Developing and Optimizing Shrub Parameters Representing Sagebrush (\u3ci\u3eArtemisia\u3c/i\u3e spp.) Ecosystems in the Northern Great Basin Using the Ecosystem Demography (EDv2.2) Model

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe ecosystem in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modeling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different plant functional types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrushsteppe ecosystem. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP) using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach. (1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. (2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. (3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two eddy covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent. Our finding on preliminary parameterization of the sagebrush shrub PFT is an important step towards subsequent studies on shrubland ecosystems using EDv2.2 or any other process-based ecosystem model

Semi-Arid Ecosystem Plant Functional Type and LAI from Small Footprint Waveform Lidar

Plant functional traits such as vegetation structure, density, and composition are indicators of ecosystem response to climate and human driven disturbances. We used small footprint waveform lidar with an ensemble random forest approach to differentiate the functional traits in a western US semi-arid ecosystem. We introduced a new gap fraction based leaf area index (LAI) estimator using lidar derived parameters. Results showed 60% - 89% accuracies discriminating plant functional types and estimating shrub LAI. These results imply the potential of waveform lidar to quantify plant functional traits in low-stature vegetation which is useful to assess climate impact in semi-arid ecosystems

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

A quick algorithmic review on management of viral infectious diseases in pediatric solid organ transplant recipients

Pediatric solid organ transplant is a life-saving procedure for children with end-stage organ failure. Viral infections are a common complication following pediatric solid organ transplantation (SOT), which can lead to increased morbidity and mortality. Pediatric solid organ transplant recipients are at an increased risk of viral infections due to their immunosuppressed state. The most commonly encountered viruses include cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus (HSV), varicella-zoster virus (VZV), adenoviruses, and BK polyomavirus. Prevention strategies include vaccination prior to transplantation, post-transplant prophylaxis with antiviral agents, and preemptive therapy. Treatment options vary depending on the virus and may include antiviral therapy and sometimes immunosuppression modification. This review provides a Quick Algorithmic overview of prevention and treatment strategies for viral infectious diseases in pediatric solid organ transplant recipient

Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem

Assessing functional diversity and its abiotic controls at continuous spatial scales are crucial to understanding changes in ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial surface and provide carbon cycling, habitat, and biodiversity, among other important ecosystem processes and services. Yet, the spatial trends and patterns of functional diversity in semi-arid ecosystems and their abiotic controls are unclear. The objectives of this study are two-fold. We evaluated the spatial pattern of functional diversity as estimated from small footprint airborne lidar (ALS) with respect to abiotic controls and fire in a semi-arid ecosystem. Secondly, we used our results to understand the capabilities of large footprint spaceborne lidar (GEDI) for future applications to semi-arid ecosystems. Overall, our findings revealed that functional diversity in this ecosystem is mainly governed by elevation, soil, and water availability. In burned areas, the ALS data show a trend of functional recovery with time since fire. With 16 months of data (April 2019-August 2020), GEDI predicted functional traits showed a moderate correlation (r = 41–61%) with the ALS predicted traits except for the plant area index (PAI) (r = 11%) of low height vegetation (<5 m). We found that the number of GEDI footprints relative to the size of the fire-disturbed areas (=< 2 km2) limited the ability to estimate the full effects of fire disturbance. However, the consistency of diversity trends between ALS and GEDI across our study area demonstrates GEDI’s potential of capturing functional diversity in similar semi-arid ecosystems. The capability of spaceborne lidar to map trends and patterns of functional diversity in this semi-arid ecosystem demonstrates its exciting potential to identify critical biophysical and ecological shifts. Furthermore, opportunities to fuse GEDI with complementary spaceborne data such as ICESat-2 or the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR), and fine scale airborne data will allow us to fill gaps across space and time. For the first time, we have the potential to monitor carbon cycle dynamics, habitats and biodiversity across the globe in semi-arid ecosystems at fine vertical scales

Remote Sensing of Drylands: Applications of Canopy Spectral Invariants

Remote sensing plays an important role in understanding the structure and function of global terrestrial ecosystems. In this project our research focus was to characterize the dryland vegetation structure and function in the western US. Sparse distribution of vegetation, low amount of leaves on the canopies and the bright soil underneath the canopy make remote sensing of drylands a challenging task. To achieve our research goal we collected aerial and ground based optical hyperspectral and lidar data concurrent to our field campaign. We studied the potential and limitations of these sensors to retrieve canopy biochemistry and structure and to map the vegetation cover at species level

Canopy structure: the link between optical and lidar remote sensing through canopy spectral invariants

Canopy structure and chemistry are the dominant factors that determine the radiation budget of vegetation. One approach to understand the role of canopy structure and disentangle it from canopy chemistry is the canopy spectral invariants theory, or p-theory. Using p-theory, the bidirectional reflectance factor (BRF) recorded by sensors can be simulated using a few spectrally-invariant variables and leaf single scattering albedo. The p-theory is originally developed for the optical domain and there are several hallenges associated with it, such as the assumption of black soil, its requirements for narrowband spectral information (e.g. hyperspectral), and limitations in very dense forests. The main question of this study is can we extend the oncepts of p-theory to lidar to overcome these limitations? To answer this question, we developed the theoretical framework in which variables associated with p-theory in the optical domain can be estimated using lidar point clouds and full-waveform information. To verify this framework, we conduct a series of experiments using the DART Monte Carlo ray-tracing model and vegetation scenes with known canopy chemistry and structure such as those offered in the Radiation Transfer Model Intercomparison (RAMI) project. Our reliminary results show that there is a strong link between information provided by optical and lidar sensors through p-theory. We show that information derived from lidar and some fixed, universal canopy chemistry (i.e. dry matter, water, and chlorophyll content) are sufficient to simulate the optical signature of a canopy with high accuracy. The results of this study advance our theoretical understanding of light interaction with canopy elements and also have significant implications for lidar-optical data fusion.Published versio

Fragment screening targeting Ebola virus nucleoprotein C-terminal domain identifies lead candidates

The Ebola Virus is a causative agent of viral hemorrhagic fever outbreaks and a potential global health risk. The outbreak in West Africa (2013-2016) led to 11,000+ deaths and 30,000+ Ebola infected individuals. The current outbreak in the Democratic Republic of Congo (DRC) with 3000+ confirmed cases and 2000+ deaths attributed to Ebola virus infections provides a reminder that innovative countermeasures are still needed. Ebola virus encodes 7 open reading frames (ORFs). Of these, the nucleocapsid protein (eNP) encoded by the first ORF plays many significant roles, including a role in viral RNA synthesis. Here we describe efforts to target the C-terminal domain of eNP (eNP-CTD) that contains highly conserved residues 641-739 as a pan-Ebola antiviral target. Interactions of eNP-CTD with Ebola Viral Protein 30 (eVP30) and Viral Protein 40 (eVP40) have been shown to be crucial for viral RNA synthesis, virion formation, and virion transport. We used nuclear magnetic response (NMR)-based methods to screened the eNP-CTD against a fragment library. Perturbations of 1

Sexual dysfunction in patients with polycystic ovary syndrome in Malaysia

Background: Polycystic ovary syndrome (PCOS) is a combination of chronic anovulation, obesity, and hyperandrogenism and can affect sexual function in women of reproductive age. It is also associated with endometrial cancer. Our aim was to evaluate the frequency and predisposing factors of sexual dysfunction in PCOS patients. Materials and methods: In this cross-sectional study, 16 married women with a definite diagnosis of PCOS were recruited. Sexual function was assessed in the domains of desire, arousal, lubrication, orgasm, satisfaction and pain using the female sexual function index (FSFI) questionnaire. Patients were also assessed for mental health using the depression, anxiety and stress (DASS-21) questionnaire. Presence of hirsutism was assessed using the Ferriman-Gallwey (FG) scoring system. Demographic data were obtained from patients during in-person interview. Results: Sexual dysfunction was present in 62.5% of patients with the domains of arousal and lubrication particularly affected (93.8% and 87.5%, respectively). Patients with symptoms of depression and anxiety were significantly more likely to suffer sexual dysfunction than those without these symptoms (p=0.04 and p=0.03 respectively). Patients with stress symptoms reported higher orgasm dysfunction than those without (p=0.02). No significant difference in any of the FSFI score domains was observed between patients with and without hirsutism. Conclusions: PCOS patients markedly suffer from sexual dysfunction and therefore it seems appropriate to be screened for intervention. Poor mental health conditions that may be the result of infertility or other complications of PCOS should also be considered as curable causes of sexual dysfunction in these patients