A novel strategy was developed for the fluorescence detection of nitrite (NO2−) in real samples. The method is based on the reaction of water-dispersible graphite-like carbon nitride (w-g-C3N4) with nitrite (NO2−) in an acidic medium to form a new kind of weak fluorescence species. Under optimal conditions, the limit of detection (LOD) for nitrite (NO2−) detection was determined to be 0.16 nM (S/N = 3), and the relative fluorescence intensity versus logarithm concentration of nitrite (NO2−) showed good linearity in the range from 0 to 58.5 μM with R2 = 0.997. This probe displayed several appealing properties including low-cost, simplicity and convenience, high sensitivity, and excellent selectivity.

Cancer classification is a key problem for identifying the genomic biomarkers and treating cancerous tumors in clinical research. The gene data in gene expression profiling are potential biomarkers and can be used to classify cancer samples. However, with the high dimensionality of the gene data, the cancer samples are difficult to classify. The identification of the significant genes is critical for the classification. To identify the significant genes, nonnegative matrix factorization (NMF) uses the sparse basis vectors of the gene data to represent gene information. However, the basis vectors with the imposed sparseness lose much of the useful information in the gene data. To more effectively represent the useful information, a method named Monte Carlo-nonnegative matrix factorization (MC-NMF) is proposed by using Monte Carlo technique in this study. The method is used to classify two cancer samples. The results show that the method can effectively estimate the significance of the genes and classify cancer samples with a high accuracy.

•The QSAR model of the pyrazolo[1,5-a]pyrimidine derivative inhibitors of Chk1 is constructed by PSO-SVM.•The stepwise multiple linear regression method is used to select the descriptors.•A stable model can be constructed by JG14, G3s, R8u+ and RDF085e.•The PSO-SVM has higher stability and better prediction performance.Checkpoint kinase 1 (Chk1) is a serine/threonine kinase that plays a key role in the response to DNA-mediated cell injury. In this paper, the quantitative structure–activity relationship (QSAR) models were constructed to predict the activity of pyrazolo[1,5-a]pyrimidine derivatives of Checkpoint kinase 1 (Chk1) by using SVM and PSO-SVM methods. The root-mean-square errors (RMSE) of the training set and the test set for the PSO-SVM model were 0.0886 and 0.1803, respectively. For the SVM model, the values were 0.2185 and 0.4023, respectively. The results showed that the performance of the PSO-SVM model was better than the corresponding SVM model. Thus, it can be inferred that the PSO-SVM analysis will be a promising method and be spread to apply in the QSAR studies.