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Aseismic and recent ruptures of persistent asperities along the Alaska-Aleutian subduction zone
<p>This repository contains GPS derived coseismic offsets and 87-day postseismic displacements associated with the 2020 Mw .78 Simeonof Island, Alaska earthquake, as well as the preferred interseismic backward slip rate distribution along the Alaska-Aleutian subduction zone in GMT psxy format. if you use the dataset, please cite the following paper:</p> <p>Zhao, B., Burgmann, R., Wang, D., Zhang, J., Yu, J., & Li, Q. (2022). Aseismic slip and recent ruptures of persistent asperities along the Alaska-Aleutian subduction zone. <em>Nature Communications</em>. https://doi.org/10.1038/s41467-022-30883-7</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
ICU post- discharge persistent symptoms, self-reported health and quality of life of COVID-19 survivors: A cohort study.
<p> </p> <p>Understanding the consequences and health impact of COVID-19 survivors discharged from the ICU is still unclear. The aim of this study was to investigated persistent symptoms, health satisfaction and health related quality of life (HRQoL) of patients that were hospitalized due COVID-19 infection after 30, 90 and 180 days from ICU discharge. This is a multicentric prospective cohort study of COVID-19 survivors discharged from 8 hospitals of Curitiba – Paraná (Brazil), between September 2020 and January 2022. Eligible COVID 19 survivors were contacted by phone and invited to answer telephone survey at 30, 90 and 180 days after ICU discharge. They responded a phone questionnaire to collect post-discharge clinical symptoms, and we also asked about health satisfaction and HRQoL. 62 COVID-19 survivors (51,6% males, mean age 50,3 years, median length of ICU stay of 13 days) responded to the telephone survey at 30, 90 and 180 days. The most persistent symptoms were fatigue (65,9%, 51,3%, 44,7%, respectively), mild dyspnea (42%, 31%, 29,8%, respectively) and myalgia (29%, 22,1%, 17%, respectively). Myalgia showed a significant reduction from 30 days to 180 days (p=0,034), and the number of symptoms also reduced significantly (30 to 90 days, <em>p=0.018</em>, 30 to 180 days<em>, p=0.001 </em>). At 30, 90 and 180 follow up days the most patients had reported “good” quality of life (59,7%, 62,9%, 51,6%, respectively), and “satisfied” with health (43,5%, 48,4%, 46,8%, respectively). We found that COVID-19 symptoms persist to 180 days, fatigue more commonly. Nevertheless, in this cohort study, most COVID-19 survivors reported good quality of life and were satisfied with health.</p>
Asynchronous Workload Balancing through Persistent Work-Stealing and Offloading for a Distributed Actor Model Library
<p>With dynamic imbalances caused by both software and ever more complex hardware, applications and runtime systems must adapt to dynamic load imbalances. We present a diffusion-based, reactive, fully asynchronous, and decentralized dynamic load balancer for a distributed actor library. With the asynchronous execution model, features such as remote procedure calls, and support for serialization of arbitrary types, UPC++ is especially feasible for the implementation of the actor model. While providing a substantial speedup for small- to medium-sized jobs with both predictable and unpredictable workload imbalances, the scalability of the diffusion-based approaches remains below expectations in most presented test cases.</p> <p>Actor-UPCXX is a high-performance computing library based on the actor model to enable the use of the actor model for HPC simulations. The source code can be found at: https://github.com/TUM-I5/Actor-UPCXX</p>
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217.
Text-fig. 4. Scanning electron micrographs (a, c, e–k) and X-ray microtomographic orthoslices (b, d) of capsular fruits compose of five carpels from Zliv-Řídká Blana locality. a–d: Taxon 4, a – fruit elliptical in shape, no. NM-F 3188, b – young fruit with reminisce of free styles at top and showing central placentation of seeds, no. NM-F 3188, c – pentacarpellate capsules in apical view, no. NM-F 3188, d – fruit with five locules, no. NM-F 3235; e, f: Taxon 6, e – elongated fruit in lateral view, the persistent perianth at the base of the fruit (arrowhead), no. NM-F 3194, f – fruit in apical view, no. NM-F 3194; g, h: Taxon 5, g – elongated fruit in lateral view, no. NM-F 3193, h – fruit showing remains of a persistent calyx in the basal part (arrowhead), no. NM-F 3193; i–k: Taxon 7, i – pentacarpellate capsules of broadly elliptical shape, no. NM-F 4091, j – fruit, apical view, no. NM-F 4091, k – fruit with five seeds (arrowheads) ellipsoidal or triangular in outline and with a thick seed coat, no. NM-F 4091. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 4. Scanning electron micrographs (a, c, e–k) and X-ray microtomographic orthoslices (b, d) of capsular fruits compose of five carpels from Zliv-Řídká Blana locality. a–d: Taxon 4, a – fruit elliptical in shape, no. NM-F 3188, b – young fruit with reminisce of free styles at top and showing central placentation of seeds, no. NM-F 3188, c – pentacarpellate capsules in apical view, no. NM-F 3188, d – fruit with five locules, no. NM-F 3235; e, f: Taxon 6, e – elongated fruit in lateral view, the persistent perianth at the base of the fruit (arrowhead), no. NM-F 3194, f – fruit in apical view, no. NM-F 3194; g, h: Taxon 5, g – elongated fruit in lateral view, no. NM-F 3193, h – fruit showing remains of a persistent calyx in the basal part (arrowhead), no. NM-F 3193; i–k: Taxon 7, i – pentacarpellate capsules of broadly elliptical shape, no. NM-F 4091, j – fruit, apical view, no. NM-F 4091, k – fruit with five seeds (arrowheads) ellipsoidal or triangular in outline and with a thick seed coat, no. NM-F 4091.
Large and non-spherical seeds are less likely to form a persistent soil seed bank
<p>There is some evidence that seed traits can affect the long-term persistence of seeds in the soil. However, findings on this topic have differed between systems. Here, we brought together a worldwide database of seed persistence data for 1474 species to test the generality of seed mass-shape-persistence relationships. We found a significant trend for low seed persistence to be associated with larger and less spherical seeds. However, the relationship varied across different clades, growth forms and species ecological preferences. Specifically, relationships of seed mass-shape-persistence were more pronounced in Poales than in other order clades. Herbaceous species that tend to be found in sites with low soil sand content and precipitation have stronger relationships between seed shape and persistence than in sites with higher soil sand content and precipitation. For the woody plants, the relationship between persistence and seed morphology was stronger in sites with high soil sand content and low precipitation than in sites with low soil sand content and higher precipitation. Improving ability to predict the soil seed bank formation process, including burial and persistence, could benefit the utilization of seed morphology-persistence relationships in management strategies for vegetation restoration and controlling species invasion across diverse vegetation types and environments.</p>
Data from: Climate change and population persistence in a hibernating marsupial
<p>Climate change has physiological consequences on organisms, ecosystems, and human societies, surpassing the pace of organismal adaptation. Hibernating mammals are particularly vulnerable as winter survival is determined by short-term physiological changes triggered by temperature. In these animals, winter temperatures cannot surpass certain threshold, above which hibernators arouse from torpor, increasing several fold their energy needs when food is unavailable. Here, we parameterized a numerical model predicting energy consumption in heterothermic species, and modeled winter survival at different climate change scenarios. As a model species, we used the arboreal marsupial monito del monte (genus <em>Dromiciops</em>) which is recognized as one of the few South America hibernators. We modeled four climate change scenarios (from optimistic to pessimistic), based on IPCC projections, predicting that northern and coastal populations (<em>Dromiciops bozinovici</em>) will decline because the minimum number of cold days needed to survive the winter will not be attained. These populations are also the most affected by habitat fragmentation and change in land use. Conversely, Andean and other highland populations at cooler environments, are predicted to persist and thrive. Given the widespread presence of hibernating mammals around the world, models based on simple physiological parameters such as this one, are becoming essential for predicting species responses to warming in the short term.</p>
R code for: Following regulation, imidacloprid persists and flupyradifurone increases in non-target wildlife
<p>After regulation of pesticides, determination of their persistence in the environment is an important indicator of effectiveness of these measures. We quantified concentrations of two types of systemic insecticides: neonicotinoids (imidacloprid, acetamiprid, clothianidin, thiacloprid, and thiamethoxam) and butenolides (flupyradifurone), in off-crop non-target media of hummingbird cloacal fluid, honey bee (<em>Apis mellifera</em>) nectar and honey, and wildflowers before and after regulation of imidacloprid on highbush blueberries in Canada in April 2021. We found that mean total pesticide load increased in hummingbird cloacal fluid, nectar, and flower samples following imidacloprid regulation. On average, we did not find evidence of a decrease in imidacloprid concentrations after regulation. However, there were some decreases, some increases and other cases with no changes in imidacloprid levels depending on the specific media, time point of sampling and site type. At the same time, we found an overall increase in flupyradifurone, acetamiprid, thiamethoxam and thiacloprid, but no change in clothianidin concentrations. In particular, flupyradifurone concentrations observed in biota sampled near to agricultural areas increased by 2-fold in honey bee nectar, 7-fold in hummingbird cloacal fluid, and 8-fold in flowers after the 2021 imidacloprid regulation. The highest residue detected in this study was flupyradifurone at 665 ng/mL (PPB) in honey bee nectar. Mean total pesticide loads were highest in honey samples (84 ± 10 PPB) followed by nectar (56 ± 7 PPB), then hummingbird cloacal fluid (1.8 ± 0.5 PPB), and least, flowers (0.51 ± 0.06 PPB). Our results highlight that limited regulation of imidacloprid does not immediately reduce residue concentrations while other systemic insecticides, possibly replacement compounds, concurrently increase in wildlife.</p>
Fig. 3 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 3 Number of human-water contact sites in persistent hot-spot and low-prevalence shehias in Unguja
Fig. 2 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 2 Map of Unguja Island, Zanzibar, showing the location of selected persistent hot-spot and low-prevalence shehias
Fig. 1 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 1 Flowchart showing the inclusion procedure for persistent hot-spot and low-prevalence shehias in Unguja
Figure 8 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 8. Effects of experimental site by burial depth by year on percentage seed viability for Amoronthus polmeri and Amoronthus tuberculotus. Vertical bars represent ± standard error of the mean.
Figure 4 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 4. Monthly soil temperature (averaged over a 15-min concurrent recording period on a daily basis for the entire experimental period) at the top and at 15 cm below the soil surface (left y-axis) and monthly soil volumetric water content at 15 cm below soil surface (right y-axis) for each of the seven sites where the seed material of Amoronthus polmeri and Amoronthus tuberculotus was exposed to burial conditions during 2014–2016. Data presented for Missouri include only two experimental years (2015 and 2016).
Figure 1 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 1. Experimental sites across Midsouth United States, where Amoronthus polmeri and Amoronthus tuberculotus seed material was exposed to burial trials for a period of 1 to 3 yr before viability test evaluation. Numbers in parentheses represent the latitude and longitude of the experimental sites.
Figure 6 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 6. Interaction of experimental site by burial depth on percentage seed damage and loss averaged across 2014–2016 for Amoronthus polmeri and Amoronthus tuberculotus. Damaged seeds include broken, shrunk, or malformed seeds and those lost due to deterioration or futile germination, which were impossible to count. Vertical bars represent ± standard error of the mean. Supplemental information is also provided in Supplementary Tables S1 and S2, where the actual values averaged across three replications per treatment and five locations of seed material origins are shown.
Figure 3 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 3. Amoronthus polmeri seeds as they appeared under a dissection microscope (A) before the seed retrieval and cleaning processes and (B) after the cleaning process. The same criteria were used for A. tuberculotus seeds.
Figure 7 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 7. Percentage seed viability as affected by burial depth and retrieval time (in months) for Amoronthus polmeri and Amoronthus tuberculotus. Vertical bars represent ± standard error of the mean (i.e., 0.612 and 0.649 for A. polmeri and A. tuberculotus, respectively).
Figure 2 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 2. Experimental layout in which the randomized arrangement of main plots (i.e., retrieval year), subplots (colored seed bags representing the site by ecotype treatments), and sub-subplot (i.e., burial depth treatment) are depicted along with details for seedbank establishment (dimensions and burial depth of PVC cage).
Figure 5 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 5. Interaction of experimental site by ecotype on seed viability for Amoronthus polmeri and Amoronthus tuberculotus averaged across 2014–2016. Vertical bars represent ± standard error of the mean. Supplemental information is also provided in Supplementary Tables S1 and S2, where the actual values averaged across three replications per treatment and five locations of seed material origins are shown.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.