Biology subjects

White, E.

Publications and source records attributed to White, E..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Jointly Modeling Species Niche and Phylogenetic Model in a Bayesian Hierarchical Framework

When studying how species will respond to climatic change, a common goal is to predict how species distributions change through time. Environmental niche models (ENMs) are commonly used to estimate a species environmental niche from observed patterns of occurrence and environmental predictors. However, species distributions are often shaped by non-environmental factors-including biotic interactions and dispersal barriers--truncating niche estimates. Though a truncated niche estimate may accurately predict present-day species distribution within the sampled area, this accuracy decreases when predicting occurrence at different places and under different environmental conditions. Modeling niche in a phylogenetic framework leverages a clades shared evolutionary history to pull species estimates closer towards phylogenetic conserved values and farther away from species specific biases. We propose a new Bayesian model of phylogenetic niche estimation implemented in R called BePhyNE (Bayesian environmental Phylogenetic Niche Estimation). Under our model, species ENM parameters are transformed into biologically interpretable continuous parameters of environmental niche optimum, breadth, and tolerance evolving as a multivariate Brownian motion. Through simulation analyses, we demonstrate model accuracy and precision that improve as phylogeny size increases. We also demonstrate our model on eastern United States Plethodontid salamanders and recover accurate estimates of species niche, even when species occurrence data is lacking and entirely informed by the evolutionary model. Our model demonstrates a novel framework where niche changes can be studied forwards and backwards through time to understand ancestral ranges, patterns of environmental specialization, and estimate niches of data-deficient species.

evolutionary biology↗

bioRxiv2022

Skin-resident immune cells engulf axonal debris in adult epidermis

Somatosensory neurons extend enormous peripheral axons to the skin, where they detect diverse environmental stimuli. Somatosensory peripheral axons are easily damaged due to their small caliber and superficial location. Axonal damage results in Wallerian degeneration, creating vast quantities of cellular debris that phagocytes must remove to maintain organ homeostasis. The cellular mechanisms that ensure efficient clearance of axon debris from stratified adult skin are unknown. Here, we establish zebrafish scales as a tractable model to study axon degeneration in the adult epidermis. Using this system, we demonstrate that skin-resident immune cells known as Langerhans cells engulf the majority of axon debris. In contrast to immature skin, adult keratinocytes do not significantly contribute to debris removal, even in animals lacking Langerhans cells. Our study establishes a powerful new model for studying Wallerian degeneration and identifies a new function for Langerhans cells in maintenance of adult skin homeostasis following injury. These findings have important implications for pathologies that trigger somatosensory axon degeneration.

cell biology↗

bioRxiv2022

A subtype of melanopsin ganglion cells encodes ground luminance

Visual input to the hypothalamus from intrinsically photosensitive retinal ganglion cells (ipRGCs) influences several functions including circadian entrainment, body temperature, and sleep. ipRGCs also project to nuclei such as the supraoptic nucleus (SON), which is involved in systemic fluid homeostasis, maternal behavior, and appetite. However, little is known about the SON-projecting ipRGCs or their relationship to well-characterized ipRGC subtypes. Using a GlyT2Cre mouse line, we identify a subtype of ipRGCs restricted to the dorsal retina that selectively project to the SON. These ipRGCs form a non-overlapping tiled mosaic that is limited to a dorsal region of the retina, forming a substrate for encoding ground luminance. Optogenetic activation of their axons demonstrates they release the neurotransmitter glutamate and that the SON is retinorecipient, receiving synaptic input from dorsal ipRGCs. Our results challenge the idea that ipRGC dendrites overlap to optimize photon capture and suggests non-image forming vision operates to sample local regions of the visual field.

neuroscience↗

bioRxiv2022

A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c

Atlantic herring in ICES Divisions 6.a, 7.b-c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographic delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 SNPs, derived from whole genome sequencing, were used to genotype 3,480 baseline spawning samples (2014-2021). A temporally stable baseline comprising 2,316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy >90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003-2004) baseline samples (270 individuals) with a high level of accuracy. Assignment of non-baseline samples (1,514 individuals) from Division 6.a, 7.b-c indicated previously unrecognised levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a toolbox that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy.

zoology↗

bioRxiv2022

Sizing up spotted lanternfly nymphs for instar determination and growth allometry

A major ongoing research effort seeks to understand the behavior, ecology and control of the spotted lanternfly (SLF) (Lycorma delicatula), a highly invasive pest in the U.S. and South Korea. These insects undergo four nymphal stages (instars) before reaching adulthood, and appear to shift host plant preferences, feeding, dispersal and survival patterns, anti-predator behaviors, and response to traps and chemical controls with each stage. However, categorizing SLF life stage is challenging for the first three instars, which have the same coloration and shape, because no comprehensive datasets exist. We present a dataset of body mass and length for SLF nymphs throughout two growing seasons and compare our results with previously-published ranges of instar body lengths. An analysis using two clustering methods revealed that 1st-3rd instar body mass and length fell into distinct clusters that were consistent between years, supporting using these metrics to stage nymphs during a single growing season. However, the ranges for 2nd-4th instars were not consistent between our results and those from earlier studies for diverse locations. The scaling of SLF nymph body mass with body length varied between isometry (constant shape) and positive allometry (growing faster than predicted by isometry) in the two years studied. Using previously published data, we also found that SLF nymph adhesive footpad area varies in direct proportion to weight, suggesting that footpad adhesion is independent of nymphal stage, while their tarsal claws display positive allometry and hence disproportionately increasing grasp (mechanical adhesion). By contrast, mouthpart dimensions are weakly correlated with body length, consistent with predictions that these features should reflect preferred host plant characteristics rather than body size. We recommend future studies use the body mass vs length growth curve as a fitness benchmark to study how SLF instar development depends on factors such as hatch date, host plant, temperature, and geographic location, to further understanding of life history patterns that help prevent further spread of this invasive insect.

zoology↗

bioRxiv2021

3D visualisation of the cardiac ryanodine receptor clusters and the molecular-scale fraying of dyads

Clusters of ryanodine receptor calcium channels (RyRs) form the primary molecular machinery in cardiomyocytes. Various adaptations of super-resolution microscopy have revealed intricate details of the structure, molecular composition and locations of these couplons. However, most optical super-resolution techniques lack the capacity for three-dimensional (3D) visualisation. Enhanced Expansion Microscopy (EExM) offers resolution (in-plane and axially) sufficient to spatially resolve individual proteins within peripheral couplons and within dyads located in the interior. We have combined immunocytochemistry and immunohistochemistry variations of EExM with 3D visualisation to examine the complex topologies, geometries and molecular sub-domains within RyR clusters. We observed that peripheral couplons exhibit variable co-clustering ratios and patterns between RyR and the structural protein, junctophilin-2 (JPH2). Dyads possessed sub-domains of JPH2 which occupied the central regions of the RyR cluster, whilst the poles were typically devoid of JPH2 and broader, and likely specialise in turnover and remodelling of the cluster. In right ventricular myocytes from rats with monocrotaline-induced right ventricular failure, we observed hallmarks of RyR cluster fragmentation accompanied by similar fragmentations of the JPH2 sub-domains. We hypothesise that the frayed morphology of RyRs in close proximity to fragmented JPH2 structural sub-domains may form the primordial foci of RyR mobilisation and dyad remodelling.

biophysics↗

bioRxiv2021

PALB2 maintains redox and mitochondrial homeostasis in the brain and cooperates with ATG7 to suppress p53 dependent neurodegeneration

The PALB2 tumor suppressor plays key roles in DNA repair and has been implicated in redox homeostasis. Autophagy maintains mitochondrial quality, mitigates oxidative stress and suppresses neurodegeneration. Here we show that Palb2 deletion in the mouse brain leads to motor deficits and that co-deletion of Palb2 with the essential autophagy gene Atg7 accelerates and exacerbates neurodegeneration induced by ATG7 loss. Palb2 deletion leads to elevated DNA damage, oxidative stress and mitochondrial markers, especially in Purkinje cells, and co-deletion of Palb2 and Atg7 results in accelerated Purkinje cell loss. Further analyses suggest that the accelerated Purkinje cell loss and severe neurodegeneration in the double deletion mice are due to oxidative stress and mitochondrial dysfunction, rather than DNA damage, and partially dependent on p53 activity. Our studies uncover a role of PALB2 in mitochondrial regulation and a cooperation between PALB2 and ATG7/autophagy in maintaining redox and mitochondrial homeostasis essential for neuronal survival.

neuroscience↗

bioRxiv2021

Simple and effective serum-free medium for sustained expansion of bovine satellite cells for cell cultured meat

Cell-cultured meat offers the potential for a more sustainable, ethical, resilient, and healthy food system. However, research and development has been hindered by the lack of suitable serum-free media that enable the robust expansion of relevant cells (e.g., muscle satellite cells) over multiple passages. Recently, a low-cost serum-free media (B8) was described for induced pluripotent stem cells. Here, we adapt this media for bovine satellite cells and show that the addition of a single component, recombinant albumin, renders B8 suitable for the long-term expansion of cells without sacrificing myogenicity. We show that this new media (Beefy-9) maintains robust cell growth over the entire culture period tested (seven passages) with an average growth rate of 39 hours per population doubling. Along with demonstrated efficacy for bovine cells, this work provides a promising starting-point for developing serum-free media for cultures from other meat-relevant species. Ultimately, this work offers a promising foundation for escaping the reliance on serum in cultured meat research, thereby accelerating the field.

bioengineering↗

bioRxiv2021

GDF8 Contributes to the Adverse Effects of Liver Injury on Skeletal Muscle Homeostasis and Regeneration

BackgroundAn emerging clinical phenomenon in patients with end stage liver disease is progressive skeletal muscle atrophy. This loss in lean mass predicts poor survival outcomes for liver disease patients and highlights an underappreciated crosstalk between injured liver and muscle that lacks defined mediators. The purpose of our study was to identify potential liver-muscle mediator(s) in pre-clinical in vivo models of liver injury which may contribute to the muscle loss observed in liver disease. MethodsUtilizing a mouse model of carbon tetrachloride CCl4-induced liver injury in the presence or absence of cardiotoxin-induced muscle injury, we evaluated whether neutralizing Activin type IIB receptor (ActRIIB) ligands, or specifically growth differentiation factor 8 (Gdf8), could preserve or reverse muscle atrophy associated with liver disease. ResultsWe found that hepatic injury via CCl4 or bile duct ligation (BDL) similarly caused significant muscle atrophy along with decreased gene expression in key myogenesis markers. This adverse effect of injured liver on muscle were completely prevented and reversed by the intervention of Activin type IIB receptor (ActRIIB)-Fc fusion protein, which neutralizes the ActRIIB ligands, including Activins and growth differentiation factor 8 (Gdf8 or myostatin). The results indicate that ActRIIB ligands promoted muscle atrophy which was manifested in response to hepatic injury/disease and conferred the negative communication of injured liver with muscle. Indeed, direct injection of exogenous Gdf8 protein into muscle along with acute focal muscle injury recapitulated similar dysregulated muscle regeneration as observed with liver injury. Furthermore, we found that hepatocytes produced Gdf8 in response to liver injury in rodents and in patients with end stage liver disease. A neutralizing antibody to Gdf8 attenuated muscle atrophy and unexpectedly ameliorated liver fibrosis in both CCl4 and BDL models. Following this observation, we demonstrated Gdf8s ability to induce fibrogenesis in stellate cells, potentially identifying a novel hepatic role for this protein. Moreover, hepatic Gdf8 promoted muscle wasting in response to liver damage and hindered skeletal muscle regeneration. ConclusionOur findings identified Gdf8 as a novel hepatomyokine contributing to injured liver-muscle negative crosstalk and liver injury progression. Moreover, we demonstrated a promising therapeutic strategy for muscle atrophy accompanying liver diseases.

pathology↗

bioRxiv2020

Comparison of changes in electrical activity, in isolated and in vivo hearts, induced by voluntary exercise in female rats

Regular mild exercise is recommended to the general population as beneficial to health. Regular exercise typically leads to structural and electrical remodelling of the heart but in human studies it is difficult to relate the extrinsic and intrinsic influences on intact hearts to changes seen at the single cell level. In this study we wished to test whether changes in electrical activity in intact hearts, in response to voluntary wheel running exercise training, were consistent with our previous observations in single cardiac myocytes and whether these changes resulted in altered susceptibility to arrhythmic stimuli. Female rats performed 5 weeks of voluntary wheel running. Implanted telemetry transmitters were used to measure electrocardiograms (ECGs) and determine heart rate variability (HRV) in conscious, unrestrained, trained (TRN) and sedentary (SED) animals. In isolated hearts, left ventricular epicardial monophasic action potentials (MAPs) were recorded and the responses to potentially arrhythmic interventions were assessed. Exercise training caused cardiac hypertrophy, as indexed by a significantly greater heart weight to body weight ratio. Consistent with previous measurements of action potential duration in single myocytes, MAPs were significantly longer at 50%, 75% and 90% repolarization. Arrhythmic susceptibility was not different between SED and TRN hearts. Trained animals displayed significantly altered HRV by week 5, in a manner consistent with reduced sympathetic tone, however resting ECG parameters, including those most associated with repolarisation duration, were unaltered. We conclude that intrinsic changes to cellular cardiac electrophysiology, induced by mild voluntary exercise, are not attenuated by the electronic loading that occurs in intact hearts. However, in vivo, extrinsic neuro-hormonal control of the heart may minimize the effects of intrinsic alterations in electrical activity.

physiology↗

bioRxiv2020

The spore of the beans: Spatially explicit models predict coffee rust spread in fragmented landscapes

Landscape structure influences the spread of plant pathogens, primarily by affecting pathogen dispersal. Coffee leaf rust (Hemileia vastatrix), a fungal disease that causes heavy economic losses in the coffee industry, is likely to be affected by landscape structure via dispersal of its wind-borne spores. Previous studies have found positive associations between leaf rust incidence and the proportion of pasture cover, suggesting that deforestation may facilitate rust spore dispersal. We explored this idea by modeling the spread of rust transmission in simulated landscapes. Specifically, we modeled within-patch transmission using a probabilistic cellular automata model, and between-patch transmission using a random walk with spore movement inhibited by forest canopy cover. We used this model to understand how the spread of coffee rust is affected by: 1) clustering of coffee plants, 2) clustering of deforestation, and 3) proportion of landscape deforestation. We found that clustering of coffee plants is the primary driver of rust transmission, affecting the likelihood and severity of rust outbreak. Deforestation is important in landscapes with high clustering of coffee: rust outbreaks are more severe in landscapes with a higher proportion of deforested areas, and more variable in landscapes where deforested areas are more evenly dispersed throughout the landscape.

ecology↗

bioRxiv2020

A super-resolution protocol to correlate structural underpinnings of fast second-messenger signalling in primary cell types

Nanometre-scale cellular information obtained through super-resolution microscopies are often unaccompanied by functional information, particularly transient and diffusible signals through which life is orchestrated in the nano-micrometre spatial scale. We describe a correlative imaging protocol which allows the ubiquitous intracellular second messenger, calcium (Ca2+), to be directly visualised against nanoscale patterns of the ryanodine receptor (RyR) Ca2+ channels which give rise to these Ca2+ signals in wildtype primary cells. This was achieved by combining total internal reflection fluorescence (TIRF) imaging of the elementary Ca2+ signals, with the subsequent DNA-PAINT imaging of the RyRs. We report a straightforward image analysis protocol of feature extraction and image alignment between correlative datasets and demonstrate how such data can be used to visually identify the ensembles of Ca2+ channels that are locally activated during the genesis of cytoplasmic Ca2+ signals.

biophysics↗

bioRxiv2020

NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

Forests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain significant technical and computational challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source dataset of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Networks Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. Tree crowns identified using this technique correspond well with hand-labeled crowns, exhibiting both high levels of overlap and good correspondence in height estimates. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses at scales of ~10,000 ha and cross-region comparisons encompassing forest types from most of the United States.

ecology↗

bioRxiv2020

Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

Sex is an important biological variable often used in analyzing and describing the functional organization of the brain during cognitive and behavioral tasks. Several prior studies have shown that blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) functional connectivity (FC) can be used to differentiate sex among individuals. Herein, we demonstrate that sex can be further classified with high accuracy using the intrinsic BOLD signal fluctuations from resting-state fMRI (rs-fMRI). We adopted the amplitude of low-frequency fluctuation (ALFF), and the fraction of ALFF (fALFF) features from the automated anatomical atlas (AAL) and Powers functional atlas as an input to different machine learning (ML) methods. Using datasets from five independently acquired subject cohorts and with eight fMRI scanning sessions, we comprehensively assessed unbiased performance using nested-cross validation for within-sample and across sample accuracies. The results demonstrated high prediction accuracies for the Human Connectome Project (HCP) dataset (area under cure (AUC) > 0.89). The yielded accuracies suggest that sex difference is embodied and well-pronounced in the low-frequency BOLD signal fluctuation. The performance degrades with the heterogeneity of the cohort and suggests that other factors.e.g. psychiatric disorders and demographics influences the BOLD signal and may interact with the classification of sex. In addition, the results revealed high learning generalizability with the HCP scan, but not across different datasets. The intraclass correlation coefficient (ICC) across HCP scans showed moderate-to-good reliability based on atlas selection (ICC = 0.65 [0.63-0.67] and ICC= 0.78 [0.76-0.80].). We also assessed the effect of scan duration on the predictability of sex and showed that sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). Moreover, we provided statistical maps of the brain regions differentially recruited by or predicting sex using Shapely values and determined an overlap with previous reports of brain response due to sex differences. Altogether, our analysis suggests that sex differences are well-pronounced in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation.

neuroscience↗

bioRxiv2019

Matching the forecast horizon with the relevant ecologicalprocesses

Most phenomenological, statistical models used to generate ecological forecasts take either a time-series approach, based on long-term data from one location, or a space-for-time approach, based on data describing spatial patterns across environmental gradients. Here we consider how the forecast horizon determines whether more accurate predictions come from the time-series approach, the space-for-time approach, or a combination of the two. We use two simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. In the short-term, dynamics reflect initial conditions and fast processes such as birth and death, and the time-series approach makes the best predictions. In the long-term, dynamics reflect the additional influence of slower processes such as evolutionary and ecological selection, colonization and extinction, which the space-for-time approach can effectively capture. At intermediate time-scales, a weighted average of the two approaches shows promise. However, making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.

ecology↗

bioRxiv2019

Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale

Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual); or (2) using remote sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: 1) image segmentation, to identify individual trees and estimate structural traits; 2) ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and 3) predictions for segmented crowns for the full remote sensing footprint at the NEON sites. The R2 values on held out test data ranged from 0.41 to 0.75 on held out test data. The ensemble approach performed better than single partial least squares models. Carbon performed poorly compared to other traits (R2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R2 of 0.62 on held out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R2 on test data of between to 0.26. We used the pipeline to produce individual level trait data for [~]5 million individual crowns, covering a total extent of [~]360 km2. This large dataset allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.

ecology↗

bioRxiv2019

Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.

ecology↗

bioRxiv2018

The Portal Project: a long-term study of a Chihuahuan desert ecosystem

This is a data paper for the Portal Project, a long-term ecological study of rodents, plants, and ants located in southeastern Arizona, U.S.A. This paper contains an overview of methods and information about the structure of the data files and the relational structure among the files. This is a living data paper and will be updated with new information as major changes or additions are made to the data. All data - along with more detailed data collection protocols and site information - is archived at: https://doi.org/10.5281/zenodo.1215988.

ecology↗