Housing fuzzy recommender system: A systematic literature review.

In recent years, significant attention has been paid to fuzzy recommender systems for housing, highlighting their ability to effectively handle the imprecision and uncertainty inherent in the real estate market. With the objective of improving the filtering of recommendations in the real estate sector, the PRISMA 2020 methodology was applied to perform new systematic reviews using its checklist on six academic databases from 1985 to 2024. RawGraph, Orange Data Minig, Jamovi and R software were used for document classification and data visualization. After classification, 1003 articles were obtained, of which 46.36% were in Scopus, and 57.82% were articles. At the end of the type, 50 articles were identified as primary, subjecting them to six research questions. It was found that 65% of the algorithms used fuzzy logic, 60% used spatial data, and 80% evaluated performance. The main difficulties were related to the integration of various sources of information. Although incorporating reclusive methods is anticipated in future systems, the need remains to address challenging areas to improve the overall performance of fuzzy recommender systems. The reviewed articles focus on enhancing fuzzy data-based recommendation systems by proposing flexible and less intrusive techniques. The significance of incorporating contextual information and exploring hybrid approaches is emphasized, along with the evaluation in real world environments, averaging artificial intelligence.

Chronic Administration of Diethylnitrosamine and 2-Acetylaminofluorene Induces Hepatocellular Carcinoma in Wistar Rats.

This study aimed to analyze the biochemical, histological, and gene expression alterations produced in a hepatocarcinogenesis model induced by the chronic administration of diethylnitrosamine (DEN) and 2-acetylaminofluorene (2-AAF) in Wistar rats. Thirteen rats weighing 180 to 200 g were divided into two groups: control and treated. Rats in the treated group were administered an intraperitoneal (i.p.) injection of DEN (50 mg/kg/week) and an intragastric (i.g.) dose of 2-AAF (25 mg/kg/week) for 18 weeks. The treated group had significant increases in their total cholesterol, HDL-C, AST, ALT, ALKP, and GGT levels. Furthermore, a histological analysis showed the loss of normal liver architecture with nuclear pleomorphism in the hepatocytes, atypical mitosis, and fibrous septa that were distributed between the portal triads and collagen fibers through the hepatic sinusoids. The gene expressions of 24 genes related to fibrosis, inflammation, apoptosis, cell growth, angiogenesis, lipid metabolism, and alpha-fetoprotein (AFP) were analyzed; only TGFß, COL1α1, CYP2E1, CAT, SOD, IL6, TNF-α, and ALB showed significant differences when both groups were compared. Additionally, lung histopathological alterations were found in the treated group, suggesting metastasis. In this model, the chronic administration of DEN+2-AAF induces characteristic alterations of hepatocellular carcinoma in Wistar rats without AFP gene expression changes, highlighting different signatures in hepatocellular carcinoma heterogeneity.

Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review.

Teacher evaluation is presented as an object of study of great interest, where multiple efforts converge to establish models from the association of heterogeneous data from academic actors, one of these is the students' community, who stands out for their contribution with rich data information for the establishment of teacher evaluation in higher education. This study aims to present the search results for references on the prediction of teacher evaluation based on the associated data provided by the performance of university students. For this purpose, a systematic literature review was carried out, established by the phases of planning (search objective, research questions, inclusion and exclusion criteria), search and selection (literature control group and keywords, the definition of the search string, results filtering), and extraction (synthesis of the contributions). As a result, a set of references on the application of predictions is obtained, focused on educational data mining techniques, such as Fuzzy logic, Fuzzy clustering, Fuzzy Neural Network (FNN), Neural networks, multilayer perceptron (MLP), Decision Trees, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier, Support Vector Machine (SVM), K-Nearest-Neighbor (KNN), and Associative classification model. In conclusion, prediction and mining techniques have been widely explored; however, teacher evaluation is in the process of growth with particular emphasis on fuzzy principles, considering that human decision-making is developed with uncertainty, which is strongly related to human behavior.

Three-Dimensional Reconstruction of the Hepatitis C Virus Envelope Glycoprotein E1E2 Heterodimer by Electron Microscopic Analysis.

Despite the development of highly effective hepatitis C virus (HCV) treatments, an effective prophylactic vaccine is still lacking. HCV infection is mediated by its envelope glycoproteins, E1 and E2, during the entry process, with E2 binding to cell receptors and E1 mediating endosomal fusion. The structure of E1E2 has only been partially resolved by X-ray crystallography of the core domain of E2 protein (E2c) and its complex with various neutralizing antibodies. Structural understanding of the E1E2 heterodimer in its native form can advance the design of candidates for HCV vaccine development. Here, we analyze the structure of the recombinant HCV E1E2 heterodimer with the aid of well-defined monoclonal anti-E1 and E2 antibodies, as well as a small-molecule chlorcyclizine-diazirine-biotin that can target and cross-link the putative E1 fusion domain. Three-dimensional (3D) models were generated after extensive 2D classification analysis with negative-stain single-particle data sets. We modeled the available crystal structures of the E2c and Fabs into 3D volumes of E1E2-Fab complexes based on the shape and dimension of the domain density. The E1E2 heterodimer exists in monomeric form and consists of a main globular body, presumably depicting the E1 and E2 stem/transmembrane domain, and a protruding structure representing the E2c region, based on anti-E2 Fab binding. At low resolution, a model generated from negative-stain analysis revealed the unique binding and orientation of individual or double Fabs onto the E1 and E2 components of the complex. Cryo-electron microscopy (cryo-EM) of the double Fab complexes resulted in a refined structural model of the E1E2 heterodimer, presented here. IMPORTANCE Recombinant HCV E1E2 heterodimer is being developed as a vaccine candidate. Using electron microscopy, we demonstrated unique features of E1E2 in complex with various neutralizing antibodies and small molecule inhibitors that are important to understanding its antigenicity and induction of immune response.

Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador.

The investigation of conventional complete blood-count (CBC) data for classifying the SARS-CoV-2 infection status became a topic of interest, particularly as a complementary laboratory tool in developing and third-world countries that financially struggled to test their population. Although hematological parameters in COVID-19-affected individuals from Asian and USA populations are available, there are no descriptions of comparative analyses of CBC findings between COVID-19 positive and negative cases from Latin American countries. In this sense, machine learning techniques have been employed to examine CBC data and aid in screening patients suspected of SARS-CoV-2 infection. In this work, we used machine learning to compare CBC data between two highly genetically distinguished Latin American countries: Brazil and Ecuador. We notice a clear distribution pattern of positive and negative cases between the two countries. Interestingly, almost all red blood cell count parameters were divergent. For males, neutrophils and lymphocytes are distinct between Brazil and Ecuador, while eosinophils are distinguished for females. Finally, neutrophils, lymphocytes, and monocytes displayed a particular distribution for both genders. Therefore, our findings demonstrate that the same set of CBC features relevant to one population is unlikely to apply to another. This is the first study to compare CBC data from two genetically distinct Latin American countries.

High frequency of the risk allele of rs4132601 and rs11978267 from the IKZF1 gene in indigenous Mexican population.

BACKGROUND: IKZF1 is a relevant gene associated with the pathogenesis of acute lymphoblastic leukemia, and the rs4132601 (T>G) and rs11978267 (A>G) polymorphisms have been associated with the development of this disease in several populations. The aim of this study was to determine the allelic and genotypic frequencies of the rs4132601 and rs11978267 polymorphisms in two indigenous Mexican groups (Cora and Huichol) and Mestizo populations from Nayarit, Mexico, and compare them with the frequencies of both polymorphisms in other populations of the world. METHODS: One hundred, 116, and 100 subjects from the Mestizo, Huichol, and Cora populations, respectively, all of them residents of the state of Nayarit, Mexico, were analyzed. The frequencies of rs4132601 and rs11978267 were determined by allelic discrimination using TaqMan assays. RESULTS: The allelic frequencies of rs4132601 were as follows: Mestizo group T = 0.74, G = 0.26; Cora T = 0.745, G = 0.255; and Huichol T = 0.47, G = 0.53. In the case of the rs11978267 polymorphism, the allelic frequencies were Mestizo A = 0.745, G = 0.255; Cora A = 0.735, G = 0.265; and Huichol A = 0.457, G = 0.543. For each population, both polymorphisms were in Hardy-Weinberg equilibrium. CONCLUSION: The Huichol population from Nayarit presented the highest frequencies of the risk allele reported to date in the whole world for both rs4132601 and rs11978267 polymorphisms.

UrbangEnCy: An emergency events dataset based on citizen sensors for monitoring urban scenarios in Ecuador.

Recently, the use of the citizen-sensors (people generating and sharing real data by social media) for detecting and disseminating emergency events in real-time have shown a considerable increase because people at the place of the event, as well as elsewhere, can quickly post relevant information on this type of alerts. Here, we present an emergency events dataset called UrbangEnCy. The dataset contains over 25500 texts in Spanish posted on Twitter from January 19th to August 19th, 2020, with emergencies and non-emergencies related content in Ecuador. We obtained, cleaned and, filtered these tweets and, then we selected the location and temporal data as well as tweet content. Besides, the data set includes annotations regarding the type of tweet (emergency / non-emergency) as well as additional nomenclature used to describe emergencies in the Center for immediate response service to emergencies (ECU 911) of Ecuador and international emergency services agencies (ESAs). UrbangEnCy dataset facilitates evaluating data science performance, machine learning, and natural language processing algorithms used with supervised and unsupervised problems re- related to text mining and pattern recognition. The dataset is freely and publicly available at https://doi.org/10.17632/4x37zz82k8.