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1,751 results for “Future”
Transforming the UK's diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)
<p>This video is the second talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Transforming the UK’s diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)</p> <p>Bio: Dimitris Grammatopoulos, PhD, FRCPath, is Professor of Molecular Medicine at Warwick Medical School and Consultant in Clinical Biochemistry and Molecular Diagnostics at the University Hospitals of Coventry and Warwickshire, NHS Trust, United Kingdom. He also leads the Novel Biomarkers theme of the Institute of Precision Diagnostics and Translational Medicine, Pathology-UHCW NHS Trust. where he combines clinical expertise in diagnostic laboratory medicine with a research track-record in application of cutting edge multidiscipline methodologies in routine clinical diagnostics. He received academic and clinical training in Newcastle, Bristol, Johns Hopkins-Baltimore and Warwick. He has expertise in biochemical/molecular diagnosis of many endocrine and metabolic disorders. His translational research interest is focused on stress hormones and homeostatic adaptations of fetal development to maternal disease as well as development of novel -omics based biomarker approaches suitable for precision medicine and better characterisation of patient phenotype. He has experience around use of AI and ML for development and refinement of clinical and diagnostic pathways for complex chronic conditions that are considered as national priorities. Dimitris is the Lead in Diagnostics, Global Health Priorities in Health, University of Warwick.</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/HiOlRzJPR7Q</p>
Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).
<p>This video is the first talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, 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.</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/I1xsig9C8w0</p>
Convection in future winter storms over Northern Europe - data
<pre><em>We provide the python code to reproduce the figures of the Environmental Research Letter entitled </em><em>"Convection in future winter storms" by S. Berthou et al.</em></pre> <p>Python library requirements:</p> <p>python 3.8.12</p> <p>pandas 1.4.1</p> <p>seaborn 0.11.2</p> <p>matplotlib 3.5.1</p> <p>numpy 1.22.3</p> <p>scipy 1.8.0</p> <p>statsmodel.api 0.13.2</p>
Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia (The genome assembly and annotion fiile)
<p>The genome assembly and annotion fiile used in the manuscript: <strong>Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia</strong></p>
Video material for: "Immersive audio inside the Greek-Roman theatre of Tyndaris: comparison between past, current and future conditions"
<p>The two folders contain 24 spherical videos with 3OA audio used for the subjective listening tests described in the paper:<br> Lavagna L, Shtrepi L, Farina A, Bevilacqua A, Astolfi A. <em>Immersive audio inside the Greek-Roman theatre of Tyndaris: comparison between past, current and future conditions</em> In: Proceedings of the 24th International Congress on Acoustics. Gyeongju; 2022.<br> <br> The listening test, structured as an AB comparison, were presented inside the virtual reality environment using a Head Mounted Display (HMD), specifically a Meta Quest 2, and open headphones (Sennheiser HD 650).<br> <br> </p>
Supplementary material 1 from: Gildenhuys E, Ellis A, Carroll S, Le Roux J (2013) The ecology, biogeography, history and future of two globally important weeds: Cardiospermum halicacabum Linn. and C. grandiflorum Sw. NeoBiota 19: 45-65. https://doi.org/10.3897/neobiota.19.5279
Supporting information for species distribution modelling of Cardiospermum species using native range presences and global pseudo absences. (doi: 10.3897/neobiota.19.5279.app) File format: Micrisoft Word Document (doc).:
Supplementary data: Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments
<p>The files in this record represent supplementary data for the article titled “Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments” in Water Resources Research. The files contain simulations of the HBV rainfall-runoff model for 14 alpine catchments in Switzerland. The model simulated different water balance components (such as runoff, snow water equivalent and evapotranspiration) for the reference period 1980-2009 and the three scenario periods (2020-2049, 2045-2074 and 2070-2099) using the A1B emission scenario.</p>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
FIGURE 2 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 2 Distributions (left), maximum likelihood (ML) phylogenetic trees (middle), principal component analysis (PCA) ordination plots from cranial measurements, photographs or drawings of the baculum and sonograms of echolocation calls (right) of selected groups of paramontane southern African bats having ranges categorized as arid (red symbols), Mediterranean (turquoise symbols), temperate-montane (blue), savanna-montane (orange), and tropical rain forest (green; see Table S1 for classification): horseshoe bats (Rhinolophus) of the R. capensis (a), R. darlingi (b), R. ferrumequinum (c), R. fumigatus (d) groups, wing-gland bats (Family Cistugidae, genus Cistugo (e), and long-eared serotine bats of the genus Laephotis (f)). Distribution maps were based on IUCN Redlist maps (open polygons), correctly identified vouchers from molecular studies (colored symbols; this study; GenBank; Curran et al., 2022; Demos et al., 2019; Dool et al., 2016; Taylor et al., 2018) and skulls measured in this study (crosses). In a few cases (see legends), GBIF records were indicated for the Angolan range of species. Gray shading indicates elevations over 1200 m a.s.l. Phylogenetic trees are shown for sub-clades (i.e., excluding outgroups) of three separate ML analyses undertaken with IQTREE of Rhinolophus, Cistugo, and Laephotis (Figures S2–S4). Values above nodes (in bold) represent median dates obtained for corresponding nodes from separate BEAST analyses in Figures S5–S7 (see text for details). Node support values for ML trees, obtained by the IQTREE program, are given below the nodes for SH-like approximate likelihood ratio tests (SH-aLRT), aBayes posterior probabilities, and ultra-fast bootstrap values (UFBS) respectively (see text for details). Tip labels marked in bold represent new sequences from this study. Underlined tip labels represent two instances of mtDNA introgression where morphologically distinct taxa from different biomes have near-identical cyt-b sequences. Species ranges of echolocation call peak frequencies were obtained from the literature for Rhinolophidae (Adams & Kwiecinski, 2018; Curran et al., 2022; Jacobs et al., 2013; Jacobs et al., 2017; Laverty & Berger, 2020; Monadjem et al., 2020; Mutumi et al., 2016; Odendaal & Jacobs, 2011; Odendaal et al., 2014; Schoeman & Jacobs, 2008), Cistugo (Monadjem et al., 2020; Schoeman & Jacobs, 2003, 2008), and long-eared Laephotis (Adams & Kwiecinski, 2018; Jacobs et al., 2005; Monadjem et al., 2020; Pierce et al., 2011). Bacula photographs and drawings were obtained from this study as well as Benda and Vallo (2012), Taylor et al. (2018), Curran et al. (2022). Abbreviation of South African province names: EC, Eastern Cape; FS, Free State; GP, Gauteng; KZN, KwaZulu-Natal; LP, Limpopo; MP, Mpumalanga; NC, Northern Cape; WC, Western Cape. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 1 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 1 Maps of southern, central, and eastern Africa showing (a) topographical features referred to in this study (see text for details), and (b) the extent of minimum monthly temperatures (bioclim6) <0°C from present and past (last glacial maximum [LGM]) models (from Worldclim; https://www.worldclim.com/; see Methods for more details). Gray or darker shading in both maps indicates mountains>1200 m in elevation. In (a), the acronym HEAN stands for the Highlands and Escarpments of Angola and Namibia (Mendelsohn et al., 2023); SEAMA stands for the South-East African Montane Archipelago (Bayliss et al., 2024); LMEE stands for the Limpopo–Mpumalanga– Eswatini Escarpment (Clark et al., 2022). The map in (b) shows distribution points of horseshoe bats, Rhinolophus (crosses), wing-gland bats, Cistugo (open triangles) and long-eared bats, Laephotis (open squares) based on morphological and molecular results from this study and from published a GenBank cyt-b sequences. In (b), minimum monthly temperatures <0°C indicated for the present (blue) and LGM (red), approximating the extent of frost (and hence temperate grasslands) currently and during the LGM (idea from Brain, 1985). Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 3 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 3 Map of southern, central, and eastern Africa showing major geographic features (as in Figure 1a) but with biogeographical barriers elucidated by this study indicated as red dashed lines, labelled as (i) to (vii) (see Discussion), and taxa specific to different ranges indicated according to the predominant biomes (green = tropical; red = arid, turquoise = Mediterranean, blue = temperate, orange = savanna). Note that only one savanna lineage is here indicated for ease of visualization. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
FIGURE 4 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 4 Maps of south-central Africa showing the distribution of Köppen–Geiger climate zones for the present (a) and projected future (2070) (b), as well as past (last glacial maximum: left panel), present (right panel), and projected future (2070; right panel) Maxent distribution models for five species groups of bats; Rhinolophus capensis group (c–e: green = R. swinnyi; blue = R. rhodesiae; orange = R. simulator; turquoise = R. capensis; red = R. denti); R. darlingi group (f–h: blue = R. cervenyi; orange = R. darlingi; red = R. damarensis), R. ferruquinum group, in part (i–k: blue = R. acrotis), Laephotis spp (l–n: blue = L. cf. botswanae; orange = L. angolensis), Cistugo spp (o–q: blue = C. lesueuri; red = C. seabrae). Details of Maxent models given in text. Ranges of species above indicated by colors corresponding to biomes recognized in this study (Tables S1 and S2) as follows: blue or green = temperate; orange = savanna; turquoise = Mediterranean; red = arid. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
Figure 4 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 4. Agricultural Research Service researchers have focused on understanding how climate change influences weeds/invasive plants and their impacts and management. Image shows a study of how precipitation change influences cheatgrass (Bromus tectorum) invasion in rangelands of northeast Wyoming, USA. (Credit: Anna Kuhne)
Figure 5. A in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 5. A conceptual framework providing context for addressing challenges for managing weeds in agroecosystems and natural areas.
Figure 3. Sorgoleone has a in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 3. Sorgoleone has a potential as a plant-incorporated protectant herbicide. The highly bioactive benzoquinone allelochemical sorgoleone is produced exclusively within root hair cells of members of the genus Sorghum. Genes of the entire sorgoleone pathway are being used to transform crops to produce sorgoloeone (Pan et al. 2021).
Figure 2 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 2. (A) Depiction of a bipartite synthetic Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) cassette (red) consisting of a Cas9 endonuclease and a guide ribonucleic acid RNA (gRNA) that is flanked by homology arms (HA) (black). (B) Following expression of the CRISPR cassette, gRNA binds to Cas9 and directs the complex to a unique sequence-specific site for DNA cleavage and homology-directed repair (HDR). Following HDR, the CRISPR cassette is copied into both genomic regions. (C) Standard Mendelian inheritance results in 50% of progeny inheriting a modified gene. In contrast, a gene drive would bias inheritance, theoretically resulting in all progenies (~99%) inheriting the modified gene, thereby "driving" the modified gene into an invasive weed population.
Figure 1 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 1. (A) BenchBot autonomous high-throughput imaging system, (B) example imagery from BenchBot after automated segmentation of weeds from background objects, (C) testing of the handheld version of the Weeds3D system at the Beltsville Agricultural Research Center, and (D) example 3D reconstruction of plant biomass from the Weeds3D systems.
Fig. 5 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 5: Size-class (S: small, M: medium and L: large) frequency distribution (%) of relevant target fishes in Unprotected (UP) and Future Protected (FP) zones at the three studied locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka), (Number of individuals used to calculate percentages is given in Supp. Mat. 2).
Fig. 3 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 3: Mean density (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
ScienceDex guides
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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.