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Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R2 = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R2 = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart. [ABSTRACT FROM AUTHOR]

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making. [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]

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]

Longevity is regulated by a network of closely linked metabolic systems. We used a combination of mouse population genetics and RNA interference in Caenorhabditis elegans to identify mitochondrial ribosomal protein S5 (Mrps5) and other mitochondrial ribosomal proteins as metabolic and longevity regulators. MRP knockdown triggers mitonuclear protein imbalance, reducing mitochondrial respiration and activating the mitochondrial unfolded protein response. Specific antibiotics targeting mitochondrial translation and ethidium bromide (which impairs mitochondrial DNA transcription) pharmacologically mimic mrp knockdown and extend worm lifespan by inducing mitonuclear protein imbalance, a stoichiometric imbalance between nuclear and mitochondrially encoded proteins. This mechanism was also conserved in mammalian cells. In addition, resveratrol and rapamycin, longevity compounds acting on different molecular targets, similarly induced mitonuclear protein imbalance, the mitochondrial unfolded protein response and lifespan extension in C. elegans. Collectively these data demonstrate that MRPs represent an evolutionarily conserved protein family that ties the mitochondrial ribosome and mitonuclear protein imbalance to the mitochondrial unfolded protein response, an overarching longevity pathway across many species. [ABSTRACT FROM AUTHOR]