NOTCH3 rs1043996 Polymorphism Is Associated with the Occurrence of Alcoholic Liver Cirrhosis Independently of PNPLA3 and TM6SF2 Polymorphisms

Alcoholic liver cirrhosis (ALC) is the most common indication for liver transplantation (LT) in Croatia and presents a risk factor for the development of hepatocellular carcinoma (HCC). However, genetic susceptibility has not yet been systematically studied. We aimed to investigate the contribution of the risk polymorphisms PNPLA3 rs738409, EGF rs4444903, TM6SF2 rs58542926, MTHFR rs1801133, previously identified in other populations and, additionally, the contribution of Notch-related polymorphisms (NOTCH1 rs3124591, NOTCH3 rs1043996 and rs1044116, NOTCH4 rs422951). The study included 401 patients. The ALC group consisted of 260 LT candidates, 128 of whom had histopathologically confirmed HCC, and 132 of whom were without HCC. The control group included 141 patients without liver disease. Genotyping was performed by PCR using Taqman assays. The patients’ susceptibility to ALC was significantly associated with PNPLA3 rs738409, TM6SF2 rs58542926, and NOTCH3 rs1043996 polymorphisms. These polymorphisms remained significantly associated with ALC occurrence in a logistic regression model, even after additional model adjustment for sex and age. Cirrhotic patients with the PNPLA3 GG genotype demonstrated higher activity of ALT aminotransferases than patients with CC or CG genotypes. The susceptibility to the development of HCC in ALC was significantly associated with PNPLA3 rs738409 and EGF rs4444903 polymorphisms, and logistic regression confirmed these polymorphisms as independent predictors.

Assessment of Adjustments Methods in Traverse Networks for Positioning

Various factors contribute to the degree of accuracy of the adjusted parameter (coordinate), one of which is the choice of adjustment model. Adjustment models seeks to eliminate (accounts) for the presence of random errors present in a given observations. The choice is critical for surveyors and other spatial analysts for optimal positioning and mapping projects since different adjustment models will yield different level of accuracy of spatial information generated irrespective of the quality of observations. For a traversing network, various adjustment models have been put forward which include; the Transit, the Bowditch, and the Crandels models. In spite of these models, internal consistency and reliability indicators of the network of positions are determined using the least squares adjustment model (observation equation and condition equation models). The aim of this work is to analyze the various traverse adjustment models. The approach deployed in this work was to compute the provisional coordinate of six traverse stations using the approximate methods of adjustment i.e., Bowditch and transit methods of traverse adjustment models. In addition, the least square adjustment models were deployed to minimize the propagation of residuals of the obtained values. The adjusted distances and directions were then compared with the observed distances and directions to obtain the residuals. The coordinate of positions was determined and the Root Mean Square Error (RMSE) associated with the traverse adjustment models are given as 0.128702264 and 0.008560954. Similarly, the RMSE of the adjusted values using the least square models are given as 0.007181432, and 0.005763969 for the observation and condition equation models respectively. The analysis of these results reveals that the traverse adjustment models are unique with capabilities embedded in the determination of the observables during data acquisition. However, for mapping and engineering survey of small locations, the transit method is more preferable to the Bowditch method.

Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic

A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making, and we exemplify the applicability of the methodology using datasets from England and France.

Variable Selection for Fault Detection Based on Causal Discovery Methods: Analysis of an Actual Industrial Case

Variable selection constitutes an essential step to reduce dimensionality and improve performance of fault detection and diagnosis in large scale industrial processes. For this reason, in this paper, variable selection approaches based on causality are proposed and compared, in terms of model adjustment of available data and fault detection performance, with several other filter-based, wrapper-based, and embedded-based variable selection methods. These approaches are applied in a simulated benchmark case and an actual oil and gas industrial case considering four different learning models. The experimental results show that obtained models presented better performance during the fault detection stage when variable selection procedures based on causality were used for purpose of model building.

Construction and application of factory digital twin model based on BIM and point cloud

The digital twin model of the factory is the basis for the construction of a digital factory, and the professional system of the factory is complex. The traditional BIM model is not completely consistent with the actual position of the corresponding component, and it is difficult to directly replace the digital twin model. In response to this situation, relying on a certain factory project, the point cloud is used to eliminate the positional deviation between the BIM model and the factory during the construction phase, improve the efficiency and accuracy and reliability of model adjustment and optimization, and , realize the conversion from BIM model to digital twin model. A novel algorithm is developed to quickly detect and evaluate the construction quality of the local structure of the factory, so as to input the initial deformation data of the structure into the corresponding model and feed back to the construction party for improvement. The results show that the digital twin model, which is highly consistent with the actual location of the factory components, not only lays a solid foundation for the construction of a digital factory, but also further deepens the integration and application of BIM and point clouds.

Desenvolvimento de Modelo que Estima o Impacto do CO2 Atmosférico nas Precipitações do Estado de Pernambuco, utilizando ARIMA

It is known that the state of Pernambuco will suffer impacts on precipitation due to the increase in CO2 in the atmosphere. In an attempt to contribute to the prognosis of these impacts, this study aims to develop a model that makes a prognosis or creates future scenarios for the state of Pernambuco. For that, the autoregressive method of moving averages, ARIMA, was used. The model adjustment was performed using the normalized Bayesian information criterion function. The results showed that the developed model presents a strong fit. The model was better adjusted for the Agreste and West of the state. The model projects a precipitation decrease trend for the western state of Pernambuco of approximately 15% below the historical average until 2027. The model projected rainfall above the historical average for the Agreste of Pernambuco, of approximately 17%, until 2027. It concludes It is believed that rainy years will occur more frequently in the Agreste region of Pernambuco, and we will have more frequent dry years in the west of the state. In applying the results of this study and simulation with the model SUPER-System of Hydrological Response Units for Pernambuco, it is concluded that there will be more flood peaks in the Mundaú basin until 2027.

Kessler Psychological Distress Scale: Internal Structure and Relation to Other Variables

This study aimed to present validity evidence based on internal structure of the Kessler Scale of Psychological Distress (K10), to show its relations with the Perceived Stress Scale (PSS-10), and to present a social distribution of distress in the present sample. Participated in the study 717 residents of Aracaju, State of Sergipe, by means of household data collection. A sociodemographic questionnaire, K10, and PSS-10 were used as instruments. Exploratory Factor Analysis was performed using the Factor software, which indicated the scale unidimensionality, explaining 69.9% of the variance. Cronbach’s alpha was 0.93, and the model adjustment indices were satisfactory. A positive and statistically significant association between K10 and PSS-10 was observed. Regarding the social distribution, the levels of distress were higher in women, patients with chronic diseases, users of controlled drugs, and unemployed participants. It was concluded that K10 presented robust psychometric properties for the detection of distress in general population.

Association between LXR-α and ABCA1 Gene Polymorphisms and the Risk of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus in a Chinese Han Population

We designed a case-control study and selected LXR-α rs7120118 C>T and ABCA1 rs2230806 A>G polymorphisms to determine the correlation between these polymorphisms and diabetic kidney disease (DKD) susceptibility in a Chinese Han population. Three hundred DKD patients and 346 type 2 diabetes mellitus (DM) patients without kidney disease were recruited. Our results showed that rs7120118 was associated with DKD (genotype, P = .027 ; allele, P < .011 ). rs7120118 was associated with a higher risk of DKD under a dominant model adjustment by age and sex ( P = .015 ) and an additive model ( P = .040 ); rs2230806 was associated with a higher risk of DKD under an recessive model ( P < .03 ); the combined effect of rs7120118 CC+rs2230806 GG genotype showed an association of DKD adjustment for age and sex ( P = .009 ). In subgroup analysis of patients without hypercholesterolemia, the rs2230806 genotype frequencies were different between the two groups ( P = .042 ). rs2230806 was associated with increased risk of DKD under a recessive model adjustment for age and sex ( P = .013 ) and an additive model ( P = .031 ). Our results suggest that LXR-α rs7120118 is significantly associated with a higher risk of DKD, and ABCA1 rs2230806 is significantly associated with a higher risk of DKD without hypercholesterolemia in Chinese Han individuals.