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169 results for “selective inference”
Data from: Low major histocompatibility complex class II variation in the endangered Indo-Pacific humpback dolphin (Sousa chinensis): inferences about the role of balancing selection
It has been widely reported that the major histocompatibility complex (MHC) is under balancing selection due to its immune function across terrestrial and aquatic mammals. The comprehensive studies at MHC and other neutral loci could give us a synthetic evaluation about the major force determining genetic diversity of species. Previously, a low level of genetic diversity has been reported among the Indo-Pacific humpback dolphin (Sousa chinensis) in the Pearl River Estuary (PRE) using both mitochondrial marker and microsatellite loci. Here, the expression and sequence polymorphism of 2 MHC class II genes (DQB and DRB) in 32 S. chinensis from PRE collected between 2003 and 2011 were investigated. High ratios of non-synonymous to synonymous substitution rates, codon-based selection analysis, and trans-species polymorphism (TSP) support the hypothesis that balancing selection acted on S. chinensis MHC sequences. However, only 2 haplotypes were detected at either DQB or DRB loci. Moreover, the lack of deviation from the Hardy–Weinberg expectation at DRB locus combined with the relatively low heterozygosity at both DQB locus and microsatellite loci suggested that balancing selection might not be sufficient, which further suggested that genetic drift associated with historical bottlenecks was not mitigated by balancing selection in terms of the loss of MHC and neutral variation in S. chinensis. The combined results highlighted the importance of maintaining the genetic diversity of the endangered S. chinensis.
FIGURE 57 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 57. Patellapis (Ch.) sabinae, male: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga T1 and T2 (scale=1mm); e. Body (scale=1mm); f. Genitalia, dorsal view (scale=100µm).
FIGURE 65 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 65. Patellapis (Ch.) cinctifera, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
Data from: Phylogenomic inference and demographic model selection suggest peripatric separation of the cryptic steppe ant species Plagiolepis pyrenaica stat. rev.
<p>The ant <em>Plagiolepis taurica</em> Santschi, 1920 (Hymenoptera, Formicidae) is a typical species of the Eurasian steppes, a large grassland-dominated biome that stretches continuously from Central Asia to Eastern Europe and is represented by disjunct outposts also in Central and Western Europe. The extent of this biome has been influenced by the Pleistocene climate, and steppes expanded recurrently during cold stages and contracted in warm stages. Consequently, stenotopic steppe species such as <em>P. taurica</em> repeatedly went through periods of demographic expansion and severe isolation. Here, we explore the impact of these dynamics on the genetic diversification within <em>P. taurica</em>. Delimitation of <em>P. taurica</em> from other Plagiolepis species has been unclear since its initial description, which raised questions on both its classification and its spatiotemporal diversification early on. We re‐evaluate species limits and explore underlying mechanisms driving speciation by using an integrative approach based on genomic and morphometric data. We found large intraspecific divergence within <em>P. taurica</em> and resolved geographically coherent western and eastern genetic groups, which likewise differed morphologically. A morphometric survey of type material showed that Plagiolepis from the western group were more similar to <em>P. barbara</em> pyrenaica Emery, 1921 than to <em>P. taurica</em>; we thus lift the former from synonymy and establish it as separate species, <em>P. pyrenaica</em> stat. rev. Explicit evolutionary model testing based on genomic data supported a peripatric speciation for the species pair, probably as a consequence of steppe contraction and isolation during the mid‐Pleistocene. We speculate that this scenario could be exemplary for many stenotopic steppe species, given the emphasized dynamics of Eurasian steppes.</p>
Data from: Tissue storage and primer selection influence pyrosequencing-based inferences of diversity and community composition of endolichenic and endophytic fungi
Next-generation sequencing technologies have provided unprecedented insights into fungal diversity and ecology. However, intrinsic biases and insufficient quality control in next-generation methods can lead to difficult-to-detect errors in estimating fungal community richness, distributions, and composition. The aim of this study was to examine how tissue storage prior to DNA extraction, primer design, and various quality-control approaches commonly used in 454 amplicon pyrosequencing might influence ecological inferences in studies of endophytic and endolichenic fungi. We first contrast 454 data sets generated contemporaneously from subsets of the same plant and lichen tissues that were stored in CTAB buffer, dried in silica gel, or freshly frozen prior to DNA extraction. We show that storage in silica gel markedly limits the recovery of sequence data and yields a small fraction of the diversity observed by the other two methods. Using lichen mycobiont sequences as internal positive controls, we next show that despite careful filtering of raw reads and utilization of current best-practice OTU clustering methods, homopolymer errors in sequences representing rare taxa artificially increased estimates of richness ca. 15-fold in a model data set. Third, we show that inferences regarding endolichenic diversity can be improved by using a novel primer that reduces amplification of the mycobiont. Together, our results provide a rationale for selecting tissue treatment regimes prior to DNA extraction, demonstrate the efficacy of reducing mycobiont amplification in studies of the fungal microbiomes of lichen thalli, and highlight the difficulties in differentiating true information about fungal biodiversity from methodological artifacts.
FIGURE 128 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 128. Patellapis (Ch.) villosicauda, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 129 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 129. Distributions of P. gruenebergensis, P. ninae, P. pastina, P. paulyi and P. villosicauda.
FIGURE 114 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 114. Patellapis (Ch.) corallina, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 112 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 112. Patellapis (Ch.) calviniensis, male: a. Head (scale=0,5mm); b. Scutellum and propodeum (scale=0,5mm); c. Metasomal terga (scale=1mm); d. Metasomal sterna (scale:1mm); e. Metasomal sterna S7 and S8 (scale=100µm); f. Genitalia, dorsal view (scale=100µm).
FIGURE 113 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 113. Patellapis (Ch.) cameroni, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 111 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 111. Patellapis (Ch.) calviniensis, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 109 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 109. Patellapis (Ch.) timpageleri, male: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Metasomal sterna (scale=1mm); f. Body (scale=1mm); g. Genitalia, dorsal view (scale=100µm); h. Metasomal sterna S7 and S8, ventral view (scale=100µm).
FIGURE 94 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 94. Patellapis (Ch.) abnormis, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 92 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 92. Patellapis (Ch.) vumbensis, male: a. Genitalia, dorsal view (scale=100µm); b. Metasomal sterna S7 and S8, ventral view (scale=100µm).
FIGURE 88 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 88. Patellapis (Ch.) semipastina, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga T1 and T2 (scale=1mm).
FIGURE 108 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 108. Patellapis (Ch.) timpageleri, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 79 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 79. Patellapis (Ch.) pearstonensis, male: a. Scutum (scale=0,5mm); b. Scutellum and propodeum (scale=0,5mm); c. Metasomal terga (scale=1mm); d. Metasomal sterna (scale=1mm); e. Body (scale=1mm); f. Metasomal sterna S7 and S8, ventral view (scale=100µm); g. Genitalia, dorsal view (scale=100µm); h. Genitalia, lateral view (scale=100µm).
FIGURE 63 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 63. Patellapis (Ch.) chubbi, female: a. Head (scale=0,5mm); b. Scutum (scale=0,5mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
FIGURE 55 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 55. Patellapis (Ch.) fynbosensis, female: a. Head (scale=0,5mm); b. Scutellum and propodeum (scale=0,5mm); c. Metasomal terga (scale=1mm); d. Basitibial plate (scale=100µm).
FIGURE 6 in Variable Selection and Inference for Multi-period Forecasting Problems
FIGURE 6. Patellapis (P.) gessorum, female: a. Head (scale=1mm); b. Scutum (scale=1mm); c. Scutellum and propodeum (scale=0,5mm); d. Metasomal terga (scale=1mm); e. Body (scale=1mm); f. Inner hind tibial spur (scale=100µm); g. Basitibial plate (scale=100µm).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.