Estabilidade de QTLs para peso de semente do feijoeiro em diferentes ambientes, utilizando regressão linear múltipla.

Os objetivos desse trabalho foram identificar, em diferentes épocas e locais de cultivo, marcadores RAPD ligados a QTLs controladores do peso de sementes do feijoeiro e avaliar a existência de interação QTLs por ambientes

Mapping and stability of QTLs for seed weight in common beans under different environments.

The objectives were: 1 - to identify, under different environments, RAPD markers linked to loci that control the 100 seed weight of common beans; 2 - to evaluate the existence of interactions involving QTL's with planting dates and locations; 3 - to compare detection procedures of markers linked to QTL's utilizing the methods of mapping and multiple regression. One hundred and minety-six recombinant inbred lines derived from the cross of cultivars Carioca x Flor de Mayo were evaluated under two tradtional sowing dates for common beans in 1996, 1997 and 1998, in Lavras and Patos de Minas, Brazil. For the phenotypic evaluation of the families, seven field experiments were conduced. The experimental design used was a 14 x 14 partially balanced Square Simple Lattice. Results indicated that interaction of QTL's by location were significant, and some stable QTL's were identified. Multiple regression analysis identified a greater number of QTL's-linked markers than the process of composite interval mapping. There was no coincidence between results obtained with the two methods studied. Molecular markers which were considered of greater potential use on marker-assisted selection for seed weight were OPN-02 (1445 pb) e OPM-06 (1096 pb)

Extracting clinical knowledge from electronic medical records

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources' importance increases because of the clinical information they contain about patients. However, the unstructured information in the form of clinical narratives present in those records, makes it hard to extract and structure useful clinical knowledge. This unstructured information limits the potential of the EMRs, because the clinical information these records contain can be used to perform important tasks inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These tasks can only be done if the unstructured clinical information from the narratives is properly extracted, structured and transformed in clinical knowledge. Usually, this extraction is made manually by healthcare practitioners, which is not efficient and is error-prone. This research uses Natural Language Processing (NLP) and Information Extraction (IE) techniques, in order to develop a pipeline system that can extract clinical knowledge from unstructured clinical information present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential.info:eu-repo/semantics/publishedVersio

Predictive analysis in healthcare: emergency wait time prediction

Emergency departments are an important area of a hospital, being the major entry point to the healthcare system. One of the most important issues regarding patient experience are the emergency department waiting times. In order to help hospitals improving their patient experience, the authors will perform a study where the Random Forest algorithm will be applied to predict emergency department waiting times. Using data from a Portuguese hospital from 2013 to 2017, the authors discretized the emergency waiting time in 5 different categories: “Really Low”, “Low”, “Average”, “High”, “Really High”. Plus, the authors considered as waiting time, the time from triage to observation. The authors expect to correctly evaluate the proposed classification algorithm efficiency and accuracy in order to be able to conclude if it is valuable when trying to predict ED waiting times.info:eu-repo/semantics/acceptedVersio

Extracting clinical information from electronic medical records

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources are each day more important because of the clinical data they contain about patients. However, the unstructured textual data in the form of narrative present in those records, makes it hard to extract and structure useful clinical information. This unstructured text limits the potential of the EMRs, because the clinical data these records contain, can be used to perform important operations inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These operations can only be done if the clinical data from the narratives is properly extracted and structured. Usually this extraction is made manually by healthcare practitioners, what is not efficient and is error-prone. The present work uses Natural Language Processing (NLP) and Information Extraction(IE) techniques in order to develop a pipeline system that can extract clinical information directly from unstructured texts present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential.info:eu-repo/semantics/acceptedVersio

Emergency waiting times data analysis

The Emergency Departments (ED) are a complex and important area of a hospital. With limited resources, it is mandatory to focus on efficiency. When hospitals are unable to deal with high demand, problems may rise leading to longer waiting times and more dissatisfaction. In this research, the authors extracted knowledge from a hospital ED, through data analysis and data mining, applying Random Forest and Naïve Bayes to study the ED patient waiting time and diseases.info:eu-repo/semantics/publishedVersio

Avaliação de linhagem e cultivares de feijoeiro comum, grupo preto, no período de inverno, em Uberlândia-MG.

Em esforço conjunto, a Universidade Federal de Uberlândia e a Embrapa, conduziram ensaios de competição de linhagens e cultivares no município de Uberlândia, MG com objetivo de avaliar o comportamento agronômico, dentre estes a produtividade, de cultivares de feijoeiro comum, do grupo preto, na época de inverno, em 2006 e 2007

Cultivares e linhagens de feijoeiro comum, do grupo carioca, no período de inverno, em Uberlândia-MG.

A Universidade Federal de Uberlândia e a Embrapa, conduziram ensaios de competição de linhagens e cultivares em Uberlândia, MG, com o objetivo de avaliar o comportamento agronômico, dentre estes a produtividade, de genótipos de feijoeiro comum, do grupo comercial carioca, na época de inverno, em 2006 e 2007