Image texture analysis and gas sensor array studies applied to vanilla encapsulation by octenyl succinic anhydride starches

Native starch derivatization with octenyl succinic anhydride (OSA) is a chemical modification designed to enhance flavor microencapsulation performance. Hi Cap 100 and Capsul are two OSA starches derived from waxy maize base, which are especially suited for encapsulation processes. This work performs for the first time the encapsulation of vanilla extract with Capsul and Hi Cap 100 using both spray and freeze drying procedures. The encapsulation efficiency was studied correlating the starch texture with the aroma retention. Texture analysis was accomplished by means of grey level co-occurrence matrix feature extraction (GLCM), yielding image parameters that clearly differ in function of the type of starch and the drying method used for the encapsulation of the flavor. In parallel, the data recorded with a gas sensor array (e-nose) and analyzed by unsupervised multivariate methods allowed to follow up the evolution of the aroma through the whole process. The joint analysis of the GLCM and sensor array recorded data indicates that Capsul shows a higher capacity for vanilla encapsulation than Hi Cap 100. In addition, the obtained converging information from GLCM and e-nose data clearly indicates that particle texture and aroma encapsulation are connected.Fil: Rodríguez, Silvio David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Wilderjans, Tom F.. Faculty of Psychology and Educational Sciences. Methodology of Educational Sciences Research Group; BélgicaFil: Sosa, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; ArgentinaFil: Bernik, Delia Leticia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentin

A retrospective report (2003–2013) of the complications associated with the use of a one-man (head and tail) rope recovery system in horses following general anaesthesia

Abstract Background The mortality rate of horses undergoing general anaesthesia is high when compared to humans or small animal patients. One of the most critical periods during equine anaesthesia is recovery, as the horse attempts to regain a standing position. This study was performed in a private equine practice in Belgium that uses a purpose-designed one-man (head and tail) rope recovery system to assist the horse during the standing process. The main purpose of the retrospective study was to report and analyse complications and the mortality rate in horses during recovery from anaesthesia using the described recovery system. Information retrieved from the medical records included patient signalment, anaesthetic protocol, duration of anaesthesia, ASA grade, type of surgery, recovery time and complications during recovery. Sedation was administered to all horses prior to recovery with the rope system. Complications were divided into major complications in which the horse was euthanized and minor complications where the horse survived. Major complications were further subdivided into those where the rope system did not contribute to the recovery complication (Group 1) and those where it was not possible to determine if the rope system was of any benefit (Group 2). Results Five thousand eight hundred fifty two horses recovered from general anaesthesia with rope assistance. Complications were identified in 30 (0.51%). Major complications occurred in 12 horses (0.20%) of which three (0.05%) were assigned to Group 1 and nine (0.15%) to Group 2. Three horses in Group 2 suffered musculoskeletal injuries (0.05%). Eighteen horses (0.31%) suffered minor complications, of which five (0.08%) were categorised as failures of the recovery system. Conclusions This study reports the major and minor complication and mortality rate during recovery from anaesthesia using a specific type of rope recovery system. Mortality associated with the rope recovery system was low. During recovery from anaesthesia this rope system may reduce the risk of lethal complications, particularly major orthopaedic injuries

The different faces of (high) sensitivity, toward a more comprehensive measurement instrument: development and validation of the sensory processing sensitivity questionnaire (SPSQ).

Health and self-regulatio

How Virtual Agents Can Learn to Synchronize: an Adaptive Joint Decision-Making Model of Psychotherapy

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations

Deriving optimal data-analytic regimes from benchmarking studies.

In benchmarking studies with simulated data sets in which two or more statistical methods are compared, over and above the search of a universally winning method, one may investigate how the winning method may vary over patterns of characteristics of the data or the data-generating mechanism. Interestingly, this problem bears strong formal similarities to the problem of looking for optimal treatment regimes in biostatistics when two or more treatment alternatives are available for the same medical problem or disease. It is outlined how optimal data-analytic regimes, that is to say, rules for optimally calling in statistical methods, can be derived from benchmarking studies with simulated data by means of supervised classification methods (e.g., classification trees). The approach is illustrated by means of analyses of data from a benchmarking study to compare two different algorithms for the estimation of a two-mode additive clustering model.Multivariate analysis of psychological dat

Integrating functional genomics data using maximum likelihood based simultaneous component analysis

<p>Abstract</p> <p>Background</p> <p>In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life <it>Escherichia coli </it>metabolomics data set.</p> <p>Results</p> <p>In the simulation study, MxLSCA-P outperforms SCA-P in terms of recovery of the true underlying scores of the common mode and of the true values underlying the data entries. MxLSCA-P further performed especially better when the simulated data blocks were subject to different noise levels. In the analysis of an <it>E. coli </it>metabolomics data set, MxLSCA-P provided a slightly better and more consistent interpretation.</p> <p>Conclusion</p> <p>MxLSCA-P is a promising addition to the SCA family. The analysis of coupled functional genomics data blocks could benefit from its ability to take different noise levels per data block into consideration and improve the recovery of the true patterns underlying the data. Moreover, the maximum likelihood based approach underlying MxLSCA-P could be extended to custom-made solutions to specific problems encountered.</p

Clusterwise Independent Component Analysis (C-ICA): using fMRI resting state networks to cluster subjects and find neurofunctional subtypes

Background: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously.New method: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs.Results: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. Comparison with other methods: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods.Conclusions: The successful performance of C-ICA indicates that it is a promising method to extract neuro-functional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.Multivariate analysis of psychological dat

Assessing the effects of a real-life contact intervention on prejudice toward LGBT people

Prejudice against sexual and gender minorities (e.g., LGBT people) is quite prevalent and is harmful. We examined an existing-and often-used-contact intervention in pre-existing groups in an educational setting and assessed its effectiveness in reducing different forms of LGBT negativity. We focused particularly on modern LGBT negativity: a relatively subtle form of prejudice, involving ambivalence, denial, and/or the belief that there is too much attention for LGBT prejudice. We used a mixed design in which condition (experimental vs. control group) was the between-participants factor, which was randomized at the group level, and time (pretest vs. posttest vs. follow-up) was the within-participants factor (N = 117). Interventions were video recorded and the behavior of LGBT educators and participants was coded. Participants responded positively to the intervention, especially to the LGBT educator's "coming-out story." Exploratory analysis of the video data indicated that the perceived effectiveness of the intervention was higher in groups where participants were more engaged, although caution is necessary in interpreting this finding. The most important measure indicated that modern LGBT negativity decreased in the intervention groups directly after the intervention, but returned to baseline levels one week later. However, in the control condition, modern LGBT negativity had increased over time. Taken together, this suggests that an actual reduction in modern LGBT negativity was short-lived (i.e., the intervention effect disappeared within 7 days).Social decision makin