Harpin-induced expression and transgenic overexpression of the phloem protein gene AtPP2-A1 in Arabidopsis repress phloem feeding of the green peach aphid Myzus persicae

<p>Abstract</p> <p>Background</p> <p>Treatment of plants with HrpN_Ea, a protein of harpin group produced by Gram-negative plant pathogenic bacteria, induces plant resistance to insect herbivores, including the green peach aphid <it>Myzus persicae</it>, a generalist phloem-feeding insect. Under attacks by phloem-feeding insects, plants defend themselves using the phloem-based defense mechanism, which is supposed to involve the phloem protein 2 (PP2), one of the most abundant proteins in the phloem sap. The purpose of this study was to obtain genetic evidence for the function of the <it>Arabidopsis thaliana </it>(Arabidopsis) PP2-encoding gene <it>AtPP2-A1 </it>in resistance to <it>M. persicae </it>when the plant was treated with HrpN_Eaand after the plant was transformed with <it>AtPP2-A1</it>.</p> <p>Results</p> <p>The electrical penetration graph technique was used to visualize the phloem-feeding activities of apterous agamic <it>M. persicae </it>females on leaves of Arabidopsis plants treated with HrpN_Eaand an inactive protein control, respectively. A repression of phloem feeding was induced by HrpN_Eain wild-type (WT) Arabidopsis but not in <it>atpp2-a1</it>/E/142, the plant mutant that had a defect in the <it>AtPP2-A1 </it>gene, the most HrpN_Ea-responsive of 30 <it>AtPP2 </it>genes. In WT rather than <it>atpp2-a1</it>/E/142, the deterrent effect of HrpN_Eatreatment on the phloem-feeding activity accompanied an enhancement of <it>AtPP2-A1 </it>expression. In PP2OETAt (<it>AtPP2-A1</it>-overexpression transgenic <it>Arabidopsis thaliana</it>) plants, abundant amounts of the <it>AtPP2-A1 </it>gene transcript were detected in different organs, including leaves, stems, calyces, and petals. All these organs had a deterrent effect on the phloem-feeding activity compared with the same organs of the transgenic control plant. When a large-scale aphid population was monitored for 24 hours, there was a significant decrease in the number of aphids that colonized leaves of HrpN_Ea-treated WT and PP2OETAt plants, respectively, compared with control plants.</p> <p>Conclusions</p> <p>The repression in phloem-feeding activities of <it>M. persicae </it>as a result of <it>AtPP2-A1 </it>overexpression, and as a deterrent effect of HrpN_Eatreatment in WT Arabidopsis rather than the <it>atpp2-a1</it>/E/142 mutant suggest that <it>AtPP2-A1 </it>plays a role in plant resistance to the insect, particularly at the phloem-feeding stage. The accompanied change of aphid population in leaf colonies suggests that the function of <it>AtPP2-A1 </it>is related to colonization of the plant.</p

Etude de la morphologie et de la distribution des neurones dans le cerveau de macaque par microscopie optique

Understanding the mechanisms involved in healthy cases, neurodegenerative diseases and the development of new therapeutic approaches is based on the use of relevant experimental models and appropriate imaging techniques. In this context, virtual microscopy offers the unique possibility of analyzing these models at a cellular scale with a very wide variety of histological markers. My thesis project consists in carrying out and applying a method of analyzing colored histological images that can segment and synthesize information corresponding to neurons using the NeuN antibody on sections of the macaque brain. In this work, we first apply the Random Forest (RF) method to segment neurons as well as tissue and background. Then, we propose an original method to separate the touching or overlapping neurons in order to individualize them. This method is adapted to process neurons presenting a variable size (diameter varying between 5 and 30 μm). It is also effective not only for so-called "simple" regions characterized by a low density of neurons but also for so-called "complex" regions characterized by a very high density of several thousands of neurons. The next work focuses on the creation of parametric maps synthesizing the morphology and distribution of individualized neurons. For this purpose, a multiscale approach is implemented in order to produce maps with lower spatial resolutions (0.22 μm original resolution and created maps offering adaptive spatial resolution from a few dozens to a few hundred of micrometers). Several dozens of morphological parameters (mean radius, surface, orientation, etc.) are first computed as well as colorimetric parameters. Then, it is possible to synthesize this information in the form of lower-resolution parametric maps at the level of anatomical regions, sections and even, eventually, the entire brains. This step transforms qualitative color microscopic images to quantitative mesoscopic images, more informative and easier to analyze. This work makes it possible to statistically analyze very large volumes of data, to synthesize information in the form of quantitative maps, to analyze extremely complex problems such as neuronal death, to test new drugs and to compare this acquired information post mortem with data acquired in vivo.La compréhension des mécanismes impliqués dans les cas sains, les maladies neurodégénératives ainsi que le développement de nouvelles approches thérapeutiques repose sur l’utilisation de modèles expérimentaux pertinents et de techniques d’imagerie adaptées. Dans ce contexte, la microscopie virtuelle offre la possibilité unique d’analyser ces modèles à l’échelle cellulaire avec une très grande variété de marquages histologiques. Mon projet de thèse consiste à mettre en place et à appliquer une méthode d’analyse d’images histologiques en couleur permettant de segmenter et de synthétiser l’information relative aux neurones à l’aide de l’anticorps NeuN sur des coupes de cerveau de macaque. Dans ce travail, nous appliquons d’abord la méthode de Random Forest (RF) pour segmenter les neurones ainsi que le tissu et le fond. Ensuite, nous proposons une méthode originale pour séparer les neurones qui sont accolés afin de les individualiser. Cette méthode s’adapte à l’ensemble des neurones présentant une taille variable (diamètre variant entre 5 et 30 μm). Elle est également efficace non seulement pour des régions dites « simples » caractérisées par une faible densité de neurones mais aussi pour des régions dites « complexes » caractérisées par une très forte densité de plusieurs milliers de neurones. Le travail suivant se concentre sur la création de cartes paramétriques synthétisant la morphologie et la distribution des neurones individualisés. Pour cela, un changement d’échelle est mis en œuvre afin de produire des cartographies présentant des résolutions spatiales plus faibles (résolution originale de 0,22 μm et cartographies créées proposant une résolution spatiale adaptative de quelques dizaines à quelques centaines de micromètres). Plusieurs dizaines de paramètres morphologiques (rayon moyen, surface, orientation, etc.) sont d’abord calculés pour chaque neurone ainsi que des paramètres colorimétriques. Ensuite, il est possible de synthétiser ces informations sous la forme de cartes paramétriques à plus basse résolution à l’échelle de régions anatomiques, de coupes voire, à terme, de cerveaux entiers. Cette étape transforme des images microscopiques qualitatives couleur en images mésoscopiques quantitatives, plus informatives et plus simples à analyser. Ce travail permet d’analyser statistiquement de très grands volumes de données, de synthétiser l’information sous la forme de cartographies quantitatives, d’analyser des problèmes extrêmement complexes tels que la mort neuronale et à terme de tester de nouveaux médicaments voire de confronter ces informations acquises post mortem avec des données acquises in vivo

Study of the morphology and distribution of neurons in the macaque brain using optical microscopy

La compréhension des mécanismes impliqués dans les cas sains, les maladies neurodégénératives ainsi que le développement de nouvelles approches thérapeutiques repose sur l’utilisation de modèles expérimentaux pertinents et de techniques d’imagerie adaptées. Dans ce contexte, la microscopie virtuelle offre la possibilité unique d’analyser ces modèles à l’échelle cellulaire avec une très grande variété de marquages histologiques. Mon projet de thèse consiste à mettre en place et à appliquer une méthode d’analyse d’images histologiques en couleur permettant de segmenter et de synthétiser l’information relative aux neurones à l’aide de l’anticorps NeuN sur des coupes de cerveau de macaque. Dans ce travail, nous appliquons d’abord la méthode de Random Forest (RF) pour segmenter les neurones ainsi que le tissu et le fond. Ensuite, nous proposons une méthode originale pour séparer les neurones qui sont accolés afin de les individualiser. Cette méthode s’adapte à l’ensemble des neurones présentant une taille variable (diamètre variant entre 5 et 30 μm). Elle est également efficace non seulement pour des régions dites « simples » caractérisées par une faible densité de neurones mais aussi pour des régions dites « complexes » caractérisées par une très forte densité de plusieurs milliers de neurones. Le travail suivant se concentre sur la création de cartes paramétriques synthétisant la morphologie et la distribution des neurones individualisés. Pour cela, un changement d’échelle est mis en œuvre afin de produire des cartographies présentant des résolutions spatiales plus faibles (résolution originale de 0,22 μm et cartographies créées proposant une résolution spatiale adaptative de quelques dizaines à quelques centaines de micromètres). Plusieurs dizaines de paramètres morphologiques (rayon moyen, surface, orientation, etc.) sont d’abord calculés pour chaque neurone ainsi que des paramètres colorimétriques. Ensuite, il est possible de synthétiser ces informations sous la forme de cartes paramétriques à plus basse résolution à l’échelle de régions anatomiques, de coupes voire, à terme, de cerveaux entiers. Cette étape transforme des images microscopiques qualitatives couleur en images mésoscopiques quantitatives, plus informatives et plus simples à analyser. Ce travail permet d’analyser statistiquement de très grands volumes de données, de synthétiser l’information sous la forme de cartographies quantitatives, d’analyser des problèmes extrêmement complexes tels que la mort neuronale et à terme de tester de nouveaux médicaments voire de confronter ces informations acquises post mortem avec des données acquises in vivo.Understanding the mechanisms involved in healthy cases, neurodegenerative diseases and the development of new therapeutic approaches is based on the use of relevant experimental models and appropriate imaging techniques. In this context, virtual microscopy offers the unique possibility of analyzing these models at a cellular scale with a very wide variety of histological markers. My thesis project consists in carrying out and applying a method of analyzing colored histological images that can segment and synthesize information corresponding to neurons using the NeuN antibody on sections of the macaque brain. In this work, we first apply the Random Forest (RF) method to segment neurons as well as tissue and background. Then, we propose an original method to separate the touching or overlapping neurons in order to individualize them. This method is adapted to process neurons presenting a variable size (diameter varying between 5 and 30 μm). It is also effective not only for so-called "simple" regions characterized by a low density of neurons but also for so-called "complex" regions characterized by a very high density of several thousands of neurons. The next work focuses on the creation of parametric maps synthesizing the morphology and distribution of individualized neurons. For this purpose, a multiscale approach is implemented in order to produce maps with lower spatial resolutions (0.22 μm original resolution and created maps offering adaptive spatial resolution from a few dozens to a few hundred of micrometers). Several dozens of morphological parameters (mean radius, surface, orientation, etc.) are first computed as well as colorimetric parameters. Then, it is possible to synthesize this information in the form of lower-resolution parametric maps at the level of anatomical regions, sections and even, eventually, the entire brains. This step transforms qualitative color microscopic images to quantitative mesoscopic images, more informative and easier to analyze. This work makes it possible to statistically analyze very large volumes of data, to synthesize information in the form of quantitative maps, to analyze extremely complex problems such as neuronal death, to test new drugs and to compare this acquired information post mortem with data acquired in vivo

A Memristor-Based Colpitts Oscillator Circuit

This paper investigates a simple memristor emulator consisting of a diode bridge and a capacitor. It exhibits pinched hysteresis loops, and what is more striking is the higher frequency, as it operates up to greater than 5 MHz. Based on the proposed memristor, a higher-frequency Colpitts circuit was established. According to the mathematical model of the system, the system only possesses one unstable equilibrium point. Period doubling bifurcation, reverse periodic doubling bifurcation, different types of periodic and chaotic orbits, transient chaos, coexisting bifurcations and offset boosting are depicted. More interestingly, it has coexisting multiple attractors with different topologies, such as a chaotic attractor accompanied with periodic orbits, period-1 orbits with bicuspid structure and periodic-2 orbits with tridentate structure. Moreover, a hardware circuit using discrete components was fabricated and experimental measurements were consistent with the MATLAB numerical results, further confirming the real feasibility of the proposed circuit

Acceleration Feature Extraction of Human Body Based on Wearable Devices

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly

Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section

International audienc

Cellular morphometric analysis: from microscopic scale to whole mouse brains

International audienceIn neurodegenerative diseases, pathological aggregates disturb cell function and morphology. Quantifying these changes is of prime interest but raises experimental and computational challenges. In this context, whole-slide imaging (WSI) offers the unique opportunity to analyze whole mouse brain sections at the cellular level using a variety of histological markers. However, this technique generates terabytes of data which is difficult to fully analyze.We developed a novel method enabling: (1) to detect cells and pathological aggregates in WSI color images at the cellular level; (2) to quantify parameters of interest such as density, shape, location or color and (3) to integrate the information within quantitative and multi-scale heat maps.This original approach enables to extract pertinent information from high-resolution qualitative images and to dramatically reduce the amount of information to be processed. A supplementary step of this work consists in extending the analysis from brain sections to the entire brains reconstructed in 3D using our in-house software BrainVISA (http://brainvisa.info).From the generated 3D parametric maps, voxel-wise statistical studies can be performed to investigate cellular structural alterations without a priori. Furthermore, correlating 3D whole-brain parametric maps with in vivo imaging modalities (MRI, fMRI, PET, in vivo microscopy, etc.) will improve the understanding of the relationship between brain structure and function in disease

Analyzing the Effect of the Intra-Pixel Position of Small PSFs for Optimizing the PL of Optical Subpixel Localization

Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement fields. With unavoidable imaging noise, there is a precision limit (PL) when estimating the target positions on image sensors, which depends on the detected photon count, noise, point spread function (PSF) radius, and PSF’s intra-pixel position. Previous studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position information. Here, we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small PSFs. To accurately estimate the PL in practical applications, we provide effective PSF (ePSF) modeling approaches and apply the Cramér–Rao lower bound. Based on the characteristics of small PSFs, we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF; we then verify these equations on real PSFs. Next, we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible, indicating that the optimum is ultimately limited by light diffraction. Finally, we apply the maximum likelihood method. Its combination with ePSF modeling allows us to successfully reach the PL in experiments, making the above theoretical analysis effective. This work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory, thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization

Transcription Regulation of <i>Tceal7</i> by the Triple Complex of Mef2c, Creb1 and Myod

Tceal7 has been identified as a direct, downstream target gene of MRF in the skeletal muscle. The overexpression of Tceal7 represses myogenic proliferation and promotes cell differentiation. Previous studies have defined the 0.7 kb upstream fragment of the Tceal7 gene. In the present study, we have further determined two clusters of transcription factor-binding motifs in the 0.7 kb promoter: CRE#2–E#1–CRE#1 in the proximal region and Mef2#3–CRE#3–E#4 in the distal region. Utilizing transcription assays, we have also shown that the reporter containing the Mef2#3–CRE#3–E#4 motifs is synergistically transactivated by Mef2c and Creb1. Further studies have mapped out the protein–protein interaction between Mef2c and Creb1. In summary, our present studies support the notion that the triple complex of Mef2c, Creb1 and Myod interacts with the Mef2#3–CRE#3–E#4 motifs in the distal region of the Tceal7 promoter, thereby driving Tceal7 expression during skeletal muscle development and regeneration

Optimal Allocation Model of Water Resources Based on the Prospect Theory

The rational allocation of water resources in the basin/region can be better assisted and performed using a suitable water resources allocation model. Rule-based and optimization-based simulation methods are utilized to solve medium- and long-term water resources allocation problems. Since rule-based allocation methods requires more experience from expert practice than optimization-based allocation methods, it may not be utilized by users that lack experience. Although the optimal solution can be obtained via the optimization-based allocation method, the highly skilled expert experience is not taken into account. To overcome this deficiency and employ the advantages of both rule-based and optimization-based simulation methods, this paper proposes the optimal allocation model of water resources where the highly skilled expert experience has been considered therein. The &ldquo;prospect theory&rdquo; is employed to analyze highly skilled expert behavior when decision-making events occur. The cumulative prospect theory value is employed to express the highly skilled expert experience. Then, the various elements of the cumulative prospect theory value can be taken as the variables or parameters in the allocation model. Moreover, the optimal water allocation model developed by the general algebraic modeling system (GAMS) has been improved by adding the decision reversal control point and defining the inverse objective function and other constraints. The case study was carried out in the Wuyur River Basin, northeast of China, and shows that the expert experience considered as the decision maker&rsquo;s preference can be expressed in the improved optimal allocation model. Accordingly, the improved allocation model will contribute to improving the rationality of decision-making results and helping decision-makers better address the problem of water shortage