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Abstract ULK1 is crucial for initiating autophagosome formation and its activity is tightly regulated by post-translational modifications and protein-protein interactions. In the present study, we demonstrate that TMEM189 (Transmembrane protein 189), also known as plasmanylethanolamine desaturase 1 (PEDS1), negatively regulates the proteostasis of ULK1 and autophagy activity. In TMEM189-overexpressed cells, the formation of autophagesome is impaired, while TMEM189 knockdown increases cell autophagy. Further investigation reveals that TMEM189 interacts with and increases the instability of ULK1, as well as decreases its kinase activities. The TMEM189 N-terminal domain is required for the interaction with ULK1. Additionally, TMEM189 overexpression can disrupt the interaction between ULK1 and TRAF6, profoundly impairs K63-linked polyubiquitination of ULK1 and self-association, leading to the decrease of ULK1 stability. Moreover, in vitro and in vivo experiments suggest that TMEM189 deficiency results in the inhibition of tumorigenicity of gastric cancer. Our findings provide a new insight into the molecular regulation of autophagy and laboratory evidence for investigating the physiological and pathological roles of TMEM189.

Four-Dimensional Cone-Beam Computed Tomography (4D-CBCT) imaging is important in upper abdomen tumor radiation treatment as it can obtain the tumor motion information immediately before treatment. However, the number of projections at a single phase for 4D-CBCT imaging is very small, and the CT images reconstructed by using conventional algorithms will be contaminated by view aliasing artifacts and noises. To address this problem, we propose a framework to jointly estimate and compensate the inter-phase motion for 4D-CBCT image reconstruction. Specifically, by introducing the intensity-based optical flow (OF) constraint into the reconstruction framework, the model can deal with the inter-frame displacements and improve the image quality simultaneously. The primal-dual algorithm method was used to optimize the cost function of the proposed model. Experiments on physical phantoms and patient data show that the proposed approach can effectively reduce the noise and artifacts in 4D–CBCT images and achieve the inter-phase motion vector fields (MVFs).

Chinese wild pepper (Capsicum frutescens L.) acts as a source of genetic variability for cultivated pepper breeding. High-resolution quantitative train locus (QTL) mapping is essential for identifying genetic regions related to important phenotype variation. In this study, a set of 120 F2 populations derived from a cross between cultivar Capsicum annuum ‘007EA’ and the wild relative Capsicum frutescens ‘P1512’ (Chinese wild pepper) were generated, and genotyping was performed with genotyping-by-sequencing. Next, an interspecific high-density genetic map containing 3708 bins was constructed using a sliding window approach. The genetic distance of the total map was 5601.79 cM, and the average interval between bins was 1.51 cM. Based on the genetic map, a QTL analysis of the pepper horticultural traits was performed. A total of 25 significant QTLs controlling nine horticultural traits were detected on 12 linkage groups (LGs), and contributed values of phenotypic variance explained (PVE) ranging from 1.90% to 59.03%, with LOD scores ranging from 4.2 to 24.6. In addition, the QTLs for the main stem length, flowering date and numbers of leaves were observed at the same position. Next, we mapped a set of significant QTLs for yield-related traits and other horticultural traits in pepper, based on a high-density genetic linkage map, using GBS. These genetic map and markers are valuable genomic resources for marker-assisted selection (MAS) of excellent agronomic traits and will provide a basis for further fine mapping and comparative genomics research involving pepper.

Plant carotenoid cleavage oxygenase (CCO) is an enzyme that catalyzes the synthesis of carotenoids and participates in many important physiological functions. The plant CCOs exist in two forms, namely carotenoid cleavage dioxygenase (CCD) and nine-cis epoxide carotenoid dioxygenase (NCED). Although studies have shown that this gene family has been identified in many species, such as Arabidopsis, grape, and tomato, the evolutionary origin of the CCO family and the expression pattern of pepper genes in response to H2O2 and other abiotic stresses are still unclear. In this study, we used the bioinformatics method to identify and analyze the members of the CCO gene family from pepper and other 13 plants from lower to higher plant species based on the whole genome sequence. A total of 158 CCO genes were identified in different plant species and further divided into two groups (e.g., groups I and II). The former was subdivided into CCD7 and CCD8 and have independent evolutionary origins, respectively, while the latter was subdivided into CCD1, CCD4, CCD-like, and NCED, which may have come from a common ancestor. In addition, the results of RNA-seq showed that the expression patterns of pepper CaCCO genes were different in the tissues tested, and only few genes were expressed at high levels such as CaCCD1a, CaCCD4a, CaNCED3, and CaCCD1b. For hydrogen peroxide (H2O2) and other abiotic stresses, such as plant hormones, heat, cold, drought, and NaCl treatments, induction of about half of the CaCCO genes was observed. Moreover, the expression patterns of CaCCOs were further investigated under heat, cold, drought, and NaCl treatments using quantitative real-time PCR (qRT-PCR), and most members were responsive to these stresses, especially some CaCCOs with significant expression changes were identified, such as CaCCD4c, CaCCD-like1, CaCCD8, and CaCCD1b, suggesting the important roles of CaCCOs in abiotic stress responses. All these results will provide a valuable analytical basis for understanding the evolution and functions of the CCO family in plants.

Capsicum frutescens, one of the domesticated species of pepper grown worldwide, is thought to be highly advantageous due to its strong resistance against plant pathogenesis, high productivity, and intense aroma. However, a shortage of molecular markers limits the efficiency and accuracy of genetic breeding for pepper. With the newly developed next-generation sequencing technology, genome sequences of C. frutescens can be generated, which are now available for identifying SSR markers via data mining. In this study, a total of 278,425 SSRs were detected from the pepper genome using MISA software. It was observed that trinucleotides were the dominant repeat motif. This was followed by dinucleotides, tetranucleotides, pentanucleotides, and the hexanucleotides repeat types. (AT)n (TTG)n (AAAT)n (AAATA)n (TATAGA)n is known to be the most common repeat motifs corresponding to dinucleotide to hexanucleotide repeats, respectively. In addition, a total of 240 SSR primers evenly distributed over all 12 chromosomes were designed and screened against 8 C. frutescens cultivars. Of these, 33 SSR markers that have high polymorphism, have been scrutinized for 147 accessions from 25 countries. The dendrogram constructed clustered these accessions into seven major groups. The groups were found to be consistent with their origins. The results obtained in this study provided resources of SSR molecular markers and insight into genetic diversity of the C. frutescens.

Myocardial reperfusion injury (MRI) induced by cardiomyocyte apoptosis plays an important role in the pathogenesis of a variety of cardiovascular diseases. New MRI treatments involving stem cells are currently being developed because these cells may exert their therapeutic effects primarily through paracrine mechanisms. Microvesicles (MVs) are small extracellular vesicles that have become the key mediators of intercellular communication. MVs derived from stem cells have been reported to play an important role in MRI. In this article, we attempted to explore the mechanisms by which MVs derived from human embryonic neural stem cells (hESC-NSC-derived MVs) rescue MRI. hESCs were differentiated into NSCs, and MVs were isolated from their supernatants by ultracentrifugation. H2O2 was used to induce apoptosis in HL-1 cardiomyocytes. Cell viability was detected by using the CCK-8 assay, apoptosis was detected by Annexin V-FITC/PI staining, and apoptosis-related proteins and signalling pathway-related proteins were detected by western blot analysis. Autophagic flux was measured using the tandem fluorescent mRFG-GFP-LC3 assay. Transmission electron microscopy and western blot analysis were adopted to evaluate autophagy levels. hESC-NSC-derived MVs increased the autophagy and inhibited the apoptosis of HL-1 cells exposed to H2O2 for 3 h in a dose-dependent manner. Additionally, hESC-NSC-derived MVs contained high levels of heat shock protein 70 (HSP-70), which can increase the level of HSP-70 in cells. Moreover, the same effect could be achieved by heat shock preconditioning of HL-1 cells overexpressing HSP-70. The benefits of NSC-MVs may be due to the involvement of AKT and mTOR signalling pathways. Importantly, hESC-NSC-derived MVs stimulated the activation of the AKTand mTOR signalling pathway in those cells by transporting HSP-70. Our results suggest that hESC-NSC-derived MVs inhibit the apoptosis of HL-1 cardiomyocytes by promoting autophagy and regulating AKT and mTOR via transporting HSP-70. However, this hypothesis requires in vivo confirmation.

Gene expression analysis in tomato fruit has drawn increasing attention nowadays. Quantitative real-time PCR (qPCR) is a routine technique for gene expression analysis. In qPCR operation, reliability of results largely depends on the choice of appropriate reference genes (RGs). Although tomato is a model for fruit biology study, few RGs for qPCR analysis in tomato fruit had yet been developed. In this study, we initially identified 38 most stably expressed genes based on tomato transcriptome data set, and their expression stabilities were further determined in a set of tomato fruit samples of four different fruit developmental stages (Immature, mature green, breaker, mature red) using qPCR analysis. Two statistical algorithms, geNorm and Normfinder, concordantly determined the superiority of these identified putative RGs. Notably, SlFRG05 (Solyc01g104170), SlFRG12 (Solyc04g009770), SlFRG16 (Solyc10g081190), SlFRG27 (Solyc06g007510), and SlFRG37 (Solyc11g005330) were proved to be suitable RGs for tomato fruit development study. Further analysis using geNorm indicate that the combined use of SlFRG03 (Solyc02g063070) and SlFRG27 would provide more reliable normalization results in qPCR experiments. The identified RGs in this study will be beneficial for future qPCR analysis of tomato fruit developmental study, as well as for the potential identification of optimal normalization controls in other plant species.

Due to its high sensitivity and reproducibility, quantitative real-time PCR (qPCR) is practiced as a useful research tool for targeted gene expression analysis. For qPCR operations, the normalization with suitable reference genes (RGs) is a crucial step that eventually determines the reliability of the obtained results. Although pepper is considered an ideal model plant for the study of non-climacteric fruit development, at present no specific RG have been developed or validated for the qPCR analyses of pepper fruit. Therefore, this study aimed to identify stably expressed genes for their potential use as RGs in pepper fruit studies. Initially, a total of 35 putative RGs were selected by mining the pepper transcriptome data sets derived from the PGP (Pepper Genome Platform) and PGD (Pepper Genome Database). Their expression stabilities were further measured in a set of pepper (Capsicum annuum L. var. 007e) fruit samples, which represented four different fruit developmental stages (IM: Immature; MG: Mature green; B: Break; MR: Mature red) using the qPCR analysis. Then, based on the qPCR results, three different statistical algorithms, namely geNorm, Normfinder, and boxplot, were chosen to evaluate the expression stabilities of these putative RGs. It should be noted that nine genes were proven to be qualified as RGs during pepper fruit development, namely CaREV05 (CA00g79660); CaREV08 (CA06g02180); CaREV09 (CA06g05650); CaREV16 (Capana12g002666); CaREV21 (Capana10g001439); CaREV23 (Capana05g000680); CaREV26 (Capana01g002973); CaREV27 (Capana11g000123); CaREV31 (Capana04g002411); and CaREV33 (Capana08g001826). Further analysis based on geNorm suggested that the application of the two most stably expressed genes (CaREV05 and CaREV08) would provide optimal transcript normalization in the qPCR experiments. Therefore, a new and comprehensive strategy for the identification of optimal RGs was developed. This strategy allowed for the effective normalization of the qPCR analysis of the pepper fruit development at the whole pepper genome level. This study not only explored the optimal RGs specific for studying pepper fruit development, but also introduced a referable strategy of RG mining which could potentially be implicated in other plant species.

Superoxide dismutases (SODs) are critical antioxidant enzymes that protect organisms from reactive oxygen species (ROS) caused by adverse conditions, and have been widely found in the cytoplasm, chloroplasts, and mitochondria of eukaryotic and prokaryotic cells. Tomato (Solanum lycopersicum L.) is an important economic crop and is cultivated worldwide. However, abiotic and biotic stresses severely hinder growth and development of the plant, which affects the production and quality of the crop. To reveal the potential roles of SOD genes under various stresses, we performed a systematic analysis of the tomato SOD gene family and analyzed the expression patterns of SlSOD genes in response to abiotic stresses at the whole-genome level. The characteristics of the SlSOD gene family were determined by analyzing gene structure, conserved motifs, chromosomal distribution, phylogenetic relationships, and expression patterns. We determined that there are at least nine SOD genes in tomato, including four Cu/ZnSODs, three FeSODs, and one MnSOD, and they are unevenly distributed on 12 chromosomes. Phylogenetic analyses of SOD genes from tomato and other plant species were separated into two groups with a high bootstrap value, indicating that these SOD genes were present before the monocot-dicot split. Additionally, many cis-elements that respond to different stresses were found in the promoters of nine SlSOD genes. Gene expression analysis based on RNA-seq data showed that most genes were expressed in all tested tissues, with the exception of SlSOD6 and SlSOD8, which were only expressed in young fruits. Microarray data analysis showed that most members of the SlSOD gene family were altered under salt- and drought-stress conditions. This genome-wide analysis of SlSOD genes helps to clarify the function of SlSOD genes under different stress conditions and provides information to aid in further understanding the evolutionary relationships of SOD genes in plants.

The Hsp20 genes are involved in the response of plants to environment stresses including heat shock and also play a vital role in plant growth and development. They represent the most abundant small heat shock proteins (sHsps) in plants, but little is known about this family in tomato (Solanum lycopersicum), an important vegetable crop in the world. Here, we characterized heat shock protein 20 (SlHsp20) gene family in tomato through integration of gene structure, chromosome location, phylogenetic relationship and expression profile. Using bioinformatics-based methods, we identified at least 42 putative SlHsp20 genes in tomato. Sequence analysis revealed that most of SlHsp20 genes possessed no intron or a relatively short intron in length. Chromosome mapping indicated that inter-arm and intra-chromosome duplication events contributed remarkably to the expansion of SlHsp20 genes. Phylogentic tree of Hsp20 genes from tomato and other plant species revealed that SlHsp20 genes were grouped into 13 subfamilies, indicating that these genes may have a common ancestor that generated diverse subfamilies prior to the mono-dicot split. In addition, expression analysis using RNA-seq in various tissues and developmental stages of cultivated tomato and the wild relative Solanum pimpinellifolium revealed that most of these genes (83%) were expressed in at least one stage from at least one genotype. Out of 42 genes, 4 genes were expressed constitutively in almost all the tissues analyzed, implying that these genes might have specific housekeeping function in tomato cell under normal growth conditions. Two SlHsp20 genes displayed differential expression levels between cultivated tomato and S. pimpinellifolium in vegetative (leaf and root) and reproductive organs (floral bud and flower), suggesting inter-species diversification for functional specialization during the process of domestication. Based on genome-wide microarray analysis, we showed that the transcript levels of SlHsp20 genes could be induced profusely by abiotic and biotic stresses such as heat, drought, salt, Botrytis cinerea and Tomato Spotted Wilt Virus, indicating their potential roles in mediating the response of tomato plants to environment stresses. In conclusion, these results provide valuable information for elucidating the evolutionary relationship of Hsp20 gene family and functional characterization of the SlHsp20 gene family in the future.

Purpose: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion prediction. Methods: We propose a deep learning framework, named as LSTM-Global Temporal Convolution-External Attention Network (LGEANet). In LGEANet, we first feed each of the univariate time series into the Long Short-Term Memory (LSTM) module respectively and utilize the strength of the global temporal convolutional layer to discover the temporal pattern of the univariate signals from hidden states of the LSTM. Then, External attention is adopted to capture the dynamic dependence of the multiple time series. Also, a traditional autoregressive linear model in parallel to the non-linear neural network part was integrated to mitigate the scale insensitivity of the networks. A total of 304 motion traces for 31 patients are acquired from a public dataset in the experiments and four representative cases were selected for model evaluation. The respiratory signals were sampled at intervals of about 37.5 ms (26 frames per second) for an average duration of 71 min. Results: The proposed LGEANet achieved better performance with higher empirical correlation coefficient value (CORRs) and lower mean absolute error value (MAEs) and relative squared error value (RSEs) than other investigated models. For the four representative datasets, when the response time is less than 231 ms, the model can achieve CORRs more than 0.96. And the averaged position error reduction by using the proposed model was about 67% in the superior–inferior (SI) direction, 41% in the anterior–posterior (AP) direction and 38% in the right–left (RL) direction compared to that without prediction. The proposed network achieved the greatest error reduction in the SI direction, which is the main direction of tumor motion. Conclusions: The LGEANet achieves promising performance in minimizing the prediction error due to system latencies during real-time tumor motion tracking. [ABSTRACT FROM AUTHOR]

Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time. We first demonstrate that nonnegative CANDECOMP/PARAFAC decomposition (NCPD) can automatically identify topics of variable persistence. We then introduce sparseness-constrained NCPD (S-NCPD) and its online variant to control the duration of the detected topics more effectively and efficiently, along with theoretical analysis of the proposed algorithms. Through an extensive study on both semi-synthetic and real-world datasets, we find that our S-NCPD and its online variant can identify both short- and long-lasting temporal topics in a quantifiable and controlled manner, which traditional topic modeling methods are unable to achieve. Additionally, the online variant of S-NCPD shows a faster reduction in reconstruction error and results in more coherent topics compared to S-NCPD, thus achieving both computational efficiency and quality of the resulting topics. Our findings indicate that S-NCPD and its online variant are effective tools for detecting and controlling the duration of topics in temporal text data, providing valuable insights into both persistent and transient trends.

This paper selects the daily data of the exchange rates of Chinese Yuan (CNY) over the currencies of 14 countries along the Belt and Road, Shanghai composite index and Shenzhen composite index to study the influence of the Belt and Road Initiative on the linkages between exchange rates and Chinese stock index based on the flow-oriented model and the stock-oriented model. To reflect the fluctuations in daily data and reduce the central bank's interference with the exchange rate, two fuzzy techniques are used to process data, that is, the centroid based measure and the integral based measure. Then we judge the relationship between exchange rate and stock index through the Pearson correlation coefficient and the Granger causality test. Besides, we further compare the results and their differences by the classic crisp method and our two fuzzy techniques, which enable us to judge their correlation more accurately, and provide a reference for a wider application of the proposed fuzzy methods. We find that there is a correlation between exchange rate and stock index under certain conditions, and the Belt and Road initiative strengthens the relationship between the Chinese foreign exchange market and the stock market, more importantly, the fuzzy techniques are effective to judge this relation.

Pauses, disfluencies and language problems in Alzheimer’s disease can be naturally modeled by fine-tuning Transformer-based pre-trained language models such as BERT and ERNIE. Using this method with pause-encoded transcripts, we achieved 89.6% accuracy on the test set of the ADReSS (Alzheimer’s Dementia Recognition through Spontaneous Speech) Challenge. The best accuracy was obtained with ERNIE, plus an encoding of pauses. Robustness is a challenge for large models and small training sets. Ensemble over many runs of BERT/ERNIE fine-tuning reduced variance and improved accuracy. We found that um was used much less frequently in Alzheimer’s speech, compared to uh. We discussed this interesting finding from linguistic and cognitive perspectives.

Fertigation technology is key to solve water pollution and inefficient fertilizer use. However, some early techniques cannot adapt to the current situation of labor shortages and large-scale planting. Therefore, it is necessary to consider farmers’ willingness to adopt more adaptive techniques. Specifically, we focus on whether early technology adoption will hinder technology renewal and whether the factors affecting the adoption of early and latest techniques are consistent. Through theoretical analysis and a survey, we find that farmers’ endowments such as income and labor force only affect the adoption intentions to the high-cost technique (Intelligent Irrigation Control System), but not early techniques (Venturi injector and Differential pressure tank), while farmers’ information processing ability and information acquisition channels affect both. Finally, the results of Propensity Score Matching show that early technology adoption will not become an obstacle to technology renewal.

Internet finance is a financial mode combining traditional financial industry with Internet technologies, which has become a crucial part of the financial field. Due to the rapid change of information technologies and public financial needs, Internet finance has produced quite a few specific operation modes, which have interested many scholars. To better appreciate its development process and innovation modes, we used bibliometrics to analyze 2,877 articles on Internet finance in Web of Science. Through the co-word network, co-citation network and various results generated by CiteSpace, we recognized six main modes of Internet finance, that is, Internet bank, peer to peer lending (P2P lending), crowdfunding, big data finance, digital currency and fintech. Emerging research topics and the development history of each mode are also detected. We find that the mainstream modes in current research are P2P lending and crowdfunding and the research on fintech and digital currency has just begun. Through the review, we also suggest some research directions for the research direction of each mode. These results will help to deepen relevant scholars’ understanding of Internet finance and provide guidance for them to choose research directions.