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In this work, cesium salts with functional anions of acetate (Ac−), fluoride (F−) and trifluoroacetate (TFA−) are explored to modulate the deposition of ZnO films in low-temperature carbon-based CsPbI2Br perovskite solar cells.

Hydrophobic organic ammonium halide salts are used to passivate perovskite defects, adjust interfacial energy level alignments, and ultimately improve the efficiency and stability of low-temperature carbon-based CsPbI2Br perovskite solar cells.

Colorectal cancer (CRC), as a result of a multistep process and under multiple factors, is one of the most common life-threatening cancers worldwide. To identify the “high risk” populations is critical for early diagnosis and improvement of overall survival rate. Of the complicated genetic and environmental factors, which group is mostly concerning colorectal carcinogenesis remains contentious. For this reason, this study collects relatively complete information of genetic variations and environmental exposure for both CRC patients and cancer-free controls; a multimethod ensemble model for CRC-risk prediction is developed by employing such big data to train and test the model. Our results demonstrate that (1) the explored genetic and environmental biomarkers are validated to connect to the CRC by biological function- or population-based evidences, (2) the model can efficiently predict the risk of CRC after parameter optimization by the big CRC-related data, and (3) our innovated heterogeneous ensemble learning model (HELM) and generalized kernel recursive maximum correntropy (GKRMC) algorithm have high prediction power. Finally, we discuss why the HELM and GKRMC can outperform the classical regression algorithms and related subjects for future study.

Currently, colorectal cancer (CRC) already becomes one of the most common cancers worldwide. Though the prognosis of CRC patients is dramatically improved due to the new advanced treatments and medical improvements, the 5-year survival rate for the CRC patient is still low. Thus, we hypothesize that CRC may result from the complicated reasons related to both genetic and environmental factors. For this reason, this study collects such big CRC data with information of genetic variations and environmental exposure for the CRC patients and cancer-free controls that are employed to train and test the predictive CRC model. Our results demonstrate that (1) the explored genetic and environmental biomarkers are validated to cause the CRC by the manually reviewed experimental evidences, (2) the model can efficiently predict the risk of CRC after parameter optimization by the big CRC-related data, (3) our innovated generalized kernel recursive maximum correntropy(GKRMC) algorithm has high predictive power. Finally, we discuss why the GKRMC can outperform the classical regression algorithms and the related future study.

The research target is to explore the key biomarkers of colorectal cancer by studying the impact of dietary factors on colorectal cancer. We first employed statistical methods to preprocess experimental data. Then, relief algorithm is employed to extract key features in the dietary data set. Finally, supporting vector machine (SVM) is used to classify the data set and compute classification accuracy. The results demonstrated that vegetables, seafood, eggs and milk have great impacts on the colorectal cancer. Therefore, we concluded that integrating relief algorithm with SVM model can explore the key biomarkers for colorectal cancer, while the investigation of the interactions among these features needs further research.