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1,620 results for “springs”
Script files for the chapter: "Spring-mass crystals and mode confinement"
<p>Data and simulations files for the chapter "Spring-mass crystals and mode confinement" of the thesis "Optimizing hybrid optomechanical crystals and thermo-optomechanical effects"</p>
Diversity, adaptation and metabolic potential of the microbiome in biofilms from a high-temperature hot spring
<p>Hot spring microbiomes have garnered significant research attention from exploring the diversity of prokaryotic communities to genes and functional potentials. While cyanobacteria-rich biofilms, characterized by warm temperatures, have been extensively studied, there is limited investigation into high-temperature streamer biofilm communities (SBC) devoid of photosynthetic ability. Here, we studied the biofilm of a Dusun Tua (DT) hot spring with a temperature of 75°C and a pH of 7.6. This grey-tan colored biofilm appeared at sites where water had slowed down following the deposition of plants and inorganic debris along the hot spring after a flood event. Amplicon sequencing of V3-V4 regions of 16S rRNA showed that dominant phyla included the Aquificota, Chloroflexota, and Desulfobacterota together with other abundant amplicon sequence variants from the Bacteroidota, Deinococcota, Hydrothermae and Armatimonadota. These microbial populations appeared to be distinct from other reported SBCs from Yellowstone National Park in the USA and Rehai Hot Springs in China. Additional shotgun sequencing of the DT biofilm revealed functional insights which were compared to counterparts obtained from low-temperature biofilms to identify possible thermophilic traits. GC content of tRNA and amino acid preferences were found to be clear indicators of thermophilicity. However, other signatures such as reverse gyrase, heat shock proteins, and average GC content of the genome may not be reliable indicators. The genome-centric analyses revealed that DT biofilm members were primarily chemo-organoheterotrophic, chemolithoautotrophic, and chemolithoheterotrophic. We speculate that the biofilm could utilize plant litter as carbon sources, but the efficiency of this process is estimated to be low due to rapid water flux that would rapidly remove dissolved organic carbon. The results of this study enhance current understanding of microbial diversity, thermal adaptation and metabolic processes related to carbon, nitrogen, sulfur and other metabolisms for hot springs in tropical climates with high allochthonous plant litter inputs.</p>
Seasonal precipitation distribution determines ecosystem CO₂ and H₂O exchange by regulating spring soil water-salt dynamics in a brackish wetland
<p>The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO<sub>2</sub> and H<sub>2</sub>O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter-seasonal precipitation distribution affects ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange compared to annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter-seasonal precipitation distribution proportions (+73%, +56%, control (CK), -56%, and -73%) to examine the effects of seasonal precipitation distribution (SPD) on ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange. Our findings revealed that the ecosystem CO<sub>2</sub> and H<sub>2</sub>O fluxes showed a trend of decreasing with the decrease of spring precipitation distribution. Among them, the annual net ecosystem CO<sub>2 </sub>exchange (NEE), evapotranspiration (ET), carbon use efficiency (CUE), and water use efficiency (WUE) were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO<sub>2</sub> and H<sub>2</sub>O exchange is primarily governed by seasonal precipitation variability, with spring soil water-salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO<sub>2</sub>-H<sub>2</sub>O exchange capacity of brackish wetlands. Our results emphasized that the spring's extreme precipitation-induced soil water-salt conditions will greatly influence CO<sub>2</sub> and H<sub>2</sub>O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water-holding capacity of brackish wetlands.</p>
Fig. 1 in Redescription of Dexiotricha colpidiopsis (Kahl, 1926) Jankowski, 1964 (Ciliophora, Oligohymenophorea) from a Hot Spring in Iceland with Identification Key for Dexiotricha species
Fig. 1. Sampling site in hot spring in Iceland (A, B) and sapropel sample (C).
Fig. 11 in Methodologicalaspects Of Study On Biologyand Development Cycles Of Dytiscus Latissimus (Coleoptera: Dytiscidae) In Laboratory Environment. Spring-Summer Period
Fig. 11. Improvised nursecage for keeping larvae
Fig.4 in Genetic Diversity Of (Brassica Napus L.) Spring Oilseed Rape
Fig.4. Analysis of Molecular Variance among cultivars and within cultivars
Figure 1 in Endemic spring snails Terrestribythinella (Mollusca) as unusual material for larval case of Crunoecia irrorata (Trichoptera: Lepidostomatidae) in Transcarpathian Ukraine
Figure 1. Map showing the locality of Crunoeсia irrorata larva sampling.
Fig. 3 in Terrace springs: habitat haven for macrobenthic fauna in the lower plain of the River Ticino (Lombardy, Northern Italy)
Fig. 3 - Cluster analysis of the macrobenthic community collected in Spring 2.
Fig. 2 in Terrace springs: habitat haven for macrobenthic fauna in the lower plain of the River Ticino (Lombardy, Northern Italy)
Fig. 2 - Cluster analysis of the macrobenthic community collected in Spring 1.
Fig. 1 in Terrace springs: habitat haven for macrobenthic fauna in the lower plain of the River Ticino (Lombardy, Northern Italy)
Fig. 1 - Study area.
Μονή Θάρρι Moni Thari, Ρόδος Rodos. Spring and water channel running east of the church.
<p>Μονή Θάρρι Moni Thari, Ρόδος Rodos. Spring and water channel running east of the church.</p>
BL41XU experiment from the beamline training at CCP4 SPring-8 school 2018
<p>Raw X-ray diffraction images collected in the beamline training in day 2 of CCP4 SPring-8 school 2018 <a href="http://www.ccp4.ac.uk/schools/Japan-2018/program.php">http://www.ccp4.ac.uk/schools/Japan-2018/program.php</a></p> <p>On <strong>BL41XU</strong>, fine/coarse phi-slicing and radiation damage were demonstrated using thermolysin crystals. Datasets were collected using EIGER X 16M detector at 1.2824 Å wavelength. Beam size was 37.0 × 23.0 μm<sup>2</sup>. Here data from group 2 are available. See an Excel file for experimental conditions. Briefly, oscillation steps of 1° (data03) and 0.1° (data01) were tested under a total dose of 0.4 MGy, and radiation damage at 100 MGy (data02) and 10 MGy (data04) was demonstrated. You may also want to try phasing by Zn-SAD? For our EIGER data file format please look at <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</a></p> <p> </p> <p>Other entries at CCP4 SPring-8 school 2018</p> <ul> <li>BL26B2 <a href="https://zenodo.org/record/1443392">https://zenodo.org/record/1443392</a></li> <li>BL32XU <a href="https://zenodo.org/record/1442922">https://zenodo.org/record/1442922</a></li> </ul>
BL32XU experiment from the beamline training at CCP4 SPring-8 school 2018
<p>Raw X-ray diffraction images collected in the beamline training in day 2 of CCP4 SPring-8 school 2018 <a href="http://www.ccp4.ac.uk/schools/Japan-2018/program.php">http://www.ccp4.ac.uk/schools/Japan-2018/program.php</a></p> <p>On <strong>BL32XU</strong>, the automatic data collection system ZOO was demonstrated using Br-lysozyme microcrystals. Multiple small-wedge (10 degrees per crystal) datasets were collected using EIGER X 9M detector at 0.9 Å wavelength. Beam size was 15.0 × 8.0 μm<sup>2</sup>. Here data from group 2 and group 3 are available (31 and 38 datasets, respectively). Due to the file size limitation, only hit images are uploaded for raster scan results. You may want to try merging and phasing by Br-SAD? For our EIGER data file format please look at <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</a></p> <p> </p> <p>Other entries at CCP4 SPring-8 school 2018</p> <ul> <li>BL26B2 <a href="https://zenodo.org/record/1443392">https://zenodo.org/record/144339</a></li> <li>BL41XU <a href="https://zenodo.org/record/1443110">https://zenodo.org/record/1443110</a></li> </ul>
BL26B2 experiment from the beamline training at CCP4 SPring-8 school 2018
<p>Raw X-ray diffraction images collected in the beamline training in day 2 of CCP4 SPring-8 school 2018 <a href="http://www.ccp4.ac.uk/schools/Japan-2018/program.php">http://www.ccp4.ac.uk/schools/Japan-2018/program.php</a></p> <p>On <strong>BL26B2</strong>, strategy calculation was demonstrated using lysozyme crystals. Datasets and snapshots were collected using MX-225HS CCD detector at 1.0 and 1.5 Å wavelengths. Beam size was 120 μm (circle shape). All datasets collected in the course are available.</p> <p>Group1:</p> <ol> <li>Snapshot of cryo-cooled lysozyme crystal (well 2 of unipuck).<br> Discussion about data collection strategy. (Max. resolution, spot overlapping, exposure time etc.)</li> <li>Data collection of 360 images (./data1/*.img)<br> wavelength = 1 Å, exposure = 0.5 sec/ 0.5 deg, distance = 100 mm (edge resolution = 1.23 Å)</li> <li>Data processing with xdsgui.<br> Evaluation and discussion about data quality.</li> </ol> <p>Group2:</p> <ol> <li>Snapshot of cryo-cooled lysozyme crystal (well 3 of unipuck).<br> Discussion about data collection strategy. (Max. resolution, spot overlapping, exposure time etc.)</li> <li>Data collection strategy with imosflm and xdsgui, and discussion.</li> </ol> <p>Group3:</p> <ol> <li>Using snapshot of former group2, discussion about data collection strategy (especially for S-SAD)</li> <li>Data collection of 720 images (./data2/*.img); <br> wavelength = 1.5 Å, exposure = 0.5 sec/ 0.5 deg, distance = 100 mm (edge resolution = 1.85 Å)</li> <li>Data processing, S-SAD phasing, and model building with xdsgui.</li> </ol> <p> </p> <p>Other entries at CCP4 SPring-8 school 2018</p> <ul> <li>BL32XU <a href="https://zenodo.org/record/1442922">https://zenodo.org/record/1442922</a></li> <li>BL41XU <a href="https://zenodo.org/record/1443110">https://zenodo.org/record/1443110</a></li> </ul>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJ-GUESS spring wheat simulations
This data set contains output data from simulations with the model LPJ-GUESS for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above groun biomass, plant day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simlations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC spring wheat simulations
This data set contains output data from simulations with the model GEPIC for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PEPIC spring wheat simulations
This data set contains output data from simulations with the model PEPIC for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU spring wheat simulations
This data set contains output data from simulations with the model EPIC-TAMU for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from pDSSAT spring wheat simulations
This data set contains output data from simulations with the model pDSSAT for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-IIASA spring wheat simulations
This data set contains output data from simulations with the model EPIC-IIASA for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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