Chemo- and Regioselectivity-Tunable Phosphination of Alkynes

A divergent intramolecular phosphination of alkynes is developed. Fine tuning of chemo- and regioselectivity has been realized by just adding LiBr or H2O to give 5-exo-dig and 6-endo-dig phosphacycles, respectively. A plausible mechanism is proposed on the basis of the control experiments and 31P NMR spectra analysis. The absorption and emission properties of the obtained 1-phosphaphenalenes were also studied

Synthesis of <i>a</i>‑Alkylated Ketones via Tandem Acceptorless Dehydrogenation/<i>a</i>‑Alkylation from Secondary and Primary Alcohols Catalyzed by Metal–Ligand Bifunctional Iridium Complex [Cp*Ir(2,2′-bpyO)(H₂O)]

A new strategy for the synthesis of α-alkylated ketones via tandem acceptorless dehydrogenation/α-alkylation from secondary and primary alcohols was proposed and accomplished. In the presence of metal–ligand bifunctional iridium complex [Cp*Ir­(2,2′-bpyO)­(H₂O)], various desirable products were obtained in high yields. Compared with previous methods for the direct dehydrogenative coupling of secondary alcohols with primary alcohols to α-alkylated ketones, this protocol has obvious advantages including complete selectivity for α-alkylated ketones and more environmentally benign conditions. Notably, the study also exhibited the potential to develop tandem reactions catalyzed using a metal–ligand bifunctional iridium complex

α‑Alkylation of Ketones with Primary Alcohols Catalyzed by a Cp*Ir Complex Bearing a Functional Bipyridonate Ligand

A Cp*Ir complex bearing a functional bipyridonate ligand was found to be a highly effective and versatile catalyst for the α-alkylation of ketones with primary alcohols under extremely environmentally benign and mild conditions (0.1 equiv of Cs₂CO₃ per substrate, reflux in <i>tert</i>-amyl alcohol under an air atmosphere for 6 h). Furthermore, this complex also exhibited a high level of catalytic activity for the α-methylation of ketones with methanol. The mechanistic investigation revealed that the carbonyl group on the ligand is of critical importance for catalytic hydrogen transfer. Notably, the results of this study revealed the unique potential of Cp*Ir complexes bearing a functional bipyridonate ligand for the development of C–C bond-forming reactions with the activation of primary alcohols as electrophiles

Effective Recognition of Different Types of Amino Groups: From Amino­benzene­sulfonamides to Amino‑(<i>N</i>‑alkyl)­benzene­sulfonamides via Iridium-Catalyzed <i>N</i>‑Alkylation with Alcohols

A simple, highly efficient, and general strategy for the direct synthesis of amino-(<i>N</i>-alkyl)­benzene­sulfonamides has been accomplished via direct <i>N</i>-alkylation of amino­benzene­sulfonamides bearing both different types of amino groups with alcohols as alkylating agents. Notably, this research exhibited the potential for the recognition of different types of amino groups in the <i>N</i>-alkylation of complex molecules with alcohols, facilitating the progress of the transition-metal-catalyzed “hydrogen autotransfer (or hydrogen-borrowing) process.

DataSheet_1_Development of the maize 5.5K loci panel for genomic prediction through genotyping by target sequencing.pdf

Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.</p

Table1_Efficacy and safety of respiratory syncytial virus vaccination during pregnancy to prevent lower respiratory tract illness in newborns and infants: a systematic review and meta-analysis of randomized controlled trials.docx

To evaluate the effectiveness and safety of respiratory syncytial virus (RSV) vaccination during pregnancy in preventing lower respiratory tract infection (LRTI) in infants and neonates, we conducted a systematic search of randomized controlled trials (RCTs) in five databases (PubMed, Embase and Cochrane Library, Web of Science, Cochrane Center Register of Controlled trial) until 1 May 2023. We performed a meta-analysis of the eligible trials using RevMan5.4.1 software. Our analysis included six articles and five RCTs. The meta-analysis revealed significant differences in the incidences of LRTI [risk ratio (RR): 0.64; 95% confidence interval (CI): 0.43, 0.96; p = 0.03)] and severe LRTI (RR: 0.37; 95% CI: 0.18, 0.79; p = 0.01) between the vaccine group and the placebo group for newborns and infants. These differences were observed at 90, 120, and 150 days after birth (p = 0.003, p = 0.05, p = 0.02, p = 0.03, p = 0.009, p = 0.05). At 180 days after birth, there was a significant difference observed in the incidence of LRTI between the two groups (RR: 0.43; 95% CI: 0.21, 0.90; p = 0.02). The safety results showed a significant difference in the incidence of common adverse events between the two groups (RR: 1.08; 95% CI: 1.04, 1.12; p < 0.0001). However, there was no significant difference observed in the incidence of serious adverse events (RR: 1.05; 95% CI: 0.97, 1.15; p = 0.23), common and serious adverse events (RR: 1.02; 95% CI: 0.96, 1.10; p = 0.23), or common and serious adverse events among pregnant women and newborns and infants (RR: 0.98; 95% CI: 0.93, 1.04; p = 0.52). In conclusion, maternal RSV vaccination is an effective and safe immunization strategy for preventing LRTI in postpartum infants, with greater efficacy observed within the first 150 days after birth.</p

Effective Recognition of Different Types of Amino Groups: From Amino­benzene­sulfonamides to Amino‑(<i>N</i>‑alkyl)­benzene­sulfonamides via Iridium-Catalyzed <i>N</i>‑Alkylation with Alcohols

A simple, highly efficient, and general strategy for the direct synthesis of amino-(<i>N</i>-alkyl)­benzene­sulfonamides has been accomplished via direct <i>N</i>-alkylation of amino­benzene­sulfonamides bearing both different types of amino groups with alcohols as alkylating agents. Notably, this research exhibited the potential for the recognition of different types of amino groups in the <i>N</i>-alkylation of complex molecules with alcohols, facilitating the progress of the transition-metal-catalyzed “hydrogen autotransfer (or hydrogen-borrowing) process.

The network architecture of GSConv.

As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network’s ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.</div

Improved part of YOLOv7.

(a) shows the ELAN network structure; (b) shows the MP network structure.</p

The precision-recall curve of our model.

As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network’s ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.</div