Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Effective Natural Killer Cell Degranulation Is an Essential Key in COVID-19 Evolution

NK degranulation plays an important role in the cytotoxic activity of innate immunity in the clearance of intracellular infections and is an important factor in the outcome of the disease. This work has studied NK degranulation and innate immunological profiles and functionalities in COVID-19 patients and its association with the severity of the disease. A prospective observational study with 99 COVID-19 patients was conducted. Patients were grouped according to hospital requirements and severity. Innate immune cell subpopulations and functionalities were analyzed. The profile and functionality of innate immune cells differ between healthy controls and severe patients; CD56dim NK cells increased and MAIT cells and NK degranulation rates decreased in the COVID-19 subjects. Higher degranulation rates were observed in the non-severe patients and in the healthy controls compared to the severe patients. Benign forms of the disease had a higher granzymeA/granzymeB ratio than complex forms. In a multivariate analysis, the degranulation capacity resulted in a protective factor against severe forms of the disease (OR: 0.86), whereas the permanent expression of NKG2D in NKT cells was an independent risk factor (OR: 3.81; AUC: 0.84). In conclusion, a prompt and efficient degranulation functionality in the early stages of infection could be used as a tool to identify patients who will have a better evolution.     

NMDA and P2X7 Receptors Require Pannexin 1 Activation to Initiate and Maintain Nociceptive Signaling in the Spinal Cord of Neuropathic Rats

Pannexin 1 (Panx1) is involved in the spinal central sensitization process in rats with neuropathic pain, but its interaction with well-known, pain-related, ligand-dependent receptors, such as NMDA receptors (NMDAR) and P2X7 purinoceptors (P2X7R), remains largely unexplored. Here, we studied whether NMDAR- and P2X7R-dependent nociceptive signaling in neuropathic rats require the activation of Panx1 channels to generate spinal central sensitization, as assessed by behavioral (mechanical hyperalgesia) and electrophysiological (C-reflex wind-up potentiation) indexes. Administration of either a selective NMDAR agonist i.t. (NMDA, 2 mM) or a P2X7R agonist (BzATP, 150 μM) significantly increased both the mechanical hyperalgesia and the C-reflex wind-up potentiation, effects that were rapidly reversed (minutes) by i.t. administration of a selective pannexin 1 antagonist (10panx peptide, 300 μM), with the scores even reaching values of rats without neuropathy. Accordingly, 300 μM 10panx completely prevented the effects of NMDA and BzATP administered 1 h later, on mechanical hyperalgesia and C-reflex wind-up potentiation. Confocal immunofluorescence imaging revealed coexpression of Panx1 with NeuN protein in intrinsic dorsal horn neurons of neuropathic rats. The results indicate that both NMDAR- and P2X7R-mediated increases in mechanical hyperalgesia and C-reflex wind-up potentiation require neuronal Panx1 channel activation to initiate and maintain nociceptive signaling in neuropathic rats.     

3′5-Dimaleamylbenzoic Acid Attenuates Bleomycin-Induced Pulmonary Fibrosis in Mice

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by parenchymal scarring, leading progressively to alveolar architecture distortion, respiratory failure, and eventually death. Currently, there is no effective treatment for IPF. Previously, 3′5-dimaleamylbenzoic acid (3′5-DMBA), a maleimide, demonstrated pro-apoptotic, anti-inflammatory, and anti-cancer properties; however, its potential therapeutic effects on IPF have not been addressed. Bleomycin (BLM) 100 U/kg was administered to CD1 mice through an osmotic minipump. After fourteen days of BLM administration, 3′5-DMBA (6 mg/kg or 10 mg/kg) and its vehicle carboxymethylcellulose (CMC) were administered intragastrically every two days until day 26. On day 28, all mice were euthanized. The 3′5-DMBA effect was assessed by histological and immunohistochemical staining, as well as by RT-qPCR. The redox status on lung tissue was evaluated by determining the glutathione content and the GSH/GSSG ratio. 3′5-DMBA treatment re-established typical lung histological features and decreased the expression of BLM-induced fibrotic markers: collagen, α-SMA, and TGF-β1. Furthermore, 3′5-DMBA significantly reduced the expression of genes involved in fibrogenesis. In addition, it decreased reduced glutathione and increased oxidized glutathione content without promoting oxidative damage to lipids, as evidenced by the decrease in the lipid peroxidation marker 4-HNE. Therefore, 3′5-DMBA may be a promising candidate for IPF treatment.     

Antibody Therapies Targeting Complex Membrane Proteins

In analyses of protein families that may serve as drug targets, membrane-associated G-protein-coupled receptors (GPCRs) dominate, followed by ion channels, transporters, and—to a lesser extent—membrane-bound enzymes. However, various challenges put such membrane proteins among key groups of underutilized opportunities for the application of therapeutic antibodies. Antibodies hold the promise of exquisite specificity, as they are able to target even specific conformations of a particular membrane protein, as well as adaptability through engineering into various antibody formats. However, the ease of raising and isolating specific, effective antibodies targeting membrane proteins depends on many factors. In particular, the generation of specific antibodies is easier when targeting larger, simpler, extracellular domains with greater uniqueness of amino acid sequence. The rareness of such ideal conditions is illustrated by the limited number of approved biologics for targeting GPCRs and other complex membrane proteins. Challenges in developing antibodies to complex membrane proteins such as GPCRs, ion channels, transporters, and membrane-bound enzymes can be addressed by the design of the antigen, antibody-generation strategies, lead optimization technologies, and antibody modalities. A better understanding of the membrane proteins being targeted would facilitate mechanism-based drug discovery. This review describes the advantages and challenges of targeting complex membrane proteins with antibodies and discusses the preparation of membrane protein antigens and antibody generation, illustrated by select examples of success.     

Up?Scalable Fabrication of -SnO2 with Multifunctional Interface for High Performance Perovskite Solar Modules

Tin dioxide -(SnO2) has been demonstrated as one of the promising electron transport layers for highefficiency perovskite solar cells (PSCs). However, scalable ...     

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

Control of attendance applied in higher education through mobile NFC technologies

This paper presents an improved attendance control system, required for continuous evaluation which has become compulsory following the Bologna Process. It provides a solution based on NFC technology and is based on a real project developed and pilot tested at the “Universidad Pontificia de Salamanca, Campus Madrid”.