Examining the role of ganglioside homeostasis in neurodegeneration and aging using MALDI imaging mass spectrometry

Gangliosides are a family of membrane lipids enriched in the central nervous system (CNS) that play an important role in cell signaling processes on the membrane. Alterations in the homeostatic distribution of the a-series family of gangliosides has been observed in both pre-clinical models and human patients with neurodegenerative diseases and injuries, such as Alzheimer’s disease (AD) and strokes. Ganglioside dysregulation has been implicated as an important mechanisms driving pathology in the aging brain, yet there is little information on where and when these lipid changes occur as well as the role of dysregulation during neurodegeneration. Matrix-Assisted Laser Desorption/Ionization (MALDI) imaging mass spectrometry (IMS) is a novel imaging technique that can map the distribution of ionizable molecules on a sample in a 2-dimensional format, making it the ideal tool for analyzing gangliosides on post-mortem brain tissue sections. A comorbid rat model of stroke and Aβ toxicity, using an endothelin-1 (ET-1) induced unilateral striatal stroke together with intracerebralventricular (icv) injections of Aβ(25-35), was used to examine ganglioside dysregulation in response to neurodegenerative injuries of varying severity. Results indicated that ganglioside dysregulation was correlated with the severity of the neurodegenerative injury and showed a characteristic pattern of depleted protective complex gangliosides with accumulated toxic simple gangliosides at the site of injury. Transgenic (Tg) rats with a mutation in the Alzheimer’s precursor protein (APP) demonstrated a similar characteristic shift in ganglioside distribution during aging compared to wild-type (Wt) rats in brain regions which are susceptible to damage in AD, such as the white matter and hippocampus. Finally, chloroquine (CQ), a pharmacological inhibitor of ganglioside catabolism, was used as a treatment for ganglioside dysregulation after injury in the rat comorbid stroke model. CQ was found to prevent ganglioside dysregulation acutely after stroke and was correlated with reduced pathology and functional impairments. These results support the hypothesis of ganglioside dysregulation as an important mechanism of neurodegeneration in the aging and injured brain and highlights the benefits associated with the restoration of ganglioside homeostasis after stroke injury

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion

Sacred fig trees promote frugivore visitation and tree seedling abundance in South India

While sacred groves (forest fragments protected for religious reasons) are widely acknowledged to have a beneficial effect on biodiversity conservation, the ecological benefits of individual sacred trees remain unknown. Fig trees are present as sacred trees in humandominated landscapes across South Asia and are considered keystone species for wildlife in tropical forests. If frugivores continue to visit fig trees in disturbed landscapes, they may deposit seeds of other tree species beneath fig canopies, ultimately facilitating forest regeneration. We studied whether sacred fig trees in Tamil Nadu, India can facilitate seed dispersal in human-dominated landscapes. We quantified abundance of sacred fig trees at the study site, assessed whether seed-dispersing frugivore visitation to fig trees is affected by human disturbance, and compared tree seedling density beneath fig trees and open areas. We found that some species of frugivorous birds and bats will visit large fig trees in conditions of high human disturbance and that tree seedling density is significantly higher under sacred trees compared to open areas. By promoting frugivore activity, sacred fig trees may have a beneficial effect on biodiversity conservation in human-dominated landscapes

Post-Fire Seed Dispersal of a Wind-Dispersed Shrub Declined with Distance to Seed Source, yet had High Levels of Unexplained Variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes

Dynamic Job Satisfaction Shifts: Implications for Manager Behavior and Crossover to Employees

In this dissertation, I investigated job satisfaction from a dynamic perspective. Specifically, I integrated the momentum model of job satisfaction with the affective shift model and crossover theory in an effort to move beyond traditional, static conceptions of job satisfaction and other constructs. Recent research and theoretical development has focused on the meaning of job satisfaction change for workers and how such change impacts their decisions to leave an organization. To extend this line of inquiry, I posited hypotheses pertaining to: (a) job satisfaction change with respect to positive work behavior (i.e., organizational citizenship behavior, family-supportive supervisor behavior); (b) the potential moderating effect of changes in negative work events (i.e., job demands, interpersonal conflict) on the relation between job satisfaction change and turnover intentions change and positive work behavior; and (c) the crossover of job satisfaction change from managers to employees and the potential underlying behavioral mechanisms. An archival dataset collected by the Work, Family & Health Network was used to investigate the aforementioned phenomena. Data were collected at two time points with a six-month interval via face-to-face computer-assisted personal interviews from individuals working at 30 facilities from a U.S. extended-healthcare organization. In total, data from 184 managers and 1,524 of their employees were used to test hypotheses. Data were analyzed using multilevel structural equation modeling. In an extension of the momentum model, I found that managers’ job satisfaction change positively related to changes in employee reports of their FSSB; in addition, I replicated prior findings in which job satisfaction change negatively related to turnover intentions change. Furthermore, based on my integration of the momentum model and the affective shift model, I tested the proposition that changes in negative work events (i.e., job demands, interpersonal conflict) would moderate the relationship between changes in job satisfaction and focal outcomes. For certain operationalizations of negative work events, hypothesis testing revealed significant interactions with respect to changes in all three outcomes: turnover intentions, OCB, and FSSB. The form of the interactions, however, deviated from my predictions for models including changes in turnover intentions and OCB, although my predictions were supported for models including changes in FSSB. In my integration of the momentum model and crossover theory, the associated hypotheses were met with very limited support. Specifically, the relationship between managers\u27 job satisfaction change and employees\u27 job satisfaction change approached significance, but the relationship between managers\u27 level of job satisfaction and their employees\u27 subsequent level of job satisfaction did not receive support. Similarly, the proposed mediational mechanisms (i.e., managers\u27 OCB and FSSB) of these crossover relations went unsupported. In sum, while my contributions to the momentum model and the affective shift model were notable, my proposed integration of the momentum model and crossover theory was met with limited support. Overall, findings from this dissertation yield important implications for both theory and practice, as they may draw more attention to changes in job satisfaction, as well as the potentially beneficial role of changes in perceived negative work events

Detecting Gold Mining Impacts on Insect Biodiversity in a Tropical Mining Frontier with SmallSat Imagery

Gold mining is a major driver of Amazonian forest loss and degradation. As mining activity encroaches on primary forest in remote and inaccessible areas, satellite imagery provides crucial data for monitoring mining-related deforestation. High-resolution imagery, in particular, has shown promise for detecting artisanal gold mining at the forest frontier. An important next step will be to establish relationships between satellite-derived land cover change and biodiversity impacts of gold mining. In this study, we set out to detect artisanal gold mining using high-resolution imagery and relate mining land cover to insects, a taxonomic group that accounts for the majority of faunal biodiversity in tropical forests. We applied an object-based image analysis (OBIA) to classify mined areas in an Indigenous territory in Guyana, using PlanetScope imagery with ~3.7 m resolution. We complemented our OBIA with field surveys of insect family presence or absence in field plots (n = 105) that captured a wide range of mining disturbances. Our OBIA was able to identify mined objects with high accuracy (\u3e90% balanced accuracy). Field plots with a higher proportion of OBIA-derived mine cover had significantly lower insect family richness. The effects of mine cover on individual insect taxa were highly variable. Insect groups that respond strongly to mining disturbance could potentially serve as bioindicators for monitoring ecosystem health during and after gold mining. With the advent of global partnerships that provide universal access to PlanetScope imagery for tropical forest monitoring, our approach represents a low-cost and rapid way to assess the biodiversity impacts of gold mining in remote landscapes

Bayesian Models for Spatially Explicit Interactions Between Neighbouring Plants

Interactions between neighbouring plants drive population and community dynamics in terrestrial ecosystems. Understanding these interactions is critical for both fundamental and applied ecology. Spatial approaches to model neighbour interactions are necessary, as interaction strength depends on the distance between neighbouring plants. Recent Bayesian advancements, including the Hamiltonian Monte Carlo algorithm, offer the flexibility and speed to fit models of spatially explicit neighbour interactions. We present a guide for parameterizing these models in the Stan programming language and demonstrate how Bayesian computation can assist ecological inference on plant–plant interactions. Modelling plant neighbour interactions presents several challenges for ecological modelling. First, nonlinear models for distance decay can be prone to identifiability problems, resulting in lack of model convergence. Second, the pairwise data structure of plant–plant interaction matrices often leads to large matrices that demand high computational power. Third, hierarchical structure in plant–plant interaction data is ubiquitous, including repeated measurements within field plots, species and individuals. Hierarchical terms (e.g. ‘random effects’) can result in model convergence problems caused by correlations between coefficients. We explore modelling solutions for these challenges with examples representing spatial data on plant demographic rates: growth, survival and recruitment. We show that ragged matrices reduce computational challenges inherent to pairwise matrices, resulting in higher efficiency across data types. We also demonstrate how metrics for model convergence, including divergent transitions and effective sample size, can help diagnose problems that result from complex nonlinear structures. Finally, we explore when to use different model structures for hierarchical terms, including centred and non-centred parameterizations. We provide reproducible examples written in Stan to enable ecologists to fit and troubleshoot a broad range of neighbourhood interaction models. Spatially explicit models are increasingly central to many ecological questions. Our work illustrates how novel Bayesian tools can provide flexibility, speed and diagnostic capacity for fitting plant neighbour models to large, complex datasets. The methods we demonstrate are applicable to any dataset that includes a response variable and locations of observations, from forest inventory plots to remotely sensed imagery. Further developments in statistical models for neighbour interactions are likely to improve our understanding of plant population and community ecology across systems and scales

Employee overqualification and manager job insecurity: Implications for employee career outcomes

This is the final version. Available on open access from Wiley via the DOI in this recordIn this study, we propose that manager job insecurity will moderate the nature of the relationship between perceived overqualification and employee career‐related outcomes (career satisfaction, promotability ratings, and voluntary turnover). We tested our hypotheses using a sample of 124 employees and 54 managers working in a large holding company in Ankara, Turkey, collected across five time periods. The results suggested that average perceived overqualification was more strongly, and negatively, related to career satisfaction of employees when managers reported higher job insecurity. Furthermore, employee perceived overqualification was positively related to voluntary turnover when manager job insecurity was high. No direct or moderated effects were found for promotability ratings. Implications for overqualification and job insecurity literatures were discussed

Forecasting Natural Regeneration of Sagebrush After Wildfires Using Population Models and Spatial Matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where postwildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (\u3e900,000 km2). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfres not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating \u3e 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfre and within-wildfre spatial heterogeneity improved out-ofsample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a fexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration

Scaling Up Sagebrush Chemistry with Near-Infrared Spectroscopy and UAS-Acquired Hyperspectral Imagery

Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 \u3c 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 \u3c 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors