Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition.

The mutant matrilineal (mtl) gene encoding patatin-like phospholipase activity is involved in in-vivo maternal haploid induction in maize. Doubling of chromosomes in haploids by colchicine treatment leads to complete fixation of inbreds in just one generation compared to 6-7 generations of selfing. Thus, knowledge of patatin-like proteins in other crops assumes great significance for in-vivo haploid induction. So far, no online tool is available that can classify unknown proteins into patatin-like proteins. Here, we aimed to optimize a machine learning-based algorithm to predict the patatin-like phospholipase activity of unknown proteins. Four different kernels [radial basis function (RBF), sigmoid, polynomial, and linear] were used for building support vector machine (SVM) classifiers using six different sequence-based compositional features (AAC, DPC, GDPC, CTDC, CTDT, and GAAC). A total of 1170 protein sequences including both patatin-like (585 sequences) from various monocots, dicots, and microbes; and non-patatin-like proteins (585 sequences) from different subspecies of Zea mays were analyzed. RBF and polynomial kernels were quite promising in the prediction of patatin-like proteins. Among six sequence-based compositional features, di-peptide composition attained > 90% prediction accuracies using RBF and polynomial kernels. Using mutual information, most explaining dipeptides that contributed the highest to the prediction process were identified. The knowledge generated in this study can be utilized in other crops prior to the initiation of any experiment. The developed SVM model opened a new paradigm for scientists working in in-vivo haploid induction in commercial crops. This is the first report of machine learning of the identification of proteins with patatin-like activity.

SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques.

The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various plant morphological features like spike and spikelet from the visual image of the wheat plant and provide a high-throughput yield estimate with great precision. Mask R-CNN outperformed previous networks in spike detection by its precise detection performance with a mean average precision (mAP) of 97.57%, a F1 score of 0.67, and an MCC of 0.91 by overcoming several natural field constraints like overlapping and background interference, variable resolution, and high bushiness of plants. The spikelet detection module's accuracy and consistency were tested with about 99% validation accuracy of the model and the least error, i.e., a mean square error of 1.3 from a set of typical and complex views of wheat spikes. Spikelet yield cumulatively showed the probable production capability of each plant. Our method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods.

Deep learning-based approach for identification of diseases of maize crop.

In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.

A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize.

Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions.

Assessment of Different File Systems for Working Time Based on Glide Path, Operating Kinetics, and the Fracture Resistance.

AIM: This study was done to assess the time to achieve the working distance based on the size of the glide path, operating kinetics, and the fracture resistance of different file systems. MATERIALS AND METHOD: One hundred and eighty mandibular premolars were divided into two groups of 90 each. Group I was subjected to continuous 360° rotary motion and group II to adaptive motion. Twisted File (TF) and Endostar E3 file methods were practiced in groups. The time (seconds) to achieving desired working length was recorded. Failures were classified as torsional failure or flexural failure. RESULTS: The time taken by glide path size 15 in group I was 5.90 ± 4.06 seconds and in group II was 6.12 ± 4.16 seconds. The time taken by glide path size 20 in group I was 5.86 ± 3.12 seconds and in group II was 4.22 ± 2.10 seconds, with 25 size the time taken in group I was 5.32 ± 2.48 seconds and in group II was 3.16 ± 3.14 seconds. The time taken by group I was less as compared to group II, and the difference was significant (p < 0.05). There was a significant difference in time taken with different number files in both groups (p < 0.05). The mean time taken reaching the working length for continuous rotation was less as compared to TF adaptive motion; however, the difference was nonsignificant (p > 0.05). CONCLUSION: We recorded higher instrument separation and deformation with the TF method and adaptive gesture. The TF system showed additional time to achieve the working distance as compared to the Endostar E3 system. CLINICAL SIGNIFICANCE: The TF system showed higher instrument separation and deformation, and it requires additional time to achieve the working distance compared to the Endostar E3 system. Hence, the Endostar E3 system is effective in achieving required clinical results.

SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.

BACKGROUND: High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. RESULTS: In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deep-learning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, "analyse particles"-function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation performance. CONCLUSION: In this study, a new approach called as SpikeSegNet has been proposed based on combined digital image analysis and deep learning techniques. A dedicated deep learning approach has been developed to identify and count spikes in the wheat plants. The performance of the approach demonstrates that SpikeSegNet is an effective and robust approach for spike detection and counting. As detection and counting of wheat spikes are closely related to the crop yield, and the proposed approach is also non-destructive, it is a significant step forward in the area of non-destructive and high-throughput phenotyping of wheat.

EdU incorporation is an alternative non-radioactive assay to [(3)H]thymidine uptake for in vitro measurement of mice T-cell proliferations.

RATIONALE: T lymphocyte proliferations can be measured by [(3)H]thymidine incorporation. However, many labs avoid this technique because of the need to use radioactive substrates. In addition, [(3)H]thymidine incorporation method does not permit simultaneous characterization of the proliferating cells. We developed the 5-ethynyl-2'-deoxyuridine (EdU) and Cu(I)-catalyzed cycloaddition "click" reaction assay to measure T-cell responses by flow cytometry. METHODS: Spleen cells from normal, immune-deficient purine nucleoside phosphorylase (PNP) defective (PNP-/-) mice or PNP-/- mice with partial immune reconstitution were stimulated with anti-CD3 antibodies. The correlation (r) between [(3)H]thymidine and EdU incorporations into stimulated T cells was measured and the stimulation index (SI), the ratio between stimulated and non-stimulated cells, was calculated. Flow cytometry was used to characterize the proliferating cells. RESULTS: EdU and [(3)H]thymidine incorporation into normal spleen cells were strongly correlated (r=0.89). Following stimulation, EdU incorporation into spleen cells from normal and immune-reconstituted PNP-/- mice was significantly increased compared to PNP-/- immune-deficient mice. Immune-deficient PNP-/- mice had increased [(3)H]thymidine and EdU incorporation into non-stimulated spleen cells, indicative of spontaneous proliferation. Analysis of EdU incorporation showed that the increased proliferation was due primarily to cells expressing CD3, CD4 and IgM. CONCLUSION: EdU-Click technology accurately measures proliferation of murine T lymphocyte and can be used as an alternative to [(3)H]thymidine assays. The EdU-Click technology also allows identification of proliferating cells.

Assessment of an anti-HIV-1 combination gene therapy strategy using the antisense RNA and multimeric hammerhead ribozymes.

A combination gene therapy strategy using an ASPsi-gag antisense RNA (targeted against the packaging signal and the gag-coding region) and a multimeric hammerhead ribozyme Rz1-9 (targeted against nine sites within the env-coding region) or Rz1-14 (targeted against 14 sites within the 5' leader and the pro-, pol-, vif- and env-coding regions) was assessed for inhibiting HIV-1 replication. A murine stem cell virus (MSCV)-based MGIN vector was used to express Rz1-9, Rz1-14, ASPsi-gag, Rz1-9ASPsi-gag, or Rz1-14ASPsi-gag RNA in a CD4+ T lymphoid cell line. Stable transductants were shown to express similar levels of interfering RNA. HIV-1 replication was inhibited in cells expressing Rz1-9 and Rz1-14. Little inhibition of HIV-1 replication was observed in cells expressing ASPsi-gag RNA. Thus, the multimeric hammerhead ribozymes inhibit HIV-1 replication better than the antisense RNA. Inhibition of HIV-1 replication in cells expressing Rz1-9ASPsi-gag or Rz1-14ASPsi-gag RNA was worse than that obtained with the multimeric ribozymes alone. This result suggests that co-expression of antisense RNA decreases the anti-HIV potential of ribozymes. The multimeric ribozymes and the antisense RNA were designed to target different sites within the HIV-1 RNA. They are not expected to interact with each other. Neither are they expected to compete with each other for binding to the HIV-1 RNA. Instead, the antisense RNA binding to its (1553 nt-long) target site may have resulted in a decreased ribozyme turn over. Furthermore, since the antisense RNA/HIV-1 RNA hybrids are degraded by the cells, the co-expressed antisense RNA may have led to ribozyme degradation.