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For over a century, numerous proposals for increasing available water in central Australia have been raised, inspired in part by the natural occurrence of the ephemeral lake, Kati Thanda‐Lake Eyre. It has also been proposed that additional rainfall generated by the lake would spread beyond the lake itself, potentially opening up large tracts of uncultivated land to dryland agriculture. Here we use a climate model to examine how adding a permanent lake to Australia's arid center might influence local and regional precipitation. Locally, evaporative cooling from the lake increases low‐level divergence, suppressing precipitation. Regionally, additional moisture from the lake is spread thinly over the Australian continent, resulting in little change to total precipitation. Overall, our results do not support the assertion that maintaining a large inland lake like Kati Thanda‐Lake Eyre in central Australia would significantly increase precipitation, either locally or regionally. Plain Language Summary: The Bradfield water scheme proposed opening central Australia up to agriculture by diverting water inland. Supporters of the plan have argued that this might also increase rainfall. We use a climate model to simulate how precipitation would be impacted by an idealized permanent lake in central Australia. The model simulations show that the presence of a large lake has negligible impact on rainfall, both locally and regionally. Key Points: A climate model is used to test the hypothesis that creating a large lake in central Australia would increase rainfallLocally, surface cooling effects of the lake suppress the formation of precipitationRegionally, moisture from the lake is exported to other areas but the amount is small compared to natural variability in Australian rainfall [ABSTRACT FROM AUTHOR]

Climate model estimates show significant groundwater depletion during the 20th century, consistent with global mean sea level (GMSL) budget analysis. However, prior to the Argo float era, in the early 2000's, there is little information about steric sea level contributions to GMSL, making the role of groundwater depletion in this period less certain. We show that a useful constraint is found in observed polar motion (PM). In the period 1993–2010, we find that predicted PM excitation trends estimated from various sources of surface mass loads and the estimated glacial isostatic adjustment agree very well with the observed. Among many contributors to the PM excitation trend, groundwater storage changes are estimated to be the second largest (4.36 cm/yr) toward 64.16°E. Neglecting groundwater effects, the predicted trend differs significantly from the observed. PM observations may also provide a tool for studying historical continental scale water storage variations. Plain Language Summary: Melting of polar ice sheets and mountain glaciers has been understood as a main cause of sea level rise associated with contemporary climate warming. It has been proposed that an important anthropogenic contribution is sea level rise due to groundwater depletion resulting from irrigation. A climate model estimate for the period 1993–2010 gives total groundwater depletion of 2,150 GTon, equivalent to global sea level rise of 6.24 mm. However, direct observational evidence supporting this estimate has been lacking. In this study, we show that the model estimate of water redistribution from aquifers to the oceans would result in a drift of Earth's rotational pole, about 78.48 cm toward 64.16°E. In combination with other well‐understood sources of water redistribution, such as melting of polar ice sheets and mountain glaciers, good agreement with PM observations serves as an independent confirmation of the groundwater depletion model estimate. Key Points: Earth's pole has drifted toward 64.16°E at a speed of 4.36 cm/yr during 1993–2010 due to groundwater depletion and resulting sea level riseIncluding groundwater depletion effects, the estimated drift of Earth's rotational pole agrees remarkably well with observations [ABSTRACT FROM AUTHOR]

X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation. [ABSTRACT FROM AUTHOR]

• Sobol' method is used to evaluate the sensitivity of APSIM-Wheat yield predictions. • Yield is most sensitive to initial nitrogen amount, soil parameters, precipitation. • Estimated yield is influenced most by the limiting resource under given conditions. • Scenarios for high fertilization level or dry seasons have lower yield variability. The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol' method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs' impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool. [Display omitted] [ABSTRACT FROM AUTHOR]

The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p < 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p < 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models. [ABSTRACT FROM AUTHOR]

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions, on how to make the most out of the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named 'Adaptive CWSI', proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday. [ABSTRACT FROM AUTHOR]

Irrigation cools near surface air temperature by increasing evapotranspiration from wetter soil. However, elevated evapotranspiration can also increase atmospheric albedo and enhance the local greenhouse effect via increased atmospheric water vapor. Their net effects on daily air temperature remains controversial. Here we show that in several considered regions, Northwest India and Central Valley of California, irrigation could result in warmer air temperature if night‐time warming is stronger than daytime cooling by irrigation. During the daytime, air temperature reduces through evaporative cooling and reduced solar radiation from increased atmospheric albedo outweighing the local greenhouse effect. At night‐time, the increased atmospheric water vapor by irrigation tends to make a stronger local greenhouse effect that increases night‐time temperature. Our results highlight the possible increase of air temperature by irrigation and the importance of considering sub‐daily processes when assessing the impact of irrigation on daily air temperature and temperature related socioeconomic phenomena. Plain Language Summary: Irrigation can influence air temperature in two contrasting ways. Evaporative cooling from wetter soil can reduce air temperature. However, evaporated water is a greenhouse gas, which can increase air temperature. The impact of these two influences differs for daytime and night‐time air temperature. Here, we examine the impact of irrigation on daytime and night‐time air temperature under two different assumptions. Our simulations show that the warming influence of irrigation on night‐time temperature could be greater than the cooling influence on daytime temperature in several considered regions, Northwest India and Central Valley of California. Our results underline that the impact of irrigation is a complex interplay of evaporative cooling and greenhouse warming and still needs to be further investigated. Key Points: Irrigation may result in warmer night‐time temperature by enhancing the local greenhouse effectThe temperature response of local irrigation can be influenced by irrigation practices over nearby areasHighlight the importance of water vapor as a greenhouse gas in climate change [ABSTRACT FROM AUTHOR]

Farm-level seasonal irrigation water usage is often highly variable across time and space in an irrigation district. Identifying the driving factors of this variation can help researchers and managers understand the underlying efficiency of water usage, identify the sources of water waste and develop best irrigation practices to facilitate the development of more efficient and sustainable irrigated cropping systems. This study explored driving factors for the seasonal irrigation water usage across an irrigation district in south-eastern Australia, using extensive climate, soil, cropping and water use data from 312 farms (a total of 1099 annual crop water use records). It focused on three irrigated crops (corn/maize, cotton and rice) for 2011–2019. Factors considered included climate, soils, irrigation practices and water allocation, from which key driving factors and their effects on the seasonal irrigation water usage were identified. A Bayesian hierarchical model averaging approach was developed to determine key predictors and estimate seasonal irrigation water usage. The Bayesian hierarchical modelling framework allowed seasonal irrigation water usage to be estimated at all crop-farm-year observation points simultaneously while also enabling different key predictors to be selected for different crop types. Results showed that the seasonal irrigation water usage was mainly driven by irrigation practices and soil, but the water use of different crop types was driven by different subsets of irrigation and soil predictors. Modelling was undertaken in natural log space and the model showed reasonable accuracy in estimating the seasonal irrigation water usage (R2 = 0.62). Our model is considered useful in 1) identifying drivers of variation of the seasonal irrigation water usage across time and space, and 2) suggesting potential adjustments to irrigation practices and quantifying their benefits to reduce irrigation water usage. • Factors driving seasonal irrigation usage were explored in an irrigation district. • A Bayesian hierarchical model (BHM) was developed for 1099 crop water use records. • Seasonal irrigation usage was mainly influenced by soil and irrigation practices. • The BHM showed good accuracy in estimating seasonal irrigation usage (R2 = 0.62). [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Targeted therapy against the epidermal growth factor receptor (EGFR) is a promising treatment approach for NSCLC. However, resistance to EGFR tyrosine kinase inhibitors (TKIs) remains a major challenge in its clinical management. EGFR mutation elevates the expression of hypoxia-inducible factor-1 alpha to upregulate the production of glycolytic enzymes, increasing glycolysis and tumor resistance. The inhibition of glycolysis can be a potential strategy for overcoming EGFR-TKI resistance and enhancing the effectiveness of EGFR-TKIs. In this review, we specifically explored the effectiveness of pyruvate dehydrogenase kinase inhibitors and lactate dehydrogenase A inhibitors in combating EGFR-TKI resistance. The aim was to summarize the effects of these natural products in preclinical NSCLC models to provide a comprehensive understanding of the potential therapeutic effects. The study findings suggest that natural products can be promising inhibitors of glycolytic enzymes for the treatment of EGFR-TKI-resistant NSCLC. Further investigations through preclinical and clinical studies are required to validate the efficacy of natural product-based glycolytic inhibitors as innovative therapeutic modalities for NSCLC. [ABSTRACT FROM AUTHOR]

Simple Summary: Protein arginine methyltransferases (PRMTs) are a group of enzymes that play pivotal roles in post-translational modifications (PTMs) and are involved in the regulation of various cellular processes. Although PRMTs are recognized for their impact on the development and prognosis of various cancers, their specific involvement in hepatocellular carcinoma (HCC) remains relatively limited. In this study, we systematically evaluated the association between PRMTs and HCC prognosis, revealing their potential as prognostic biomarkers. We also highlighted the importance of PRMT1 and elucidated its role in HCC progression, especially in the context of T cell exhaustion (Tex). Our study underscores the prospective utility of PRMTs, particularly PRMT1, as promising prognostic indicators for HCC, as well as therapeutic targets for this malignancy. HCC is a major contributor to cancer-related mortality worldwide. Curative treatments are available for a minority of patients diagnosed at early stages; however, only a few multikinase inhibitors are available and are marginally effective in advanced cases, highlighting the need for novel therapeutic targets. One potential target is the protein arginine methyltransferase, which catalyzes various forms of arginine methylation and is often overexpressed in various cancers. However, the diverse expression patterns and clinical values of PRMTs in HCC remain unclear. In the present study, we evaluated the transcriptional expression of PRMTs in HCC cohorts using publicly available datasets. Our results revealed a significant association between PRMTs and prognosis in HCC patients with diverse clinical characteristics and backgrounds. This highlights the promising potential of PRMTs as prognostic biomarkers in patients with HCC. In particular, single-cell RNA (scRNA) sequencing analysis coupled with another human cohort study highlighted the pivotal role of PRMT1 in HCC progression, particularly in the context of Tex. Translating these findings into specific therapeutic decisions may address the unmet therapeutic needs of patients with HCC. [ABSTRACT FROM AUTHOR]

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales. [ABSTRACT FROM AUTHOR]

Animal models have been utilized to understand the pathogenesis of Zellweger spectrum disorders (ZSDs); however, the link between clinical manifestations and molecular pathways has not yet been clearly established. We generated peroxin 5 homozygous mutant zebrafish (pex5−/−) to gain insight into the molecular pathogenesis of peroxisome dysfunction. pex5−/− display hallmarks of ZSD in humans and die within one month after birth. Fasting rapidly depletes lipids and glycogen in pex5−/− livers and expedites their mortality. Mechanistically, deregulated mitochondria and mechanistic target of rapamycin (mTOR) signaling act together to induce metabolic alterations that deplete hepatic nutrients and accumulate damaged mitochondria. Accordingly, chemical interventions blocking either the mitochondrial function or mTOR complex 1 (mTORC1) or a combination of both improve the metabolic imbalance shown in the fasted pex5−/− livers and extend the survival of animals. In addition, the suppression of oxidative stress by N-acetyl L-cysteine (NAC) treatment rescued the apoptotic cell death and early mortality observed in pex5−/−. Furthermore, an autophagy activator effectively ameliorated the early mortality of fasted pex5−/−. These results suggest that fasting may be detrimental to patients with peroxisome dysfunction, and that modulating the mitochondria, mTORC1, autophagy activities, or oxidative stress may provide a therapeutic option to alleviate the symptoms of peroxisomal diseases associated with metabolic dysfunction. [ABSTRACT FROM AUTHOR]

A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods. [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]

Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited. [ABSTRACT FROM AUTHOR]

Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort. [ABSTRACT FROM AUTHOR]

Angiotensin II (AngII) has potent cardiac hypertrophic effects mediated through activation of hypertrophic signaling like Wnt/β-Catenin signaling. In the current study, we examined the role of protein arginine methyltransferase 7 (PRMT7) in cardiac function. PRMT7 was greatly decreased in hypertrophic hearts chronically infused with AngII and cardiomyocytes treated with AngII. PRMT7 depletion in rat cardiomyocytes resulted in hypertrophic responses. Consistently, mice lacking PRMT7 exhibited the cardiac hypertrophy and fibrosis. PRMT7 overexpression abrogated the cellular hypertrophy elicited by AngII, while PRMT7 depletion exacerbated the hypertrophic response caused by AngII. Similar with AngII treatment, the cardiac transcriptome analysis of PRMT7-deficient hearts revealed the alteration in gene expression profile related to Wnt signaling pathway. Inhibition of PRMT7 by gene deletion or an inhibitor treatment enhanced the activity of β-catenin. PRMT7 deficiency decreases symmetric dimethylation of β-catenin. Mechanistic studies reveal that methylation of arginine residue 93 in β-catenin decreases the activity of β-catenin. Taken together, our data suggest that PRMT7 is important for normal cardiac function through suppression of β-catenin activity. [ABSTRACT FROM AUTHOR]

Brown adipose tissue (BAT) is a major site for uncoupling protein 1 (UCP1)-mediated non-shivering thermogenesis. BAT dissipates energy via heat generation to maintain the optimal body temperature and increases energy expenditure. These energetic processes in BAT use large amounts of glucose and fatty acid. Therefore, the thermogenesis of BAT may be harnessed to treat obesity and related diseases. In mice and humans, BAT levels decrease with aging, and the underlying mechanism is elusive. Here, we compared the transcriptomic profiles of both young and aged BAT in response to thermogenic stimuli. The profiles were extracted from the GEO database. Intriguingly, aging does not cause transcriptional changes in thermogenic genes but upregulates several pathways related to the immune response and downregulates metabolic pathways. Acute severe CE upregulates several pathways related to protein folding. Chronic mild CE upregulates metabolic pathways, especially related to carbohydrate metabolism. Our findings provide a better understanding of the effects of aging and metabolic responses to thermogenic stimuli in BAT at the transcriptome level. [ABSTRACT FROM AUTHOR]