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Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water's pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2's limits is 86%⁻94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.

Killer cell immunoglobulin-like receptors (KIRs) modulate natural killer (NK) and T-cell function by human leukocyte antigen class I interaction and have been implicated in celiac disease (CD). Qualitative expression of 16 KIR genes was determined in biopsies from 22 CD patients at diagnosis and after >2 years on a gluten-free diet (GFD). Quantitative expression analysis of KIR2DL4, KIR3DL1, KIR3DL3, and KLRC2 (a marker of an NK-reprogrammed T-cell subpopulation augmented in CD) was performed in 35 additional CD biopsy pairs and 14 non-CD control biopsies. No specific KIR expression profile was observed in CD. KIR3DL1 was more frequently expressed in active CD compared with GFD (p = 0.0312) and controls (p = 0.0008), with slightly increased levels in active disease. KLRC2 was overexpressed in active (p = 0.0037) and GFD (p = 0.0469) patients compared with non-CD controls and coexpressed with KIR3DL1. Results suggest the participation of KIR3DL1 overexpression in the overall immune activation seen in CD mucosa, which could be partly explained by the NK-like T-cell subpopulation increase., (Copyright © 2011 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Celiac disease is a complex, immune-mediated disorder of the intestinal mucosa with a strong genetic component. HLA-DQ2 is the major determinant of risk, but other minor genes, still to be identified, also are involved., Methods: We designed a strategy that combines gene expression profiling of intestinal biopsy specimens, linkage region information, and different bioinformatics tools for the selection of potentially regulatory single-nucleotide polymorphisms (SNPs) involved in the disease. We selected 361 SNPs from 71 genes that fulfilled stringent functional (changes in expression level) and positional criteria (located in regions that have been linked to the disease, other than HLA). These polymorphisms were genotyped in 262 celiac patients and 214 controls., Results: We detected strong evidence of association with several SNPs (the most significant were rs6747096, P = 2.38 x 10(-5); rs7040561, P = 6.55 x 10(-5); and rs458046, P = 1.35 x 10(-4)) that pinpoint novel candidate determinants of predisposition to the disease in previously identified linkage regions (eg, SERPINE2 in 2q33, and PBX3 or PPP6C in 9q34)., Conclusions: Our study shows that the combination of function and position is a valid strategy for the genetic dissection of complex traits.