CucumberAI: Cucumber Fruit Morphology Identification System Based on Artificial Intelligence.

Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide. Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits. At present, several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits; however, these manual methods are time-consuming and inefficient. Therefore, in this paper, we propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features, including 32 newly defined parameters. The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques. The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition. Additionally, the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation, a MobileNetV2 model for carpel classification, a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification. The relationships between the image-based manual and algorithmic traits are highly correlated, and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness, and a fruit appearance cluster analysis was also performed. In brief, CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements.

Single-nucleus RNA and multiomics in situ pairwise sequencing reveals cellular heterogeneity of the abnormal ligamentum teres in patients with developmental dysplasia of the hip.

Developmental dysplasia of the hip (DDH) is the most common hip deformity in pediatric orthopedics. One of the common pathological changes in DDH is the thickening and hypertrophy of the ligamentum teres. However, the underlying pathogenic mechanism responsible for these changes remains unclear. This study represents the first time that the heterogeneity of cell subsets in the abnormal ligamentum teres of patients with DDH has been resolved at the single-cell and spatial levels by snRNA-Seq and MiP-Seq. Through gene set enrichment and intercellular communication network analyses, we found that receptor-like cells and ligament stem cells may play an essential role in the pathological changes resulting in ligamentum teres thickening and hypertrophy. Eight ligand-receptor pairs related to the ECM-receptor pathway were observed to be closely associated with DDH. Further, using the Monocle R package, we predicted a differentiation trajectory of pericytes into two branches, leading to junctional ligament stem cells or fibroblasts. The expression of extracellular matrix-related genes along pseudotemporal trajectories was also investigated. Using MiP-Seq, we determined the expression distribution of marker genes specific to different cell types within the ligamentum teres, as well as differentially expressed DDH-associated genes at the spatial level.

Single-cell transcriptomic analysis of the role of HPV16-positive macrophages in cervical cancer prognosis.

Almost all cases of cervical cancer (CC) can be attributed to high-risk human papillomavirus (HPVs) infections in keratinocytes. However, it is unknown whether HPV invades immune cells such as macrophages and T cells. We analyzed the single-cell transcriptome of the CC and its adjacent tissues and found that HPV16 genes, including E1, E6, and E7, expressed in the macrophages and CD8+ T cells in addition to the malignant cells. HPV16+ macrophages highly expressed the genes that promote cell adhesion and the favorable genes such as WAS, IQCB1, MYO1F, and PDZD11 in CC prognosis. The transcription factor KLF5 potentially accounted for the induction of these protective genes and thus facilitated the infiltration of the immune cells in tumor tissues. Our single-cell transcriptome analysis suggests the potential value of the HPV16+ macrophage in CC prognosis. However, extensive experimental studies investigating the characteristics and functions of the HPV+ immune cells are still required.

Activin A attenuates apoptosis of granulosa cells in atretic follicles through ERß-induced autophagy.

It is well known that approximately 99% of ovarian follicles in mammals suffer from a degenerative process known as atresia, which is a huge waste of genetic resource in female animals. Studies have shown that activin A (ACT-A) is located in ovarian granulosa cells and has different effects in granulosa cell depending on species. Although granulosa cells play a critical role during follicular atresia, the mechanism of action of ACT-A in bovine ovarian granulosa cells (BGC) is poorly understood. In this study, we first determined the apoptosis of BGCs isolated from growth follicles and atretic follicles respectively. Then, BGC isolated from atretic follicles were used as a model to elucidate the role of ACT-A in cattle ovary. The results showed that apoptosis occurred in both growing follicles and atretic follicles, and the percentage of apoptotic cells in atretic follicles was higher than that in growing follicles. The current results indicated that ACT-A can attenuate apoptosis of BGC by maintaining the function of BGC in atretic follicles. Increased ERß induced by ACT-A promoted BGC autophagy but had no effect on apoptosis. In summary, this study suggests that ACT-A attenuates BGC apoptosis in atretic follicles by ERß-mediated autophagy signalling.