Extensive effort has been expended to utilize π-allyl palladium-complexes as electrophilic allyl donor intermediates in cooperative dual catalysis, but their counter anions such as carboxylates or alkoxides are almost always discarded as waste. We have developed a cooperative Pd(0)/Rh(II) dual catalysis system that utilizes both the electrophilic allyl and nucleophilic counter anion functionalities inherent in the starting allylic substrates. In this cooperative catalysis, redox compatible Pd(0) and Rh(II) catalysts selectively activate allylic substrates and N-sulfonyl-1,2,3-triazoles to generate π-allyl Pd(II)-complexes and 1,3-ambivalent equivalent aimino Rh(II)-carbenoid intermediates, respectively. The counter anion of the π-allyl Pd(II)-complex acts as a nucleophile transferring to the electrophilic carbenic carbon to form Pd/Rh-associated zwitterionic intermediates, in which the cationic palladium species may coordinate with both counter anion and imine nitrogen in the same plane establishing the (Z)-geometry of the products.

A silica monolithic column chemically modified with L-pipecolic acid as chiral stationary phase has been developed for chiral separation of dansyl amino acids by capillary electrochromatography-mass spectrometry (CEC-MS). The monolithic column was prepared by a sol-gel process and subsequent chemical modification by L-pipecolic acid as chiral selector with 3-glycidoxypropyltrimethoxysilane as spacer. Interestingly, it was found that the L-pipecolic acid-modified monolithic column can hold copper(II) ions tightly after loading Cu(II) ions during column preparation and conditioning and allows CEC separation to be conducted based on chiral ligand exchange with MS detection by a mobile phase without copper ions. It has been demonstrated that the chiral monolithic column operates well for enantioseparation of several dansyl amino acids by CEC-MS.

A straightforward route toward construction of α-quaternary chiral β-lactam moiety via Rh(II)/Pd(0)catalyzed stereoselective relay catalytic reaction is reported. This asymmetric dual relay catalysis involves Rh(II)-catalyzed enantioselective intramoluecular C−H insertions of αdiazoamides, and sequential Pd(0)-catalyzed diastereoselective intermolecular allylic alkylation. Under mild reaction conditions, a broad range of α-quaternary allylated chiral βlactams have been synthesized in high yields (up to 99%) with excellent stereoselectivities [up to diastereomeric ratio (dr) >99:1, up to 98% enantiomeric excess (ee)].

Subseasonal predictions of the regional summer rainfall over several tropical Asian ocean and land domains are examined using hindcasts by the NCEP CFSv2. Higher actual and potential forecast skill are found over oceans than over land. The forecast for Arabian Sea (AS) rainfall is most skillful, while that for Indo-China (ICP) rainfall is most unskillful. The rainfall–surface temperature (ST) relationship over AS is characterized by strong and fast ST forcing but a weak and slow ST response, while the relationships over the Bay of Bengal, the South China Sea (SCS), and the India subcontinent (IP) show weak and slow ST forcing, but apparently strong and rapid ST response. Land–air interactions are often less noticeable over ICP and southern China (SC) than over IP. The CFSv2 forecasts reasonably reproduce these observed features, but the local rainfall–ST relationships often suffer from different degrees of unrealistic estimation. Also, the observed local rainfall is often related to the circulation over limited regions, which gradually become more extensive in forecasts as lead time increases. The prominent interannual differences in forecast skill of regional rainfall are sometimes associated with apparent disparities in forecasts of local rainfall–ST relationships. Besides, interannual variations of boreal summer intraseasonal oscillation, featured by obvious changes in frequency and amplitude of certain phases, significantly modulate the forecasts of rainfall over certain regions, especially the SCS and SC. It is further discussed that the regional characteristics of rainfall and model’s deficiencies in capturing the influences of local and large-scale features are responsible for the regional discrepancies of actual predictability of rainfall.

Background Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. Methods The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). Conclusions The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.

This paper proposed the optical weighting combined mode of Least Square Support Vector Machine (LS-SVM) and BP Neural network. According to the measured data, this paper compared and analyzed the accuracy of LS-SVM model, BP Neural network model; quadratic polynomial curve surface fitting based on Total least-square algorithm and optimal weighting combined model, the data shows that the optimal weighting combined model has higher precision then others.

Atmospheric water vapor is an important part of the earth's atmosphere, and it has a great relationship with the formation of precipitation and climate change. In CNSS-derived precipitable water vapor (PWV), atmospheric weighted mean temperature, T m , is the key factor in the progress of retrieving PWV. In this study, using the profiles of Guilin radiosonde station in 2017, the spatiotemporal variation characteristics and relationships between T m and surface temperature (T s ) are analyzed in Guilin, an empirical T m model suitable for Guilin is constructed by regression analysis. Comparing the T m values calculated from Bevis model, Li Jianguo model and new model, it is found that the root mean square error (RMSE) of new model is 2.349 K, which are decreased by 14% and 19%, respectively. Investigating the impact of different T m models on GNSS-PWV, the T m -induced error from new model has a smaller impact on PWV than other two models. The results show that the new T m model in Guilin has a relatively good performance and it can improve the reliability of the regional GNSS water vapor retrieval to some extent.This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W10-1155-2020 | © Authors 2020. CC BY 4.0 License.

BACKGROUND Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. OBJECTIVE The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. METHODS The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. RESULTS A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). CONCLUSIONS The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.