Biology subjects

White, E. P.

Publications and source records attributed to White, E. P..

At least 19 recordsLinked to original sources

bioRxiv2026

BLTP3A dysfunction unleashes lysosomal stress-induced antitumor immunity through STING

High-grade serous ovarian cancer progresses within a lipid-rich ascites microenvironment that engages stress-adaptation programs supporting malignant growth, yet how failed stress resolution influences tumor progression remains incompletely defined. Here, we show that unresolved lysosomal stress restrains tumor progression by sustaining tumor cell-intrinsic STING activity and reorganizing antitumor immunity. We identify BLTP3A as a lysosomal stress-resolution factor in ovarian cancer and show that the common germline missense variant BLTP3A M1098T increases stress-induced lysosomal damage. Patients carrying M1098T had longer overall survival and tumors enriched for cytotoxic T cell-myeloid neighborhoods. In syngeneic tumors, Bltp3a loss or orthologous variant expression delayed progression and generated Cxcl9+ antigen-presenting macrophage niches; M1098T-expressing human tumors similarly promoted interferon-responsive myeloid and T-cell enrichment in humanized mice. Mechanistically, ascites-induced lysosomal stress activated a STING-ATF4-BLTP3A feedback program. BLTP3A dysfunction disrupted CASM-associated lysosomal stress resolution, resulting in prolonged STING activity and sustained tumor-derived IFN-{lambda} without increased cGAMP. Tumor-cell STING and IFN-{lambda} were required for immune remodeling and tumor control. Thus, BLTP3A-dependent lysosomal stress resolution constrains the persistence of stress-induced STING signaling, whereas failure of this response sustains a tumor-intrinsic inflammatory program that organizes protective antitumor immunity.

cancer biology↗

bioRxiv2025

A dietary pan-amino acid dropout screen in vivo reveals a critical role for histidine in T-ALL

Dietary interventions show therapeutic potential in cancer, but systematic comparisons are lacking. We performed a dietary pan-amino acid dropout screen in an orthotopic model of NOTCH1-driven T-cell acute lymphoblastic leukemia and identified histidine depletion as uniquely antileukemic. Histidine-restricted diets extended survival of leukemic mice in a dose-dependent manner, while remaining well-tolerated. Mechanistically, multiomic profiling revealed that histidine deprivation-induced ribosome stalling activates GCN2 to suppress cholesterol biosynthesis pathways critical for leukemic proliferation. Dietary cholesterol supplementation partially reverted the antileukemic effects of histidine restriction in vivo. These findings couple histidine levels and translational control to cholesterol metabolism, which can be therapeutically exploited for cancer treatment. Our results suggest that defined dietary amino acid restrictions may expose broader therapeutic opportunities in diseases beyond cancer.

cancer biology↗

bioRxiv2025

Using time-series remote sensing to identify and track individual bird nests at large scales

The challenges of monitoring wildlife often limits the scales and intensity of the data that can be collected. New technologies - such as remote sensing using unoccupied aircraft systems (UAS) - can collect information more quickly, over larger areas, and more frequently than is feasible using ground-based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual-based demographic information is less explored. Repeat airborne imagery to generate an imagery time-series provides the potential to track individuals over time to collect information beyond one-off counts, but doing so necessitates automated approaches to handle the resulting high-frequency large-spatial scale imagery. We develop an automated time-series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time-series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology-informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human assessment of the same imagery shows that our primary approach identifies nests with comparable performance to human photo assessment, and that a secondary approach designed to find quick-fail nests resulted in high false positive rates. We also assessed the ability of both human photo assessment and our primary algorithm to find ground-verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results show that automating nest detection, a key first step towards estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches.

ecology↗

bioRxiv2024

Near real-time monitoring of long legged wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real-time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end-to-end workflow for conducting near real-time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual level species data using a Retinanet-50 object detector, post-processed, archived, and presented on a web-based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species-level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post-survey processing, data on the populations of these species is available in near real-time (< 1 week from the date of the survey) providing information at the time-scales needed for ecological forecasting and active management.

ecology↗

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↗

bioRxiv2023

Restoring adiponectin via rosiglitazone ameliorates tissue wasting in mice with lung cancer.

The cancer associated cachexia syndrome (CACS) is a systemic metabolic disorder resulting in loss of body weight due to skeletal muscle and adipose tissues atrophy. CACS is particularly prominent in lung cancer patients, where it contributes to poor quality of life and excess mortality. Using the Kras/Lkb1 (KL) mouse model, we found that CACS is associated with white adipose tissue (WAT) dysfunction that directly affects skeletal muscle homeostasis. WAT transcriptomes showed evidence of reduced adipogenesis, and, in agreement, we found low levels of circulating adiponectin. To preserve adipogenesis and restore adiponectin levels, we treated mice with the PPAR-{gamma} agonist, rosiglitazone. Rosiglitazone treatment increased serum adiponectin levels, delayed weight loss, and preserved skeletal muscle and adipose tissue mass, as compared to vehicle-treated mice. The preservation of muscle mass with rosiglitazone was associated with increases in AMPK and AKT activity. Similarly, activation of the adiponectin receptors in muscle cells increased AMPK activity, anabolic signaling, and protein synthesis. Our data suggest that PPAR-{gamma} agonists may be a useful adjuvant therapy to preserve tissue mass in lung cancer. Key points- The PPAR-{gamma} agonist, rosiglitazone, restores circulating adiponectin levels in mice with lung cancer. - Rosiglitazone preserves skeletal muscle and adipose tissue mass in mice with lung cancer. - The preservation of muscle mass with rosiglitazone is associated with increases in AMPK and AKT activity. - Stimulation of adiponectin signaling increases AMPK activity, anabolic signaling, and protein synthesis in muscle cell culture.

cancer biology↗

bioRxiv2023

Ketogenic diet promotes tumor ferroptosis but induces relative corticosterone deficiency that accelerates cachexia

The dependency of cancer cells on glucose can be targeted with high-fat low- carbohydrate ketogenic diet (KD). However, hepatic ketogenesis is suppressed in IL-6 producing cancers, which prevents the utilization of this nutrient source as energy for the organism. In two IL-6 associated murine models of cancer cachexia we describe delayed tumor growth but accelerated onset of cancer cachexia and shortened survival when mice are fed KD. Mechanistically, we find this uncoupling is a consequence of the biochemical interaction of two simultaneously occurring NADPH-dependent pathways. Within the tumor, increased production of lipid peroxidation products (LPPs) and, consequently, saturation of the glutathione (GSH) system leads to ferroptotic death of cancer cells. Systemically, redox imbalance and NADPH depletion impairs the biosynthesis of corticosterone, the main regulator of metabolic stress, in the adrenal glands. Administration of dexamethasone, a potent glucocorticoid, improves food intake, normalizes glucose homeostasis and utilization of nutritional substrates, delays onset of cancer cachexia and extends survival of tumor-bearing mice fed KD, while preserving reduced tumor growth. Our study highlights that the outcome of systemic interventions cannot necessarily be extrapolated from the effect on the tumor alone, but that they have to be investigated for anti{-}cancer and host effects. These findings may be relevant to clinical research efforts that investigate nutritional interventions such as KD in patients with cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/528937v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@613bb6org.highwire.dtl.DTLVardef@19746a4org.highwire.dtl.DTLVardef@9ef652org.highwire.dtl.DTLVardef@3db934_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIKetogenic diet delays tumor growth but accelerates cancer cachexia and shortens survival C_LIO_LIIn the tumor, accumulation of lipid peroxidation products results in saturation of the GSH detoxifying pathway and ferroptotic death of cancer cells C_LIO_LIIn the host organism, systemic redox state imbalance causes NADPH depletion, GDF-15 elevations, and relative corticosterone deficiency C_LIO_LIDexamethasone coadministration with ketogenic diet delays onset of cancer cachexia by improving food intake, glucose homeostasis and utilization of nutritional substrates C_LI

cancer biology↗

bioRxiv2021

Continental-scale Hyperspectral tree species classification in the National Ecological Observatory Network

Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species). On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.

ecology↗

bioRxiv2021

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for delineation of individual crowns and classification to determine species identity. This competition included data from multiple sites to assess the methods ability to generalize learning across multiple sites simultaneously, and transfer learning to novel sites where the methods were not trained. Six teams, representing 4 countries and 9 individual participants, submitted predictions. Methods from a previous competition were also applied and used as the baseline to understand whether the methods are changing and improving over time. The best delineation method was based on an instance segmentation pipeline, closely followed by a Faster R-CNN pipeline, both of which outperformed the baseline method. However, the baseline (based on a growing region algorithm) still performed well as did the Faster R-CNN. All delineation methods generalized well and transferred to novel forests effectively. The best species classification method was based on a two-stage fully connected neural network, which significantly outperformed the baseline (a random forest and Gradient boosting ensemble). The classification methods generalized well, with all teams training their models using multiple sites simultaneously, but the predictions from these trained models generally failed to transfer effectively to a novel site. Classification performance was strongly influenced by the number of field-based species IDs available for training the models, with most methods predicting common species well at the training sites. Classification errors (i.e., species misidentification) were most common between similar species in the same genus and different species that occur in the same habitat. The best methods handled class imbalance well and learned unique spectral features even with limited data. Most methods performed better than baseline in detecting new (untrained) species, especially in the site with no training data. Our experience further shows that data science competitions are useful for comparing different methods through the use of a standardized dataset and set of evaluation criteria, which highlights promising approaches and common challenges, and therefore advances the ecological and remote sensing field as a whole.

ecology↗

bioRxiv2021

A general deep learning model for bird detection in high resolution airborne imagery

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increase these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

ecology↗

bioRxiv2021

Disentangling the roles of inter and intraspecific variation on leaf trait distributions across the eastern United States

Functional traits are influenced by phylogenetic constraints and environmental conditions, but previous large-scale studies modeled traits either as species weighted averages or directly from the environment, precluding analyses of the relative contributions of inter- and intraspecific variation across regions. We developed a joint model integrating phylogenetic and environmental information to understand and predict the distribution of eight leaf traits across the eastern USA. This model explained 68% of trait variation, outperforming both species-only and environment-only models, with variance attributable to species alone (23%), the environment alone (13%), and their combined effects (25%). The importance of the two drivers varied by trait. Predictions for the eastern USA produced accurate estimates of intraspecific variation and deviated from both species-only and environment-only models. Predictions revealed that intraspecific variation holds information across scales, affects relationships in the leaf economic spectrum and is key for interpreting trait distributions and ecosystem processes within and across ecoregions.

ecology↗

bioRxiv2020

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Networks Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.

ecology↗

bioRxiv2020

Influence of climate forecasts, data assimilation, and uncertainty propagation on the performance of near-term phenology forecasts

Evaluation of ecological forecasts is a vital step in the continuous improvement of near-term ecological forecasts. Here we performed a thorough evaluation of a near-term phenological forecast system which has been operating for several years. We evaluated point forecast accuracy and the reliability of the prediction intervals. We also tested the contribution of upstream climate forecasts on phenology forecast proficiency. We found that 9 month climate forecasts contributed little skill overall, though some species did benefit from them. The assimilation of observed winter and spring temperature provided the largest improvement of forecast skill throughout the spring. We also found that phenology forecast prediction intervals were most robust when uncertainty was propagated from climate, phenological model, and model parameters as opposed to using climate uncertainty alone. Our analysis points the way toward several potential improvements to the forecasting system, which can be re-evaluated at a future date in a continuous cycle of forecast refinement.

ecology↗

bioRxiv2020

A simulation study of the use of temporal occupancy for identifying core and transient species

Transient species, which do not maintain self-sustaining populations in a system where they are observed, are ubiquitous in nature and their presence often impacts the interpretation of ecological patterns and processes. Identifying transient species from temporal occupancy, the proportion of time a species is observed at a given site over a time series, is subject to classification errors as a result of imperfect detection and source-sink dynamics. We use a simulation-based approach to assess how often errors in detection or classification occur in order to validate the use of temporal occupancy as a metric for inferring whether a species is a core or transient member of a community. We found that low detection increases error in the classification of core species, while high habitat heterogeneity and high detection increase error in classification of transient species. These findings confirm that temporal occupancy is a valid metric for inferring whether a species can maintain a self-sustaining population, but imperfect detection, low abundance, and highly heterogeneous landscapes may yield high misclassification rates.

ecology↗

bioRxiv2019

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization of proposed approaches, and limits tree detection at broad scales. Using data from the National Ecological Observatory Network we extend a recently developed semi-supervised deep learning algorithm to include data from a range of forest types, determine whether information from one forest can be used for tree detection in other forests, and explore the potential for building a universal tree detection algorithm. We find that the deep learning approach works well for overstory tree detection across forest conditions, outperforming conventional LIDAR-only methods in all forest types. Performance was best in open oak woodlands and worst in alpine forests. When models were fit to one forest type and used to predict another, performance generally decreased, with better performance when forests were more similar in structure. However, when models were pretrained on data from other sites and then fine-tuned using a small amount of hand-labeled data from the evaluation site, they performed similarly to local site models. Most importantly, a universal model fit to data from all sites simultaneously performed as well or better than individual models trained for each local site. This result suggests that RGB tree detection models that can be applied to a wide array of forest types at broad scales should be possible.

ecology↗

bioRxiv2019

Hotspot prioritizations show sensitivity to data type

Prioritizing regions for conservation is essential for effectively allocating limited conservation resources. One of the most common approaches to prioritization is identifying regions with the highest biodiversity, or hotspots, typically using global range map data. Range maps are readily available at large scales for an array of taxa, but are also known to differ from local-scale survey data in the same regions. We examined how prioritizations may differ between range map and survey data using the North American Breeding Bird survey (BBS) and BirdLife International range maps as a case study. Hotspot prioritizations were generated for species richness and the richness of rare species at two scales.\n\nTotal species richness patterns differed substantially between data types with at most a 41% overlap in identified hotspots. Some regions had few or no hotspots for one data type and a significant number for the other. Hotspots for rare species were more similar across the data types with 44% overlap at the larger scale. Future efforts to prioritize areas for conservation should consider differences between local-scale survey data and range maps, match data to the scale of interest, and develop methods to better downscale range map-based prioritizations to the scale of conservation decisions.

ecology↗

bioRxiv2019

Automated data-intensive forecasting of plant phenology throughout the United States

Phenology - the timing of cyclical and seasonal natural phenomena such as flowering and leaf out - is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there has been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here we describe a new near-term phenology forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.

ecology↗

bioRxiv2018

Developing a modern data workflow for living data

Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. \"Evolving data\" present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a evolving data workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.

ecology↗