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1,919 results for “trigger”
Rapid mapping inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023
<p>The dataset contains the spatial explicit inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023. Hybrid pixel-based and object-based image analysis approach was used to generate the rapid mapping products. The fully automated supervised procedure relies on change detection analysis, based on quantitative variation of vegetation cover in pre-event and post-event remote sensing imagery. Two separate inventories were generated using:<br>- Copernicus Sentinel-2 MSI satellite imagery<br>- PlanetScope SuperDove satellite imagery</p> <p>The dataset is a spatial representation vector polygons representing potential landslides, distributed in GeoPackage format, complemented with confidence measure. It contains modified Copernicus Sentinel data, available at no cost from Copernicus Open Access Hub. Use of PlanetScope images is under license: ©Planet Labs PBC, CC BY-NC-SA 2.0.</p>
Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework
<p>Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework</p> <p>DOI: 10.1002/adma.202110027</p> <p>Boix-Constant, Carla; Garcia-Lopez, Victor; Navarro-Moratalla, Efren; Clemente-Leon, Miguel; Zafra, Jose Luis; Casado, Juan; Guinea, Francisco; Manas-Valero, Samuel; Coronado, Eugenio</p> <p> Adv. Mater. 34, 2110027 (2022)</p>
Experiment package for Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment
<p>Full experimental materials, scripts, and results for "Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment"</p>
Sustainable Smart Tags with Two-Step Verification for Anticounterfeiting Triggered by the Photothermal Response of Upconverting Nanoparticles
<p>Dataset accompanying figures published in the publication DOI: https://doi.org/10.5281/zenodo.6245930</p>
Immune and genetic signature of HER2-driven breast carcinomas triggering anti-Yo paraneoplastic cerebellar degeneration
<ul> <li>Supplementary eTables from: Immune and genetic signature of HER2-driven breast carcinomas triggering anti-Yo paraneoplastic cerebellar degeneration: <ul> <li><strong>eTables 1</strong>: Results of DESeq2 on the differential gene expression analysis between Yo-PCD and control tumours for all genes and for differentially expressed genes.</li> <li><strong>eTables 2</strong>: Results of the Gene Ontology enrichment performed by clusterProfiler on genes over-expressed in Yo-PCD tumours</li> <li><strong>eTables 3</strong>: Results of the Gene Ontology enrichment performed by clusterProfiler on genes under-expressed in Yo-PCD tumours</li> </ul> </li> <li>Gene counts</li> <li>Gene TPMs</li> </ul>
Replication Package for "TriggerBench: A Performance Benchmark for Serverless Function Triggers"
<p>This replication package contains the code (`aws-triggers` and `azure-trigger`), data analysis scripts (`data-analysis`), and dataset (`data`) of the TriggerBench cross-provider serverless benchmark.</p> <p>It also bundles a customized extension of the `serverless-benchmarker` tool to automate and analyze serverless performance experiments.</p> <p><strong>TriggerBench</strong></p> <p>The Github repository <a href="https://github.com/joe4dev/trigger-bench/">joe4dev/trigger-bench</a> contains the last version of TriggerBench. This replication package describes the version for the paper "TriggerBench: A Performance Benchmark for Serverless Function Triggers".</p> <p>TriggerBench currently supports three triggers on AWS and eight triggers on Microsoft Azure.</p> <p><strong>Dataset</strong></p> <p>The `data/aws` and `data/azure` directories contain data from benchmark executions from April 2022.</p> <p>Each execution is a separate directory with a timestamp in the format `yyyy-mm-dd-HH-MM-SS` (e.g., `2022-04-15_21-58-52`) and contains the following files:</p> <ul> <li>`k6_metrics.csv`: Load generator HTTP client logs in CSV format (see [K6 docs](https://k6.io/docs/results-visualization/csv/))</li> <li>`sb_config.yml`: serverless benchmarker execution configuration including experiment label.</li> <li>`trigger.csv`: analyzer output CSV per trace. <ul> <li>`root_trace_id`: The trace id created by k6 and adopted by the invoker function</li> <li>`child_trace_id`: The trace id newly created by the receiver function if trace propagation is not supported (this is the case for most asynchronous triggers)</li> <li>`t1`-`t4`: Timestamps following the trace model (see paper)</li> <li>`t5`-`t9`: Additional timestamps for measuring timestamping overhead</li> <li>`coldstart_f1=True|False`: coldstart status for invoker (f1) and receiver (f2) functions</li> <li>`trace_ids.txt`: text file with each pair of `root_trace_id` and `child_trace_id` on a new line.</li> </ul> </li> <li>`traces.json`: raw trace JSON representation as retrieved from the provider tracing service. For AWS, see [X-Ray segment docs](https://docs.aws.amazon.com/xray/latest/devguide/xray-api-segmentdocuments.html). For Azure, see [Application Insights telemetry data model](https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model).</li> <li>`workload_options.json`: [K6 load scenario](https://k6.io/docs/using-k6/scenarios/) configuration.</li> </ul> <p><strong>Replicate Data Analysis</strong></p> <p><strong>Installation</strong></p> <p>1. Install [Python](https://www.python.org/downloads/) 3.10+</p> <p>2. Install Python dependencies `pip install -r requirements.txt`</p> <p><strong>Create Plots</strong></p> <p>1. Run `python plots.py` generates the plots and the statistical summaries presented in the paper.</p> <p>By default, the plots will be saved into a `plots` sub-directory.<br> An alternative output directory can be configured through the environment variable `PLOTS_PATH`.</p> <p>> Hint: For interactive development, we recommend the VSCode [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) in [interactive mode](https://youtu.be/lwN4-W1WR84?t=107).</p> <p> </p> <p><strong>Replicate Cloud Experiments</strong></p> <p>The following experiment plan automates benchmarking experiments with different types workloads (constant and bursty).</p> <p>This generates a new dataset in the same format as described above.</p> <p> </p> <ol> <li> Set up a load generator as vantage point following the description in [LOADGENERATOR](./serverless-benchmarker/docs/LOADGENERATOR.md).</li> <li> Choose the `PROVIDER` (aws or azure) in the [constant.py](./experiment-plans/constant.py) experiment plan</li> <li> Run the [constant.py](./experiment-plans/constant.py) experiment plan <ol> <li> Open tmux</li> <li> Activate virtualenv `source sb-env/bin/activate`</li> <li> Run `./constant.py 2>&1 | tee -a constant.log`</li> </ol> </li> </ol> <p><strong>Contributors</strong></p> <p>The initial trigger implementations for AWS and Azure are based on two master thesis projects at Chalmers University of Technology in Sweden supervised by Joel:</p> <ul> <li>AWS + Azure: <a href="https://odr.chalmers.se/handle/20.500.12380/302822">Performance Comparison of Function-as- a-Service Triggers: A Cross-Platform Performance Study of Function Triggers in Function-as-a-Service</a> by Marcus Bertilsson and Oskar Grönqvist, 2021.</li> <li>Azure Extension: <a href="https://odr.chalmers.se/handle/20.500.12380/305138">Serverless Function Triggers in Azure: An Analysis of Latency and Reliability</a> by Henrik Lagergren and Henrik Tao, 2022.</li> </ul> <p>Joel contributed many improvements to their original source code as documented in the import commits <a href="https://github.com/joe4dev/trigger-bench/commit/a00b67a1dd8476ca77d026e59adf2674c7807e68">a00b67a</a> and <a href="https://github.com/joe4dev/trigger-bench/commit/6d2f5ef8bda0596b3f295cb6c6cbeba212c6ef43">6d2f5ef</a> and developed TriggerBench as an integrated benchmark suite (see commit history for detailed changelog).</p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
Materials for "GRB221009A: INTEGRAL detection of Hard X-ray emission at least 38 hours after trigger"
<p>Images and data for "GRB221009A: INTEGRAL detection of Hard X-ray emission at least 38 hours after trigger". Text of the <a href="https://www.astronomerstelegram.org/?read=15663">ATel #15663</a> and <a href="https://gcn.gsfc.nasa.gov/gcn3/32691.gcn3">GCN #32691</a> below.</p> <p> </p> <pre>Volodymyr Savchenko (UNIGE, EPFL), Carlo Ferrigno, Enrico Bozzo (UNIGE), D. Gotz (CEA Paris Saclay), S. Mereghetti (INAF/IASF Milano), Antonio Martin Carrillo, Lorraine Hanlon (UCD), Elisabeth Jourdain, Jean-Pierre Roques (IRAP), Thomas Siegert (University of Würzburg), Erik Kuulkers, Celia Sanchez (ESA) Following the detection of the record-breaking GRB221009A by Swift/BAT (GCN #32632,#32635), Fermi/GBM (GCN #32636), INTEGRAL/SPI-ACS (GCN #32660), we have performed INTEGRAL pointed observations of the GRB221009A location. INTEGRAL pointed observation lasted from 2022-10-10T14:31:40 (T0 + 25.2 hours, where T0 is 2022-10-09T13:17:00) to 2022-10-11T03:18:20 (T0 + 38.0 hours) with a total exposure time of 30.2 ks (for ISGRI). In the complete observation, the source is clearly detected in JEM-X1, JEM-X2 (3-30 keV), and ISGRI (28 - 80 keV), with S/N of 27.8, 27.3, 15.0 respectively. The joint JEM-X and ISGRI spectra can be satisfactorily modeled between 3 and 80 keV with a single powerlaw of slope 2.15 +/- -0.07 (90% confidence) with a flux of 4.4e-10 +/- -2.1e-11 erg/cm2/s (3 - 80 keV). This might indicate a single spectral component spanning from from hard X-ray to Fermi/LAT (GCN #32658). Combination of bright Hard X-ray afterglow with gamma-ray emission was also found in GRB120711A (Martin-Carrillo et al. 2014 A&A 567, 84) and GRB130427A (Kouveliotou et al. 2013 ApJ 779L, 1K) - in fact GRB221009A appears rather similar to GRB120711A, but at 10 times smaller distance. Within the relatively short JEM-X not ISGRI lightcurves, we do not observe any evidence for flux decrease. Further INTEGRAL observations are scheduled between 2022-10-11 13:52:21 and 2022-10-13 00:58:26. These observations will overlap with the planned observation of IXPE (GCN #32690). We are grateful to the INTEGRAL Ground Segment team for quickly scheduling the observations. Images and reduced data related to this publication can be found here: https://zenodo.org/record/7186289 </pre>
Triggering Sustainable Biogas Energy Communities through Social Innovation- ISABEL ---- Social Innovation and Community energy best preactices, methods and tools across Europe ----Semi-structured interviews from communities
<p>Having identified through the literature review various success and failure factors for social innovation applied to community energy projects, ISABEL has further conducted 18 semi-structured interviews of a range of stakeholders. The interviewee sample was a convenience sample of participants in existing projects and thus, inevitably, they are able to speak more to successful than unsuccessful projects and they likely have had less exposure to obstacles to the success of their projects. T The interviews have focused on identifying answers to the questions: <em>What were the key success factors? What obstacles were overcome? How? Participants were also asked to specify the type of renewable energy and community energy model. </em></p>
Relief from nitrogen starvation triggers a transient destabilization of glycolytic mRNAs in Saccharomyces cerevisiae cells
<p>Dataset supporting "Relief from nitrogen starvation triggers a transient destabilization of glycolytic mRNAs in Saccharomyces cerevisiae cells" (2018) Molecular Biology of the Cell 29:377-522. DOI: 10.1091/mbc.E17-01-0061</p>
Structural Homology of Epitope Pair Candidates for Molecular Mimicry Trigger of Type 1 Diabetes Mellitus
<p><strong><em><span>Background:</span></em></strong><span> </span><span>Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E<sub>INF</sub>) and T1DM epitopes (E<sub>T1D</sub>) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potential surfaces of the epitope pairs identified in previous work from our laboratory. </span></p> <p><strong><span> </span></strong><strong><em><span>Results:</span></em></strong><span> </span><span> For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold software program. </span><span>Of the 52 epitope pairs considered here only 10 do not exhibit any matching (i.e. less than 3 residues overlap). When considering all residues the RMSD ranges from 0.33 Å to 11.66 Å with an average of 2.68 Å. Twenty-two pairs (42%) have RMSD of less than 1.5 Å and 30 (58%) less than 3 Å. Even some of the matching pairs show some electrostatic similarities that need to be considered. In general there is good agreement between the folding predicted for the isolated </span><span>E<sub>INF</sub></span><span> and E<sub>T1D</sub> epitopes and the folding of the corresponding amino acid sequence in the parent antigen, but in some cases there are deviation that need to be considered, even when the RMDS is small.</span></p> <p><span> </span><strong><em><span>Conclusions:</span></em></strong><span> </span><span>Despite differences, most of the E<sub>INF</sub><span>/</span>E<sub>T1D </sub>pairs selected by sequence homology show similar structural and electrostatic distributions, indicating that the E<sub>INF</sub> may bind to the same protein targets, the major histocompatibility complex molecules, for T1DM, leading to molecular mimicry onset of the disease. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for molecular mimicry that should be considered for further study. Still structure and full docking calculations will be necessary to advance the in-silico molecular mimicry predictions. </span> Here we presnt the following files:</p> <p><span><span>·<span> </span></span></span>Fasta files of all epitopes studied.</p> <p><span><span>·<span> </span></span></span>Alphafold calculated Structures of all epitopes.</p> <p><span><span>·<span> </span></span></span>Antigen structures.</p> <p><span><span>·<span> </span></span></span>Epitope pair structure comparison and their electrostatics.</p> <p> </p> <p> </p>
Pesticide exposure triggers sex-specific inter- and trans-generational effects conditioned by past sexual selection
<p>Environmental variation often induces plastic responses in organisms that can trigger changes in subsequent generations through non-genetic inheritance mechanisms. Such transgenerational plasticity thus consists of environmentally-induced non-random phenotypic modifications that are transmitted through generations. Transgenerational effects may vary according to the sex of the organism experiencing the environmental perturbation, the sex of their descendants, or both, but whether they are affected by past sexual selection is unknown. Here we use experimental evolution on an insect model system to conduct a first test of the involvement of sexual selection history in shaping transgenerational plasticity in the face of rapid environmental change (exposure to pesticides). We manipulated evolutionary history in terms of the intensity of sexual selection for over 80 generations before exposing individuals to the toxicant. We found that sexual selection history constrained adaptation under rapid environmental change. We also detected intergenerational and transgenerational effects of pesticide exposure in the form of increased fitness and longevity. These cross-generational influences of toxicants were sex-dependent (they affected only male descendants), and intergenerational, but not transgenerational, plasticity was modulated by sexual selection history. Our results highlight the complexity of intragenerational, intergenerational, and transgenerational influences of past selection and environmental stress on phenotypic expression.</p>
Fig. 2 in Phylogenetic placement of a new paleoendemic pill scarab from the Udzungwa Mountains, Tanzania, triggers biogeographic interpretations (Coleoptera: Hybosoridae, Ceratocanthinae)
Fig. 2 – Balleriodes sphaera gen. et sp. nov., paratypes (A-R, specimen 8969, S, specimen 8752), details. A-B, head, fronto-dorsal (A) and frontal (B); C, maxilla; D, labrum; E-F, mandibles; G, antenna; H: labium; I-J, elytron in ventral view showing phoretic mites (J, enlarged); K, prothorax, ventral view; L-N, fore, middle and hind left legs; O, pterothorax, ventral view; P-R, phallobase and parameres, right lateral (P), left lateral (Q) and dorsal (R) views; abdomen, with male genitalia in situ. Images are not to scale.
Fig. 4 in Phylogenetic placement of a new paleoendemic pill scarab from the Udzungwa Mountains, Tanzania, triggers biogeographic interpretations (Coleoptera: Hybosoridae, Ceratocanthinae)
Fig. 4 – Habitus of select ingroup and outgroup pill scarabs, left lateral view; images are to scale.
Fig. 5 in Phylogenetic placement of a new paleoendemic pill scarab from the Udzungwa Mountains, Tanzania, triggers biogeographic interpretations (Coleoptera: Hybosoridae, Ceratocanthinae)
Fig. 5 – Maximum likelihood tree of Ceratocanthinae pill scarabs (Coleoptera: Hybosoridae), as reconstructed by RAxML from the three-fragment concatenated matrix.Balleriodes sphaera gen. et sp. nov. is inside the monophyletic Synarmostes group. Branches with ML bootstrap support percentage ≥90 are marked by black circles; those with bootstrap <90 and ≥75 are marked by open rhombi. Colours of Ceratocanthinae taxa and branches indicate their biogeographical region. Habitus images are to scale; small black arrows indicate imaged specimens.
Fig. 1 – A in Phylogenetic placement of a new paleoendemic pill scarab from the Udzungwa Mountains, Tanzania, triggers biogeographic interpretations (Coleoptera: Hybosoridae, Ceratocanthinae)
Fig. 1 – A, Distribution of Balleriodes sphaera gen. et sp. nov. and potentially related and/or similar pill scarabs in Africa and Madagascar. B-F: Balleriodes sphaera gen. et sp. nov., holotype, habitus (B: left lateral, C: dorsal, D: ventral; E: anterior, F: posterior).
Fig. 2 in Sucrose triggers honeydew preference in the ghost ant, Tapinoma melanocephalum (Hymenoptera: Formicidae)
Fig. 2. Foraging preference of ghost ants. The data are presented as the mean ± SE, and an asterisk above the bars indicates statistically significant differences between mealybug and aphid (paired-sample t-test, P = 0.05).
Fig. 3 in Sucrose triggers honeydew preference in the ghost ant, Tapinoma melanocephalum (Hymenoptera: Formicidae)
Fig. 3. Foraging preferences of ghost ants (A) when different sugars were offered simultaneously (Xy, Gl, Fr, Su, Tr, Me, Ra, Rh, M-H, and A-H denote xylose, glucose, fructose, sucrose, trehalose, melezitose, raffinose, rhamnose, mealybug honeydew, and aphid honeydew, respectively); and (B) when different concentrations of sucrose were offered simultaneously. The data are presented as the mean ± SE, and different letters above the bars indicate statistically significant differences between the treatments (Mann–Whitney U test, P = 0.05).
Copepod diapause duration estimation code for manuscript: Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging
<p>This is a script and data upload by Frederic Maps (Université Laval) which creates figure 10 as well as other exploratory plots for publication Schmid, Maps, Fortier 2018: Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging, to be published in the journal of plankton research (JPR).</p>
The skin commensal yeast Malassezia triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation
<p>Data accompanying the publication: Sparber et al. The skin commensal yeast <em>Malassezia</em> triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation. 2019. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2019.02.002</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.