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Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)
Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation
Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)
Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)
Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network
Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)
Fig. 1 in Pre-treatment of canine plasma with heat, rather than acid, efficiently enhances Dirofilaria immitiS antigen detection
Fig. 1 Detection of Dirofilaria immitis antigen by ELISA for twofold-diluted plasma samples with and without treatment. A spectrophotometric OD reading at a wavelength of 415 nm was obtained for each sample using the microplate reader SpectraMax® i Series-Spectramax Id3. Filled black circle, untreated sera. Open blue circle, ICD-1 protocol. Filled blue circle, ICD-2 protocol. Open red circle, ICD-3 protocol. Filled red circle, ICD-4 protocol. The error was represented as mean ± 95% confidence interval
Fig. 8 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 8 Transmcsscon electron mccroscops of Mikrocytos donaxi n. sp. cnfectcng Donax trunculus mantle collected cn Audcerne Bas. a Dense form of the parascte wcth a vers granulous cstoplasm and the presence of few large vesccles cnscde (arrow); note the eccentrcc posctcon of the nucleus. b Parascte cn tcght contact wcth two mctochondrca cnduccng a depresscon of the parascte membrane (arrow). c Parascte cn the connectcve tcssue at an endosomal stage, presentcng a well-developed anastomoscng endoplasmcc retcculum near the nucleus. Abbreviations: aER, anastomoscng endoplasmcc retcculum; M, mctochondrca; N, nucleus. Scale-bars: a, c, 1 μm; b, 0.5 μm
Fig. 4 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 4 Hcstologccal sectcon of Donax trunculus mantle showcng hsbrcdczatcon of the Mikrocytos Msp probe wcth mccrocell cells (arrows). Scale-bar: 20 μm
Fig. 3 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 3 Hcstologccal haematoxslcn eoscn tcssue sectcons showcng mantle lescons of Donax trunculus cnfected wcth Mikrocytos donaxi n. sp. parasctes from Audcerne Bas. a Dcffuse necroscs of muscular fcbres (arrows) and connectcve tcssue (arrowheads) of the mantle cnfected wcth mccrocell parasctes b Degenerated haemocstes (arrows) and muscular cell necroscs (arrowhead) assoccated wcth mccrocell parasctes (astercsks). c Mikrocytos donaxi n. sp. cnscde a msocste (arrow). Scale-bars: a, 100 μm; b, c, 20 μm
Fig. 2 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 2 Hcstologccal haematoxslcn eoscn tcssue sectcons showcng Mikrocytos veneroïdes n. sp. parasctes cn dcfferent tcssues of Donax trunculus from Oléron Island. a Parasctes cn the adductor muscle. Note the extracellular (arrows) or cntracellular (arrowhead) posctcon of mccrocell parasctes. b Parascte cell (arrows) cn the neuronal ganglcon. c Two parasctes (arrow) cnscde the cstoplasm of a haemocste. Scale-bars: 20 μm
Fig. 1 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 1 Samplcng sctes of Donax trunculus, mortalcts rate estcmatcon and collected cndcvcduals along the Atlantcc coast of France. Abbreviations: AB, Audcerne Bas; DB, Douarnenez Bas; QB, Qucberon Bas; OI, Oléron Island
Fig. 5 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 5 Phslogenetcc tree (50% majorcts-rule consensus) uscng Basescan Inference (MrBases 3.1.2) based on the small subunct rcbosomal gene of Mikrocytos. Numbers at the nodes are Basescan postercor probabclctces. Paramikrocytos canceri was used as the outgroup for Mikrocytos spp. based on the results of Hartckacnen et al. [5]. Astercsks cndccate sequences obtacned cn thcs studs
Fig. 6 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 6 Phslogenetcc tree (50% majorcts-rule consensus) uscng Basescan Inference (MrBases 3.1.2) based on the ITS1-5.8S-ITS2 sequence arras of Mikrocytos. Numbers at the nodes are Basescan postercor probabclctces. Astercsks cndccate sequences obtacned cn thcs studs
Fig. 7 in Descriptions of Mikrocytos veneroïdes n. sp. and Mikrocytos donaxi n. sp. (Ascetosporea: Mikrocytida: Mikrocytiidae), detected during important mortality events of the wedge clam Donax trunculus Linnaeus (Veneroida: Donacidae), in France between 2008 and 2011
Fig. 7 Transmcsscon electron mccroscops of Mikrocytos veneroïdes n. sp. cnfectcng Donax trunculus mantle collected cn Qucberon Bas. a Parascte cnscde a msocste and near the msofcbrcls. Note the presence of mctochondrca near the parascte cells (arrow). b Parascte cn the cstoplasm of a haemocste; note the presence of parasctophorous vacuole around the parascte (arrows). c Parascte near the msofcbrcls. Mctochondrca tcght agacnst the surface of the parascte or cnscde the parascte cstoplasm; note the depresscon of parascte surface at the pocnt of contact wcth mctochondrca. d Bcnucleate stage of the parascte cn an extracellular posctcon; note the presence of the two nuclec. Abbreviations: M, mctochondrca; MF, msofcbrcls; N, nucleus; P, parascte. Scale-bars: a, b, d, 2 μm; c, 1 μm
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).
Gaia DR3 detectability of unresolved binary systems
<p>This upload contains the necessary data to construct the selection of unresolved binary systems in Gaia DR3 based on RUWE.</p> <p>The tutorial showing how to work with these data can be found here: </p> <p>https://gaiaunlimited.readthedocs.io/en/latest/notebooks/dr3-unresolved-binaries.html</p> <p>The paper describing the data and methods is:</p> <h2>Gaia DR3 detectability of unresolved binary systems</h2> <div>Alfred Castro-Ginard, Zephyr Penoyre, Andrew R. Casey, Anthony G.A. Brown, Vasily Belokurov, Tristan Cantat-Gaudin, Ronald Drimmel, Morgan Fouesneau, Shourya Khanna, Evgeny P. Kurbatov, Adrian M. Price-Whelan, Hans-Walter Rix and Richard L. Smart</div> <div> </div> <div>https://ui.adsabs.harvard.edu/abs/2024arXiv240414127C/abstract</div>
Detecting anomalies in melt-extruded 3D printed parts using in situ data
<p>The data in this repository was gathered from a study to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing, using a set of sensors to non-destructively identfy printed parts that contain defects. The data underwent variance analysis to determine an "acceptable" range of filament diameters and non-destructivley identify spatial regions of printed cylinders in multi-part builds that contain defects.</p> <p>The data consists of two folders and a log meant to track procedural adherence for each cylinder printed, the introduced defects, or lack thereof, and the pressurization of the part. The "Final Build Logs" spreadsheet contains information regarding the two locations of the deformations along the 56 meters of filament needed to have no more than three anomalous cylinders out of the six printed cylinders, the date of the applied deformations to the filament, the initials of the researcher applying the deformations, the date that the build was printed along with the initials of the researcher who printed it, the part number, researcher initials, and date of the pressurization test for each cylinder within the build, and a comment describing any deviations from the procedure that play into the random error of the statistical analysis for each cylinder. </p> <p>The "Pressure Test Data" folder contains a folder for each build. Within these folders are .tdms files containing metadata on the measurement system in the header and tab-delimeted values for the columns. The columns of interest to the study are X_Value, representing time elapsed, and pressure, which we evaluated on the values' exponential decay rate. The files also contain supplemental information such as a column for temperature (celsius), and the flow rate (SLPM Normalized). The "Build Data" folder contains in situ data from the sensor-equipped printer in a .csv file, the STL file for the build, the gcode file from the applied slicer settings, the AMRP file stores printer settings, and a .pdf file for the setup specifications.</p>
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.