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1,065
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1,065 results for “1 year”
Association of Presurgical Circulating MicroRNAs with 1-Year Postsurgical Pain Reduction in Lumbar Spinal Stenosis Patients with Spine Facet Osteoarthritis
GEO Series GSE158825. Homo sapiens. 60 samples. Type: Non-coding RNA profiling by high throughput sequencing.
X-linked recessive TLR7 deficiency in 1% of men under 60 years with life-threatening COVID-19
GEO Series GSE181787. Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing.
Creld1 deficient CD4+ T cells from 1-year-old animals under homeostatic
GEO Series GSE153210. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Independent validation of circulating microRNAs as biomarkers in a case-control study of adolescents with type 1 diabetes for more than 8 years
GEO Series GSE315292. Homo sapiens. 48 samples. Type: Expression profiling by RT-PCR.
Gene expression analysis of Influenza vaccine response in Young and Old - Year 1 - B and T cells
GEO Series GSE65440. Homo sapiens. 215 samples. Type: Expression profiling by array.
Long-Term TIL Engraftment and Clonal Expansion After Multiple TIL and Anti-PD-1 Therapy: A Five-Year Immunogenomic Case Study
GEO Series GSE295713. Homo sapiens. 25 samples. Type: Other.
Vascular and renal response to neonatal hyperoxia in 1-year old rats
GEO Series GSE234635. Rattus norvegicus. 16 samples. Type: Expression profiling by high throughput sequencing.
Real-time quantitative PCR analysis of primary human dermal fibroblasts_comparing two young samples (fibroblasts isolated from a 5 month old vs. those from a 1 year old)
GEO Series GSE100409. Homo sapiens. 2 samples. Type: Expression profiling by RT-PCR.
APPENDIX 1 in 1802 - 2018: 220 ans d'histoire des périodiques au Muséum 1802 - 2018: a 220 - year history of the Muséum periodicals
APPENDIX 1. — Suite/Continuation.
APPENDIX 1 in 1802 - 2018: 220 ans d'histoire des périodiques au Muséum 1802 - 2018: a 220 - year history of the Muséum periodicals
APPENDIX 1. — Suite/Continuation.
Figure 1 from: Gogoi J, Rana TS (2020) The rediscovery of Uraria lacei Craib (Leguminosae) after 67 years from India. PhytoKeys 160: 99-107. https://doi.org/10.3897/phytokeys.160.54237
Figure 1 Map of Manipur showing the locality where Uraria lacei was rediscovered.
Figure 8 from: Vahtera V, Stoev P, Akkari N (2020) Five million years in the darkness: A new troglomorphic species of Cryptops Leach, 1814 (Chilopoda, Scolopendromorpha) from Movile Cave, Romania. ZooKeys 1004: 1-26. https://doi.org/10.3897/zookeys.1004.58537
Figure 8 Likelihood tree with bootstrap values > 50% shown for each node.
Figure 2 from: Nedeva I, Vodenicharov V, Karamfilova V, Assyov Y (2024) A 36-year-old woman with a parathyroid cyst. Pharmacia 71: 1-3. https://doi.org/10.3897/pharmacia.71.e115504
Figure 2 Ultrasound image of the formation 1 month after sclerotherapy.
Figure 1 from: Nedeva I, Vodenicharov V, Karamfilova V, Assyov Y (2024) A 36-year-old woman with a parathyroid cyst. Pharmacia 71: 1-3. https://doi.org/10.3897/pharmacia.71.e115504
Figure 1 Ultrasound image of the formation under the left lobe of the thyroid gland.
Increasing Contribution of Adolescent Type 1 Diabetes Drives Incidence Rates in Poland - a 40-year-long Observational Study
<div> <div> <p><span><span>Aims/hypothesis</span></span><span><span>: </span><span>40-year-long longitudinal observation</span> <span>of long-term trends of type 1 diabetes</span><span> incidence</span><span> and prevalence</span> <span>in children </span><span>in Central P</span><span>o</span><span>land </span></span><span> </span></p> </div> <div> <p><span><span>Methods</span></span><span><span>: This was a prospective observational study performed </span><span>by </span><span>a reference </span><span>regional </span><span>center</span><span> for pediatric diabetes care for Lodz Province (currently 2</span><span>·</span><span>4M </span><span>inhabitants</span><span>, 360K children). </span><span>We </span><span>registered </span><span>e</span><span>ach case of new-onset </span><span>type 1 </span><span>diabetes </span><span>admitted</span> <span>to regional pediatric diabetes centers </span><span>between </span><span>the </span><span>years 1983 and 2022 in children between 0 and 14 </span><span>y.o.</span> <span>The diagnosis</span><span> was based on </span><span>currently </span><span>available guidelines</span><span>.</span> <span>C</span><span>ases of other types of diabetes (e.g.</span><span>,</span><span> monogenic) were excluded</span><span> upon identification from incidence and prevalence rates</span><span>. Yearly data on the at</span><span>-</span><span>risk</span> <span>population were </span><span>acquired</span><span> from Poland`s General Statistical Office. Sex-specific data </span><span>on population structure </span><span>were available from 1989 onwards.</span><span> </span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: In the </span><span>analyzed</span> <span>period, </span><span>the </span><span>incidence rate</span><span> of type 1 diabetes</span><span> increased </span><span>tenfold </span><span>from 3</span><span>·</span><span>29/100</span><span>,</span><span>000 (95%CI: </span><span>1</span><span>·</span><span>85</span><span>-</span><span>4</span><span>·</span><span>73</span><span>) in 1983 to 3</span><span>2.43</span><span> (2</span><span>6</span><span>·</span><span>42-38</span><span>·</span><span>44</span><span>) in 2022, with </span><span>an </span><span>average annual percentage change of 5</span><span>·</span><span>73</span><span>% (95%CI: 4</span><span>·</span><span>9</span><span>9</span><span>%-6</span><span>·</span><span>44</span><span>%). Joinpoint analysis detected two distinct periods of increase</span><span>:</span> <span>rapid in 1983-200</span><span>5</span><span> (annual percentage increase </span><span>of </span><span>7</span><span>·</span><span>38</span><span>%</span><span>, 95%CI: </span><span>6</span><span>·</span><span>30</span><span>-1</span><span>0</span><span>·</span><span>52</span><span>%</span><span>) and </span><span>a </span><span>slower </span><span>one</span><span> in </span><span>200</span><span>5</span><span>-2022 (3</span><span>·</span><span>65</span><span>%</span><span>, 95%CI: -0</span><span>·</span><span>86</span><span>-</span><span>5</span><span>·</span><span>13</span><span>%</span><span>). </span><span>I</span><span>ncidence rates among the youngest children (0-4</span> <span>y.o.</span><span>) were significantly lower than in 5-9</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>5</span><span>67</span><span>±</span><span>0</span><span>·</span><span>059</span><span>, p<0</span><span>·</span><span>0001</span><span>)</span><span> and 10-14</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>520</span><span>±</span><span>0</span><span>·</span><span>0</span><span>60</span><span>, p<0</span><span>·</span><span>0001)</span><span>. </span><span>The </span><span>incidence </span><span>growth dynamic </span><span>for</span> <span>the two older groups showed </span><span>a </span><span>consistent </span><span>increase</span><span>, </span><span>whereas</span><span> the incidence in </span><span>0-4 year-olds</span><span> plateaued after 2007.</span> <span>Incidence rates varied </span><span>seasonally,</span><span> with the </span><span>most cases diagnosed </span><span>during the </span><span>winter months (December, January, </span><span>and </span><span>February</span><span>;</span> <span>mean difference from remaining seasons of 29</span><span>±11</span><span>·</span><span>6 percentage points</span><span>, p</span><span><0</span><span>·</span><span>0001</span><span>). </span><span>Corresponding with increasing incidence rate, estimated prevalence of type 1 diabetes increased over the years and reached </span><span>177</span><span>·</span><span>21</span><span>/100</span><span>,</span><span>000 (95%CI: 163</span><span>·</span><span>18-191</span><span>·</span><span>24)</span><span> for children 0-14 </span><span>y.o.</span><span>,</span><span> and</span><span> </span> <span>1</span><span>7</span><span>·</span><span>11</span> <span>(95%CI:</span><span> 9</span><span>·</span><span>2</span><span>-</span><span>2</span><span>5</span><span>·</span><span>02</span><span>),</span><span> 1</span><span>90</span><span>·</span><span>54</span><span> (95%CI:</span> <span>16</span><span>5</span><span>·</span><span>03</span><span>-</span><span>21</span><span>5</span><span>·</span><span>75</span><span>),</span><span> 2</span><span>38</span><span>·</span><span>73</span><span> (95%CI: </span><span>21</span><span>1</span><span>·</span><span>7</span><span>-26</span><span>5</span><span>·</span><span>76</span><span>)</span><span> for 0-4</span><span>,</span><span> 5-9</span><span>, </span><span>and 10-14</span> <span>y.o.</span><span>, respectively.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusion/i</span><span>nterpretation</span></span><span><span>: </span><span>Over the past 40 years, the incidence of </span><span>type 1 diabetes</span><span> in </span><span>children in </span><span>Central Poland </span><span>has increased significantly, but the rate of increase </span><span>appears to be</span><span> slowing.</span><span> As</span> <span>majority</span><span> of patients with type 1 diabetes are 10 years old or older, with the </span><span>most new</span><span> cases occurring in that age group</span><span> the healthcare systems should prepare for care of young adults who are extensive users of new diabetes technologies</span><span>.</span></span><span> </span></p> </div> </div>
Sample Weather Observation Website Data (6 Sites, 1 Year each)
<p><strong>Contains public sector information licensed under the Open Government Licence v3.0</strong></p> <p>An example of data that can be downloaded from <a href="https://wow.metoffice.gov.uk/">WOW</a>, used to demonstrate data surrounding the Met Office in a <a href="https://github.com/informatics-lab/spaceapps-2022-mo-bootcamp">data tooling and analysis tutorial</a>. File names are adapted from the source by giving the WOW site id and the year of the downloaded data.</p>
Mistletoe chronobiology: 27 years of daily metabolomic fingerprinting (dataset 1)
<p>Chronobiological aspects of mistletoe have been studied for decades <span>(Fyfe, 1969, Scheer et al., 1992, Flückiger and Baumgartner, 2002, Dorka et al., 2007, Urech et al., 2009, Derbidge et al., 2013, Derbidge et al., 2016)</span>.<span> One of the pioneers of such studies was Agnes Fyfe, a researcher at Hiscia Research Institute (Society for Cancer Research, Arlesheim, Switzerland). Already in the 1950s, with the aim of identifying optimal harvesting times, she started to build up a dataset of chromatograms using a form of metabolomic fingerprinting based on pattern formation peculiar in terms of ease, rapidity and affordability. She conducted daily chromatograms of <em><span>Viscum album </span></em>ssp. <em><span>album</span></em> L. for nearly 30 years (from November 1958 to October 1985) creating a unique heritage.</span></p> <p><span>In 2020, half of it, namely the “Gold Fyfe dataset”, was digitized. A total of 27 979 chromatograms showing recurring variations of different patterns compose the Gold Fyfe dataset. </span><span>Part of the chromatograms were made with 50% mistletoe extract concentration (n= 19 109; here referred to as "dataset 1"). </span></p> <p><span>The following information was always recorded on each chromatography paper: sample name and concentration, harvest date and a numerical identification code. Additional information can be reported concerning the sample, as extract rising time, humidity and temperature. But also reagent information such as name and concentration, reagent rising time, humidity and temperature. Moreover, additional handwritten notes can appear on the right upper corner of the chromatographic paper.</span></p> <p><span>Chromatograms were scanned in reflective mode using an Epson Perfection V600 scanner (Epson, Kloten, Switzerland). Since the chromatograms are colour pictures,<span> </span>colours were calibrated using: IT 8.7/2 RF target (Wolf Faust, Frankfurt, Germany) and SilverFast Ai Studio 8.8 (LaserSoft Imaging AG, Kiel, Germany) as calibration program. Images were saved in TIFF format, 300 DPI, RGB, 16 cm x 17 cm.</span></p> <p>Each TIFF file was named in the following way YYYYMMDD_50_dx. In particular:</p> <ul> <li>YYYYMMDD: refers to sample harvest date.</li> <li>50: refers to sample extract concentration (50%).</li> <li>dx: refers to duplicate number (from 1 up to 8). Chromatograms are considered duplicates if the harvesting date and hour is the same. The standard harvest time is 8 am and it may/not be specified on the chromatograms. Harvesting time is specified in the TIFF file name only if it is different than the standard (8 am). In that case, the name of the TIFF file is: YYYYMMDD_50/10_HHHH_dx (where HHHH stands for harvesting time).</li> </ul> <p> </p>
Fig 1 from: Kenyeres Z, Takács G, Bauer N (2019) Response of orthopterans to macroclimate changes: A 15-year case study in Central European humid grasslands. Journal of Orthoptera Research 28(2): 187-193. https://doi.org/10.3897/jor.28.34102
Fig 1 Location map of the study area in Hungary.
Figure 2 from: Kress W, Knapp S, Stoev P, Penev L (2012) On the front line of modern data-management and Open Access publishing: Two years of PhytoKeys – the fastest growing journal in plant systematics. PhytoKeys 19: 1-8. https://doi.org/10.3897/phytokeys.19.4501
Figure 2 - Taxonomic distribution by family of the published nomenclatural novelties in PhytoKeys.
Figure 1 from: Stoev P, Erwin T, Georgiev T, Penev L (2011) ZooKeys 150: Three and a half years of innovative publishing and growth. ZooKeys 150: 5-14. https://doi.org/10.3897/zookeys.150.2431
Figure 1 - Total number of published articles and pages on six-month intervals.
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.