+</sup> contributes significantly to the baseline tolerance mechanisms. However, the observed variance in overall tolerance potential across a meaningful diversity panel were more significantly attributed to antioxidant capacity, maintenance of stomatal conductance, chlorophyll content, and divalent cation (Mg<sup>2+</sup>) contents other than Ca<sup>2+</sup> through a complex interaction with Na<sup>+</sup> homeostasis. The multi-tier macro-physiological, biochemical and molecular data generated in this study, and the networks of interactions uncovered strongly suggest that a complex physiological and biochemical synergy beyond the first-line-of defense (Na<sup>+</sup> sequestration and mobilization) accounts for the total phenotypic variance across the primary germplasm of <i>Gossypium hirsutum</i>. These findings are consistent with the recently proposed Omnigenic Theory for quantitative traits and should contribute to a modern look at phenotypic selection for salt tolerance in cotton breeding.</p>
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Networks of physiological adjustments and defenses, and their synergy with sodium (Na+) homeostasis explain the hidden variation for salinity tolerance across the cultivated Gossypium hirsutum germplasm
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Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
<p>Loss of habitats and native species, introduction of invasive species, and changing climate regimes lead to the homogenization of landscapes and communities, affecting the availability of habitats and resources for economically important guilds, such as pollinators. Understanding how pollinators and their interactions vary along resource diversity gradients at different scales may help to determine their adaptability to current diversity loss related to global change. We used data on 20 plant-pollinator communities along gradients of flower richness (local diversity) and landscape heterogeneity (landscape diversity) to understand how the diversity of resources at local and landscape scales affected: (1) wild pollinator abundance and richness (accounting also for honeybee abundance); (2) the structure of plant-pollinator networks; (3) the proportion of actively selected interactions (those not occurring by neutral processes) and (4) pollinator diet breadth and species' specialization in networks. Wild pollinator abundance was higher overall in flower-rich and heterogeneous habitats, while wild pollinator richness increased with flower richness (more strongly for beetles and wild bees) and decreased with honeybee abundance. Network specialization (H2'), modularity, and functional complementarity were all positively related to floral richness and landscape heterogeneity, indicating niche segregation as the diversity of resources increases at both scales. Flower richness also increased the proportion of actively selected interactions (especially for wild bees and flies), whereas landscape heterogeneity had a weak negative effect on this variable. Overall, network-level metrics responded to larger landscape scales than pollinator-level metrics did. Higher floral richness resulted in a wider taxonomic and functional diet for all the study guilds, while functional diet increased mainly for beetles. Despite this, specialization in networks (d') increased with flower richness for all the study guilds, because pollinator species fed on a narrower subset of plants as communities became richer in species. Our study indicates that pollinators are able to adapt their diet to resource changes at local and landscape scales. However, resource homogenization might lead to poor and generalist pollinator communities, where functionally specialized interactions are lost. This study highlights the importance of including different scales to understand the effects of global change on pollination service through changes in resource diversity.</p>
Data from: On the Helmert-blocking Technique: its acceleration by Block Choleski decomposition and formulae to insert observations into an adjusted network
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Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
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