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118 results for “qPCR”
eDNAssay: a machine learning tool that accurately predicts qPCR cross-amplification
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Niche partitioning between planktivorous fish in the pelagic Baltic Sea assessed by DNA metabarcoding, qPCR and microscopy: Data and Analyses
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qPCR results from design and partial validation of three novel eDNA qPCR assays for several common North American tick (Arachnida: Ixodida) species
<p>The range expansion of ticks to higher latitudes poses a severe threat to human health exposing human populations who had no prior contact with ticks to several harmful tick-borne diseases. Early detection of ticks in new areas is critical to help inform the public and medical professionals of the dangers associated with tick encounters. Environmental DNA represents a novel survey method that could provide reliable records of tick occurrences and timely warnings of their range expansions. In this study, we designed three novel eDNA qPCR assays for three common North American tick species (<em>Dermacentor variabilis</em>, <em>Amblyomma americanum</em>, and <em>Ixodes scapularis</em>) and tested them on samples of grasses collected from grasslands and forests in Illinois. We provide <em>in silico</em> and <em>in vitro </em>validation of all three assays, however we were unable to generate any positive detections from field samples. Our lack of eDNA detections likely stems from low eDNA deposition rates coupled with rapid degradation in grasslands and forests, a problem exacerbated by terrestrial eDNA sampling methods that are limited by volume of substrate. We provide recommendations for improving sample collection methods to increase detection probability in future efforts. Continued research should focus on the viability of eDNA to detect small terrestrial invertebrates, like ticks, and it potential as early warning indicator of the spread of vector-borne diseases.</p>
The qPCR results of Asian arowana eDNA detection in Muda Lake, Kedah, Malaysia
<p>We present a qPCR-based eDNA method to detect the presence of the endangered Asian arowana (Teleostei: Osteoglossidae: <em>Scleropages formosus</em>) in Muda Lake, where resides one of its last wild populations on the west side of Peninsular Malaysia. Flanked by a pair of newly developed species-specific primer, a 163-base pair fragment of the cytochrome oxidase subunit I (COI) gene was selected as the eDNA marker. We applied our eDNA method at five sampling sites throughout Muda Lake and at five different periods. The study observed a marked seasonal variation in detection rates, with significantly higher rates during the wet months of August, January, and December compared to the dry season spanning May and June. The Asian arowana is known to breed during the rainy season and we therefore hypothesize its reproductive biology influences the concentration of eDNA in the lake. Our results also highlight the possible impact of abiotic factors in tropical freshwater ecosystems (such as high-water temperature) on eDNA persistence. In conclusion, future applications of qPCR-based eDNA methods in tropical environments would benefit from locally evaluating the effects of both biotic and abiotic factors on species detection.</p>
Otter qPCR Data at SAFE
<b>Description: </b><p>All data collected during my project at the SAFE project, March-April 2017. This document contains metadata that should summarise all data collected.</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/166"><b>Using environmental DNA (eDNA) as a tool for monitoring the biodiversity of tropical otter species</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=13">here</a></p><p><b>Data worksheets: </b>There are 2 data worksheets in this dataset:</p><ol><li><p><b>eDNA</b> (Worksheet eDNA)</p><p>Dimensions: 66 rows by 33 columns</p><p>Description: eDNA' contains all data related to the qPCR elements of this project</p><p>Fields: </p><ul><li><b>Date</b>: Date Collected (Field type: Date)</li><li><b>Session</b>: Morning or Evening (Field type: Categorical)</li><li><b>Site</b>: Catchment Site; indicates metres upriver from SAFE Project hydrology datalogger (Field type: ID)</li><li><b>Code</b>: Unique Code given to each samplea and subsample (1a, 1b, 1c…) (Field type: ID)</li><li><b>Location</b>: Riparian transect sample collected from (Field type: Location)</li><li><b>Time_Start</b>: Time arrived at site (Field type: Time)</li><li><b>Time_Finish</b>: Time left site (Field type: Time)</li><li><b>eDNA_Start</b>: Sample collection starting time (Field type: Time)</li><li><b>eDNA_Stop</b>: Sample collection ending time (Field type: Time)</li><li><b>T_w</b>: Water Temperature at time of sample (Field type: Numeric)</li><li><b>T_a</b>: Air Temperature at time of sample (Field type: Numeric)</li><li><b>pH</b>: pH of surface water (Field type: Numeric)</li><li><b>R_H</b>: Relative humidty at site (Field type: Numeric)</li><li><b>Lux</b>: Lux score at time of sample (Field type: Numeric)</li><li><b>Precip</b>: Precipitation Present (Yes or No) (Field type: Categorical)</li><li><b>Shade</b>: Presence of shade during collection (Field type: Categorical)</li><li><b>Leaf_Litter</b>: Presence of leaf litter during collection (Field type: Categorical)</li><li><b>Substrate</b>: Type of substrate (Field type: Categorical)</li><li><b>Time1</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Time2</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Time3</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>TimeAv</b>: Average time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Depth1</b>: Depth of sample (Field type: Numeric)</li><li><b>Depth2</b>: Depth of sample (Field type: Numeric)</li><li><b>Depth3</b>: Depth of sample (Field type: Numeric)</li><li><b>DepthAv</b>: Average_depth of sample (Field type: Numeric)</li><li><b>River_Dist</b>: Distance "bottle" travelled at sample site ; used to calculate flow rate (Field type: Numeric)</li><li><b>Flow</b>: Calculated flow (speed = distance/ time) (Field type: Numeric)</li><li><b>Presence</b>: Presence of otters detected (to a level deemed above background noise on qPCR) (Field type: Categorical)</li><li><b>N. Wells Pos</b>: Presence of otters detected (to a level deemed above background noise on qPCR) (Field type: Abundance)</li><li><b>Positive NTC</b>: Number of positive negative controls out of 12 (Field type: Numeric)</li><li><b>Notes</b>: Any important notes during sampling (Field type: Comments)</li></ul><br></li><li><p><b>Traditional</b> (Worksheet Traditional)</p><p>Dimensions: 122 rows by 12 columns</p><p>Description: Traditional' contains data collected using traditional surveys</p><p>Fields: </p><ul><li><b>Location</b>: Riparian transect sample collected from (Field type: Location)</li><li><b>Session</b>: Morning or Evening (Field type: Categorical)</li><li><b>Date</b>: Date of sampling (Field type: Date)</li><li><b>Time</b>: Time of sampling (Field type: Time)</li><li><b>Site</b>: Catchment Site; indicates metres upriver from SAFE Project hydrology datalogger (Field type: ID)</li><li><b>Otter</b>: Otter observed at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Spraint</b>: Otter faeces at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Den</b>: Den present at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Footprints</b>: Footprints at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Remains</b>: Remains of prey present? 0=no, 1=yes (Field type: Abundance)</li><li><b>Notes </b>: Any additional notes deemed important (Field type: Comments)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-14 to 2017-04-11</p><p><b>Latitudinal extent: </b>4.6508 to 4.7255</p><p><b>Longitudinal extent: </b>117.5765 to 117.5980</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> -  -  - Carnivora<br> -  -  -  - Mustelidae<br></div><p></p>
qPCR cytokines upon treatment
<p>Raw data (Ct values) of qPCR performed on zebrafish rag1+/+ (wt) and rag1-/- (mutant) intestines dissected 6 hours after i.p. injection with PBS, Vibrio anguillarum or Anisakis simplex extract. </p>
Figure 1 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 1. Distribution of Cq values of candidate reference genes in date mussel (L. lithophaga).
Figure 2 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 2. Average expression stability (M-value) of reference genes evaluated by geNorm.
Quantitative monitoring of diverse fish communities on a large scale combining eDNA metabarcoding and qPCR
<p>eDNA metabarcoding is an effective method for studying fish communities but allows only an estimation of relative species abundance (density / biomass). Here, we combine metabarcoding with an estimation of the total abundance of eDNA amplified by our universal marker (teleo) using a qPCR approach to infer the absolute abundance of fish species. We carried out a 2,850 km eDNA survey within the Danube catchment using a spatial integrative sampling protocol coupled with traditional electrofishing for fish biomass and density estimation. Total fish eDNA concentrations and total fish abundance were highly correlated. The correlation between eDNA concentrations per taxon and absolute specific abundance was of comparable strength when all sites were pooled and remained significant when the sites were considered separately. Furthermore, a non-linear mixed model showed that species richness was underestimated when the amount of teleo-DNA extracted from a sample was below a threshold of 0.65.106 copies of eDNA. This result, combined with the decrease in teleo-DNA concentration by several orders of magnitude with river size, highlights the need to increase sampling effort in large rivers. Our results show a comprehensive description of longitudinal changes in fish communities and underline our combined metabarcoding/qPCR approach for biomonitoring and bioassessment surveys when a rough estimate of absolute species abundance is sufficient.</p>
Original qPCR data from cutaneous microbiomes of Central European amphibians
<p>This data set contains a table with original qPCR values obtained from an analysis of the cutaneous microbiome of Central European amphibians. Also, the original figures used in the accompanying publication have been added for convenience.</p>
RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
<p>Plants respond to environmental stresses through controlled stem cell maintenance and meristem activity. One level of transcriptional control is RNA alternative splicing. However the mechanistic link between stress, meristem function and RNA splicing is poorly understood. The MERISTEM-DEFECTIVE (MDF)/DEFECTIVELY ORGANIZED TRIBUTARIES (DOT2) gene of Arabidopsis encodes a SR-related family protein, required for meristem function and leaf vascularization, and is the likely orthologue of the human SART1 and yeast snu66 splicing factors. MDF is required for the correct splicing and expression of key transcripts associated with root meristem function. We identified RSZ33 and ACC1, both known to regulate cell patterning, as splicing targets required for MDF function in the meristem. MDF expression is modulated by osmotic and cold stress, associated with differential splicing and specific isoform accumulation and shuttling between nucleus and cytosol, and acts in part via a splicing target SR34. We propose a model in which MDF controls splicing in the root meristem to promote stemness and repress stress response and cell differentiation pathways.</p>
CocciEnv qPCR data and accompanying metadata from soil and settled dust in the San Joaquin Valley
<p>Coccidioidomycosis is a typically respiratory fungal disease that, in the United States, occurs primarily in Arizona and California. In California, most coccidioidomycosis cases occur in the San Joaquin Valley, a primarily agricultural region where the disease poses a risk for outdoor workers. We collected 710 soil samples and 265 settled dust samples from nine sites in the San Joaquin Valley and examined how <em>Coccidioides</em> detection varied by month, site, and the presence and abundance of other fungal species. We detected <em>Coccidioides</em> in 89 of 238 (37.4%) rodent burrow soil samples at five undeveloped sites and were unable to detect <em>Coccidioides</em> in any of 472 surface and subsurface soil samples at four agricultural sites. In what is the largest sampling effort undertaken on agricultural land, our results provide no evidence that agricultural soils in the San Joaquin Valley harbor <em>Coccidioides</em>. We found no clear association between <em>Coccidioides</em> and the greater soil fungal community, but we identified 19 fungal indicator species that were significantly associated with <em>Coccidioides</em> detection in burrows. We also did not find a seasonal pattern in <em>Coccidioides</em> detection in the rodent burrow soils we sampled. These findings suggest both the presence of a spore bank and that coccidioidomycosis incidence may be more strongly associated with <em>Coccidioides</em> dispersal than <em>Coccidioides</em> growth. Finally, we were able to detect <em>Coccidioides</em> in only five of our 265 near-surface settled dust samples, one from agricultural land, where <em>Coccidioides</em> was undetected in soils, and four from undeveloped land, where <em>Coccidioides</em> was common in the rodent burrow soils we sampled. Our ability to detect <em>Coccidioides</em> in few settled dust samples indicates that improved methods are likely needed moving forward, though raises questions regarding aerial dispersal in <em>Coccidioides</em>, whose key transmission event likely occurs over short distances in rodent burrows from soil to naïve rodent lungs.</p>
Data from: Bromodomain-containing protein 4 regulates innate inflammation via modulation of alternative splicing (images and qPCR data)
<p class="MsoNormal">Bromodomain-containing Protein 4 (BRD4) is a transcriptional regulator which coordinates gene expression programs controlling cancer biology, inflammation, and fibrosis. In the context of virus-infection, BRD4-specific inhibitors (BRD4i) block the release of pro-inflammatory cytokines and prevent downstream epithelial plasticity<a>. </a>Although the chromatin modifying functions of BRD4 in inducible gene expression have been extensively investigated, its roles in post-transcriptional regulation are not well understood. Given BRD4's interaction with the transcriptional elongation complex and spliceosome, we hypothesize that BRD4 is a functional regulator of mRNA processing. To address this question, we combine data-independent analysis - parallel accumulation-serial fragmentation (diaPASEF) with RNA-sequencing to achieve deep and integrated coverage of the proteomic and transcriptomic landscapes of human small airway epithelial cells exposed to viral challenge and treated with BRD4i. The transcript-level data was further interrogated for alternative splicing analysis, and the resulting datasets were correlated to identify pathways subject to post-transcriptional regulation. We discover that BRD4 regulates alternative splicing of key genes, including Interferon-related Developmental Regulator 1 (IFRD1) and X-Box Binding Protein 1 (XBP1), related to the innate immune response and the unfolded protein response. These findings extend the transcriptional elongation-facilitating actions of BRD4 in control of post-transcriptional RNA processing in innate signaling.</p>
Development and application of a qPCR-based genotyping assay for Ophidiomyces ophidiicola to investigate the epidemiology of ophidiomycosis
Ophidiomycosis (snake fungal disease) is an infectious disease caused by the fungus Ophidiomyces ophidiicola to which all snake species appear to be susceptible. Significant variation has been observed in clinical presentation, progression of disease, and response to treatment, which may be due to genetic variation in the causative agent. Recent phylogenetic analysis based on whole-genome sequencing identified that O. ophidiicola strains from the United States formed a clade distinct from European strains, and that multiple clonal lineages of the clade are present in the United States. The purpose of this study was to design a qPCR-based genotyping assay for O. ophidiicola, then apply that assay to swab-extracted DNA samples to investigate whether the multiple O. ophidiicola clades and clonal lineages in the United States have specific geographic, taxonomic, or temporal predilections. To this end, six full genome sequences of O. ophidiicola representing different clades and clonal lineages were aligned to identify genomic areas shared between subsets of the isolates. Eleven hydrolysis-based Taqman primer-probe sets were designed to amplify selected gene segments and produce unique amplification patterns for each isolate, each with a limit of detection of 10 or fewer copies of the target sequence and an amplification efficiency of 90–110%. The qPCR-based approach was validated using samples from strains known to belong to specific clades and applied to swab-extracted O. ophidiicola DNA samples from multiple snake species, states, and years. When compared to full-genome sequencing, the qPCR-based genotyping assay assigned 75% of samples to the same major clade (Cohen's kappa = 0.360, 95% Confidence Interval = 0.154–0.567) with 67–77% sensitivity and 88–100% specificity, depending on clade/clonal lineage. Swab-extracted O. ophidiicola DNA samples from across the United States were assigned to six different clonal lineages, including four of the six established lineages and two newly defined groups, which likely represent recombinant strains of O. ophidiicola. Using multinomial logistic regression modeling to predict clade based on snake taxonomic group, state of origin, and year of collection, state was the most significant predictor of clonal lineage. Furthermore, clonal lineage was not associated with disease severity in the most intensely sampled species, the Lake Erie watersnake (Nerodia sipedon insularum). Overall, this assay represents a rapid, cost-effective genotyping method for O. ophidiicola that can be used to better understand the epidemiology of ophidiomycosis.
Data from: Compared with conventional PCR assay, qPCR assay greatly improves the detection efficiency of predation
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Repository of raw qPCR data to assess Sin Nombre hantavirus presence in lung tissues and replication in vitro
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Development and application of a qPCR-based genotyping assay for Ophidiomyces ophidiicola to investigate the epidemiology of ophidiomycosis
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The qPCR results of Asian arowana eDNA detection in Muda Lake, Kedah, Malaysia
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RNA-seq and RT-qPCR data showing MDF role in RNA splicing and gene expression control in Arabidopsis
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Quantitative monitoring of diverse fish communities on a large scale combining eDNA metabarcoding and qPCR
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