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33,754 results for “Enhancers”
Dataset: Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra
<p>Dataset accompanying publication:</p> <p>Rafał Białek, Thomas Vasileiadis, Mikołaj Pochylski, Bartłomiej Graczykowski, Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra, Photoacoustics, Volume 30, 2023, 100478, ISSN 2213-5979, https://doi.org/10.1016/j.pacs.2023.100478.</p>
Data for: Thermoregulation enhances survival but not reproduction in a plant-feeding insect
<p>Temperature influences nearly all aspects of fitness. However, reproduction is often more thermally sensitive than survival. Thermoregulation must maintain performance in both components of fitness to buffer populations from environmental change. We assessed the fitness benefits of thermoregulation in <em>Enchenopa binotata</em> treehoppers. Under realistic mesocosm conditions, we quantified fine-scale microclimates using 3D-printed operative temperature models. We then compared operative temperatures to treehopper body temperatures and translated patterns of thermoregulation into variation in survival and reproduction. We also assessed two thermoregulatory mechanisms: precise microclimate choice and heat escape behaviors. Finally, we applied our results to evaluate if arthropod thermoregulation is accurately characterized by two theoretical models commonly used to simulate responses to environmental change. We found substantial thermal variation at fine spatial scales relevant to insects: at a single point in time, temperatures within 30cm-tall plants spanned ranges up to 19ºC (23-42ºC). Lethal operative temperatures were common when air temperatures were high. However, heat escapes allowed treehoppers to almost entirely avoid lethal temperatures. By contrast, individuals thermo-conformed in the absence of lethal operative temperatures. This finding suggests that precise microclimate choice imposes high costs due to thermal uncertainty at fine spatial scales. Furthermore, given the narrow range of temperatures in which reproduction occurs, thermoregulation is unlikely to maintain reproduction. Thermoregulation was most effective in the lowest-quality and most spatially variable thermal habitats. Treehopper thermoregulation therefore more closely follows cost-benefit models of thermoregulation compared to models that account for inhibited movement at extreme temperatures. Overall, even if thermoregulation can prevent lethal heat stress, thermoregulation may have limited capacity to buffer arthropods and other small ectotherms from environmental change if it cannot maintain reproductive performance.<strong> </strong></p>
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
T A B L E 1 in Fruit production in coffee (Coffea arabica L.) crops is enhanced by the behaviour of wild bees (Hymenoptera: Apidae)
T A B L E 1 Relationship of management type (conventional and agroecological) on the diversity of floral visitors (abundance and Chao-1) and the most abundant floral visitors. Values in bold are those that were found to be statistically significant.
F I G U R E 1 in Fruit production in coffee (Coffea arabica L.) crops is enhanced by the behaviour of wild bees (Hymenoptera: Apidae)
F I G U R E 1 Location of all study sites. The map shows location of the study sites in the central and south-western regions of Guatemala, where pollination experiments and pollinator observations were performed. Blue crosses represent conventional sites, and lilac crosses represent agroecological sites.
F I G U R E 3 in Fruit production in coffee (Coffea arabica L.) crops is enhanced by the behaviour of wild bees (Hymenoptera: Apidae)
F I G U R E 3 Relationship between the weight of fruits and (a) the percentage of Apis mellifera that carried pollen (PolTran) on their legs/bodies. Relationship between fruit set and (b) nectary, the percentage of P. bilineata observed touching the nectary of the coffee flowers, and (c) the average number of flowers visited by P. bilineata. Lilac colour is used for agroecological sites 'a', and blue is used for conventional sites 'c'. Shaded lines indicate 95% confidence interval.
FIGURE 2 in Comparative performance of a multi-locus barcoding approach to enhance taxonomic resolution of New Zealand mosquitoes (Diptera: Culicidae)
FIGURE 2 The statistical parsimony network showing the genetic relationships between Culex asteliae (n = 3), Culex pervigilans (n = 15) and Culex rotoruae (n = 3) based on their (a) COI sequences and (b) ITS2 sequences derived from the current study. Each COI/ITS2 singleton sequence is represented by one circle with size proportional to their frequency. The colours refer to the mosquito species to which each individual belongs. Small white circles connecting coloured circles indicate 'missing' or hypothetical singletons. The line linked two circles indicates one basepair difference on the sequence.
FIGURE 1 in Comparative performance of a multi-locus barcoding approach to enhance taxonomic resolution of New Zealand mosquitoes (Diptera: Culicidae)
FIGURE 1 The phylogenetic relationships of New Zealand endemic and exotic mosquito species based on (a) COI and (b) ITS2 sequences using maximum likelihood method. Anopheles annulipes was outgroup taxon for both trees. Only bootstrap support values greater than 50% is present at branches on the tree. The sequences collected for the current study and derived from New Zealand endemic, introduced or recently eradicated species are highlighted in bold. The specimens collected outside of New Zealand were highlighted by underlines. The complete phylogenetic trees of New Zealand endemic and exotic mosquito species based on COI and ITS2 sequences are available in Figure S3.
F I G U R E 2 in Fruit production in coffee (Coffea arabica L.) crops is enhanced by the behaviour of wild bees (Hymenoptera: Apidae)
F I G U R E 2 Distribution under conventional (blue) and agroecological (lilac) management of (a) abundance of floral visitors, (b) abundance of A. mellifera and (c) abundance of P. bilineata. The differences between variables considering management were analysed with paired -F-test. *p <0.05.
Dataset related to article "Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study"
<p><span>The dataset provided here contains the raw data used in the study discussed in this article. The primary objective of the study was to conduct a comparative analysis of the performance of dual energy subtraction (DES) images acquired using contrast-enhanced mammography (CEM) systems produced by three different manufacturers. The comparison, facilitated by a CEM-specific phantom, focused on the assessment of radiation dose and image quality.</span></p> <p><span>Composed of three separate CSV files, the dataset is structured as follows:</span></p> <p><span>1) "Dose-related data": This file provides exposure parameters (including A/F combination, tube voltage and exposure) and the corresponding mean glandular dose (MGD for low-energy (LE) and high-energy (HE) images. Data are given for each CEM system and automatic exposure mode (AEC).</span></p> <p><span>2) "CNR-related data": Encapsulated in this file are data extracted from the phantom DES images. These include measurements of the mean pixel value (MPV) for each iodinated contrast detail, as well as the MPV and standard deviation (SD) for the surrounding background. These data were used to calculate the contrast-to-noise ratio (CNR) for the iodinated contrast details.</span></p> <p><span>3) “Residual CNR data”: This file includes MPV and SD data extracted from phantom DES images, which were useful for calculating the residual CNR after cancellation of the normal background tissue by the DES algorithm.</span></p>
Supplementary data for the paper: "Enhancing concrete durability in chloride-rich environments through manual application of healing agents"
<p>Supplementary data for the paper: “Enhancing concrete durability in chloride-rich environments through manual application of healing agents”.<br><br>Open data concerning experimental work. The paperinvestigates the use of three potential healing agents: water-repellent agent (WRA), sodium silicate (SS), and polyurethane (PU). The agents are tested separately under two conditions: exposure to a 3.3% NaCl concentration at 20°C and exposure to cyclic freeze-thaw conditions. The agents are manually injected into the cracks and evaluated for their healing efficiency using the capillary absorption test and microscopy analysis. Additionally, their resistance to freeze-thaw cycles by monitoring mass loss and chloride bulk diffusion is measured using sprayed silver nitrate, titration, and EDX mapping. </p>
Figure 5 in Early development of Gongolaria montagnei (Fucales, Phaeophyta) germlings under laboratory conditions, with a view to enhancing restoration potential in the Eastern Mediterranean
Figure 5: Growth of Gongolaria montagnei embryos and germlings during incubation (31 days). Increase in relative growth rates (RGR) was observed in two phases (red lines), from days 8–15 (0.2 day−1) and days 21–31 (0.1 day−1).
Figure 4 in Early development of Gongolaria montagnei (Fucales, Phaeophyta) germlings under laboratory conditions, with a view to enhancing restoration potential in the Eastern Mediterranean
Figure 4: Divisions and developmental stages of Gongolaria montagnei. (A) Zygote after release from the conceptacle. (B) First division. (C) Second, most common, division. (D) Second, rare, division. (E) Development of the rhizoids (arrow). (F) Elongation of the rhizoids. (G) Fertilization membrane (arrow). (H) Hyaline apical hairs. (I) Germling after five weeks of culture. Scale bar = 100 μm.
Figure 2 in Early development of Gongolaria montagnei (Fucales, Phaeophyta) germlings under laboratory conditions, with a view to enhancing restoration potential in the Eastern Mediterranean
Figure 2: Thallus of Gongolaria montagnei during the reproductive period (A, scale bar = 3 cm), mature apical fronds (B, scale bar = 3 cm) and receptacles (C, scale bar = 2 mm).
Figure 1 in Early development of Gongolaria montagnei (Fucales, Phaeophyta) germlings under laboratory conditions, with a view to enhancing restoration potential in the Eastern Mediterranean
Figure 1: Map showing locations (marked with symbols and) where the reproductive phenology of Gongolaria montagnei was followed in sitU from February to May 2021. Mature receptacles were collected at Saronida ().
Figure 3 in Early development of Gongolaria montagnei (Fucales, Phaeophyta) germlings under laboratory conditions, with a view to enhancing restoration potential in the Eastern Mediterranean
Figure 3: Cross section of a receptacle of Gongolaria montagnei, showing antheridia (A) and oogonia (O). Scale bar = 100 μm.
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop.
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
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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.