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In this article, we researched the existence of the solution to the fractional Navier-Stokes equations with the Coriolis force under initial data, which belong to the Lei-Lin-Gevrey spaces. Moreover, we showed a blow-up criterion, i.e., when the maximal time of existence T*{T}^{* } is finite, we proved that the norm of this same solution, in a specific Lei-Lin-Gevrey space, goes to infinity, as time tends to the maximal time of its existence.

Objective: To investigate the association between thyroid dysfunction or thyroid autoimmunity (TAI) and diminished ovarian reserve (DOR). Methods: A total of 2,867 women undergoing their first in-vitro fertilization (IVF) cycle at Shenzhen Zhongshan Obstetrics & Gynecology Hospital between January 1, 2013 and June 30, 2021, were enrolled in this study. The participants had documented thyroid and ovarian reserve metrics. They were categorized into three groups based on their thyroid function: normal thyroid function (N = 2,540), subclinical/overt hypothyroidism (SCH/OH) (N = 290), and subclinical/overt hyperthyroidism (N = 37). Anti-Mullerian hormone (AMH) and antral follicle count (AFC) were assessed and collected. Women with AMH <1.2 ng/mL and AFC < 5 were diagnosed with DOR. Basic characteristics and ovarian reserve-related parameters were compared among the three groups. The association between thyroid function and ovarian reserve function was further analyzed using logistical regression analyses. In addition, the euthyroid population was stratified using a thyroid-stimulating hormone (TSH) threshold of 2.5 µIU/mL, and the ovarian reserve-related parameters were compared among women with low-normal TSH (TSH < 2.5 µIU/mL), high-normal TSH (2.5 µIU/mL ≤ TSH ≤ 4.2 µIU/mL) and SCH/OH. Results: Women with SCH/OH had lower AMH levels (2.79 ng/mL vs. 3.41 ng/mL, P < 0.001) and a significantly higher prevalence of AMH level < 1.2ng/mL (17.2% vs. 12.1%, P = 0.015) compared to those with normal thyroid function. The prevalence of DOR was also higher among women with SCH/OH (10.0% vs. 6.5%, P = 0.036). There were no significant differences in ovarian reserve between women with normal thyroid function and those with subclinical/overt hyperthyroidism. Logistic regression analyses showed that the odds ratio (OR) of women with SCH/OH suffering from DOR was 1.666 (95% CI: 1.079-2.572) compared to those with normal thyroid function, after adjusting for TAI status and basic clinical characteristics. When the euthyroid group was stratified according to TSH levels, women with SCH/OH showed significantly lower AMH levels compared to women with low-normal TSH (2.79 ng/mL vs. 3.44 ng/mL, P < 0.001) and a significantly higher prevalence of DOR (10.0% vs. 6.0%, P = 0.010). Logistic regression analyses showed that the women with SCH/OH had an increased prevalence of DOR (OR: 1.819, 95% CI: 1.158-2.858) compared to those with low-normal TSH, after adjusting for TAI status and basic clinical characteristics. However, the OR for DOR among women with high-normal TSH was not significantly elevated compared to those with low-normal TSH (OR: 1.310, 95% CI: 0.936-1.832). Conclusion: SCH/OH may be associated with DOR, irrespective of TAI status. [ABSTRACT FROM AUTHOR]

To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 patients (492 in training set and 123 in test set) who underwent the first single and fresh blastocyst transfer in the first IVF or intracytoplasmic sperm injection cycle performed in fertility centre of Shenzhen Zhongshan Obstetrics & Gynecology Hospital from July 2015 to June 2021 were enrolled in this study. Conventional method such as logistic regression (LR), individual machine learning methods including naive bayesian (NB), K-nearest neighbours (K-NN), support vector machine (SVM), decision tree (DT), and ensemble learning methods including random forest (RF), XGBoost, LightGBM, Voting were used to establish the clinical pregnancy outcome prediction model, and the efficacy among different models was compared. Three major types of prediction models, including conventional method: LR (AUC = 0.707), individual machine learning classifiers: NB (AUC = 0.741), K-NN (AUC = 0.719), SVM (AUC = 0.761), DT (AUC = 0.728), ensemble models: RF (AUC = 0.790), XGBoost (AUC = 0.799), LightGBM (AUC = 0.794), Voting (AUC = 0.845) were established. It was found that the performance of the voting model was best. This study revealed that a voting classifier can provide a more accurate estimate of IVF outcome, which can assist clinicians to make individual patient counselling. [ABSTRACT FROM AUTHOR]

Introduction: Individuals who develop new‐onset diabetes have been identified as a high‐risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this specific cohort presents a promising opportunity for early pancreatic cancer detection. We aimed to develop and validate a novel model capable of identifying high‐risk individuals among those with new‐onset diabetes. Methods: Employing the UK Biobank cohort, we focused on those developing new‐onset diabetes during follow‐up. Genetic and clinical characteristics available at registration were considered as candidate predictors. We conducted univariate regression analysis to identify potential indicators and used a 5‐fold cross‐validation method to select optimal predictors for model development. Five machine learning algorithms were used for model development. Results: Among 12,735 patients with new‐onset diabetes, 100 (0.8%) were diagnosed with PC within 2 years. The final model (area under the curve, 0.897; 95% confidence interval, 0.865–0.929) included 5 clinical predictors and 24 single nucleotide polymorphisms. Two threshold cut‐offs were established: 1.28% and 5.26%. The recommended 1.28% cut‐off, based on model performance, reduces definitive testing to 13% of the total population while capturing 76% of PC cases. The high‐risk threshold is 5.26%. Utilizing this threshold, only 2% of the population needs definitive testing, capturing nearly half of PC cases. Conclusions: We, for the first time, combined clinical and genetic data to develop and validate a model to determine the risk of pancreatic cancer in patients with new‐onset diabetes using machine learning algorithms. By reducing the number of unnecessary tests while ensuring that a substantial proportion of high‐risk patients are identified, this tool has the potential to improve patient outcomes and optimize healthcare sources. [ABSTRACT FROM AUTHOR]

Rapid urbanization in developing countries leads to significant land-use and land-cover change (LULCC), which contributes to increased carbon dioxide (CO2) emissions and the degradation of carbon storage. Studying spatio-temporal changes in carbon storage is crucial for guiding sustainable urban development toward carbon neutrality. This study integrates machine-learning random forest algorithm, CA–Markov, and InVEST models to predict carbon storage distribution in Shenzhen, China, under various scenarios. The findings indicate that, over the past two decades, Shenzhen has experienced significant land-use changes. The transformation from high- to low-carbon-density land uses, particularly the conversion of forestland to construction land, is the primary cause of carbon storage loss. Forestland is mainly influenced by natural factors, such as digital elevation model (DEM) and precipitation, while other land-use and land-cover (LULC) types are predominantly affected by socio-economic and demographic factors. By 2030, carbon storage is projected to vary significantly across different development scenarios, with the greatest decline expected under the natural development scenario (NDS) and the least under the ecological priority scenario (EPS). The RF-CA–Markov model outperforms the traditional CA–Markov model in accurately simulating land use, particularly for small and scattered land-use types. Our conclusions can inform future low-carbon city development and land-use optimization. [ABSTRACT FROM AUTHOR]

The rapid development of biotechnology has provided a greater understanding of the biological functions of major candidate genes that have important functions regarding economic traits, and new materials for livestock breeding have been obtained through gene editing (GE) and embryo manipulation with the purpose of improving quality and output and reducing the costs and risk of disease. Public concerns, particularly over safety risks and production performance, must be addressed. Evaluation is the most important component of the regulation of gene-edited livestock and is a crucial guarantee of public safety before the marketing of gene-edited animal products. Here, the system of evaluation of gene-edited livestock is discussed in terms of public safety and production performance. The search for safe and ethical applications in the GE of livestock, a case-by-case evaluation strategy, and classification and simplification are used in order to promote a more efficient, objective, comprehensive and operable evaluation system.

Confucian Image Politics: Masculine Morality in Seventeenth-Century China. By YING ZHANG. Seattle: UNIVERSITY OF WASHINGTON PRESS, 2016. Pp. xvi + 306. $50. Chinese ruling elites, including both officials and the [...]