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518 results for “useful plants”
Fig. 3 in Laboratory evaluations of the foraging success of Tamarixia radiata (Hymenoptera: Eulophidae) on flowers and extrafloral nectaries: potential use of nectar plants for conservation biological control of Asian citrus psyllid (Hemiptera: Liviidae)
Fig. 3. Choice of cups with either unscented sucrose solution or with bananascented sucrose solution made by Tamarixia radiata following a pre-test exposure to either 1.0 M sucrose solution or 1.0 M sucrose solution and banana flavor extract (G-test; ** = P ≤ 0.01; NS = not significant).
Fig. 1 in Laboratory evaluations of the foraging success of Tamarixia radiata (Hymenoptera: Eulophidae) on flowers and extrafloral nectaries: potential use of nectar plants for conservation biological control of Asian citrus psyllid (Hemiptera: Liviidae)
Fig. 1. Diagrammatic representation of nectary architectures presented to Tamarixia radiata in foraging evaluations. Location of nectaries shown in red. A. Cy- athium of euphorbiaceous species with exposed nectaries. B. Partially exposed nectaries as found in buckwheat. C. Partially hidden nectaries as found in alyssum. D. Partially exposed nectaries covered with trichomes as found in marjoram. E. Hidden nectaries as found in composites. Drawings are only indicative of size and spatial relationships and are not to scale.
Fig. 2 in Laboratory evaluations of the foraging success of Tamarixia radiata (Hymenoptera: Eulophidae) on flowers and extrafloral nectaries: potential use of nectar plants for conservation biological control of Asian citrus psyllid (Hemiptera: Liviidae)
Fig. 2. Mean (± SE) feeding time of Tamarixia radiata when presented with different concentrations of sugars commonly occurring in nectar (sucrose, fructose, glucose) and honeydew (melizitose, raffinose). Bars within the same concentration having different letters are different at P ≤ 0.05 (ANOVA).
K-mer databases of plant virus sequences for use with the Kodoja workflow
<p><strong>Details</strong></p> <p>This is a gzipped tar file that includes the plant virus database files required to run the Kodoja workflow (https://github.com/abaizan/kodoja)[1]. Kodoja is a workflow for the detection of plant virus sequences in RNA-seq data files that uses two previoulsy published tools Kraken[2] and Kaiju[3].</p> <p>This file contains databases for Kraken [2] and Kaiju [3]. The file includes the kraken database files: database.idx, database.kdb, nodes.dmp, names.dmp and the kaiju database file kaij_library.fmi.</p> <p>These k-mer databases are based on virus sequences in RefSeq [4] (ttps://www.ncbi.nlm.nih.gov/refseq/) with plant hosts as defined in the Virus-Host Database [5] (https://www.genome.jp/virushostdb/).</p> <p><strong>Version</strong><strong> 1.0</strong></p> <p>kodojaDB_v1.0 is based on RefSeq v89 and the Virus-Host Database (accessed 03/09/2018 which is based on RefSeq 89 and Genbank 226.0). The viral partition of RefSeq v89 genome comprises 7946 viruses (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/viral/assembly_summary.txt).</p> <p>kodojaDB_v1.0 was created using kodoja_retrieve.py which is part of the kodoja workflow (v0.05) (https://github.com/abaizan/kodoja).</p> <p><strong>References</strong></p> <p>[1] Baizan-Edge, A, Cock, P, MacFarlane, S, McGavin, W, Torrance, T, Jones, S. Kodoja: A workflow for virus detection in plants using k-mer analysis of RNA-sequencing data (under review Nucleic Acids Research). </p> <p>[2] Wood,D.E. and Salzberg,S.L. (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. <em>Genome Biol.</em>, <strong>15</strong>, R46</p> <p>[3] Menzel,P., Ng,K.L. and Krogh,A. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em>, <strong>7</strong>, 1–9.</p> <p>[4] O’Leary,N.A., Wright,M.W., Brister,J.R., Ciufo,S., Haddad,D., McVeigh,R., Rajput,B., Robbertse,B., Smith-White,B., Ako-Adjei,D., <em>et al.</em> (2016) Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. <em>Nucleic Acids Res.</em>, <strong>44</strong>, D733–D745.</p> <p>[5] Mihara,T., Nishimura,Y., Shimizu,Y., Nishiyama,H., Yoshikawa,G., Uehara,H., Hingamp,P., Goto,S. and Ogata,H. (2016) Linking virus genomes with host taxonomy. <em>Viruses</em>, <strong>8</strong>, 10–15</p>
Fig. 5. Native plant hosting M in Distribution, habitat use and plant associations of Moluchia brevipennis (Saussure, 1864) (Blattodea: Ectobiidae): an endemic cockroach from Chilean Mediterranean Matorral biome
Fig. 5. Native plant hosting M. brevipennis cockroaches at our study site. (A) Cockroaches feeding pollen from Oenothera acaulis (Onagraceae) flowers, (B) Puya venusta (Bromeliaceae) inflorescences with cockroaches reaching nectar sources, (C) Carpobrotus chilensis (Aizoaceae) with cockroach inside inflorescence and climbing branch, (D) Haplopappus chrysantemifolius (Asteraceae) with adult and nymphs feeding on florets from floral head, black bar indicates a 1 cm scale.
Fig. 2 in Distribution, habitat use and plant associations of Moluchia brevipennis (Saussure, 1864) (Blattodea: Ectobiidae): an endemic cockroach from Chilean Mediterranean Matorral biome
Fig. 2. Distribution of sampled sites at Los Molles. Right panel show South America map highlighting central Los Molles locality in Chile's Mediterranean Matorral area. Left panel correspond to a detailed map of Los Molles showing sampling details. Black dots correspond to active collecting and gray dots to trapping, see Table 1 for more details.
A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning
<div> <div> <div> <div> <p>India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India’s electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India’s coal power generation as the country transitions towards its renewable energy targets.</p> </div> </div> </div> </div>
Linked collectors and determiners for: ABC Useful Plants of Malawi: Economical & Commercial.
Natural history specimen data linked to collectors and determiners held within, "ABC Useful Plants of Malawi: Economical & Commercial". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/da39fadf-94c9-4d9d-ba65-64750b58cfe7">https://bionomia.net/dataset/da39fadf-94c9-4d9d-ba65-64750b58cfe7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/da39fadf-94c9-4d9d-ba65-64750b58cfe7">https://gbif.org/dataset/da39fadf-94c9-4d9d-ba65-64750b58cfe7</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care.
Natural history specimen data linked to collectors and determiners held within, "ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a>. Formatted as a Frictionless Data package.
Fig. 4 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 4. Distribution of specimen ages and the number of loci recovered in the phylogenomic study of Ochnaceae. A, Histogram of the collection years of all Ochnaceae specimens; B & C, Relationship between the year of collection of the specimens and the number of loci recovered for tissue obtained from herbarium material (excluding specimens with silica-dried leaf material), analysed for Ochneae and all the remaining Ochnaceae separately, either using the consensus-alignment (B) or the sample-specific (C) reference-based assembly approach. Pearson correlation coefficients and confidence intervals are given for each group.
Fig. 2 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 2. RAxML trees based on the concatenated nuclear loci of Ochnaceae. A, Early-diverging branches of Ochnaceae and relationships within Quiinoideae based on the FAM dataset; B, Phylogenetic relationships of Sauvagesieae, Luxemburgieae and Testuleeae based on the SLT dataset. Numbers on the branches are bootstrap values>50%. Numbers in parentheses after species names correspond to the specimen IDs (only for species with multiple accessions). The indicated classification of subfamilies and tribes follows Schneider & al. (2014).
Fig. 1 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 1. Overview of the phylogenetic relationships of the major clades of Ochnaceae based on the FAM dataset together with images of representative species. The classification follows Schneider & al. (2014). Ochninae is by far the most species-rich clade comprising about two-thirds of the family's species and six genera (Brack. = Brackenridgea; Cmp. = Campylospermum, clades A and B; I. = Idertia; Ochna; Ouratea; Rh. = Rhabdophyllum). Letters around the tree refer to the photos (mostly flowers except where indicated) and the relative position of the displayed taxa on the tree. A, Medusagyne oppositifolia (Medusagynoideae); B, Froesia venezuelensis (Quiinoideae); C, Luxemburgia schwackeana (Luxemburgieae); D, Rhytidanthera sulcata; E, Cespedesia spathulata; F, Poecilandra retusa; G, Godoya antioquiensis; H, Wallacea insignis; I, Sauvagesia semicylindrifolia; J, Sauvagesia erecta (Sauvagesieae); K, Infructescence of Lophira lanceolata with accrescent sepals (Lophirinae); L, Flower of Elvasia kollmannii (Elvasiinae); M, Perissocarpa umbellifera; N, Fruiting Rhabdophyllum arnoldianum; O, Brackenridgea nitida; P, Campylospermum glaberrimum; Q, Ochna serrulata; R, Fruit of Ochna integerrima with drupelets sitting on enlarged receptacle; S, Fruit of Ouratea sp. with enlarged red receptable; T, Ouratea sp. — Photos: A, K & N from www.africanplants.senckenberg.de (Dressler & al., 2014–); B by Julio Schneider; C by William Milliken/ Royal Botanic Gardens, Kew; D by Sandra Reinales; E by Reinaldo Aguilar; F, H & M by Francisco Farroñay; G by John Clark; I, J, S & T by Domingos Cardoso; L by Claudio Nicoletti de Fraga; O by John Elliott; P by Warran McCleland; Q by Marja Broersma; R by Pierre Grard.
Data and scripts used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Images, script and data used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Molecular assays of pollen use consistently reflect pollinator visitation patterns in a system of flowering plants
<p>Determining how pollinators visit plants versus how they carry and transfer pollen is an ongoing project in pollination ecology. The current tools for identifying the pollens that bees carry have different strengths and weaknesses when used for ecological inference. In this study we use three methods to better understand a system of congeneric, co-flowering plants in the genus <i>Clarkia </i>and their bee pollinators: observations of plant-pollinator contact in the field, and two different molecular methods to estimate the relative abundance of each <i>Clarkia </i>pollen in samples collected from pollinators. We use these methods to investigate if observations of plant-pollinator contact in the field correspond to the pollen bees carry; if individual bees carry <i>Clarkia </i>pollens in predictable ways, based on previous knowledge of their foraging behaviors; and how the three approaches differ for understanding plant-pollinator interactions. We find that observations of plant-pollinator contact are generally predictive of the pollens that bees carry while foraging, and network topologies using the three different methods are statistically indistinguishable from each other. Results from molecular pollen analysis also show that while bees can carry multiple species of <i>Clarkia </i>at the same time, they often carry one species of pollen. Our work contributes to the growing body of literature aimed at resolving how pollinators use floral resources. We suggest our novel relative amplicon quantification method as another tool in the developing molecular ecology and pollination biology toolbox.</p>
Figs 35–41. Doubledaya sinuata Zia, 1934 in Morphology of adult and immature stages, and host plant use of Doubledaya sinuata (Coleoptera: Erotylidae: Languriinae)
Figs 35–41. Doubledaya sinuata Zia, 1934, adult male and female (35), adult female (36–37) and oviposition hole (38–39) on recently dead Sinobambusa sat bamboo culm and larva inside S. sat internode cavity (40–41). 35 – mating; 36 – making an oviposition hole; 37 – inserting the ovipositor into an oviposition hole; 38 – outer opening; 39 – inner opening; 40 – young instar larva; 41 – mature larva. Arrows indicate pith tissue. Scale bars = 1.0 mm (38–39), 5.0 mm (40–41).
Figs 1–18. Doubledaya sinuata Zia, 1934 in Morphology of adult and immature stages, and host plant use of Doubledaya sinuata (Coleoptera: Erotylidae: Languriinae)
Figs 1–18. Doubledaya sinuata Zia, 1934, adult female (1–12) and male (13–18). 2, 14 – head, dorsal view; 3 – right antenna, dorsal view; 4, 15 – pronotum; 5 – prosternal process; 6 – anterior part of elytron with scutellum; 7 – apices of elytron; 8 – abdominal segment VII, ventral view; 9 – abdominal segment VIII, dorsal view; 10 – apex of ovipositor, left lateral view; 11 – apex of ovipositor, ventral view; 12 – spermatheca; 16 – tegmen, ventral view; 17–18 – median lobe, dorsal (17) and ventral (18) views. An arrow indicates gonostylus. Scale bars = 2.0 mm (1, 13), 1.0 mm (2–7, 14–18) 0.5 mm (8–11), 0.1 mm (12).
Figs 19–34. Doubledaya sinuata Zia, 1934 in Morphology of adult and immature stages, and host plant use of Doubledaya sinuata (Coleoptera: Erotylidae: Languriinae)
Figs 19–34. Doubledaya sinuata Zia, 1934, larva (19–28) and pupa (29–34). 19–20 – larva, dorsal (19) and left lateral (20) views; 21 – head; 22 – right antenna, dorsal view; 23 – left mandible, ventral view; 24–25 – left maxilla, dorsal (24) and ventral (25) views; 26 – labium, ventral view; 27 – right front leg, anterior view; 28 – spiracle of abdominal segment I; 29–31 – pupa, ventral (29), left lateral (30) and dorsal (31) views; 32 – spines on abdominal tergite VII, posterior view; 33 – spines on abdominal sternite VII, posterior view; 34 – urogomphi, dorsal view. Scale bars = 5.0 mm (19–20, 29–31), 0.5 mm (21, 27), 0.2 mm (22–26, 32–34), 0.1 mm (28).
Fig. 2 in A Camera Trapping Inventory For Mammals In A Mixed Use Planted Forest In Sarawak
Fig. 2. Some of the photographs taken by camera during the study period showing how animals respond to a lure. A, Helarctos malayanus, Malayan Sun Bear; B, Sus barbatus, Bearded Pig; C, Viverra tangalunga, Malay Civet / Tangalung.
Fig. 1 in A Camera Trapping Inventory For Mammals In A Mixed Use Planted Forest In Sarawak
Fig. 1. Location of Planted Forest Zone within Bintulu Division in Sarawak, Malaysia. Planted Forest Zone is indicated in dark striped areas in center of map.
Public resources about PHYVV and TMV detection in Jalapeño pepper plants using CNNs
<p>This repository contains a dataset of Jalapeño pepper leaves which where divided into three classes labeles as "healthy", "PHYVV-infected" and "TMV-infected. In addition, the folder labeled as model contains all the information of the artificial intelligence model that was utilized to classify between the aforementioned classes.</p>
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.