检索结果-南通市图书馆

Autophagy in neuroinflammation after traumatic brain injury

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Neural Regeneration Research 2024年第5期19卷 951-952页

作者： Chinmoy sarkar Marta M.Lipinski Shock Trauma and Anesthesiology Research(STAR)CenterDepartment of AnesthesiologyUniversity of Maryland School of MedicineBaltimoreMDUSA Department of Anatomy and Neurobiology University of Maryland School of MedicineBaltimoreMDUSA

Traumatic brain injury(TBI)is an acquired injury of the brain caused by the impact of external forces on the brain(Maas et al.,2008).It is a major cause of death and disability among people of all ages(Maas et al.,2008).The primary mechanical injury to the brain initiates a cascade of secondary biochemical events that lead to acute and chronic neurodegeneration and activation of inflammatory pathways(Maas et al.,2008).Both brain-resident microglia and blood-derived myeloid cells-macrophages and monocytes that infiltrate the brain due to injury-induced blood-brain barrier damage,contribute to the inflammatory responses after TBI(Morganti et al.,2015).

关键词： damage death after

Two-dimensional magnetic materials for spintronic applications

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Nano Research 2024年第2期17卷 743-762页

作者： Shivam N.Kajale Jad Hanna Kyuho Jang Deblina sarkar MIT Media Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Information Technology and Electrical Engineering ETH ZürichZürich8092Switzerland

Spintronic devices are driving new paradigms of bio-inspired,energy efficient computation like neuromorphic stochastic computing and in-memory computing.They have also emerged as key candidates for non-volatile memories for embedded systems as well as alternatives to persistent memories.To meet the growing demands from such diverse applications,there is need for innovation in materials and device designs which can be scaled and adapted according to the application.Two-dimensional(2D)magnetic materials address challenges facing bulk magnet systems by offering scalability while maintaining device integrity and allowing efficient control of magnetism.In this review,we highlight the progress made in experimental studies on 2D magnetic materials towards their integration into spintronic devices.We provide an account of the various relevant material discoveries,demonstrations of current and voltage-based control of magnetism and reported device systems,while also discussing the challenges and opportunities towards integration of 2D magnetic materials in commercial spintronic devices.

关键词： spintronics van der Waals magnetic tunnel junction(MTJ) spin-orbit torque magnetism

Early Paleozoic basement diorite of arc-magmatism from Kutch basin, Western India

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Geochimica 2024年第2期43卷 296-307页

作者： Piyush Gupta Sandeep Singh Shakti Singh Rathore Argha Narayan sarkar Geochronology Laboratory KDMIPEOil and Natural Gas Corporation Ltd.9Kaulagarh RoadDehradun 248195India Department of Earth Sciences Indian Institute of TechnologyRoorkee 247667India

In this study,we report for theﬁrst time an Early Palaeozoic basement diorite from the drilled well Nirona-A in the Banni Half-Graben of the Kutch basin,western India.The 40Ar–39Ar dates provided a plateau age of 441.84±2.66 Ma and another pseudo plateau of 441.28±5.82 to 388.08±16.65 Ma for the basement diorite.These ages constrain the basement formation age to the Late Ordovician-Early Silurian period.The obtained basement ages are correlatable with the later part of Cambro-Ordovician alkaline magmatism that has been reported from the Huqf area in Central Oman,whereas their lithological and petrographic correlativity with base-ment diorites occurring in the Dinsi Body of Nagar Parkar igneous complex in Pakistan can also be envisaged.The geochemical studies characterized the diorite with enrich-ment of LILE(Rb,Ba,and K)and LREE(La,Ce,Nd),strong depletion of HFSE(Nb,Sr,P,and Ti),along with weakly negative Eu anomalies.The geochemical signatures indicate their petrogenetic afﬁliation with mantle-derived magmas,as well as their tectonic setting to be arc-related,having post-collisional continental-arc type afﬁnity.The*440 Ma basement of Kutch,therefore,appears to rep-resent the later thermal event associated with the reworked Neoproterozoic subduction-related suite from Greater India’s northwest edge,which has implications for Gond-wana assembly in the northwest Indian subcontinent.

关键词： Kutch basin 40Ar–39Ar ages Early palaeozoic basement Continental-arc magmatism Calc-alkaline magmatism

3D convolutional selective autoencoder for instability detection in combustion systems

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

能源与人工智能(英文)

能源与人工智能(英文) 2021年第2期4卷 80-90页

作者： Tryambak Gangopadhyay Vikram Ramanan Adedotun Akintayo Paige K Boor Soumalya sarkar Satyanarayanan R Chakravarthy soumik sarkar Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Indian Institute of Technology Madras ChennaiIndia Intel Corporation FolsomCAUSA Raytheon Technologies East HartfordCTUSA

While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3D convolutional selective autoencoder(3D-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3D-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

关键词： 3D deep learning Convolutional autoencoder Hi-speed video analytics Combustion instability Gas turbine engines Early detection Instability precursors Physics-based validation

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

npj Computational Materials 2019年第1期5卷 302-312页

作者： Balaji Sesha Sarath Pokuri Sambuddha Ghosal Apurva Kokate soumik sarkar Baskar Ganapathysubramanian Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Department of Computer Science Iowa State UniversityAmesIAUSA

The microstructure determines the photovoltaic performance of a thin film organic semiconductor film.The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus making microstructure optimization challenging.Here,we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks.We characterize this approach in terms of two critical metrics,its generalizability(has it learnt a reasonable map?),and its intepretability(can it produce meaningful microstructure characteristics that influence its prediction?).A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration.We illustrate this by using the surrogate model for both manual exploration(that verifies known domain insight)as well as automated microstructure optimization.We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.

关键词： microstructure property meaningful

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline(SNAP)

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Plant Phenomics 2021年第1期3卷 197-208页

作者： Talukder Zaki Jubery Clayton N.Carley Arti Singh soumik sarkar Baskar Ganapathysubramanian Asheesh K.Singh Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Department of Agronomy Iowa State UniversityAmesIAUSA

Nodules form on plant roots through the symbiotic relationship between soybean(Glycine max L.Merr.)roots and bacteria(Bradyrhizobium japonicum)and are an important structure where atmospheric nitrogen(N2)is fixed into bioavailable ammonia(NH3)for plant growth and development.Nodule quantification on soybean roots is a laborious and tedious task;therefore,assessment is frequently done on a numerical scale that allows for rapid phenotyping,but is less informative and suffers from subjectivity.We report the Soybean Nodule Acquisition Pipeline(SNAP)for nodule quantification that combines RetinaNet and UNet deep learning architectures for object(i.e.,nodule)detection and segmentation.SNAP was built using data from 691 unique roots from diverse soybean genotypes,vegetative growth stages,and field locations and has a good model fit(R2=0:99).SNAP reduces the human labor and inconsistencies of counting nodules,while acquiring quantifiable traits related to nodule growth,location,and distribution on roots.The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors,and their interactions on nodulation from an early development stage.The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars,as well as enhanced insight into the plant-Bradyrhizobium relationship.

关键词： soybean breeding cultivar

Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Plant Phenomics 2020年第1期2卷 149-171页

作者： Kevin G.Falk Talukder Zaki Jubery Jamie A.O’Rourke Arti Singh soumik sarkar Baskar Ganapathysubramanian Asheesh K.Singh Department of Agronomy Iowa State UniversityAmesIowaUSA Department of Mechanical Engineering Iowa State UniversityAmesIowaUSA USDA-Agricultural Research Service Corn Insects and Crop Genetics Research UnitAmesIowaUSA

We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

关键词： neural breeding Root

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Plant Phenomics 2021年第1期3卷 286-297页

作者： Luis GRiera Matthew ECarroll Zhisheng Zhang Johnathon MShook Sambuddha Ghosal Tianshuang Gao Arti Singh Sourabh Bhattacharya Baskar Ganapathysubramanian Asheesh K.Singh soumik sarkar Department of Mechanical Engineering Iowa State UniversityAmesIowaUSA Department of Agronomy Iowa State UniversityAmesIowaUSA Department of Computer Science Iowa State UniversityAmesIowaUSA

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.

关键词： breeding soybean cultivar

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Plant Phenomics 2019年第1期1卷 106-120页

作者： Kyle Parmley Koushik Nagasubramanian soumik sarkar Baskar Ganapathysubramanian Asheesh K.Singh Department of Agronomy Iowa StateUniversityAmesIAUSA Department of Electrical Engineering Iowa State UniversityAmesIAUSA Department of Mechanical Engineering Iowa State UniversityAmesIAUSA

The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs.Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements.Random forest,a machine learning(ML)algorithm,was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation(CV)scenarios consistent with breeding challenges.To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives,feature importance in tandem with a genetic algorithm(GA)technique allowed selection of a subset of phenotypic traits,specifically optimal wavebands.The results illuminated the capability of fusingML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping(e.g.,seed yield harvest).While we illustrate with soybean,this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective。

关键词： breeding soybean forest