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Rizwan, Muhammad,Ali, Shafaqat,Qayyum, Muhammad Farooq,Ok, Yong Sik,Adrees, Muhammad,Ibrahim, Muhammad,Zia-ur-Rehman, Muhammad,Farid, Mujahid,Abbas, Farhat Elsevier 2017 Journal of hazardous materials Vol.322 No.1
<P><B>Abstract</B></P> <P>The concentrations of engineered metal and metal oxide nanoparticles (NPs) have increased in the environment due to increasing demand of NPs based products. This is causing a major concern for sustainable agriculture. This review presents the effects of NPs on agricultural crops at biochemical, physiological and molecular levels. Numerous studies showed that metal and metal oxide NPs affected the growth, yield and quality of important agricultural crops. The NPs altered mineral nutrition, photosynthesis and caused oxidative stress and induced genotoxicity in crops. The activities of antioxidant enzymes increased at low NPs toxicity while decreased at higher NPs toxicity in crops. Due to exposure of crop plants to NPs, the concentration of NPs increased in different plant parts including fruits and grains which could transfer to the food chain and pose a threat to human health. In conclusion, most of the NPs have both positive and negative effects on crops at physiological, morphological, biochemical and molecular levels. The effects of NPs on crop plants vary greatly with plant species, growth stages, growth conditions, method, dose, and duration of NPs exposure along with other factors. Further research orientation is also discussed in this review article.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Metal and metal oxide nanoparticles (NPs) are widely used worldwide. </LI> <LI> NPs has both positive and negative effects of crop plants. </LI> <LI> NPs toxicity decreased growth, biomass and yield of food crops. </LI> <LI> This review discussed the NPs effects and toxicity mechanisms in food crops. </LI> </UL> </P>
( Muhammad Ibrahim Rajoka ),( Sobia Idrees ),( Usman Ali Ashfaq ),( Beenish Ehsan ),( Asma Haq ) 한국미생물 · 생명공학회 2015 Journal of microbiology and biotechnology Vol.25 No.1
Thermostable enzymes derived from Thermotoga maritima have attracted worldwide interest for their potential industrial applications. Structural analysis and docking studies were preformed on T. maritima β-glucosidase enzyme with cellobiose and pNP-linked substrates. The 3D structure of the thermostable β-glucosidase was downloaded from the Protein Data Bank database. Substrates were downloaded from the PubCehm database and were minimized using MOE software. Docking of BglA and substrates was carried out using MOE software. After analyzing docked enzyme/substrate complexes, it was found that Glu residues were mainly involved in the reaction, and other important residues such as Asn, Ser, Tyr, Trp, and His were involved in hydrogen bonding with pNP-linked substrates. By determining the substrate recognition pattern, a more suitable β-glucosidase enzyme could be developed, enhancing its industrial potential.
IoT Enabled Intelligent System for Radiation Monitoring and Warning Approach using Machine Learning
Muhammad Saifullah,Imran Sarwar Bajwa,Muhammad Ibrahim,Mutyyba Asgher International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.