Biology subjects

Townsend, P.

Publications and source records attributed to Townsend, P..

4 recordsLinked to original sources

bioRxiv2024

Automated liquid handling extraction and rapid quantification of underivatized amino acids and tryptophan metabolites from human serum and plasma using dual-column U(H)PLC-MRM-MS and its application to prostate cancer study.

Free amino acids (AAs) and their metabolites are important building blocks, energy sources and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Robust and reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification of AA and TRP metabolites are required to identify candidate biomarkers and to improve current screening methods. We developed a validated semi-automated extraction and sample processing workflow using a robotic liquid handling platform and a rapid method for the absolute quantification of 20 free, underivatized AAs and 6 TRP metabolites using dual-column U(H)PLC-MRM-MS. The automated extraction and sample preparation workflow is designed for use in a 96-well plate format, allowing robust and reproducible high sample throughput without the need for further SPE, evaporation and/or buffer exchange. Samples extracted from serum and/or plasma in 96-well plates can be transferred directly to the U(H)PLC autosampler. The dual-column U(H)PLC-MRM-MS method, using a mixed- mode reversed-phase anion exchange column with formic acid as mobile phase modifier and a high- strength silica reversed-phase column with difluoroacetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification (LLOQ) for all metabolites except glycine (LLOQ: 2.46 {micro}M) in only 7.9 minutes. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify amino acids responsible for the statistical separation between healthy controls and prostate cancer patients on active surveillance.

biochemistry↗

bioRxiv2023

Individual tree crown maps for the National Ecological Observatory Network

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

ecology↗

bioRxiv2022

Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model

Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground-based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, the majority of classification models only include the most abundant species, leading to biased predictions at broad scales. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. In addition, large landscapes often require multiple acquisition events, leading to significant within-species variation in reflectance spectra. Using a multi-temporal hierarchical model, we demonstrate the ability to include species predicted at less than 1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670,000 individual trees at the Ordway Swisher Biological Station within the National Ecological Observatory Network. We estimate the relative abundance of the species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. These maps provide the first estimates of canopy tree diversity within NEON sites to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales.

ecology↗

bioRxiv2018

Generalized sample verification models to estimate ecological state variables with detection-nondetection data while accounting for imperfect detection and false positive errors.

Spatially-indexed repeated detection-nondetection data is widely collected by ecologists interested in estimating parameters associated with species distribution, relative abundance, phenology, and more while accounting for imperfect detection. Recent model development has focused on accounting for false positive error as well, given growing recognition that misclassification is common across many sampling protocols. To date, however, the development of model-based solutions to false positive error has been largely restricted to occupancy models. We describe a general form of the observation confirmation protocol originally described for occupancy estimation that permits investigators to flexibly and intuitively extend several models for detection-nondetection data to account for false positive error. Simulation results demonstrate that estimators for relative abundance and arrival time exhibit relative bias greater than 20% under realistic levels of false positive prevalence (e.g., 5% of detections are false positive). Bias increases as true and false positives occur in more distinct places or times, but can also be sensitive to the values of the state variables of interest, sampling design, and sampling efficiency. Results from an empirical study focusing on patterns of gray fox relative abundance across Wisconsin, USA suggest that false positive error can also distort estimated spatial patterns often used to guide decision-making. The extended estimators described within typically improve performance at any level of confirmation, and when false positive error occurs at random and constitutes less than 10% of all detections, the estimators are essentially unbiased when more than 50 observations can be confirmed as true or false positives. The generalized form of the observation-confirmation protocol is a flexible model-based solution to false positive error useful for researchers collecting data with sampling devices like trail or smartphone cameras, acoustic recorders, or other techniques where classifications can be reviewed post-hoc.

ecology↗