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opencc-by-4.0May 2024View details →

Appendices for the paper "Decision Support Systems Adoption in Pesticide Management"

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dryad40/100

Infection-nutrition feedbacks: fat supports pathogen clearance but pathogens reduce fat in a wild mammal

Though far less obvious than direct effects (clinical disease or mortality), the indirect influences of pathogens are difficult to estimate but may hold fitness consequences. Here, we disentangle the directional relationships between infection and energetic reserves, evaluating the hypotheses that energetic reserves influence infection status of the host and that infection elicits costs to energetic reserves. Using repeated measures of fat reserves and infection status in individual bighorn sheep (Ovis canadensis) in the Greater Yellowstone Ecosystem, we documented that fat influenced ability to clear pathogens (Mycoplasma ovipneumoniae) and infection with respiratory pathogens was costly to fat reserves. Costs of infection approached, and in some instances exceeded, costs of rearing offspring to independence in terms of reductions to fat reserves. Fat influenced probability of clearing pathogens, pregnancy, and over-winter survival; from an energetic perspective, an animal could survive for up to 23 days on the amount of fat that was lost to high levels of infection. Cost of pathogens may amplify tradeoffs between reproduction and survival. In the absence of an active outbreak, the influence of resident pathogens often is overlooked. Nevertheless, the energetic burden of pathogens likely has consequences for fitness and population dynamics, especially when food resources are insufficient.

opencc-zeroJun 2024View details →

Supporting Information Proteins Manuscript (Irfan Khawar)

This file is basically a Supporting information (SI) of my research paper. It consists of all the tables of the datasets for Chicken protein-water, Fish protein-water, Combined Muscle protein-water, and Bovine serum albumin-water partitioning. It also consists of the R codes used to generate figures and results. Supporting information is available free of charge (section 3 in SI).

FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"

This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.

opencc-by-4.0May 2024View details →

Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

The exchange of carbon between the soil and the atmosphere is an important factor in climate change.  Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change.  Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model.  However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections.  For example, climate projections are often only available at scales of 10s to 100s of km2.  To overcome this, we use a climate projection dataset downscaled to <1 km2  (~18,000 cells).  We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region.  We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models.   In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.

Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"

Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions". <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>

Figure 7 in Developing an advanced information system to support ballast water management

Figure 7. The application of the on-demand visualization tool for assessing the risk of ballast water sourced from Providence, USA and discharged in Louisbourg, Canada.

Figure 4 in Developing an advanced information system to support ballast water management

Figure 4. An example two-level report for Incoming Vessels by Arrival Date. Level one is displayed on the left and level two is displayed on the right.

Figure 6 in Developing an advanced information system to support ballast water management

Figure 6. An example Risk Map showing environmental risk between source port Boston, USA and destination port Saint John, Canada.

Figure 5 in Developing an advanced information system to support ballast water management

Figure 5. An example two-level report for Ballast Discharge Amount (m3) by Tank Discharge Date. Level one is displayed on the left and level two is displayed on the right.

Figure 2 in Developing an advanced information system to support ballast water management

Figure 2. The Ballast Water Information System provides services to Operational Team, Data Users, Vessel Operators, and System Administrators such as: 1) facilitating data entry, validation and verification for Ballast Water Reporting Forms (BWRFs); 2) providing a search functionality for BWRFs; 3) providing basic reports on BWRF data; and 4) implementing research on risk assessment to build decision-support reports.

PREMIERE Tools and databases of tools in support of co-creation

This database is a draft intermediary overview of relevant tools or databases of tools that could have potential in being of relevance in a multi-actor project proposal preparation process

Figure 4 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 4: Crop sequence in rotation 3: standard sugar beets variety "Mixer"; Cereals; WOSRWWOSR; Cereals; Oil radish; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.

Figure 8 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 8: SBN population dynamic under the semi-tolerant variety "Rosalinda" followed by non-host crops at The SBN initial population Pi (eggs g−1 soil) = 4, Tolerance (T) = 0.273, proportion of the population survived (s) = 0.35 and reproduction factor (Rf) = 3.8. The suggested number of waiting years by SBN-Watch to the next "Rosalinda" crop is four years (Pf <T).

Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.

Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.

Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.

Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.

Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production

Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.