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31 results for “hybridization capture”
Plethodon hybrid zone capture-mark-recapture survey plots at the Coweeta Hyrdologic Laboratory, Otto, NC.
A major goal of the Coweeta LTER is to understand the interactions between climate and land use on the ecology of southern Appalachia biota. Southern Appalachia is the global hotspot for salamander diversity, with most of that diversity situated at mid and upper elevations of mountains where species are functionally trapped by their dependence of a narrow, cool climatic zone. The ranges of the two terrestrial salamander species, Plethodon teyahalee (southern Appalachian salamander) and Plethodon shermani (red-legged salamander) meet at a unique hybrid zone at the Coweeta LTER in Macon County, NC. The hybrid zone is unique because it appears to be shifting up in elevation, possibly due to climate change. This research will be situated in the hybrid zone of these two species to assess the differential effects of climate change and land use on both species. To evaluate the effects of climate on the local ecology of salamanders, we will conduct an intensive mark-recapture study to measure surface activity on 6 plots distributed in pairs at 3 elevations that provide a range of warmer to cooler climates. Every two weeks, each plot is searched for 30 min by two investigators using head lamps. Animals are hand captured, identified to species, scored for color and patterning, measured, and marked using a unique combination of visible implant elastomers (VIE). This will enable us to estimate individual capture probabilities and rates of temporary surface emigration (an indication of avoidance of climatically unsuitable conditions).
Pilot output of a hybrid micro-CPV solar panels with integrated micro-tracking and diffuse capture
<p>Dataset of power output of four final generation Insolight/Hiperion modules on the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are micro CPV modules using integrated planar tracking and hybrid diffuse collection. The modules have a four terminal output: the "CPV" output corresponds to triple junction micro solar cells under ~100X concentration using integrated planar tracking and the "SI" output corresponds to an array of IBC silicon solar cells which ocupies the rest of hte backplane area and harvests light not captured by the concentrator. See References for more information</p><p><strong>Monitoring campaign:</strong></p><ul><li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li><li>Starting date: 19 Oct 2022</li><li>End date: 1 Dec 2022</li></ul><p><strong>Measurement setup:</strong></p><ul><li>The four modules included in this data set were mounted on an 8 module pilot array.</li><li>The four modules have the following ID numbers: 195, 196, 197, and 198</li><li>The modules were mounted at a fixed mounting angle: Due South, Slope Angle = 30°</li><li>All modules were connected to two Enphase IQ7+ microinverters (one per output) to place them at MPPT.</li><li>CPV and SI voltages and currents wer measured with the Enphase monitoring gateway (Envoy)</li><li><strong>NOTE: Some shading on array in mornings due to time of year.</strong></li></ul><p><strong>Description of data file:</strong></p><ul><li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li><li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously).<ul><li>Date Time (dd/mmm/yyyy HH:MM:SS): time in local civil time (data set begins in CEST / UTC+2 but on 30-Oct-22 transitions to CET / UCT+1 with the end of central european summer time.</li></ul></li><li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times.<ul><li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li><li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li><li>DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li><li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li><li>T_Amb (°C): ambient temperature</li><li>Wind Speed (m/s): wind speed</li><li>Wind Dir. (m/s): wind direction</li></ul></li><li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience<ul><li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun's known declination and hour angle from the time.</li><li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li><li>SMR_Top_Mid (n.d.): "Spectral Matching Ratio". This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li></ul></li><li><strong>Measured module data:</strong> The MP voltage and current are provided for each output of each module. XXX indicates ID number of module. The power in watts may be found by multiplying these values<ul><li>V_CPV_XXX(V):</li><li>V_SI_XXXi (V)</li><li>I<i>_</i>CPV_XXX(A):</li><li>I<i>_</i>SI_XXXi (A)</li></ul></li></ul>
Non-invasive genomics of respiratory pathogens infecting wild great apes using hybridization capture
<p>This dataset complements a manuscript reporting genomic analyses of respiratory pathogens cuasing lethal outbreaks in the wild chimpanzee community living in Tai National Park, Ivory Coast.</p>
Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture
<p>Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are measurements of a module of the same type as “<a href="zenodo.org/record/2667772">Outdoor monitoring data of an Insolight B-series module - CPV sub-module</a>” however, in the previous measurements the module was mounted on a two-axis tracker to benchmark its performance, while in these measurements, the module’s integrated planar micro tracking system was used. This data was presented at IEEE PVSC 46 in June 2019 in Chicago. <strong><a href="https://zenodo.org/record/3349781">See preprint of conference article</a>.</strong></p> <p><strong>Monitoring campaign:</strong></p> <ul> <li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li> <li>Fixed Mounting Angle: Due South, Slope Angle = 30°</li> <li>Starting date: 30 May 2019</li> <li>End date: 14 June 2018</li> </ul> <p><strong>Description of data file:</strong></p> <ul> <li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li> <li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously). <ul> <li>Date Time (dd/mmm/yyyy HH:MM:SS): time in CEST / UTC+2</li> </ul> </li> <li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times. <ul> <li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li> <li>G(41°) (W/m2): Global Inclined Irradiance as measured with a pyranometer mounted facing due south and at a slope angle of 41° (near to local latitude). Spectral range: 305 – 2800 nm.</li> <li>T_Amb (°C): ambient temperature </li> <li>Wind Speed (m/s): wind speed</li> <li>Wind Dir. (m/s): wind direction</li> </ul> </li> <li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience <ul> <li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun’s known declination and hour angle from the time.</li> <li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li> <li>SMR_Top_Mid (n.d.): “Spectral Matching Ratio”. This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li> </ul> </li> <li><strong>Measured module data:</strong> The module was placed in a short-ciruit condition and allowed to track using its integrated tracking system. The short circuit current was measured using shut resistors and integrated A/D channels. This hybrid module features both III-V micro cells (under concentration, with planar microtracking) and large area silicon solar cells (for diffuse capture). <ul> <li>ISC_measured_IIIV (A):</li> <li>ISC_measured_Si (A)</li> </ul> </li> <li><strong>Estimated module data:</strong> As is explained in the IEEE PVSC 46 manuscript (<a href="https://zenodo.org/record/3349781">see Preprint</a>) the following values are estimated using the previously listed measured data. <ul> <li>T_Backplane (°C)</li> <li>PMP_estimated_IIIV (W)</li> <li>PMP_estimated_Si (W)</li> </ul> </li> </ul>
Database for comparison between Hybrid Capture and PCR techniques for HPV-HR detection
<p>This database displays the results from the HPV-HR detection with the gold standard technique Hybrid Capture 2 (HC2) and different PCR-based techniques. These results are compared in order to determine the degree of agreement between theses techniques. </p>
Patterns of pollen dispersal and pollen capture in the hybridizing cattails, Typha latifolia and T. angustifolia
<p><span>Pollen dispersal regulates the formation of the invasive, wind-pollinated hybrid cattail T. × glauca, the F1 offspring of the broadleaf (T. latifolia) and narrowleaf (T. angustifolia cattail. An earlier study suggested that pollen dispersal by T. latifolia might be spatially restricted, with most dispersal occurring over distances less than 2 m. Restricted pollen dispersal would imply that hybrid formation primarily occurs within mixed stands of cattails. Hybrid formation might also be affected by preferential receipt of conspecific pollen, but this has not been investigated for cattails. We compared patterns of pollen dispersal for T. latifolia and T. angustifolia using a wind tunnel. We then tested whether patterns of pollen receipt were biased toward the capture of conspecific versus heterospecific pollen using monospecific cattail stands with a single local pollen source. Results from the wind tunnel partially supported the previous finding of spatially restricted pollen dispersal for T. latifolia, the paternal parent of F1 hybrids. Pollen receipt by T. angustifolia was biased toward the capture of conspecific pollen. Localized pollen dispersal by T. latifolia and preferential conspecific pollen capture by T. angustifolia should reduce rates of hybrid formation below that expected under random mating.</span></p>
Targeted DNA methylation from cell free DNA using hybrid probe capture
<p>CSV files contain beta value and coverage per base & per region, as indicated in the file name.<br> Columns are samples, rows are CpGs or target region - depending on the file.</p> <p>Files were generated from our bismark/bsseq pipeline as described in the manuscript.</p> <p>Contact: dnbuckle@usc.edu</p>
Solidago hybrid-sequence capture probe set
<p><em>Premise of the study</em>: The phylogenetic relationships among the ca. 138 species of goldenrods (<em>Solidago</em>; Asteraceae) have been difficult to infer due to species richness, and shallow interspecific genetic divergences. This study aims to overcome these obstacles by combining extensive sampling of goldenrod herbarium specimens with the use of a custom <em>Solidago</em> hybrid-sequence capture probe set.</p> <p><em>Methods</em>: A set of tissues from herbarium samples comprising ca. 90% of <em>Solidago</em> species was assembled, and DNA was extracted. A custom hybrid-sequence capture probe set was designed, and data from 854 nuclear regions were obtained and analyzed from 209 specimens. Maximum likelihood and coalescent approaches were used to estimate the genus phylogeny for 157 diploid samples.</p> <p><em>Key results</em>: Although DNAs from older specimens were both more fragmented and produced fewer sequencing reads, there was no relationship between specimen age and our ability to obtain sufficient data at the target loci. The <em>Solidago</em> phylogeny was generally well supported, with 88/155 (57%) nodes receiving ≥95% bootstrap support. <em>Solidago</em> was supported as monophyletic, with <em>Chrysoma</em> <em>pauciflosculosa</em> identified as sister. A clade comprising <em>Solidago</em> <em>ericameriodes</em>, <em>Solidago</em> <em>odora</em>, and <em>Solidago</em> <em>chapmanii</em> was identified as the earliest diverging <em>Solidago</em> lineage. The previously segregated genera <em>Brintonia</em> and <em>Oligoneuron</em> were identified as placed well within <em>Solidago</em>. These and other phylogenetic results were used to establish four subgenera and fifteen sections within the genus.</p> <p><em>Conclusions</em>: The combination of expansive herbarium sampling and hybrid-sequence capture data allowed us to quickly and rigorously establish the evolutionary relationships within this difficult, species-rich group.</p>
Solidago hybrid-sequence capture probe set
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Patterns of pollen dispersal and pollen capture in the hybridizing cattails, Typha latifolia and T. angustifolia
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A novel approach for pollen identification and quantification using hybrid capture-based DNA metabarcoding
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Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Quantifying and reducing cross-contamination in single- and multiplex hybridization capture of ancient DNA
<p>The use of hybridization capture has enabled a massive upscaling in sample sizes for ancient DNA studies, allowing the analysis of hundreds of skeletal remains (Mathieson et al., 2015; Narasimhan et al., 2019) or sediments (Vernot et al., 2021; Wang et al., 2021; Zavala et al., 2021) in single studies. Yet demands in throughput continue to grow, and hybridization capture has become a limiting step in sample preparation due to the large consumption of reagents, consumables and time. Here we explore the possibility of improving the economics of sample preparation via multiplex capture, i.e. the hybridization capture of pools of double-indexed ancient DNA libraries. We demonstrate that this strategy is feasible for small genomic targets, such as mitochondrial DNA, if the annealing temperature is increased and PCR cycles are limited in post-capture amplification to avoid index swapping by jumping PCR, which manifests as cross-contamination in resulting sequence data. We also show that the re-amplification of double-indexed libraries to PCR plateau before or after hybridization capture can sporadically lead to small, but detectable cross-contamination even if libraries are amplified in separate reactions. We provide protocols for both manual capture and automated capture in 384-well format that are compatible with single- and multiplex capture and effectively suppress cross-contamination and artefact formation. Last, we provide a simple computational method for quantifying cross-contamination due to index swapping in double-indexed libraries, which we recommend using for routine quality checks in studies that are sensitive to cross-contamination. </p>
Quantifying and reducing cross-contamination in single- and multiplex hybridization capture of ancient DNA
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Data from: An empirical assessment of a single family-wide hybrid capture locus set at multiple evolutionary timescales in Asteraceae
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Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
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Data from: Evaluating hybridization capture with RAD probes as a tool for museum genomics with historical bird specimens
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Multistate hybrid time-dependent density functional theory with surface hopping accurately captures ultrafast thymine photodeactivation
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Data from: Cost-effective enrichment hybridization capture of chloroplast genomes at deep multiplexing levels for population genetics and phylogeography studies
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Sensitive and unbiased genome-wide profiling of base-editor-induced off-target activity with CHANGE-seq-BE [Hybrid Capture Sequencing for CBE and ABE]
GEO Series GSE308237. Homo sapiens. 36 samples. Type: Other.
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