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With the impact of climate change in recent years, the instability of precipitation and the increase of evaporation have made water resources in Shaanxi Province more and more stressed. How to maintain stable economic development while ensuring the sustainable development of agriculture within the limited water resources has become an important issue facing Shaanxi Province. This paper analyzes the temporal and spatial distribution characteristics of maize irrigation water requirement in Shaanxi Province based on meteorological data and obtains the ideal irrigation water requirement. However, considering the agricultural water scarcity issue in this region, this paper establishes different water scarcity scenarios, adopts the Jensen model to construct an objective function aimed at minimizing crop yield reduction, and solves it using a genetic algorithm to obtain the optimized maize irrigation scheduling under different water scarcity scenarios. The results analysis indicates the following: (1) The effective rainfall in Shaanxi Province from 1960 to 2019 shows a slight upward trend, while the maize water requirement and maize irrigation water requirement shows a downward trend. (2) The spatial distribution of maize irrigation water requirement in Shaanxi Province decreases gradually from north to south. High-value areas are mainly distributed in the northern regions of Yulin City and Yan'an City in Shanbei. Low-value areas are distributed in Ankang City in Shannan. (3) In the face of water scarcity, spring maize should ensure water use during the middle growth period, fast development period, and initial growth periods. Under the same water scarcity conditions, the yield reduction rate is the highest in Guanzhong, followed by the Shannan, and the lowest in Shanbei. This paper aims to provide a scientific and reasonable optimization plan for maize irrigation in Shaanxi Province by calculating and analyzing the maize irrigation water requirement in Shaanxi Province, combined with optimization tools such as genetic algorithms, to address the challenges brought by water resource constraints. [ABSTRACT FROM AUTHOR]

Mechanical force is the basis of cardiovascular development, homeostasis, and diseases. The perception and response of mechanical force by the cardiovascular system are crucial. However, the molecular mechanisms mediating mechanotransduction in the cardiovascular system are not yet understood. PIEZO1, a novel transmembrane mechanosensitive cation channel known for its regulation of touch sensation, has been found to be widely expressed in the mammalian cardiovascular system. In this review, we elucidate the role and mechanism of PIEZO1 as a mechanical sensor in cardiovascular development, homeostasis, and disease processes, including embryo survival, angiogenesis, cardiac development repair, vascular inflammation, lymphangiogenesis, blood pressure regulation, cardiac hypertrophy, cardiac fibrosis, ventricular remodeling, and heart failure. We further summarize chemical molecules targeting PIEZO1 for potential translational applications. Finally, we address the controversies surrounding emergent concepts and challenges in future applications. [ABSTRACT FROM AUTHOR]

STUDY QUESTION What was the performance of different pretreatment prediction models for IVF, which were developed based on UK/US population (McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model), in wider populations? SUMMARY ANSWER For a patient in China, the published pretreatment prediction models based on the UK/US population provide similar discriminatory power with reasonable AUCs and underestimated predictions. WHAT IS KNOWN ALREADY Several pretreatment prediction models for IVF allow patients and clinicians to estimate the cumulative probability of live birth in a cycle before the treatment, but they are mostly based on the population of Europe or the USA, and their performance and applicability in the countries and regions beyond these regions are largely unknown. STUDY DESIGN, SIZE, DURATION A total of 26 382 Chinese patients underwent oocyte pick-up cycles between January 2013 and December 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS UK/US model performance was externally validated according to the coefficients and intercepts they provided. Centre-specific models were established with XGboost, Lasso, and generalized linear model algorithms. Discriminatory power and calibration of the models were compared as the forms of the AUC of the Receiver Operator Characteristic and calibration curves. MAIN RESULTS AND THE ROLE OF CHANCE The AUCs for McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model were 0.69 (95% CI 0.68–0.69), 0.67 (95% CI 0.67–0.68), 0.69 (95% CI 0.68–0.69), and 0.67 (95% CI 0.67–0.68), respectively. The centre-specific yielded an AUC of 0.71 (95% CI 0.71–0.72) with key predictors including age, duration of infertility, and endocrine parameters. All external models suggested underestimation. Among the external models, the rescaled McLernon 2022 model demonstrated the best calibration (Slope 1.12, intercept 0.06). LIMITATIONS, REASONS FOR CAUTION The study is limited by its single-centre design and may not be representative elsewhere. Only per-complete cycle validation was carried out to provide a similar framework to compare different models in the sample population. Newer predictors, such as AMH, were not used. WIDER IMPLICATIONS OF THE FINDINGS Existing pretreatment prediction models for IVF may be used to provide useful discriminatory power in populations different from those on which they were developed. However, models based on newer more relevant datasets may provide better calibrations. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Natural Science Foundation of China [grant number 22176159], the Xiamen Medical Advantage Subspecialty Construction Project [grant number 2018296], and the Special Fund for Clinical and Scientific Research of Chinese Medical Association [grant number 18010360765]. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the contribution of the cleavage stage morphological parameters to the prediction of blastocyst transfer outcomes. Methods: A retrospective study was conducted on 8383 single-blastocyst transfer cycles including 2246 fresh and 6137 vitrified-warmed cycles. XGboost, LASSO, and GLM algorithms were employed to establish models for assessing the predictive value of the cleavage stage morphological parameters in transfer outcomes. Four models were developed using each algorithm: all-in model with or without day 3 morphology and embryo quality-only model with or without day 3 morphology. Results: The live birth rate was 48.04% in the overall cohort. The AUCs of the models with the algorithm of XGboost were 0.83, 0.82, 0.63, and 0.60; with LASSO were 0.66, 0.66, 0.61, and 0.60; and with GLM were 0.66, 0.66, 0.61, and 0.60 respectively. In models 1 and 2, female age, basal FSH, peak E2, endometrial thickness, and female BMI were the top five critical features for predicting live birth; In models 3 and 4, the most crucial factor was blastocyst formation on D5 rather than D6. In model 3, incorporating cleavage stage morphology, including early cleavage, D3 cell number, and fragmentation, was significantly associated with successful live birth. Additionally, the live birth rates for blastocysts derived from on-time, slow, and fast D3 embryos were 49.7%, 39.5%, and 52%, respectively. Conclusions: The value of cleavage stage morphological parameters in predicting the live birth outcome of single blastocyst transfer is limited. [ABSTRACT FROM AUTHOR]

Associations between tropospheric ozone (O3) and climate variations have been extensively investigated worldwide. However, given the lack of historical O3 monitoring data, the knowledge gaps regarding the influences of climate variations on long-term O3 trends in China remain. The present study used a tropospheric O3 dataset from the summers of 1999 to 2017 simulated by an atmospheric chemistry model to explore the linkage between summer O3 and a dominant atmospheric circulation system – the Western Pacific Subtropical High (WPSH) pressure – on an interannual basis in China. During this period, both WPSH strength and O3 concentrations in eastern and central China illustrated a growing trend. An EOF analysis was conducted to examine significant summer O3 characteristics and patterns and their potential connections with the WPSH. We find that the correlation between the first principal component of summer ozone concentration in the EOF analysis and the WPSH reached 0.56 (P≤0.01) in China from 1999 to 2017. We show that the WPSH determines interannual fluctuations of summer O3 , whereas O3 precursor emissions contribute primarily to the O3 long-term trend. Our results reveal that the WPSH plays a vital role in O3 perturbation in the eastern seaboard regions and inland China. Precursor emissions made more significant contributions of up to 60 % to increasing O3 trends in the inland urban agglomerations than coastal regions in eastern and southern China. The strongest contribution of meteorological conditions associated with the WPSH to summer O3 occurred in the Yangtze River Delta (YRD), accounting for over 9 % to ozone perturbations from 1999 to 2017. We find that the effect of the WPSH on regional O3 depends on the spatial proximity to the WPSH. We attributed the effects of the WPSH on O3 interannual variations to the changes in air temperature, precipitation, and winds associated with the WPSH's intensity and positions. [ABSTRACT FROM AUTHOR]