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Fig. 1 in Detection of Sarcocystis albifronsi, Eimeria alpacae, and Cystoisospora felis in Eurasian lynx (Lynx lynx) in northwestern China

Fig. 1. Phylogenetic tree based on partial gene of the COI sequence of Sarcocystis albifronsi (▴) from Eurasian lynx #2 obtained in this study in northwestern China. The evolutionary history was inferred using the neighbor-joining method (bootstrap replicates: 1000) with MEGA 7.0.

Fig. 1 in Dirofilaria immitis and Dirofilaria striata (Spirurida: Onchocercidae) detected in wild carnivores from Texas, United States

Fig. 1. Map of Texas, US indicating the origin and number of animals sampled by species. Counties of Texas from which samples originated are in blue. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2. Phylogenetic tree created using a in Dirofilaria immitis and Dirofilaria striata (Spirurida: Onchocercidae) detected in wild carnivores from Texas, United States

Fig. 2. Phylogenetic tree created using a maximum likelihood method (2000 bootstrap replicates) showing the relationship of identified Dirofilaria immitis and one isolate of Dirofilaria striata. Brugia pahangi sequence was used as outgroup.

FIGURE 11 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 11. Fossils (bones and teeth) and a mixture of fossils and sediment (sand and gravel) from Sample 3. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image (grayscale) under UV light. 4, Black and white image after segmentation of the blue channel image.

FIGURE 9. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 9. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3.

FIGURE 10 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 10. Examples of spectral images of Sample 3 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.

FIGURE 4 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 4. Examples of spectral images of Sample 1 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB image computed using the sRGB - standard RGB colour space is also provided.

FIGURE 8 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 8. RGB images of Sample 2 with ferrous particles, bones-teeth, sand-gravel and a mixture of bonesteeth and sand-gravel. 1, Under daylight (D65). 2, Under UV Light.

FIGURE 3. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 3. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1. 2, Fluorescence spectra (radiance in W/sr·cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1.

FIGURE 6. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 6. 1, Reflectance spectra of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2.

FIGURE 1. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 1. 1, Wet sieving process of palaeontological samples using Freudenthal's technique. 2, Visual recognition and separation of teeth and bones using a binocular microscope and pincers.

FIGURE 7 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 7. Examples of spectral images of Sample 2 through spectral bands (420, 520, 620, 670 and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.

FIGURE 5 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

FIGURE 5. Mixture of bones-teeth and sand-gravel from Sample 1. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image under UV light. 4, Green channel image (grayscale) under UV light. 5, Black and white image after segmentation of the green channel image.

opencc-by-nc-nd-4.0Sep 2024View details →

High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory

The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation

Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives. Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2  based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020)  (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used. Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done.  To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used.  We have curated two datasets Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation" Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation" *Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"

TUD-IPB Dataset for Detecting the Interaction between Particles and Biomass

This dataset contains the data used for the following publications: <div> <pre>(1) Jia, T., Peng, Z., Yu, J., Piaggio, A. L., Zhang, S., & de Kreuk, M. K. (2024). Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method. Science of The Total Environment, 175813. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.175813" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.175813</a> (2) Jia, T., Yu, J., Sun, A., Wu, Y., Zhang, S., & Peng, Z. (2025). Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment. Journal of Environmental Management, 375, 124268. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jenvman.2025.124268" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jenvman.2025.124268</a></pre> </div> The "TU Delft-Interaction between Particles and Biomass” (TUD-IPB) dataset is for detecting the interaction between microparticles and biomass in biological wastewater treatment process  with computer vision. We created this dataset from the experiments conducted in water lab of TU Delft (Stevinweg 1, Delft, Netherlands). The experiment details can be found in the publication. This dataset includes microscope images and mask annotations, that shows the shape and location of free particles and entrapped particles in images. The images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the classes of the annotation (i.e., entrap particle and free particle) is stored in the classes.txt file. The filenames of images and annotations includes three parts: Sampling time (the first part), the diameter of biomass (the middle part), and image number (the last part). Details of the first two parts and their meanings are provided in the following table. Explanation of Filename Components and Their Meanings <table> <tbody> <tr> <td>The first part of filename</td> <td>Sampling time </td> <td>The middle part of filename</td> <td>Diameter of biomass (mm)</td> </tr> <tr> <td>A10</td> <td>10 min</td> <td>bigger3-0</td> <td>>3.1</td> </tr> <tr> <td>A30</td> <td>30 min</td> <td>2-0-3-0</td> <td>2.0-3.1</td> </tr> <tr> <td>A60</td> <td>60 min</td> <td>1-0-2-0</td> <td>1.0-2.0</td> </tr> <tr> <td> </td> <td> </td> <td>0-2-1-0</td> <td>0.2-1.0</td> </tr> <tr> <td> </td> <td> </td> <td>smaller0-2</td> <td>< 0.2</td> </tr> </tbody> </table>   If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: <pre>@article{jia2024detecting, title={Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method}, author={Jia, Tianlong and Peng, Zhaoxu and Yu, Jing and Piaggio, Antonella L and Zhang, Shuo and de Kreuk, Merle K}, journal={Science of The Total Environment}, pages={175813}, year={2024}, publisher={Elsevier} } @article{jia2025semi,   title={Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment},   author={Jia, Tianlong and Yu, Jing and Sun, Ao and Wu, Yipeng and Zhang, Shuo and Peng, Zhaoxu},   journal={Journal of Environmental Management},   volume={375},   pages={124268},   year={2025},   publisher={Elsevier} }</pre>

Black Soldier Fly detection, phenotyping and sex prediction dataset

BSF_Larvae for training the detection and segmentation model consists of 321 images with over 5000 larvae instances.  Single_Larvae_data consists of 1608 single larvae images for the measurement of body weight, length and width of the individual larva.  The Larvae size  excel sheet contains the size and sex data corresponding to the individual larva.  The Larvae sex classification dataset includes images cropped from the Single_Larvae_data for sex classification.

EOL computer vision pipelines: Object Detection for Image Cropping: Aves

Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s).  388,166 rows

EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon

Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>. <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>