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The Role of Viral Infection in Inducing Variability in Virus-Free Progeny in Tomato

Journal of Integrative Plant Biology 2009年第5期51卷 476-488页

作者： liliana Marii gheorghe Chiriac Institute of Genetics and Plant Physiology Academy of Sciences of Moldova

The effect of virus-host interactions on subsequent generations is poorly understood. The evaluation of the effects of viral infection on inheritance of quantitative traits in the progeny of infected plants and elucidation of a possible relationship between chiasma frequency in the infected plants and variability of traits in the progeny were investigated. The current study involved genotypes of four intraspecific hybrids of tomato （Solanum lycopersicum L.）, their parental forms and two additional cultivars. Used as infection were the tobacco mosaic virus （TMV） and potato virus X （PVX）. The consequences of the effect of viral infection were evaluated based on chromosome pairing in diakinesis and/or by examining quantitative and qualitative traits in the progeny of the infected tomato plants. Tomato plants infected with TMV ＋ PVX were found to differ in chiasma frequency per pollen mother cell or per bivalent. Deviations have been observed for genotypes of both F1 hybrids and cultivars. At the same time, differences in mean values of the traits under study have only been found for progeny populations （F2-F4） derived from virus-infected F1 hybrids, but not in the case of progeny of the infected cultivars. The rate of recombinants combining traits of both parents increased significantly （2.22-8.24 times） in progeny populations of hybrids infected with TMV＋PVX. The above suggests that the observed effects could be the result of modification of recombination frequencies that can be manifested in heterozygous hybrids and make small contributions to variability in cases of ＇homozygous＇ tomato genotypes （i.e. cultivars）.

关键词： chiasma recombination Solanum lycopersicum traits variability viral infection.

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The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 300-304页

作者： Tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana Grigorovici Stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Department of Surgery I Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixt

关键词： Artificial Intelligence Cervical Cancer Thyroid Cancer MRI Images CT Images

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Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

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Journal of Computer and Communications 2021年第7期9卷 8-20页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici Stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Departament Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Iasi Departament of Surgery I Regional Institute of Oncology Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into

关键词： Combined Model of U-Net-Based Architectures Medical Image Segmentation 2D/3D/CT/RMN Images

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Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

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E-Health Telecommunication Systems and Networks 2021年第2期10卷 41-74页

作者： Tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana Grigorovici Stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Department of Surgery I Regional Institute of Oncology Iasi Romania Department of Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

关键词： Medical Image Analysis Data Types Labels Deep Learning Models

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The Use of Artificial Intelligence on Colposcopy Images, in the Diagnosis and Staging of Cervical Precancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 266-270页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici Stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Iasi Department of Surgery I Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition an

关键词： Artificial Intelligence Colposcopy Images Cervical Precancer

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Energy transfer and luminescent properties of Tb^(3+)and Tb^(3+),Yb^(3+)doped CNGG phosphors

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Journal of Rare Earths 2022年第9期40卷 1445-1453,I0004页

作者： S.Hau G.Stanciu D.Avram L.gheorghe C.gheorghe National Institute for Laser Plasma and Radiation PhysicsSolid State Quantum Electronics Laboratory409 Atomistilor StreetP.O.Box MG-36Magurele077125IlfovRomania

In this work,calcium niobium gallium garnet(Ca_(3)Nb_(1.6875)Ga_(3.1875)O_(12)-CNGG)ceramic samples singledoped with Tb^(3+)and co-doped with Tb^(3+)and Yb^(3+)ions were sintered by the solid-state reaction method.The structural characterization of the samples was carried out by X-ray diffraction measurements.The optimal concentration of Tb^(3+)ions corresponding to the maximum luminescence in the green spectral range in CNGG:x at%Tb(x=0.1,0.5,1,2,3,4,and 5)was determined to be 4 at%.The timeresolved luminescence of the^(5)D_(4)level(Tb^(3+))in the CNGG:x at%Tb samples was analysed to explore the quenching mechanisms involved in the Tb^(3+)green emission.Co-doped CNGG:4 at%Tb,y at%Yb(y=0.5,2,4,6,8,and 10)ceramics were prepared and investigated.It is shown that CNGG:4 at%Tb,y at%Yb phosphors exhibit intense green luminescence under ultra-violet(UV),visible(VIS),and near-infrared(NIR)excitation,thus demonstrating the presence of simultaneous down-conversion(DC)and upconversion(UC)processes.The dependence of the UC luminescence intensity on the diode laser pumping power was measured and the results indicate a two-photon process based on cooperative energy transfer(CET).Under UV excitation,the lifetime of the^(5)D_(4)(Tb^(3+))level slowly increases with increase of Yb^(3+)concentration,suggesting the energy transfer from Yb^(3+)to Tb^(3+)ions,while under NIR excitation,the lifetime of the^(5)D_(4)(Tb^(3+))level decreases with increase of Yb^(3+)ions concentration,indicating the presence of a strong energy transfer from Tb^(3+)to Yb^(3+)ions.The highest energy transfer efficiency ofη_(ET)≈42%was determined for the CNGG:4 at%Tb,10 at%Yb sample.The obtained results indicate that CNGG:(Tb^(3+),Yb^(3+))could be efficient new yellowish-green-emitting phosphors.

关键词： Phosphors Tb^(3+)and Tb^(3+)-Yb^(3+)luminescence properties Up-conversion Down-conversion Quantum cutting process Rare earths

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Medical management of symptomatic severe aortic stenosis in patients non-eligible for transcatheter aortic valve implantation

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Journal of Geriatric Cardiology 2020年第11期17卷 704-709页

作者： Gabriela Silvia gheorghe Andreea Simona Hodorogea Andrei Cristian Dan gheorghe Ioan Tiberiu Nanea Ana Ciobanu University of Medicine and Pharmacy Carol Davila BucharestProf.Dr.Th.Burghele HospitalBucharestRomania

1 Transcatheter aortic valve implantation in symptomatic severe aortic stenosis: where do we stand? Aortic stenosis occurs in 2%-9% of patients over the age of 65, the most common cause being degenerative.^([1,2]) The preferred treatment in symptomatic severe aortic stenosis(SAS) is surgical aortic valve replacement(SAVR), but in the elderly, the surgical risk can be greater than the benefit.([3]).

关键词： Angiotensin-converting enzyme inhibitors Beta-blockers Calcium channel blockers Diuretics Symptomatic severe aortic stenosis Transcatheter aortic valve implantation

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Investigations of a nanostructured FeMnSi shape memory alloy produced via severe plastic deformation

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International Journal of Minerals,Metallurgy and Materials 2016年第11期23卷 1315-1322页

作者： gheorghe Gurau Carmela Gurau Vedamanickam Sampath Leandru gheorghe Bujoreanu Faculty of Engineering "Dun?rea de Jos" University of Galati Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Faculty of Materials Science and Engineering "Gheorghe Asachi" Technical University of Ia?i

Low-cost iron-based shape memory alloys（SMAs） show great potential for engineering applications. The developments of new processing techniques have recently enabled the production of nanocrystalline materials with improved properties. These developments have opened avenues for newer applications for SMAs. The influence of severe plastic deformation induced by the high-speed high-pressure torsion（HSHPT） process on the microstructural evolution of an Fe–Mn–Si–Cr alloy was investigated. Transmission electron microscopic analysis of the alloy revealed the existence of nanoscale grains with an abundance of stacking faults. The high density of dislocations characteristic of severe plastic deformation was not observed in this alloy. X-ray diffraction studies revealed the presence of ε-martensite with an HCP crystal structure and γ-phase with an FCC structure.

关键词： iorn manganese silicon alloys shape memory effect nanostructured materials martensite plastic deformation

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THE MAIN INVARIANTS OF A COMPLEX FINSLER SPACE

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Acta Mathematica Scientia 2014年第4期34卷 995-1011页

作者： Nicoleta ALDEA gheorghe MUNTEANU Department of Mathematics and Informatics Transilvania UniversityIuliu Maniu 50Bra?sov 500091Romania

In this paper we extend the results obtained in [3], where are investigated the general settings of the two-dimensional complex Finsler manifolds, with respect to a local complex Berwahl frame. The geometry of such manifolds is controlled by three real invari- ants which live on T＇M： two horizontal curvature invariants K and W and one vertical curvature invariant I. By means of these invariants are defined both the horizontal and the vertical holomorphic sectional curvatures. The complex Landsberg and Berwald spaces are of particular into, rest. Complex Berwald spaces coincide with K/ihler spaces, in the two - dimensional case, We establish the necessary and sufficient condition under which K is a constant and we obtain a characterization for the Kghler purely Hermitian spaces by the fact K = W=constant and I = 0. For the class of complex Berwald spaces we have K =W = 0. Finally, a classitication of two-dimensional complex Finsler spaces for which the horizontal curvature satisfies a special property is obtained.

关键词： Berwald frame complex Landsberg space complex Berwald space holomorphic sectional curvature

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The Space Infrastructure Vulnerability Cadastre:Orbital Debris Critical Loads

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International Journal of Disaster Risk Science 2015年第4期6卷 359-371页

作者： Adrian V.gheorghe Daniel E.Yuchnovicz Department of Engineering Management and Systems Engineering Old Dominion University

Orbital debris from over 50 years of human activity in space are threatening the operations of existing and future satellites and the sustainability of high-value satellite orbits. This technical memorandum calls for the development of a cadastre that depicts the vulnerability of critical satellite orbits to accumulating orbital debris. A space infrastructure vulnerability cadastre could serve as a governance tool for use by developers and operators of critical space infrastructures to better communicate the current and future vulnerability of high-value orbits to the accumulation of orbital debris. These high-value orbits are susceptible to 'loss' for decades or centuries if generation of orbital debris continues unabated. The concept of environmental critical loads is applied to heavily used orbits as a way to indicate acceptable debris density for satellite operations, and when debris density/risk thresholds approach unacceptable levels that reduce the probability of sustaining spacecraft operations in those orbits.

关键词：轨道的碎片卫星轨道空间地籍图空间基础结构

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