Adjusting aggregation modes and photophysical and photovoltaic properties of diketopyrrolopyrrole-based small molecules by introducing B←N bonds

The packing mode of small-molecular semiconductors in thin films is an important factor that controls the performance of their optoelectronic devices. Designing and changing the packing mode by molecular engineering is challenging. Three structurally related diketopyrrolopyrrole (DPP)-based compounds were synthesized to study the effect of replacing C−C bonds by isoelectronic dipolar B←N bonds. By replacing one of the bridging C−C bonds on the peripheral fluorene units of the DPP molecules by a coordinative B←N bond and changing the B←N bond orientation, the optical absorption, fluorescence, and excited-state lifetime of the compounds can be tuned. The substitution alters the preferential aggregation of the molecules in the solid state from H-type (for C−C) to J-type (for B←N). Introducing B←N bonds thus provides a subtle way of controlling the packing mode. The photovoltaic properties of the compounds were evaluated in bulk heterojunctions with a fullerene acceptor and showed moderate performance as a consequence of suboptimal morphologies, bimolecular recombination, and triplet-state formation

Clinical meaning of serum trimethylamine oxide, N-terminal-pro-brain natriuretic peptide, hypoxia-inducible factor-1a and left ventricular function and pregnancy outcome in patients with pregnancy-induced hypertension

Background: To figure out the clinical meaning of serum trimethylamine oxide (TMAO), N-terminal-pro-brain natriuretic peptide (NT-proBNP) and hypoxia-inducible factor-1a (HIF-1a) with left ventricular function and pregnancy outcome in patients with pregnancy-induced hypertension. Methods: From January 2018 to October 2020, 117 patients with gestational hypertension were taken as the research objects and grouped into the gestational hypertension (pregnancy-induced hypertension, 55 cases), mild preeclampsia (mild PE, 43 cases) and severe preeclampsia (severe PE, 19 cases) in the light of the severity of the disease. Analysis of the relation of serum TMAO, NT-proBNP and HIF-1a with the severity of disease and cardiac function indexes in patients with gestational hypertension was conducted. All patients were followed up to the end of pregnancy, and the predictive value of serum TMAO, NT-proBNP and HIF-1a on pregnancy outcome in patients was analyzed. Results: Serum TMAO and NT-proBNP of patients were elevated, while HIF-1a was reduced with the severity of the disease (P < 0.05). Serum TMAO and NT-proBNP in patients with gestational hypertension were positively correlated but HIF-1a was negatively correlated with the severity of the disease (P < 0.05). Left ventricular end-diastolic volume (LVEDV) and left ventricular end-systolic volume (LVESV) were elevated in gestational hypertension patients, while ejection fraction (LVEF) was reduced with the severity of disease (P < 0.05). Serum TMAO, NT-proBNP and HIF1a were associated with LVEDV, LVESV and LVEF values in patients with gestational hypertension (P < 0.05). Serum TMAO and NT-proBNP were elevated but HIF-1a was reduced in patients with a poor pregnancy outcome (P < 0.05). The AUC of the combined detection of serum TMAO, NT-proBNP and HIF-1a on pregnancy outcome was greater (P < 0.05). Conclusions: Serum TMAO, NT-proBNP and HIF-1a in patients with gestational hypertension are associated with disease severity and cardiac function, and have predictive and evaluative values for disease severity and pregnancy outcome

Single-base methylome profiling of the giant kelp Saccharina japonica reveals significant differences in DNA methylation to microalgae and plants

Brown algae have convergently evolved plant-like body plans and reproductive cycles, which in plants are controlled by differential DNA methylation. This contribution provides the first single-base methylome profiles of haploid gametophytes and diploid sporophytes of a multicellular alga. Although only c. 1.4% of cytosines in Saccharina japonica were methylated mainly at CHH sites and characterized by 5-methylcytosine (5mC), there were significant differences between life-cycle stages. DNA methyltransferase 2 (DNMT2), known to efficiently catalyze tRNA methylation, is assumed to methylate the genome of S. japonica in the structural context of tRNAs as the genome does not encode any other DNA methyltransferases. Circular and long noncoding RNA genes were the most strongly methylated regulatory elements in S. japonica. Differential expression of genes was negatively correlated with DNA methylation with the highest methylation levels measured in both haploid gametophytes. Hypomethylated and highly expressed genes in diploid sporophytes included genes involved in morphogenesis and halogen metabolism. The data herein provide evidence that cytosine methylation, although occurring at a low level, is significantly contributing to the formation of different life-cycle stages, tissue differentiation and metabolism in brown algae

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products