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We present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation. Key Points: We developed a novel Bayesian Hierarchical Space‐time Model for fields over a large domainWe applied modeling and reconstruction of equatorial Pacific SST at contemporary and Holocene time scalesThe framework offers bright prospects for modeling and reconstruction of other climate fields [ABSTRACT FROM AUTHOR]

Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%. Key Points: Fusion of multiple remote sensing estimates is a promising approach to generate high resolution daily estimates of snow coverA new machine learning approach outperforms a state‐of‐the‐art competitorSpatially‐varying fitted random forests allow for geographically‐distributed feature importance [ABSTRACT FROM AUTHOR]

Lipid remodeling, the modification of cell membrane chemistry via structural rearrangements within the lipid pool of an organism, is a common physiological response amongst all domains of life to alleviate environmental stress and maintain cellular homeostasis. Whereas culture experiments and environmental studies of phytoplankton have demonstrated the plasticity of lipids in response to specific abiotic stressors, few analyses have explored the impacts of multi-environmental stressors at the community-level scale. Here, we study changes in the pool of intact polar lipids (IPLs) of a phytoplanktonic community exposed to multi-environmental stressors during a ∼ 2-month-long mesocosm experiment deployed in the eastern tropical South Pacific off the coast of Callao, Peru. We investigate lipid remodeling of IPLs in response to changing nutrient stoichiometries, temperature, pH, and light availability in surface and subsurface water masses with contrasting redox potentials, using multiple linear regressions, classification and regression trees, and random forest analyses. We observe proportional increases in certain glycolipids (namely mono- and diglycosyldiacylglycerol – MGDG and DGDG, respectively) associated with higher temperatures and oxic conditions, consistent with previous observations of their utility to compensate for thermal stress and their degradation under oxygen stress. N-bearing (i.e., betaine lipids and phosphatidylethanolamine – BLs and PE) and non-N-bearing (i.e., MGDG; phosphatidylglycerol, PG; and sulfoquinovosyldiacylglycerol, SQDG) IPLs are anti-correlated and have strong positive correlations with nitrogen-replete and nitrogen-depleted conditions, respectively, which suggests a substitution mechanism for N-bearing IPLs under nitrogen limitation. Reduced CO 2 (aq) availability and increased pH levels are associated with greater proportions of DGDG and SQDG IPLs, possibly in response to the lower concentration of CO 2 (aq) and the overall lower availability of inorganic carbon for fixation. A higher production of MGDG in surface waters corresponds well with its established photoprotective and antioxidant mechanisms in thylakoid membranes. The observed statistical relationships between IPL distributions, physicochemical parameters, and the composition of the phytoplankton community suggest evidence of lipid remodeling in response to environmental stressors. These physiological responses may allow phytoplankton to reallocate resources from structural or extrachloroplastic membrane lipids (i.e., phospholipids and betaine lipids) under high-growth conditions to thylakoid and/or plastid membrane lipids (i.e., glycolipids and certain phosphatidylglycerols) under growth-limiting conditions. Further investigation of the exact mechanisms controlling the observed trends in lipid distributions is necessary to better understand how membrane reorganization under multi-environmental stressors can affect the pools of cellular C, N, P, and S, as well as their fluxes to higher trophic levels in marine environments subjected to increasing environmental pressure. Our results suggest that future studies addressing the biogeochemical consequences of climate change in the eastern tropical South Pacific Ocean must take into consideration the impacts of lipid remodeling in phytoplankton. [ABSTRACT FROM AUTHOR]

Introduction: It is well-established that obesity plays a significant role in the development of metabolic syndrome. Visfatin is a novel adipocytokine predominantly secreted in adipose tissue, associated with a wide range of biological effects including glucose and lipid metabolism. Visfatin levels are significantly linked to inflammation and the development of obesity-related metabolic disorders. Unfortunately, the roles of visfatin in obesity, particularly in the Indian population, are scarce. Aim: To study the role of serum visfatin in diagnosing metabolic syndrome in overweight and obese adults. Materials and Methods: A comparative cross-sectional study was conducted at the Department of Biochemistry, Tomo Riba Institute of Health and Medical Sciences, Naharlagun, Arunachal Pradesh, India, between September 2022 and October 2023. A total of 200 subjects (50 controls, 50 overweight individuals, 50 obese individuals without metabolic syndrome, and 50 obese individuals with metabolic syndrome), aged 20-70 years, were enrolled as study participants. Anthropometric parameters, lipid profiles, and fasting glucose were analysed using an auto analyser. Serum visfatin levels were measured by Enzymelinked Immunosorbent Assay (ELISA). Statistical analysis was performed using the t-test, and categorical data were analysed using the Chi-square test. Correlation analysis was done by Pearson’s correlation at a significance level of 5%. Results: The control group consisted of 17 males and 33 females with a mean age of 41.5±13.4 years, the overweight group consisted of 13 males and 37 females with a mean age of 37.1±10.9 years, the obese without metabolic syndrome group consists of 16 males and 34 females with a mean age of 40.6±12.7 years, and obese with metabolic syndrome group had 23 males and 27 females with a mean age of 42.0±9.2 years. Serum visfatin levels (ng/mL) were significantly elevated in the overweight (1.7±0.3), obese without metabolic syndrome (4.3±3.2), and obese with metabolic syndrome (10.9±6.6) groups compared to the controls (1.0±0.2). Serum visfatin levels were positively correlated with Body Mass Index (BMI) (r=0.51, p<0.001), Waist to Hip Ratio (WHR) (r=0.41, p<0.001), Neck Circumference (NC) (r=0.50, p<0.001), Fasting glucose (r=0.44, p<0.001), Total Cholesterol (TC) (r=0.41, p<0.001), Triglycerides (TG) (r=0.39, p<0.001), LowDensity Lipoprotein-cholesterol (LDL-c) (r=0.39, p<0.001), Very Low-Density Lipoprotein (VLDL) (VLDL) (r=0.39, p<0.001), Systolic Blood Pressure (SBP) (r=0.52, p<0.001), and Diastolic Blood Pressure (DBP) (r=0.45, p<0.001), and negatively correlated with High-Density Lipoprotein-cholesterol (HDL-c) (r=-0.20, p<0.002). Conclusion: The present study revealed a good relationship between serum visfatin and the anthropometric and biochemical parameters. The current data belief is that visfatin may be a promising biomarker for predicting metabolic syndrome and its associated disorders particularly in overweight and obese adults. [ABSTRACT FROM AUTHOR]