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2,967 results for “secondary”
Fig. 1 in Agrilozodes suarezi (Coleoptera: Buprestidae) as secondary colonizer of a Sclerolobium sp. branch girdled by Oncideres saga (Coleoptera: Cerambycidae)
Fig. 1. Sclerolobium sp. branch girdled by the twig girdler (A), the twig girdler beetle Oncideres saga (Coleoptera: Cerambycidae) (B), and secondary borer beetles, Tropibidion signatum signatum (Coleoptera: Cerambycidae) (C), Agrilozodes suarezi (D), and Agrilus sp. 1 (Coleoptera: Buprestidae) (E), emerged from this branch.
Secondary Data for: Enhanced Modeling of Back-Mixing in Chemical Reactor Networks
<p>Secondary data for the results presented in the preprint "Enhanced Modeling of Back-Mixing in Chemical Reactor Networks" by L. Gossel, M. Fricke and D. Bothe (2023). </p> <p>https://arxiv.org/abs/2305.11591</p> <p>Tables containing the secondary data of the results presented in Figure 5, a-d are provided. </p> <p>The used code is confidential and thus not included in the repository. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols
<p><span>This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA</span><span>’</span><span>s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.</span></p> <p><span> </span></p> <p><span>The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:</span></p> <p><span> </span></p> <p><strong><span>Model code.zip:</span></strong><span> Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.</span></p> <p><span> </span></p> <p><strong><span>PD_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E20C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions and climate conditions; </span></p> <p><strong><span>20L20E20C</span></strong><span> represents simulations with present-day land use, emissions, and climate conditions.</span></p> <p><span>Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (<strong>DBF2CRO</strong>), evergreen broadleaf forest to cropland (<strong>EBF2CRO</strong>), evergreen needleleaf forest to cropland (<strong>ENF2CRO</strong>), grassland to cropland (<strong>GRA2CRO</strong>), shrubland to cropland (<strong>SHR2CRO</strong>), deciduous broadleaf forest to grassland (<strong>DBF2GRA</strong>), evergreen broadleaf forest to grassland (<strong>EBF2GRA</strong>), and evergreen needleleaf forest to grassland (<strong>ENF2GRA</strong>).</span></p> <p><span>Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>N, <strong>LLAT</strong>), mid-latitude (50</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>S and 20</span><span>°</span><span>N</span><span>–</span><span>50</span><span>°</span><span>N, <strong>MLAT</strong>), and high-latitude (south of 50</span><span>°</span><span>S and north of 50</span><span>°</span><span>N, <strong>HLAT</strong>) regions.</span></p> <p><span> </span></p> <p><strong><span>FU_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E21C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>18L21E21C</span></strong><span> represents simulations with pre-industrial land use, future emissions, and future climate conditions;</span></p> <p><strong><span>20L20E21C</span></strong><span> represents simulations with present-day land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>20L21E21C</span></strong><span> represents simulations with present-day land use, future emissions, and future climate conditions.</span></p>
Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine"
<p>raw LC-MS/MS data (Bruker Daltonics - timsTOF Pro and tims TOF SCP) and ESI-MS data (Bruker Daltonics - 15T ESI/MALDI FT-ICR MS) and related MASCOT generic files (<em>.mgf) and search results (</em>.csv) plus corresponding custom databases (*.fasta). Protein identifications were performed using MASCOT or PEAKS programs.</p> <p>Data were used to:</p> <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP, ProAlanase or Neprosin. Target samples were: protein mixture (BSA, cytC, bCA2, myoglobin, 14-3-3) or human serum from a healthy donor or HEK cell lysate or human insulin or oxidized bovine insulin beta chain</li> <li>profile protein content of Clarity Ferm AnPEP and quantify the proteins</li> </ul> <p>Additional data are under Zenodo entry 10.5281/zenodo.13985598</p> <p>Linked to the publication: <br>Postproline Cleaving Enzymes also Show Specificity to Reduced Cysteine. <br>Kalaninová Z, Portašiková JM, Jirečková B, Polák M, Nováková J, Kavan D, Novák P, Man P. <br>Anal. Chem. 2024, 96, 48, 19084–19092 <br>doi: 10.1021/acs.analchem.4c04277. </p> <p> </p>
Linked collectors and determiners for: Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Otonga.
Natural history specimen data linked to collectors and determiners held within, "Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Otonga". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/08a79618-374c-4014-9e71-3194ef3cf69a">https://bionomia.net/dataset/08a79618-374c-4014-9e71-3194ef3cf69a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/08a79618-374c-4014-9e71-3194ef3cf69a">https://gbif.org/dataset/08a79618-374c-4014-9e71-3194ef3cf69a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa.
Natural history specimen data linked to collectors and determiners held within, "Tropical epiphyte diversity under human impact ? Comparing primary forests, secondary forests, and forest fragments in Ecuador - Bilsa". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://bionomia.net/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0">https://gbif.org/dataset/42962fd5-34ee-4666-abdb-04e8ba4d23b0</a>. Formatted as a Frictionless Data package.
Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine" Part2
<p>raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS<em> search results (*</em>.csv) plus corresponding custom database (*.fasta). Protein identifications were performed using PEAKS programs.</p> <p>Data were used to:</p> <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.</li> </ul> <p>These data are related to https://doi.org/10.5281/zenodo.13938580.</p>
Integrated Omics-Based Discovery of Novel Genes, Secondary Metabolites Clusters, and Small Molecules in Penicillium spp. with Disparate Fungal Isolates
<p><em><span>Penicillium expansum</span></em><span> is a ubiquitous postharvest pathogen of pome fruit that causes blue mold decay of apple fruit while another member of the genus, <em>P. chrysogenum</em><span>,</span><em> </em>is a well-studied saprophyte used for antibiotic and small molecule production. While these two fungi have been investigated individually, the recent discovery of <em>P. chrysogenum </em>hindering <em>P. expansum</em> apple fruit infection has not been well studied. To shed light on this interaction between the two species, we conducted a comparative transcriptomic, metabolomic, and genomic study. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for the <em>P. chrysogenum </em>isolates, and different between <em>P. expansum </em>isolates. Further, the two <em>P. chrysogenum</em> genomes revealed secondary metabolite gene clusters that differed from <em>P. expansum</em>. This included the absence of an intact patulin gene cluster in <em>P. chrysogenum</em>, which corroborates the metabolomic data regarding the species’ inability to produce patulin. Additionally, <em>P. expansum </em>virulence gene homologues were identified in <em>P. chrysogenum </em>and were similarly transcriptionally regulated <em>in vitro</em>. Molecules with potential antimicrobial activity, and phytohormones like indole-3-acetic acid (IAA), were detected for the first time in <em>P. expansum</em> while pharmacological compounds like the well-studied antibiotic penicillin G were identified in <em>P. chrysogenum</em> culture supernatants. Our findings provide new omics-based resources that enable the study of small molecule production of interest, the potential of <em>Penicillium</em>-derived antimicrobials for postharvest decay control, and <em>P.</em> <em>expansum’s</em> metabolites roles in host-pathogen interactions. </span></p>
Figure 2 in Parallelism in secondary loss of sex from a heterogonic life cycle on different host plants in the Andricus mukaigawae complex (Hymenoptera: Cynipidae), with taxonomic notes
Figure 2. Representative karyotype of Andricus targionii (s. lat.) on Quercus dentata, Kitami. Scale bar: 10 mm.
Figure 1 in Parallelism in secondary loss of sex from a heterogonic life cycle on different host plants in the Andricus mukaigawae complex (Hymenoptera: Cynipidae), with taxonomic notes
Figure 1. Andricus targionii (s. lat.) populations on Quercus dentata used for the present chromosome study. 1, Kitami; 2, Minami-chitose; 3, Aomori; 4, Mt Haruna; 5, Lake Yamanaka; 6, Lake Shirakaba.
Dataset related to article "ANTIHYPERTENSIVE DRUGS FOR SECONDARY PREVENTION AFTER ISCHEMIC STROKE OR TRANSIENT ISCHEMIC ATTACK"
<p>we performed a systematic review and meta-analysis in order to summarize the current evidence on blood pressure (BP)-lowering drugs for secondary prevention in patients with ischemic stroke or transient ischemic attack. We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials up to January 31, 2020. We included randomized controlled trials comparing any specific BP-lowering drug, as monotherapy or combination, with either a control or another BP-lowering drug. Eight studies that enrolled 33 774 patients with ischemic stroke or transient ischemic attack were included in the meta-analysis. Primary outcomes were all-cause mortality and the proportion of patients who developed a stroke following BP-lowering drug use, irrespective of its nature (ischemic or hemorrhagic) and severity. Secondary outcomes included the proportion of patients who developed an ischemic stroke; an ischemic stroke or TIA irrespective of severity; a hemorrhagic stroke, defined as an acute extravasation of blood into and around the brain parenchyma (subdural hematoma and epidural hematoma were excluded); a cardiovascular event defined as any sudden death, fatal or nonfatal acute coronary syndrome, stroke, intracranial hemorrhage, or pulmonary embolism; a fatal cardiovascular event defined as any death due to any vascular cause, including unexplained sudden death; and serious adverse events of hypotension, syncope, injurious falls, electrolyte abnormalities, bradycardia, or acute renal failure. We recorded the outcomes at the longest available follow-up for all analyses. We considered the following potential sources of heterogeneity (effect modifiers): inclusion limited to hypertensive patients (normotensive and hypertensive patients versus hypertensive patients only) or noncardioembolic ischemic strokes (all ischemic strokes versus noncardioembolic ischemic strokes only), time from the index ischemic event to randomization (acute patients treated within the first week versus stabilized patients treated after the first week), and trial risk of bias.</p>
Figs. 6–10. 6 in Nomenclatural changes removing secondary homonymy in the genus Oxytelus (Coleoptera: Staphylinidae: Oxytelinae)
Figs. 6–10. 6 – syntype of Anisopsis guineensis Bernhauer, 1932, female, head and pronotum (scale = 0.5 mm). 7 – holotype of Paranisopsis congoensis Fagel, 1960, male, head and pronotum (scale = 0.5 mm). 8–10 – holotype of Anisopsis minima Bernhauer, 1927, female: 8 – head (scale = 0.5 mm); 9 – pronotum (scale = 0.5 mm); 10 – elytra (scale = 0.5 mm).
Figs 1–5. 1–2 in Nomenclatural changes removing secondary homonymy in the genus Oxytelus (Coleoptera: Staphylinidae: Oxytelinae)
Figs 1–5. 1–2 – Oxytelus leleupi (Fagel, 1960), male: 1 – head and pronotum (scale = 0.2 mm); 2 – sternites VII and VIII (scale = 0.1 mm). 3 – O. luteus (Fagel, 1960), male, head and pronotum (scale = 0.2 mm). 4–5 – syntype of Anisopsis guineensis Bernhauer, 1932, female: 4 – head and pronotum (scale = 0.5 mm); 5 – antenna (scale = 0.3 mm).
Fig. 1 in Secondary removal of seeds dispersed by gibbons (Hylobates lar) in a tropical dry forest in Thailand
Fig. 1. Distribution of experimental sites where seeds were dispersed by 4 groups of white-handed gibbons (Hylobates lar). Home range maps of the gibbons are based on Light (2016) and Phiphatsuwannachai et al. (2018), plus newly-discovered areas (extended home ranges) by the author. Fruiting trees and gibbon defecation locations were recorded in a GPS. Each site when active contained a camera trap and a paired control/treatment.
Fig 2 in Secondary removal of seeds dispersed by gibbons (Hylobates lar) in a tropical dry forest in Thailand
Fig 2. Estimates of beta-coefficients from binomial regressions with parameter estimates derived from model averaging with 95% confidence intervals. A variable is considered significant if the confidence interval does not overlap zero.
Fig. 13. Secondary growth bordering a in The Mammals Of Paracou, French Guiana: A Neotropical Lowland Rainforest Fauna Part 2. Nonvolant Species
Fig. 13. Secondary growth bordering a narrow dirt road through our study area at Paracou. Our only specimen of Gracilinanus emiliae was taken in this habitat, but primary forest occurs only a few meters away and it is not known which successional stage is typical of this rarely collected species. More commonly encountered inhabitants of secondary growth at Paracou include Didelphis marsupialis, Marmosa murina, Micoureus demerarae, Philander opossum, Dasypus novemcinctus, Saguinas midas, Potos flavus, Neacomys paracou, Oligoryzomys fulvescens, Oryzomys megacephalus, and Proechimys cuvieri.
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.) in Cartilago Teniformis And Its Derivatives: Additional Information On The Basic Composition And Evolution Of The Skull
Text-fig. 9. Tachyglossus aculeatus. Transverse section through the orbital region of the head of a juvenile specimen. ms: sphenobturatory membrane; plb: palatine bone; psw: primary endocranial sidewall; sll: secondary lateral lamella of Kuhn (1971); this endocranial process (blue) is here interpreted as a derivative of the cartilago teniformis. (From Kuhn and Zeller 1987.)
Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
<p>A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.</p>
Dataset with survey answers about enginering studies opinion in each kind of high school Spanish studies (Compulsory Secondary Education, Vocational Education and Upper Secondary Education)
<p>The first line includes each question and the rest of the tuples include one answer per each filled survey. Depending on the type of high school studies, you find one different survey, because the questions are adapted to each particular high school education.</p> <p> </p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
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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.