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| 1 | Understanding Brown Planthopper Resistance in Rice: Genetics, Biochemical and Molecular Breeding Approaches显示文摘Brown planthopper(BPH,Nilaparvata lugens St自I)is the most devastating pest of rice in Asia and causes significant yield loss annually.Around 37 BPH resistance genes have been identified so far in indica,African rice varieties along with wild germplasms such as Oryza officinalis,O.minuta,O.nivara,O.punctata,O.rufipogon and O,latifolia.Genes/QTLs involved in BPH resistance,including Bph1,bph2/BPH26,Bph3,Bph6,bph7,BPH9,Bph12,Bph14,Bph15,Bph17,BPH18,bph19,Bph20,Bph21(t),Bph27,Bph27©Bph28(t),BPH29,QBph3,QBph4,QBph4.2,Bph30,Bph32,Bph33,Bph35 and Bph36,have been fine-mapped by different researchers across the globe.The majority of genes/QTLs are located on rice chromosomes 1,3,4,6,11 and 12.Rice plants respond to BPH attack by releasing various endogenous metabolites like proteinase inhibitors,callose,secondary metabolites(terpenes,alkaloids,flavonoid,etc.)and volatile compounds.Besides that,hormonal signal pathways mediating(antagonistic/synergistic)resista nee responses in rice have been well studied.Marker-assisted breedi ng and genome editi ng techniq ues can also be adopted for improving resista nee to novel BPH biotypes. | Lakesh MUDULI Sukanta Kumar PRADHAN Abinash MISHRA Debendra Nath BASTIA Kailash Chandra SAMAL Pawan Kumar AGRAWAL Manasi DASH | 2021 | Rice science2021，28，6： | 2 |
| 2 | Barnyard millet global core collection evaluation in the submontane Himalayan region of India using multivariate analysis显示文摘Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters. | Salej Sood Rajesh K.Khulbe Arun Kumar R. Pawan K. Agrawal Hari D.Upadhyaya | 2015 | The Crop Journal2015，3，6： | 1 |
| 3 | A Trailblazing Framework of Security Assessment for Traffic Data Management显示文摘Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior. | Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Neha Yadav Syed Anas Ansar Pawan Kumar Chaurasia Alka Agrawal | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
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