Multiomics analyses and machine learning of nuclear receptor coactivator 6 reveal its essential role in hepatocellular carcinoma.

Nuclear receptor coactivator 6 (NCOA6), a coactivator of numerous nuclear receptors and transcription factors, regulates multiple critical cellular functions. Nuclear receptor coactivator 6 is dysregulated in various cancers, including hepatocellular carcinoma (HCC); however, its role remains largely unknown. Here we reported that NCOA6 was highly expressed in HCC compared to the adjacent liver tissue, and NCOA6 overexpression was significantly correlated with poor HCC prognosis. Experiments revealed that the knockdown of NCOA6 damaged the proliferation, migration, and invasion of HCC cells. Multiomics and immune infiltration analyses showed a close relationship between NCOA6 expression, multiple cancer-related malignant pathways, and the immunosuppressive microenvironment. Finally, we established an effective NCOA6-related microRNA (miRNA) signature to distinguish HCC from hepatitis\liver cirrhosis patients. To the best of our knowledge, this study is the first to provide a comprehensive analysis of NCOA6 expression in HCC. We found that NCOA6 plays an important role in HCC development and has a potential mechanism of action. Establishing an NCOA6-related miRNA signature will help develop novel diagnostic strategies for HCC patients.

Advancements in predicting and modeling rare event outcomes for enhanced decision-making.

Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showcased in this issue aims to provide valuable insights and strategies to enhance the accuracy of rare event prediction and modeling.

Graph convolutional network approach to discovering disease-related circRNA-miRNA-mRNA axes.

Non-coding RNAs are gaining prominence in biology and medicine, as they play major roles in cellular homeostasis among which the circRNA-miRNA-mRNA axes are involved in a series of disease-related pathways, such as apoptosis, cell invasion and metastasis. Recently, many computational methods have been developed for the prediction of the relationship between ncRNAs and diseases, which can alleviate the time-consuming and labor-intensive exploration involved with biological experiments. However, these methods handle ncRNAs separately, ignoring the impact of the interactions among ncRNAs on the diseases. In this paper we present a novel approach to discovering disease-related circRNA-miRNA-mRNA axes from the disease-RNA information network. Our method, using graph convolutional network, learns the characteristic representation of each biological entity by propagating and aggregating local neighbor information based on the global structure of the network. The approach is evaluated using the real-world datasets and the results show that it outperforms other state-of-the-art baselines on most of the metrics.

TGF-ß1 induced deficiency of linc00261 promotes epithelial-mesenchymal-transition and stemness of hepatocellular carcinoma via modulating SMAD3.

Emerging evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the metastasis and recurrence of hepatocellular carcinoma (HCC). A kinds of lncRNAs were found to be involved in regulating epithelial-mesenchymal transition (EMT) or stem-like traits in human cancers, however, the molecular mechanism and signaling pathways targeting EMT and stemness remains largely unknown. Previously, we found that linc00261 was down-regulated in HCC and associated with multiple worse clinical pathological parameters and poor prognosis. Here, we show that linc00261 was down-regulated in TGF-ß1 stimulated cells, and forced expression of linc00261 attenuated EMT and stem-like traits in HCC. Linc00261 also inhibited the tumor sphere forming in vitro and decreased the tumorigenicity in vivo. Furthermore, we revealed that linc00261 suppressed the expression and phosphorylation of SMAD3 (p-SMAD3), which could be core transcriptional modulator in TGF-ß1 signaling mediated EMT and the acquisition of stemness traits. A negative correlation between linc00261 and p-SMAD3 was determined in HCC samples. Conclusion: Our study revealed that linc00261 suppressed EMT and stem-like traits in HCC cells by inhibiting TGF-ß1/SMAD3 signaling.

Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data.

BACKGROUND: For differential abundance analysis, zero-inflated generalized linear models, typically zero-inflated NB models, have been increasingly used to model microbiome and other sequencing count data. A common assumption in estimating the false discovery rate is that the p values are uniformly distributed under the null hypothesis, which demands that the postulated model fit the count data adequately. Mis-specification of the distribution of the count data may lead to excess false discoveries. Therefore, model checking is critical to control the FDR at a nominal level in differential abundance analysis. Increasing studies show that the method of randomized quantile residual (RQR) performs well in diagnosing count regression models. However, the performance of RQR in diagnosing zero-inflated GLMMs for sequencing count data has not been extensively investigated in the literature. RESULTS: We conduct large-scale simulation studies to investigate the performance of the RQRs for zero-inflated GLMMs. The simulation studies show that the type I error rates of the GOF tests with RQRs are very close to the nominal level; in addition, the scatter-plots and Q-Q plots of RQRs are useful in discerning the good and bad models. We also apply the RQRs to diagnose six GLMMs to a real microbiome dataset. The results show that the OTU counts at the genus level of this dataset (after a truncation treatment) can be modelled well by zero-inflated and zero-modified NB models. CONCLUSION: RQR is an excellent tool for diagnosing GLMMs for zero-inflated count data, particularly the sequencing count data arising in microbiome studies. In the supplementary materials, we provided two generic R functions, called rqr.glmmtmb and rqr.hurdle.glmmtmb, for calculating the RQRs given fitting outputs of the R package glmmTMB.

Model diagnostics for censored regression via randomized survival probabilities.

Residuals in normal regression are used to assess a model's goodness-of-fit (GOF) and discover directions for improving the model. However, there is a lack of residuals with a characterized reference distribution for censored regression. In this article, we propose to diagnose censored regression with normalized randomized survival probabilities (RSP). The key idea of RSP is to replace the survival probability (SP) of a censored failure time with a uniform random number between 0 and the SP of the censored time. We prove that RSPs always have the uniform distribution on (0, 1) under the true model with the true generating parameters. Therefore, we can transform RSPs into normally distributed residuals with the normal quantile function. We call such residuals by normalized RSP (NRSP residuals). We conduct simulation studies to investigate the sizes and powers of statistical tests based on NRSP residuals in detecting the incorrect choice of distribution family and nonlinear effect in covariates. Our simulation studies show that, although the GOF tests with NRSP residuals are not as powerful as a traditional GOF test method, a nonlinear test based on NRSP residuals has significantly higher power in detecting nonlinearity. We also compared these model diagnostics methods with a breast-cancer recurrent-free time dataset. The results show that the NRSP residual diagnostics successfully captures a subtle nonlinear relationship in the dataset, which is not detected by the graphical diagnostics with CS residuals and existing GOF tests.

Mathematical Model and microCT-Based Kinematic Analysis of the Rostrum Mouthparts in Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae).

To uncover the chewing mechanism of Cyrtotrachelus buqueti Guer, a mathematical model was created and a kinematic analysis of its rostrum mouthparts was conducted for, to our knowledge, the first time. To reduce noise and improve the quality of scanning electron micrographs of the weevil's mouthparts, nonlocal means and integral nonlocal means algorithms were proposed. Additionally, based on a comparison and analysis of five classical edge detection algorithms, a multiscale edge detection algorithm based on the B-spline wavelet was used to obtain the boundaries of structural features. The least squares method was used to analyze the data of the mouthparts to fit the mathematical model and fitted curves were obtained using Gaussian equations. The results show that curvature and concave-convex variations of the weevil's mouthparts can highlight fluctuations in friction effects when it chews bamboo shoots, which is helpful in preventing debris from bamboo shoots or other debris from sticking to the mouthpart surfaces. Moreover, this paper highlights the utility of micro-computed tomography (microCT) for three-dimensional (3D) reconstruction and a flowchart is suggested. The reconstructed slices were 9.0 µm thick and an accurate 3D rendered model was obtained from a series of microCT slices. Finally, a real model of the rostrum mouthparts was analyzed using finite-element analysis. The results provide a biological template for the design of a novel bionic drilling mechanism.

A comparison of residual diagnosis tools for diagnosing regression models for count data.

BACKGROUND: Examining residuals is a crucial step in statistical analysis to identify the discrepancies between models and data, and assess the overall model goodness-of-fit. In diagnosing normal linear regression models, both Pearson and deviance residuals are often used, which are equivalently and approximately standard normally distributed when the model fits the data adequately. However, when the response vari*able is discrete, these residuals are distributed far from normality and have nearly parallel curves according to the distinct discrete response values, imposing great challenges for visual inspection. METHODS: Randomized quantile residuals (RQRs) were proposed in the literature by Dunn and Smyth (1996) to circumvent the problems in traditional residuals. However, this approach has not gained popularity partly due to the lack of investigation of its performance for count regression including zero-inflated models through simulation studies. Therefore, we assessed the normality of the RQRs and compared their performance with traditional residuals for diagnosing count regression models through a series of simulation studies. A real data analysis in health care utilization study for modeling the number of repeated emergency department visits was also presented. RESULTS: Our results of the simulation studies demonstrated that RQRs have low type I error and great statistical power in comparisons to other residuals for detecting many forms of model misspecification for count regression models (non-linearity in covariate effect, over-dispersion, and zero inflation). Our real data analysis also showed that RQRs are effective in detecting misspecified distributional assumptions for count regression models. CONCLUSIONS: Our results for evaluating RQRs in comparison with traditional residuals provide further evidence on its advantages for diagnosing count regression models.

Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection.

BACKGROUND: Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR's biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data. RESULTS: The proposed method, DNP-AAP (deep neural pursuit - average activation potential), was tested on a Neisseria gonorrhoeae dataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively. CONCLUSIONS: DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.

Nanoindentation Properties and Finite Element Analysis of the Rostrum of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae).

This work focuses on the application of nanoindentation measurements and the finite element method for analyzing the mechanical properties of the rostrum of the outstanding driller Cyrtotrachelus buqueti Guer. Nanoindentation tests were carried out to measure the Young's modulus and hardness of the rostrum, with the results for the "dry" samples being 13.886 ± 0.75 and 0.368 ± 0.0445 GPa, respectively. The values for the "fresh" samples showed no clear difference from those of the "dry" ones. Moreover, field observation was conducted to determine the motion behaviors of the rostrum on the weevil. Micro-computed tomography technology was employed to obtain structural information about the rostrum, using 9 µm slices. A real three-dimensional model of the rostrum was created using the MIMICS application. Finally, the mechanical properties of the rostrum were determined by finite element analysis. It was concluded that the rostrum provides an ideal biological template for the design of biocomposite materials and lightweight tube-shaped structures. The properties determined in this study can potentially be applied in different fields, such as in the design of automotive hybrid transmission shafts, helicopter tail drive shafts, robotic arms, and other sandwich structures in aerospace engineering.

Knockdown of Mir-135b Sensitizes Colorectal Cancer Cells to Oxaliplatin-Induced Apoptosis Through Increase of FOXO1.

BACKGROUND/AIMS: Aberrant expression of microRNAs (miRNAs) is found to be responsible for tumorigenesis, cancer development and chemoresistance. Although oxaliplatin is an effective chemotherapeutic drug for treatment of colorectal cancer (CRC), CRC cells can develop some mechanisms to evade oxaliplatin-induced cell death. It is urgent to explore the novel strategies to increase the chemosensitivity of CRC cells. METHODS: QRT-PCR analysis was performed to detect the expression of miR-135b in CRC patients' serum and CRC cell lines. MTT assays were used to evaluate the effect of anti-miR-135b on oxaliplatin-induced cell death in CRC cell lines. Western blot, flow cytometry and luciferase reporter assays were performed to evaluate the potential mechanism and pathway of anti-miR-135b-promoted apoptosis in oxaliplatin-treated CRC cells. RESULTS: Significant upregulation of miR-135b was observed in CRC cell lines and CRC patients' serum. Knockdown of miR-135b was found to sensitize colorectal cancer cells to oxaliplatin-induced cytotoxicity. Mechanically, knockdown of miR-135b increased the expression level of FOXO1 in CRC. As the downstream, the increased FOXO1 induced by anti-miR-135b promoted the expression of Bim and Noxa. Since Bim and Noxa act as key pro-apoptotic proteins in mitochondrial apoptosis, anti-miR-135b was able to enhance the oxaliplatin-induced apoptosis dependent on the anti-miR-135b/FOXO1 axis. CONCLUSIONS: Anti-miR-135b enhanced the anti-tumor effect of oxaliplatin on CRC. Combination with miR-135b antisense nucleotides may represent a novel strategy to sensitize CRC to oxaliplatin-based treatment.

Sleep duration and sleep disturbances in association with falls among the middle-aged and older adults in China: a population-based nationwide study.

BACKGROUND: Falls pose major health problems to the middle-aged and older adults and may potentially lead to various levels of injuries. Sleep duration and disturbances have been shown to be associated with falls in literature; however, studies of the joint and distinct effects of those sleep problems are still sparse. To fill this gap, we aimed to determine the association between sleep duration, sleep disturbances and falls among middle-aged and older adults in China controlling for psychosocial, lifestyle, socio-demographical factors and comorbidity. METHODS: Data were derived from the China Health and Retirement Longitudinal Study (CHARLS) based on multi-stage sampling designs, with respondents aged 50 and older. Associations were evaluated by using multiple logistic regression adjusting for confounders and complex survey design. To further determine if the association of sleep duration/disturbance and falls depends on age groups, the study data were divided into two samples (age 50-64 vs. age 65+) and comparison was made between the two age groups. RESULTS: Of the 12,759 respondents, 2172 (17%) had falls within the last 2 years. Our findings indicated that the participants who had nighttime sleep duration ≤5 were more likely to report falls than those who had nighttime sleep duration ≥6 h; whereas no association between nighttime sleep duration > 8 h and falls. Participants having sleep disturbances 1-2 days, or 3-4 days, and 5-7 days per week were also more likely to report falls than those who had no sleep disturbance. The nap sleep duration was not significantly associated with falls. Although the combined sample found both sleep duration and sleep disturbance to be strongly associated with falls after adjusting for various confounders, sleep disturbance was not significantly related to falls among participants aged 65 + . CONCLUSIONS: Our study suggested that there is an independent association between falls and short sleep duration and disturbed sleep among middle-aged and older adults in China. Findings underscore the need for evidence-based prevention and interventions targeting sleep duration and disturbance among this study population.

Computational identification of harmful mutation regions to the activity of transposable elements.

BACKGROUND: Transposable elements (TEs) are interspersed DNA sequences that can move or copy to new positions within a genome. TEs are believed to promote speciation and their activities play a significant role in human disease. In the human genome, the 22 AluY and 6 AluS TE subfamilies have been the most recently active, and their transposition has been implicated in many inherited human diseases and in various forms of cancer. Therefore, understanding their transposition activity is very important and identifying the factors that affect their transpositional activity is of great interest. Recently, there has been some work done to quantify the activity levels of active Alu TEs based on variation in the sequence. Given this activity data, an analysis of TE activity based on the position of mutations is conducted. RESULTS: A method/simulation is created to computationally predict so-called harmful mutation regions in the consensus sequence of a TE; that is, mutations that occur in these regions decrease the transpositional activity dramatically. The methods are applied to the most active subfamily, AluY, to identify the harmful regions, and seven harmful regions are identified within the AluY consensus with q-values less than 0.05. A supplementary simulation also shows that the identified harmful regions covering the AluYa5 RNA functional regions are not occurring by chance. This method is then applied to two additional TE families: the Alu family and the L1 family, to computationally detect the harmful regions in these elements. CONCLUSIONS: We use a computational method to identify a set of harmful mutation regions. Mutations within the identified harmful regions decrease the transpositional activity of active elements. The correlation between the mutations within these regions and the transpositional activity of TEs are shown to be statistically significant. Verifications are presented using the activity of AluY elements and the secondary structure of the AluYa5 RNA, providing evidence that the method is successfully identifying harmful mutation regions.

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd.

The Chemokine CXCL7 is Correlated with LDH-A and Predicts the Prognosis of Patients with Colorectal Cancer.

OBJECTIVE: The aims of this study were to evaluate the correlation between chemokine (C-X-C) ligand 7 (CXCL7) expression and glycolysis and to explore the prognostic significance of CXCL7 in colorectal cancer (CRC). METHODS: The expression of CXCL7 and lactate dehydrogenase A (LDH-A) was measured by immunohistochemistry in tissue from 158 CRC patients. Patients were divided into high expression and low expression groups based on receiver operating characteristic curves and a cut-off value. The correlation between CXCL7 and LDH-A expression was evaluated. The overall survival (OS) times of CRC patients were explored. The risk factors related to prognosis were assessed. RESULTS: Significantly higher expression of CXCL7 and LDH-A was detected in CRC tissue than in non-CRC tissue, and was associated with N stage and tumor-node-metastasis (TNM) stage. CXCL7 expression was strongly correlated with LDH-A expression in CRC tissue. High expression of CXCL7 was validated as an independent risk factor for OS. CONCLUSION: Increased expression of CXCL7 was positively correlated with LDH-A expression and was an independent risk factor for CRC prognosis.

Multiscale Characterization and Biomimetic Design of Porcupine Quills for Enhanced Mechanical Performance.

The mechanical properties of porcupine quills have attracted the interest of researchers due to their unique structure and composition. However, there is still a knowledge gap in understanding how these properties can be utilized to design biomimetic structures with enhanced performance. This study delves into the nanomechanical and macro-mechanical properties of porcupine quills, unveiling varied elastic moduli across different regions and cross sections. The results indicated that the elastic moduli of the upper and lower epidermis were higher at 8.13 ± 0.05 GPa and 7.71 ± 0.14 GPa, respectively, compared to other regions. In contrast, the elastic modulus of the mid-dermis of the quill mid-section was measured to be 7.16 ± 0.10 GPa. Based on the micro- and macro-structural analysis of porcupine quills, which revealed distinct variations in elastic moduli across different regions and cross sections, various biomimetic porous structures (BPSs) were designed. These BPSs were inspired by the unique properties of the quills and aimed to replicate and enhance their mechanical characteristics in engineering applications. Compression, torsion, and impact tests illustrated the efficacy of structures with filled hexagons and circles in improving performance. This study showed enhancements in maximum torsional load and crashworthiness with an increase in filled structures. Particularly noteworthy was the biomimetic porous circular structure 3 (BPCS_3), which displayed exceptional achievements in average energy absorption (28.37 J) and specific energy absorption (919.82 J/kg). Finally, a response surface-based optimization method is proposed to enhance the design of the structure under combined compression-torsion loads, with the goal of reducing mass and deformation. This research contributes to the field of biomimetics by exploring the potential applications of porcupine quill-inspired structures in fields such as robotics, drive shafts, and aerospace engineering.

The Design of a Biomimetic Hierarchical Thin-Walled Structure Inspired by a Lotus Leaf and Its Mechanical Performance Analysis.

Inspired by the macro- and microstructures of the lotus leaf, a series of biomimetic hierarchical thin-walled structures (BHTSs) was proposed and fabricated, exhibiting improved mechanical properties. The comprehensive mechanical properties of the BHTSs were evaluated using finite element (FE) models constructed in ANSYS, which were validated by the experimental results. Light-weight numbers (LWNs) were used as an index to assess these properties. The simulation results were compared with the experimental data to validate the findings. The compression results indicated that the maximum load carried by each BHTS was very similar, with the highest bearing load being 32,571 N and the lowest being 30,183 N, resulting in only a 7.9% difference between them. In terms of the LWN-C values, the BHTS-1 exhibited the highest value at 318.51 N/g, while the BHTS-6 had the lowest value at 295.16 N/g. For the torsion and bending results, these findings suggested that increasing the bifurcation structure at the end side of the thin tube branch significantly improved the torsional resistance properties of the thin tube. For the impact characteristics of the proposed BHTSs, enhancing the bifurcation structure at the end of the thin tube branch significantly increased the energy absorption capacity and improved the energy absorption (EA) and the specific energy absorption (SEA) values of the thin tube. The BHTS-6 had the best structural design in terms of both the EA and SEA among all the BHTSs, but its CLE value was slightly lower than that of the BHTS-7, indicating slightly lower structural efficiency. This study provides a new idea and method for developing new lightweight and high-strength materials as well as designing more effective energy absorption structures. At the same time, this study has important scientific value in understanding how biological structures in nature exhibit their unique mechanical properties.

Study on Erosion Behavior of Laser Wire Feeding Cladding High-Manganese Steel Coatings.

High-manganese steel (HMnS) coating was prepared using laser wire feeding cladding technology. Erosion damage behavior and erosion rate of both the HMnS coating and the HMnS substrate were investigated at room temperature using an erosion testing machine. SEM/EDS, XRD, EPMA, and microhardness analyses were used to characterize the cross sections of the coating and matrix, as well as the morphology, phase composition, and microhardness of the eroded surface. The phase composition, orientation characteristics, and grain size of the eroded surfaces of both the coating and substrate were examined by using the EBSD technique. The erosion mechanism under different erosion angles was revealed. By analyzing the plastic deformation behavior of the subsurface of the HMnS coating, the impact hardening mechanism of the high-manganese steel coating during the erosion process was investigated. The results demonstrated that the HMnS coating, prepared through laser wire feeding cladding, exhibited excellent metallurgical bonding with the substrate, featuring a dense microstructure without any cracks. The erosion rate of the coatings was lower than that of the substrate at different erosion angles, with the maximum erosion rate occurring at 35° and 50°. The damage to the coating and substrate under low-angle erosion was primarily attributed to the micro-cutting of erosion particles and a minor amount of hammering. At the 90° angle, the dominant factor was hammering. After erosion, the microhardness of both the coating and substrate sublayer increased to 380HV0.3 and 359HV0.3, respectively. Dendrite segregation, refined grains, low-angle grain boundaries, and localized dislocations, generated by laser wire feeding cladding, contributed to the deformation process of HMnS. These factors collectively enhance the hardening behavior of HMnS coatings, thereby providing excellent erosion resistance.

Selective C(sp2)-H bond functionalization of olefins via visible-light-induced photoredox-quinuclidine dual catalysis.

The site selective C(sp2)-H bond functionalization of olefins has been achieved through a visible-light-induced photoredox-quinuclidine dual catalysis upon merging the quinuclidinium radical cation addition to alkene strategy and the distal heteroaryl ipso-migration strategy. This synthetic protocol features a simple operation with readily available starting materials in good step-economy to access alkenylheteroaromatic products in moderate to good yields under mild conditions. A plausible cascade catalytic reaction mechanism is also proposed.

Visible-light-induced dual catalysis for N-α C(sp3)-H amination and alkenylation of N-alkyl benzamides.

The amination and alkenylation of the C(sp3)-H bond at the N-α position of secondary benzamides were both realized in this work by using N-hydroxyphthalimide (NHPI) imidate esters as substrates under a dual catalysis involving a photoredox catalyst and hydrogen atom transfer (HAT) catalyst. The developed methods significantly extended the scope of applications of the N-α position C(sp3)-H bond functionalization with regard to secondary N-alkylamides. More importantly, new reaction models in photoredox catalysis have been established. Based on corresponding experiments and density functional theory (DFT) calculations on the critical reaction steps combined with information reported previously, we proposed a synergistic photo- and organocatalytic reaction process for the C(sp3)-H bond functionalization and also clarified the occurrence of a chain process in the reaction pathway.