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Fig. 5 in A List of Pselaphine Species (Insecta, Coleoptera, Staphylinidae) Collected from Mt. Phia Oac, North Vietnam in 2017 with a Corrected Checklist of the Pselaphine Species Known from Vietnam
Fig. 5. SEM photos of Triomicrus spp. A, E. T. vietus Löbl et al. from Mt. Tam Dao, male; B, F. ditto, female; C, G. T. sp. 1 from Mt. Phia Oac, male; D, H. ditto, female. A–D. head and pronotum in dorsal view; E–H. apical segments of antenna.
Figure 2 in New Data On Distribution Of Two Palaearctic Leptaleus Laferté-Sénectère, 1849 (Coleoptera: Anthicidae) Species With New Records And A Correction
Figure 2. Leptaleus chaudoiri (Kolenati, 1846), male aedeagus. A – specimen from Cyprus, 'CYP RUS TROODOS Mts. Dhiarizos Riv., on light eastAgios Georgios leg. PREISS', 18 VII 99; B – specimen from 'Greece, Rodos, Koljmbia, 18– 25.06.2001 Leg. L. Fancello'; C – specimen from Iran, 'IRAN, Gilan Province S Astara, 5 km W Lavandvil: Koteh Komeh, 180 m 38°18'26" N, 048°47'14" E 10.10.2011, leg. Frisch'; D – specimen from Nepal, 'NEPAL, Prov. Bheri 28°38'23" N, 81°17'14" E Chisapani, KarnaliUfer 17.VI.2005; 180 m leg.: J. Weipert'; E – specimen from Pakistan, Islamabad sect. 7 (see studied material).
Fig. 2 in Additions and corrections to the list of Psocoptera of Unguja Island (Zanzibar, Tanzania)
Fig. 2. Light microscope photograph of the chaetotaxy of prothorax of a female L. puber (A) and L. plesiopuber (B) from Unguja Island (40x). The much shorter bristle of plesiopuber is shown by arrows.
FIGURE 5 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 5. Simplified overview of factors that influence the preservation and destruction of the cellular, soft tissue, and mineral content of bone. Diagenesis of these materials is more complex than is shown here. Additional factors also have influence, and multiple levels and modes of preservation and destruction may occur in different regions of a single bone (see text for details).
FIGURE 4 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 4. The geologic column according to science vs. YEC ideology. Time periods are not shown to scale. The dates according to science are from radiometric dating (Schmitz, 2020). The dates according to YEC ideology are based on biblical genealogies (Jones, 2016). YEC identifications of Paleozoic, Mesozoic, and pre-Quaternary Cenozoic strata as Flood deposits (e.g., Clarey, 2020; Oard and Carter, 2021) are based on misinterpretations of geologic data (Senter, 2011; Willoughby, 2016; Prothero, 2017; Senter, 2019).
FIGURE 2 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 2. Microstructure of bone matrix. A. Part of a collagen molecule, showing its triple helical structure (based on figure 2 of Bella (2016), with modifications), with each of the three helices shown in a different color: black, dark gray, and light gray. B. A collagen microfibril and associated bone mineral crystallites, showing that the microfibril consists of five staggered collagen molecules and that the crystallites form between the tips of the collagen molecules in the microfibrils (based on figure 1d of Alexander et al. (2012), with modifications). C. Part of a collagen fibril, showing that bone mineral crystallites form both within microfibrils (unshaded crystallites) and between microfibrils (shaded crystallites).
FIGURE 1 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 1. Cells and soft tissues from bones of the hadrosaurid dinosaur Edmontosaurus annectens, from the Standing Rock Hadrosaur Site (SHRS) in South Dakota (Upper Cretaceous: Maastrichtian). The images are reprinted from figure 2 of Cretaceous Research vol. 99, Ullmann et al., "Patterns of soft tissue and cellular preservation in relation to fossil bone tissue structure and overburden depth at the Standing Rock Hadrosaur Site, Maastrichtian Hell Creek Formation, South Dakota, USA" (2019), with permission from Elsevier. A. Osteocyte from fragment of ossified tendon. B. Osteocyte from caudal vertebra SRHS-DU-220. C. Blood vessels with spherical structures in the lumen, from metatarsal SHRS-DU-274. D. Blood vessel (right) and sheets of CBM (lower left) from fragment of ossified tendon. E. Sheet of CBM with embedded osteocytes, from metatarsal SHRS-DU-274.
FIGURE 3 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 3. Histology of bone. A. Macroscopic view of compact and spongy bone in a cross-section of the humerus of a domestic cow (Bos taurus). B. Arrangement of microstructures in compact and spongy bone. C. Human compact bone viewed through a compound microscope, with cells boiled away and voids filled with black ink, to make lacunae and canaliculi stand out.
FIGURE 6 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 6. Recrystallization of bone mineral. Note that through geologic time, the crystallite has become enlarged, and many of its original ions have been replaced by other ions from groundwater. Here, ions are not shown to scale with respect to each other or to the size of the crystallite. For details on relative abundances of the various ions in fossil bone, see Hubert et al. (1996); Kiseleva et al. (2019); Ullman et al. (2021); Schroeter et al. (2022); and Ullmann et al. (2022). REE = rare earth elements.
FIGURE 7 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 7. Spherical objects in a blood vessel from fossil bone, and items with which such structures have been hypothetically identified. The scale bar applies to A, C, and the smaller version of the image in B. The correct identity of the spherical objects in blood vessels of fossil bone remains unknown. A. Spherical objects in a blood vessel from fossil bone of the theropod dinosaur Beipiaosaurus inexpectus, from the Yixian Formation of Liaoning, China (Lower Cretaceous: Barremian–Aptian). This image is used with the permission of the journal PeerJ. It is from figure 2C of "Putative fossil blood cells reinterpreted as diagenetic structures," PeerJ, vol. 9: e12651, Korneisel et al. (2019). B. Pyrite framboids, shown to scale with A and C (left) and enlarged (right). This image is used with the permission of the journal PALAIOS. It is from figure 1 of "Rapid formation of framboidal sulfides on bone surfaces from a simulated marine carcass fall," PALAIOS, vol. 30: 327-334, Vietti et al. (2015). C. Red blood cells of the crocodilian species Caiman yacare (spectacled caiman). This image is reprinted by permission from Springer, from figure 1A of "Hepatozoon caimani Carini, 1909 (Adeleina: Hepatozoidae) in wild population of Caiman yacare Daudin, 1801 (Crocodylia: Alligatoridae), Pantanal, Brazil," Parasitology Research, vol. 116: 1907-1916 (2017).
Dataset for "Meteorological drivers of vineyard water vapour loss and water use efficiency during dry days" [corrected version]
<p>Dataset for "Meteorological drivers of vineyard water vapour loss and water use efficiency during dry days".</p>
Fig. 1 in Correction of the type locality of Gylippus rickmersi Kraepelin, 1899 (Solpugida: Gylippidae)
Fig. 1. Type locality of Gylippus rickmersi: true site (yellow symbol) vs. traditionally assumed but wrong site (red symbol).
Figure 1 in The mysterious Widdringtonia wallichii (Cupressaceae) and the correct scientific name for the Clanwilliam cedar
Figure 1. Specimen of Widdringtonia nodiflora (K75280) sent by N. Wallich to J.D. Hooker, and the origin of the name W. wallichii.
Normalized and batch-corrected concentration of 43 immune markers from 248 subjects with stress-related mental disorders and 36 healthy controls
<h1>Abstract</h1> <p>In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small markers panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.</p> <h1>Methods</h1> <p>The normalized and batch-corrected concentration of 43 immune markers from 237 subjects with stress-related mental disorders and 36 healthy controls was determined in plasma. This data was used in the initial analysis. Additionally, the same measurements are provided for 11 subjects with stress-related mental disorders used in a replication analysis.</p> <p>- blood was collected in the morning under fasted conditions and plasma stored at -80°C until further processing<br>- samples were randomized into 96 well plates<br>- immune marker concentration was measured with the Meso Scale Diagnostics V-PLEX Human Biomarker 54-Plex Kit and the MESO QuickPlex SQ 120 imager according to the manufacturer's instructions<br>- additionally, high-sensitivity C-reactive protein (Tecan Group Ltd.), cortisol (Tecan Group Ltd.), interleukin (IL)-6 (Thermo Fisher Scientific), IL-6 soluble receptor (Thermo Fisher Scientific) and IL-13 (Thermo Fisher Scientific) was measured via ELISA according to the manufacturer's instructions<br>- values below the detection limit in markers measured with ELISA were set to zero and values above the detection limit to the upper limit<br>- the data was quantile-normalized (values were ranked and mapped to the quantiles of a standard normal distribution)<br>- the normalized concentration was corrected for the biobank storage position (batch_variable) via a linear model and the residuals reported as the concentration</p> <p>The following markers were measured:<br>fibroblast growth factor 2 (FGF2 or bFGF), cortisol, C-C motif chemokine 11 (CCL11 or eotaxin), CCL26 (eotaxin-3), vascular endothelial growth factor receptor 1 (VEGFR1 or Flt-1), hsCRP, intercellular adhesion molecule (ICAM)-1, interferon (IFN)-gamma, IL-1alpha, IL-1 receptor antagonist (IL-1RA), IL-10, IL-12/IL-23p40, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-17B, IL-2, IL-27, IL-31, IL-5, IL-6 high sensitivity (IL-6HS), IL-7, IL-8HS, C-X-C motif chemokine 10 (CXCL10 or IP-10), CCL2 (MCP-1), CCL13 (MCP-4), CCL22 (MDC), CCL3 (MIP-1alpha), CCL4 (MIP-1beta), placental growth factor (PlGF), serum amyloid A (SAA), sIL-6R, CCL17 (TARC), angiopoietin-1 receptor (Tie-2), tumor necrosis factor (TNF or TNF-alpha), lymphotoxin-alpha (LT-alpha or TNF-beta), thymic stromal lymphopoietin (TSLP), vascular cell adhesion protein 1 (VCAM-1), vascular endothelial growth factor (VEGF)-A-HS, VEGF-C, VEGF-D</p> <p>The data is provided as a tab separated file.</p>
Draft version of poposed images and plotting code of manuscript: Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite
<p>This documentation provides a detailed description of the folder structure and image contents uploaded to the website. It aims to help users understand the purpose and organization of the files. The corresponding manuscript is titled,<strong><em> Stray light correction and enhancement of nocturnal low-light image of early-morning-orbiting Fengyun-3E satellite</em></strong>.</p>
Fig. 1 in Updated and corrected list of the inland molluscs of Samothraki Island (N Aegean, Greece)
Fig. 1. Tandonia kusceri in its habitat on Samothraki: under a stone in a Platanus orientalis forest along the stream near Pachia Ammos Beach.
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</p>
Dynamic HD Model Relative Orography Corrections
<p>Relative orography corrections for MPI's Dynamic HD model version 3.0.</p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Distances for the Gaia RV set with corrected parallaxes
<p>The datasets are described in <a href="https://arxiv.org/abs/1902.02355">https://arxiv.org/abs/1902.02355</a> , which is submitted to MNRAS.</p> <p>Each file contains a distance derivation under different assumptions for the Gaia RV dataset.</p> <p>Please note the following: to allow for freedom in choice of cuts, we have limited ourselves to just minimal cuts: parallax/parallaxerr > 3, detected G,G_BP,G_RP magnitudes > 0 mag, vlos_err < 10 km/s, n_vis > 5, reasonably measured RV (<5550 km/s), distance and 1/parallax < 10 kpc</p> <p>Note that these cuts are NOT sufficient to ensure quality of the sample. We strongly advise to use the quality criteria laid out in Schoenrich, McMillan and Eyer (2019), and test for the effects of a cut in galactic latitude b > 10 degrees for most applications.</p> <p>The file format is .csv (comma separated) with the following columns (a key is in row 1)<br> index: running number in our sample<br> sourceid: Gaia Source ID<br> E_dist: expectation value for the distance, unit is kpc<br> distm2: second moment of distance probability distribution (use the usual formula to get the variance/dispersion)<br> distm3: third moment of distance probability distribution<br> distm4: fourth moment """"<br> parallax: The Gaia parallax _including_ the offset correction chosen, unit is mas<br> parallax/parallaxerr : ratio of parallax to parallax uncertainty. Again, both parallax and parallaxerror contain the corrections. Use this and the last column to obtain the parallax uncertainty for quality cuts<br> x: x coordinate (radial coordinate in local cartesian frame), unit is kpc<br> y: y coordinate in the azimuthal direction, unit is kpc<br> z: z coordinate (perpendicular to the plane), relative to the Sun, unit is kpc. Please add the solar offset from the plane that you favor (0.02 kpc, Joshi et al., or the Gerhard & Bland-Hawthorn review are good values)<br> R: galactocentric radius (in-plane, cylindrical coordinates), unit is kpc<br> U_g: cylindrical radial velocity in a galactocentric frame (positive taken inwards), unit is km/s<br> v_phi: azimuthal velocity (V_g, azimuthal direction), unit is km/s<br> v_z: velocity component upwards perpendicular to the plane, unit is km/s<br> vlos: line-of-sight velocity as provided in the Gaia RV datasets<br> vloserr: line-of-sight velocity error as provided in the Gaia RV datasets<br> gl: Galactic longitude l (degrees)<br> gb: Galactic latitude b (degrees)<br> pml: proper motion in l (mas/yr)<br> pmb: proper motion in b (mas/yr)<br> pml_err: proper motion error/uncertainty in l (mas/yr)<br> pmb_err: proper motion error in b (mas/yr)<br> E_bprp: BP-RP excess flux factor (provided in Gaia catalogue)<br> excessnoise: astrometric excess noise<br> Gmag: Gaia G magnitude<br> Rmag: Gaia G_RP magnitude<br> B-Rcolour: G_BP - G_RP (colour in mag)<br> nvis: number of visibility periods<br> eclat: ecliptic latitude (degrees)</p> <p>Each file contains a different set of assumptions that result in a near-zero average distance bias, as discussed in the paper:</p> <p>We recommend using either:<br> gaiaRVdelp54delsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for parallax offset of 0.054 mas</p> <p>gaiaRVdelpeqspdelsp43 : Increased parallax uncertainty by 0.043 mas in quadrature, corrected for a parallax offset = modified parallax error</p> <p>For comparison and applications that need long-range in distance s, it may be interesting to use:<br> gaiaRVdelp48delsp00 : only increased parallaxes by an offset of 0.048 mas. Distance bias on average is stable out to 4kpc or more, but you have to be wary of random errors.</p> <p><br> All distances were derived with the standard prior described in Schoenrich, McMillan and Eyer (2019); the parameter for the additional exponential function in the prior at very large s > 4 kpc is 0.06/kpc</p> <p>If you need specific subsamples of stars, we advice to draw a new prior with the method of Schoenrich & Aumer (2017) and then re-derive the distances. We are also happy to help with that and provide you with the necessary code.</p> <p>Please note that our definition (angular distance per time in the direction of l, b) of pml and pmb is frequently noted with a * in other derivations.</p> <p>Please also note that why we used z_Sun = 0.02 kpc in the underlying analysis and distance prior, the value in the catalogue is given relative to the Sun (i.e. add the Solar offset that you favour).</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.