Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8–20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10–20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20–14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.
This paper presents a mathematical procedure for modeling rectangular (N rows with M modules each) and non-rectangular photovoltaic (PV) arrays in Total Cross-Tied (TCT) configuration operating in uniform and mismatching conditions. The proposed model uses the simple single diode representation for each PV module; then each row of the TCT array is represented as an equivalent non-linear PV circuit with a bypass diode, which allows to represent the TCT array as one string of equivalent PV circuits. The inflection voltages (array voltages that turn off the bypass diodes) of the string are calculated in order to solve only the non-linear equation system related to the active equivalent PV circuits for calculating the array current for a given voltage. Such a strategy reduces the computational burden and improves calculation speed. A TCT array of 4×2 with deep mismatching conditions was implemented in PSIM software to validate the proposed model, obtaining a correlation between model predicted data and the circuital simulation. The accuracy and improved calculation speed of the proposed model allow its use altogether with reconfiguration techniques as well as to reduce the time of energetic evaluations of TCT arrays for PV planning.Peer ReviewedPostprint (published version
Maize crops occupy an important place in world food security. However, different conditions, such as abiotic stress factors, can affect the productivity of these crops, requiring technologies that facilitate their monitoring. One such technology is spectroscopy, which measures the energy reflected and emitted by a surface along the electromagnetic spectrum. Spectral data can help to identify abiotic factors in plants, since the spectral signature of vegetation has discriminating features associated with the plant’s health condition. This paper introduces a spectral library captured on maize crops under different nitrogen-deficiency stress levels. The datasets will be of potential interest to researchers, ecologists, and agronomists seeking to understand the spectral features of maize under nitrogen-deficiency stress. The library includes three datasets captured at different growth stages of 10 tropical maize genotypes. The spectral signatures collected were in the visible to near-infrared range (450–950 nm). The data were pre-processed to reduce noise and anomalous signatures. This study presents a spectral library of the effects of nitrogen deficiency on ten maize genotypes, highlighting that some genotypes show tolerance to this type of stress at different phenological stages. Most of the evaluated genotypes showed discriminate spectral features 4–6 weeks after sowing. Higher reflectance was obtained at approximately 550 nm for the lowest nitrogen fertilization treatments. Finally, we describe some potential applications of the spectral library of maize leaves under nitrogen-deficiency stress.
Acute myeloid leukemia (AML) is a malignant disorder of the hematopoietic stem and progenitor cells, which results in the build-up of immature blasts in the bone marrow and eventually in the peripheral blood of affected patients. Accurately assessing a patient´s prognosis is very important for clinical management of the disease, which is why there are several prognostic factors such as age, performance status at diagnosis, platelet count, serum creatinine and albumin that are taken into account by the clinician when deciding the course of treatment. However, proteomic changes related to treatment response in this patient group have not been widely explored. Here, we make available a set of 22 two-dimensional gel electrophoresis (2DGE) images obtained from the peripheral blood samples of 11 patients with AML, taken at the time of diagnosis and after induction therapy (approximately 21–28 days after starting treatment). The same set of 2DGE images is also made available after a preprocessing stage (an additional 22 2DGE pre-processed images), which was performed using algorithms developed in Python, in order to improve the visualization of characteristic spots and facilitate proteomic analysis of this type of images.
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