Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments.

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

Early pregnancy protein multiplex screening reflects circulating and urinary divergences associated with the development of preeclampsia.

BACKGROUND: Preeclampsia, a pregnancy disorder characterized by hypertension and proteinuria, represents the leading cause of fetal and maternal morbidity and mortality in developing countries. The identification of novel and accurate biomarkers that are predictive of preeclampsia is necessary to improve the prognosis of patients with preeclampsia. OBJECTIVE: To evaluate the preeclampsia predictive value of 34 angiogenic-related proteins. METHODS: We performed a nested cohort case-control study of pregnant women. The profile of the 34 proteins was evaluated at 12, 16, and 20 gestational weeks (GWs), using urine/plasma from 16 women who developed preeclampsia and 20 normotensive pregnant controls by Bio-Plex ProTM Human Cancer Biomarker Panels 1 and 2. RESULTS: The urine concentration of soluble epidermal growth factor receptor (sEGFR), hepatocyte growth factor (HGF), angiopoietin-2 (ANG-2), endoglin (ENG), soluble fas ligand (sFASL), interleukin 6 (IL-6), placental growth factor (PLGF), and vascular endothelial growth factor A (VEGF-A) at 12 GW, prolactin (PRL), ANG-2, transforming growth factor alpha (TGF-α), and VEGF-A at 16 GW, and soluble IL-6 receptor alpha (sIL-6Rα), ANG-2 and sFASL at 20 GW, were different between groups (p < 0.05). The concentration cut-off values calculated in this study for the mentioned proteins, predicted an increased risk to developing preeclampsia in a range of 3.8-29.8 times in the study population. CONCLUSION: The proteins sEGFR, HGF, ANG-2, sFASL, IL-6, PLGF, VEGF-A, PRL, TGF-α FGF-b, sHER2/Neu sIL-6Rα, ENG, uPA, and insulin-like growth factor binding protein 1 (IGFBP-1), were predictive of the development of preeclampsia and their use as markers for this disease should be considered.

Matrix metalloproteinase multiplex screening identifies increased MMP-2 urine concentrations in women predicted to develop preeclampsia.

BACKGROUND: Preeclampsia, a pregnancy disorder characterized by hypertension and proteinuria, represents the leading cause of fetal and maternal morbidity and mortality in developing countries. The identification of novel and accurate biomarkers that are predictive of preeclampsia is necessary to improve the prognosis of patients with preeclampsia. OBJECTIVE: The objective of this study is to evaluate the usefulness of nine urinary metalloproteinases to predict the risk of preeclampsia development. METHODS: MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12 and MMP-13 were analyzed in urine (early-pregnancy) from 17 women predicted to develop preeclampsia and 48 controls using the Bio-Plex Pro-Human MMP panel (Bio-Rad, Hercules, CA). RESULTS: Urinary MMP-2 showed differences between groups which allowed us to calculate an increased risk for PE development of up to 20 times among the study population. CONCLUSION: Increased urinary concentration of MMP-2 at 12 and 16 weeks of gestation predicted an increased risk of developing preeclampsia in the study population.

Circulating levels of specific members of chromosome 19 microRNA cluster are associated with preeclampsia development.

PURPOSE: To perform serum microRNA expression profiling to identify members of chromosome 19 miRNA cluster involved in preeclampsia development. METHODS: Serum chromosome 19 miRNA cluster microRNA expression profiling was evaluated at 12, 16, and 20 gestational weeks and at the time of preeclampsia diagnosis, in women who developed preeclampsia (WWD-PE; n = 16) and controls (n = 18) using TaqMan low density array plates. RESULTS: A total of 51 chromosome 19 microRNA cluster members were evaluated. The circulating hsa-miRs 512-3p, 518f-3p, 520c-3p, and 520d-3p, were differentially expressed between groups (P < 0.05). Compared with controls, serum levels of hsa-miR-518f-3p at 20 GW were useful for identifying WWD-Mild-PE (P = 0.035) and WWD-Severe-PE (P = 0.007). CONCLUSIONS: Serum hsa-miRs 512-3p, 518f-3p, 520c-3p, and 520d-3p, are differentially expressed between WWD-PE and controls and their role in the development of preeclampsia should be investigated further.