Total Synthesis of Penicibilaenes Enabled by a Tandem Double Conia-ene Type Reaction.

The total syntheses of penicibilaenes A and B are described. The key step is the tBuOK/DMSO-mediated tandem 5-exo-dig Conia-ene type reaction and 6-exo-dig Conia-ene type reaction to install the tricyclic [6.3.1.01,5] dodecane core of penicibilaenes from dibutynyl cyclohexanone in a single step, together with a sequence of copper-mediated conjugate addition and Crabtree's hydrogenation to forge the stereogenic centers at C5 and C2, respectively.

Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize.

Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Combined population transcriptomic and genomic analysis reveals cis-regulatory differentiation of non-coding RNAs in maize.

KEY MESSAGE: Long intergenic non-coding RNA (lincRNA), cis-acting expression quantitative trait locus (cis-eQTL), maize, regulatory evolution. The law of genetic variation during domestication explains the evolutionary mechanism and provides a theoretical basis for improving existing varieties of maize. Previous studies focused on exploiting regulatory variations controlling the expression of protein-coding genes rather than of non-protein-coding genes. Here, we examined the genetic and evolutionary features of long non-coding RNAs from intergenic regions (long intergenic non-coding RNAs, lincRNAs) using population-scale transcriptome data and identified 1168 lincRNAs with cis-acting expression quantitative trait loci (cis-eQTLs). We found that lincRNAs are more likely to be regulated by cis-eQTLs, which exert stronger effects than the protein-coding genes. During maize domestication and improvement, upregulated alleles of lincRNAs, which originated from both standing variation and new mutation, accumulate more frequently and show larger effect sizes than the coding genes. A stronger signature of genetic differentiation was observed in their regulatory regions compared to those of randomly sampled lincRNAs. In addition, we found that cis-regulatory differentiation of lincRNAs is related to the sequence conservation of lincRNA transcripts. Non-conserved lincRNAs more tend to gain upregulated alleles and show a stronger relationship with selected traits than conserved lincRNAs between maize and its wild relatives. Our findings in maize improve the understanding of cis-regulatory variation in lincRNA genes during domestication and improvement and provide an effective approach for prioritizing candidates for further investigation.

Dek504 Encodes a Mitochondrion-Targeted E+-Type Pentatricopeptide Repeat Protein Essential for RNA Editing and Seed Development in Maize.

In flowering plants, RNA editing is a post-transcriptional process that selectively deaminates cytidines (C) to uridines (U) in organellar transcripts. Pentatricopeptide repeat (PPR) proteins have been identified as site-specific recognition factors for RNA editing. Here, we report the map-based cloning and molecular characterization of the defective kernel mutant dek504 in maize. Loss of Dek504 function leads to delayed embryogenesis and endosperm development, which produce small and collapsed kernels. Dek504 encodes an E+-type PPR protein targeted to the mitochondria, which is required for RNA editing of mitochondrial NADH dehydrogenase 3 at the nad3-317 and nad3-44 sites. Biochemical analysis of mitochondrial protein complexes revealed a significant reduction in the mitochondrial NADH dehydrogenase complex I activity, indicating that the alteration of the amino acid sequence at nad3-44 and nad3-317 through RNA editing is essential for NAD3 function. Moreover, the amino acids are highly conserved in monocots and eudicots, whereas the events of C-to-U editing are not conserved in flowering plants. Thus, our results indicate that Dek504 is essential for RNA editing of nad3, which is critical for NAD3 function, mitochondrial complex I stability, and seed development in maize.

Nuclear-Encoded Maturase Protein 3 Is Required for the Splicing of Various Group II Introns in Mitochondria during Maize (Zea mays L.) Seed Development.

Splicing of plant organellar group II introns from precursor-RNA transcripts requires the assistance of nuclear-encoded splicing factors. Maturase (nMAT) is one such factor, as its three homologs (nMAT1, 2 and 4) have been identified as being required for the splicing of various mitochondrial introns in Arabidopsis. However, the function of nMAT in maize (Zea mays L.) is unknown. In this study, we identified a seed development mutant, empty pericarp 2441 (emp2441) from maize, which showed severely arrested embryogenesis and endosperm development. Positional cloning and transgenic complementation assays revealed that Emp2441 encodes a maturase-related protein, ZmnMAT3. ZmnMAT3 is highly expressed during seed development and its protein locates to the mitochondria. The loss of function of ZmnMAT3 resulted in the reduced splicing efficiency of various mitochondrial group II introns, particularly of the trans-splicing of nad1 introns 1, 3 and 4, which consequently abolished the transcript of nad1 and severely impaired the assembly and activity of mitochondrial complex I. Moreover, the Zmnmat3 mutant showed defective mitochondrial structure and exhibited expression and activity of alternative oxidases. These results indicate that ZmnMAT3 is essential for mitochondrial complex I assembly during kernel development in maize.

Asymmetric Total Synthesis of (+)-Waihoensene.

The asymmetric total synthesis of (+)-waihoensene, which has a cis-fused [6,5,5,5] tetracyclic core bearing an angular triquinane, a cis-fused six-membered ring, and four contiguous quaternary carbon atoms, was achieved through a sequence of chemical reactions in a stereochemically well-defined manner. The total synthesis features the following: (1) Cu-catalyzed asymmetric conjugated 1,4-addition; (2) diastereoselective Conia-ene type reaction; (3) diastereoselective intramolecular Pauson-Khand reaction; (4) Ni-catalyzed diastereoselective conjugated 1,4-addition; and (5) radical-initiated intramolecular hydrogen atom transfer (HAT). Control experiments and density functional theory calculations support the proposed HAT process.

Bibliometric analysis of endoplasmic reticulum stress in hepatocellular carcinoma: trends and future directions.

BACKGROUND: Over the past three decades, endoplasmic reticulum (ER) stress has gained considerable attention in the field of hepatocellular carcinoma (HCC) with an increasing number of publications. It is crucial to reveal the global status, research hotspots and future research trends of ER stress in HCC. The aim of this study is to analyze the publications related to ER stress in HCC through bibliometric analysis in order to better understand the current status of ER stress research in HCC and to identify potential new research directions. METHODS: In this study, articles and reviews on ER stress in HCC up to December 31, 2023 were searched and downloaded from the Science Citation Index-Expanded (SCIE) of the Web of Science Core Collection (WoSCC), Pubmed, Scopus and Embase databases. Using CiteSpace 6.2.R6, VOSviewer 1.6.19, Scimago Graphica and Microsoft Office Excel 2019, the knowledge networks of a variety of countries, regions, authors, references, keywords and journals were analyzed. RESULTS: A total of 1239 publications were retrieved, including 843 articles and 396 review articles. The number of global publications is increasing every year, with the majority of publications coming from China and the USA. Ih-Jen Su, Wenya Huang and Wei Wei are the top 3 prolific authors. "Progression", "inflammation", "cell cycle arrest", "metabolism", "snsignaling pathways", "pathogenesis" and "non-alcoholic fatty liver disease" have emerged as research hotspots in recent years. The journal with the greatest co-citation is Hepatology. CONCLUSIONS: Based on current global trends, the total number of publications on ER stress in HCC research will continue to increase, but there is a need for more cooperation between authors and countries/regions. ER stress in HCC will continue to be a research priority. CONCLUSIONS: Based on current global trends, the total number of publications on ER stress in HCC research will continue to increase, but there is a need for more cooperation between authors and countries/regions. ER stress in HCC will continue to be a research priority.

Development and multi-institutional validation of a deep learning model for grading of vesicoureteral reflux on voiding cystourethrogram: a retrospective multicenter study.

Background: Voiding cystourethrography (VCUG) is the gold standard for the diagnosis and grading of vesicoureteral reflux (VUR). However, VUR grading from voiding cystourethrograms is highly subjective with low reliability. This study aimed to develop a deep learning model to improve reliability for VUR grading on VCUG and compare its performance to that of clinicians. Methods: In this retrospective study in China, VCUG images were collected between January 2019 and September 2022 from our institution as an internal dataset for training and 4 external data sets as external testing set for validation. Samples were divided into training (N = 1000) and validation sets (N = 500), internal testing set (N = 168), and external testing set (N = 280). An ensemble learning-based model, Deep-VCUG, using Res-Net 101 and the voting methods was developed to predict VUR grade. The grading performance was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score in the internal and external testing set. The performances of four clinicians (2 pediatric urologists and 2 radiologists) with and without the Deep-VCUG assisted to predict VUR grade were explored in external testing sets. Findings: A total of 1948 VCUG images were collected (Internal dataset = 1668; multi-center external dataset = 280). For assessing unilateral VUR grading, the Deep-VCUG achieved AUCs of 0.962 (95% confidence interval [CI]: 0.943-0.978) and 0.944 (95% [CI]: 0.921-0.964) in the internal and external testing sets, respectively, for bilateral VUR grading, the Deep-VCUG also achieved high AUCs of 0.960 (95% [CI]: 0.922-0.983) and 0.924 (95% [CI]: 0.887-0.957). The Deep-VCUG model using voting method outperformed single model and clinician in terms of classification based on VCUG image. Moreover, Under the Dee-VCUG assisted, the classification ability of junior and senior clinicians was significantly improved. Interpretation: The Deep-VCUG model is a generalizable, objective, and accurate tool for vesicoureteral reflux grading based on VCUG imaging and had good assistance with clinicians to VUR grading applicability. Funding: This study was supported by Natural Science Foundation of China, "Fuqing Scholar" Student Scientific Research Program of Shanghai Medical College, Fudan University, and the Program of Greater Bay Area Institute of Precision Medicine (Guangzhou).

Theory and application of robust linear phase-shift algorithm for phase-shift deflectometry method.

Based on the polynomial theory, the error propagation characteristics of the widely used N-step discrete Fourier transform (N-DFT) phase-shift algorithm were analyzed via theoretical analysis, under the effect of Gamma distortion and phase detuning. The results showed that the N-DFT algorithm could not simultaneously suppress both types of error. A robust linear phase-shift (RLPS) algorithm was designed, the performance of the RLPS and 8-DFT algorithms in terms of spectral response, detuning robustness, and G S / N was briefly analysis by Manuel Servin method. The Simulation analysis and comparison of the results show that the RLPS algorithm could suppress both types of error simultaneously, which exhibited better stability and accuracy than N-DFT and exponential algorithms, particularly in gradient measurement stability, peak-to-valley (PV) and root-mean-square (RMS) error suppression. Moreover, a physical experiment apparatus was built to test unidirectionally inclined plane mirror and concave mirror using the RLPS, N-DFT, and exponential algorithms. The results showed that the RLPS algorithm could significantly improve the measurement stability and accuracy in the presence of detuning and without screen Gamma calibration.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.