Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains ...unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to −34% (−17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to −37% loss by extreme drought (−32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.
This study reveals that excessive rainfall can adversely affect maize yield as much as extreme drought in the US, especially at regional scale. However, current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate crop yield under wet conditions. The results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.
Methionine is an aliphatic, sulfur-containing, essential amino acid, and a precursor of succinyl-CoA, homocysteine, cysteine, creatine, and carnitine. Recent research has demonstrated that methionine ...can regulate metabolic processes, the innate immune system, and digestive functioning in mammals. It also intervenes in lipid metabolism, activation of endogenous antioxidant enzymes such as methionine sulfoxide reductase A, and the biosynthesis of glutathione to counteract oxidative stress. In addition, methionine restriction prevents altered methionine/transmethylation metabolism, thereby decreasing DNA damage and carcinogenic processes and possibly preventing arterial, neuropsychiatric, and neurodegenerative diseases. This review focuses on the role of methionine in metabolism, oxidative stress, and related diseases.
Gastric cancer (GC) is the fourth largest cancer in the world, with a 5‐year survival rate of <30%. Thus, this study intends to investigate the effects of inhibin βA (INHBA) gene silencing on the ...migration and invasion of GC cells via the transforming growth factor‐β (TGF‐β) signaling pathway. Initially, this study determined the expression of INHBA and the TGF‐β signaling pathway‐related genes in GC tissues. After that, to assess the effect of INHBA silencing on GC progression, GC cells were transfected with short hairpin RNAs that targeted INHBA in order to detect the expression of INHBA and the TGF‐β signaling pathway‐related genes, as well as cell migration, invasion, and proliferation abilities. Finally, a tumor xenograft model in nude mice was constructed to verify the effect that the silencing of INHBA had on tumor growth. Highly expressed INHBA and activated TGF‐β signaling pathways were observed in GC tissues. In response to shINHBA‐1 and shINHBA‐2, the TGF‐β signaling pathway was inhibited in GC cells, whereas the GC cell migration, invasion, proliferation, and tumor growth were significantly dampened. On the basis of the observations and findings of this study, INHBA gene silencing inhibited the progression of GC by inactivating the TGF‐β signaling pathway, which provides a potential target in the treatment of GC.
•Machine learning algorithms have been applied to estimate wheat yield for Australia.•Combining climate and satellite data achieves high performance for yield prediction.•The unique and overlapping ...information of climate and satellite data is quantified.•Optimal prediction performance can be achieved two-month lead time before maturity.
Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction.
Seasonal agricultural production forecasting is essential for agricultural supply chain and economic prediction. However, to what extent seasonal climate prediction and remote sensing observations ...can improve crop yield forecasting at regional scale remains unknown. Using a statistical seasonal forecasting framework for U.S. county‐level maize yield, we demonstrated that (1) incorporating satellite‐based enhanced vegetation index (EVI) significantly improved the yield forecasting performance, compared with other climate‐only models using monthly air temperature (T), precipitation (P), and vapor pressure deficit (VPD). (2) The bias‐corrected climate prediction from the Coupled Forecast System model version 2 (CFSv2) showed better yield forecasting performance than the historical climate ensemble. (3) Using the “T + P + VPD + EVI” model with climate prediction from bias‐corrected climate prediction from CFSv2 outperformed the yield forecast in the World Agricultural Supply and Demand Estimates reports released by the United States Department of Agriculture, with root‐mean‐square error of 4.37 bushels per acre (2.79% of multiyear averaged yield) by early August.
Plain Language Summary
Given the significant advances in both seasonal climate prediction and satellite remote sensing, these data have not been fully used in crop yield forecasting at regional scale, and their benefits are to be quantified compared to survey‐based approaches. Here we evaluated the benefits of using seasonal climate prediction and satellite remote sensing data in forecasting U.S. maize yield at both national and county levels. To achieve this goal, we built a seasonal forecasting system for U.S. maize yield by bridging the most advanced seasonal climate prediction products from National Centers for Environmental Prediction (NCEP) with a statistical crop modeling framework. We found we could not achieve a better forecasting performance than the official survey‐based forecast from United States Department of Agriculture until we used both climate and remote sensing observations in our model. Compared with using historical climate information for the unknown future in each growing season, using climate prediction from NCEP gave better forecasting performance once we corrected the bias in the seasonal climate prediction products. Using our climate‐remote sensing combined model and bias‐corrected climate prediction from NCEP, we achieved a better forecasting performance than the United States Department of Agriculture forecast. Our system will be useful for the stakeholders in the agriculture industry and commodity market.
Key Points
Incorporating remote sensing observations significantly improves maize yield forecasting skill
Bias‐corrected seasonal climate prediction performs better than the historical climate ensemble in maize yield forecasting
The “climate + remote sensing” approach achieves high within‐season yield forecasting performance
Sulfur amino acids are a kind of amino acids which contain sulfhydryl, and they play a crucial role in protein structure, metabolism, immunity, and oxidation. Our review demonstrates the oxidation ...resistance effect of methionine and cysteine, two of the most representative sulfur amino acids, and their metabolites. Methionine and cysteine are extremely sensitive to almost all forms of reactive oxygen species, which makes them antioxidative. Moreover, methionine and cysteine are precursors of S-adenosylmethionine, hydrogen sulfide, taurine, and glutathione. These products are reported to alleviate oxidant stress induced by various oxidants and protect the tissue from the damage. However, the deficiency and excess of methionine and cysteine in diet affect the normal growth of animals; thereby a new study about defining adequate levels of methionine and cysteine intake is important.
This study examines the extent to which entrepreneurial alertness mediates the effects of students' proactive personalities and creativity on entrepreneurial intention. Drawing on a field survey of ...735 Chinese undergraduates at 26 universities, this study provides evidence for the argument that entrepreneurial alertness has a fully mediation effect on the relationship between creativity, a proactive personality, and entrepreneurial intention. The findings shed light on the mechanisms that underpin entrepreneurial alertness and contribute to the literature on key elements of the entrepreneurial process.
•A DNA walker-based electrochemical ratiometric platform was developed.•These dual signal ratiometric strategy overcome the changes of a variety of factors.•A detection limit of 0.31 fg/mL for ...ochratoxin A (OTA) was achieved.•The method was applied for determining OTA content in red wine samples.
Ochratoxin A (OTA) is a naturally occurring mycotoxin that poses serious threats, such as kidney damage, to human health. Therefore, we developed a DNA walker-based dual-signal electrochemical ratiometric platform for OTA detection, which could overlook the variations in environmental and instrumental factors and DNA load densities. Cobalt metal–organic frameworks (Co-MOFs) and toluidine blue were used as the electrochemical signal tag and internal reference probe, respectively. In the presence of OTA, this developed machine resulted in the DNA labelled-Co-MOFs far away from the electrode. Thus, Co-MOFs signal at −1.18 V decreased, while toluidine blue at −0.28 V increased. This proposed strategy has displayed superior sensitivity (limit of detection = 0.31 fg/mL, linear range = 1–50 ng/mL) and high reproducibility. The sensor was also applied for determining OTA content in red wine samples and the results were comparable to those of commercial enzyme-linked immunoassay kits with satisfactory results.
•Monthly VPD and precipitation in spline form, combined with EVI, give the best prediction model.•Model’s performance shows regional and interannual variations, which are related to spatial and ...temporal yield variability.•Model’s prediction shows increasingly larger RMSE toward wetter years and extremely dry years.•Inconsistent model evaluation practices undermine the comparability between statistical modeling studies.
Statistical crop models have been a major tool in identifying critical drivers of crop yield, forecasting short-term crop yield, and assessing long-term climate change impacts on agricultural productivity. However, few studies focus specifically on fundamental issues encountered in developing a high-performance statistical crop model for yield prediction. Such issues include: how to select predictors and fitting functions, how to effectively address the spatiotemporal scale issue, weather it is beneficial to include satellite data as explanatory variables, and how to reconcile different model evaluation procedures. In this study, we present our statistical modeling practices for predicting rainfed corn yield in the Midwest U.S. and address the aforementioned issues through comprehensive diagnostic analysis. Our results show that vapor pressure deficit and precipitation at a monthly scale, in spline form with customized knots, define the “Best Climate-only” model among alternative climate variables (e.g., air temperature) and fitting functions (e.g., linear or polynomial), with an out-of-sample (leave-one-year-out) median R2 of 0.79 and RMSE of 1.04 t/ha (16.6 bu/acre) from 2003 to 2016. Satellite variables, such as MODIS land surface temperature and Enhanced Vegetation Index (EVI), when used as predictors alone, reduce the model’s RMSE to 0.93 t/ha (14.8 bu/acre). Adding satellite variables (i.e., EVI in polynomial form) to the “Best Climate-only” model gives the “Best Climate + EVI” model, which has the highest prediction performance of this study, with a median R2 of 0.85 and RMSE of 0.90 t/ha (14.3 bu/acre). Such a model trained using all data (so-called “global model”) in most cases leads to better predictions than the state-specific trained models. However, the global model’s prediction performance exhibits considerable regional and interannual variations. The regional-varying performance is related to states’ spatiotemporal variability in yield, where states with larger spatial yield variability show higher R2, and states with smaller temporal yield variability show lower RMSE. Interannual variations in prediction performance are linked to yield variability and degree of wetness, with higher R2 in years with larger yield variability but increasingly larger RMSE toward wetter years and extreme dry years. These identified spatial and temporal variations of model’s performance, together with inconsistent evaluation practices undermine the comparability between statistical modeling studies. Alleviating such comparability issues requires more transparency and open data practices. The statistical model presented in this study provides a benchmark for further development and can be applied to future research related to yield prediction or assessment of climate change impact.
Interaction between the acidic motif (AM) of protein kinase WNK4 and the Kelch domain of KLHL3 are involved in the pathogenesis of pseudohypoaldosteronism type II, a hereditary form of hypertension. ...This interaction is disrupted by some disease‐causing mutations in either WNK4 or KLHL3, or by angiotensin II‐ and insulin‐induced phosphorylation of KLHL3 at serine 433, which is also a site frequently mutated in patients. However, the mechanism by which this phosphorylation disrupts the interaction is unclear. In this study, we approached this problem using molecular dynamics simulation with structural, dynamical and energetic analyses. Results from independent simulations indicate that when S433 was phosphorylated, the electrostatic potential became more negative in the AM binding site of KLHL3 and therefore was unfavorable for binding with the negatively charged AM. In addition, the intermolecular hydrogen bond network that kept the AM stable in the binding site of KLHL3 was disrupted, and the forces for the hydrophobic interactions between the AM of WNK4 and KLHL3 were also reduced. As a result, the weakened interactions were no longer capable of holding the AM of WNK4 at its binding site in KLHL3. In conclusion, phosphorylation of KLHL3 at S433 disrupts the hydrogen bonds, hydrophobic and electrostatic interactions between the Kelch domain of KLHL3 and the AM of WNK4. This study provides a key molecular understanding of the KLHL3‐mediated regulation of WNK4, which is an integrative regulator of electrolyte homeostasis and blood pressure regulation in the kidney.
Significances Statement: WNK4 is an integrative regulator of electrolyte homeostasis, which is important in the blood pressure regulation by the kidney. Interaction between WNK4 and KLHL3 is a key physiological process that is impaired in a hereditary form of hypertension. This study provides substantial new insights into the role of phosphorylation of KLHL3 in regulating the interaction with WNK4, and therefore advances our understanding of molecular pathogenesis of hypertension and the mechanism of blood pressure regulation.
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