Acid hydrolysis conditions do affect the non-extractable phenolic compounds composition from grape peel and seed.

This study aimed to evaluate the effect of hydrolysis conditions on non-extractable phenolic compounds (NEPC) composition of grape peel and seed powder. The effect of temperature (50-90 °C), hydrochloric acid concentration (0.1-15.0 %), and time (5-20 min) were evaluated to understand their impact on NEPC release/extraction and degradation. The use of 1.0 and 8.0 % of HCl concentrations (v/v) and temperatures of 65 and 80 °C produced extracts with higher concentrations and a larger set of compounds. These conditions promoted a balance between release/extraction and degradation processes, thereby maximizing the NEPC content in the extracts. Furthermore, the results suggest that hydrolysis conditions can be set to modulate the release of specific classes. Non-extractable proanthocyanidins showed higher concentrations when intermediate values of temperature and acid concentration were applied. Hydrolysable tannins and hydroxybenzoic acids, on the other hand, were better extracted using higher acid concentrations and higher temperatures. The results suggest that the concentration and composition of NEPC are influenced by the hydrolysis conditions and the type of matrix. Hence, it is crucial to account for this compositional variation when conducting research on the biological effects of NEPC and when using this fraction as supplements or food ingredients.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data.

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative. In this paper we investigate the performance of the Random Forest (RF) classifier associated with several wavelength importance ranking approaches on the task of classifying product samples into categories, such as quality levels or authenticity. Our propositions are tested using six NIR datasets comprised of two or more classes of food and pharmaceutical products, as well as illegal drugs. Our proposed classification model, an integration of the χ2 ranking score and the RF classifier, substantially reduced the number of wavelengths in the dataset, while increasing the classification accuracy when compared to the use of complete datasets. Our propositions also presented good performance when compared to competing methods available in the literature.

Identifying the most relevant tablet regions in the image detection of counterfeit medicines.

This paper proposes a novel image-based approach to detect counterfeit medicines and identify the most relevant regions of the tablet in the task of classification. Images of medicine tablets undergo an initial pre-processing step which (i) removes the background to find the region of interest, (ii) clusters individual pixels into super-pixels, and (iii) extracts features containing color and texture information. The classification relying on Support Vector Machine (SVM) defines the class the respective image will be inserted into. The task of identifying the relevant regions of the tablets for counterfeiting detection is performed using the concept of support vectors, generating a heat map that indicates the regions that contribute the most to the classification purpose. Two datasets containing images of authentic and counterfeit tablets of Cialis and Viagra were used to validate our propositions, achieving correct classification rates of 100% on both datasets. Regarding the task of identifying the most relevant regions, our proposition outperformed the traditional LIME (Local Interpretable Model-agnostic Explanations) method by yielding more robust explanations.

Man vs. machine: Predicting hospital bed demand from an emergency department.

BACKGROUND: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems. OBJECTIVE: Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED. METHODS: This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC). RESULTS: All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77-0.87], 0.80 (95% CI: 0.75-0.85), 0.76 (95% CI: 0.71-0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR. CONCLUSIONS: Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends.

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Management of ophthalmic surgical instruments and processes optimization: mixed method study.

AIM: Analysis of the use of ophthalmic instruments during surgical procedures in order to propose a material management method. METHOD: Mixed method study, sequential exploratory design, performed from January to June 2015, at a university hospital in southern Brazil. First, a qualitative approach was held from brainstorming and field observation. Themes were grouped into thematic categories. By connection, the quantitative stage happened through matrix arrangement and linear programming, culminating in the instrument management proposal. RESULTS: Given categories - instruments reorganization according to the time of the surgical procedure and the need surgical instruments for in each procedure - guided the definition of existing restrictions and application of mathematical models. There was an average reduction of 13.10% in the number of surgical instruments per tray and an increase of 17.88% in surgical production. FINAL CONSIDERATIONS: This proposal allowed the rationalization and optimization of ophthalmic instruments, favoring sustainability of the organization.

Strategies for synchronizing chocolate conching batch process data using dynamic time warping.

In batch processing, process control is typically carried out comparing trajectories of process variables with those in an in-control set of batches that yielded products within specifications. However, one strong assumption of these schemes is that all batches have equal duration and are synchronized, which is often not satisfied in practice. To overcome that, dynamic time warping (DTW) methods may be used to synchronize stages and align the duration of batches. In this paper, three DTW methods are compared using supervised classification through the k-nearest neighbor technique to determine the in-control set in a milk chocolate conching process. Four variables were monitored over time and a set of 62 batches with durations between 495 and 1170 min was considered; 53% of the batches were known to be conforming based on lab test results and experts' evaluations. All three DTW methods were able to promote the alignment and synchronization of batches; however, the KMT method (Kassidas et al. in AIChE J 44(4):864-875, 1998) outperformed the others, presenting 93.7% accuracy, 97.2% sensitivity, and 90.3% specificity in batch classification as conforming and non-conforming. The drive current of the main motor was the most consistent variable from batch to batch, being deemed the most important to promote alignment and synchronization of the chocolate conching dataset.

Sistemática para racionalização de instrumentais de bandejas cirúrgicas / A systematic approach for the rationalization of surgical tray instruments / Sistemática para racionalización de instrumentales de bandejas quirúrgicas

Objetivo: Relatar a experiência de desenvolver uma sistemática para racionalização de instrumentais em bandejas cirúrgicas. Método: Estudo de desenvolvimento de sistemática para racionalização de instrumentais, realizado em 2015, a partir do método qualitativo, em um centro de materiais e esterilização (CME) de um hospital universitário federal de Porto Alegre, Brasil. Resultados: Houve redução média do quantitativo de instrumentais em bandejas institucionais em 10,92%; diminuição de bandejas de propriedade das equipes médicas, sendo 84,06% pertencentes à equipe da otorrinolaringologia; e inativação definitiva de 369 instrumentais da cirurgia ortopédica, o que significou 72,84% do total dos instrumentais inativados. Além disso, houve condução de melhorias no gerenciamento de instrumentais, otimização do tempo de preparo e redução da esterilização por expiração do prazo de utilização. Conclusão: A realocação de instrumentais e o acréscimo de peças em bandejas específicas permitiu a reavaliação das solicitações de compras de instrumentais e a melhoria das relações entre as equipes. Essa sistemática contribuiu significativamente para o gerenciamento de instrumentais, otimizando processos e envolvendo as equipes cirúrgicas no trabalho do CME e evidenciou que pode ser aplicada em outras instituições.

Objective: To report the experience of developing a systematic approach for the rationalization of instruments in surgical trays. Method: Study of the development of a systematic approach for the rationalization of instruments, carried out in 2015, using a qualitative method, in the Central Sterile Supply Department (CSSD) of a federal university hospital in Porto Alegre, Brazil. Results: There was a 10.92% average reduction in the number of instruments in institutional trays, a reduction in the number of trays owned by medical teams ­ 84.06% belonged to the otorhinolaryngology team ­ and a definitive inactivation of 369 orthopedic surgery instruments, which represented 72.84% of the total number of inactivated instruments. In addition, improvements were made to the management of instruments, the optimization of preparation time and the reduction of sterilization by expiration date. Conclusion: The relocation of instruments and the addition of items in specific trays allowed for the reappraisal of requests for purchase of instruments and the improvement of relationships between the teams. This systematic approach contributed significantly to the management of instruments, the optimizing processes and the involvement of the surgical teams in the work of the CSSD, thus demonstrating that it can be applied in other institutions.

Objetivo: Relatar la experiencia de desarrollar una sistemática para racionalización de instrumentales en bandejas quirúrgicas. Método: Estudio de desarrollo de sistemática para racionalización de instrumentales, realizado en 2015, desde el método cualitativo, en un centro de materiales y esterilización (CSSD) de un hospital universitario federal de Porto Alegre, Brasil. Resultados: Hubo reducción media del cuantitativo de instrumentales en bandejas institucionales en el 10,92%; disminución de bandejas de propiedad de los equipos médicos, siendo el 84,06% pertenecientes al equipo de la otorrinolaringología; e inactivación definitiva de 369 instrumentales de la cirugía ortopédica, lo que significó el 72,84% del total de los instrumentales inactivados. Además, hubo conducción de mejoras en el gerenciamiento de instrumentales, optimización del tiempo de preparo y reducción de la esterilización por expiración del plazo de utilización. Conclusión: La reubicación de instrumentales y el incremento de piezas en bandejas específicas permitió la reevaluación de las solicitaciones de compras de instrumentales y la mejora de las relaciones entre los equipos. Esa sistemática contribuyó significativamente para el gerenciamiento de instrumentales, perfeccionando procesos e involucrando a los equipos quirúrgicos en el trabajo de CSSD y evidenció que puede aplicarse en otras instituciones.

A multivariate-based conflict prediction model for a Brazilian freeway.

Real-time collision risk prediction models relying on traffic data can be useful in dynamic management systems seeking at improving traffic safety. Models have been proposed to predict crash occurrence and collision risk in order to proactively improve safety. This paper presents a multivariate-based framework for selecting variables for a conflict prediction model on the Brazilian BR-290/RS freeway. The Bhattacharyya Distance (BD) and Principal Component Analysis (PCA) are applied to a dataset comprised of variables that potentially help to explain occurrence of traffic conflicts; the parameters yielded by such multivariate techniques give rise to a variable importance index that guides variables removal for later selection. Next, the selected variables are inserted into a Linear Discriminant Analysis (LDA) model to estimate conflict occurrence. A matched control-case technique is applied using traffic data processed from surveillance cameras at a segment of a Brazilian freeway. Results indicate that the variables that significantly impacted on the model are associated to total flow, difference between standard deviation of lanes' occupancy, and the speed's coefficient of variation. The model allowed to asses a characteristic behavior of major Brazilian's freeways, by identifying the Brazilian typical heterogeneity of traffic pattern among lanes, which leads to aggressive maneuvers. Results also indicate that the developed LDA-PCA model outperforms the LDA-BD model. The LDA-PCA model yields average 76% classification accuracy, and average 87% sensitivity (which measures the rate of conflicts correctly predicted).

[A data mining method for breast cancer identification based on a selection of variables]. / Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis.

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection. This paper presents a method for selection of variables for the classification of cases into two classes, benign or malignant, based on cytopathological analysis of breast cell samples of patients. The variables are ranked according to a new index of importance of variables that combines the weighting importance of Principal Component Analysis and the explained variance based on each retained component. Observations from the test sample are categorized into two classes using the k-Nearest Neighbor algorithm and Discriminant Analysis, followed by elimination of the variable with the index of lowest importance. The subset with the highest accuracy is used to classify observations in the test sample. When applied to the Wisconsin Breast Cancer Database, the proposed method led to average of 97.77% in classification accuracy while retaining an average of 5.8 variables.

Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis / A data mining method for breast cancer identification based on a selection of variables

Na maioria dos países, o câncer de mama entre as mulheres é predominante. Se diagnosticado precocemente, apresenta alta probabilidade de cura. Diversas abordagens baseadas em Estatística foram desenvolvidas para auxiliar na sua detecção precoce. Este artigo apresenta um método para a seleção de variáveis para classificação dos casos em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. As variáveis são ordenadas de acordo com um novo índice de importância de variáveis que combina os pesos de importância da Análise de Componentes Principais e a variância explicada a partir de cada componente retido. Observações da amostra de treino são categorizadas em duas classes através das ferramentas k-vizinhos mais próximos e Análise Discriminante, seguida pela eliminação da variável com o menor índice de importância. Usa-se o subconjunto com a máxima acurácia para classificar as observações na amostra de teste. Aplicando ao Wisconsin Breast Cancer Database, o método proposto apresentou uma média de 97,77% de acurácia de classificação, retendo uma média de 5,8 variáveis.

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection. This paper presents a method for selection of variables for the classification of cases into two classes, benign or malignant, based on cytopathological analysis of breast cell samples of patients. The variables are ranked according to a new index of importance of variables that combines the weighting importance of Principal Component Analysis and the explained variance based on each retained component. Observations from the test sample are categorized into two classes using the k-Nearest Neighbor algorithm and Discriminant Analysis, followed by elimination of the variable with the index of lowest importance. The subset with the highest accuracy is used to classify observations in the test sample. When applied to the Wisconsin Breast Cancer Database, the proposed method led to average of 97.77% in classification accuracy while retaining an average of 5.8 variables.