139 research outputs found
Isolation and Bioefficacy of β-Carotene from an Unexplored Plant Source
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Growth and characterization of glycinium 3-nitrophthalate nonlinear optical single crystal
No full textData from: Distribution models predict climate-related range alteration or extinction of eleven threatened tropical rainforest trees in the Western Ghats
No full text<p>This dataset contains information related to species occurence data and species distribution modeling (SDM) analysisr of eleven threatened tree species. Occurrences are compiled from extensive field surveys in the Anamalai Hills along with data from the Global Biodiversity Information Facility (GBIF.org) and earlier work done within the southern Western Ghats, India.</p>
<p>References:<br>Page, N. V., & Shanker, K. (2020). Climatic stability drives latitudinal trends in range size and richness of woody plants in the Western Ghats, India. PLOS ONE, 15(7), e0235733. https://doi.org/10.1371/journal.pone.0235733</p>
<p>GBIF.org (2022) GBIF Occurrence Download, 2 August 2022. DOI:10.15468/dl.gnvuxj</p>
<p><br>AUTHOR #1<br>1. Name: A.P. Madhavan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: [email protected]<br>4. ORCID: https://orcid.org/0009-0009-2754-8256</p>
<p>AUTHOR #2<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: [email protected]<br>4. ORCID: ORCID: https://orcid.org/0000-0002-6190-2687</p>
<p>AUTHOR #3<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: [email protected]<br>4. ORCID: https://orcid.org/0000-0001-7323-6653</p>
<p>AUTHOR #4<br>1. Name: Divya Mudappa <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: [email protected] <br>4. ORCID: https://orcid.org/0000-0001-9708-4826</p>
<p>AUTHOR #5<br>1. Name: Navendu Page<br>2. Work Address: Wildlife Institute of India, Post Box No. 18, Chandrabani, Dehradun, Uttarakhand 248001, India<br>3. Email address: [email protected]<br>4. ORCID: ORCID: https://orcid.org/0000-0002-9413-7571</p>
<p>AUTHOR #6<br>1. Name: T. R. Shankar Raman <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: [email protected] <br>4. ORCID: https://orcid.org/0000-0002-1347-3953</p>
<p>Keywords: tropical rainforest, climate change, tree distributions, species distribution models, range shifts, Western Ghats</p>
<p><br>Geographic Coverage:<br>1. Location/Study Area: Southern Western Ghats Montane Rain Forests, Southern Western Ghats Moist Deciduous Forests, India<br>2. GPS coordinates: SWG (73.95° – 80.33° E, 8.06° – 13.11°N) </p>
<p>Temporal coverage<br>Starts: 2020-08-01<br>Ends: 2024-03-28</p>
<p>Besides this README.txt file, the dataset includes three comma-delimited text files (csv); two R scripts, and 1 kml file of surveyed trails.</p>
<p>CSV files with the data in columns as explained below:</p>
<p>1) Focal_Tree_Dat.csv</p>
<p>Comp: Number identifier<br>FT_ID: Unique tree no for each individual<br>Focal_tree: Scientific name of species<br>Date: Date of occurrence observation<br>Place: Area/locality description<br>Trail: Unique trail ID<br>Waypoint: Waypoint number <br>Time: Time in hh:mm format <br>Location: Specific description of occurrence locality <br>Latitude: Latitude in decimal degrees N <br>Longitude: Longitude in decimal degrees E <br>Elevation: Elevation in metres <br>Slope: Cateory of slope <br>ID_Notes: Notes on identification<br>Phenophase: Phenophase expression at the time of observation <br>GBH: Girth at breast height in centimetres (comma separated list of numbers in case of multi-stemmed trees) <br>Tree_ht: Tree height in metres<br>Canopy_ht: Maximimum height of the surrounding canopy in metres<br>Substrate: Soil substrate composition<br>Invasives: Name of invasive species (if present) <br>Stature: Vegetation strata position <br>Relatively: Stature of focal individual relative to other surrounding individuals <br>Deadwood: Description of deadwood on the tree <br>Damage: Description of damage on the bole <br>Shape: Description of tree canopy shape<br>Closure: Canopy closure at focal tree <br>Seedlings: Number of conspecific seedlings present in 5 m radius of focal tree <br>Saplings: Number of conspecific saplings present in 5 m radius of focal tree<br>Trees: Number of conspecific trees present in 5 m radius of focal tree<br>Remarks: Remarks </p>
<p>2) Ffspecies.csv</p>
<p>Source: Source of occurrence <br>ID: State/location of occurrence<br>Region: Biogeographic region of occurrence <br>decimalLatitude: Latitude in decimal degrees N<br>decimalLongitude: Longitude in decimal degrees E<br>species: Scientific name of species</p>
<p>4) ft_surveys.csv</p>
<p>Date: Date of survey of sample trail<br>Prot_type: Category indicating whether protected area or fragment <br>Place: Area/locality description<br>Route_description: Specific landmark description of trail<br>Trail: Unique trail ID <br>Trail_distance: Tracked distance of trail in km <br>Corrected_trail_distance: Corrected distance of trail in km<br>Track_filename_kml: File name of gps track<br>Sample_collected: Name of species if sample collected <br>Observers: Name of observers <br>Remarks: Remarks</p>
<p>ANALYSES SCRIPTS<br>flexsdm_script.R<br>Script containing the analysis of all maxent distribution modeling and associated analysis</p>
<p>Franklinia_density.Rmd<br>Script of density and abundance related analysis</p>
<p> </p><p><strong>Funding</strong></p>
<p>We thank Fondation Franklinia, Rohini Nilekani Philanthropies, Rainmatter Foundation, and AMM Murugappa Chettiar Research Centre for funding support.</p>
Shared genetic influences between blood analyte levels and risk of severe COVID-19
No full textGenome-wide association studies (GWASs) show that genetic factors contribute to the risk of severe coronavirus disease 2019 (COVID-19) and blood analyte levels. Here, we utilize GWAS summary statistics to study the shared genetic influences (pleiotropy) between severe COVID-19 and 344 blood analytes at the genome, gene, and single-nucleotide polymorphism (SNP) levels. Our pleiotropy analyses genetically link blood levels of 71 analytes to severe COVID-19 in at least one of the three levels of investigation—suggesting shared biological mechanisms or causal relationships. Six analytes (alanine aminotransferase, alkaline phosphatase, apolipoprotein B, C-reactive protein, triglycerides, and urate) display evidence of pleiotropy with severe COVID-19 at all three levels. Causality analyses indicate that higher triglycerides levels causally increase the risk of severe COVID-19, thereby providing important support for the use of lipid-lowering drugs such as statins and fibrates to prevent severe COVID-19
Mitochondrial Metabolism of Human Kidney Cancers
No full textThe general metadata -- e.g., title, author, abstract, subject headings, etc. -- is publicly available, but access to the submitted files is restricted to UT Southwestern campus access and/or authorized UT Southwestern users.Metabolism is often dysregulated in cancer. Pinpointing the exact metabolic requirements critical for cancer cell survival has been the subject of intense study for the last 100 years. However, very few metabolic targets have successfully translated to effective therapies for patients. Progress in clinical translation has been limited as the vast majority of cancer metabolism studies are currently conducted in preclinical models of cell culture and mice. How relevant these preclinical studies are to disease biology in humans is almost entirely unknown. I use a multidisciplinary approach to infuse 13C-labeled nutrients during surgical tumor resection in over 80 patients with kidney cancer. Labeling from [U-13C]glucose varies across cancer subtypes, indicating that the kidney environment alone cannot account for all metabolic reprogramming in these tumors. Compared to the adjacent kidney, clear cell renal cell carcinomas (ccRCC) display suppressed labelling of tricarboxylic acid (TCA) cycle intermediates in vivo and in organotypic slices cultured ex vivo, indicating that suppressed labeling is tissue intrinsic. Infusions of [1,2-13C]acetate and [U-13C]glutamine in patients, coupled with respiratory flux of mitochondria isolated from kidney and tumor tissue, reveal primary defects in mitochondrial function in human ccRCC. However, ccRCC metastases unexpectedly have enhanced labeling of TCA cycle intermediates compared to primary ccRCCs, indicating a divergent metabolic program during ccRCC metastasis in patients. In mice, stimulating respiration in ccRCC cells is sufficient to promote metastatic colonization. Altogether, these findings indicate that metabolic properties evolve during human kidney cancer progression, and suggest that mitochondrial respiration may be limiting for ccRCC metastasis but not for ccRCC growth at the site of origin
First Complete Genome Sequence Resource of a Potyvirus lactucae Isolate from the United States of America
No full textPotyvirus lactucae, lettuce mosaic virus (LMV), is a devastating pathogen that impacts commercial lettuce production. The complete genome sequence of an LMV isolate from a romaine lettuce plant (LMV-R) collected in 1994 in the Salinas Valley of California was determined by RNA-seq and Sanger sequencing, followed by 5′ rapid amplification of cDNA ends (RACE). The genome of LMV-R consists of 10,080 nucleotides and shares the highest nucleotide identity with LMV isolate CL208 from Chile (97.2%) (GenBank accession KJ161176.1). The LMV-R genome encodes for a single large polypeptide (UED15646) with putative proteolytic cleavage sites and showed 98.9% amino acid (aa) identity to a Turkish isolate (UEP55410) and 98.71% aa identity a to Chilean isolate (AIB00275). The sequence and phylogenetic analysis highlight the close association between LMV-R from the United States and CL208 of Chile, indicating that LMV-R might have been exchanged between North America and South America through international trade. [Figure: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license
30-m HRSC DTM Mosaic of Gale Crater, Mars
No full textDigital terrain model (DTM) mosaic of Gale crater, Mars, processed from High-Resolution Stereo Camera (HRSC) stereo images using the modification of DLR-VICAR described by Kim and Muller (2009).
Format: GeoTiff
Projection: Equidistant cylindrical
Datum: Spheroid (r = 3396.190 km)
Bit depth: Float32
Grid-spacing: 30 m/pixel
Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018)
HRSC source images: H1938_0000, H1927_0000, and H1916_0000The first author is now at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California. Contact: [email protected]
Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India
No full text<p><strong>TITLE</strong></p><p><strong>Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India</strong><br> </p><p><strong>DESCRIPTION</strong></p><p>This dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).</p><p><strong>Publications</strong></p><p>Specific portions of the dataset have been used in the following publications:</p><ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet <i>Paradoxurus jerdoni</i> in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. <i>Biodiversity and Conservation</i> 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. <i>Journal of Tropical Ecology</i> 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. <i>BioRxiv</i> 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365<br> </li></ul><p>A related dataset is the following:<br>Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. <i>Dryad Dataset.</i> https://doi.org/10.5061/dryad.4mw6m907q<br> </p><p><strong>Curation and corrections</strong></p><p>Data were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.</p><p><strong>Acknowledgements</strong></p><p>We are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF.</p><p><br><strong>CONTACTS</strong><br> </p><p>CONTACT #1<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: [email protected]<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: [email protected]<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: Hari Sridhar<br>2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: [email protected]<br>5. ORCID: https://orcid.org/0000-0003-3286-0120</p><p>CONTACT #4<br>1. Name: Akshay Surendra<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458<br>3. Work Phone: +91 821 2515601<br>4. Email address: [email protected]<br>5. ORCID: https://orcid.org/0000-0003-2719-7432<br> </p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2000-01-01 (Year, Month, Day)<br>2. Ends: 2019-12-31 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.</p><p>PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).</p><p>All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25 to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor.</p><p><br><strong>FILES INCLUDED</strong><br>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)<br> </p><p><strong>01) sites.csv -- Details of study sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation<br>Description: Description of the place<br> </p><p><strong>02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year<br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4)<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>locationRemarks: Code for site name as originally used<br>SpCode: Species code as originally used during data entry<br>TreeHeight: Tree height in metres (only available in 2019 survey)<br>identificationRemarks: Notes related to identification if available<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth<br> </p><p><strong>03) pcqlocations.csv -- Locations of sample PCQ points</strong><br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>note: Site name code<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres<br> </p><p><strong>04) allhabitat.csv -- Data on habitat structure variables</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point: ID of replicate survey point within the Fragment<br>0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground<br>1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground<br>2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground<br>4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground<br>8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground<br>16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground<br>24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground<br>over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground<br>VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns)<br>CanopyHeight: Canopy height in metres<br>CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer<br>CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer<br>CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible<br>UC: Canopy overlap rank as above, for understorey vegetation only<br>MC: Canopy overlap rank as above, for the midstorey only<br>CC: Canopy overlap rank as above, for the upper canopy only<br>Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device<br>RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>TotalShrub: Total number of shrubs within 2 m radius of point<br>Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point<br>Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point<br>Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point<br>Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point<br>LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe<br>CutTrees: Number of cut trees within 5 m radius of point<br> </p><p><strong>05) gbifnames.csv -- Results of GBIF name matching tool</strong><br>sno: Serial number<br>verbatimScientificName: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level<br>sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool<br>key: GBIF key as provided by GBIF name matching tool<br>matchType: Type of match as provided by GBIF name matching tool<br>confidence: Confidence as provided by GBIF name matching tool<br>status: Status as accepted name or synonym as provided by GBIF name matching tool<br>rank: Taxonomic rank as provided by GBIF name matching tool<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>class: Class as provided by GBIF name matching tool<br>order: Order as provided by GBIF name matching tool<br>family: Family as provided by GBIF name matching tool<br>genus: Genus as provided by GBIF name matching tool<br>species: Species as provided by GBIF name matching tool<br>canonicalName: Canonical name as provided by GBIF name matching tool<br>authorship: Author of name as provided by GBIF name matching tool<br> </p><p><strong>06) plots2000.csv -- Data from 5 m radius circular plots in select sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>PlotID: ID of 5 m radius plot<br>Treeno: Serial number of tree in the plot<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification</p><p> </p><p><strong>07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language</strong> </p><p>This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh</p><p> </p><p><strong>Changes in Version 2</strong></p><p>In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for Puthuthottam</p><p>Added the anampcqs4gbif.rmd file with R code</p>
Trajecting Territories: A Spatial Reconfiguration towards Multipurpose Foodscapes
No full textWith an average cheese production of 947 mln kg/year, the dairy industry (in the Netherlands) is responsible for 6.3 % of agricultural/dairy/commodity greenhouse gas (GHG) emissions in Northwestern (NW) Europe. This report brings the production of dairy and its effects on the spatiotemporal and environmental footprints. By performing a material analysis flow of an everyday consumption product-cheese, a by-product from the milk produced by cattle raised on the vast flat pasture lands in the Netherlands, we determine its harmful role in GHG emissions. Using a mixed-method approach, this study combines qualitative and quantitative analysis methodologies, extensive literature reviews, group discussions, available QGIS datasets, farmers sharing their experiences and knowledge on YouTube channels, case studies and a stakeholder interview. This led us to the formulation of a sustainable polyculture agriculture catalogue and toolbox where the dairy sector shifts from a core polluter and extractor role to a regenerative one. A future for farming is formulated where healthy soil is at the core of agricultural thinking. We outline a cow reduction spectrum resulting in opportunities for NW Europe leading to ecological improvements of the soil. Applying this toolbox to the South-Holland scale led to a multipurpose foodscape using an Integrated Crop-Livestock System (ICLS), where cows play the primary role of fertilisers of the land and secondarily, the role of milk producers. In conclusion, the research proves that the adoption of ICLS can significantly reduce GHG emissions in dairy production territories and optimise the existing land use. Implementing this system requires a shift in mindset and has significant implications for the dairy industry, policymakers and society at large. The strategy and action plan in this research seeks to inform policymakers, urban planners and other stakeholders in the dairy farming industry on how to transition towards a more regenerative and sustainable system that benefits the environment, society and the economy in the long duree. It suggests a socially just transition to the groups of farmers via a symbiotic approach.AR2U086 R&D Studio – Spatial Strategies for the Global MetropolisArchitecture, Urbanism and Building Sciences | Urbanis
Multisectoral interventions for urban health in Africa: a mixed-methods systematic review
No full textIncreasing evidence suggests that urban health objectives are best achieved through a multisectoral approach. This approach requires multiple sectors to consider health and well-being as a central aspect of their policy development and implementation, recognising that numerous determinants of health lie outside (or beyond the confines of) the health sector. However, collaboration across sectors remains scarce and multisectoral interventions to support health are lacking in Africa. To address this gap in research, we conducted a mixed-method systematic review of multisectoral interventions aimed at enhancing health, with a particular focus on non-communicable diseases in urban African settings. Africa is the world’s fastest urbanising region, making it a critical context in which to examine the impact of multisectoral approaches to improve health. This systematic review provides a valuable overview of current knowledge on multisectoral urban health interventions and enables the identification of existing knowledge gaps, and consequently, avenues for future research. We searched four academic databases (PubMed, Scopus, Web of Science, Global Health) for evidence dated 1989–2019 and identified grey literature from expert input. We identified 53 articles (17 quantitative, 20 qualitative, 12 mixed methods) involving collaborations across 22 sectors and 16 African countries. The principle guiding the majority of the multisectoral interventions was community health equity (39.6%), followed by healthy cities and healthy urban governance principles (32.1%). Targeted health outcomes were diverse, spanning behaviour, environmental and active participation from communities. With only 2% of all studies focusing on health equity as an outcome and with 47% of studies published by first authors located outside Africa, this review underlines the need for future research to prioritise equity both in terms of research outcomes and processes. A synthesised framework of seven interconnected components showcases an ecosystem on multisectoral interventions for urban health that can be examined in the future research in African urban settings that can benefit the health of people and the planet.This research/project is supported/funded by the British Academy’s Urban Infrastructures of Well-Being 2019 Programme, supported under the UK Government’s Global Challenges Research Fund (Grant reference UWB190032). LF, EM, FA, TO are supported by the National Institute for Health Research (NIHR) (16/137/34) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.Peer Reviewed"Article signat per 19 autors/es: Meelan Thondoo, Ebele R. I. Mogo, Lambed Tatah, Monica Muti, Kim R. van Daalen, Trish Muzenda, Rachel Boscott, Omar Uwais, George Farmer, Adelaide Yue, Sarah Dalzell, Gudani Mukoma, Divya Bhagtani, Sostina Matina, Philip M. Dambisya, Kufre Okop, Charles Ebikeme, Lisa Micklesfield and Tolu Oni"Postprint (published version
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