Photoinduced radical tandem annulation of 1,7-diynes: an approach for divergent assembly of functionalized quinolin-2(1H)-ones.

The first photocatalytic trichloromethyl radical-triggered annulative reactions of amide-linked 1,7-diynes with polyhalomethanes were established for the flexible assembly of functionalized quinolin-2(1H)-ones with generally acceptable yields. With the installation of the aryl group (R1) into the alkynyl moiety, C-center radical-initiated Kharasch-type addition/nucleophilic substitution/elimination cascade to produce quinolin-2(1H)-ones-incorporating gem-dihaloalkene, whereas three examples of polyhalogenated quinolin-2(1H)-ones were afforded when amide-linked 1,7-diynes bearing two terminal alkyne units were subjected to BrCX3 by exploiting dry acetonitrile as a solvent.

Photoinduced Alkylsulfonylation and Cyanoalkylsulfonylation of Morita-Baylis-Hillman Adducts via Multicomponent Insertion of Sulfur Dioxide.

General and convenient visible-light-promoted alkylsulfonylation and cyanoalkylsulfonylation of MBH adducts have been developed through the multicomponent insertion of sulfur dioxide, enabling the assembly of two C-S bonds to generate structurally diverse allylic alkylsulfones (43 examples in total). The reaction of MBH adducts with potassium alkyltrifluoroborates and 1,4-diazabicyclo[2.2.2]octane bis(sulfur dioxide) adduct afforded sulfones with generally good yields. Notably, the addition of N,N,N',N'-tetramethylethylenediamine as a base into the photocatalytic system led to yielding an alkyl sulfonyl unit and cyano group-anchored trisubstituted alkenes by utilizing cycloketone oxime esters as C-radical precursors. Both of these reactions have constructed two C-S bonds, and all desired products were obtained in moderate to excellent yields with complete stereospecificity.

[Fluorescence discrimination technique for phytoplankton based on the wavelet analysis].

Daubechies7 (db7) wavelet was selected to decompose the 3-D fluorescence spectra of 27 species of phytoplankton belonging to 22 generas of 6 divisions found in major lakes, then the scale vectors and time-series vectors were obtained as candidates for feature spectra. The third scale vector (Ca3) of db7 was chosen as feature spectra by Bayesian discriminant analysis, and the reference spectra were obtained via hierarchical cluster analysis to feature spectra. Based on the above data, a fluorescence discrimination technique was developed by multiple linear regression resolved by non-negative least squares. For single species algae cultures, the average correct discrimination ratio (CDR) was 98.6%, with the average relative content of 90.8% at division level. Furthermore, the noise immunity of reference spectra was tested by adding noise at different proportions. For the dominant division of laboratory mixed samples, the average CDR was 97.0%, with the average relative content of 67.7% at division level, and the average CDR of subdominant division was 90.7%, with the average relative content of 32.3%. The results showed that the technique is feasible to some extent.

[Research on the 3D discrete fluorescence spectrum technique for differentiation of phytoplankton population].

The present research was targeted to develop a fluorescence analyser for phytoplankton population which uses a series of LEDs as the light source. So the 3D discrete fluorescence spectra with 12 excitation wavelengths (400, 430, 450, 460,470, 490, 500, 510, 525, 550, 570 and 590 nm) were determined by fluorescence spectrophotometer for 43 phytoplankton species. Then, the wavelet, Daubechies-7 (Db7), and Bayes Classifier were applied to extract the characteristics for each classes from the 3D discrete fluorescence spectra. Lastly, the fluorescence differentiation method for phytoplankton populations was established by multivariate linear regression and non-negative least squares, which could differentiate phytoplankton populations at the levels of both divisions and genus. This method was tested: for simulatively mixed samples(the dominant species accounted for 70%, 80%, 90% and 100% of the gross biomass, respectively) from 32 red tide algal species, and the correct discrimination rates at the level of genus were 67.5%, 75.8%, 81.4% and 79.4%, respectively. For simulatively mixed samples (the dominant divisions algae accounted for 50%, 75% and 100% of the gross biomass, respectively) from 43 algal species, the discrimination rates at the level of division were 95.2%, 99.7% and 91.9% with average relative content of 38.1%, 63.2% and 90.5%, respectively.

[Research on the 3D fluorescence spectra differentiation of phytoplankton by coiflet2 wavelet].

In the present paper, the authors utilize the wavelet base function coiflet2 (coif2) to analyze the 3D fluorescence spectra of 37 phytoplankton species belonging to 30 genera of 7 divisions, and these phytoplankton species include common species frequently causing harmful algal blooms and most predominant algal species in the inshore area of China Sea. After the Rayleigh and Raman scattering peaks were removed by the Delaunay triangulation interpolation, the fluorescence spectra of those phytoplankton species were transformed with the coiflet2 wavelet, and the scale vectors and the wavelet vectors were candidate for the feature spectra. Based on the testing results by Bayesian analysis, the 3rd scale vectors were the best feature segments at the division level and picked out as the fluorescence division feature spectra of those phytoplankton species, and the group of the 3rd scale vectors, the 2nd and 3rd wavelet vectors were the best feature segments at the genus level and chosen as the fluorescent genus feature spectra of those phytoplankton species. The reference spectra of those phytoplankton species at the division level and that at the genus level were obtained from these feature spectra by cluster analysis, respectively. The reference spectra base for 37 phytoplankton species was composed of 107 reference spectra at the division level and 155 ones at the genus level. Based on this reference spectra base, a fluorometric discriminating method for phytoplankton populations was established by multiple linear regression resolved by the nonnegative least squares. For 1 776 samples of single phytoplankton species, a correct discriminating rate of 97.0% at genus level and 98.1% at division level can be obtained; The correct discriminating rates are more than 92.7% at the genus level and more than 94.8% at the division level for 384 mixed samples from two phytoplankton species.