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A total of 10 benzopyrylium salts, 7 being novel, were synthesized by Claisen‐Schmidt condensation reaction and characterized by 1H and 13C NMR, High‐Resolution Mass Spectrometry, FT‐IR, and UV‐Vis spectroscopy. The purity of these synthesized compounds was determined by HPLC. The in‐vitro anti‐cancer evaluation of these synthesized benzopyrylium salts was done at National Cancer Institute (NCI), USA, against 60 human cancer cell lines. The compounds 4 and 8 showed promising anti‐cancer activities against various human cancer cell lines. The docking studies of the compounds were carried out against the oncogenic target JNK and their docking scores were compared to that of the known JNK inhibitor SP600125 (Pyrazolanthrone). Compound 4 exhibited an outstanding docking score of −9.0 kcal/mol, comparable to the docking score of SP600125 (−9.3 kcal/mol). The ex‐vivo studies in the hind‐gut of Drosophila were carried out and the inhibitory activity of compound 4 supported the findings of the docking studies. Furthermore, the potent compounds 4 and 8 were taken up for their in‐vivo anti‐cancer evaluation in the colorectal cancer model (CRC) of Drosophila melanogaster. The toxicity studies of these potent compounds were carried out both in wild type and mutant type Drosophila and the lethal doses were determined to be 200 μM. [ABSTRACT FROM AUTHOR]

SUMMARY: Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection. [ABSTRACT FROM AUTHOR]

This study investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in smart distribution system integrated with electric vehicles (EVs) and wind power generation. A two-stage framework is proposed for real and reactive power management and DFR. In the first stage, demand dispatch of EVs is coordinated to utilise available wind power and minimise the total load variations. In the second stage, a comprehensive problem formulation is developed to minimise the losses in the system with simultaneous EVs reactive power management and DFR. A genetic algorithm is used to solve this two-stage optimisation problem. A case study of 33-bus radial distribution system is presented to highlight the efficacy of proposed approach. The obtained results are quantitatively assessed in terms of losses and node voltages. A comparative benchmarking analysis is presented to substantiate the role of DFR and reactive power capability of EVs in improving the system performance. [ABSTRACT FROM AUTHOR]

Diffuse midline gliomas (DMGs) are rare and devastating tumors with limited therapeutic options. Programmed death‐ligand 1 (PD‐L1) expression represents a potential predictive biomarker for immunotherapy. One hundred and twenty‐six DMGs (89 adult and 37 pediatric) were assessed for immune profile (PD‐L1, cluster of differentiation (CD3, CD8) and genetic markers (mutation in 27th amino acid of histone H3 (H3K27M), alpha thalassemia/mental retardation syndrome X‐linked (ATRX), isocitrate dehydrogenase 1 (IDH1), p53) by immunohistochemistry. Sanger sequencing was done for IDH1 and H3K27M. The thalamus was the commonest site. Four molecular subgroups of DMGs were identified. H3K27M mutation was more frequent in children (P = 0.0001). The difference in median overall survival (OS) was not significant in any of the four molecular subgroups (P > 0.05). PD‐L1 expression was significantly higher in H3K27M/IDH1 double‐negative adult glioblastomas (GBMs) (P = 0.002). Strong PD‐L1 expression was more frequent in grade IV tumors and thalamic location, although the difference was not significant (P = 0.14 and P = 0.19 respectively). Positive PD‐L1 expression was significantly associated with high tumor‐infiltrating lymphocytes count (P < 0.05). There was no significant difference in median OS in PD‐L1‐positive versus negative cases among four genetic subgroups (P > 0.05). On univariate analysis, there was no direct correlation of PD‐L1 with any genetic alteration, except H3K27M mutation (P = 0.01). CD3 infiltration was similar in both adults and pediatric ages (84.3% and 78.4%, respectively) while CD8 expression was significantly greater in adults compared to children (74.1% vs 37.8%, P = 0.0001). This is the first comprehensive analysis highlighting molecular and immune profiles of DMGs. Despite molecular and clinicopathological diversity, overall survival in DMGs remains dismal. Multicentric studies with larger numbers of cases should be undertaken for stratifying DMGs according to their age, immune and molecular profiles, to develop effective immunotherapies. [ABSTRACT FROM AUTHOR]

The ongoing and anticipated growth in the use of electric vehicles (EVs) in transportation brings new opportunities for the development of new smart grids. EVs can transfer power from vehicle‐to‐grid (V2G) to potentially contribute towards improving grid functionality and stability. Coordinated charging/discharging of EVs is a possible solution to the challenges imposed by random charging and potential ways to extract benefits from the V2G functionality of EVs in the grid. Furthermore, coordinated scheduling can be augmented with effective operative tools to improve the operation of system. Towards addressing this goal, this study proposes a two‐stage optimisation to investigate the impact on the distribution system in terms of losses and voltage when distribution feeder reconfiguration (DFR) is employed with different scheduling strategies of EVs. In the first stage, the optimal charging/discharging schedule of EVs is developed on the basis of the technical and economic objective. A genetic algorithm‐based approach is used to model EV demands. The DFR problem is solved in the second stage with a new, improved version of a grey wolf optimisation algorithm considering the optimal EV load demand obtained from the first stage. The efficacy of proposed methodology is demonstrated on 69‐bus distribution system and 118‐bus distribution system. [ABSTRACT FROM AUTHOR]

A three-parameter sigmoidal 'local' model (climate-specific) and a 'regional' model (common for all climates) have been developed for measuring the monthly average diffuse solar radiation fraction from atmospheric transmissivity by using large, 21-year datasets (1973-1993) over four stations (Jodhpur, New Delhi, Nagpur and Kolkata). These stations represent the four 'prime' climates (arid, semi-arid, sub-humid and humid) over the Indian sub-tropics. The models have been validated with the longer time-series (10 years) of independent datasets (1994-2003) of 4 'prime' climates as well as datasets from 16 stations using one-year datasets (termed as secondary stations) of the Indian region. The monthly diffuse fraction estimates were also compared with seven globally existing models. The 'regional' model showed more accurate estimates than 'local' models over three (semi-arid, sub-humid and humid) of the four 'prime' climates. However, the different error statistics showed that the 'regional' model outperformed the globally existing models which failed to capture diffuse fraction variability over the Indian sub-tropics. The extendibility of the 'regional' model over 'secondary' stations in India showed an overall good performance with R2: 0.78-0.96 and RMSE: 0.017-0.125, except for two stations. These models are unique for Indian sub-tropics and can undoubtedly be used for predicting future diffuse solar radiation fraction from transmissivity datasets of climate simulations, and also for other meteorological, climatological, solar energy-based applications. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]