Asymmetric Drying and Wetting Trends in Eastern and Western China

As an important factor that directly affects agricultural production, the social economy, and policy implementation,observed changes in dry/wet conditions have become a matter of widespread concern. However, previous research has mainly focused on the long-term linear changes of dry/wet conditions, while the detection and evolution of the non-linear trends related to dry/wet changes have received less attention. The non-linear trends of the annual aridity index, obtained by the Ensemble Empirical Mode Decomposition(EEMD) method, reveal that changes in dry/wet conditions in China are asymmetric and can be characterized by contrasting features in both time and space in China. Spatially, most areas in western China have experienced transitions from drying to wetting, while opposite changes have occurred in most areas of eastern China. Temporally, the transitions occurred earlier in western China compared to eastern China. Research into the asymmetric spatial characteristics of dry/wet conditions compensates for the inadequacies of previous studies, which focused solely on temporal evolution;at the same time, it remedies the inadequacies of traditional research on linear trends over centennial timescales. Analyzing the non-linear trend also provides for a more comprehensive understanding of the drying/wetting changes in China.

CIB1 as a Potential Diagnosis and Prognosis Biomarker in Uveal Melanoma

Background: Uveal melanoma (UVM) is the most common primary intraocular tumor in adults. However, identification of the effective biomarker for the diagnosis and treatment of UVM remains to be explored. Calcium and integrin-binding protein 1 (CIB1) is emerging as an important factor in tumor progression. Purpose: To determine the contribution of CIB1 in the diagnosis of UVM. Method: Immunohistochemical staining is used to detect the CIB1 expression level, while Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and UALCAN online tools were used to analyze patient survival and CIB1 correlation genes in UVM. Integrative analysis using STRING and GeneMANIA predicted the correlated genes with CIB1 in UVM. Results: CIB1 expression level in UVM was significantly enhanced when compared with that in paracancerous tissues. A higher CIB1 expression level resulted in a significantly worse disease-free survival as well as overall survival. Moreover, the survival probability of patients was associated with body weight and gender of the patients with UVM. The correlated genes with CIB1 in UVM, and the similarity of the genes in UVM expression and survival heatmap were verified. Furthermore, Gene ontology enrichment analysis revealed that CIB1 and its correlated genes are significantly enriched in ITGA2B-ITGB3-CIB1 complex, regulation of intracellular protein transport and regulation of ion transport. Conclusions: Our novel findings suggested that CIB1 might be a potential diagnostic predictor for UVM, and might contribute to the potential strategy for UVM treatment by targeting CIB1.

NLRP3 Inflammasome in Relation to Glucose and Lipid Metabolism, and Insulin Resistance in Diabetes and Pre-Diabetes

Aims: To investigate the relationship among NLRP3 inflammasome, glucose and lipid metabolism, and insulin resistance (IR) in the serum of patients with diabetes and pre-diabetes. Methods: A total of 100 patients with abnormal blood glucose divided into the pre-diabetes mellitus (PDM) group (N = 46) and the type 2 diabetes mellitus (T2DM) group (N = 54). 20 normoglycemic subjects (NG, N = 20) were selected as a control group. The serum levels of glucose and lipid metabolism, IR, and the expression of NLRP3, ASC and Caspase-1 were measured. Besides, the correlations of NLRP3 inflammasome with glucose and lipid metabolism, and IR were analyzed. Results: Compared with the NG group, the levels of NLRP3, ASC, Caspase-1, FBG, HbA1C, TG, LDL-C, FINs, and HOMA-IR were higher (P β were lower (P P β were seen (P P β. Regression analysis further showed that blood glucose related indexes, FINs, and NLRP3 have made a decisive contribution to IR. Conclusions: Collectively, this evidence suggested that NLRP3 is closely related to glucose and lipid metabolism, and IR, and activated in PDM and T2DM.

Yoga as an Exercise Prescription for the Pregnancy or Postpartum Period: Recent Advances and Perspective

The aim of this study is to examine studies published from 2015 to 2020 and to determine whether yoga can be an efficacious approach during the pregnancy or postpartum period. PubMed, EBSCO, Wiley and Science Direct databases were searched for studies published from January 2015 to June 2020. A total of 14 studies met the inclusion criteria, covering 1116 participants were identified. The results demonstrated that yoga intervention could significantly reduce depression (P < 0.001), anxiety (P = 0.003), labor pain (P = 0.001), back pain and the percentage of cesarean section (P = 0.002). There was significant improvement in psychological well-being (P < 0.5), immune function and the intrauterine fetal growth (P < 0.5). Moreover, the yoga intervention group has lower salivary cortisol (P < 0.001), salivary alpha-amylase and salivary a-amylase levels (P < 0.5) and higher immunoglobulin A (P < 0.001) levels when compared with that in control groups. The findings suggested that yoga is well benefited for either pregnant women or postpartum women. More high-quality and well-controlled randomized controlled trials are required to provide more information regarding the utility of yoga interventions for different stages of pregnancy women.

关键审计事项披露与上市公司IPO溢价

IPO公司自2018年开始适用新的审计报告准则,需要对2017年及之后的财务报表审计业务披露关键审计事项。本文以此为契机,比较研究了新审计报告准则实施前后首次信息披露中关键审计事项披露对上市公司IPO溢价的影响。实证结果表明,IPO时,根据新审计报告准则规定披露关键审计事项的公司,溢价更高,表明关键审计事项的披露,增进了投资者对财务报告的信任,提高了投资者对公司股票的估价。研究结果在控制内生性等多种稳健性检验后仍然成立,在区分涉及收入和资产减值的关键审计事项后也依然成立。该研究结论不仅有助于更全面地认知关键审计事项的沟通价值,而且进一步扩展了IPO溢价影响因素的相关研究。

On the sensitive areas for targeted observations in ENSO forecasting

Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.

Identification of Genetic Relationship of Hybrid Rice Varieties to Their Parents with PCR Products

[Objectives]To establish a simple,rapid and accurate method for identifying the genetic relationship of hybrid rice varieties to their parents.[Methods]Taking F1 hybrids Liangyou 336,Deliangyou Huazhan,and the parents of Liangyou 336,i.e.,C815S(♀)and R336(♂),as experimental materials,the genetic relationship of the hybrid rice varieties to the parental materials was identified by way of PCR amplification with the 48 pairs of SSR primers of Protocol for Identification of Rice Varieties:SSR Marker Method(NY/T 1433-2014).[Results]The genetic relationship of the hybrid rice varieties could be determined by comparing the PCR amplification products of the mixed DNA of the parents and the DNA of the F1 hybrids.[Conclusions]This method not only reduced the number of samples required but also had a good visual effect and high accuracy.

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19.However,numerous factors influence the development of pandemic outbreaks,and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19.Direct estimation of the role of environmental factors without removing the confounding effects will be biased.To overcome this critical problem,we developed a Double Machine Learning(DML)causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities.Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors.Environmental factors are not the dominant cause of widespread outbreaks in China in 2022.In addition,by further analyzing the causal effects of environmental factors,it was verified that there is significant heterogeneity in the role of environmental factors.The causal effect of environmental factors on COVID-19 changes with the regional environment.It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics,confounding factors must be handled carefully in order to obtain clean quantitative results.This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic,as well as a framework for more accurately quantifying the factors influencing the outbreak.

Theory of three-pattern decomposition of global atmospheric circulation

This paper reviews the three-pattern decomposition of global atmospheric circulation(3P-DGAC)developed in recent years,including the decomposition model and the dynamical equations of global horizontal,meridional,and zonal circulations.Compared with the traditional two-dimensional(2D)circulation decomposition method,the 3P-DGAC can effectively decompose the actual vertical vorticity into two components that are caused by the horizontal circulation and convergent/divergent movement(associated with the meridional and zonal circulations).It also decomposes the vertical velocity into the components of the meridional vertical circulation and the zonal vertical circulation,thus providing a new method to study the dynamical influences of convergent/divergent motions on the evolution of actual vertical vorticity and an accurate description of local vertical circulations.The 3P-DGAC is a three-dimensional(3D)circulation decomposition method based on the main characteristics of the actual atmospheric movements.The horizontal,meridional,and zonal circulations after the 3P-DGAC are the global generalization of Rossby waves in the middle-high latitudes and Hadley and Walker circulations in low latitudes.Therefore,the three-pattern decomposition model and its dynamical equations provide novel theoretical tools for studying complex interactions between middle-high and low latitude circulations as well as the physical mechanisms of the abnormal evolution of large-scale atmospheric circulations under the background of global warming.

A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously

Machine learning methods are effective tools for improving short-term climate prediction.However,commonly used methods often carry out classification and regression prediction modeling separately and independently.Such a single modeling approach may obtain inconsistent prediction results in classification and regression and thus may not meet the needs of practical applications well.To address this issue,this study proposes a selective Naive Bayes ensemble model(SENB-EM)by introducing causal effect and voting strategy on Naive Bayes.The new model can not only screen effective predictors but also perform classification and regression prediction simultaneously.After being applied to the area prediction of summer western North Pacific subtropical high(WNPSH)from 2008 to 2021,it is found that the accuracy classification score(a metric to assess the overall classification prediction accuracy)and the time correlation coefficient(TCC)of SENB-EM can reach 1.0 and 0.81,respectively.After integrating the results of different models[including multiple linear regression ensemble model(MLR-EM),SENB-EM,and Chinese Multimodel Ensemble Prediction System(CMME)used by National Climate Center(NCC)]for 2017-2021,the TCC of the ensemble results of SENB-EM and CMME can reach 0.92(the highest result among them).This indicates that the prediction results of the summer WNPSH area provided by SENB-EM have a high reference value for the real-time prediction.It is worth noting that,except for the numerical prediction results,the SENB-EM model can also give the range of numerical prediction intervals and predictions for anomalous degrees of the WNPSH area,thus providing more reference information for meteorological forecasters.Overall,as a new hybrid machine learning model,the SENB-EM has a good prediction ability;the approach of performing classification prediction and regression prediction simultaneously through integration is informative to short-term climate prediction.