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opencc-by-4.0Jan 2018View details →

Evolved eavesdropping: sympatric but not allopatric honey bee species can detect and use hornet alarm pheromone for defence

Eavesdropping is predicted to evolve between sympatric, but not allopatric, predator and prey. The evolutionary arms race between Asian honey bees and their hornet predators has led to a remarkable defence, heat-balling, which suffocates hornets with heat and carbon dioxide. We show that the sympatric Asian species, Apis cerana(Ac), formed heat balls in response to Ac and hornet (Vespavelutina) alarm pheromones, demonstrating eavesdropping. The allopatric species, Apismellifera(Am), only weakly responded to a live hornet and Am alarm pheromone, butnot to hornet alarm pheromone. We observed typical hornet alarm pheromone releasing behaviour, hornet sting extension, when guard bees initially attacked. Once heat balls were formed, guards released honey bee sting alarm pheromones: isopentyl acetate, octyl acetate, (E)-2-decen-1-yl acetate, and benzyl acetate. Only Ac heat-balled in response to realistic bee alarm pheromone component levels, <1 bee-equivalent (1 µg), of isopentyl acetate. Detailed eavesdropping experiments showed that Ac, but not Am, formed heat-balls in response to a synthetic blend of hornet alarm pheromone. Only Ac antennae showed strong, consistent responses to hornet alarm pheromone compounds and venom volatiles. These data provide the first evidence that the sympatric Ac, but not the allopatric Am, can eavesdrop upon hornet alarm pheromone and uses this information, in addition to bee alarm pheromone, to heat-ball hornets. Evolution has likely given Ac this eavesdropping ability, an adaptation that the allopatric Am does not possess.

opencc-by-4.0Dec 2019View details →

Fig. 4 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife

Fig. 4. qPCR detection of intracellular stages of Eimeria in cecum and ileum from Mus musculus. -Delta Ct value (CtEimeria - CtMouse) from each tissue for 164 mice are plotted on the graph. The dotted line indicate the threshold of −5, values above the line are considered positive for the corresponding tissue. Circles represent negative samples, triangles indicate samples with Eimeria species identification and colors correspond to the Eimeria species identified in those samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Dec 2019View details →

Fig. 1 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife

Fig. 1. Geographical localization of house mice (Mus musculus) collected for this study and comparison of diagnostic methods for Eimeria. A) Localization from the 378 mice included in the present study, colors indicate the Eimeria species identified for each. B) Venn diagram showing the overlap between detection methods and successful genotyping identification of the isolates. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Dec 2019View details →

Fig. 3 in Detection and quantification of house mouse Eimeria at the species level - Challenges and solutions for the assessment of coccidia in wildlife

Fig. 3. Morphological and morphometrical characteristics of Eimeria oocyst isolated from Mus musculus. a) Photomicrographs at 1000x amplification of Eimeria oocyst from the three species isolated from Mus musculus (red = E. falciformis; green = E. ferrisi and yellow = E. vermiformis). Length/Width ratio from b) oocyst and c) sporocysts corresponding to each species (E. falciformis n = 31; E. ferrisi n = 127 and E. vermiformis n = 35). Mean ± 95% Confidence Interval is plotted. * Represent significant difference (Tukey HSD, p <0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Detection and molecular characterization of Cryptosporidium species in wildcaught pet spiny-tailed lizards

Fig. 2. Phylogenetic relationships between Cryptosporidium sp. from the mastigures and other Cryptosporidium spp. The mid-point tree was generated with the neighbor-joining method using Tamura 3-parameter plus Gamma distribution. Nodal values represent boot strap values (> 50%) for neighbor-joining (left) and ML (right). Circle represents the major reptile-associated species. Bar represents the number of nucleotide substitutions per sites.

Fig. 3 in Detection and molecular characterization of Cryptosporidium species in wildcaught pet spiny-tailed lizards

Fig. 3. Phylogenetic relationships of Cryptosporidium detected from the Arabian blue mastigure and closely-related species/subtypes as inferred by a maximum likelihood analysis of concatenated sequences were constructed from the partial DNA sequences of SSU (left), actin (middle), and HSP70 (right) loci. Numbers represent the boot strap values for NJ (left) and ML (Right). Bar represents the number of nucleotide substitutions per sites. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Detection and molecular characterization of Cryptosporidium species in wildcaught pet spiny-tailed lizards

Fig. 1. Histological findings of the deceased Arabian blue mastigure. (A) jejunoileum showing villous atrophy, muscle layer edema, and degenerated epithelial cells in the lumen. Bar = 500 μm. (B) Cryptosporidium appear as rounded purple structures (arrowheads) on the microvilli of the epithelial cells. HE staining. Bar = 50 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species

Fig. 2. Coccidia-specific PCR to confirm the presence of DNA of various parasite species used to test the analytic specificity of the BdanjoRT1 real-time PCR. (1) Besnoitia darlingi, (2) B. neotomofelis, (3) B. oryctofelisi, (4) B. besnoiti (Evora isolate), (5) B. bennetti (Texas), (6) B. tarandi (Bt-CA-Quebec1), (7) Toxoplasma gondii, (8) Hammondia hammondi, (9) Neospora caninum, (10) H. heydorni, (11) Cystoisospora felis, (12) C. rivolta, (13) C. burrowsi, (14) C. canis, (15) Sarcocystis cruzi and (16) Crytosporidium parvum. Presence of (17) Giardia duodenalis and (18) Tritrichomonas foetus DNA was shown by amplification using species or genus-specific primers, respectively. C, negative control; M, marker.

Fig. 3 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species

Fig. 3. Analytic sensitivity (A) and standard curve (B) obtained for the threshold cycle (Cq) values obtained in the BdanjoRT1 real-time PCR using varying amounts of genomic Besnoitia darlingi DNA (approximately equivalent to the DNA content of 10.000 [blue], 1000 [green], 100 [red], 10 [brown] and 1 [black] B. darlingi tachyzoites) diluted in 100 ng/μl mouse DNA. Cq values used for regression are displayed as circles. Results on samples resembling DNA of 0.1 tachyzoite were not included in regression, since only two of four samples had reacted with a Cq value of 37.8 or 38.3 (displayed grey in A and as crosses in B). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species

Fig. 1. Location of the primers and the probe of the Besnoitia darlingi/B.neotomofelis /B. oryctofelisi-specific real-time PCR assay BdanjoRT1 within the ITS-1 region of the rRNA gene. The sequences of the ITS-1 region of B. akodoni (AY545987, bold), B. jellisoni (AF076860, bold), B. neotomofelis (HQ909085, bold), B. darlingi (AF489696, bold) and B. oryctofelisi (AY182000, bold), were aligned relative to sequences of other Besnoitia spp. including B. besnoiti from Portugal, Spain and Germany, B. bennetti reported from the USA and Belgium, B. tarandi from Canada and Finland and those of Neospora caninum, Hammondia heydorni, Toxoplasma gondii and H. hammondi by using Clustal V (DNAStar, Madisin, Wisconsin, USA). Deletions and substitutions in the sequences relative to and within the clade of B. acodoni, B. jellisoni, B. neotomofelis, B. darlingi and B. oryctofelisi are indicated by black background. Sequences of the primer BdanjoRev and the Probe Bb11-12 are displayed in their complementary form. The probe Bb11-12 was established for a real-time PCR to detect B. besnoiti, but it is universal and can be used for the detection of all Besnoitia spp. mentioned here.

opencc-by-4.0Dec 2020View details →

Fig. 3. Maximum likelihood tree for Culex species showing the 5 clades representing 5 subgroups. Clade I in Mosquito identification and haemosporidian parasites detection in the enclosure of the African penguins (Spheniscus demersus) at the SANBI zoological garden

Fig. 3. Maximum likelihood tree for Culex species showing the 5 clades representing 5 subgroups. Clade I is the Trifilatus Subgroup (Mattingly and Rageau, 1958) for Cx. torrentium; Clade II and III are the Pipiens Complex; Clade IV the Theileri Subgroup (Sirivanakarn, 1976) for Cx. theileri; and Clade V is the Tarsalis (Edwards, 1932) for Cx. declaratory and Apicinus Subgroups (Edwards, 1932) for Cx. mollis. Lutzia sp. used as outgroups. Sequences from this study are indicated by asterisks (*).

opencc-by-4.0Aug 2024View details →

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"

opencc-by-4.0Oct 2022View details →

A deep learning dataset for underwater object detection of tropical freshwater fish species in northern Australia

This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia.  Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the <a href="https://www.dcceew.gov.au/science-research/supervising-scientist">Supervising Scientists</a> annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found <a href="https://www.dcceew.gov.au/sites/default/files/documents/ss-atr-2020-21.pdf">here</a>. <ul> <li>All images are in .jpg format and are 1920x1080 in dimension.</li> <li>Bounding box annotations are in COCO format. </li> </ul> Two .zip files are included: <ul> <li><a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-CompactModel.zip">202210-KakaduFishAI-CompactModel.zip</a>: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. </li> <li> <a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-TrainingData.zip">202210-KakaduFishAI-TrainingData.zip</a>: includes all images and one COCO (.json) file with annotations. </li> </ul> Fish taxa include:  <ol> <li>Ambassis agrammus</li> <li>Ambassis macleayi</li> <li>Amniataba percoides</li> <li>Craterocephalus stercusmuscarum</li> <li>Denariusa bandata</li> <li>Glossamia aprion</li> <li>Glossogobius spp.</li> <li>Hephaestus fuliginosus</li> <li>Lates calcarifer</li> <li>Leiopotherapon unicolor</li> <li>Liza ordensis</li> <li>Megalops cyprinoides</li> <li>Melanotaenia nigrans</li> <li>Melanotaenia splendida inornata</li> <li>Mogurnda mogurnda</li> <li>Nemetalosa erebi</li> <li>Neoarius spp.</li> <li>Neosilurus spp.</li> <li>Oxyeleotris spp.</li> <li>Scleropages jardinii</li> <li>Strongylura kreffti</li> <li>Syncomistes butleri</li> <li>Toxotes chatareus</li> </ol> If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.

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Hourly detections of echolocation clicks in Hawaiian Island HARP data from Hawai`i, Kaua`i, and Manawai with species labels

This dataset consists of counts of detections of echolocation clicks at three sites in the Hawaiian Islands Archipelago. These sites are Hawaii, Kauai, and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into one-minute bins and each bin was given a species' label. The data provided here is further binned at an hourly level, where counts of a given species represent the number of one-minute bins within a given hour that were labeled as that species (up to a maximum of 60). One file is provided per site, and files are in .csv format that can be read using any desired coding language.

opencc-zeroDec 2022View details →

opencc-by-4.0Apr 2023View details →

Species detection histories used in Killion et al. (2023): Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models

Camera trap species detection histories used for occupancy models in "Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models".

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Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks

In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HyDe to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program bpp to reanalyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HyDe, and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data.

opencc-zeroJun 2023View details →

Fig. 3 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 3. Phylogenetic tree based on D1/D2 LSU rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.

Fig. 5. A in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 5. A comparison of the line drawings in Stein (1883) and micrographs of Tripos eugrammus (A) and Dinophysis tripos (B). The arrows point the Stein's drawing style with acuter appendices than in the real morphology of the cells.

Fig. 2 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 2. Phylogenetic tree based on SSU rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.

Fig. 4 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 4. Phylogenetic tree based on ITS rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.