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Obesity predisposes to type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD), but underlying mechanisms are incompletely understood. Potassium channel tetramerization domain-containing protein 17 (Kctd17) levels are increased in livers from obese mice and humans. In this study, we investigated the mechanism of increased Kctd17 and whether it is causal to obesity-induced metabolic complications. We transduced Rosa26-LSL-Cas9 knockin mice with AAV8-TBG-Cre (Control), AAV8-U6-Kctd17 sgRNA-TBG-Cre (L-Kctd17), AAV8-U6-Oga sgRNA-TBG-Cre (L-Oga), or AAV8-U6-Kctd17/Oga sgRNA-TBG-Cre (DKO). We fed mice a high-fat diet (HFD) and assessed for hepatic glucose and lipid homeostasis. We generated Kctd17 , O-GlcNAcase (Oga) , or Kctd17/Oga -knockout hepatoma cells by CRISPR-Cas9, and Kctd17 -directed antisense oligonucleotide to test therapeutic potential in vivo. We analyzed transcriptomic data from patients with NAFLD. Hepatocyte Kctd17 expression was increased in HFD-fed mice due to increased Srebp1c activity. HFD-fed L-Kctd17 or Kctd1 7 antisense oligonucleotide–treated mice show improved glucose tolerance and hepatic steatosis, whereas forced Kctd17 expression caused glucose intolerance and hepatic steatosis even in lean mice. Kctd17 induced Oga degradation, resulting in increasing carbohydrate response element-binding protein (Chrebp) protein, so concomitant Oga knockout negated metabolic benefits of hepatocyte Kctd17 deletion. In patients with NAFLD, KCTD17 messenger RNA was positively correlated with expression of Chrebp target and other lipogenic genes. Srebp1c-induced hepatocyte Kctd17 expression in obesity disrupted glucose and lipid metabolism by stabilizing Chrebp, and may represent a novel therapeutic target for obesity-induced T2D and NAFLD. Hepatocyte Kctd17 expression is increased in obesity, which predisposes to insulin resistance and liver fat accumulation. Blocking excess Kctd17 by either genetic or pharmacologic means reverses these pathologic processes, by decreasing Chrebp protein stability, a key factor that regulates both glucose and lipid metabolism. [ABSTRACT FROM AUTHOR]

Accurate specification of spatiotemporal errors of remotely sensed soil moisture (SM) data is essential for a correct assessment of their utility and optimally integrating multiple SM products or assimilating them into hydrological models. Although Triple Collocation Analysis (TCA) has been widely used to provide SM errors, the impact of rescaling technique on the TCA error estimates has not received major attention, which can lead to biased and inaccurate error estimates. Moreover, current knowledge about time-variant SM errors derived from TCA is still very limited, which hampers the advance of applying time-variant errors in data merging and data assimilation studies efficiently. Based on these considerations, this work aims to advance the use of the TCA for characterizing errors with a focus on the rescaling techniques, and validating TCA-based time-variant errors using global ground measurements in 759 grid cells. To this end, the Advanced Scatterometer (ASCAT) and four passive-based SM products, including Soil Moisture and Ocean Salinity Level-3 (SMOS-L3), SMOS INRA-CESBIO (SMOS-IC), Soil Moisture Active Passive Level-3 (SMAP-L3), and SMAP INRAE BORDEAUX (SMAP-IB) SM products, were considered. The time-variant error term here denotes an aggregate error magnitude over a 101-day moving-time-window. It is found that different selection of the rescaling technique considered in TCA led to TCA error estimates with significantly different accuracy when ground-based errors are regarded as the benchmark. The optimal combination strategy to implement TCA is applying TCA to SM anomalies and rescaling the errors by coefficients derived from the TCA model. Pearson's correlation with ground-based time-variant errors is 0.62, 0.72, 0.83, 0.89, and 0.93 for SMAP-IB, SMAP-L3, SMOS-IC, SMOS-L3, and ASCAT SM, respectively. Considering time-variant errors in applications is necessary since time-variant errors deviate from time-invariant errors by 50% when errors are rescaled by the TCA model parameters. Time-invariant errors are greater than time-variant errors when SM products are rescaled against a reference dataset while the opposite conclusion can be drawn when errors are rescaled by the TCA coefficients. TCA- and ground-based methods provide consistent evaluations in 74.7% (77.3%), 75.8% (79.8%), 79.6% (81.1%), and 78.6% (79.7%) of the analysis period on average (median) for the TCA implementations with SMAP-L3, SMAP-IB, SMOS-L3, and SMOS-IC SM, respectively. The error analysis reveals that TCA typically underestimated ASCAT errors while overestimated passive SM errors when considering ground-based evaluation as the benchmark. Moreover, TCA was found to have relatively less power to efficiently characterize SM errors in croplands when compared with other land cover types. This study validated TCA time-variant errors using ground measurements and compared TCA- and ground-based evaluation performances on a global scale. Our work arouses particular attention to the rescaling technique selection considered in TCA, which is crucial for accurately characterizing SM errors and efficiently using them in various hydrometeorological applications. • Rescaling techniques have a great impact on the final TCA error estimates. • Noticeable differences are observed for time-variant and time-invariant errors. • TCA has less power to efficiently characterize SM errors in croplands. • Temporal interpolations have a relatively small impact on the TCA errors. • TCA underestimates ASCAT and overestimates passive-based SM errors. [ABSTRACT FROM AUTHOR]

Background & Aims To date, no pharmacological therapy has been approved for non-alcoholic fatty liver disease (NAFLD). The aim of the present study was to evaluate the therapeutic potential of poly ADP-ribose polymerase (PARP) inhibitors in mouse models of NAFLD. Methods As poly ADP-ribosylation (PARylation) of proteins by PARPs consumes nicotinamide adenine dinucleotide (NAD + ), we hypothesized that overactivation of PARPs drives NAD + depletion in NAFLD. Therefore, we assessed the effectiveness of PARP inhibition to replenish NAD + and activate NAD + -dependent sirtuins, hence improving hepatic fatty acid oxidation. To do this, we examined the preventive and therapeutic benefits of the PARP inhibitor (PARPi), olaparib, in different models of NAFLD. Results The induction of NAFLD in C57BL/6J mice using a high-fat high-sucrose (HFHS)-diet increased PARylation of proteins by PARPs. As such, increased PARylation was associated with reduced NAD + levels and mitochondrial function and content, which was concurrent with elevated hepatic lipid content. HFHS diet supplemented with PARPi reversed NAFLD through repletion of NAD + , increasing mitochondrial biogenesis and β-oxidation in liver. Furthermore, PARPi reduced reactive oxygen species, endoplasmic reticulum stress and fibrosis. The benefits of PARPi treatment were confirmed in mice fed with a methionine- and choline-deficient diet and in mice with lipopolysaccharide-induced hepatitis; PARP activation was attenuated and the development of hepatic injury was delayed in both models. Using Sirt1 hep−/− mice, the beneficial effects of a PARPi-supplemented HFHS diet were found to be Sirt1-dependent. Conclusions Our study provides a novel and practical pharmacological approach for treating NAFLD, fueling optimism for potential clinical studies. Lay summary Non-alcoholic fatty liver disease (NAFLD) is now considered to be the most common liver disease in the Western world and has no approved pharmacological therapy. PARP inhibitors given as a treatment in two different mouse models of NAFLD confer a protection against its development. PARP inhibitors may therefore represent a novel and practical pharmacological approach for treating NAFLD. [ABSTRACT FROM AUTHOR]

Water Energy Balance (WEB) Soil Vegetation Atmosphere Transfer (SVAT) modelling can be used to estimate soil moisture by forcing the model with observed data such as precipitation and solar radiation. Recently, an innovative approach that assimilates remotely sensed thermal infrared (TIR) observations into WEB-SVAT to improve the results has been proposed. However, the efficacy of the model-observation integration relies on the model's realistic representation of soil water processes. Here, we explore methods to improve the soil water processes of a simple WEB-SVAT model by adopting and incorporating an exponential root water uptake model with water stress compensation and establishing a more appropriate soil-biophysical linkage between root-zone moisture content, above-ground states and biophysical indices. The existing WEB-SVAT model is extended to a new Multi-layer WEB-SVAT with Dynamic Root distribution (MWSDR) that has five soil layers. Impacts of plant root depth variations, growth stages and phenological cycle of the vegetation on transpiration are considered in developing stages. Hydrometeorological and biogeophysical measurements collected from two experimental sites, one in Dookie, Victoria, Australia and the other in Ponca, Oklahoma, USA, are used to validate the new model. Results demonstrate that MWSDR provides improved soil moisture, transpiration and evaporation predictions which, in turn, can provide an improved physical basis for assimilating remotely sensed data into the model. Results also show the importance of having an adequate representation of vegetation-related transpiration process for an appropriate simulation of water transfer in a complicated system of soil, plants and atmosphere. [ABSTRACT FROM AUTHOR]

Summary In recent years, tetracyclines, such as doxycycline, have become broadly used to control gene expression by virtue of the Tet-on/Tet-off systems. However, the wide range of direct effects of tetracycline use has not been fully appreciated. We show here that these antibiotics induce a mitonuclear protein imbalance through their effects on mitochondrial translation, an effect that likely reflects the evolutionary relationship between mitochondria and proteobacteria. Even at low concentrations, tetracyclines induce mitochondrial proteotoxic stress, leading to changes in nuclear gene expression and altered mitochondrial dynamics and function in commonly used cell types, as well as worms, flies, mice, and plants. Given that tetracyclines are so widely applied in research, scientists should be aware of their potentially confounding effects on experimental results. Furthermore, these results caution against extensive use of tetracyclines in livestock due to potential downstream impacts on the environment and human health. [ABSTRACT FROM AUTHOR]

Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. It can provide insights for retrieval development and validation, and inform suitable strategies for data fusion and assimilation. Su et al. (2013a) proposed a potential Fourier method for quantifying the errors based on the difference between the empirical power spectra of these SM data and a water balance model via spectral fitting (SF), circumventing the need for any ancillary data. This work first evaluates its utility by estimating the errors in two passive and active microwave satellite SM over Australia, and comparing the results against the triple collocation (TC) estimator. The SF estimator shows very good agreement with TC in terms of error standard deviation and signal-to-noise ratio, with strong linear correlations of 0.80–0.92 but with lower error estimates. As the two estimators are not strictly comparable, their strong agreement suggests a strong complementarity between time-domain and frequency-domain analyses of errors. A better understanding of the spectral characteristics of the error is still needed to understand their differences. Next, spatial analyses of the derived (SF and TC) error maps, in terms of error standard deviation and noise-to-signal ratio, for the two satellite data are performed with principal component analysis to identify influence of vegetation/leaf-area index (LAI), rainfall, soil wetness, and spatial heterogeneity in topography and soil type on retrieval errors. Lastly, seasonal analysis of the errors discovers systematic temporal variability in errors due to variability in rainfall amount, and less so with changing LAI. [ABSTRACT FROM AUTHOR]

Summary Mitochondrial activity is controlled by proteins encoded by both nuclear and mitochondrial DNA. Here, we identify Sirt7 as a crucial regulator of mitochondrial homeostasis. Sirt7 deficiency in mice induces multisystemic mitochondrial dysfunction, which is reflected by increased blood lactate levels, reduced exercise performance, cardiac dysfunction, hepatic microvesicular steatosis, and age-related hearing loss. This link between SIRT7 and mitochondrial function is translatable in humans, where SIRT7 overexpression rescues the mitochondrial functional defect in fibroblasts with a mutation in NDUFSI . These wide-ranging effects of SIRT7 on mitochondrial homeostasis are the consequence of the deacetylation of distinct lysine residues located in the hetero- and homodimerization domains of GABPβ1, a master regulator of nuclear-encoded mitochondrial genes. SIRT7-mediated deacetylation of GABPβ1 facilitates complex formation with GABPα and the transcriptional activation of the GABPα/GABPβ heterotetramer. Altogether, these data suggest that SIRT7 is a dynamic nuclear regulator of mitochondrial function through its impact on GABPβ1 function. [ABSTRACT FROM AUTHOR]

Summary Reverse cholesterol transport (RCT) is an antiatherogenic process in which excessive cholesterol from peripheral tissues is transported to the liver and finally excreted from the body via the bile. The nuclear receptor liver receptor homolog 1 (LRH-1) drives expression of genes regulating RCT, and its activity can be modified by different posttranslational modifications. Here, we show that atherosclerosis-prone mice carrying a mutation that abolishes SUMOylation of LRH-1 on K289R develop less aortic plaques than control littermates when exposed to a high-cholesterol diet. The mechanism underlying this atheroprotection involves an increase in RCT and its associated hepatic genes and is secondary to a compromised interaction of LRH-1 K289R with the corepressor prospero homeobox protein 1 (PROX1). Our study reveals that the SUMOylation status of a single nuclear receptor lysine residue can impact the development of a complex metabolic disease such as atherosclerosis. [ABSTRACT FROM AUTHOR]

Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops. [Display omitted] • Crop model is a tool to simulate crop growth, seek solutions for mitigating negative environmental impacts on production. • APSIM Classic has been used as an example to explore wheat yield prediction performance and the influential factors. • Reviewed and established a meta-database from 76 published studies. • Overall: the model predicts wheat yield with RMSE=1 t/ha. Fully calibrated and under stress-free condition: RMSE=0.64 t/ha. • Calibration method, heat, frost, water, and nitrogen stresses were identified as factors causing the model to mis-simulate. [ABSTRACT FROM AUTHOR]

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NO X , NH 4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies. [Display omitted] • A multi-model approach reveals the key drivers for the spatial variation in water quality. • Catchment natural characteristics have the greatest impact on most constituents. • Land use has an important impact on dissolved nutrients. • Our results enable prediction of catchment water quality across space. A multi-model approach indicated that catchment natural characteristics have the greatest impact on water quality overall, but land use has an important impact on dissolved nutrients. [ABSTRACT FROM AUTHOR]

Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI. • Time-variant daily error is globally evaluated for ASCAT and SMAP soil moisture. • Lag correlation analysis for daily soil moisture error, LAI, and rainfall. • Rainfall better explains temporal variability of soil moisture errors than LAI. • Soil moisture error peak comes before rainfall and LAI peaks in savannas. [ABSTRACT FROM AUTHOR]

Background & Aims Obesity-induced nonalcoholic fatty liver disease (NAFLD) develops, in part, via excess insulin-stimulated hepatic de novo lipogenesis, which increases, paradoxically, in patients with obesity-induced insulin resistance. Pleckstrin homology domain leucine-rich repeat protein phosphatase 2 (PHLPP2) terminates insulin signaling by dephosphorylating Akt; levels of PHLPP2 are reduced in livers from obese mice. We investigated whether loss of hepatic PHLPP2 is sufficient to induce fatty liver in mice, mechanisms of PHLPP2 degradation in fatty liver, and expression of genes that regulate PHLPP2 in livers of patients with NAFLD. Methods C57BL/6J mice (controls), obese db/db mice, and mice with liver-specific deletion of PHLPP2 ( L-PHLPP2 ) fed either normal chow or high-fat diet (HFD) were analyzed for metabolic phenotypes, including glucose tolerance and hepatic steatosis. PHLPP2-deficient primary hepatocytes or CRISPR/Cas9-mediated PHLPP2-knockout hepatoma cells were analyzed for insulin signaling and gene expression. We performed mass spectrometry analyses of liver tissues from C57BL/6J mice transduced with Ad-HA-Flag-PHLPP2 to identify posttranslational modifications to PHLPP2 and proteins that interact with PHLPP2. We measured levels of mRNAs by quantitative reverse transcription polymerase chain reaction in liver biopsies from patients with varying degrees of hepatic steatosis. Results PHLPP2-knockout hepatoma cells and hepatocytes from L-PHLPP2 mice showed normal initiation of insulin signaling, but prolonged insulin action. Chow-fed L-PHLPP2 mice had normal glucose tolerance but hepatic steatosis. In HFD-fed C57BL/6J or db/db obese mice, endogenous PHLPP2 was degraded by glucagon and PKA-dependent phosphorylation of PHLPP2 (at Ser1119 and Ser1210), which led to PHLPP2 binding to potassium channel tetramerization domain containing 17 (KCTD17), a substrate-adaptor for Cul3-RING ubiquitin ligases. Levels of KCTD17 mRNA were increased in livers of HFD-fed C57BL/6J or db/db obese mice and in liver biopsies patients with NAFLD, compared with liver tissues from healthy control mice or patients without steatosis. Knockdown of KCTD17 with small hairpin RNA in primary hepatocytes increased PHLPP2 protein but not Phlpp2 mRNA, indicating that KCTD17 mediates PHLPP2 degradation. KCTD17 knockdown in obese mice prevented PHLPP2 degradation and decreased expression of lipogenic genes. Conclusions In mouse models of obesity, we found that PHLPP2 degradation induced lipogenesis without affecting gluconeogenesis. KCTD17, which is up-regulated in liver tissues of obese mice and patients with NAFLD, binds to phosphorylated PHLPP2 to target it for ubiquitin-mediated degradation; this increases expression of genes that regulate lipogenesis to promote hepatic steatosis. Inhibitors of this pathway might be developed for treatment of patients with NAFLD. [ABSTRACT FROM AUTHOR]

River discharge is a critical component for understanding hydrological processes and sustainable management of water resources. The importance of discharge observation has increased due to its potential extreme variation resulting from the projected climate change and stronger variability of precipitation and temperature in some large basins. However, inherent difficulties in ground-based observations and decreasing number of gauge stations hinder accurate measurement of global river discharge and its spatio-temporal variations. Various remote sensing methods have been examined as alternatives, however, they require ground measurements to convert their proxy measurements into the actual river discharge. In this study, we estimate the discharge at the Óbidos station and the mouth of the Amazon basin using the water storage variations derived from GRACE gravity data without relying on any auxiliary ground observations. We extract the water mass signal along the main stem of the river by applying the Empirical Orthogonal Function (EOF) for water storage variations over the basin. The relative water storage variations along the main stem derived from the EOF decomposition are highly correlated with in-situ discharge at the Óbidos. However, in high water season, the GRACE-based discharge is estimated larger than the in-situ observations, and the difference is particularly significant during the 2009 extreme flood season. We argue that the in-situ river discharge in 2009 was underestimated due to the missed water volume for the flow detouring around the Óbidos gauge station during the high-flow event. Net river discharge of the Amazon Basin to Atlantic Ocean is also estimated, and its annual discharge is about 23% larger than that of the Óbidos. In particular, 2009 river discharge to Atlantic Oceans is estimated as 1050 Gton. [ABSTRACT FROM AUTHOR]

Floodplains are a key ecological feature of arid and semi-arid regions and flooding is the main source of vegetation productivity. Characterizing the spatiotemporal flood dynamics, such as surface water and changes in subsequent vegetation growth vigour and biomass, is essential in better understanding of ecology and hydrology of these regions. Recent remotely sensed flooding studies of arid zone floodplains have concentrated on improving classification of surface water, particularly in mixed water-vegetation areas; less is known about the effect of surface water flooding pattern on emergent vegetation dynamics. We use an integrated framework for mapping both flood extents and the persistence of floodplain response changes of water, vegetation and moisture in Cooper Creek, Australia. We analysed pixel-based time series of multiple indices generated by daily MODIS data for 14 highly variable flow flood events between 2000 and 2012. Results indicate that for the extremely flat Cooper Creek floodplain, mapping inundation area by changes in vegetation growth vigour and biomass was significantly larger than surface water mapping area (16.2% for the 2004 flood event) and the difference in inundation mapping mainly occurs around the inundated edges. In addition, by studying surface water and subsequent vegetation response together, it is possible to generate new information, such as the lag time between flooding and peak vegetation growth vigour and biomass, and persistence time of surface water and green vegetation, which provide important insights to arid zone floodplain behaviour. The large extent and high frequency of MODIS images provide advantages in characterizing inundation dynamics for large-scale floodplains where instantaneous (daily) inundation extent is considerably smaller than total cumulative inundation extent, compared with sensors with higher spatial resolution but lower temporal resolution; however, the coarse resolution of MODIS (500 m) limits its performance for small flood events. Globally, this approach is suitable for other large, low-gradient floodplains in arid zones that show similar, long duration vegetation responses as observed in Cooper Creek. [ABSTRACT FROM AUTHOR]

The Advanced Microwave Scanning Radiometer 2 (AMSR2), a follow-up microwave sensor to the AMSR for Earth Observing System (AMSR-E), was launched on the Global Change Observation Mission 1 – Water (GCOM-W1) satellite in May 2012. It is as yet unclear if instrumental improvements in AMSR2 over AMSR-E have led to better soil moisture (SM) estimates, especially since there is no overlapping period of data between the sensors. This study focuses on comparing the results of AMSR2 and AMSR-E SM over Australia, distinguishing four Köppen climate zones to determine if AMSR2 is better than AMSR-E. This is achieved by selecting two year-long comparative time periods from the operating periods of AMSR-E and AMSR2, based on their statistical similarities in modeled SM as a proxy, using Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L). The AMSR2 and AMSR-E C- and X-band SM derived from the Land Parameter Retrieval Model (LPRM) was evaluated. Both AMSR2 C- and X-band SM products were found to show similar temporal patterns and spatial agreement with AMSR-E C- and X-band SM, supported by unbiased root mean square difference (ubRMSD) and R-values with MERRA-L SM, respectively. Using lag-based instrumental variable analysis to estimate the random error component of SM retrievals, the noise-to-signal ratios in AMSR2 X-band SM were found to be slightly higher than their AMSR-E counterparts. The improvements in AMSR2, such as the superior radiometric sensitivity and spatial resolution, have therefore not led to statistically significant differences in performance for LPRM retrievals at 1/2° × 1/2° grid resolution, when compared with AMSR-E. However, similarities in the metrics for AMSR2 and AMSR-E SM suggest that AMSR2 provides a valuable continuation to AMSR-E. [ABSTRACT FROM AUTHOR]

[Display omitted] • In asynchronous climates, the forest growth response to rainfall is not well known. • Lagged relationship of forest growth to antecedent rainfall was explored. • Vegetation in the asynchronous climate have a long memory to the rainfall. • The memory of vegetation to rain could indicate soil/rooting depth across the landscape. The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months). [ABSTRACT FROM AUTHOR]

Real-time de-noising of satellite-derived soil moisture observations presents opportunities to deliver more accurate and timely satellite data for direct satellite users. So far, the most commonly used techniques for reducing the impact of noise in the retrieved satellite soil moisture observations have been based on moving average filters and Fourier based methods. This paper introduces a new alternative wavelet based approach called Wiener-Wavelet-Based Filter (WiW), which uses an entropy based de-noising method to design a causal version of the filter. WiW is used as a post-retrieval processing tool to enhance the quality of observations derived from one active (the Advanced Scatterometer, ASCAT) and one passive (the Advanced Microwave Scanning Radiometer for Earth Observing System, AMSRE) satellite sensors. The filter is then compared with two candidate de-noising techniques, namely: i) a Wiener causal filter introduced by Su et al. (2013) and ii) a conventional moving average filter. The validation is carried out globally at 173 (for AMSRE) and 243 (for ASCAT) soil moisture stations. Results show that all the three de-noising techniques can increase the agreement between satellite and in situ measurements in terms of correlation and signal-to-noise ratio. The Wiener-based methods show least signal distortion and demonstrate to be conservative in retaining the signal information in de-noised data. Importantly, the Wiener filters can be calibrated with the data at hand, without the need for auxiliary data. [ABSTRACT FROM AUTHOR]

Background & Aims Mitochondrial dysfunction, oxidative stress, inflammation, and metabolic reprograming are crucial contributors to hepatic injury and subsequent liver fibrosis. Poly(ADP-ribose) polymerases (PARP) and their interactions with sirtuins play an important role in regulating intermediary metabolism in this process. However, there is little research into whether PARP inhibition affects alcoholic and non-alcoholic steatohepatitis (ASH/NASH). Methods We investigated the effects of genetic deletion of PARP1 and pharmacological inhibition of PARP in models of early alcoholic steatohepatitis, as well as on Kupffer cell activation in vitro using biochemical assays, real-time PCR, and histological analyses. The effects of PARP inhibition were also evaluated in high fat or methionine and choline deficient diet-induced steatohepatitis models in mice. Results PARP activity was increased in livers due to excessive alcohol intake, which was associated with decreased NAD + content and SIRT1 activity. Pharmacological inhibition of PARP restored the hepatic NAD + content, attenuated the decrease in SIRT1 activation and beneficially affected the metabolic-, inflammatory-, and oxidative stress-related alterations due to alcohol feeding in the liver. PARP1 −/− animals were protected against alcoholic steatohepatitis and pharmacological inhibition of PARP or genetic deletion of PARP1 also attenuated Kupffer cell activation in vitro . Furthermore, PARP inhibition decreased hepatic triglyceride accumulation, metabolic dysregulation, or inflammation and/or fibrosis in models of NASH. Conclusion Our results suggests that PARP inhibition is a promising therapeutic strategy in steatohepatitis with high translational potential, considering the availability of PARP inhibitors for clinical treatment of cancer. Lay summary Poly(ADP-ribose) polymerases (PARP) are the most abundant nuclear enzymes. The PARP inhibitor olaparib (Lynparza) is a recently FDA-approved therapy for cancer. This study shows that PARP is overactivated in livers of subjects with alcoholic liver disease and that pharmacological inhibition of this enzyme with 3 different PARP inhibitors, including olaparib, attenuates high fat or alcohol induced liver injury, abnormal metabolic alteration, fat accumulation, inflammation and/or fibrosis in preclinical models of liver disease. These results suggest that PARP inhibition is a promising therapeutic strategy in the treatment of alcoholic and non-alcoholic liver diseases. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products. [ABSTRACT FROM AUTHOR]

Active and passive microwave satellite remote sensing are enabling sub-daily global observations of surface soil moisture (SM) for hydrological, meteorological and climatological studies. Because the retrieved SM data can be quite noisy, post-retrieval processing such as de-noising can play an important role to aid interpretation of the observed dynamics or enhance their utility for data assimilation. To date, the merits of such techniques have not yet been fully evaluated. Here we consider the applications of Fourier-based de-noising filters of Su et al. (2013a) for improving SM retrieved by AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) and ASCAT (Advanced Scatterometer of MetOp-A) sensors. The filters are calibrated in the frequency domain based on a water-balance model, without the need for ancillary data. The evaluation of the de-noising methods was conducted globally against in situ data distributed via the International Soil Moisture Network (ISMN) at 277 AMSR-E and 385 ASCAT pixels. Systematic improvements were found for all considered metrics, namely root-mean-square deviation, linear correlation and signal-to-noise ratio, for both SM products, with improvements more striking for AMSR-E. However, the originally proposed implementation of the filters can induce undesirable over-smoothing and distortion of SM timeseries. To overcome this, based on a simple heuristic argument, we propose the use of ancillary precipitation data in the filtering process, although at some expense of overall agreements with the in situ data. [ABSTRACT FROM AUTHOR]

Pancreatic beta cell dysfunction is a hallmark of type 2 diabetes. Growth differentiation factor 15 (GDF15), which is an energy homeostasis regulator, has been shown to improve several metabolic parameters in the context of diabetes. However, its effects on pancreatic beta-cell remain to be identified. We, therefore, performed experiments using cell models and histological sectioning of wild-type and knock-out GDF15 mice to determine the effect of GDF15 on insulin secretion and cell viability. A bioinformatics analysis was performed to identify GDF15-correlated genes. GDF15 prevents glucotoxicity-mediated altered glucose-stimulated insulin secretion (GSIS) and connexin-36 downregulation. Inhibition of endogenous GDF15 reduced GSIS in cultured mouse beta-cells under standard conditions while it had no impact on GSIS in cells exposed to glucolipotoxicity, which is a diabetogenic condition. Furthermore, this inhibition exacerbated glucolipotoxicity-reduced cell survival. This suggests that endogenous GDF15 in beta-cell is required for cell survival but not GSIS in the context of glucolipotoxicity. • High glucose and palmitate treatment increase GDF15 in cultured pancreatic beta-cells and INS-1E cells. • GDF15 prevents glucose-stimulated insulin secretion inhibited by glucolipotoxicity in the beta-cell and INS-1E cells. • GDF15 prevent glucotoxicity induced connexin-36 downregulation in INS-1E cells. • GDF15 is a potential therapeutic agent which can delay the need of insulin under glucolipotoxic conditions. [ABSTRACT FROM AUTHOR]

The commencement of SEAS5 model for operational seasonal climate forecasting by the European Centre for Medium-Range Weather Forecasts (ECMWF) is a new development. It replaces the ECMWF System 4, which had a large international community of seasonal climate forecast users. To assist potential users of SEAS5 forecasts, a systematic and detailed evaluation of forecast skill and reliability of climate variables over land areas is valuable. In this regional study, we evaluate SEAS5 performance in forecasting precipitation and daily minimum temperature (Tmin) and daily maximum temperature (Tmax) for the Australian continent, based on 36 years of re-forecast data. We evaluate forecasts after simple mean-corrections, and statistically calibrated forecasts using the Bayesian joint probability (BJP) modelling approach. We also provide a comparison with System 4. A new simpler and more efficient BJP algorithm is introduced to facilitate this study and support wider use of the algorithm in other applications. • Seasonal climate forecasts of the new ECMWF SEAS5 model are evaluated for Australia. • SEAS5 offers considerable skill for forecasting the coming month. • The skill drops significantly beyond one month forecast horizon. • A simpler and more efficient Bayesian joint probability (BJP) algorithm is introduced. • Forecasts are more skillful and reliable after calibration using the BJP algorithm. [ABSTRACT FROM AUTHOR]