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Isozymic and protein diversity among five sugarcane varieties viz., Co 6304, Co 85019, Co 8371, Co 89003 and Co 91010 were studied to understand the varietal interrelationship and to identify the biochemical marker for the disease resistance and stress tolerance. The standard technique of vertical gel electrophoresis PAGE was employed for size separation of isozymes. The gel was stained with different staining solutions for different isozyme systems viz. peroxidase, esterase, acid phosphatase, alkaline phosphatase and proteins. Rf values of the banding profiles, similarity index and variation between the varieties were analysed. Among the four enzyme systems, peroxidase profile reveals the difference between the disease resistant / susceptible and abiotic stress tolerant / non tolerant varieties. The two isoperoxidase bands with Rf values 0.62 and 0.66 showed their presence in disease resistant and abiotic tolerant varieties. The presence of two marker bands (0.62, 0.66) of resistant and stress tolerant varieties suggest that the variety Co 6304 may also be resistant to smut, wilt and moderately resistant to red rot and tolerant to drought.

The present study was aimed to produce the isoperoxidase and protein marker for the maize cultivars from India and to find the isoperoxidase and protein expression on the various developmental stages. The leaf samples of Zea mays L. were harvested on 3rd, 7th, 11th, 15th, 19th, 23rd and 27th d for electrophoretic analysis of isoperoxidase and protein. The total protein and isoperoxidase were isolated using the standard procedure described by Sadasivam and Manickam. Isozyme and protein separation was carried out using 10% non-denaturing polyacrylamide gel electrophoresis. The banding pattern suggested several similarities as well as presence of unique isoperoxidase and protein in each stage. These studies will be useful for the identification of the major proteins present at different developmental stages of Z. mays.

Medical imaging is crucial in examinations and for providing appropriate patient care. Medical images are used to diagnose and identify specific conditions. However, the diagnostic procedures are confounded by the complexity of these images, which have several overlapping characteristics. Various techniques have enhanced biomedical image segmentation, and strategies to improve the contrast and accuracy of medical images have been presented in the literature. This article presents a recurrent grasshopper herd-based optimized histogram-based contrast enhancement technique, in which sharp-edge morphological characteristics can be enhanced using inclusive Laplacian fractional order Savitzky–Golay differentiation. An adaptive-sized residual fusion network approach is used to isolate the target tissue in the biomedical image. The effectiveness of the proposed system is verified using different medical data sets and a high level of accuracy is achieved. The proposed method outperforms other state-of-the-art methodologies in terms of the scale, shape and morphological attributes of the medical image. [ABSTRACT FROM AUTHOR]

Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency subbands using Discrete Wavelet Transform (DWT). The sub-band wavelet coefficients of SSC and other Structure Features (SF) are extracted using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) techniques. Here, the maximum and minimum margin between hyperplane values of the kernel parameters are adjusted periodically as a result of storing the SF values of the SVs in the IoT devices. The performance characteristics of internal cross-validation and its statistical properties are evaluated by cross-entropy measures and compared by nonparametric Mann-Whitney U test. The significant differences in classification performance between the techniques are analyzed using the receiver operating characteristics (ROC) curve. The combination of QDA + SVM technique will be required for intelligent cancer diagnosis in the future, and it gives reduced statistic parameter feature set with greater classification accuracy. The IoT network based QDA + SVM classification technique has led to the improvement of SS cancer prognosis in medical industry applications using blockchain technology. [ABSTRACT FROM AUTHOR]

Summary: In addition to proteins, microRNAs, and lipids, plant-derived exosome-like nanovesicles (ENVs) are also enriched with host plant bioactives. Both curcumin and piperine are water insoluble, lack bioavailability, and are extracted by non-ecofriendly solvents. Herein, we present an eco-friendly protocol for co-isolating both curcumin and piperine in the form of hybrid ENVs. We describe steps for sample pre-processing, combined homogenization of plant materials, filtration, and differential centrifugation. We then detail procedures for polyethylene glycol-based fusion and precipitation of hybrid ENVs.For complete details on the use and execution of this protocol, please refer to Kumar et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.