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Figure 6 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 6 Neighbor-Joining tree (Kimura 2 parameter, built with MEGA 6; cf. Tamura et al. 2013) of Eidophasia spp. and type species of selected Plutellidae genera. The width of the triangles represents the sample size, and the depth the genetic variation within the cluster. Source: DNA Barcode data from BOLD (Barcode of Life Database, cf. Ratnasingham and Hebert 2007).
Figure 4 from: Huemer P, Sohn J-C (2020) Eidophasia assmanni sp. nov., the first alpine representative of the genus, detected in the Russian Altai Mountains (Lepidoptera, Plutellidae). ZooKeys 959: 99-111. https://doi.org/10.3897/zookeys.959.54259
Figure 4 Eidophasia vanella (Walsingham, 1881), male genitalia, Canada, gen. slide 20/1586 P. Huemer. Scale bar: 0.2 mm.
Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations
<b>Description: </b><p>Species detection data for 28 medium-large mammal species obtained using camera trap methods across a logging-induced degradation gradient. Cameras were deployed using a paired design across 74 sampling locations. Data were used to explore species-habitat associations with LiDAR-derived measures of forest structure.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/26"><b>Understanding covariation between mammalian diversity and forest carbon across a human-modified tropical landscape</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, NE/K016407/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FK016407%2F1&classtype=ENRIs&classification=Biodiversity&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FK016407%2F1&classtype=ENRIs&classification=Biodiversity&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4010757">here</a></p><p><b>Files: </b>This consists of 1 file: DeereEtAl2020_PNAS.xlsx</p><p><b>DeereEtAl2020_PNAS.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Site metadata and deployment details</b> (described in worksheet Deployment)</p><p>Description: Details of sampling locations, operational dates and survey effort for each camera trap station (N=126)</p><p>Number of fields: 9</p><p>Number of data rows: 126</p><p>Fields: </p><ul><li><b>Site_ID</b>: Unique alphanumeric identifier of the location camera traps were deployed (Field type: location)</li><li><b>Habitat_Class</b>: Forest condition relative to logging-indiced degradation. Follows Putz and Redford Classification scheme (Putz, Francis E., and Kent H. Redford. "The importance of defining 'forest': tropical forest degradation, deforestation, long‐term phase shifts, and further transitions." Biotropica 42.1 (2010): 10-20) (Field type: categorical)</li><li><b>Latitude</b>: Geographic coordinate of camera trap location (Field type: latitude)</li><li><b>Longitude</b>: Geographic coordinate of camera trap location (Field type: longitude)</li><li><b>Date_On</b>: Date camera traps were deployed (Field type: date)</li><li><b>Time_On</b>: Time camera traps were deployed (Field type: time)</li><li><b>Date_Off</b>: Date camera traps were collected (Field type: date)</li><li><b>Time_Off</b>: Time camera traps were collected (Field type: time)</li><li><b>CTNs</b>: Total survey effort for camera trap station (Field type: numeric)</li></ul></li><li><p><b>Species detection data</b> (described in worksheet Detection)</p><p>Description: Raw camera trap detection data for 28 medium-large mammal species obtained from 126 camera trap stations deployed using a paired design across 74 sampling locations </p><p>Number of fields: 7</p><p>Number of data rows: 29008</p><p>Fields: </p><ul><li><b>Site</b>: Unique alphanumeric identifier of the location camera traps were deployed (Field type: location)</li><li><b>common_name</b>: Mammal species identifier (Field type: taxa)</li><li><b>Sp_ID</b>: Numeric species identifier, used to coerce dataframe into a 4D array (Field type: id)</li><li><b>Site_ID</b>: Numeric site identifier, used to coerce dataframe into a 4D array (Field type: id)</li><li><b>Spatial_Rep</b>: Spatial replicate indicative of the number of camera trap stations deployed at a site. Also used to coerce dataframe into a 4d array (Field type: replicate)</li><li><b>Temporal_Rep</b>: Temporal replicate, each comprising six camera trap nights (Field type: replicate)</li><li><b>Detection</b>: Presence/absence of species during survey period (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2014-06-20 to 2017-10-09</p><p><b>Latitudinal extent: </b>4.5536 to 4.8121</p><p><b>Longitudinal extent: </b>117.4122 to 117.7398</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Rheithrosciurus</i> <br> -  -  -  -  -  -  -  <i>Rheithrosciurus macrotis</i> <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Arctictis</i> <br> -  -  -  -  -  -  -  <i>Arctictis binturong</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  -  <i>Neofelis</i> <br> -  -  -  -  -  -  -  <i>Neofelis diardi</i> <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Martes</i> <br> -  -  -  -  -  -  -  <i>Martes flavigula</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  -  Herpestidae <br> -  -  -  -  -  -  <i>Herpestes</i> <br> -  -  -  -  -  -  -  <i>Herpestes brachyurus</i> <br> -  -  -  -  -  Mephitidae <br> -  -  -  -  -  -  <i>Mydaus</i> <br> -  -  -  -  -  -  -  <i>Mydaus javanensis</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br></div><p></p>
Figures 2-7 from: Abram PK, McPherson AE, Kula R, Hueppelsheuser T, Thiessen J, Perlman SJ, Curtis CI, Fraser JL, Tam J, Carrillo J, Gates M, Scheffer S, Lewis M, Buffington M (2020) New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. Journal of Hymenoptera Research 78: 1-17. https://doi.org/10.3897/jhr.78.55026
Figures 2-7 Ganaspis brasiliensis (2, 4); Leptopilina japonica (3, 5); Pachycrepoideus vindemmiae (6); Asobara sp. (7).
Figure 1 from: Abram PK, McPherson AE, Kula R, Hueppelsheuser T, Thiessen J, Perlman SJ, Curtis CI, Fraser JL, Tam J, Carrillo J, Gates M, Scheffer S, Lewis M, Buffington M (2020) New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. Journal of Hymenoptera Research 78: 1-17. https://doi.org/10.3897/jhr.78.55026
Figure 1 Map of sites where Leptopilina japonica (red circles), or both L. japonica and Ganaspis brasiliensis (green circles) were found in British Columbia, Canada in 2016 and 2019. The red box in the inset shows where the mapped area is situated in North America. Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
Automated detection of Hainan gibbon calls for passive acoustic monitoring
<p>Data accompanying the paper: "Automated detection of Hainan gibbon calls for passive acoustic monitoring"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Durbach, Ian and Hansford, James and Hoepfner, Amanda and Ma, Heidi and Bryant, Jessica and Stender, Christina and Li, Wenyong and Liu, Zhiwei and Chen, Qing and Zhou, Zhaoli and Turvey, Samuel. (2020). Automated detection of Hainan gibbon calls for passive acoustic monitoring. BioRxiv doi: https://doi.org/10.1101/2020.09.07.285502</p> </blockquote> <p>The Hainan gibbon is the world's rarest primate and one of the world's rarest mammals, with only a single population of about 30 individuals surviving in Bawangling National Nature Reserve (BNNR), Hainan, China. Eight Song Meter SM3 recorders (Wildlife Acoustics, Maynard, Massachusetts) were used to collect acoustic data from 1 March to 20 August 2016 within BNNR. Recorders were attached to trees at approximately 1.5 meters from the ground in tropical evergreen forest. Recorders were set to record for eight hours each day from the time of sunrise, which varied between approximately 05:00 and 06:00 during the study period. Devices did not record continuously throughout the entire survey period due to logistical and technical issues; in total, survey days per recorder varied between 79 and 129 days, and roughly 6,000 hours of recordings were collected. The majority of recordings were made with a sampling rate of 9,600Hz and bit depth of 16, with isolated recordings at 28,800Hz.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <p>Train.zip - contains the training .wav audio files</p> <p>Train_Labels.zip - contains the labels for the training data</p> <p>Test.zip - contains the testing .wav audio files</p> <p>Test_Labels.zip - contains the labels for the test data</p> <p>Extra_Labelled_Data.zip - contains extra data that was labelled and non-gibbon calls used for training</p> <p>Extra_Labels.zip - contains the labels for the extra labelled data</p> <p>Unlabelled_Data.zip - contains additional .wav audio files which have not been labelled. These are split into various files (1-15) and can be downloaded individually.</p> <p>Code.zip - contains all the software scripts and notebooks</p> <p>Manual-zip - contains the user manual</p> <p><strong>Labels</strong></p> <p>The names of the labelled files start with either "g_" or "n_", for example "g_HGSM3D_0+1_20160429_051600.data" and "n_HGSM3D_0+1_20160429_051600.data". Files starting with "g_" contain the timestamps of the gibbon calls, and files starting with "n_" contain the timestamps of non-gibbon calls (e.g. background noise and bird calls). An audio file will have both a "g_" and "n_" file. Each file has the following format: Start,End,Duration,Type,Notes, where "start" denotes the start time in seconds, "end" denotes the end time in seconds, "duration" denotes the duration (end - start) in seconds, "type" denotes the type of call/noise and "notes" are additional notes which we labelled.</p> <p><strong>Types</strong></p> <p>The legend for the "type" column in the labelled files is defined as follows. The types for gibbon and non-gibbon files are different and we distinguish this below.</p> <p> </p> <p>Gibbon files ("g_")</p> <p>type 1 = one pulse gibbon call</p> <p>type 2 = multiple pulse gibbon call (check "notes column" below)</p> <p>type 3 = duet gibbon call</p> <p> </p> <p><em>Notes column (only available in gibbon files)</em></p> <p>One of the following: 2 pulse call, 3 pulse call, 4 pulse call, 5 pulse call or 6 pulse call.</p> <p> </p> <p>Non-gibbon files ("n_")</p> <p>type 1 = rain</p> <p>type 2 = other species (e.g. birds)</p> <p>type 3 = rain and other species</p> <p>type 4 = rain and external noise (e.g. aircraft)</p> <p>type 5 = natural sounds and external noise</p> <p>type 6 = natural sounds and other species</p> <p><strong>Training files (containing gibbon calls):</strong></p> <p>HGSM3AC_0+1_20160309_055600<br> HGSM3AC_0+1_20160312_055400<br> HGSM3A_0+1_20160304_060000<br> HGSM3BD_0+1_20160305_060000<br> HGSM3AC_0+1_20160314_055200<br> HGSM3B_0+1_20150616_050500<br> HGSM3BD_0+1_20160402_053600<br> HGSM3D_0+1_20160429_051600<br> HGSM3B_0+1_20160305_060000<br> HGSM3C_0+1_20160501_051500<br> HGSM3SOL_0+1_20160320_054700<br> HGSM3SOL_0+1_20160405_053400<br> HGSM3BD_0+1_20160401_053700</p> <p><strong>Testing files:</strong></p> <p>HGSM3B_0+1_20160323_054500<br> HGSM3B_0+1_20160321_054700<br> HGSM3B_0+1_20160306_055900<br> HGSM3B_0+1_20160308_055700<br> HGSM3B_0+1_20160309_055600<br> HGSM3B_0+1_20160316_055100<br> HGSM3B_0+1_20160311_055500<br> HGSM3B_0+1_20160304_060000<br> HGSM3B_0+1_20160322_054600</p>
Genome-wide SNPs detect no evidence of genetic population structure for reef manta rays (Mobula alfredi) in southern Mozambique
Little is known about the extent of genetic connectivity along continuous coastlines in manta rays or whether site visitation is influenced by relatedness. Such information is pertinent to defining population boundaries and understanding localised dispersal patterns and behaviour. Here, we use 3057 genome-wide single nucleotide polymorphisms (SNPs) to evaluate population genetic structure and assess levels of relatedness at aggregation sites of reef manta rays (Mobula alfredi) in southern Mozambique (n = 114). Contrary to indications of limited dispersal along the southern Mozambican coastline inferred from photo identification and telemetry studies, our results show no evidence of population structure (non-significant FST <0.001) for M. alfredi along this coast. We also found no evidence that individuals sampled at the same site were more related than expected by chance for males, females or across both sexes, suggesting kinship may not influence visitation pattern s at these sites. We estimated the effective population size (Ne) of this population to be 375 (95% CI = 369-380). Comparison to a distant eastern Indian Ocean site (Western Australia; n = 15) revealed strong genetic differentiation between Mozambique and Western Australia (FST = 0.377), identifying the Indian Ocean basin as a barrier to dispersal. Our findings show that genetic connectivity in M. alfredi extends for several hundred kilometres along continuous coastlines. We therefore recommend the population in Mozambique be considered a discrete management unit and future conservation plans should prioritize integrated strategies along the entire southern coastline.
A novel colorimetric method for Salmonella spp. detection based on the peroxidase activity of Cu(II)-modified reduced graphene oxide nanoparticles and PCR
<p>Figure 1s. The zeta potential of nanomaterials in sodium acetate buffer (pH 4)</p>
Novel immunoassay detecting p-Tau Thr217 distinguishes Alzheimer's Disease from other dementias
<p><b>Objective</b></p> <p>To investigate whether p-tau T217 assay in cerebrospinal fluid (CSF) can distinguishes Alzheimer's disease from other dementias and healthy controls.</p> <p><b>Methods</b></p> <p>We developed and validated a novel Simoa immunoassay to detect p-tau T217 in CSF. There was a total of 190 participants from three cohorts with AD (n = 77) and other neurodegenerative diseases (n = 69) as well as healthy subjects (n = 44).</p> <p><b>Results</b></p> <p>The p-tau T217 assay (cut-off 242 pg/ml) identified AD subjects with accuracy of 90%, with 78% positive predictive value (PPV), 97% negative predictive value (NPV), 93% sensitivity, 88% specificity compared favorably with p-tau T181 ELISA (52 pg/ml) showing 78% accuracy, 58% PPV, 98% NPV, 71% specificity; 97% sensitivity. The assay distinguished AD patients from age-matched healthy subjects (cut-off 163 pg/ml, sensitivity 98%, specificity 93%) similarly to p-tau T181 ELISA (cut-off 60 pg/ml, 96% sensitivity and 86% specificity). In AD patients, we found a strong correlation between p-tau T217-tau and p-tau T181, t-tau and Aβ40 but not with Aβ42.</p> <p><b>Conclusions</b></p> <p>This study demonstrates that p-tau T217 displayed better diagnostic accuracy than p-tau T181. The data suggests that new p-tau T217 assay has a potential as an AD diagnostic test in the clinical evaluation.</p> <p><b>Classification of Evidence</b>: </p> <p>This study provides Class III evidence that a CSF immunoassay for p-tau T217 distinguishes AD from other dementias and healthy controls.</p>
Raw data associated with the article: "Single-molecule DNA sequencing of widely varying GC-content using nucleotide release, capture and detection in microdroplets.", NAR, Puchtler et.al.
<p>All data taken in the production of the corresponding paper: "Single-molecule DNA sequencing of widely varying GC-content using nucleotide release, capture and detection in microdroplets."</p> <p>The associated manuscript describes a method for DNA sequencing which involves the sequential release of nucleotides from a single, immobilised strand of DNA via pyrophosphorolysis (PPL). Released nucleotides, in the form of dNTPs, are captured in microdroplets which are manipulated using an optical-EWOD platform. A detection chemistry within each droplet releases a specific dye depending on which dNTPs are present, allowing the optical read-out of bases within each droplet. Hence, by capturing bases sequentially within droplets as they are cleaved from the strand of DNA, the sequence can be optically identified.</p>
Multi year social stability and social information use in reef sharks with diel fission-fusion dynamics: Raw acoustic detection
<p><span>Animals across vertebrate taxa form social communities and often exist as fission-fusion societies. Central place foragers (CPF) may form social groups, from which they will predictably disperse to forage, either individually or in smaller groups, before returning to fuse with the larger group. However, the function and stability of social associations in predatory fish acting as CPFs is unknown, as individuals do not need to return to a shelter, yet show fidelity to core areas. Using dynamic social networks generated from acoustic tracking data, we document spatially structured sociality in CPF grey reef sharks at a Pacific Ocean atoll. We show that sharks form stable, social groups over multi-year periods, with some dyadic associations consistent for up to four years. Groups primarily formed during the day, increasing in group size before sharks dispersed from the reef to forage at night. Our simulations suggest that multiple individuals sharing a central place and using social information while foraging (i.e. local enhancement), will outperform non-CPF social foragers. We show multiyear social stability in sharks and suggest that social foraging with information transfer could provide a generalisable mechanism for the emergence of sociality with group central place foraging.</span></p>
From leaf to label: A robust automated workflow for stomata detection: Light microscope images of stomata.
<p>All light microscope images used for training and testing of the deep learning model developed in the study: Meeus S., Van den Bulcke J., wyffels F. (2020) From leaf to label: A robust automated workflow for stomata detection. Ecol. Evol. <a href="https://doi.org/10.1002/ece3.6571">https://doi.org/10.1002/ece3.6571</a>.</p>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>smell_search.py</em> is used for searching review comments that contain code smell terms.</li> </ul> <p> </p> <p><strong>3. project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul>
Figure 7 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 7 COI Molecular phylogeny. Molecular phylogeny of COI-gene sequences (474 bp: 193 variable and 281 conserved) of ocellate and non- ocellate octopus species. Ocellated octopuses are bold-faced. Purple rectangle indicates the clade containing the specimens evaluated in this study.
Figure 4 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 4 Coloration patterns. Coloration patterns observed in live individuals of Octopus oculifer from the Revillagigedo Archipelago A pale body with few reddish/brown spots randomly placed throughout the mantle and arms, entire body with a rugose aspect B brown and smooth body with large well-defined white ovals throughout mantle and arms C rugose and reddish body with large cream ovals of different size D red and smoother (still rugose) body with lesser number of cream ovals of different size E dark red body without ovals and a smooth skin.
Figure 6 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 6 COIII Molecular phylogeny. Molecular phylogeny of COIII-gene sequences (273 bp: 136 variable and 137 conserved) of ocellate and non- ocellate octopus species. Ocellated octopuses are bold-faced. Purple rectangle indicates the clade containing the specimens evaluated in this study.
Figure 5 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 5 Length-weight relationship of Octopus oculifer from the Revillagigedo Archipelago A number of individuals per size class B length-weight plot.
Figure 3 from: Valdez-Cibrián A, Díaz-Santana-Iturrios M, Landa-Jaime V, Michel-Morfín JE (2020) First detection of an ocellate octopus in the Revillagigedos ecoregion, a biodiversity hotspot located in the Tropical East Pacific Province. ZooKeys 986: 81-100. https://doi.org/10.3897/zookeys.986.53250
Figure 3 Morphological features. Morphological features of Octopus oculifer from the Revillagigedo Archipelago A dorsal view; H: hectocotylus, LI-IVA: left arms I-IV B ventral view; LI-IVA: left arms I-IV C ligula Lg and calamus Cl D H: hectocotylus, radulae E funnel organ shape F demibranch G upper and lower beaks.
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
MGMT promoter methylation and IDH mutation detected in 301 patients with GBM
<p>The datasets had listed the information of IDH mutation or wildtype at c.395G>A and MGMT promoter methylation status in 301 samples with GBM.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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OpenNeuro
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