Predictive Ability of the SYNergy Between Percutaneous Coronary Intervention with TAXus and Cardiac Surgery Score Ⅱ for Long-term Mortality in Patients with Three-vessel Coronary Artery Disease Undergoing Percutaneous Coronary Intervention Treated

Background：The SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery Score Ⅱ （SS-Ⅱ） can well predict 4-year mortality in patients with complex coronary artery disease （CAD）,and guide decision-making between coronary artery bypass graft surgery and percutaneous coronary intervention （PCI）.However,there is lack of data regarding the utility of the SS-Ⅱ in patients with three-vessel CAD undergoing PCI treated with second-generation drug-eluting stents （DES）.The purpose of the present study was to evaluate the ability of the SS-Ⅱ to predict long-term mortality in patients with three-vessel CAD undergoing PCI with second-generation DES.Methods：Totally,573 consecutive patients with de novo three-vessel CAD who underwent PCI with second-generation DES were retrospectively studied.According to the tertiles of the SS-Ⅱ,the patients were divided into three groups：The lowest SS-Ⅱ tertile （SS-Ⅱ ≤20）,intermediate SS-Ⅱ tertile （SS-Ⅱ of 21-31）,and the highest SS-Ⅱ tertile （SS-Ⅱ ≥32）.The survival curves of the different groups were estimated by the Kaplan-Meier method.Univariate and multivariate Cox proportional hazard regression analyses were performed to evaluate the relationship between the SS-Ⅱ and 5-year mortality.The performance of the SS-Ⅱ with respect to predicting the rate of mortality was studied by calculating the area under the receiver operator characteristic （ROC） curve.The predictive ability of the SS-Ⅱ for 5-year mortality was evaluated and compared with the SS alone.Results：The overall SS-Ⅱ was 27.6 ± 9.0.Among patients in the lowest,intermediate and the highest SS-Ⅱ tertiles,the 5-year rates of mortality were 1.6%,3.2%,and 8.6%,respectively （P =0.003）;the cardiac mortality rates were 0.5%,1.9%,and 5.2%,respectively （P =0.014）.By multivariable analysis,adjusting for the potential confounders,the SS-Ⅱ was an independent predictor of 5-year mortality （hazard ratio：2.45,95% confidence interval：1.38-4.36;P=0.002）.The SS-Ⅱ demonstrated a higher predictive accuracy for 5-year mortality compared with the SS alone （the area under the ROC curve was 0.705 and 0.598,respectively）.Conclusion：The SS-Ⅱ is an independent predictor of 5-year mortality in patients with three-vessel CAD undergoing PCI treated with second-generation DES,and demonstrates a superior predictive ability over the SS alone.

Comparative research on the prognostic ability of improved early warning and APACHE Ⅱ evaluation for hospitalized patients in the emergency department

Objective： To compare the feasibility and applicability of predicting the prognosis of patients using the Early Warning Score（MEWS） system and the Acute Physiology and Chronic Health Evaluation（APACHE Ⅱ） system in the Emergency Department.Methods： Using a prospective study method, the APACHE Ⅱ and MEWS data for 640 patients hospitalized in the Emergency Internal Medicine Department were collected. The prognoses, two scores to predict the corresponding prediction index of sensitivity, specificity and positive predictive value for the prognosis,the negative predictive value and the ROC curve for predicting the prognosis were analyzed for all patients.Results： In the prediction of the risk of mortality, the MEWS system had a high resolution. The MEWS area under the ROC curve was 0.93. The area under the ROC curve for the APACHE score was 0.79, and the difference was statistically significant（Z =4.348, P 〈 0.01）.Conclusions： Both the MEWS and APACHE Ⅱ systems can be used to determine the severity of emergency patients and have a certain predictive value for the patient＇s mortality risk. However, the MEWS system is simple and quick to operate, making it a useful supplement for APACHE Ⅱ score.

THE IMPACT OF INITIAL FORCED WIND ON THE PREDICTABILITY OF THE ZEBIAK-CANE COUPLED OCEAN-ATMOSPHERE MODEL

With simultaneous observed sea surface temperature anomaly （SSTA）, the difference between NCEP/NCAR 925hPa reanalysis wind stress anomaly （NCEPWSA） and FSU wind stress anomaly （FSUWSA） is analyzed, and the prediction abilities of Zebiak-Cane coupled ocean-atmosphere model （ZC coupled model） with NCEPWSA and FSUWSA serving respectively as initialization wind are compared. The results are as follows. The distribution feature of NCEPWSA matches better with that of the observed SSTA than counterpart of FSUWSA both in 1980s and in 1990s; The ZC ocean model has a better skill under the forcing of NCEPWSA than that of FSUWSA, especially in 1990s. Meanwhile, the forecast abilities of the ZC coupled model in 1990s as well as in 1980s have been improved employing NCEPWSA as initialization wind instead of FSUWSA. Particularly, it succeeded in predicting 1997/1998 E1 Nino 6 to 8 months ahead, further analysis shows that on the antecedent and onset stages of the 1997/1998 E1 Nino event, the horizontal cold and warm distribution characteristics of the simulated SSTA from ZC ocean model, with NCEPWSA forcing compared to FSUWSA forcing, match better with counterparts of the corresponding observed SSTA, whereby providing better predication initialization conditions for ZC coupled model, which, in turn, is favorable to improve the forecast ability of the coupled model.

Prediction Ability of Cash Flows, Net Income （NI）, and Auditors

The accounting information should help investors and creditors evaluate the amounts, timing, and uncertainty of firms＇ future cash receipts and disbursements. The Financial Accounting Standards Board （FASB） contends that accrual-based historical earnings are superior to cash flows in predicting future cash flows. But, Bowen, Burgstahler, and Daley （1986） showed that traditional measures of cash flows （net income （NI） plus depreciation and working capital from operations） appear to be better predictors of future cash flows than accrual accounting earnings. Since then, many researchers have articulated the importance of accounting data, especially cash flows and NI, in the predictive and forecasting processes. In this study, we empirically re-examined the ability of cash flows from operating activities （CFO） and accrual-based NI in predicting firms＇ bankruptcy. In the past, the results of this type of research were mixed. Differently from previous research, we focus on the timing of predictive ability, i.e., which indicator, cash flows or NI, is faster in predicting a firm＇s bankruptcy. We also investigate the timing of auditors＇ issuance of a going-concern opinion. The preliminary results show that the accrual-based NI is more accurate and faster than either CFO or audit opinion in predicting firms＇ failures. On average, NI signals a firm＇s bankruptcy 2.41 years before the bankruptcy filing, while CFO signals 1.48 years before filing. Auditors issued a going-concern opinion, another signal for firms＇ failure, to only 16 out of 41 bankrupt firms one year before bankruptcy, and no auditor issued the going-concern opinion two years before bankruptcy.

A promising resilience parameter for breeding:the use of weight and feed trajectories in growing pigs

Background Increasing resilience is a priority in modern pig breeding.Recent research shows that general resilience can be quantified via variability in longitudinal data.The collection of such longitudinal data on weight,feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations.The goal of this study was to investigate resilience traits,which were estimated as deviations from longitudinal weight,feed intake and feeding behaviour data during the finishing phase.A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Pietrain pigs with known pedigree and genomic information was used.We provided guidelines for a rigid quality control of longitudinal body weight data,as we found that outliers can significantly affect results.Gompertz growth curve analysis,linear modelling and trajectory analyses were used for quantifying resilience traits.Results To our knowledge,this is the first study comparing resilience traits from longitudinal body weight,feed intake and feeding behaviour data in pigs.We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight(h2=2.9%–20.2%),in feed intake(9.4%–23.3%)and in feeding behaviour(16.2%–28.3%).Additionally,these traits have good predictive abilities in cross-validation analyses.Deviations in individual body weight and feed intake trajectories are highly correlated(rg=0.78)with low to moderate favourable genetic correlations with feed conversion ratio(rg=0.39–0.49).Lastly,we showed that some resilience traits,such as the natural logarithm of variances of observed versus predicted body weights(lnvarweight),are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase.Conclusions Our results will help future studies investigating resilience traits and resilience-related traits.Moreover,our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data.Our findings will be valuable for breeding organizations as they offer evidence that pigs’general resilience can be selected on with good accuracy.Moreover,this methodology might be extended to other species to quantify resilience based on longitudinal data.

Genomic selection: A breakthrough technology in rice breeding

Rice(Oryza sativa)provides a staple food source for more than half the world population.However,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global population.Genomic selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured.Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding.However,several affecting factors and limitations require careful consideration when performing GS.In this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding.We also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic data.Finally,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

Many rice-growing areas are affected by high concentrations of arsenic(As).Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health.Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits.We explored the predictive ability(PA)of genomic prediction with single-environment models,accounting or not for trait-specific markers,multi-environment models,and multi-trait and multi-environment models,using the genotypic(1600K SNPs)and phenotypic(grain As content,grain yield and days to flowering)data of the Bengal and Assam Aus Panel.Under the base-line single-environment model,PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content,respectively;the three prediction methods(Bayesian Lasso,genomic best linear unbiased prediction and reproducing kernel Hilbert spaces)were considered to perform similarly,and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17K,without negative effect on PA of genomic predictions.Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%.Multi-environment models,accounting for genotype×environment interactions,and multi-trait and multi-environment models outperformed the base-line models by up to 47%and 61%,respectively.Among the multi-trait and multi-environment models,the Bayesian multi-output regressor stacking function obtained the highest predictive ability(0.831 for grain As)with much higher efficiency for computing time.These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel.Genomic prediction can also be applied to breeding for other complex traits under multiple environments.

An Effective Approach for Improving the Real-Time Prediction of Summer Rainfall over China

This paper has two purposes. One is to evaluate the ability of an atmospheric general circulation model (IAP9L-AGCM) to predict summer rainfall over China one season in advance. The other is to propose a new approach to improve the predictions made by the model. First, a set of hindcast experiments for summer climate over China during 1982-2010 are performed from the perspective of real-time prediction with the IAP9L-AGCM model and the IAP ENSO prediction system. Then a new approach that effectively combines the hind-cast with its correction is proposed to further improve the model's predictive ability. A systematic evaluation reveals that the model's real-time predictions for 41 stations across China show significant improvement using this new approach, especially in the lower reaches between the Yellow River and Yangtze River valleys.

Validation and evaluation of clinical prediction systems for first and repeated transarterial chemoembolization in unresectable hepatocellular carcinoma: A Chinese multicenter retrospective study

BACKGROUND The treatment outcome of transarterial chemoembolization(TACE)in unresectable hepatocellular carcinoma(HCC)varies greatly due to the clinical heterogeneity of the patients.Therefore,several prognostic systems have been proposed for risk stratification and candidate identification for first TACE and repeated TACE(re-TACE).AIM To investigate the correlations between prognostic systems and radiological response,compare the predictive abilities,and integrate them in sequence for outcome prediction.METHODS This nationwide multicenter retrospective cohort consisted of 1107 unresectable HCC patients in 15 Chinese tertiary hospitals from January 2010 to May 2016.The Hepatoma Arterial-embolization Prognostic(HAP)score system and its modified versions(mHAP,mHAP2 and mHAP3),as well as the six-and-twelve criteria were compared in terms of their correlations with radiological response and overall survival(OS)prediction for first TACE.The same analyses were conducted in 912 patients receiving re-TACE to evaluate the ART(assessment for re-treatment with TACE)and ABCR(alpha-fetoprotein,Barcelona Clinic Liver Cancer,Child-Pugh and Response)systems for post re-TACE survival(PRTS).RESULTS All the prognostic systems were correlated with radiological response achieved by first TACE,and the six-and-twelve criteria exhibited the highest correlation(Spearman R=0.39,P=0.026)and consistency(Kappa=0.14,P=0.019),with optimal performance by area under the receiver operating characteristic curve of 0.71[95%confidence interval(CI):0.68-0.74].With regard to the prediction of OS,the mHAP3 system identified patients with a favorable outcome with the highest concordance(C)-index of 0.60(95%CI:0.57-0.62)and the best area under the receiver operating characteristic curve at any time point during follow-up;whereas,PRTS was well-predicted by the ABCR system with a C-index of 0.61(95%CI:0.59-0.63),rather than ART.Finally,combining the mHAP3 and ABCR systems identified candidates suitable for TACE with an improved median PRTS of 36.6 mo,compared with non-candidates with a median PRTS of 20.0 mo(logrank test P<0.001).CONCLUSION Radiological response to TACE is closely associated with tumor burden,but superior prognostic prediction could be achieved with the combination of mHAP3 and ABCR in patients with unresectable liver-confined HCC.

Validation of a Technique for Estimating Alfalfa (Medicago sativa) Biomass from Canopy Volume

Determining biomass production of individual alfalfa (Medicago sativa L.) plants in space planted evaluation studies is generally not feasible. Clipping plants is time consuming, expensive, and often not possible if the plants are subjected to grazing. A regression function (B′ = 0.72558 + 0.11638 × V′) was developed from spaced plants growing on rangeland in northwestern South Dakota near Buffalo to nondestructively estimate individual plant biomass (B) from canopy volume (V). However, external validation is necessary to effectively apply the model to other environments. In the summer of 2015, new data to validate the model were collected from spaced plants near Brookings, South Dakota. Canopy volume and clipped plant biomass were obtained from ten alfalfa populations varying in genetic background, growth habit, and growth stage. Fitted models for the model-building and validation data sets had similar estimated regression coefficients and attributes. Mean squared prediction errors (MSPR) were similar to or smaller than error mean square (MSE) of the model-building regression model, indicating reasonable predictive ability. Validation results indicated that the model reliably estimated biomass of plants in another environment. However, the technique should not be utilized where individual plants are not easily distinguished, such as alfalfa monocultures. Estimating biomass from canopy volume values that are extrapolations (>2.077 × 106 cm3) of the model-building data set is not recommended.

A STUDY ON THE RELATIONSHIP BETWEEN SPRING SOIL MOISTURE OVER CHINA AND EAST ASIA SUMMER MONSOON

The correlation analysis has been used to study the relationship between spring soil moisture over China and East Asian summer monsoon (EASM). It is shown that EASM has a strong positive correlation with spring soil moisture over southwest China and the Great Bend region of the Yellow River. A standard soil moisture index (SMI) has been defined using the observed soil moisture of the two regions. The results show that SMI has a strong correlation with EASM. The years of strong (weak) SMI are associated with stronger (weaker) summer monsoon circulation. In the years of strong SMI, the west Pacific subtropical high is much northward in position and weaker in intensity;the westerlies zone is also more to the north. All of these make EASM circulation move northward and cause the rainfall belt to relocate to North China and Northeast China. SMI can reflect the variation of the summer rainfall anomaly over eastern China. In the years of strong SMI, the rainfall belt is mainly located over the northern part of China. However, during the weak years, the summer rainfall belt is largely located over the mid-and lower-reaches of the Yangtze River. Additionally, the SMI has obvious oscillations of quasi 4-6 years and quasi 2 years. Moreover, negative SMI predicts EASM better than positive SMI.

Underlying Mechanisms of Tai-Chi-Chuan Training for Improving Balance Ability in the Elders

Objective： To compare balance ability between elderly individuals who practiced Tai-Chi-Chuan （TCC） for average 9.64 years and elderly individuals who did not practice TCC and its relationship with lower extremity muscle strength and ankle proprioception. Methods： Twenty-five elderly volunteers were divided into two groups according to their TCC practcing experience. Sixteen were TCC group and the other nine were control population. Subjects completed a static balance test and ankle proprioception test using a custom-designed evaluation system, and concentric and eccentric knee extensor and flexor muscle strength tests. Subjects stood on the plate form to measure the proprioception in functional standing position which was differed from the previous studies. Multiple linear regressions were also used to predict the important factor affecting balance. Results： TCC group performed better than the control group in balance, proprioception, and muscle strength of lower extremity. The proprioception was the most important factor related to balance ability and it can be accounted for explaining 44% of variance in medial-lateral sway direction, and 53% of variance in antero-posterior sway direction. The proprioception may be a more important factor which affecting the balance ability. Conclusion： TCC training is recommended to the elders; as it can improve balance ability through better proprioception.

THE UNDERSTANDING OF ENSO CYCLE MECHANISM AND ENSO POTENTIAL PREDICTION ABILITY

In recent years,the dynamic coupled models of ocean-atmosphere and statistical models have been used in routine operation for issuing long-lead forecasts.The dynamic coupled models consist of models with varying degrees of complexity,ranging from simplified coupled models of the shallow water to coupled general circulation models.During the period of 1980—1992,some models performed considerably better than the persistence forecast on predicting typical indices of ENSO for lead time of 6 to 12 months.It seems that ENSO is predictable at least one year in advance.However.nearly all the models have lost their skill of forecasting sea surface temperature (SST)changes in the eastern equatorial Pacific since 1992.It is a challenge not only to the dynamic models but also to the understanding of the ENSO cycle mechanism.This paper examines multiple time-space scales of the ocean-atmosphere interactions and potential prediction ability of ENSO event by using data analysis and model study.

A comparison of genomic selection methods for breeding value prediction

Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.

The Clinical Value of Artery Umbilical Cord Blood Gas in Predicting Neonate Condition:A Prospective Cohort Study

Objective:To evaluate the predictive ability of neonate condition through the traditional parameters and artery umbilical cord blood gas(aUCBG).Methods:A prospective cohort study was conducted in obstetrics and gynecology department between October 2017 and August 2018 at Tongji Hospital in Wuhan,China,and 360 aUCBG samples were collected.The average age of pregnant women was(29.50±4.42)years,range from 19 to 48 years old.The gestational age range from 28+4 weeks to 41+3 weeks at admission.Logistic regression and area under the curve(AUC)from Receiver operating characteristic curves were used to identify risk factors,such as,premature rupture of membranes(PROM),high blood pressure,premature delivery(PD),low 1-minute Apgar scores(Apgar 1),low 5-minute Apgar scores(Apgar 5),pH,base excess,bicarbonate,neonatal blood sugar(NBS),and so on,to predict neonatal condition and evaluate the predictive ability of traditional and aUCBG parameters.Results:In all cases,PROM,PD,Apgar 1,Apgar 5,pH,base excess,bicarbonate,total carbon dioxide,and neonatal blood sugar were risk factors and were associated with poor condition of neonate.Apgar 1 were an independent risk factor.Combined traditional and aUCBG parameters had higher AUC of 0.895(95%confidence interval(C/):0.830-0.960,P<0.001).In cesarean section subgroup,high blood pressure,PD,and Apgar 1 were risk factors and were associated with poor condition of neonate.Apgar 1 and low pH were the independent risk factors.Combined traditional and aUCBG parameters had highest AUC of 0.940(95%C/:0.886-0.993,P<0.001).In vaginal delivery subgroup,maternal age above 35 years,PROM,PD,Apgar 1,Apgar 5,and male newborn were risk factors and were associated with poor condition of neonate.Maternal age above 35 years was an independent risk factor.Combined traditional and aUCBG parameters had highest AUC of 0.897(95%Cl:0.828-0.965,P<0.001).For pregnant women without comorbidities and complications of pregnancy,aUCBG may not be necessat7.Conclusion:In high-risk pregnancies,especially lower Apgar scores,PD,and maternal age above 35-year old,aUCBG is recommended.Traditional parameters combined with aUCBG might increase the predicting ability of neonate condition.

From laboratory to on-site operation:Reevaluation of empirically based electric water chiller models

Chiller model is a key factor to building energy simulation and chiller performance prediction.With spread of new types of electric water chillers that have higher performance and wider operating range,new challenges have been faced by building energy simulation tools and their chiller models.This work takes a new type of electric water chiller as a case study and reevaluates eight typical empirically based models for predicting the energy performance of electric water chiller to verify whether they are suitable for the new type of chiller,using both laboratory test data from chiller manufacturer and online monitoring data from on-site operation of a central cooling plant with chillers of the same type.The prediction ability of the chiller models(including model prediction accuracy and generation ability)in laboratory test and on-site operation situations are examined.The results show that the existing models can well describe the chiller performance in the laboratory test situation but perform poorly in the on-site operation situation.As the best two models in the laboratory dataset,the overall prediction errors of DOE-2 and GN model increase more than 250%and 75%respectively in the field dataset.The big discrepancy of model prediction accuracy in the two situations is mainly due to the differences of evaporator and condenser water flow rates between the laboratory and on-site operation datasets,which indicates the limitations of the empirical chiller models and implies further research in future in order to improve the suitability and reliability of chiller model.