Long noncoding RNA H19: functions and mechanisms in regulating programmed cell death in cancer.

Long noncoding RNAs (lncRNAs) are a group of noncoding RNAs with transcript lengths of >200 nucleotides. Mounting evidence suggests that lncRNAs are closely associated with tumorigenesis. LncRNA H19 (H19) was the first lncRNA to function as an oncogene in many malignant tumors. Apart from the established role of H19 in promoting cell growth, proliferation, invasion, migration, epithelial-mesenchymal transition (EMT), and metastasis, it has been recently discovered that H19 also inhibits programmed cell death (PCD) of cancer cells. In this review, we summarize the mechanisms by which H19 regulates PCD in cancer cells through various signaling pathways, molecular mechanisms, and epigenetic modifications. H19 regulates PCD through the Wnt/ß-catenin pathway and the PI3K-Akt-mTOR pathway. It also acts as a competitive endogenous RNA (ceRNA) in PCD regulation. The interaction between H19 and RNA-binding proteins (RBP) regulates apoptosis in cancer. Moreover, epigenetic modifications, including DNA and RNA methylation and histone modifications, are also involved in H19-associated PCD regulation. In conclusion, we summarize the role of H19 signaling via PCD in cancer chemoresistance, highlighting the promising research significance of H19 as a therapeutic target. We hope that our study will contribute to a broader understanding of H19 in cancer development and treatment.

Effect of Warm-Water Retting Pretreatment on the Physical Properties of Banana Stem and Its Fibre.

In this paper, warm-water flax retting was used as a pretreatment method for banana-fibre extraction. To determine the optimum conditions for flax retting, the physical properties of various parts of stems and fibres in the process of flax retting were analysed. By studying the tensile strength, elongation at break, diameter, moisture regain, and other characteristics of the fibres, the influences of bacteria and enzymes in the retting liquor on the fibre characteristics in different retting stages were determined. Through mechanical-property tests and microscopic observation of the stem skin, the change rules of the mechanical properties and degumming state of the stems were examined. The results showed that the fibre tensile strength of banana stems reached the maximum value of 45 ± 16 cN·tex-1 after 11 days of retting. As most resins had not been hydrolysed, fibre extraction was difficult. After 21-25 days of retting, the tensile strength of fibres was about 34 ± 10 cN·tex-1, elongation at break was about 1.71%, and moisture regain was about 13.56%. The fibre characteristics met the process requirements, and the tensile separation stress of the stem was small, about 0.034 MPa. This time point could be used as the optimum endpoint for retting flax in warm water, which could provide theoretical support and research basis for the recycling of banana straw. The functional groups of the extracted fibres were studied by FTIR, which confirmed the observed change rule of each component during degumming. The experimental results showed that a longer retting time corresponded with a lower content of fibre impurities, more thorough degumming, and less difficult extraction; however, strength and toughness decreased.

Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism.

An improved lightweight network (Improved YOLOv5s) was proposed based on YOLOv5s in this study to realise all-weather detection of dragon fruit in a complex orchard environment. A ghost module was introduced in the original YOLOv5s to realise the lightweight of the model. The coordinate attention mechanism was joined to make the model accurately locate and identify the dense dragon fruits. A bidirectional feature pyramid network was built to improve the detection effect of dragon fruit at different scales. SIoU loss function was adopted to improve the convergence speed during model training. The improved YOLOv5s model was used to detect a dragon fruit dataset collected in the natural environment. Results showed that the mean average precision (mAP), precision (P) and recall (R) of the model was 97.4%, 96.4% and 95.2%, respectively. The model size, parameters (Params) and floating-point operations (FLOPs) were 11.5 MB, 5.2 M and 11.4 G, respectively. Compared with the original YOLOv5s network, the model size, Params and FLOPs of the improved model was reduced by 20.6%, 18.75% and 27.8%, respectively. Meanwhile, the mAP of the improved model was improved by 1.1%. The results prove that the improved model had a more lightweight structure and better detection performance. Moreover, the average precision (AP) of the improved YOLOv5s for dragon fruit under the front light, back light, side light, cloudy day and night was 99.5%, 97.3%, 98.5%, 95.5% and 96.1%, respectively. The detection performance met the requirements of all-weather detection of dragon fruit and the improved model had good robustness. This study provides a theoretical basis and technical support for fruit monitoring based on unmanned aerial vehicle technology and intelligent picking based on picking robot technology.

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm.

The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91 cm, and the average registration error was 0.66 cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.

Near-infrared-persistent luminescence/bioluminescence imaging tracking of transplanted mesenchymal stem cells in pulmonary fibrosis.

Human mesenchymal stem cells (hMSCs) are promising in the treatment of pulmonary fibrosis (PF). However, the behavior of hMSCs after transplantation, including dynamic translocation, location and survival, impeding the clinical application of hMSCs in PF is still ambiguous. Herein, we report an effective dual-labeling strategy combining endogenous bioluminescence imaging (BLI) and exogenous near-infrared-persistent luminescence (NIR-PL) imaging for in situ visualization of the transplanted stem cells. The long persistent luminescence nanoparticles (LPLNPs), Zn1.1Ga1.8Ge0.1O4:Cr3+,Eu3+, were developed to track the dynamic translocation, position and distribution of the transplanted hMSCs, taking advantage of their long-lasting NIR-PL imaging ability and minimal autofluorescence background interference. Moreover, in virtue of their ability to express red-emitting firefly luciferase (RfLuc), the living stem cells after transplantation could be discriminated from the dead cells by BLI. This facile pattern contributes to the in situ monitoring of stem cells regarding their spontaneous behavior in vivo and therefore deepening our knowledge in the role played by the transplanted hMSCs in PF therapy.

CT/Bioluminescence Dual-Modal Imaging Tracking of Mesenchymal Stem Cells in Pulmonary Fibrosis.

Human mesenchymal stem cells (hMSCs), due to their immune regulation and collateral secretion effects, are currently explored for potential therapy of idiopathic pulmonary fibrosis (IPF). Understanding the migration, homing, functions, and survival of transplanted hMSCs in vivo is critical to successful IPF treatment. Therefore, it is highly desired to develop noninvasive and effective imaging technologies to track the transplanted hMSCs, providing experimental basis for improving the efficacy of hMSCs in the treatment of IPF. The rational design and development of a dual-labeling strategy are reported by integrating gold nanoparticle (AuNP)-based computed tomography (CT) nanotracers and red-emitting firefly luciferase (RfLuc)-based bioluminescence (BL) tags for CT/BL multimodal imaging tracking of the transplanted hMSCs in a murine model of IPF. In this approach, the CT nanotracer is prepared by sequential coupling of AuNPs with polyethylene glycol and trans-activator of transcription (TAT) peptide (Au@TAT), and employed it to monitor the location and distribution of the transplanted hMSCs in vivo by CT imaging, while RfLuc is used to monitor hMSCs viability by BLI. This facile strategy allows for visualization of the transplanted hMSCs in vivo, thereby enabling profound understanding of the role of hMSCs in the IPF treatment, and advancing stem cell-based regenerative medicine.

Stress response of Caenorhabditis elegans induced by space crowding in a micro-column array chip.

Crowding stress has been reported to play an important role in affecting physiological behaviour. To study this process, a reliable analytical method under confined space is essential. In this work, we demonstrated a microfluidic approach for investigating physiological responses of C. elegans to confined spaces. The PDMS microfluidic chip consisting of arrays of micro-columns enabled us to mimic different crowding conditions by changing the intervals among micro-columns. C. elegans were transferred into this micro-column array and the subcellular distribution of DAF-16, which is a well-known transcription factor regulating different stress responses, was monitored for analysing the physiological responses to the confined spaces. We found that the worms exhibited a gradual increase in DAF-16 nuclear localization in the micro-column array with intervals from 200 µm to 40 µm. Moreover, the results showed that the absence of food and crowding stress could cooperate to promote increased DAF-16 nuclear localization. Finally, loss-of-function mutations in mec-4 and mec-10, which are amiloride-sensitive Na(+) channel genes expressed in all six gentle touch neurons, accelerated the velocity of DAF-16 nuclear localization, induced by confined space, revealing that mec-4/mec-10 were not required for this stress response. Thus, this device will provide a versatile, reliable and controllable platform for crowding stress studies.