One-pot asymmetric living copolymerization-induced chiral self-assemblies and circularly polarized luminescence.

Controlled synthesis of conjugated block polymers enables the optimization of their self-assembly and may lead to distinct optical properties and functionalities. Herein, we report a direct chain extension of one-handed helical poly(acyl methane) with 1-ethynyl-4-iodo-2,5-bis(octyloxy)benzene, affording well-defined π-conjugated poly(acyl methane)-b-poly(phenylene ethynylene) copolymers. Although the distinct monomers are polymerized via different mechanisms, the one-pot copolymerization follows a living polymerization manner, giving the desired optically active block copolymers with controllable molar mass and low distribution. The block copolymerization induced chiral self-assembly simultaneously due to the one-handed helicity of the poly(acyl methane) block, giving spherical nanoparticles, one-handed helices, and chiral micelles with controlled dimensions regarding the composition of the generated copolymers. Interestingly, the chiral assemblies exhibit clear circularly polarized luminescence with tunable handedness and a high dissymmetric factor.

Learnable Distribution Calibration for Few-Shot Class-Incremental Learning.

Few-shot class-incremental learning (FSCIL) faces the challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study, we propose a learnable distribution calibration (LDC) approach, to systematically solve these two challenges using a unified framework. LDC is built upon a parameterized calibration unit (PCU), which initializes biased distributions for all classes based on classifier vectors (memory-free) and a single covariance matrix. The covariance matrix is shared by all classes, so that the memory costs are fixed. During base training, PCU is endowed with the ability to calibrate biased distributions by recurrently updating sampled features under supervision of real distributions. During incremental learning, PCU recovers distributions for old classes to avoid 'forgetting', as well as estimating distributions and augmenting samples for new classes to alleviate 'over-fitting' caused by the biased distributions of few-shot samples. LDC is theoretically plausible by formatting a variational inference procedure. It improves FSCIL's flexibility as the training procedure requires no class similarity priori. Experiments on CUB200, CIFAR100, and mini-ImageNet datasets show that LDC respectively outperforms the state-of-the-arts by 4.64%, 1.98%, and 3.97%. LDC's effectiveness is also validated on few-shot learning scenarios.

Structural equation modeling analysis of determinants of barriers to breast self-examination among Eastern Chinese women.

BACKGROUND: This study aimed to establish a structural equation model to determine the associations among knowledge of breast cancer, beliefs about breast self-examination (BSE), BSE practice, socio-economic status (SES), and barriers to BSE among Eastern Chinese women. METHODS: An online cross-sectional correlational survey of 2026 women from Eastern China was undertaken by a self-administered questionnaire in 2020. Structural equation modeling was utilized for examining the interrelationships between BSE barriers and other variables. RESULTS: Our results suggested that BSE barriers were significantly and negatively related to SES, BSE beliefs, and BSE practices (ß = -0.176, p < 0.001 and ß = -0.118, p < 0.001 and ß = -0.435, p < 0.001, respectively). Among them, participants' BSE practices had the strongest effects on BSE barriers, while the effect of breast cancer knowledge on BSE barriers was in an opposite direction (ß = 0.177, p < 0.001). CONCLUSIONS: BSE barriers were influenced by SES, breast cancer knowledge, BSE beliefs and BSE practices. Our results warn that breast cancer prevention education should strengthen knowledge of practical methods rather than general knowledge. Therefore, intervention strategies designed to develop BSE and breast cancer prevention should take into account multiple factors, in particular finding more effective ways for the public to turn knowledge into a motivator rather than a barrier.

Dynamic Support Network for Few-Shot Class Incremental Learning.

Few-shot class-incremental learning (FSCIL) is challenged by catastrophically forgetting old classes and over-fitting new classes. Revealed by our analyses, the problems are caused by feature distribution crumbling, which leads to class confusion when continuously embedding few samples to a fixed feature space. In this study, we propose a Dynamic Support Network (DSN), which refers to an adaptively updating network with compressive node expansion to "support" the feature space. In each training session, DSN tentatively expands network nodes to enlarge feature representation capacity for incremental classes. It then dynamically compresses the expanded network by node self-activation to pursue compact feature representation, which alleviates over-fitting. Simultaneously, DSN selectively recalls old class distributions during incremental learning to support feature distributions and avoid confusion between classes. DSN with compressive node expansion and class distribution recalling provides a systematic solution for the problems of catastrophic forgetting and overfitting. Experiments on CUB, CIFAR-100, and miniImage datasets show that DSN significantly improves upon the baseline approach, achieving new state-of-the-arts.

N-Heterocyclic Carbene-Catalyzed [3 + 2] Annulation of 3,3'-Bisoxindoles with α-Bromoenals: Enantioselective Construction of Contiguous Quaternary Stereocenters.

An NHC-catalyzed enantio- and diastereoselective [3 + 2] annulation of α-bromoenals with bisoxindoles is developed, affording efficient access to various spirocyclic bisoxindole alkaloids. This protocol tolerates a broad substrates scope, with various spirocyclic bisoxindoles obtained in generally excellent enantioselectivities. More importantly, two contiguous sterically congested all-carbon quaternary stereocenters are successfully created during this process.

Downregulated expression of S2-RNase attenuates self-incompatibility in "Guiyou No. 1" pummelo.

Self-incompatibility (SI) substantially restricts the yield and quality of citrus. Therefore, breeding and analyzing self-compatible germplasm is of great theoretical and practical significance for citrus. Here, we focus on the mechanism of a self-compatibility mutation in 'Guiyou No. 1' pummelo (Citrus maxima), which is a spontaneous mutant of 'Shatian' pummelo (Citrus maxima, self-incompatibility). The rate of fruit set and the growth of pollen tubes in the pistil confirmed that a spontaneous mutation in the pistil is responsible for the self-compatibility of 'Guiyou No. 1'. Segregation ratios of the S genotype in F1 progeny, expression analysis, and western blotting validated that the reduced levels of S2-RNase mRNA contribute to the loss of SI in 'Guiyou No. 1'. Furthermore, we report a phased assembly of the 'Guiyou No. 1' pummelo genome and obtained two complete and well-annotated S haplotypes. Coupled with an analysis of SV variations, methylation levels, and gene expression, we identified a candidate gene (CgHB40), that may influence the regulation of the S2-RNase promoter. Our data provide evidence that a mutation that affects the pistil led to the loss of SI in 'Guiyou No. 1' by influencing a poorly understood mechanism that affects transcriptional regulation. This work significantly advances our understanding of the genetic basis of the SI system in citrus and provides information on the regulation of S-RNase genes.

Highly Enantioselective Synthesis of Pyrroloindolones and Pyrroloquinolinones via an N-Heterocyclic Carbene-Catalyzed Cascade Reaction.

In this work, the NHC-catalyzed Michael/Mannich/lactamization cascade reaction of enals with either indole-2-carboxaldehyde-derived aldimines or indole-7-carboxaldehyde-derived aldimines is described. This protocol enables the rapid assembly of optically active pyrroloindolones and pyrroloquinolinones derivatives under mild conditions with high yields, excellent enantioselectivities, and a broad substrate scope.

Harmonic Feature Activation for Few-Shot Semantic Segmentation.

Few-shot semantic segmentation remains an open problem because limited support (training) images are insufficient to represent the diverse semantics within target categories. Conventional methods typically model a target category solely using information from the support image(s), resulting in incomplete semantic activation. In this paper, we propose a novel few-shot segmentation approach, termed harmonic feature activation (HFA), with the aim to implement dense support-to-query semantic transform by incorporating the features of both query and support images. HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way. HFA incorporates a low-rank decomposition procedure, which speeds up bilinear feature activation with negligible performance cost. In addition, a semantic diffusion procedure is fused with HFA, which further improves the global harmony and local consistency of the feature activation. Extensive experiments on commonly used datasets (PASCAL VOC and MS COCO) show that HFA improves the state-of-the-arts with significant margins. Code is available at https://github.com/Bibikiller/HFA.

A systematic review and meta-analysis on influencing factors of failure of oral implant restoration treatment.

BACKGROUND: With the continuous improvement of human living standards, more and more dental patients are requiring oral implant restoration treatment. However, there is still controversy regarding the influence of risk factors such as osteoporosis, radiotherapy, diabetes, and smoking on the failure of oral implants. This study aimed to explore the correlation between risk factors and failure of oral implant restoration treatment. METHODS: The databases of China National Knowledge Infrastructure (CNKI), Baidu Academic, Weipu, Wanfang, PubMed, EBSCO, Medline, Web of knowledge, Ovid, and the Cochrane Library were searched. The search strategies included: subject terms related to research results such as survival, osseointegration, failure, removal, replacement, and loss; related to risk factors: osteoporosis, head and neck cancer, diabetes, and smoking; and oral implantology as a keyword. RESULTS: Thirty-two articles were included in meta-analysis, there was a high heterogeneity between radiotherapy and dental implant failure (I2=71.6%, P=0.000), and there was an obvious correlation between radiotherapy and dental implant failure [relative risk (RR) =2.09, 95% confidential interval (CI): 1.68-2.61]. There was heterogeneity between diabetes and oral implant failure in the included articles (I2=59.6%, P=0.084). There was no remarkable correlation between osteoporosis and dental implant failure (RR =1.19, 95% CI: 0.81-1.74). There was a high heterogeneity between smoking and dental implant failure in the included articles (I2=33.8%, P=0.092), showing obvious correlation (RR =1.80, 95% CI: 1.53-2.11). DISCUSSION: The results of meta-analysis confirmed that radiotherapy and smoking were greatly associated with oral implant failure.

Complete Genome Sequence of a Chinese Strain of "Candidatus Liberibacter asiaticus".

We report here the complete genome sequence of "Candidatus Liberibacter asiaticus" (strain Guangxi-1). The 1,268,237-bp genome with a 36.5% G+C content comprises 1,141 open reading frames, 44 tRNAs, and 3 complete rRNAs in a circular chromosome.