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BackgroundThis study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy.MethodsIn this work, computed tomography (CT) scans of 110 patients who underwent modified radical mastectomies were collected. The CTV contours were confirmed by two experienced oncologists. A novel CNN was constructed to automatically delineate the CTV. Quantitative evaluation metrics were calculated, and a clinical evaluation was conducted to evaluate the performance of our model.ResultsThe mean Dice similarity coefficient (DSC) of the proposed model was 0.90, and the 95th percentile Hausdorff distance (95HD) was 5.65 mm. The evaluation results of the two clinicians showed that 99.3% of the chest wall CTV slices could be accepted by clinician A, and this number was 98.9% for clinician B. In addition, 9/10 of patients had all slices accepted by clinician A, while 7/10 could be accepted by clinician B. The score differences between the AI (artificial intelligence) group and the GT (ground truth) group showed no statistically significant difference for either clinician. However, the score differences in the AI group were significantly different between the two clinicians. The Kappa consistency index was 0.259. It took 3.45 s to delineate the chest wall CTV using the model.ConclusionOur model could automatically generate the CTVs for breast cancer. AI-generated structures of the proposed model showed a trend that was comparable, or was even better, than those of human-generated structures. Additional multicentre evaluations should be performed for adequate validation before the model can be completely applied in clinical practice.

Allelopathy is a central process in crop–weed interactions and is mediated by the release of allelochemicals that result in adverse growth effects on one or the other plant in the interaction. The genomic mechanism for the biosynthesis of many critical allelochemicals is unknown but may involve the clustering of non-homologous biosynthetic genes involved in their formation and regulatory gene modules involved in controlling the coordinated expression within these gene clusters. In this study, we used the transcriptomes from mono- or co-cultured rice and barnyardgrass to investigate the nature of the gene clusters and their regulatory gene modules involved in the allelopathic interactions of these two plants. In addition to the already known biosynthetic gene clusters in barnyardgrass we identified three potential new clusters including one for quercetin biosynthesis and potentially involved in allelopathic interaction with rice. Based on the construction of gene networks, we identified one gene regulatory module containing hub transcription factors, significantly positively co-regulated with both the momilactone A and phytocassane clusters in rice. In barnyardgrass, gene modules and hub genes co-expressed with the gene clusters responsible for 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) biosynthesis were also identified. In addition, we found three genes in barnyardgrass encoding indole-3-glycerolphosphate synthase that regulate the expression of the DIMBOA cluster. Our findings offer new insights into the regulatory mechanisms of biosynthetic gene clusters involved in allelopathic interactions between rice and barnyardgrass, and have potential implications in controlling weeds for crop protection.

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BackgroundNon-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs) and circular RNAs (circRNAs), accomplish remarkable variety of biological functions. However, the composition of ncRNAs and their interactions with coding RNAs in modulating and controlling of cellular process in plants is largely unknown. Using a diverse group of high-throughput sequencing strategies, the mRNA, miRNA, lncRNA and circRNA compositions of tobacco (Nicotiana tabacum) roots determined and their alteration and potential biological functions in response to topping treatment analyzed.ResultsA total of 688 miRNAs, 7423 non-redundant lncRNAs and 12,414 circRNAs were identified, among which, some selected differentially expressed RNAs were verified by quantitative real-time PCR. Using the differentially expressed RNAs, a co-expression network was established that included all four types of RNAs. The number of circRNAs identified were higher than that of miRNAs and lncRNAs, but only two circRNAs were present in the co-expression network. LncRNAs appear to be the most active ncRNAs based on their numbers presented in the co-expression network, but none of them seems to be an eTM (endogenous Target Mimicry) of miRNAs. Integrated with analyses of sequence interaction, several mRNA-circRNA-miRNA interaction networks with a potential role in the regulation of nicotine biosynthesis were uncovered, including a QS-circQS-miR6024 interaction network. In this network miR6024 was significantly down-regulated, while the expression levels of its two targets, circQS and its host gene QS, were sharply increased following the topping treatment.ConclusionsThese results illustrated the transcriptomic profiles of tobacco roots, the organ responsible for nicotine biosynthesis. mRNAs always play the most important roles, while ncRNAs are also expressed extensively for topping treatment response, especially circRNAs are the most activated in the ncRNA pool. These studies also provided insights on the coordinated regulation module of coding and non-coding RNAs in a single plant biological sample. The findings reported here indicate that ncRNAs appear to form interaction complex for the regulation of stress response forming regulation networks with transcripts involved in nicotine biosynthesis in tobacco.