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1,221 results for “Aggregators”
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
The GBA variant E326K is associated with alpha-synuclein aggregation and lipid droplet accumulation in human cell lines.
<p>Raw data corresponding to graphs in publication: The GBA variant E326K is associated with alpha-synuclein aggregation and lipid droplet accumulation in human cell lines. Files are titled with the figure number. Each graph is on a different tab within the excel spreadsheet.</p>
FIGURES – 8. Cylindrocystis brebissonii (2–4), apical view (3), zygospore (4); Cylindrocystis brebissonii var. turgida (5–6), variability (6); Cylindrocystis crassa (7–8), cell aggregate (8); Mesotaenium caldariorum (9); Mesotaenium chlamydosporum (10–mucilage–wrapped cells); Mesotaenium endlicherianum (11); Mesotaenium macrococcum var. minus (12); Netrium digitus (13–16), N.digitus var. latum? (14), N. digitus var. lamelosum? (15), zigospore (16); Netrium interruptum var. minor (17–18), morphological variability (18); Gonatozygon kinahani (19–20), apex detail (20); Closterium acerosum (21–22), apex detail (22); Closterium dianae var. brevius (23–25), apex detail (24), young superior semicell without thickening (25); Closterium malinvernianiforme (26–28), apex detail (27), isthmus with a girdle band (28). Scale bar 10µm (2–12, 24–25, 27–28), 50 µm (13–23); 100 µm (26). in The forgotten world of subaerial desmids (Zygnematophyceae) in the Atlantic Forest, southeast Brazil
FIGURES – 8. Cylindrocystis brebissonii (2–4), apical view (3), zygospore (4); Cylindrocystis brebissonii var. turgida (5–6), variability (6); Cylindrocystis crassa (7–8), cell aggregate (8); Mesotaenium caldariorum (9); Mesotaenium chlamydosporum (10–mucilage–wrapped cells); Mesotaenium endlicherianum (11); Mesotaenium macrococcum var. minus (12); Netrium digitus (13–16), N.digitus var. latum? (14), N. digitus var. lamelosum? (15), zigospore (16); Netrium interruptum var. minor (17–18), morphological variability (18); Gonatozygon kinahani (19–20), apex detail (20); Closterium acerosum (21–22), apex detail (22); Closterium dianae var. brevius (23–25), apex detail (24), young superior semicell without thickening (25); Closterium malinvernianiforme (26–28), apex detail (27), isthmus with a girdle band (28). Scale bar 10µm (2–12, 24–25, 27–28), 50 µm (13–23); 100 µm (26).
FIGURE. Cladosporium bambusicola (VIC 44237, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–F. Conidiophore and bigger conidia. G–H. Conidiophores and smaller conidia. I. Stromatic hyphal aggregation. J–K. Micronematous conidiophores. L. Ramoconidia and conidia. M. Microcyclic conidiogenesis. Scale bars: E = 50 µM; F–M = 20 µM. in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. Cladosporium bambusicola (VIC 44237, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–F. Conidiophore and bigger conidia. G–H. Conidiophores and smaller conidia. I. Stromatic hyphal aggregation. J–K. Micronematous conidiophores. L. Ramoconidia and conidia. M. Microcyclic conidiogenesis. Scale bars: E = 50 µM; F–M = 20 µM.
FIGURE. Cladosporium aulonemiae (VIC 44413, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–G. Macronematous conidiophores and numerous conidia; H–I. Formation of loci in close succession; I. Spread polysaccharide-like material; J. Micronematous conidiophores; K. Ramoconidia and conidia; L. Microcyclic conidiogenesis; M. Stromatic hyphal aggregation. Scale bars: E–M = 20 µM. in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. Cladosporium aulonemiae (VIC 44413, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–G. Macronematous conidiophores and numerous conidia; H–I. Formation of loci in close succession; I. Spread polysaccharide-like material; J. Micronematous conidiophores; K. Ramoconidia and conidia; L. Microcyclic conidiogenesis; M. Stromatic hyphal aggregation. Scale bars: E–M = 20 µM.
Protein aggregation and calcium dysregulation are the earliest hallmarks of familial Parkinson's disease in human midbrain dopaminergic neurons
<p>Mutations in the <em>SNCA</em> gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an <em>SNCA</em> mutation during development, to end stage pathology is unknown. Utilising human induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the initial formation of small β-sheet rich oligomeric aggregates, in <em>SNCA</em>-mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34-41 post differentiation. Once midbrain identity fully developed, at day 48-62 post differentiation, <em>SNCA</em>-mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD, and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease.</p>
Aggregate TB diagnosis and treatment data, Uganda 2017 and 2019
<p>To accelerate tuberculosis (TB) control and elimination, reliable data is needed to improve the quality of TB care. We assessed agreement between a surveillance dataset routinely collected for Uganda's national TB program and a high-fidelity dataset collected from the same source documents for a research study from 32 health facilities in 2017 and 2019 for six measurements: 1) Smear-positive and 2) GeneXpert-positive diagnoses, 3) bacteriologically confirmed and 4) clinically diagnosed treatment initiations, and the number of people initiating TB treatment who were also 5) living with HIV or 6) taking antiretroviral therapy. We measured agreement as the average difference between the two methods, expressed as the average ratio of the surveillance counts to the research data counts, its 95% limits of agreement (LOA), and the concordance correlation coefficient. We used linear mixed models to investigate whether agreement changed over time or was associated with facility characteristics. We found good overall agreement with some variation in the expected facility-level agreement for the number of smear-positive diagnoses (average ratio [95% LOA]: 1.04 [0.38-2.82]; CCC: 0.78), bacteriologically confirmed treatment initiations (1.07 [0.67-1.70]; 0.82), and people living with HIV (1.11 [0.51-2.41]; 0.82). The agreement was poor for Xpert positives, with surveillance data undercounting relative to research data (0.45 [0.099-2.07]; 0.36). Although surveillance data overcounted relative to research data for clinically diagnosed treatment initiations (1.52 [0.71-3.26]) and the number of people taking antiretroviral therapy (1.71 [0.71-4.12]), their agreement as assessed by CCC was not poor (0.82 and 0.62, respectively). The average agreement was similar across study years for all six measurements, but facility-level agreement varied yearly and was not explained by facility characteristics. In conclusion, the agreement of TB surveillance data with high-fidelity research data was highly variable across measurements and facilities. To advance the use of routine TB data as a quality improvement tool, future research should elucidate and address reasons for variability in its quality.</p>
Supplementary material 2 from: Cheng S, Chan K-M, Ishak S-F, Khoo V, Chew MY (2017) Elucidating food plants of the aggregative, synchronously flashing Southeast Asian firefly, Pteroptyx tener Olivier (Coleoptera, Lampyridae). BioRisk 12: 25-39. https://doi.org/10.3897/biorisk.12.14061
Supporting Information S2. Cytochrome oxidase subunit 1 sequence alignment : Data type: molecular data
Scripts for theory figures in: Spatial Correlations Drive Long-Range Transport and Trapping of Excitons in Single H-Aggregates: Experiment and Theory
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AMO-aware Aggregates in Answer Set Programming
<p>Dataset for the paper "AMO-aware Aggregates in Answer Set Programming" accepted at IJCAI 2024.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2005
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2005.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone
<p>The measured data for the manuscript "Treasure Bowl: PM2.5 Aggregation in the Eye of a Tropical Cyclone"</p>
Fragmentation and aggregation of cyanobacterial colonies under a cone and plate shear
<p>Brief description of experimental setup: <br>Colonies of the cyanobacteria Microcystis in liquid medium BG-11 are place and a cone-and-plate shear at various values of energy dissipation rate and total biovolume fraction. Brightfield microscopy is perfomed simulanoeusly to the shear flow thorugh the transparente lower plate.</p> <p>Files:<br>rawimages.zip: Raw images (.tiff) for the dataset Fragmentation-eps-5.8m2/s3-phi100ppm. Pixel resolution is 1.63 um/px.<br>features.zip : Processed data from raw images for all data sets are present. Title inform initial distribution ("Bre" = large colonies; "Agg" = single cells), nominal shear rate and total biovolume fraction. Csv files contain background image, detected features using a routine in scikit-image scikit-image and initial parameters. Script for analizing data can be found at https://github.com/FluidLab/CyanoB_Agg_Frag.git</p> <p><br>Contact: Yuri Sinzato - y.z.sinzato@uva.nl / Mazi Jalaal - m.jalaal@uva.nl</p>
Energy landscapes for the aggregation of A beta (17-42)
<p>Data set for the paper "Energy landscapes for the aggregation of A beta (17-42)" in JACS, 2018 (doi:10.1021/jacs.7b12896)</p>
How Modern News Aggregators Help Development Communities Shape and Share Knowledge: Appendix
<p>This package contains the appendix of our ICSE 2018 paper "How Modern News Aggregators Help Development Communities Shape and Share Knowledge".</p> <p>Content:</p> <ul> <li>The qualitative analysis of the interviews as well as the interview guide</li> <li>Data from HackerNews and Reddit used in our quantitative analysis</li> <li>Results from our survey</li> <li>The qualitative analysis on HN and Reddit posts</li> </ul>
HEK cell aggregation in flow conditions using 3 electrodes in a 8 electrode configuration design
<p>HEK cell aggregation in flow conditions using 3 electrodes out of a 8 electrode configuration design.</p>
Aggregated Spatial Data by province GPKG Format | Biodiversity Publication Bias Compromises Setting Conservation Priorities
<p>Result of running https://github.com/raffael-hickisch/provincer</p>
Repurposing Major Metabolites of Lamiaceae Family as Potential Inhibitors of α-Synuclein Aggregation to Alleviate Neurodegenerative Diseases: An In Silico Approach
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Fig. 8 in The sperm aggregation in a whirligig beetle (Coleoptera, Gyrinidae): structure, functions, and comparison with related taxa
Fig. 8 Phylogenetic relationships of Adephaga and the sperm aggregation. Phylogenies of (A) Gyrinidae (Obtained and modified from Miller & Bergsten, 2012) and (B) Adephaga (Obtained and modified from Gustafson et al., 2020). Forms of sperm aggregation are represented in the phylogenetic trees. Schematic representations based from (a) this study, (b) Dallai & Afzelius, 1985, (c) Breland & Simmons,
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