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637 results for “Population analysis”
Fig. 2 in Fossil population structure and mortality analysis of the cave bears from Urşilor Cave, north-western Romania
Fig. 2. Mortality profile of cave bears from Urşilor (~45–40 calendar kyrs BP). A. Right M1 (N = 44). B. Right M2 (N = 36). C. Left mandible (N = 82).
Fig. 1 in Fossil population structure and mortality analysis of the cave bears from Urşilor Cave, north-western Romania
Fig. 1. Geographic localization (A) and plan (C) of Urşilor Cave of Chişcău. B. Long profile (section) of the Excavation Chamber from the Scientific Reserve. D. Plan of the lower level of the cave (= Scientific Reserve).
Fig. 4 in Fossil population structure and mortality analysis of the cave bears from Urşilor Cave, north-western Romania
Fig. 4.Transverse diameters of all of the adult lower (A, N = 74) and upper (B, N = 105) cave bear canines from Urşilor (~45–40 calendar kyrs BP).
REPIN population analysis in 4 Dokdonia genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar dokdonia/in/ dokdonia/out/ 4h-3-7-5.fas 55 21 in/yafM_Ecoli.faa dokdonia.nwk 1e-10 true 1"</p> <p>All input files are located in the folder dokdonia/in/, all output data is located in dokdonia/out/.</p> <p>The input files include the 4 fasta formatted <em>Dokdonia</em> genome files (*.fas) and a RAYT protein sequence called yafM_Ecoli.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "dokdonia.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the 4h-3-7-5 genome: 4h-3-7-5.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the 4h-3-7-5 genome: 4h-3-7-5.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all yafM_Ecoli.faa relatives identified with BLAST+ in the <em>Dokdonia </em>species: yafM_relatives.fna</p> <p>maxREPIN_0.txt Contains the most frequent REPIN identified for each sequence type in each <em>Dokdonia</em> strain.</p> <p> presAbs_0.txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>D. </em>sp. 4h-3-7-5. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_4h-3-7-5_*.out and .out.fas. There is also a 4h-3-7-5_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the 4h-3-7-5 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the 4h-3-7-5 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 4 genomes.</p> <p> </p> <p>For each genome there is one output folder (ending in _0), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p><strong>The dataset was generated using the following external tools:</strong></p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
Fig. 2. Principal Component Analysis plot showing the 42 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. 2. Principal Component Analysis plot showing the 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Fig. S1. Principal Component Analysis plot showing 28 in Evidence of genetic connectivity between fragmented pig populations in a tropical urban city-state
Fig. S1. Principal Component Analysis plot showing 28 out of 42 individuals from the Central Catchment Nature Reserve (CCNR) and the Northeast with kinship values <0.2. Individuals are differentiated by sex and age class. Individuals exhibiting genetic admixture are labelled. Percentage variation accounted for by each principal component is indicated in brackets.
Fig. 1 in Populations analysis of the Brazilian Sharpnose Shark Rhizoprionodon lalandii (Chondrichthyes: Carcharhinidae) on the São Paulo coast, Southern Brazil: inferences from mt DNA sequences
Fig. 1. Median-joining haplotype network. The haplotypes are represented by circles, with the width proportional to their frequencies. Black circles correspond to Praia Grande, white to Ubatuba, and gray to Itanhaém samples. Each branch corresponds to a single mutation, except line a (with 2 mutations) and line b (with 3 mutations).
Fig. 3 in A multi-approach analysis of the genetic diversity in populations of Astyanax aff. bimaculatus Linnaeus, 1758 (Teleostei: Characidae) from Northeastern Brazil
Fig. 3. Giemsa-stained karyotypes of Astyanax aff. bimaculatus (2n = 50, FN = 96) from sites A (a), B (b) and C (c). In (d), a somatic metaphase after silver nitrate staining in a specimen from Contas River, showing four positive signals (arrows). The bar equals 5µm.
Fig. 2 in A multi-approach analysis of the genetic diversity in populations of Astyanax aff. bimaculatus Linnaeus, 1758 (Teleostei: Characidae) from Northeastern Brazil
Fig. 2. Partial view of collection sites of Astyanax aff. bimaculatus in the State of Bahia, Brazil: (a) Contas River, upstream Pedra Dam, Porto Alegre County – site A, (b) Contas River, downstream Pedra Dam, city of Jequié – site B, and (c) Mineiro stream, Recôncavo Sul Basin, city of Itamari – site C. In (d), view of Pedra Dam reservoir in Middle Contas River, city of Jequié.
Fig. 1 in A multi-approach analysis of the genetic diversity in populations of Astyanax aff. bimaculatus Linnaeus, 1758 (Teleostei: Characidae) from Northeastern Brazil
Fig. 1. Map of the studied area in the State of Bahia, Brazil, showing the hydrographic system and collection sites of Astyanax aff. bimaculatus: (a) site A - Contas River, upstream of Pedra Dam, Porto Alegre County (b) site B - Contas River, downstream of Pedra Dam, city of Jequié (Contas River Basin), (c) site C - Mineiro stream, city of Itamari (Recôncavo Sul Basin) and (*) location of Pedra Dam in Contas River. A specimen of Astyanax aff. bimaculatus is illustrated in detail (total length = 6.65 cm).
Fig. 1 in Comparative dietary analysis of two populations of Mimagoniates rheocharis (Characidae: Glandulocaudinae) from two streams of Southern Brazil
Fig. 1. Percent composition of dietary items found in the stomachs of Mimagoniates rheocharis. (a) Station 1, March 1998 to March 1999 and (b) Station 2, January 1998 to February 1999.
Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
<p>The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed.</p>
Global burden of non-tuberculous mycobacteria in the cystic fibrosis population: A systematic review and meta-analysis
<p><span><strong>Background</strong>:</span><span> People living with cystic fibrosis have an increased risk of lung infection with non-tuberculous mycobacteria (NTM), which is reportedly increasing. We conducted a systematic review of the literature to estimate the burden (prevalence and incidence) of non-tuberculous mycobacteria in the cystic fibrosis population. </span></p> <p><span><strong>Methods</strong>: Electronic databases, registries, and grey literature sources were searched for cohort and cross-sectional studies reporting epidemiological measures (incidence and prevalence) of NTM infection or NTM pulmonary disease (NTM-PD) in cystic fibrosis. The last search was conducted in September 2021; we included reports since database creation and registry reports published since 2010. The methodological quality of studies was appraised with the Joanna Briggs Institute tool. A random-effects meta-analysis was conducted to summarize the prevalence of NTM infection, and the remaining results are presented in a narrative synthesis. </span></p> <p><span><strong>Results</strong>: Ninety-five studies were included in this review. All 95 studies reported on NTM infection, and 14 of these also reported on NTM-PD. The pooled estimate for the point prevalence of NTM infection was 7.9% (CI 95%, 5.1–12.0%). In meta-regression, sample size and geographical location of the study modified the estimate. Longitudinal analysis of registry reports showed an increasing trend in NTM infection prevalence between 2010 and 2019. </span></p> <p><span><strong>Conclusions</strong>: The overall prevalence of NTM infection in CF is 7.9% and is increasing over time based on international registry reports. Future studies should report screening frequency, microbial identification methods, and incidence rates of progression from NTM infection to pulmonary disease.</span></p>
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
<p>Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.</p>
Fig 1 in Invertebrate Communities Associated To Parastacus Pugnax (Decapoda, Parastacidae) Northern Patagonian Populations (38° S, Araucania, Chile): A First Exploratory Analysis
Fig 1. Cluster analysis for invertebrate communities reported for sites included in the present study.
Analysis - Threatened North African seagrass meadows have supported green turtle populations for millennia
<p>Scripts and data to run bagplots and discriminant analysis associated with the paper:</p> <p>Threatened North African seagrass meadows have supported green turtle populations for millennia, de Kock et al.</p>
Fig. 7 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 7 – Comparison of the sizes of foraging areas of polycalic colonies and supercolonies of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges: a, Crimea, b, – Rostov-on-Don, c, Tashkent.
Fig. 6 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 6 – Average values of the average size of the foraging areas of Crematogaster subdentata (a) and Lasius neglectus (b) in the primary and secondary ranges. C – Crimea, T – Tashkent, R – Rostov-on-Don.
Fig. 5 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 5 – Relation of tree and shrub species visited by Crematogaster subdentata (a, Crimea, b, Rostov-on-Don, c, Tashkent) and Lasius neglectus (d, – Crimea, e, – Rostov-on-Don, f, – Tashkent). Trees: Ac – Acer sp., Ah – Aesculus hippocastanum, Aj – Albizia julibrissin, Al – Ailanthus altissima, An – Acer negundo, Cl – Cedrus libani, Co – Cydonia oblonga, Cs – Cupressus sempervirens, Ea – Elaeagnus angustifolia, Fe – Fraxinus excelsior, Fr – Fraxinus sp., Gl – Gleditsia triacanta, Jr – Juglans regia, M – Morus sp., Md – Malus domestica, Mn – Morus nigra, Pa – Prunus americana, Pb - Pinus brutia, Pc – Prunus cerasus, Pd – Prunus domestica, Pi – Pinus pallasiana, Pp – Populus niger, Po – Populus alba, Pr – Prunus cerasifera, Ps – Prunus spinosa, Py - Populus pyramidalis, Ra – Robinia pseudoacacia, Sa – Salix sp., Sj – Styphnolobium japonicum, Tl – Tilia sp., Ul – Ulmus sp., Us – Ulmus laevis.
Fig. 1 in Comparative analysis of the population structure of Crematogaster subdentata and Lasius neglectus in the primary and secondary ranges (Hymenoptera: Formicidae)
Fig. 1 – Distribution of Crematogaster subdentata and Lasius neglectus in Tashkent C. subdentata, polycalic colonies, C. subdentata, monocalic colonies, L. neglectus,> 20 nests per 100 m, L. neglectus, 10-20 nests per 100 m, L. neglectus, <10 nests per 100 m.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.