Long non-coding RNA LUCAT1 promotes cell proliferation and invasion in melanoma

Abstract Background: Melanoma is a serious malignant cancer with a low survival rate. On a global scale, breast cancer is the most frequent malignancy and leading cause of cancer death in women. Long non-coding RNAs (lncRNAs) can be used effectively as regulators and biomarkers in several cancers. Accordingly, the treatment plans of cancer patients could be made easier because of this. It has been reported that lncRNAs can play regulatory functions in various cancers, including melanoma. It is necessary to improve melanoma research programs and health policies, including in poor countries, around the world. Objective: The function of lncRNA lung cancer associated transcript 1 (LUCAT1) in melanoma has still not been identified. In the present study, large-scale screening for the differentially expressed lncRNAs was performed by lncRNAs microarray and finding the relationship between LUCAT1 and stemness marker. Methods and materials: LncRNA LUCAT1 expression was assessed in cancer tissues by in situ hybridization. Sphere-formation assay and colony-formation assay were used to detect cell self-renewal and proliferation, respectively. RNA pull-down and luciferase reporter assays were used to identify LUCAT1. Results: Silenced LUCAT1 can reduce cell growth, migration and invasion, and promote cell apoptosis, of melanoma. Conversely, over-expressed LUCAT1 can promote the progression of melanoma cells. Conclusions: The expression of LUCAT1 in melanoma cells was detected via quantitative real-time polymerase chain reaction (qRT-PCR) assay. We found that lncRNA LUCAT1 was significantly upregulated in melanoma cells. Then, we further searched the role of lncRNA LUCAT1 in melanoma. [Ethiop. J. Health Dev. 2020; 34(4):293-300] Key words: LUCAT1, melanoma, QRT-PCR assay, transwell migration, health scienc

Determination of selected physical and mechanical properties of Chinese jujube fruit and seed

Some of physical characteristics and mechanical properties of two widely commercialized varieties of Chinese jujube (Zizyphus jujube cv. junzao and Zizyphus jujube cv. huizao) were studied at 62.2% and 35.4% w.b. for fruits and seeds of junzao and 70.3% and 25.2% w.b. for fruits and seeds of huizao. The results showed that fruits and seeds of junzao were larger in all the dimensions and heavier than that of huizao while the fruits of junzao were smaller in true density, bulk density and porosity than that of huizao. The aspect ratio and sphericity of both cultivars fruits were spherical and more likely to roll than slide. And all the physical parameters measured and calculated of both cultivars fruits and seeds were significant different to each other. The rupture force of junzao was higher than that of huizao at both orientations under compression. Greater rupture force and higher hardness were found at the horizontal orientation of both cultivars

Non-coding RNA regulation of Magang geese skeletal muscle maturation via the MAPK signaling pathway

Skeletal muscle is a critical component of goose meat and a significant economic trait of geese. The regulatory roles of miRNAs and lncRNAs in the maturation stage of goose skeletal muscle are still unclear. Therefore, this study conducted experiments on the leg muscles of Magang geese at two stages: 3-day post-hatch (P3) and 3 months (M3). Morphological observations revealed that from P3 to M3, muscle fibers mainly underwent hypertrophy and maturation. The muscle fibers became thicker, nuclear density decreased, and nuclei moved towards the fiber edges. Additionally, this study analyzed the expression profiles of lncRNAs, miRNAs, and mRNAs during the skeletal muscle fiber maturation stage, identifying 1,949 differentially expressed mRNAs (DEMs), 21 differentially expressed miRNAs (DEMIs), and 172 differentially expressed lncRNAs (DELs). Furthermore, we performed enrichment analyses on DEMs, cis-regulatory genes of DELs, and target DEMs of DEMIs, revealing significant enrichment of signaling pathways including MAPK, PPAR, and mTOR signaling pathways. Among these, the MAPK signaling pathway was the only pathway enriched across all three types of differentially expressed RNAs, indicating its potentially more significant role in skeletal muscle maturation. Finally, this study integrated the targeting relationships between DELs, DEMs, and DEMIs from these two stages to construct a ceRNA regulatory network. These findings unveil the potential functions and mechanisms of lncRNAs and miRNAs in the growth and development of goose skeletal muscle and provide valuable references for further exploration of the mechanism underlying the maturation of Magang geese leg muscle

Advancement in artificial intelligence for on-farm fruit sorting and transportation

On-farm sorting and transportation of postharvest fruit include sorting out defective products, grading them into categories based on quality, distributing them into bins, and carrying bins to field collecting stations. Advances in artificial intelligence (AI) can speed up on-farm sorting and transportation with high accuracy and robustness and significantly reduce postharvest losses. The primary objective of this literature review is to provide an overview to present a critical analysis and identify the challenges and opportunities of AI applications for on-farm sorting and transportation, with a focus on fruit. The challenges of on-farm sorting and transportation were discussed to specify the role of AI. Sensors and techniques for data acquisition were investigated to illustrate the tasks that AI models have addressed for on-farm sorting and transportation. AI models proposed in previous studies were compared to investigate the adequate approaches for on-farm sorting and transportation. Finally, the advantages and limitations of utilizing AI have been discussed, and in-depth analysis has been provided to identify future research directions. We anticipate that this survey will pave the way for further studies on the implementation of automated systems for on-farm fruit sorting and transportation

Background and roles: myosin in autoimmune diseases

The myosin superfamily is a group of molecular motors. Autoimmune diseases are characterized by dysregulation or deficiency of the immune tolerance mechanism, resulting in an immune response to the human body itself. The link between myosin and autoimmune diseases is much more complex than scientists had hoped. Myosin itself immunization can induce experimental autoimmune diseases of animals, and myosins were abnormally expressed in a number of autoimmune diseases. Additionally, myosin takes part in the pathological process of multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, autoimmune myocarditis, myositis, hemopathy, inclusion body diseases, etc. However, research on myosin and its involvement in the occurrence and development of diseases is still in its infancy, and the underlying pathological mechanisms are not well understood. We can reasonably predict that myosin might play a role in new treatments of autoimmune diseases

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera

Scifresh Apple Orignial and DepthFilter RGB Images

Dataset comprises 1,600 images. Image data were collected during two harvesting seasons (2017 and 2018) in a commercial orchard from a fixed distance of 0.5 m using an RGB-D camera. A series of sensor data were acquired, including aligned RGB and depth information. Trees and apples from nontarget rows in the RGB images (i.e. Original-RGB images) were removed using the depth feature with a distance threshold of 1.2 m (i.e. Foreground-RGB images)

A Refined Apple Binocular Positioning Method with Segmentation-Based Deep Learning for Robotic Picking

An apple-picking robot is now the most widely accepted method in the substitution of low-efficiency and high-cost labor-intensive apple harvesting. Although most current research on apple-picking robots works well in the laboratory, most of them are unworkable in an orchard environment due to unsatisfied apple positioning performance. In general, an accurate, fast, and widely used apple positioning method for an apple-picking robot remains lacking. Some positioning methods with detection-based deep learning reached an acceptable performance in some orchards. However, apples occluded by apples, leaves, and branches are ignored in these methods with detection-based deep learning. Therefore, an apple binocular positioning method based on a Mask Region Convolutional Neural Network (Mask R-CNN, an instance segmentation network) was developed to achieve better apple positioning. A binocular camera (Bumblebee XB3) was adapted to capture binocular images of apples. After that, a Mask R-CNN was applied to implement instance segmentation of apple binocular images. Then, template matching with a parallel polar line constraint was applied for the stereo matching of apples. Finally, four feature point pairs of apples from binocular images were selected to calculate disparity and depth. The trained Mask R-CNN reached a detection and segmentation intersection over union (IoU) of 80.11% and 84.39%, respectively. The coefficient of variation (CoV) and positioning accuracy (PA) of binocular positioning were 5.28 mm and 99.49%, respectively. The research developed a new method to fulfill binocular positioning with a segmentation-based neural network

Interactive Teaching of Digital Electronics in Agricultural Universities in China

This article exemplifies the establishment of digital electronic course in Northwest A&F University, which is a typical agricultural university in China, for students who are majoring in electronic information engineering. Three major existing problems were systematically analyzed and discussed, and three potential and feasible solutions were also provided. This article aims at improving the quality of digital electronic course in agricultural universities in China, which can in turn better serve for agricultural engineering