Skin lesion classification from dermoscopic images using deep learning techniques

The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient’s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.Postprint (author's final draft

Advances in mapping ice-free surfaces within the Northern Antarctic peninsula region using polarimetric RADARSAT-2 data

Ice-free areas within the Northern Antarctic Peninsula region are of interest for studying changes occurring to surface covers, including those related to glacial coverage, raised beach deposits and periglacial processes and permafrost. The objective of this work is to map the main surface covers within ice-free areas of King George Island, the largest island of the South Shetlands archipelago, using fully polarimetric RADARSAT-2 SAR data. Surface covers such as rock outcrops and glacial till, stone fields, patterned ground, and sand and gravel deposits form the most representative classes and account for 84 km2 of the ice-free areas on the island. A distribution of complex geomorphological features and landforms was obtained, being some of them considered indicators of periglacial processes and presence of permafrost.Published versio

Mixing in convective thermal fluxes in unsteady nonhomogeneous flows generating complex three dimensional vorticity patterns

Diffusion and scaling of the velocity and vorticity in a thermoelectric driven heating and cooling experimental device is presented in order to map the different patterns and transitions between two and three dimensional convection in an enclosure with complex driven flows. The size of the water tank is of 0.2 x 0.2 x 0.1 m and the heat sources or sinks can be regulated both in power and sign [1-3]. The thermal convective driven flows are generated by means of Peltier effects in 4 wall extended positions of 0.05 x 0.05 cm each. The parameter range of convective cell array varies strongly with the Topology of the boundary conditions. Side heat and momentum fluxes are a function of Rayleigh, Peclet and Nusselt numbers, [4-6] Visualizations are performed by PIV, Particle tracking and shadowgraph. The structure of the flow is shown by setting up a convective flow generated by buoyant heat fluxes. The experiments described here investigate high Prandtl number mixing using brine and fresh water in order to form a density interface and low Prandtl number mixing with temperature gradients. The evolution of the mixing fronts are compared and the topological characteristics of the merging of the convective structures are examined for different configurations. Based on two dimensional Vorticity spectral analysis, new techniques can be very useful to determine the evolution of scales considering the multi-fractal structure of the convective flows.Peer ReviewedPostprint (published version

Multiscaling properties on sequences of turbulent plumes images

A multifractal analysis on a finite-range-scale of the plume concentration images at different experimental conditions (the height of the source Ho), where the measure is the grey value of the image (from 0 to 255), was applied to study its structure through time. The multifractal spectrum showed the characteristic inverse U-shape and a similar evolution in all Ho. The variation of the Hölder exponent (¿a) presented different amplitudes at different moments and increased with time. The symmetry of the spectrum (¿f) decreased with time achieving negative values (from left hand asymmetry evolving to right asymmetry). We show the different behaviour of axial velocity (W) with ¿a and ¿f. There is a linear relation of entrainment coefficient (ae) and the entropy dimension (a1). Therefore, the multifractal spectrum and the derived parameters can be used as markers of plume evolution as well as to study the effect of experimental conditions.Postprint (published version

Determination of the Number of Epoxides Groups by FTIR-HATR and Its Correlation with 1H NMR, in Epoxidized Linseed Oil

Artículo completo publicadoBy varying the conditions of the epoxidation reaction, samples of epoxidized linseed oil with different epoxidation degrees were obtained. The epoxidation of linseed oil was carried out by the formation of peracetic acid in situ using acetic acid, hydrogen peroxide solution, Amberlite IR-120H as the catalyst, and toluene as solvent. The quantification of the number of epoxides was carried out by 1H-NMR and the values obtained were correlated with the absorbance of the epoxy group signal in the FTIR-HATR spectrum. The method used to quantify the absorbance of the epoxy group signal by FTIR-HATR is based on the analysis of two regions: the area between 765-854 cm-1, and the net absorbance at 821 cm-1. Both signals analysis showed high linear correlation coefficients corroborating that this methodology represents an easy, fast, and reliable technique in the quantification of epoxides in natural oils.The authors thank to project SIEA-UAEM 4965/2020CIB, CONACYT for the Master fellowship, and to M.C. María de las Nieves Zavala from CCIQS, UAEM-UNAM for her technical support in the 1H-NMR analysis, through the intern project of the CCIQS SHL-2018

Fungicide Sensitivity of \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e Isolates from Five States with Different Fungicide Treatments

Sclerotinia sclerotiorum is a plant pathogenic fungus that causes a disease called white mold that can infect more than 450 plant species including soybeans, dry beans, green beans, canola, and sunflower. This pathogen is capable of up to $252M in losses every year (U.S. Canola Association, 2014). Fungicides are widely used in developed agricultural systems to control disease. However, resistance to the most effective fungicides has emerged and spread in pathogen populations and there have been multiple reports of S. sclerotiorum isolates becoming resistant to certain fungicides. Since different fields in different states use different fungicide treatments on plants and different numbers of applications depending on environmental conditions, we hypothesize that isolates with the lowest fungicide sensitivity will be those that come from fields with more intensive fungicide applications. We aim to determine the fungicide sensitivity of S. sclerotiorum isolates from five states to assess the risk of resistance. Isolates were selected from dry bean fields from five states from the selection of isolates in the Evertart lab. Isolates were screened against boscalid, tetraconazole, picoxystrobin, and thiophanate methyl fungicides using discriminatory concentrations previously determined by members of the Everhart lab, and their EC50(D) was calculated. Differences in EC50(D) in different states hints at S. sclerotiorum developing resistance to commonly used fungicides. However, the hypothesis was only partially supported by the data since the baseline isolates (isolates that have never been exposed to fungicides) EC50(D) is not the lowest for all fungicides

Fungicide Sensitivity of \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e Isolates from Five States with Different Fungicide Treatments

Sclerotinia sclerotiorum is a plant pathogenic fungus that causes a disease called white mold that can infect more than 450 plant species including soybeans, dry beans, green beans, canola, and sunflower. This pathogen is capable of up to $252M in losses every year (U.S. Canola Association, 2014). Fungicides are widely used in developed agricultural systems to control disease. However, resistance to the most effective fungicides has emerged and spread in pathogen populations and there have been multiple reports of S. sclerotiorum isolates becoming resistant to certain fungicides. Since different fields in different states use different fungicide treatments on plants and different numbers of applications depending on environmental conditions, we hypothesize that isolates with the lowest fungicide sensitivity will be those that come from fields with more intensive fungicide applications. We aim to determine the fungicide sensitivity of S. sclerotiorum isolates from five states to assess the risk of resistance. Isolates were selected from dry bean fields from five states from the selection of isolates in the Evertart lab. Isolates were screened against boscalid, tetraconazole, picoxystrobin, and thiophanate methyl fungicides using discriminatory concentrations previously determined by members of the Everhart lab, and their EC50(D) was calculated. Differences in EC50(D) in different states hints at S. sclerotiorum developing resistance to commonly used fungicides. However, the hypothesis was only partially supported by the data since the baseline isolates (isolates that have never been exposed to fungicides) EC50(D) is not the lowest for all fungicides

Fungicide Sensitivity of \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e Isolates from Five States with Different Fungicide Treatments

Sclerotinia sclerotiorum is a plant pathogenic fungus that causes a disease called white mold that can infect more than 450 plant species including soybeans, dry beans, green beans, canola, and sunflower. This pathogen is capable of up to $252M in losses every year (U.S. Canola Association, 2014). Fungicides are widely used in developed agricultural systems to control disease. However, resistance to the most effective fungicides has emerged and spread in pathogen populations and there have been multiple reports of S. sclerotiorum isolates becoming resistant to certain fungicides. Since different fields in different states use different fungicide treatments on plants and different numbers of applications depending on environmental conditions, we hypothesize that isolates with the lowest fungicide sensitivity will be those that come from fields with more intensive fungicide applications. We aim to determine the fungicide sensitivity of S. sclerotiorum isolates from five states to assess the risk of resistance. Isolates were selected from dry bean fields from five states from the selection of isolates in the Evertart lab. Isolates were screened against boscalid, tetraconazole, picoxystrobin, and thiophanate methyl fungicides using discriminatory concentrations previously determined by members of the Everhart lab, and their EC50(D) was calculated. Differences in EC50(D) in different states hints at S. sclerotiorum developing resistance to commonly used fungicides. However, the hypothesis was only partially supported by the data since the baseline isolates (isolates that have never been exposed to fungicides) EC50(D) is not the lowest for all fungicides