Relation of vocational identity statuses to interest structure among Swiss adolescents

Vocational identity is one core component of identity construction in adolescence. The current study investigated whether vocational interest structure in terms of differentiation, coherence, elevation, and interest-aspiration congruence would differentiate among students in vocational identity achievement, foreclosure, moratorium, and diffusion. Swiss students at the beginning of eighth grade (N = 341) participated in the study. Groups were created using cluster analysis based on the dimensions of career exploration and career commitment, and group differences were explored with discriminant analysis. Controlling for sociodemographic variables, higher interest differentiation and elevation distinguished students in achievement/moratorium from those in diffusion. More interest elevation differentiated moratorium from foreclosure. (Contains 1 figure and 3 tables.)

Milk fatty acids as possible biomarkers to early diagnose elevated concentrations of blood plasma nonesterified fatty acids in dairy cows

Most cows encounter a state of negative energy balance during the periparturient period, which may lead to metabolic disorders and impaired fertility. The aim of this study was to assess the potential of milk fatty acids as diagnostic tools of detrimental levels of blood plasma nonesterified fatty acids (NEFA), defined as NEFA concentrations beyond 0.6 mmol/L, in a data set of 92 early lactating cows fed a glucogenic or lipogenic diet and subjected to 0-, 30-, or 60-d dry period before parturition. Milk was collected in wk 2, 3, 4, and 8 (n = 368) and blood was sampled weekly from wk 2 to 8 after parturition. Milk was analyzed for milk fatty acids and blood plasma for NEFA. Data were classified as "at risk of detrimental blood plasma NEFA" (NEFA ≥ 0.6 mmol/L) and "not at risk of detrimental blood plasma NEFA" (NEFA <0.6 mmol/L). Concentrations of 45 milk fatty acids and milk fat C18:1 cis-9-to-C15:0 ratio were subjected to a discriminant analysis. Milk fat C18:1 cis-9 revealed the most discriminating variable to identify detrimental blood plasma NEFA. A false positive rate of 10% allowed us to diagnose 46% of the detrimental blood plasma NEFA cases based on a milk fat C18:1 cis-9 concentration of at least 230 g/kg of milk fatty acids. Additionally, it was assessed whether the milk fat C18:1 cis-9 concentrations of wk 2 could be used as an early warning for detrimental blood plasma NEFA risk during the first 8 wk in lactation. Cows with at least 240 g/kg of C18:1 cis-9 in milk fat had about 50% chance to encounter blood plasma NEFA values of 0.6 mmol/L or more during the first 8 wk of lactation, with a false positive rate of 11.4%. Profit simulations were based on costs for cows suffering from detrimental blood plasma NEFA, and costs for preventive treatment based on daily dosing of propylene glycol for 3 wk. Given the relatively low incidence rate (8% of all observations), continuous monitoring of milk fatty acids during the first 8 wk of lactation to diagnose detrimental blood plasma NEFA does not seem cost effective. On the contrary, milk fat C18:1 cis-9 of the second lactation week could be an early warning of cows at risk of detrimental blood NEFA. In this case, selective treatment may be cost effective.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Disparities in foodborne illness in Harris County, Texas, 2005--2010

Using a retrospective cross-sectional approach, this study quantitatively analyzed foodborne illness data, restaurant inspection data, and census-derived socioeconomic and demographic data within Harris County, Texas between 2005 and 2010. The main research question investigated involved determining the extent to which contextual and regulatory conditions distinguish outbreak and non-outbreak establishments within Harris County. Two groups of Harris County establishments were analyzed: outbreak and non-outbreak restaurants. STATA 11 was employed to determine the average profiles of each category across both the regulatory and socioeconomic (contextual) variables. Cross tabulations of all of the non-quantitative variables were also performed, and finally, a discriminant analysis was conducted to assess how well the variables were able to allocate the restaurants into their respective categories. Contextual and regulatory conditions were found to be minimally associated with the occurrence of foodborne outbreaks within Harris County. Across both the categories (outbreak and non-outbreak establishments), variables included were extremely similar in means, and when possible to observe, distributions. The variables analyzed in this study, both regulatory and contextual, were not found to significantly allocate the establishments into their correct outbreak or non-outbreak categories. The implications of these findings are that regulatory processes and guidelines in place in Harris County do not effectively to distinguish outbreak from non-outbreak restaurants. Additionally, no socioeconomic or racial/ethnic patterns are apparent in the incidence of foodborne disease in the county. ^

Una metodología diferente para el estudio de un nuevo curriculum. Facultad de Ciencias Agrarias, UNCuyo.

Este trabajo se realizó en el contexto del Plan de estudios que la Facultad de Ciencias Agrarias (UNCuyo) implementó a partir de 1994 para las carreras de Ingeniería Agronómica y Licenciatura en Bromatología. Se calcularon algunos indicadores educativos (Tasa de Aprobación, Tasa de Deserción, Tasa de Recursado y Tasa de Permanencia) con el objeto de efectuar un seguimiento en la aplicación de dicho Plan de estudios, a través de una nueva metología. Para el análisis de los datos se utilizaron cuatro métodos: "Determinación de distancias entre matrices y sus respectivos valores ideales", que permitió elaborar un índice de lejanía del sistema en su conjunto; "Análisis de Componentes Principales (ACP)" seguido de un "Análisis de Conglomerados (AC)", que permitieron agrupar las materias de acuerdo con sus similitudes. Finalmente, se realizó un "Análisis Discriminante", que permitió concluir que las tasas calculadas no establecen diferencias entre las asignaturas de los ciclos Básico e Instrumental (únicamente en el caso de Ingeniería Agronómica).

Postglacial deposition of sand in the Eckernfoerder Bay, western Baltic Sea

At the NW-slope of Eckernforder Bay (Western Baltic) between 14 and 21 m water depth 7 sand cores were taken with a vibrocorer. The cores were between 85 and 250 cm long. The sand was analysed for grain size distribution, proportions of organic carbon and carbonate, and contents of microfossils. The radiometric age and stable carbon isotope ratios were determined on organic material from 14 sample. With regard to benthic foraminifera and other microorganisms four different types of depositional conditions could be distinguished: Types 1 and 2: two types of offshore sand areas. Type 3: lagoon and nearshore. Type 4: subaerial or limnic. Using sedimentological and geochemical parameters two formation areas could be distinguished with the aid of a discriminant analysis: offshore (types 1 and 2) and nearshore (types 3 and 4). A juxtaposition of core sections indicated two distinct profiles. Their ages fit into the picture of the assumed postglacial sea-level rise. The lagoon- and nearshore sands are interpreted as the result of sea-level stagnation at 17-18 m below present sea-level. The accumulation rates of the sand in the offshore areas are, with a maximum of 0.15 mm/yr., an order of magnitude smaller than in the mud areas, located several hundred metres away.

Single beam survey data and links to sea-bottom video files off Helgoland

Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.

Continuous measurements at Schwingbachtal Studienlandschaft in the growing seasons 2013-2014

The recent development of in-situ monitoring devices, such as UV-spectrometers, makes the study of short-term stream chemistry variation relevant, especially the study of diurnal cycles, which are not yet fully understood. Our study is based on high-frequency data from an agricultural catchment (Studienlandschaft Schwingbachtal, Germany). We propose a novel approach, i.e. the combination of cluster analysis and Linear Discriminant Analysis, to mine from these data nitrate behavior patterns. As a result, we observe a seasonality of nitrate diurnal cycles, that differs from the most common cycle seasonality described in the literature, i.e. pre-dawn peaks in spring. Our cycles appear in summer and the maximum and minimum shift to a later time in late summer/autumn. This is observed both for water- and energy-limited years, thus potentially stressing the role of evapotranspiration. This concluding hypothesis on the role of evapotranspiration on nitrate stream concentration, which was obtained through data mining, broadens the perspective on the diurnal cycling of stream nitrate concentrations.

Determinant factors to discriminate the body weight loss in overweight people

Introduction. Most studies have described how the weight loss is when different treatments are compared (1-3), while others have also compared the weight loss by sex (4), or have taken into account psychosocial (5) and lifestyle (6, 7) variables. However, no studies have examined the interaction of different variables and the importance of them in the weight loss. Objective. Create a model to discriminate the range of weight loss, determining the importance of each variable. Methods. 89 overweight people (BMI: 25-29.9 kg?m-2), aged from 18 to 50 years, participated in the study. Four types of treatments were randomly assigned: strength training (S), endurance training (E), strength and endurance training (SE), and control group (C). All participants followed a 25% calorie restriction diet. Two multivariate discriminant models including the variables age, sex, height, daily energy expenditure (EE), type of treatment (T), caloric restriction (CR), initial body weight (BW), initial fat mass (FM), initial muscle mass (MM) and initial bone mineral density (BMD) were performed having into account two groups: the first and fourth quartile of the % of weight loss in the first model; the groups above and below the mean of the % of weight loss in the second model. The discriminant models were built using the inclusion method in SPSS allowing us to find a function that could predict the body weight loss range that an overweight person could achieve in a 6 months weight loss intervention.Results. The first discriminant analysis predicted that a combination of the studied variables would discriminate between the two ranges of body weight loss with 81.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.475, p=0.003): Discriminant score=-18.266-(0.060xage)- (1.282xsex[0=female;1=male])+(14.701xheight)+(0.002xEE)- (0.006xT[1=S;2=E;3=SE;4=C])-(0.047xCR)- (0.558xBW)+(0.475xFM)+(0.398xMM)+(3.499xBMD) The second discriminant model obtained would discriminate between the two groups of body weight loss with 74.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.725, p=0.005): Discriminant score=-5.021-(0.052xage)- (0.543xsex[0=female;1=male])+(3.530xheight)+(0.001xEE)- (0.493xT[1=S;2=E;3=SE;4=C])+(0.003xCR)- (0.365xBW)+(0.368xFM)+(0.296xMM)+(4.034xBMD) Conclusion. The first developed model could predict the percentage of weight loss in the following way: if the discriminant score is close to 1.051, the range of weight loss will be from 7.44 to -4.64% and if it is close to - 1.003, the range will be from -11.03 to -25,00% of the initial body weight. With the second model if the discriminant score is close to 0.623 the body weight loss will be above -7.93% and if it is close to -0.595 will be below - 7.93% of the initial body weight. References. 1. Brochu M, et al. Resistance training does not contribute to improving the metabolic profile after a 6-month weight loss program in overweight and obese postmenopausal women. J Clin Endocrinol Metab. 2009 Sep;94(9):3226-33. 2. Del Corral P, et al. Effect of dietary adherence with or without exercise on weight loss: a mechanistic approach to a global problem. J Clin Endocrinol Metab. 2009 May;94(5):1602-7. 3. Larson-Meyer DE, et al. Caloric Restriction with or without Exercise: The Fitness vs. Fatness Debate. Med Sci Sports Exerc. 2010;42(1):152-9. 4. Hagan RD, et al. The effects of aerobic conditioning and/or caloric restriction in overweight men and women. Medicine & Science in Sports & Exercise. 1986;18(1):87-94. 5. Teixeira PJ, et al. Mediators of weight loss and weight loss maintenance in middle-aged women. Obesity (Silver Spring). 2010 Apr;18(4):725-35. 6. Bautista-Castano I, et al. Variables predictive of adherence to diet and physical activity recommendations in the treatment of obesity and overweight, in a group of Spanish subjects. Int J Obes Relat Metab Disord. 2004 May;28(5):697-705.

Stress Detection by means of Stress Physiological Template

This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications

Conceptual framework for the design, implementation and validation of psychomotor skills’ assessment systems in minimally invasive surgery

Las técnicas de cirugía de mínima invasión (CMI) se están consolidando hoy en día como alternativa a la cirugía tradicional, debido a sus numerosos beneficios para los pacientes. Este cambio de paradigma implica que los cirujanos deben aprender una serie de habilidades distintas de aquellas requeridas en cirugía abierta. El entrenamiento y evaluación de estas habilidades se ha convertido en una de las mayores preocupaciones en los programas de formación de cirujanos, debido en gran parte a la presión de una sociedad que exige cirujanos bien preparados y una reducción en el número de errores médicos. Por tanto, se está prestando especial atención a la definición de nuevos programas que permitan el entrenamiento y la evaluación de las habilidades psicomotoras en entornos seguros antes de que los nuevos cirujanos puedan operar sobre pacientes reales. Para tal fin, hospitales y centros de formación están gradualmente incorporando instalaciones de entrenamiento donde los residentes puedan practicar y aprender sin riesgos. Es cada vez más común que estos laboratorios dispongan de simuladores virtuales o simuladores físicos capaces de registrar los movimientos del instrumental de cada residente. Estos simuladores ofrecen una gran variedad de tareas de entrenamiento y evaluación, así como la posibilidad de obtener información objetiva de los ejercicios. Los diferentes estudios de validación llevados a cabo dan muestra de su utilidad; pese a todo, los niveles de evidencia presentados son en muchas ocasiones insuficientes. Lo que es más importante, no existe un consenso claro a la hora de definir qué métricas son más útiles para caracterizar la pericia quirúrgica. El objetivo de esta tesis doctoral es diseñar y validar un marco de trabajo conceptual para la definición y validación de entornos para la evaluación de habilidades en CMI, en base a un modelo en tres fases: pedagógica (tareas y métricas a emplear), tecnológica (tecnologías de adquisición de métricas) y analítica (interpretación de la competencia en base a las métricas). Para tal fin, se describe la implementación práctica de un entorno basado en (1) un sistema de seguimiento de instrumental fundamentado en el análisis del vídeo laparoscópico; y (2) la determinación de la pericia en base a métricas de movimiento del instrumental. Para la fase pedagógica se diseñó e implementó un conjunto de tareas para la evaluación de habilidades psicomotoras básicas, así como una serie de métricas de movimiento. La validación de construcción llevada a cabo sobre ellas mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. Adicionalmente, los resultados obtenidos en la validación de apariencia fueron en general positivos en todos los grupos considerados (noveles, residentes, expertos). Para la fase tecnológica, se introdujo el EVA Tracking System, una solución para el seguimiento del instrumental quirúrgico basado en el análisis del vídeo endoscópico. La precisión del sistema se evaluó a 16,33ppRMS para el seguimiento 2D de la herramienta en la imagen; y a 13mmRMS para el seguimiento espacial de la misma. La validación de construcción con una de las tareas de evaluación mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. La validación concurrente con el TrEndo® Tracking System por su parte presentó valores altos de correlación para 8 de las 9 métricas analizadas. Finalmente, para la fase analítica se comparó el comportamiento de tres clasificadores supervisados a la hora de determinar automáticamente la pericia quirúrgica en base a la información de movimiento del instrumental, basados en aproximaciones lineales (análisis lineal discriminante, LDA), no lineales (máquinas de soporte vectorial, SVM) y difusas (sistemas adaptativos de inferencia neurodifusa, ANFIS). Los resultados muestran que en media SVM presenta un comportamiento ligeramente superior: 78,2% frente a los 71% y 71,7% obtenidos por ANFIS y LDA respectivamente. Sin embargo las diferencias estadísticas medidas entre los tres no fueron demostradas significativas. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la definición de sistemas de evaluación de habilidades para cirugía de mínima invasión, a la utilidad del análisis de vídeo como fuente de información y a la importancia de la información de movimiento de instrumental a la hora de caracterizar la pericia quirúrgica. Basándose en estos cimientos, se han de abrir nuevos campos de investigación que contribuyan a la definición de programas de formación estructurados y objetivos, que puedan garantizar la acreditación de cirujanos sobradamente preparados y promocionen la seguridad del paciente en el quirófano. Abstract Minimally invasive surgery (MIS) techniques have become a standard in many surgical sub-specialties, due to their many benefits for patients. However, this shift in paradigm implies that surgeons must acquire a complete different set of skills than those normally attributed to open surgery. Training and assessment of these skills has become a major concern in surgical learning programmes, especially considering the social demand for better-prepared professionals and for the decrease of medical errors. Therefore, much effort is being put in the definition of structured MIS learning programmes, where practice with real patients in the operating room (OR) can be delayed until the resident can attest for a minimum level of psychomotor competence. To this end, skills’ laboratory settings are being introduced in hospitals and training centres where residents may practice and be assessed on their psychomotor skills. Technological advances in the field of tracking technologies and virtual reality (VR) have enabled the creation of new learning systems such as VR simulators or enhanced box trainers. These systems offer a wide range of tasks, as well as the capability of registering objective data on the trainees’ performance. Validation studies give proof of their usefulness; however, levels of evidence reported are in many cases low. More importantly, there is still no clear consensus on topics such as the optimal metrics that must be used to assess competence, the validity of VR simulation, the portability of tracking technologies into real surgeries (for advanced assessment) or the degree to which the skills measured and obtained in laboratory environments transfer to the OR. The purpose of this PhD is to design and validate a conceptual framework for the definition and validation of MIS assessment environments based on a three-pillared model defining three main stages: pedagogical (tasks and metrics to employ), technological (metric acquisition technologies) and analytical (interpretation of competence based on metrics). To this end, a practical implementation of the framework is presented, focused on (1) a video-based tracking system and (2) the determination of surgical competence based on the laparoscopic instruments’ motionrelated data. The pedagogical stage’s results led to the design and implementation of a set of basic tasks for MIS psychomotor skills’ assessment, as well as the definition of motion analysis parameters (MAPs) to measure performance on said tasks. Validation yielded good construct results for parameters such as time, path length, depth, average speed, average acceleration, economy of area and economy of volume. Additionally, face validation results showed positive acceptance on behalf of the experts, residents and novices. For the technological stage the EVA Tracking System is introduced. EVA provides a solution for tracking laparoscopic instruments from the analysis of the monoscopic video image. Accuracy tests for the system are presented, which yielded an average RMSE of 16.33pp for 2D tracking of the instrument on the image and of 13mm for 3D spatial tracking. A validation experiment was conducted using one of the tasks and the most relevant MAPs. Construct validation showed significant differences for time, path length, depth, average speed, average acceleration, economy of area and economy of volume; especially between novices and residents/experts. More importantly, concurrent validation with the TrEndo® Tracking System presented high correlation values (>0.7) for 8 of the 9 MAPs proposed. Finally, the analytical stage allowed comparing the performance of three different supervised classification strategies in the determination of surgical competence based on motion-related information. The three classifiers were based on linear (linear discriminant analysis, LDA), non-linear (support vector machines, SVM) and fuzzy (adaptive neuro fuzzy inference systems, ANFIS) approaches. Results for SVM show slightly better performance than the other two classifiers: on average, accuracy for LDA, SVM and ANFIS was of 71.7%, 78.2% and 71% respectively. However, when confronted, no statistical significance was found between any of the three. Overall, this PhD corroborates the investigated research hypotheses regarding the definition of MIS assessment systems, the use of endoscopic video analysis as the main source of information and the relevance of motion analysis in the determination of surgical competence. New research fields in the training and assessment of MIS surgeons can be proposed based on these foundations, in order to contribute to the definition of structured and objective learning programmes that guarantee the accreditation of well-prepared professionals and the promotion of patient safety in the OR.

Control by ATR-FTIR of surface treatment of cork stoppers and its effect on their mechanical performance

Fourier transform infrared (FTIR) spectroscopy was applied to determine the type of surface treatment and dose used on cork stoppers and to predict the friction between stopper and bottleneck. Agglomerated cork stoppers were finished with two different doses and using two surface treatments: P (paraffin and silicone), 15 and 25 mg/stopper, and S (only silicone), 10 and 15 mg/stopper. FTIR spectra were recorded at five points for each stopper by attenuated total reflectance (ATR). Absorbances at 1,010, 2,916, and 2,963 cm -1 were obtained in each spectrum. Discriminant analysis techniques allowed the treatment, and dose applied to each stopper to be identified from the absorbance values. 91.2% success rates were obtained from individual values and 96.0% from the mean values of each stopper. Spectrometric data also allowed treatment homogeneity to be determined on the stopper surface, and a multiple regression model was used to predict the friction index (If = Fe/Fc) (R 2 = 0.93)

Models for Internal Quality Fruit Sorting, Based on Time- Domain Laser Reflectance Spectroscopy (TDRS)

Time domain laser reflectance spectroscopy (TDRS) was applied for the first time to evaluate internal fruit quality. This technique, known in medicine-related knowledge areas, has not been used before in agricultural or food research. It allows the simultaneous non-destructive measuring of two optical characteristics of the tissues: light scattering and absorption. Models to measure firmness, sugar & acid contents in kiwifruit, tomato, apple, peach, nectarine and other fruits were built using sequential statistical techniques: principal component analysis, multiple stepwise linear regression, clustering and discriminant analysis. Consistent correlations were established between the two parameters measured with TDRS, i.e. absorption & transport scattering coefficients, with chemical constituents (sugars and acids) and firmness, respectively. Classification models were built to sort fruits into three quality grades, according to their firmness, soluble solids and acidity.

Optical detection of mealiness in apples using laser TDRS

Mealiness is a textural attribute related to an internal fruit disorder that involves quality loss. It is characterised by the combination of abnormal softness of the fruit and absence of free juiciness in the mouth when eaten by the consumer. Recent research concluded with the development of precise instrumental procedure to measure a scale of mealiness based on the combination of several rheological properties and empirical magnitudes. In this line, time-domain laser reflectance spectroscopy (TDRS) is a medical technology, new in agrofood research, which is capable of obtaining physical and chemical information independently and simultaneously, and this can be of interest to characterise mealiness. Using VIS & NIR lasers as light sources, TDRS was applied in this work to Golden Delicious and Cox apples (n=90), conforming several batches of untreated samples and storage-treated (20°C & 95%RH) to promote the development of mealiness. The collected database was clustered into different groups according to their instrumental test values (Barreiro et al, 1998). The optical coefficients were used as explanatory variables when building discriminant analysis functions for mealiness, achieving a classification score above 80% of correctly identified mealy versus fresh apples.

Fruit quality sensing:Post-harvest ripeness

Firmness sensing of selected varieties of apples, pears and avocado fruits has been developed using a nondestructive impact technique. In addition to firmness measurements, postharvest ripeness of apples and pears was monitored by spectrophotometric reflectance measurements, and that of avocadoes by Hunter colour measurements. The data obtained from firmness sensing were analyzed by three analytical procedures: principal component, correlation and regression, and stepwise discriminant analysis. A new software was developed to control the impact test, analyse the data, and sort the fruit into specified classes, based on the criteria obtained from a training procedure. Similar procedures were used to analyse the reflectance and colour data. Both sensing systems were able to classify fruits w i th good accuracy.