High-efficiency all fluorescence white OLEDs with high color rendering index by manipulating excitons in co-host recombination layers.

All fluorescence white organic light-emitting diodes (WOLEDs) based on thermally activated delayed fluorescence (TADF) emitters are an attractive route to realize highly efficient and high color quality white light sources. However, harvesting triplet excitons in these devices remains a formidable challenge, particularly for WOLEDs involving conventional fluorescent emitters. Herein, we report a universal design strategy based on a co-host system and a cascaded exciton transfer configuration. The co-host system furnishes a broad and charge-balanced exciton generation zone, which simultaneously endows the devices with low efficiency roll-off and good color stability. A yellow TADF layer is put forward as an intermediate sensitizer layer between the blue TADF light-emitting layer (EML) and the red fluorescence EML, which not only constructs an efficient cascaded Förster energy transfer route but also blocks the triplet exciton loss channel through Dexter energy transfer. With the proposed design strategy, three-color all fluorescence WOLEDs reach a maximum external quantum efficiency (EQE) of 22.4% with a remarkable color rendering index (CRI) of 92 and CIE coordinates of (0.37, 0.40). Detailed optical simulation confirms the high exciton utilization efficiency. Finally, by introducing an efficient blue emitter 5Cz-TRZ, a maximum EQE of 30.1% is achieved with CIE coordinates of (0.42, 0.42) and a CRI of 84 at 1000 cd m-2. These outstanding results demonstrate the great potential of all fluorescence WOLEDs in solid-state lighting and display panels.

Inhibitor screening using microarray identifies the high capacity of neutralizing antibodies to Spike variants in SARS-CoV-2 infection and vaccination.

Rationale: Mutations of SARS-CoV-2, which is responsible for coronavirus disease 2019 (COVID-19), could impede drug development and reduce the efficacy of COVID-19 vaccines. Here, we developed a multiplexed Spike-ACE2 Inhibitor Screening (mSAIS) assay that can measure the neutralizing effect of antibodies across numerous variants of the coronavirus's Spike (S) protein simultaneously. Methods: The SARS-CoV-2 spike variant protein microarrays were prepared by printing 72 S variants onto a chemically-modified glass slides. The neutralization potential of purified anti-S antibodies and serum from convalescent COVID-19 patients and vaccinees to S variants were assessed with the mSAIS assay. Results: We identified new S mutations that are sensitive and resistant to neutralization. Serum from both infected and vaccinated groups with a high titer of neutralizing antibodies (NAbs) displayed a broader capacity to neutralize S variants than serum with low titer NAbs. These data were validated using serum from a large vaccinated cohort (n = 104) with a tiled S peptide microarray. In addition, similar results were obtained using a SARS-CoV-2 pseudovirus neutralization assay specific for wild-type S and five prevalent S variants (D614G, B.1.1.7, B.1.351, P.1, B.1.617.2), thus demonstrating that high antibody diversity is associated with high NAb titers. Conclusions: Our results demonstrate the utility of the mSAIS platform in screening NAbs. Moreover, we show that heterogeneous antibody populations provide a more protective effect against S variants, which may help direct COVID-19 vaccine and drug development.

Correlation between BMI and metagenomics concentration of gut microbiota of college students in Inner Mongolia / 中国学校卫生

Objective@#To explore the correlation between BMI and gut microbiota of college students in Inner Mongolia,and to provide a reference basis for revealing the relationship between intestinal flora and obesity.@*Methods@#Totally 88 college students from Inner Mongolia Medical University were enrolled, Height and weight were measured,and the feces samples were collected. The bacterial metagenome was extracted from dry feces samples for the concentration detection in per gram of dry feces,expressed as μg/μL. Correlation between BMI and metagenomics concentration of gut microbiota was statistically analyzed. Meanwhile,the metagenomics concentration of gut microbiota in different BMI groups was compared with each other.@*Results@#There was a negative correlation between BMI and the metagenomics concentration of gut microbiota(r=-0.27,P<0.05). Significant difference in the concentration of gut microflora was observed between the normal group and the obesity group,the normal group and the overweight/obesity group(F=3.62,P<0.05). Among the female volunteers,there were significant differences between normal group and overweight group,between normal group and obesity group(F=1.87,P<0.05). No significant differences in metagenomics concentration of gut microbiota were found in different BMI groups(F=0.60, P>0.05).@*Conclusion@#There is a correlation between BMI and gut microbiota of college students in Inner Mongolia，the concentration of gut microflora metagenome in overweight and obese people decreased significantly.

Recognition of Mitochondrial Proteins in Plasmodium Based on the Tripeptide Composition.

Mitochondria play essential roles in eukaryotic cells, especially in Plasmodium cells. They have several unusual evolutionary and functional features that are incredibly vital for disease diagnosis and drug design. Thus, predicting mitochondrial proteins of Plasmodium has become a worthwhile work. However, existing computational methods can only predict mitochondrial proteins of Plasmodium falciparum (P. falciparum for short), and these methods have low accuracy. It is highly desirable to design a classifier with high accuracy for predicting mitochondrial proteins for all Plasmodium species, not only P. falciparum. We proposed a novel method, named as PM-OTC, for predicting mitochondrial proteins in Plasmodium. PM-OTC uses the Support Vector Machine (SVM) as the classifier and the selected tripeptide composition as the features. We adopted the 5-fold cross-validation method to train and test PM-OTC. Results demonstrate that PM-OTC achieves an accuracy of 94.91%, and performances of PM-OTC are superior to other methods.