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Fig. 5 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 5 Western blot analysis of MSP in E. brevis. a MSP has unusual mobility in gel and is found as protein with weight 36–38 kDa. Both male and female samples reveal MSP signal, because the latter include inseminated females. α-Tubulin was used as a loading control (approximate weight 55 kDa). b Peptide competition assay confirms reactivity of anti-MSP antibodies with protein band of 36–38 kDa

Fig. 2 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 2 Western blot analysis of MSP in P. redivivus. In adult animals, MSP is detected as double band with approximate weight 15 and 16 kDa. a Both male and female samples reveal MSP signal, because the latter include mated females. α-Tubulin was used as a loading control (approximate weight 55 kDa). b Analysis of young males and females. MSP is not detected in females, because most of them are unmated. Abbreviations: m, males; f, females

Fig. 3 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 3 Schematic representation of P. redivivus spermatozoa based on transmission electron microscopy. a Morphology of immature and mature spermatozoa. Immature spermatozoon is an unpolarized cell with nucleus devoid of nuclear envelope, mitochondria, and membranous organelles. Mature spermatozoon in female reproductive system is a bipolar cell with anterior pseudopodium and posterior main cell body containing chromatin, mitochondria, and membranous organelles that attached to cell membrane and open to the exterior via pores. Reproduced from Zograf (2014) with the permission from copyright holder (Russian Journal of Nematology). b Chain of conjugated mature spermatozoa in female reproductive system. Abbreviations: N, nucleus; mt, mitochondria; mo, membranous organelles; ch, nuclear chromatin; ps, pseudopodium; mcb, mail cell body

Fig. 1 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 1 Phylogeny of nematodes and MSP-based sperm motility. Phylogenetic relationships within phylum Nematoda derived primarily from SSU rDNA sequence data are given according to De Ley and Blaxter (2002). Suborders of the order Rhabditida, in which representatives highly homologous MSPs are found at DNA, RNA, or protein levels, are marked by underlining. Taxa whose species used in this study are marked with asterisks. Orders Trefusi- ida, Isolaimida, Dioctophyma- tida, Muspiceida, Marimermith- ida, and Desmoscolecida are not shown in this tree

Fig. 8 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 8 Putative MSPs those are most similar to peptide antigen. a P. redivivus MSPs aligned with peptide antigen. Protein sequences (Pan_g61.t1, Pan_g6018.t1, Pan_g6424.t1, Pan_g9068.t1, Pan_ g19433.t1, and Pan_g21178.t1) were found by Blast using peptide

Fig. 4 in Analysis of major sperm proteins in two nematode species from two classes, Enoplus brevis (Enoplea, Enoplida) and Panagrellus redivivus (Chromadorea, Rhabditida), reveals similar localization, but less homology of protein sequences than expected for Nematoda phylum

Fig. 4 Immunolocalization of MSP in P. redivivus sperm. a Immature spermatozoa extracted from male. MSP localizes in granules. In some cells, MSP has strongest signals in the periphery (arrowheads) (scale bar 10 µm). b Chain of mature spermatozoa extracted from female.

Figure 5 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 5. Gene family and time-calibrated evolution analyses of 28 litostomateans and two spirotricheans. Ages are given as million years ago (Mya). Calibrated nodes are indicated with a star. Numbers asser "+" and "-" represent the expanded or contracted gene families in each branch* respectively. MRCA* the most recent common ancestor; H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia; CZ* Cenozoic; MZ* Mesozoic; NP* Neoproterozoic; PZ* Palaeozoic.

Figure 6 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 6. Heatmap showing the bias of stop codon usage among 28 litostomatean ciliates. H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia.

Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans

Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans by maximum likelihood (ML) and Bayesian inference (BI) methods. Sequences from the present study are in bold. Ŋe numbers at the nodes are the bootstrap values of ML out of 1000 pseudoreplicates and the posterior probability of Bayesian analysis* respectively. Ŋe black dots represent full support values both in the ML and in the BI trees. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.

Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B* GO enrichment analysis of conserved orthogroups in Litostomatea. C* heatmap showing the number of shared genes among 28 litostomatean ciliates. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.

Figure 7 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 7. Ŋe length distribution (A–C) and motif (C–E) of the introns detected in Monodinium sp.* Myriokaryon sp.* and Apodileptus visscheri.

Figure 1 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 1. Maps showing the location of the sampling sites and photomicrographs showing the in vivo morphology of the 14 litostomatean ciliates for which omics' data were newly obtained.

Figure 4 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 4. Carbohydrate-Active Enzymes Database (CAZy) annotation results of 28 litostomatean ciliates. A* comparison of the number of CAZymes in 28 litostomatean ciliates. B* CAZy function classification diagrams of three newly sequenced litostomateans (Didinium sp.1* Myriokaryon sp.* and Apodileptus visscheri). º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.

Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity

Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B*

opencc-by-4.0Jun 2024View details →

Dangerous Items Dataset for 5-Class Object Detection (Pascal VOC annotation)

This repository contains the data from the manuscript "Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN". It contains 4,000 images representing 5 classes of objects: baseball bat, gun, knife, machete and rifle (<a href="https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1aG30Hpupctnp8ywhAprz313jmYbFnZk3/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger one is no longer than 1,000 pixels. The full set was randomly divided into training and testing parts (75% and 25% of the full set respectively). As a result, the training part contains 3,000 images, and the testing part – 1,000 images. Both parts are balanced, that is, they contain similar number of objects to be detected. Images were annotated using bounding boxes in the Pascal VOC format, according to which the description about each image is included in the corresponding XML file. Using this dataset please cite: Omiotek, Z. (2025). Dangerous items’ detection in surveillance camera images using Faster R‑CNN. Przegląd Elektrotechniczny, 101(5), 156-168. https://www.red.pe.org.pl/articles/2025/5/36.pdf

opencc-by-4.0Jul 2024View details →

Popular Animals Dataset for 6-Class Object Detection (Pascal VOC annotation)

This dataset contains images used in the monograph titled Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej (Application of Selected Deep Learning Methods in Computer Vision) to build the Faster R-CNN model. The full collection consists of 600 image files showing 6 classes of objects: kot (cat), krowa (cow), pies (dog), koń (horse), człowiek (human), owca (sheep) (<a title="Popular Animals Dataset" href="https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1g3O1Qq2YqmCb8WSMDHaoFJPoHe6yujlQ/view?usp=drive_link</a>). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger side is no longer than 1000 pixels. The set was randomly divided into a training part (75% of the full set) and a test part (25% of the full set). As a result, the training part contains 450 files, and the test part - 150. Both parts are balanced - they contain a similar number of detected objects. The images are labeled with bounding boxes in the Pacal VOC format, according to which the description of each file is contained in an XML file with the same name. The dataset can be used to build models for object detection.

opencc-by-4.0Sep 2024View details →

Electronic Devices Dataset for 2-Class Semantic Segmentation

This dataset contains images used in the monograph titled Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej (Application of Selected Deep Learning Methods in Computer Vision) to build the U-Net model. The full collection consists of 600 image files of resolution 512x512 pixels showing small electronic devices and office accessories (<a title="Electronic Devices Dataset" href="https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link</a>). The set was randomly divided into a training part (50% of the full set), validation and test part (each accounted for 25% of the full set). As a result, the training part contains 300 files, validation part – 150 and test part - 150. The collection was created by augmenting the original set of 100 images with vertical and horizontal flip, random rotation from -45 to 45 degrees, and a combination of both flips and random rotation. The images are labeled with masks representing 2 kind of objects – REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.

opencc-by-4.0Jul 2024View details →

Class-specific data augmentation for plant stress classification

This is a companion dataset for the paper titled "Class-specific data augmentation for plant stress classification" by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, and Baskar Ganapathysubramanian published in The Plant Phenome Journal, https://doi.org/10.1002/ppj2.20112 Abstract: Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.