Identification of Cocoa Pods with Image Processing and Artificial Neural Networks

Cocoa pods harvest is a process where peasant makes use of his experience to select the ripe fruit. During harvest, the color of the pods is a ripening indicator and is related to the quality of the cocoa bean. This paper proposes an algorithm capable of identifying ripe cocoa pods through the processing of images and artificial neural networks. The input image pass in a sequence of filters and morphological transformations to obtain the features of objects present in the image. From these features, the artificial neural network identifies ripe pods. The neural network is trained using the scaled conjugate gradient method. The proposed algorithm, developed in MATLAB ®, obtained a 91% of assertiveness in the identification of the pods. Features used to identify the pods were not affected by the capture distance of the image. The criterion for selecting pods can be modified to get similar samples with each other. For correct identification of the pods, it is necessary to take care of illumination and shadows in the images. In the same way, for accurate discrimination, the morphology of the pod was important

Geometrie analysis and scaling properties of calcite e-twins in the Cameros Basin (NW Iberian Chain, Spain)

One dimensional geometric analysis has been carried out in several scan lines from 885 measures of twins in calcite grains to determine grain width (in microns) and twin density (number of twins.mm-1 ) distributions. Grain width and twin density have a good fit to the log-normal frequency distribution. Twinning in calcite implies intracrystaline deformation mechanism with low shear stress. When the process begins low grain width and calcite twins are developed with a probably random distribution what could be supported by a negative exponential distribution tendency. The twinning process continues until a "critical" value of grain width and density which is going to influence in the scaling process, and becoming the distribution to log-normal type. But some data also conform to a power-law (fractal) frequency distribution from determined range or sizes (300 to WOO mm) and density (2 to W twins.mm-1) with some superimposed random (negative-exponential) elements, possible due to the irregularities at grain scale, but also because this systems show multifractal behavior

Cleavages of photochromic compounds derived from heterocycles under electrospray tandem mass spectrometry : study of the influence of the heteroatom in fragmentation mechanisms

In this paper we report the fragmentation pathways of chromenes derived from carbazole, dibenzofuran and dibenzothiophene, under ESI-MS/MS experimental conditions, and their relationship with structural features, specially focused on the heteroatom’s effect on the fragmentation mechanisms.Fundação para a Ciência e Tecnologia (FCT

Crambescin C1 Acts as A Possible Substrate of iNOS and eNOS Increasing Nitric Oxide Production and Inducing In Vivo Hypotensive Effect

Crambescins are guanidine alkaloids from the sponge Crambe crambe. Crambescin C1 (CC) induces metallothionein genes and nitric oxide (NO) is one of the triggers. We studied and compared the in vitro, in vivo, and in silico effects of some crambescine A and C analogs. HepG2 gene expression was analyzed using microarrays. Vasodilation was studied in rat aortic rings. In vivo hypotensive effect was directly measured in anesthetized rats. The targets of crambescines were studied in silico. CC and homo-crambescine C1 (HCC), but not crambescine A1 (CA), induced metallothioneins transcripts. CC increased NO production in HepG2 cells. In isolated rat aortic rings, CC and HCC induced an endothelium-dependent relaxation related to eNOS activation and an endothelium-independent relaxation related to iNOS activation, hence both compounds increase NO and reduce vascular tone. In silico analysis also points to eNOS and iNOS as targets of Crambescin C1 and source of NO increment. CC effect is mediated through crambescin binding to the active site of eNOS and iNOS. CC docking studies in iNOS and eNOS active site revealed hydrogen bonding of the hydroxylated chain with residues Glu377 and Glu361, involved in the substrate recognition, and explains its higher binding affinity than CA. The later interaction and the extra polar contacts with its pyrimidine moiety, absent in the endogenous substrate, explain its role as exogenous substrate of NOSs and NO production. Our results suggest that CC serve as a basis to develop new useful drugs when bioavailability of NO is perturbed.Fil: Rubiolo, Juan Andrés. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; Argentina. Ministerio de Ciencia, Tecnologia E Innovacion Productiva (santa Fe). - Gobierno de la Provincia de Santa Fe. Ministerio de Ciencia, Tecnologia E Innovacion Productiva (santa Fe).; Argentina. Universidad de Santiago de Compostela; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Lence, Emilio. Universidad de Santiago de Compostela; EspañaFil: González Bello, Concepción. Universidad de Santiago de Compostela; EspañaFil: Roel, María. Universidad de Santiago de Compostela; EspañaFil: Gil Longo, José. Universidad de Santiago de Compostela; EspañaFil: Campos Toimil, Manuel. Universidad de Santiago de Compostela; EspañaFil: Ternon, Eva. Université Nice Sophia Antipolis. Laboratoire Jean-alexandre Dieudonné.; FranciaFil: Thomas, Olivier P.. National University of Ireland Galway; IrlandaFil: González Cantalapiedra, Antonio. Universidad de Santiago de Compostela; EspañaFil: López Alonso, Henar. Universidad de Santiago de Compostela; EspañaFil: Vieytes, Mercedes R.. Universidad de Santiago de Compostela; EspañaFil: Botana, Luis M.. Universidad de Santiago de Compostela; Españ

Analysis of gated myocardial perfusion SPECT images based on computational image registration

Myocardial perfusion is commonly studied based on the evaluation of the leftventricular function using stress-rest gated myocardial perfusion single photon emissioncomputed tomography (GSPECT), which provides a suitable identification of the myocardialregion, facilitating the localization and characterization of perfusion abnormalities. Theprevalence and clinical predictors of myocardial ischemia and infarct can be assessed fromGSPECT images.Here, techniques of image analysis, namely image segmentation and registration, areintegrated to automatically extract a set of features from myocardial perfusion SPECT imagesthat are automatically classified as related to myocardial perfusion disorders or not. Thesolution implemented can be divided into two main parts: 1) building of a template image,segmentation of the template image and computation of its dimensions; 2) registration of theimage under study with the template image previously built, extraction of the image features,statistical analysis and classification. It should be noted that the first step just needs to beperformed once for a particular population. Hence, algorithms of image segmentation,registration and classification were used, specifically of k-means clustering, rigid anddeformable registration and classification.The computational solution developed was tested using 180 3D images from 48 patients withhealthy cardiac condition and 72 3D images from 12 patients with cardiac diseases, whichwere reconstructed using the filtered back projection algorithm and a low pass Butterworthfilter or iterative algorithms. The images were classified into two classes: abnormalitypresent and abnormality not present. The classification was assessed using fiveparameters: sensitivity, specificity, precision, accuracy and mean error rate.The results obtained shown that the solution is effective, both for female and male cardiacSPECT images that can have very different structural dimensions. Particularly, the solutiondemonstrated reasonable robustness against the two major difficulties in SPECT imageanalysis: image noise and low resolution. Furthermore, the classifier used demonstrated goodspecificity and accuracy, Table 1

Traffic State Prediction Using 1-Dimensional Convolution Neural Networks and Long Short-Term Memory

Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic’s spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison

Lipidomic approach of the biological activity of tacrine and two tacrine-analogues on non-synaptic rat brain mitochondria

Foundation for Science and Technology (FCT), FEDER and COMPET

Multitemporal monitoring of plant area index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff ) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances