Development and validation of potential phenotypes of serum electrolyte disturbances in critically ill patients and a Web-based application.

BACKGROUND: Electrolyte disturbances are highly heterogeneous and severely affect the prognosis of critically ill patients. Our study was to determine whether data-driven phenotypes of seven electrolytes have prognostic relevance in critically ill patients. METHODS: We extracted patient information from three large independent public databases, and clustered the electrolyte distribution of ICU patients based on the extreme value, median value and coefficient of variation of electrolytes. Three plausible clinical phenotypes were calculated using K-means clustering algorithm as the basic clustering method. MIMIC-IV was considered a training set, and two others have been designated as verification set. The robustness of the model was then validated from different angles, providing dynamic and interactive visual charts for more detailed characterization of phenotypes. RESULTS: 15,340, 12,445 and 2147 ICU patients with electrolyte records during early ICU stay in MIMIC-IV, eICU-CRD and AmsterdamUMCdb were enrolled. After clustering, three reasonable and interpretable phenotypes are defined as α, ß and γ according to the order of clusters. The α and γ phenotype, with significant differences in electrolyte distribution and clinical variables, higher 28-day mortality and longer length of ICU stay (p < 0.001), was further demonstrated by robustness analysis. The α phenotype has significant kidney injury, while the ß phenotype has the best prognosis. In addition, the assignment methods of the three phenotypes were developed into a web-based tool for further verification and application. CONCLUSIONS: Three different clinical phenotypes were identified that correlated with electrolyte distribution and clinical outcomes. Further validation and characterization of these phenotypes is warranted.

Application of K-Means Clustering Algorithm to Commercial Parameters of Pleurotus spp. Cultivated on Representative Agricultural Wastes from Province of Guayas.

Data of the commercial parameters of Pleurotus ostreatus and Pleurotus djamor were analyzed using the data mining technique: K-means clustering algorithm. The parameters evaluated were: biological efficiency, crop yield ratio, productivity rate, nutritional composition, antioxidant and antimicrobial activities in the production of fruit bodies of 50 strains of Pleurotus ostreatus and 50 strains of Pleurotus djamor, cultivated on the most representative agricultural wastes from the province of Guayas: 80% sugarcane bagasse and 20% wheat straw (M1), and 60% wheat straw and 40% sugarcane bagasse (M2). The database of the parameters obtained in experimental procedures was grouped into three clusters, providing a visualization of the strains with a higher relation to each parameter (vector) measured.

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data.

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil.

This data-driven work aims to analyze and classify the spatiotemporal distribution of all Brazilian states considering data so diverse as the number of Covid-19 cases, deaths, confirmed cases per 100 k inhabitants, mortality per 100 k inhabitants and case fatality rates as health indicators. We also considered population, area and population density as geographic indicators. Finally, GDP and HDI were taken into account as economic and social criteria. For this task data were collected from April 3rd until August 8th, 2020, corresponding to epidemiological weeks 14-32, reaching three million cases and a hundred thousand deaths. With this data it was possible to classify Brazilian states using multivariate methods into possible groups by means of non-hierarchical (k-means) cluster as well as factor analysis. It was possible to group all states plus the Federal District into five clusters, taking into account these 10 variables over the first five months of the epidemic. Group changes between states were observed over time and clusters, and between three and four factors were found. However, even with great difference on health indicators during days, the number of clusters remains fixed. Also, São Paulo and Rio de Janeiro states were ranked at top list taking into account all epidemiological weeks. Correlations were observed between variables, such as the number of Covid cases and deaths with GDP for most of epidemiological weeks. Some clusters were more critical due to specific variables, including cities that are main hotspots. These multivariate findings would provide a comprehensive description of the ongoing Covid-19 epidemic and may help to guide subsequent studies to understand and control virus transmission.

Microporosity Clustering Assessment in Calcium Phosphate Bioceramic Particles.

There has been an increase in the application of different biomaterials to repair hard tissues. Within these biomaterials, calcium phosphate (CaP) bioceramics are suitable candidates, since they can be biocompatible, biodegradable, osteoinductive, and osteoconductive. Moreover, during sintering, bioceramic materials are prone to form micropores and undergo changes in their surface topographical features, which influence cellular physiology and bone ingrowth. In this study, five geometrical properties from the surface of CaP bioceramic particles and their micropores were analyzed by data mining techniques, driven by the research question: what are the geometrical properties of individual micropores in a CaP bioceramic, and how do they relate to each other? The analysis not only shows that it is feasible to determine the existence of micropore clusters, but also to quantify their geometrical properties. As a result, these CaP bioceramic particles present three groups of micropore clusters distinctive by their geometrical properties. Consequently, this new methodological clustering assessment can be applied to advance the knowledge about CaP bioceramics and their role in bone tissue engineering.

Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua.

We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015. Interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, microcredit, technical training, university education stipends, and use of the Internet. In 2004, a survey of basic health and demographic information was performed in the whole population, followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals. Information included the house material (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, food security, household assets, and women's self-rated health were collected in 2014. A K-means algorithm was used to cluster the household data (56 variables) in six clusters. The poverty ranking of household clusters using the unsatisfied basic needs index variables changed when including variables describing basic capabilities. The households in the fairly rich cluster with assets such as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity, were labeled vulnerable. Poor and poorest clusters of households were traditional, e.g., in using horses for transport. Results displayed a society transforming from traditional to modern, where the forerunners were not the richest but educated, had more working members in household, had fewer children, and were food secure. Those lagging were the poor, traditional, and food insecure. The approach may be useful for an improved understanding of poverty and to direct local policy and interventions.

Discrimination of Picea chihuahuana Martinez populations on the basis of climatic, edaphic, dendrometric, genetic and population traits.

BACKGROUND: Picea chihuahuana, which is endemic to Mexico, is currently listed as "Endangered" on the Red List. Chihuahua spruce is only found in the Sierra Madre Occidental (SMO), Mexico. About 42,600 individuals are distributed in forty populations. These populations are fragmented and can be classified into three geographically distinct clusters in the SMO. The total area covered by P. chihuahuana populations is less than 300 ha. A recent study suggested assisted migration as an alternative to the ex situ conservation of P. chihuahuana, taking into consideration the genetic structure and diversity of the populations and the predictions regarding the future climate of the habitat. However, detailed background information is required to enable development of plans for protecting and conserving species and for successful assisted migration. Thus, it is important to identify differences between populations in relation to environmental conditions. The genetic diversity of populations, which affect vigor, evolution and adaptability of the species, must also be considered. In this study, we examined 14 populations of P. chihuahuana, with the overall aim of discriminating the populations and form clusters of this species. METHODS: Each population was represented by one 50 × 50 m plot established in the center of its respective location. Climate, soil, dasometric, density variables and genetic and species diversities were assessed in these plots for further analyses. The putatively neutral and adaptive AFLP markers were used to calculate genetic diversity. Affinity Propagation (AP) clustering technique and k-means clustering algorithm were used to classify the populations in the optimal number of clusters. Later stepwise binomial logistic regression was applied to test for significant differences in variables of the southern and northern P. chihuahuana populations. Spearman's correlation test was used to analyze the relationships among all variables studied. RESULTS: The binomial logistic regression analysis revealed that seven climate variables, the geographical longitude and sand proportion in the soil separated the southern from northern populations. The northern populations grow in more arid and continental conditions and on soils with lower sand proportion. The mean genetic diversity using all AFLP studied of P. chihuahuana was significantly correlated with the mean temperature in the warmest month, where warmer temperatures are associated to larger genetic diversity. Genetic diversity of P. chihuahuana calculated with putatively adaptive AFLP was not statistically significantly correlated with any environmental factor. DISCUSSION: Future reforestation programs should take into account that at least two different groups (the northern and southern cluster) of P. chihuahuana exist, as local adaptation takes place because of different environmental conditions.

Exploratory Investigation of Bacteroides fragilis Transcriptional Response during In vitro Exposure to Subinhibitory Concentration of Metronidazole.

Bacteroides fragilis, member from commensal gut microbiota, is an important pathogen associated to endogenous infections and metronidazole remains a valuable antibiotic for the treatment of these infections, although bacterial resistance is widely reported. Considering the need of a better understanding on the global mechanisms by which B. fragilis survive upon metronidazole exposure, we performed a RNA-seq transcriptomic approach with validation of gene expression results by qPCR. Bacteria strains were selected after in vitro subcultures with subinhibitory concentration (SIC) of the drug. From a wild type B. fragilis ATCC 43859 four derivative strains were selected: first and fourth subcultures under metronidazole exposure and first and fourth subcultures after drug removal. According to global gene expression analysis, 2,146 protein coding genes were identified, of which a total of 1,618 (77%) were assigned to a Gene Ontology term (GO), indicating that most known cellular functions were taken. Among these 2,146 protein coding genes, 377 were shared among all strains, suggesting that they are critical for B. fragilis survival. In order to identify distinct expression patterns, we also performed a K-means clustering analysis set to 15 groups. This analysis allowed us to detect the major activated or repressed genes encoding for enzymes which act in several metabolic pathways involved in metronidazole response such as drug activation, defense mechanisms against superoxide ions, high expression level of multidrug efflux pumps, and DNA repair. The strains collected after metronidazole removal were functionally more similar to those cultured under drug pressure, reinforcing that drug-exposure lead to drastic persistent changes in the B. fragilis gene expression patterns. These results may help to elucidate B. fragilis response during metronidazole exposure, mainly at SIC, contributing with information about bacterial survival strategies under stress conditions in their environment.

Spatial variation and area definition of nutrient extraction by Marandu grass biomass / Variação espacial e definição de zonas de extração de nutrientes pela fitomassa do capim-Marandu

O objetivo foi quantificar, descrever e identificar zonas de extração de nutrientes pela fitomassa da Brachiaria brazantha cv. Marandu em sistemas de integração floresta-pasto em região de transição Cerrado-Amazônia sobre Neossolo Quartzarênico Órtico típico, por meio de técnicas de geoestatística, de análise de componentes principais e da lógica de agrupamento não hierárquica de fuzzy k-médias. As avaliações foram realizadas em dois sistemas de integração floresta-pasto originários da associação de Brachiaria brizantha cv. Marandu e vegetação nativa raleada com 50% e 75% (IFP-I e IFP-II, respectivamente) de sombreamento e em pastagem de Brachiaria brizantha cv. Marandu em monocultivo. Para cada tratamento foi demarcada uma área de 4.000 m² (40 x 100 m) que continham 32 pontos de coleta dispostos em malha de 4 x 25 m. Em cada ponto previamente marcado nos tratamentos avaliados se estimou as taxas de alongamento de lâminas foliares, senescência foliar e de alongamento de colmo. Ao final de cada ciclo produtivo foram determinados nas lâminas foliares e no colmo os teores de nutrientes (N, P, K, Ca e Mg). A extração de nutrientes foi calculada em função das taxas de produção bruta de forragem, de acúmulo de forragem e de folhas. Zonas de extração de nutrientes minerais pela fitomassa da Brachiaria brizantha cv. Marandu são definidas utilizando-se técnicas de geoestatística, de análise de componentes principais e da lógica de agrupamento não hierárquica de fuzzy k-médias. Assim o uso desses procedimentos é viável na definição e delimitação de zonas homogêneas dentro e entre os sistemas de produção de gramínea estudados.(AU)

The present study aimed to quantify, describe and identify areas of nutrient extraction by Brachiaria brizantha cv. Marandu biomass in integrated forest-pasture systems from a Cerrado-Amazon transition region with Typic Quartzipsamment soil by using geostatistical techniques, principal components analysis and non-hierarchical fuzzy k-means clustering. The evaluations were conducted in two integrated forest-pasture systems from an association with Brachiaria brizantha cv. Marandu and native vegetation thinned with 50% and 75% (integrated forest production-I (IFP-I) and IFP-II, respectively) shading and in Brachiaria brizantha cv. Marandu monoculture. For each treatment, an area of 4,000 m² (40 x 100 m) was demarcated containing 32 collection points arranged in a 4 x 25 m mesh. At each point, the rates of leaf elongation, senescence and stem elongation were estimated. At the end of each production cycle, the nutrient content (N, P, K, Ca and Mg) was determined in the leaf blades and stem. The nutrient uptake was calculated according to the rates of gross forage production, forage accumulation and leaf accumulation. The nutrient extraction zones of Brachiaria brizantha cv. Marandu biomass were defined using geostatistical techniques, principal components analysis and non-hierarchical fuzzy k-means clustering. Thus, the use of these procedures is feasible for the definition and delimitation of homogeneous zones within and between the pasture production systems studied.(AU)

Spatial variation and area definition of nutrient extraction by Marandu grass biomass / Variação espacial e definição de zonas de extração de nutrientes pela fitomassa do capim-Marandu

O objetivo foi quantificar, descrever e identificar zonas de extração de nutrientes pela fitomassa da Brachiaria brazantha cv. Marandu em sistemas de integração floresta-pasto em região de transição Cerrado-Amazônia sobre Neossolo Quartzarênico Órtico típico, por meio de técnicas de geoestatística, de análise de componentes principais e da lógica de agrupamento não hierárquica de fuzzy k-médias. As avaliações foram realizadas em dois sistemas de integração floresta-pasto originários da associação de Brachiaria brizantha cv. Marandu e vegetação nativa raleada com 50% e 75% (IFP-I e IFP-II, respectivamente) de sombreamento e em pastagem de Brachiaria brizantha cv. Marandu em monocultivo. Para cada tratamento foi demarcada uma área de 4.000 m² (40 x 100 m) que continham 32 pontos de coleta dispostos em malha de 4 x 25 m. Em cada ponto previamente marcado nos tratamentos avaliados se estimou as taxas de alongamento de lâminas foliares, senescência foliar e de alongamento de colmo. Ao final de cada ciclo produtivo foram determinados nas lâminas foliares e no colmo os teores de nutrientes (N, P, K, Ca e Mg). A extração de nutrientes foi calculada em função das taxas de produção bruta de forragem, de acúmulo de forragem e de folhas. Zonas de extração de nutrientes minerais pela fitomassa da Brachiaria brizantha cv. Marandu são definidas utilizando-se técnicas de geoestatística, de análise de componentes principais e da lógica de agrupamento não hierárquica de fuzzy k-médias. Assim o uso desses procedimentos é viável na definição e delimitação de zonas homogêneas dentro e entre os sistemas de produção de gramínea estudados.

The present study aimed to quantify, describe and identify areas of nutrient extraction by Brachiaria brizantha cv. Marandu biomass in integrated forest-pasture systems from a Cerrado-Amazon transition region with Typic Quartzipsamment soil by using geostatistical techniques, principal components analysis and non-hierarchical fuzzy k-means clustering. The evaluations were conducted in two integrated forest-pasture systems from an association with Brachiaria brizantha cv. Marandu and native vegetation thinned with 50% and 75% (integrated forest production-I (IFP-I) and IFP-II, respectively) shading and in Brachiaria brizantha cv. Marandu monoculture. For each treatment, an area of 4,000 m² (40 x 100 m) was demarcated containing 32 collection points arranged in a 4 x 25 m mesh. At each point, the rates of leaf elongation, senescence and stem elongation were estimated. At the end of each production cycle, the nutrient content (N, P, K, Ca and Mg) was determined in the leaf blades and stem. The nutrient uptake was calculated according to the rates of gross forage production, forage accumulation and leaf accumulation. The nutrient extraction zones of Brachiaria brizantha cv. Marandu biomass were defined using geostatistical techniques, principal components analysis and non-hierarchical fuzzy k-means clustering. Thus, the use of these procedures is feasible for the definition and delimitation of homogeneous zones within and between the pasture production systems studied.

Non-invasive method to analyse the risk of developing diabetic foot.

Foot complications (diabetic foot) are among the most serious and costly complications of diabetes mellitus. Amputation of all or part of a lower extremity is usually preceded by a foot ulcer. To prevent diabetic foot, an automatic non-invasive method to identify patients with diabetes who have a high risk of developing diabetic foot is proposed. To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the K-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. Both real and simulated data were used to evaluate the method in which promising results were achieved with accuracy of 0.97 ± 0.06 for simulated data and 0.68 ± 0.16 considering the classification of specialists as the gold standard for real data. The method requires a simple computational processing and can be useful for basic health units to triage diabetic patients helping the health-care team to reduce the number of cases of diabetic foot.