Nesting Success of Kemp’s Ridley Sea Turtles, Lepidochelys kempi, at Rancho Nuevo, Tamaulipas, Mexico, 1982–2004

The Kemp’s ridley sea turtle, Lepidochelys kempi, was on the edge of extinction owing to a combination of intense egg harvesting and incidental capture in commercial fishing trawls. Results from a cooperative conservation strategy initiated in 1978 between Mexico and the United States to protect and restore the Kemp’s ridley turtle at the main nesting beach at Rancho Nuevo, Tamaulipas, Mexico are assessed. This strategy appears to be working as there are signs that the species is starting to make a recovery. Recovery indicators include: 1) increased numbers of nesting turtles, 2) increased numbers of 100+ turtle nesting aggregations (arribadas), 3) an expanding nesting season now extending from March to August, and 4) significant nighttime nesting since 2003. The population low point at Rancho Nuevo was in 1985 (706 nests) and the population began to significantly increase in 1997 (1,514 nests), growing to over 4,000 nests in 2004. The size and numbers of arribadas have increased each year since 1983 but have yet to exceed the 1,000+ mark; most arribadas are still 200–800+ turtles

Application of machine learning to prediction of vegetation health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern

Nutrient Concentrations in Timbalier Bay and the Louisana Oil Patch

Paper by H. P. Burchfield, R. J. Wheeler, and W. Subr

The role of the Niemann-Pick disease, type C1 protein in adipocyte insulin action.

The Niemann-Pick disease, type C1 (NPC1) gene encodes a transmembrane protein involved in cholesterol efflux from the lysosome. SNPs within NPC1 have been associated with obesity and type 2 diabetes, and mice heterozygous or null for NPC1 are insulin resistant. However, the molecular mechanism underpinning this association is currently undefined. This study aimed to investigate the effects of inhibiting NPC1 function on insulin action in adipocytes. Both pharmacological and genetic inhibition of NPC1 impaired insulin action. This impairment was evident at the level of insulin signalling and insulin-mediated glucose transport in the short term and decreased GLUT4 expression due to reduced liver X receptor (LXR) transcriptional activity in the long-term. These data show that cholesterol homeostasis through NPC1 plays a crucial role in maintaining insulin action at multiple levels in adipocytes

Mitochondrial oxidative stress causes insulin resistance without disrupting oxidative phosphorylation.

Mitochondrial oxidative stress, mitochondrial dysfunction, or both have been implicated in insulin resistance. However, disentangling the individual roles of these processes in insulin resistance has been difficult because they often occur in tandem, and tools that selectively increase oxidant production without impairing mitochondrial respiration have been lacking. Using the dimer/monomer status of peroxiredoxin isoforms as an indicator of compartmental hydrogen peroxide burden, we provide evidence that oxidative stress is localized to mitochondria in insulin-resistant 3T3-L1 adipocytes and adipose tissue from mice. To dissociate oxidative stress from impaired oxidative phosphorylation and study whether mitochondrial oxidative stress per se can cause insulin resistance, we used mitochondria-targeted paraquat (MitoPQ) to generate superoxide within mitochondria without directly disrupting the respiratory chain. At ≤10 μm, MitoPQ specifically increased mitochondrial superoxide and hydrogen peroxide without altering mitochondrial respiration in intact cells. Under these conditions, MitoPQ impaired insulin-stimulated glucose uptake and glucose transporter 4 (GLUT4) translocation to the plasma membrane in both adipocytes and myotubes. MitoPQ recapitulated many features of insulin resistance found in other experimental models, including increased oxidants in mitochondria but not cytosol; a more profound effect on glucose transport than on other insulin-regulated processes, such as protein synthesis and lipolysis; an absence of overt defects in insulin signaling; and defective insulin- but not AMP-activated protein kinase (AMPK)-regulated GLUT4 translocation. We conclude that elevated mitochondrial oxidants rapidly impair insulin-regulated GLUT4 translocation and significantly contribute to insulin resistance and that MitoPQ is an ideal tool for studying the link between mitochondrial oxidative stress and regulated GLUT4 trafficking

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression.

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells

The RELAMPAGO Lightning Mapping Array: Preliminary Scientific Results and Application to GLM Calibration and Validation

During November 2018 through April 2019, an 11-station NASA lightning mapping array (LMA) was installed in the Cordoba region of Argentina, in support of GOES-16 Geostationary Lightning Mapper (GLM) calibration and validation, as well as the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. This region of Argentina is well known for frequent, intense thunderstorms and severe weather. The LMA was monitored remotely via the Internet throughout its deployment, but due to bandwidth limitations no real-time data were available. Custom GOES-16 imagery provided by NASA SPoRT assisted with monitoring of thunderstorm cases. Occasional site visits were done to obtain data disks, perform routine maintenance, and troubleshoot problems. During the deployment the network captured lightning in a variety of storm modes, including ordinary and severe multicells, supercells, and mesoscale convective systems. Many examples of normal-polarity thunderstorms, as well as a few examples of anomalously charged thunderstorms, were observed. Long (100+ km) horizontally stratified lightning flashes, as well as lightning in overshooting tops, also were frequently observed. Supporting research radar observations were available through January 2019, with operational radar coverage available after that time. Some cases featured supporting ABI meso scanning. This presentation will report on the LMA deployment in context with the RELAMPAGO field campaign, show results from some representative case studies, and will provide initial comparisons to GLM observations

Proteomic Analysis of GLUT4 Storage Vesicles Reveals Tumor Suppressor Candidate 5 (TUSC5) as a Novel Regulator of Insulin Action in Adipocytes.

Insulin signaling augments glucose transport by regulating glucose transporter 4 (GLUT4) trafficking from specialized intracellular compartments, termed GLUT4 storage vesicles (GSVs), to the plasma membrane. Proteomic analysis of GSVs by mass spectrometry revealed enrichment of 59 proteins in these vesicles. We measured reduced abundance of 23 of these proteins following insulin stimulation and assigned these as high confidence GSV proteins. These included established GSV proteins such as GLUT4 and insulin-responsive aminopeptidase, as well as six proteins not previously reported to be localized to GSVs. Tumor suppressor candidate 5 (TUSC5) was shown to be a novel GSV protein that underwent a 3.7-fold increase in abundance at the plasma membrane in response to insulin. siRNA-mediated knockdown of TUSC5 decreased insulin-stimulated glucose uptake, although overexpression of TUSC5 had the opposite effect, implicating TUSC5 as a positive regulator of insulin-stimulated glucose transport in adipocytes. Incubation of adipocytes with TNFα caused insulin resistance and a concomitant reduction in TUSC5. Consistent with previous studies, peroxisome proliferator-activated receptor (PPAR) γ agonism reversed TNFα-induced insulin resistance. TUSC5 expression was necessary but insufficient for PPARγ-mediated reversal of insulin resistance. These findings functionally link TUSC5 to GLUT4 trafficking, insulin action, insulin resistance, and PPARγ action in the adipocyte. Further studies are required to establish the exact role of TUSC5 in adipocytes