•Procalcitonin is elevated in patients with preeclampsia.•Procalcitonin is elevated in umbilical vein samples of patients with preeclampsia.•High-sensitivity C-reactive protein (hs-CRP) is elevated ...in patients with preeclampsia.•hs-CRP is elevated in umbilical vein samples of patients with preeclampsia.•Interleukin-6 is elevated in patients with preeclampsia.
To report maternal and umbilical vein levels of procalcitonin (PCT) in patients with preeclampsia (PE) compared to controls. As secondary aims, we measured high-sensitivity C-reactive protein (hs-CRP), and interleukin-6 (IL-6). Moreover, correlation analyses were performed between the inflammatory biomarkers and mean arterial pressure (MAP).
This was a single center, cross-sectional study.
After Institutional Review Board approval and written informed consent, patients with or without PE were enrolled. PCT, hs-CRP, and IL-6 levels were compared between groups using multiple linear regression models. We calculated the adjusted ratios of geometric means (aRGM) for the comparison of patients with and without PE. Correlation analysis between the inflammatory biomarkers and MAP was performed using Spearman’s method.
A total of 156 participants were enrolled, yielding 156 venous blood samples and umbilical venous blood samples. Seventy-six patients were in the PE group, and 80 patients were in the control group. Maternal plasma and serum concentrations of PCT (aRGM 3.35 (95% confidence interval CI: 2.26, 4.95; p < 0.001)), hs-CRP (aRGM 1.85 (95% CI: 1.30, 2.63; p = 0.003)), and IL-6 (aRGM 1.49 (95% CI: 1.08, 2.04; p = 0.045)) were higher in the PE group. In umbilical venous samples, the concentrations of PCT (aRGM 2.54 (95% CI: 1.46, 4.44; p = 0.003)) and hs-CRP (aRGM 1.45 (95% CI: 1.13, 1.87; p = 0.012)) in the PE group were higher than the controls. No difference in umbilical venous IL-6 concentrations were detected between PE vs. control groups (aRGM 1.46; 95% CI: 1.07, 1.98; p = 0.051). There was positive correlation for both PCT and hs-CRP with MAP in maternal and umbilical venous samples. However, there was no correlation between IL and 6 and MAP in maternal or umbilical venous samples.
PCT levels were elevated in maternal and umbilical venous samples of patients with PE, and correlated with disease severity.
Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar ...patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.
Meta-heuristic algorithms inspired by nature have been used in a wide range of optimization problems. These types of algorithms have gained popularity in the field of artificial neural networks ...(ANN). On the other hand, spiking neural networks are a new type of ANN that simulates the behaviour of a biological neural network in a more realistic manner. Furthermore, these neural models have been applied to solve some pattern recognition problems. In this paper, it is proposed the use of the particle swarm optimization (PSO) algorithm to adjust the synaptic weights of a spiking neuron when it is applied to solve a pattern classification task. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal. Then, the spiking neuron is stimulated during T ms and the firing rate is computed. After adjusting the synaptic weights of the neural model using the PSO algorithm, input patterns belonging to the same class will generate similar firing rates. On the contrary, input patterns belonging to other classes will generate firing rates different enough to discriminate among the classes. At last, a comparison between the PSO algorithm and a differential evolution algorithm is presented when the spiking neural model is applied to solve non-linear and real object recognition problems.
A view-based method for 3D object recognition based on some biological aspects of infant vision is proposed in this paper. The biological hypotheses of this method are based on the role of the ...response to low frequencies at early stages as well as some conjectures concerning how an infant detects subtle features (stimulating points) from an object. In order to recognize an object from different images of it (at different orientations from 0° to 360°), we make use of a dynamic associative memory (DAM). As the infant vision responds to low frequencies of the signal, a low-filter is first used to remove high frequency components from the image. Then, we detect subtle features in the image by means of a random feature selection detector. At last, the DAM is fed with this information for training and recognition. To test the accuracy of the proposed model, we use the Columbia Object Image Library (COIL 100) database.
Abstract Objective A systematic histological, morphometric and immunohistochemical (PAI-1 and TGF-β1) study of umbilical vessels in normal and pathological conditions was undertaken in order to ...describe and compare the lesions found. Methods Segments of umbilical cords were obtained from 92 pregnancies/107 newborns from normal gestations (n = 20) or from gestational diabetes mellitus (n = 13), chronic hypertension (n = 14), preeclampsia (n = 9), intrahepatic cholestasis (n = 13), antiphospholipid syndrome (n = 11), fetal growth restriction (n = 9), oligohydramnios (n = 6), premature rupture of membranes (n = 12), antiphospholipid antibodies (n = 11) and fetal distress (n = 23). Thirty-four of these patients presented combined pathologies. Results “Pathological” umbilical cords presented perivascular/intraparietal hemorrhages with wall dissections, parietal recent thrombosis and focal moderate or extensive Wharton’s jelly hemorrhages. Pathological pregnancies presented more microscopic lesions (35/73; 48%) than normal pregnancies (4/20; 20%; p = 0.039). The wall:lumen ratio of arteries was significantly higher in all pathologies (32.6 ± 16) as compared to 3.1 ± 0.6 in the control group (p < 0.0001), also due to the significantly higher values belonging to outer plus inner layer areas in opposition to much less increases in luminal areas (p = 0.03). Concerning veins, wall:lumen ratio was also higher in the pathological groups (p = 0.0086) due to a 2-fold increase in wall areas. Conclusion Quantitative histomorphometry of the pathological alterations and pathophysiologic disorders of the umbilical cord has the potential to enhance investigation and treatment of maternal and fetal diseases.
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•Selecting an appropriate passband in the preprocessing improves the classification.•The selection of the passband affects regardless if a linear/nonlinear feature is used.•Fractal ...dimension enhance the accuracy of electroencephalography signal classification.
Brain signals classification is an interesting topic due its different applications not only in the medical field, but also in the development of technology. Due to the features of the brain signals such as high variability and complexity, the stages previous to the classification: pre-processing and feature extraction, are crucial. Particularly, the pre-processing stage has different objectives such as segmentation, artifact removal or the selection of desired frequency bands. The latter is useful since different oscillatory patterns are present when different mental tasks are performed. Therefore, in this work, we propose an analysis of the accuracy during the classification when electroencephalographic (EEG) signals related to motor imagery tasks, are filtered by a band pass filter with different cutoff frequencies. The results are compared against those achieved when the signals are filtered using the frequency bands related to the performed tasks, in this case: alpha and beta bands. Furthermore, different feature extraction techniques are evaluated: the coefficients of an auto-regressive model and the fractal dimension computed through two different methods, Higuchi and Katz. The classification is achieved by a linear discriminant. Based on a statistical analysis is possible to say that a significant increase on the accuracy is achieved when the cutoff frequencies of the band pass filter are properly selected. Moreover, the results suggest that the fractal dimension provides better results than AR coefficients and for computing the fractal dimension, Higuchi's method is better option than Katz since provides similar accuracy values but its computational complexity is lower.
The molecular dipole moments, their derivatives, and the fundamental IR intensities of the X2CY (X = H, F, Cl; Y = O, S) molecules are determined from QTAIM atomic charges and dipoles and their ...fluxes at the MP2/6-311++G(3d,3p) level. Root-mean-square errors of ±0.03 D and ±1.4 km mol-1 are found for the molecular dipole moments and fundamental IR intensities calculated using quantum theory of atoms in molecules (QTAIM) parameters when compared with those obtained directly from the MP2/6-311++G(3d,3p) calculations and ±0.05 D and 51.2 km mol-1 when compared with the experimental values. Charge (C), charge flux (CF), and dipole flux (DF) contributions are reported for all the normal vibrations of these molecules. A large negative correlation coefficient of −0.83 is calculated between the charge flux and dipole flux contributions and indicates that electronic charge transfer from one side of the molecule to the other during vibrations is accompanied by a relaxation effect with electron density polarization in the opposite direction. The characteristic substituent effect that has been observed for experimental infrared intensity parameters and core electron ionization energies has been applied to the CCFDF/QTAIM parameters of F2CO, Cl2CO, F2CS, and Cl2CS. The individual atomic charge, atomic charge flux, and atomic dipole flux contributions are seen to obey the characteristic substituent effect equation just as accurately as the total dipole moment derivative. The CH, CF, and CCl stretching normal modes of these molecules are shown to have characteristic sets of charge, charge flux, and dipole flux contributions.
Median associative memories (MED-AMs) are a special type of associative memory based on the median operator. This type of associative model has been applied to the restoration of gray scale images ...and provides better performance than other models, such as morphological associative memories, when the patterns are altered with mixed noise. Despite of his power, MED-AMs have not been applied in problems involving true-color patterns. In this paper we describe how a median hetero-associative memory (MED-HAM) could be applied in problems that involve true-color patterns. A complete study of the behavior of this associative model in the restoration of true-color images is performed using a benchmark of 14400 images altered by different type of noises. Furthermore, we describe how this model can be applied to an image categorization problem.
Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of ...training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this paper is to show which of these two algorithms provides the best accuracy during the learning phase of an ANN. First of all, we explain how the ANN training phase could be seen as an optimization problem. Then, we explain how PSO and DE could be applied to find the best synaptic weights of the ANN. Finally, we perform a comparison between PSO and DE approaches when used to train an ANN applied to different non-linear problems.
Several meta-heuristic algorithms have been pro posed in the last years for solving a wide range of optimization problems. Cuckoo Search Algorithm (CS) is a novel meta heuristic based on the obligate ...brood parasitic behaviour of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. This algorithm has been applied in a wide range of optimization problems; nonetheless, their promising results suggest its application in the field of artificial neural networks, specially during the adjustment of the synaptic weights. On the other hand, spiking neurons are neural models that try to simulate the behavior of biological neurons when they are excited with an input current (input pattern) during a certain period time. Instead of generating a response in its output every iteration, as classical neurons do, this model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current. To perform a classification task the model ought to exhibit the next behavior: patterns from the same class must generate similar firing rates and patterns from other classes have to generate firing rates sufficiently dissimilar to differentiate among the classes. The model needs of a training phase aimed to adjust their synaptic weights and exhibit the desired behavior. In this paper, we describe how the CS algorithm can be useful to train a spiking neuron to be applied in a pattern classification task. The accuracy of the methodology is tested using several pattern recognition problems.
