Utilización de redes neuronales para formular grasas técnicas

Neural networks are a branch of artificial intelligence based on the structure and development of biological systems, having as its main characteristic the ability to learn and generalize knowledge. They are used for solving complex problems for which traditional computing systems have a low efficiency. To date, applications have been proposed for different sectors and activities. In the area of fats and oils, the use of neural networks has focused mainly on two issues: the detection of adulteration and the development of fatty products. The formulation of fats for specific uses is the classic case of a complex problem where an expert or group of experts defines the proportions of each base, which, when mixed, provide the specifications for the desired product. Some conventional computer systems are currently available to assist the experts; however, these systems have some shortcomings. This article describes in detail a system for formulating fatty products, shortenings or special fats, from three or more components by using neural networks (MIX). All stages of development, including design, construction, training, evaluation, and operation of the network will be outlined.Las redes neuronales son una rama de la inteligencia artificial basadas en la estructura y funcionamiento de sistemas biológicos, teniendo como principal característica la capacidad de aprender y generalizar conocimiento. Estas son utilizadas en la resolución de problemas complejos, en los cuales los sistemas computacionales tradicionales presentan una eficiencia baja. Hasta la fecha, han sido propuestas aplicaciones para los más diversos sectores y actividades. En el área de grasas y aceites, la utilización de redes neuronales se ha concentrado principalmente en dos asuntos: la detección de adulteraciones y la formulación de productos grasos. La formulación de grasas para uso específico es el caso clásico de problema complejo donde un experto o grupo de expertos definen las proporciones de cada base, que al ser mezcladas proporcionaran características especificadas para el producto deseado. Algunos sistemas computacionales convencionales están disponibles actualmente para auxiliar a los expertos, sin embargo, estos sistemas presentan algunas deficiencias. En este artículo será descrito con detalles, un sistema para la formulación de productos grasos por redes neuronales (MIX) a partir de 3 o más componentes. Todas las etapas del desarrollo, incluyendo el diseño, construcción, entrenamiento, evaluación y operación de la red serán mostradas

Estimation of Cachexia among Cancer Patients Based on Four Definitions

Objectives. Estimate and compare the proportion of cancer patients with cachexia using different definitions from available clinical data. Methods. Electronic medical records were examined to estimate the proportion of cancer patients with cachexia using 4 definitions: (1) ICD-9 diagnostic code of 799.4 (cachexia), (2) ICD-9 diagnosis of cachexia, anorexia, abnormal weight loss, or feeding difficulties, (3) prescription for megestrol acetate, oxandrolone, somatropin, or dronabinol, and (4) ≥5% weight loss. Patients with cancer of the stomach, pancreas, lung, colon/rectum, head/neck, esophagus, prostate, breast, or liver diagnosed between 1999 and 2004 were followed for cachexia. Results. Of 8541 cancer patients (60% men and 55% Caucasian), cachexia was observed in 2.4% of patients using the cachexia diagnostic code, 5.5% expanded diagnoses, 6.4% prescription medication definition, and 14.7% with ≥5% weight loss. Conclusions. The proportion of patients with cachexia varied considerably depending upon the definition employed, indicating that a standard operational definition is needed

Further Validation of an Individualized Migraine Treatment Satisfaction Measure

AbstractObjectiveTo assess individualized satisfaction with migraine treatment, patient expectations, importance rankings, treatment outcomes, and overall satisfaction were combined using a four-part conceptual model. This article describes the measurement properties of the Migraine Treatment Satisfaction Measure (MTSM) using participants from a randomized controlled trial evaluating a Headache Management Program (HMP).MethodsParticipants completed the first two parts of the MTSM upon enrollment and the final two parts at 6 months. Internal consistency reliability was computed within each of the four modules. Discriminant validity was ascertained using Migraine Disability Assessment Survey (MIDAS), Patient Health Questionnaire-9, and MSFB scores. Convergent validity was established by hypothesized positive correlations between MTSM scores, Medical Outcomes Study Short-Form (SF-36), MIDAS, and Migraine Symptom Frequency Bother (MSFB).ResultsIn total, 124 participants (mean age 45.4 years, 75% women, 59.7% Caucasian) enrolled. Internal consistency for expectations, importance rankings, outcomes, and satisfaction measures was 0.83, 0.95, 0.86, and 0.95, respectively. As the severity of depression increased, MTSM scores decreased significantly. ANOVA between MTSM scores and symptom bothersomeness and symptom frequency tertiles showed a significant decrease in satisfaction in the moderate-to-severe groups. MTSM scores showed expected associations with MSFB scores (−0.301; P < 0.01), MIDAS (−0.267; P < 0.01), general health (0.253; P < 0.05), mental health (0.217; P < 0.05), and vitality subscales of SF-36 (0.214; P < 0.05). Patients in the HMP reported significantly higher MTSM scores (43.2 vs. 31.4; P < 0.001). Patients on triptans reported a significantly higher satisfaction compared to patients on analgesics (39.5 vs. 32.9; P < 0.05).ConclusionThe MTSM is a valid and reliable patient-reported outcome that can be used to evaluate differences in treatment satisfaction associated with migraine therapies

Classifying antibiotics in the WHO Essential Medicines List for optimal use—be AWaRe

International audienc

Determination of the magnetocaloric entropy change by field sweep using a heat flux setup

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESWe report on a simple setup using a heat flux sensor adapted to a Quantum Design Physical Property Measurement System to determine the magnetocaloric entropy change (Delta S). The major differences for the existing setups are the simplicity of this assembly and the ease to obtain the isothermal entropy change either by a field sweep or a temperature sweep process. We discuss the use of these two processes applied to Gd and Gd5Ge2Si2 samples. The results are compared to the temperature sweep measurements and they show the advantages of this setup and of the field sweep procedure. We found a significant reduction of DS and on the refrigerating cooling power (RCP) at low field changes in a field sweep process when the sample is not driven to the same initial state for each temperature. We show that the field sweep process without any measuring protocol is the only correct way to experimentally determine DS and RCP for a practical regenerative refrigerator.105715FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçãoSem informaçãoSem informaçã