Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory

Productivity stabilization is a critical issue facing plant factories. As such, researchers have been investigating growth prediction with the overall goal of improving productivity. The projected area of a plant (PA) is usually used for growth prediction, by which the growth of a plant is estimated by observing the overall approximate movement of the plant. To overcome this problem, this study focused on the time-series movement of plant leaves, using optical flow (OF) analysis to acquire this information for a lettuce. OF analysis is an image processing method that extracts the difference between two consecutive frames caused by the movement of the subject. Experiments were carried out at a commercial large-scale plant factory. By using a microcomputer with a camera module placed above the lettuce seedlings, images of 338 seedlings were taken every 20 min over 9 days (from the 6th to the 15th day after sowing). Then, the features of the leaf movement were extracted from the image by calculating the normal-vector in the OF analysis, and these features were applied to machine learning to predict the fresh weight of the lettuce at harvest time (38 days after sowing). The growth prediction model using the features extracted from the OF analysis was found to perform well with a correlation ratio of 0.743. Furthermore, this study also considered a phenotyping system that was capable of automatically analyzing a plant image, which would allow this growth prediction model to be widely used in commercial plant factories

In Vivo Safety and Persistence of Endoribonuclease Gene-Transduced CD4+ T Cells in Cynomolgus Macaques for HIV-1 Gene Therapy Model

BACKGROUND: MazF is an endoribonuclease encoded by Escherichia coli that specifically cleaves the ACA sequence of mRNA. In our previous report, conditional expression of MazF in the HIV-1 LTR rendered CD4+ T lymphocytes resistant to HIV-1 replication. In this study, we examined the in vivo safety and persistence of MazF-transduced cynomolgus macaque CD4+ T cells infused into autologous monkeys. METHODOLOGY/PRINCIPAL FINDINGS: The in vivo persistence of the gene-modified CD4+ T cells in the peripheral blood was monitored for more than half a year using quantitative real-time PCR and flow cytometry, followed by experimental autopsy in order to examine the safety and distribution pattern of the infused cells in several organs. Although the levels of the MazF-transduced CD4+ T cells gradually decreased in the peripheral blood, they were clearly detected throughout the experimental period. Moreover, the infused cells were detected in the distal lymphoid tissues, such as several lymph nodes and the spleen. Histopathological analyses of tissues revealed that there were no lesions related to the infused gene modified cells. Antibodies against MazF were not detected. These data suggest the safety and the low immunogenicity of MazF-transduced CD4+ T cells. Finally, gene modified cells harvested from the monkey more than half a year post-infusion suppressed the replication of SHIV 89.6P. CONCLUSIONS/SIGNIFICANCE: The long-term persistence, safety and continuous HIV replication resistance of the mazF gene-modified CD4+ T cells in the non-human primate model suggests that autologous transplantation of mazF gene-modified cells is an attractive strategy for HIV gene therapy

Sequencing and Bioinformatics-Based Analyses of the microRNA Transcriptome in Hepatitis B–Related Hepatocellular Carcinoma

MicroRNAs (miRNAs) participate in crucial biological processes, and it is now evident that miRNA alterations are involved in the progression of human cancers. Recent studies on miRNA profiling performed with cloning suggest that sequencing is useful for the detection of novel miRNAs, modifications, and precise compositions and that miRNA expression levels calculated by clone count are reproducible. Here we focus on sequencing of miRNA to obtain a comprehensive profile and characterization of these transcriptomes as they relate to human liver. Sequencing using 454 sequencing and conventional cloning from 22 pair of HCC and adjacent normal liver (ANL) and 3 HCC cell lines identified reliable reads of more than 314000 miRNAs from HCC and more than 268000 from ANL for registered human miRNAs. Computational bioinformatics identified 7 novel miRNAs with high conservation, 15 novel opposite miRNAs, and 3 novel antisense miRNAs. Moreover sequencing can detect miRNA modifications including adenosine-to-inosine editing in miR-376 families. Expression profiling using clone count analysis was used to identify miRNAs that are expressed aberrantly in liver cancer including miR-122, miR-21, and miR-34a. Furthermore, sequencing-based miRNA clustering, but not individual miRNA, detects high risk patients who have high potentials for early tumor recurrence after liver surgery (P = 0.006), and which is the only significant variable among pathological and clinical and variables (P = 0,022). We believe that the combination of sequencing and bioinformatics will accelerate the discovery of novel miRNAs and biomarkers involved in human liver cancer

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers. Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index, calculated from leaf wilting and environmental data. However, these studies have not addressed the specific role of leaf wilting in the estimation. Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method. In this paper, we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network. To clarify the role of leaf wilting, this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting, quantified by optical flow, and stem diameter variations as a water stress index. The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations, which were water stress responses in plants. As the results were consistent with known plant water transport mechanisms, it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations

Implementing and Evaluating Priority Control Mechanism for Heterogeneous Remote Monitoring IoT System

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Evaluation of Performance of TCP on Mobile IP SHAKE

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