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Fig. 3 in Evaluation of blood and muscle tissues for molecular detection and characterization of hematozoa infections in northern pintails (Anas acuta) wintering in California

Fig. 3. Phylogenetic assignment of hematozoa mitochondrial DNA cytochrome b sequences originating from northern pintails collected from the Central Valley of California (asterisks). Reference sequences for Leucocytozoon (white circles), Haemoproteus (grey circles), and Plasmodium (black circles) parasites were obtained from the National Center for Biotechnology Information. Bootstrap support values for differentiation of broad taxonomic groups are indicated. Values reported for Haemoproteus and Plasmodium show support for phylogenetic differentiation from the mixed subclade formed by reference sequences a and b.

Fig. 4. Minimum spanning network for hematozoa mitochondrial DNA cytochrome b in Evaluation of blood and muscle tissues for molecular detection and characterization of hematozoa infections in northern pintails (Anas acuta) wintering in California

Fig. 4. Minimum spanning network for hematozoa mitochondrial DNA cytochrome b haplotypes detected in Central Valley northern pintails. Circles are drawn proportional to the frequency at which haplotypes were observed. Shading represents the sample collection from which haplotypes originated: white (2006– 2007 wing muscle), grey (2011–2012 wing muscle), and black (2011–2012 blood). A single mutation separates nodes unless explicitly indicated by number. Lines separating nodes are drawn to scale unless indicated by a break. Parasite taxa have been abbreviated in haplotype names (Leu = Leucocytozoon, Hae = Haemoproteus and Pla = Plasmodium).

Fig. 1 in Evaluation of blood and muscle tissues for molecular detection and characterization of hematozoa infections in northern pintails (Anas acuta) wintering in California

Fig. 1. Locations in the Central Valley of California from which northern pintail tissue samples were collected. Samples (n in 2006–2007, n in 2011–2012) were collected in the Sacramento Valley sub-region at: (A) Sacramento National Wildlife Refuge (NWR; 44,92), (B) Delevan NWR (35,7), (C) Colusa NWR (0,2), (D) Sutter NWR (0,1), (E) Little Dry Creek State Wildlife Area (SWA; 0,5), (F) Howard Slough SWA (0,1), (G) Yolo SWA (0,8) and (H) a private duck hunting club (0,1). Samples were collected in the San Joaquin Valley sub-region at: (I) San Luis NWR (7,0), (J) Kesterson NWR (4,0), (K) Los Banos SWA (14,0), (L) Volta SWA (10,0), (M) a private duck hunting club (2,0), and (N) Mendota SWA (30,40).

Fig. 2 in Evaluation of blood and muscle tissues for molecular detection and characterization of hematozoa infections in northern pintails (Anas acuta) wintering in California

Fig. 2. Estimated prevalence of Leucocytozoon (white bars), Haemoproteus (grey bars), and Plasmodium (black bars) parasites in northern pintails sampled in the Central Valley of California in 2006–2007 and 2011–2012 using occupancy modeling. Error bars represent 95% confidence intervals around point estimates.

opencc-by-4.0Dec 2014View details →

Fig. 2 in Restricted evaluation of Trichodectes canis (Phthiraptera: Trichodectidae) detection methods in Alaska gray wolves

Fig. 2. Divisions of wolf hide into 100 cm2 subsections for lice density analysis utilizing potassium hydroxide digestion. Each hide subsection square represents 10 cm by 10 cm, one representative section from each region was examined: 1 from the neck; 2 from the shoulder; 3 from the groin; 4 from the rump.

opencc-by-4.0Dec 2014View details →

Fig. 1 in Restricted evaluation of Trichodectes canis (Phthiraptera: Trichodectidae) detection methods in Alaska gray wolves

Fig. 1. Divisions of wolf half hides designated for lice proportion analysis utilizing potassium hydroxide digestion. Wolf hides were cut in half and the right and left subdivided into four relatively equal sections and numbered sequentially from the neck to the tail base.

Fig. 4 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 4. Phylogenetic relationships of species of Rugopharynx and Rugonema labiatum based on a neighbor-joining analysis of the sequence data of the ITS+ nuclear ribosomal DNA. Values above and below branches represent the NJ and MP bootstrap values (respectively) that were greater than 70%.

Fig. 3 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 3. Phylogenetic relationships of species of Rugopharynx and Rugonema labiatum based on a Bayesian analysis of the sequence data of the ITS+ nuclear ribosomal DNA. Values above branches indicate posterior probabilities that were greater than 0.8. Abbreviations of Australian state names are provided in Table 1.

Fig. 2 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 2. Morphological buccal capsule types in the genus Rugopharynx. I, simple cylindrical buccal capsule, R. macropodis; IIA, bilobed buccal capsule with subequal divisions, R. epsilon; IIB, bilobed buccal capsule with anterior lobe shorter, R. rufogrisea; III, trilobed buccal capsule, R. longibursaris.

Fig. 1 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 1. Localities within Australia at which specimens of Rugopharynx used in this study were collected. Coordinates for each locality are provided in Table 1. 1, Lake Clifton; 2, Waroona; 3, Collie, Wellington Dam; 4, Perup River; 5, Kalgoorlie; 6, Wallerberdina Station; 7, Port Augusta; 8, Ashbourne; 9, Kangaroo Island; 10, Naracoorte; 11, Hattah Lakes National Park; 12, Yan Yean; 13, The Gurdies; 14, Launceston; 15, Emu Flat, Bondo State Forest; 16, Trangie; 17, Grafton; 18, Lamington National Park; 19, Miles; 20, Dawes; 21, Mt Sebastopol; 22, Rockhampton; 23, Winton; 24, Proserpine; 25, Bowen; 26, Magnetic Island; 27, Lake Barrine.

Fig. 5 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 5. Molecular phylogeny of species of Rugopharynx and Rugonema labiatum based on a consensus of the BI, NJ and MP trees (Figs. 3 and 4), and the relationships of their hosts. This figure includes species of Macropus (M. agilis, M. antilopinus, M. bernardus) which are not hosts to species of Rugopharynx, as well as M. parma, from which no material could be obtained for genetic studies. Only those species of Petrogale included in this study are shown on the host tree. The morphology of the buccal capsule for each nematode taxon is also shown.

opencc-by-4.0Jul 2024View details →

Datasets for Crowd-based Requirements Engineering and aspect-based detection of learning-centered emotion from the text in Serbian language

<h1>Datasets for the paper "Enhancing Software and Learning with Serbian Student Feedback Corpora"</h1> These datasets include student feedback on an Intelligent Tutoring System written in Serbian, annotated with categories for Crowd-based Requirements Engineering (CrowdRE) and aspect-based detection of learning-centered emotions. Four annotators manually annotated each sentence.  The CrowdRE dataset includes two JSON files: <ul> <li>crowdre_english.json - annotated text with columns and classes written in English. Columns are: <ul> <li>Comment - the entire student feedback</li> <li>Sentence - sentence extracted from the feedback that was annotated</li> <li>Intention - class representing the intention of the sentence</li> <li>Topic - class representing the topic of the sentence</li> </ul> </li> <li>crowdre_srpski.json - annotated text with columns and classes written in Serbian. Columns are: <ul> <li>Komentar - the entire student feedback</li> <li>Recenica - sentence extracted from the entire feedback that was annotated</li> <li>Namera - class representing the intention of the sentence</li> <li>Tema - class representing the topic of the sentence.</li> </ul> </li> </ul> The dataset for the aspect-based detection of learning-centered emotions includes two JSON files: <ul> <li>emotions_english.json - annotated text with columns and classes written in English. Columns are: <ul> <li>Comment - the entire student feedback</li> <li>Sentence - sentence extracted from the feedback that was annotated</li> <li>Aspect - class representing the aspect of the sentence</li> <li>Emotion - class representing the learning-centered emotion of the sentence</li> </ul> </li> <li>emocije_srpski.json - annotated text with columns and classes written in Serbian. Columns are: <ul> <li>Komentar - the entire student feedback</li> <li>Recenica - sentence extracted from the entire feedback that was annotated</li> <li>Aspekt - class representing the aspect of the sentence</li> <li>Emotion - class representing the learning-centered emotion of the sentence.</li> </ul> </li> </ul> Annotators annotated the dataset based on the annotation procedure and guidelines available <a href="https://github.com/Clean-CaDET/student-feedback-mining">here</a>.  <h2>Citation</h2> If you use this in your research, please cite: <blockquote> Vidaković, D., Luburić, N., Kovačević, A., & Slivka, J. Enhancing software and learning with Serbian student feedback corpora. Language Resources & Evaluation (2025). https://doi.org/10.1007/s10579-025-09855-y </blockquote>

opencc-by-4.0Jul 2024View details →

Transfer learning with generative models for object detection on limited datasets

The provided datasets are used for the analysis in the work "Transfer learning with generative models for object detection on limited datasets" (https://doi.org/10.1088/2632-2153/ad65b5). The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine. The provided datasets are built with the help of Gligen and the already existing NuImages, Ozfish and Deepfish datasets. The file "CarGenerated.zip" contains images generated with Gligen and with provided bounding boxes around cars in an urban environment. The file "fishes_on_bkg.zip" provides fish images generated with fishes from Deepfish inpainted with Gligen on generated backgrounds. The file "fish_text.zip" contains images completely generated with Gligen containing fishes with annotated bounding boxes. Finally, the file "oz_masked_512.zip" contains a simpler dataset of copy paste images of Deepfish fishes on Ozfish backrounds. All the files contains the images saved in different folders for training and validation, plus an index file called gt_fish.csv for the bounding boxes.

opencc-by-4.0Apr 2019View details →

Figure 2. a in Evidence of Ehrlichia chaffeensis in Argentina through molecular detection in marsh deer (Blastocerus dichotomus)

Figure 2. a_Summary of the E. chaffeensis molecular amplification of the 16SrRNA and the VLPT genes in marsh deer and tick samples at different time points. b_ Alignment of a fragment of the 16SrRNA nucleotide sequences from reference Anaplasma and Ehrlichia strains and those amplified from deer (C2, C7, D11 and C12) and a tick (tick1_C7) in the present study. The red line marks the hypervariable V1 region.

opencc-by-4.0Apr 2019View details →

Fig. 1 in Evidence of Ehrlichia chaffeensis in Argentina through molecular detection in marsh deer (Blastocerus dichotomus)

Fig. 1. Map of the two marsh deer populations (Paraná River Delta and Ibera Wetlands) located along the alluvial plain of the Paraná River (Argentina).

opencc-by-4.0Aug 2018View details →

Fig. 3. Seasonal detection rate for E. uekii, type B in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps

Fig. 3. Seasonal detection rate for E. uekii, type B, and mixed Eimeria spp. oocyst infection in both adults and chicks in 2006 and 2007. Numbers in parentheses below months indicate the total number of fecal samples analyzed. Data for the number of chicks (23 in 2006 and 11 in 2007) were only available for August.

opencc-by-4.0Apr 2018View details →

Fig. 3 in Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer

Fig. 3. Phylogenetic relationship of Plasmodium sequences from Brazilian pampas deer within ungulate Plasmodium spp. The tree was constructed using concatenated partial nucleotide sequences of cytb and cox1 by maximum likelihood (ML) method based on the GTR + I + G model. Bootstrap values (BV) for ML with 1000 replicates of ultrafast bootstrap analysis and Bayesian posterior probability (BPP) are indicated for each internal branch. The compositions of collapsed clades are described in Fig. 2 legend. The length for the substitutions/site (0.07) is indicated.

opencc-by-4.0Apr 2018View details →

Fig. 2 in Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer

Fig. 2. Phylogenetic relationships of Plasmodium sequences from Brazilian pampas deer within Haemosporidia. The tree was constructed using ∼3.4 kb of partial mitochondrial nucleotide sequences by the maximum likelihood (ML) method based on the GTR + I + G model. Bootstrap values (BV) for ML with 1000 replicates of ultrafast bootstrap analysis and Bayesian posterior probability (BPP) are indicated for each internal branch. The compositions of collapsed clades are Leucocytozoon (L. fringillinarium, L. majoris, and L. sabrasezi); Haemoproteus and Parahaemoproteus (Haemoproteus sp. jb1.JA27, Haemoproteus sp. jb2.SEW5141, and Parahaemoproteus vireonis); and bird, lizard and non-ungulate mammalian Plasmodium (P. gallinaceum, P. relictum, P. juxtinucleare, P. lutzi, P. floridense, P. mexicanum, P. falciparum, P. vivax, P. malariae, P. ovale, P. coatneyi, P. cynomolgi, P. fieldi, P. gonderi, P. inui, P. knowlesi, P. fragile, P. simiovale, P. simium, P. hylobati, P. reichenowi, P. billicollinsi, P. billbrayi, P. berghei, P. chabaudi, P. vinckei, and P. yoelii). Mitochondrial DNA sequences (including cytb and cox1) used in this study were listed in the Supplementary Table S1 of Templeton et al. (2016a). Nucleotide sequences of Plasmodium sp. in the North American white-tailed deer was based on Table S4 of Martinsen et al. (2016). Nucleotide positions containing indels or undetermined nucleotides, or those where the alignment was not clearly made were excluded. Nucleotide positions corresponding to the P. falciparum mtDNA (NC_002375.1) 974–1502, 1509–1576, 1578–1628, 1637–1678, 1698–1760, 1762–1769, 1774–1800, 1806–1831, 1834–1867, 1870–1909, 1914–2031, 2050–3474, and 3486–4444 were used. The length for the substitutions/site (0.04) is indicated.

opencc-by-4.0Aug 2018View details →

Fig. 1 in Detecting co-infections of Echinococcus multilocularis and Echinococcus canadensis in coyotes and red foxes in Alberta, Canada using real-time PCR

Fig. 1. Standard curve for qPCR assays to detect E. canadensis and E. multilocularis using Cox143 and Nad234 primers/probes, respectively.