Isolation, Selection And Characterization Of Parthenocarpic Fruit Somaclonal Variant In Brinjal (Solanum Melongena L. Var. PPL)

Brinjal (Solanum melongena L.) also known as eggplant (England) or Aubergine (France) belonging to the Solanaceae family is one of the most important vegetable crops of India (Daunay, 2008). Important brinjal growing countries are India, Japan, Indonesia, China, Bulgaria and many African countries (Vavilov, 1928). In India, brinjal is referred by various names viz., Baigan (Hindi), Badanekai (Kannada), Vangi (Marathi), Katharikai (Tamil), Vankai (Telugu) (De Candolle,1883). Brinjal fruits are a good source of calcium, phosphorus, iron and vitamins (Singh & Kumar 2006). Its green leaves are the main source of vitamin C (Gurbuza et.al., 2018). Brinjal has got much potential as raw material in pickle making. (Asaolu & Asaolu 2002). The dark purple brinjal are stated to be rich in vitamin C in comparison with white brinjal (Tabing & Tiwari, 2018)

Role of Ayurveda in the Conservative Management of Avascular Necrosis of Femur Head - A Case Report

Avascular necrosis describes the death of bone due to impairment of its blood supply. Non-traumatic or idiopathic osteonecrosis is thought to be the result of an ischemic episode affecting the bone and marrow tissue and may cause a progressive collapse of the femoral head in adults. Treatment is usually surgical and is determined by the stage and extent of the disease. Here is the case of a 36-year-old female who was diagnosed with avascular necrosis one and half years ago and was admitted with chief complaints of severe pain, stiffness in the left hip with radiating pain from left hip to lower limb and occasional pain in right hip. She was given Ksheera Vaitarana Basti and oral medications for period of 12 days. After the treatment pain, stiffness in left hip has reduced and gait was improved. This case shows that Ayurveda treatment is helpful in the management of avascular necrosis and helps in improving the quality of life.&nbsp

Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare

Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies

Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare

Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare

Preparation and characterization of polypyrrole/graphene nanocomposite films and their electrochemical performance

A one-step electrochemical process had been employed to synthesize nanocomposite films of polypyrrole/graphene (PPy/GR) by electrochemical polymerisation on indium tin oxide (ITO) from an aqueous solution containing pyrrole monomer, graphene oxide (GO) nanosheets and sodium p-toluenesulfonate (NapTS). The X-ray diffraction (XRD) patterns showed that the typical peak of GO at 9.9o was missing from the nanocomposite’s diffraction pattern, suggesting that the GO had been stripped off of its oxygenous groups after the reaction. We postulated that a nanocomposite film was produced through a layer-by-layer deposition based on field emission scanning electron microscope (FESEM) images. The Raman spectroscopy profiles exhibited that the D/G intensity ratio (ID/IG) of PPy was not altered by the inclusion of GO due to the low concentration of the material used. However, the concentration was sufficient to increase the specific capacitance of the nanocomposite by 20 times compared to that of pure PPy, reflecting a synergistic effect between PPy and GR, as analysed by a three-electrode electrochemical cell. The electrochemical performance of the nanocomposites was affected by varying the deposition parameters such as concentrations of pyrrole and GO, scan rate, deposition time and deposition potential

Double Imprinted Nanoparticles for Sequential Membrane-to-Nuclear Drug Delivery

\ua9 2024 The Author(s). Advanced Science published by Wiley-VCH GmbH. Efficient and site-specific delivery of therapeutics drugs remains a critical challenge in cancer treatment. Traditional drug nanocarriers such as antibody-drug conjugates are not generally accessible due to their high cost and can lead to serious side effects including life-threatening allergic reactions. Here, these problems are overcome via the engineering of supramolecular agents that are manufactured with an innovative double imprinting approach. The developed molecularly imprinted nanoparticles (nanoMIPs) are targeted toward a linear epitope of estrogen receptor alfa (ERα) and loaded with the chemotherapeutic drug doxorubicin. These nanoMIPs are cost-effective and rival the affinity of commercial antibodies for ERα. Upon specific binding of the materials to ERα, which is overexpressed in most breast cancers (BCs), nuclear drug delivery is achieved via receptor-mediated endocytosis. Consequentially, significantly enhanced cytotoxicity is elicited in BC cell lines overexpressing ERα, paving the way for precision treatment of BC. Proof-of-concept for the clinical use of the nanoMIPs is provided by evaluating their drug efficacy in sophisticated three-dimensional (3D) cancer models, which capture the complexity of the tumor microenvironment in vivo without requiring animal models. Thus, these findings highlight the potential of nanoMIPs as a promising class of novel drug compounds for use in cancer treatment