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Early blight (EB), caused by Alternaria linariae (Neerg.) (syn. A. tomatophila) Simmons, is a disease that affects tomatoes (Solanum lycopersicum L.) throughout the world, with tremendous economic implications. The objective of the present study was to map the quantitative trait loci (QTL) associated with EB resistance in tomatoes. The F2 and F2:3 mapping populations consisting of 174 lines derived from NC 1CELBR (resistant) × Fla. 7775 (susceptible) were evaluated under natural conditions in the field in 2011 and in the greenhouse in 2015 by artificial inoculation. In all, 375 Kompetitive Allele Specific PCR (KASP) assays were used for genotyping parents and the F2 population. The broad-sense heritability estimate for phenotypic data was 28.3%, and 25.3% for 2011, and 2015 disease evaluations, respectively. QTL analysis revealed six QTLs associated with EB resistance on chromosomes 2, 8, and 11 (LOD 4.0 to 9.1), explaining phenotypic variation ranging from 3.8 to 21.0%. These results demonstrate that genetic control of EB resistance in NC 1CELBR is polygenic. This study may facilitate further fine mapping of the EB-resistant QTL and marker-assisted selection (MAS) to transfer EB resistance genes into elite tomato varieties, including broadening the genetic diversity of EB resistance in tomatoes. [ABSTRACT FROM AUTHOR]

The emergence of deep neural networks has allowed the development of fully automated and efficient diagnostic systems for plant disease and pest phenotyping. Although previous approaches have proven to be promising, they are limited, especially in real-life scenarios, to properly diagnose and characterize the problem. In this work, we propose a framework which besides recognizing and localizing various plant abnormalities also informs the user about the severity of the diseases infecting the plant. By taking a single image as input, our algorithm is able to generate detailed descriptive phrases (user-defined) that display the location, severity stage, and visual attributes of all the abnormalities that are present in the image. Our framework is composed of three main components. One of them is a detector that accurately and efficiently recognizes and localizes the abnormalities in plants by extracting region-based anomaly features using a deep neural network-based feature extractor. The second one is an encoder--decoder network that performs pixel-level analysis to generate abnormalityspecific severity levels. Lastly is an integration unit which aggregates the information of these units and assigns unique IDs to all the detected anomaly instances, thus generating descriptive sentences describing the location, severity, and class of anomalies infecting plants. We discuss two possible ways of utilizing the abovementioned units in a single framework. We evaluate and analyze the efficacy of both approaches on newly constructed diverse paprika disease and pest recognition datasets, comprising six anomaly categories along with 11 different severity levels. Our algorithm achieves mean average precision of 91.7% for the abnormality detection task and a mean panoptic quality score of 70.78% for severity level prediction. Our algorithm provides a practical and costefficient solution to farmers that facilitates proper handling of crops. [ABSTRACT FROM AUTHOR]

Aim: This work aims to formulate topical hybrid gel containing chitosan-coated moxifloxacin (MXF) HCl nanoparticles (NPs) with enhanced antibacterial and healing activity. Methods: MXF HCl NPs prepared by the ionic gelation method were loaded onto a hybrid chitosan carbomer gel. Size analysis of the prepared NPs was performed using SEM and Zeta-sizer. Further characterisation was done using Fourier transforms infra-red spectroscopy (FTIR), X-ray diffraction (XRD), and Thermogravimetric analysis (TGA). Prepared gel was evaluated for its in vitro drug release, biocompatibility, antibacterial activity, and stability studies under storage conditions. In-vivo wound healing was measured by observing percentage reduction in wound. Results: NPs have 359 ± 79 nm mean particle size, 31.01 mV zeta potential with 0.008 polydispersity index (PD1), 63.5% drug entrapment and 83 ± 3.5% drug release at pH 5.5. Hybrid chitosan carbomer gel showed good biocompatibility, antibacterial, in-vivo wound healing properties and stable properties. Conclusions: NP-loaded hybrid gel can be an effective treatment for acute and challenged topical wounds. [ABSTRACT FROM AUTHOR]

In the present study nanotechnology approach, i.e., a cyclodextrin (CD) based carbonate nanosponge was used to improve the solubility and dissolution of ibuprofen. Solvent and ultrasound assisted methods were used to prepare nanosponges using two CDs (β-CD and 2-hydroxypropyl-β-CD (2HP-β-CD)) and a cross-linker (CL) diphenyl carbonate (DPC) in varying molar ratios. Nanosponges were investigated for their solubilizing efficiency and phase solubility studies. Structural analysis by Fourier transform infrared (FTIR) and powder X-ray diffraction (PXRD), thermoanalytical characterization by differential scanning calorimetry (DCS), morphology by scanning electron microscopy (SEM). In-vitro drug release followed by in-vivo analgesic and anti-inflammatory studies were performed. 2HP-β-CD based nanosponges (molar ratio 0.01:0.04) prepared by ultrasound assisted method showed the highest solubilizing efficiency (i.e., 4.28 folds). Stability constant values showed that all complexes were stable. Inclusion complexes of drug was confirmed by PXRD and DSC. SEM images showed porous structures confirming the formation of cross-linked network. Particle size was in the range of 296.8±64 to 611.7±32nm. In-vitro release studies showed enhanced dissolution profile from nanosponge formulation (~94% from I11) as compared to the pure drug (~45% Ibuprofen) in 120min. Significant (p<0.05) extent of pain inhibition and anti-inflammatory activity was observed for nanosponge formulation when compared with the pure drug. CD based carbonate nanosponges with better solubility, enhanced release profile, improved analgesic and anti-inflammatory activity were successfully formulated for ibuprofen. [ABSTRACT FROM AUTHOR]

Keeping in view the importance of lattice theory, ring theory and soft sets, in this article the notion of lattice ordered double framed soft rings is acquainted and some basic properties of the defined notion are discussed. Additional to this the behavior of different operations of lattice ordered double framed soft sets is discussed under the atmosphere of rings. Wherever necessary the concepts for the defined notion are elaborated by examples. [ABSTRACT FROM AUTHOR]

Omega-3 fatty acids are long chain polyunsaturated fatty acids (LCPUFAs) have been the source of interest because of their beneficial pharmacological activities. These omega 3 LCPUFAs, especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have shown anti-inflammatory activity, which increased the research interest on EPA and DHA for their possible effectiveness against cancer. EPA and DHA offer safe and non-toxic approach (as compared to anti-cancer drugs) to prevent or inhibit carcinoma cell growth. The present review article outlines research on omega-3 fatty acids on various carcinomas to understand the mechanism and effectiveness of EPA and DHA against different cancerous diseases. In this article, the increased interest in fish oil and the positive outcome of omega-3 fatty acids in the cancer research is highlighted. It suggests that the research on the natural compounds like omega-3 fatty acids should be encouraged to understand the mechanisms against tumor and to improve the quality of life. [ABSTRACT FROM AUTHOR]