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Synthesis,characterization and antimicrobial activity of some new biquinoline derivatives containing a thiazole moiety

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Chinese Chemical Letters 2012年第4期23卷 454-457页

作者： Nirav k.Shah Nimesh M.Shah manish P.Patel Ranjan G.Patel Department of Chemistry Sardar Patel UniversityVallabh Vidyanagar 388120GujaratIndia

A series of new biquinoline derivatives containing a thiazole moiety were synthesized by a one-pot,base-catalyzed cyclocondensation reaction of 2-chloro-3-formyl quinoline,malononitrile and *** the synthesized compounds were characterized by elemental analysis,FT-IR,~1H NMR and ^（13）C NMR *** the synthesized compounds were screened against three bacterial pathogens,namely Bacillus cereus,*** and Escherichia coli and for antifungal activity against three fungal pathogens,Aspergillus niger,Fusarium oxisporum and Rhizopus using the disc diffusion method.

关键词： Quinoline Multicomponent reaction Thiazole Antimicrobial activity

Ecological niche modelling of a critically endangered species Commiphora wightii (Arn.) Bhandari using bioclimatic and non-bioclimatic variables

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Ecological Processes 2023年第1期12卷 110-139页

作者： manish Mathur Preet Mathur Harshit Purohit ICAR-Central Arid Zone Research Institute Jodhpur 342003India Jodhpur Institute of Engineering and Technology JodhpurIndia Neal Analytics PuneMaharashtraIndia

Back.round The aim of this study is to examine the effects of four different bioclimatic predictors(current,2050,2070,and 2090 under Shared Socioeconomic Pathways SSP2-4.5)and non-bioclimatic variables(soil,habitat heterogeneity index,land use,slope,and aspect)on the habitat suitability and niche dimensions of the critically endangered plant species Commiphora wightii in *** also evaluate how niche modelling affects its extent of occurrence(EOO)and area of occupancy(AOO).Results The area under the receiver operating curve(AUC)values produced by the maximum entropy(Maxent)under various bioclimatic time frames were more than 0.94,indicating excellent model ***-bioclimatic characteristics,with the exception of terrain slope and aspect,decreased the accuracy of our ***,Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables(AUC=0.75 to 0.78).With current,2050,and 2070 bioclimatic projections,our modelling revealed the significance of water availability parameters(BC-12 to BC-19,*** and seasonal precipitation as well as precipitation of wettest,driest,and coldest months and quarters)on habitat suitability for this ***,with 2090 projection,energy variables such as mean temperature of wettest quarter(BC-8)and isothermality(BC-3)were identified as governing *** salt,rooting conditions,land use type(grassland),characteristics of the plant community,and slope were also noticed to have an impact on this *** distribution modelling of this species in both its native(west-ern India)and exotic(North-east,Central Part of India,as well as northern and eastern Ghat)habitats,we were also able to simulate both its fundamental niche and its realized *** EOO and AOO analysis reflects the possibility of many new areas in India where this species can be planted and *** According to the calculated area under the various suitability classes,we can conclude

关键词： Commiphora wightii Critically endangered Maxent Habitat heterogeneity index Niche hypervolume

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Artificial neural network.based modeling of liquid membranes for separation of dysprosium

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Journal of Rare Earths 2023年第3期41卷 440-445,I0005页

作者： Jawad Iqbal Arjun Tyagi manish Jain Department of Applied Chemistry Delhi Technological UniversityDelhi110042India

In recent years,the liquid membrane process has been widely investigated to remove rare earth ***,transport modeling of this process requires the accurate values of several parameters,which are difficult to ***,the accurate simulation of this process is a challenging *** this study,the artificial neural network.ANN)based approach is used to model the liquid membrane process for removing *** results from a previous study were used to train the ***,the number of neurons in the hidden layer was *** minimum mean squared error between experimental results and model predictions is found with ten *** predictions were successfully validated with experimental results with correlation factor(R)of 0.9987,which confirms the authenticity of the trained *** ANN was then used to study the effects of different operating parameters on transport *** higher volume ratio of membrane solution to feed solution(3-4)with 50-60 min of operation,higher feed pH(5),HCl concentration in stripping solution of 2 mol/L,and moderate concentration of carrier species(0.5 mol/L)with 0.5×10^(-4) mol/L dysprosium initial concentration are found to be optimum values of operating conditions for maximizing the transport rate.

关键词： Rareearth metal separation Artificial neural network Dysprosium separation Liquid membranes

Unsupervised machine learning-based clustering identifies unique molecular signatures of colorectal cancer with distinct clinical outcomes

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Genes & Diseases 2023年第6期10卷 2270-2273页

作者： manish Pratap Singh Sandhya Rai Sarvesh k.Gupta Nand k.Singh Sameer Srivastava Motilal Nehru National Institute of Technology Allahabad Prayagraj 211004India Deen Dayal Upadhyay Gorak.pur University Gorakhpur 273001India

Colorectal cancer(CRC)is k.own to harbor considerable heterogeneity.1 Consequently,it could be hypothesized that similar-appearing tumors might exhibit substantial genetic differences while diverse-appearing tumors may have a similar genetic landscape.2 Due to these differences at the molecular level,they behave or respond differently to therapies as *** progression is a multistep process and involves the accumulation of substantial genetic and epigenetic events in a stage-dependent manner.

关键词： cancer colorectal signature

Pixel-Level Feature Extraction Model for Breast Cancer Detection

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Computers, Materials & Continua 2023年第2期74卷 3371-3389页

作者： Nishant Behar manish Shrivastava Guru Ghasidas Vishvavidyalaya Bilaspur45009India

Breast cancer is the most prevalent cancer among women,and diagnosing it early is vital for successful *** examination of images captured during biopsies plays an important role in determining whether a patient has cancer or ***,the stochastic patterns,varying intensities of colors,and the large sizes of these images mak. it challenging to identify and mark.malignant regions in *** this back.rop,this study proposes an approach to the pixel categorization based on the genetic algorithm(GA)and principal component analysis(PCA).The spatial features of the images were extracted using various filters,and the most prevalent ones are selected using the GA and fed into the classifiers for pixel-level *** classifiers—random forest(RF),decision tree(DT),and extra tree(ET)—were used in the proposed *** parameters of all modelswere separately tuned,and their performance was *** results show that the features extracted by using the GA+PCA in the proposed model are influential and reliable for pixel-level classification in service of the image annotation and tumor ***,an image from benign,malignant,and normal classes was randomly selected and used to test the proposed *** proposed modelGA-PCA-DT has delivered accuracies between 0.99 to 1.0 on a reduced feature *** predicted pixel sets were also compared with their respective ground-truth values to assess the overall performance of the method on two metrics—the universal image quality index(UIQI)and the structural similarity index(SSI).Both quality measures delivered excellent results.

关键词： Breast cancer machine learning classification feature extraction feature selection

Hybrid Deep VGG-NET Convolutional Classifier for Video Smok. Detection

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Computer Modeling in Engineering & Sciences 2019年第6期119卷 427-458页

作者： Princy Matlani manish Shrivastava Department of Computer Science&Engineering Institute of TechnologyGuru Ghasidas UniversityBilaspurChhattisgarh495009India

Real-time wild smok. detection utilizing machine based identification method is not produced proper accuracy,and it is not suitable for accurate ***,various video smok. detection approaches involve minimum lighting,and it is required for the cameras to identify the existence of smok. particles in a *** overcome such challenges,our proposed work.introduces a novel concept lik. deep VGG-Net Convolutional Neural Network.CNN)for the classification of smok. *** Deep Feature Synthesis algorithm automatically generated the characteristics for relational *** hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is *** proposed real-time algorithm uses some pre-processing for the image enhancement and next to the image enhancement processing;foreground and back.round regions are separated with Otsu ***,to regulate the linear combination of foreground and back.round components alpha channel is applied to the image ***,Farneback.optical flow evaluation technique diminishes the false finding rate and finally smok. particles are classified with the VGG-Net CNN *** the end,the investigational outcome shows better statistical stability and performance regarding classification *** algorithm has better smok. detection performance among various video scenes.

关键词： Smok. detection foreground extraction optical flow estimation classification filtering thresholding

ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

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Computer Modeling in Engineering & Sciences 2022年第2期130卷 823-839页

作者： Nishant Behar manish Shrivastava Department of Computer Science&Engineering GGVBilaspur495009India

Breast cancer is considered an immense threat and one of the leading causes of mortality in *** is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology *** depends on the accurate identification of the landmark. in the visual artefacts present in the slide *** are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour ***,computerbased automated systems play an important role in cancer image analysis and help healthcare experts mak. rapid and correct inferences about the type of *** study proposes an effective convolutional neural network.ased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology ***50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test *** result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,*** accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data *** precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

关键词： Classification histopathology images convolutional neural network breast cancer break.is

Time course of physiological,biochemical,and gene expression changes under short-term salt stress in Brassica juncea L.

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The Crop Journal 2017年第3期5卷 219-230页

作者： manish Pandey Suprasanna Penna Plant Stress Physiology and Biotechnology Section Nuclear Agriculture and Biotechnology DivisionBhabha Atomic Research CentreTrombayMumbai 400085India

Salinity-imposed limitations on plant growth are manifested through osmotic and ionic imbalances. However, because salinity-induced responses vary considerably among crop plants, monitoring of such responses at an early stage has relevance. In this study,physiological(seed germination, seed vigor index, root length, shoot length, fresh weight,dry weight) and biochemical attributes(osmoprotectants, k.+/Na^+ ratio) were analyzed for a time-course assessment of salt responses in Indian mustard(Brassica juncea L.) with an emphasis on early monitoring. The results showed strong correlations for total soluble sugars at germination phase(24 h), proline content in the seedling establishment phase(48 h) and various physiological parameters including seed vigor index(R^2= 0.901), shoot length(R^2= 0.982), and fresh weight(R^2= 0.980) at 72 h(adaptation under stress). In addition, transcriptional changes were observed under NaCl treatment for k.y genes belonging to the family of selective ion transporters(NHX, Hk.) and abscisic acid synthesis(AAO-3). The status of mitochondrial respiration was also examined as a probe for salinity tolerance at an early stage. The results suggested that although all the analyzed parameters showed correlations(negative or positive) with salt stress magnitude, their critical response times differed, with most of the studied biochemical, physiological, or molecular mark.rs providing valuable information only after radicle emergence, whereas mitochondrial respiration via alternative oxidase was useful for the early detection of salt responses.

关键词： Alternative oxidase Brassica Osmolytes Salinity Seed germination

Machine Learning-based Electric Load Forecasting for Peak.Demand Control in Smart Grid

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Computers, Materials & Continua 2023年第3期74卷 4785-4799页

作者： manish k.mar Nitai Pal Department of Electrical Engineering Indian Institute of Technology(ISM)Dhanbad826004India Department of Nuclear Science&Technology Pandit Deendayal Energy UniversityGandhinagarIndia

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy *** ways of energy generation and energy consumption lik. smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of *** integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants *** the integration of distributed energy sources and increase in electric demand enhance instability in the ***-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the *** load forecasting in advance on the basic historical data modelling plays a crucial role in peak.electrical demand control,reinforcement of the grid demand,and generation balancing with cost *** accurate forecasting of electrical data is a very challenging task.due to the nonstationary and nonlinearly nature of the *** learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load *** purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is tak.n for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among *** on this modelling of data the network.will be able to forecast the peak.load of the electric grid one day *** study is very helpful in grid stability and peak.load control management.

关键词： Artificial intelligence electric load forecasting machine learning peak.load control renewable energy smart grids