Optimization of methods and treatment conditions for studying effects of fatty acids on cell growth

Antiproliferative properties of molecular regulators of lipid metabolism have been increasingly studied during recent years. Discussion is ongoing concerning optimal treatment conditions and assays used for monitoring proliferation and cytotoxicity. The objective of the present work was to optimize methods and treatment conditions used for studying antiproliferative effects of fatty acids and analogs, represented by palmitic acid (PA) and the β-oxidation-restricted fatty acid analog tetradecylthioacetic acid (TTA), in rat (BT4Cn) and human (D54Mg and GaMg) glioma cell lines. Changes in [³H]thymidine incorporation preceded changes in cell number in TTA-treated glioma cell cultures, and the growth inhibition was more significantly expressed by [³H]thymidine incorporation than cell number. Addition of bovine serum albumin decreased cellular fatty acid uptake and reduced the effects of TTA and PA on [³H]thymidine incorporation. Determination of the antiproliferative effect of TTA in BT4Cn cells by MTT conversion and [³H]thymidine incorporation yielded concordant results. TTA-mediated reduction in cell number corresponded to reduction in cellular protein and total DNA content in BT4Cn cells. Reduced growth potential in TTA-treated multicellular D54Mg and GaMg spheroids supported the findings from monolayer cultures. In conclusion, cell density, treatment period, fatty acid administration, and methods for growth determination may profoundly influence the outcome of cell growth experiments. Thus, experimental conditions should be carefully controlled when performing cell growth experiments, and effects on cell growth should preferably be confirmed by different methods. Karl Johan Tronstad and Kjetil Berge contributed equally to this article.     

The computer attributes for learning scale (CALS) among university students: Scale development and relationship with actual computer use for learning

The purpose of this study was twofold. First to develop an instrument, the computer attributes for learning scale (CALS) for assessing how university students perceive the attributes of computer use for learning, and secondly to examine the predictive value of the CALS in relation to the actual use students made of a computer for learning. The research was based on innovation diffusion theory, and was set in the context of higher education in Flanders, the Dutch speaking part of Belgium. The first step in the development of the computer attributes for learning scale, was to draw on the literature to determine a set of attributes which could be used in relation to the use of computers for learning. Secondly, working with a stratified sample of university students (n = 237), exploratory factor analysis was used to develop a one-dimensional nine item-scale for measuring students’ perceptions towards these specified attributes of a computer for learning. Next, using another similar stratified sample of the same size, confirmatory factor analysis was used to assess the stability of the one-factor structure. Finally, having developed the instrument, the predictive value of the CALS was assessed by examination of the relationship of the CALS with actual computer use for learning, when controlling for related computer variables, including computer self-efficacy, perceived usefulness of computers and computer use for personal purposes. The CALS was found to have a significant predictive value in terms of computer use for learning. In view of this it is argued that the CALS is an instrument which can be used in the future to assess the likely use students will make of a computer application in relation to their learning.     

Applied relaxation: an experimental analogue study of therapist vs. computer administration

This experimental analog component study compared two ways of administrating relaxation, either via a computer or by a therapist. The second phase of applied relaxation was used, which is called “release-only relaxation”. Sixty participants from a student population were randomized to one of three groups: computer-administered relaxation, therapist-administered relaxation, or a control group in which participants surfed on the Internet. Outcome was measures using psychophysiological responses and self-report. Objective psychophysiological data and results on the subjective visual analogue scale suggest that there was no difference between the two forms of administration. Both experimental groups became significantly more relaxed than the control group that surfed on the Internet. Practical applications and future directions are discussed.     

Discriminative touch and emotional touch.

Somatic sensation comprises four main modalities, each relaying tactile, thermal, painful, or pruritic (itch) information to the central nervous system. These input channels can be further classified as subserving a sensory function of spatial and temporal localization, discrimination, and provision of essential information for controlling and guiding exploratory tactile behaviours, and an affective function that is widely recognized as providing the afferent neural input driving the subjective experience of pain, but not so widely recognized as also providing the subjective experience of affiliative or emotional somatic pleasure of touch. The discriminative properties of tactile sensation are mediated by a class of fast-conducting myelinated peripheral nerve fibres--A-beta fibres--whereas the rewarding, emotional properties of touch are hypothesized to be mediated by a class of unmyelinated peripheral nerve fibres--CT afferents (C tactile)--that have biophysical, electrophysiological, neurobiological, and anatomical properties that drive the temporally delayed emotional somatic system. CT afferents have not been found in the glabrous skin of the hand in spite of numerous electrophysiological explorations of this area. Hence, it seems reasonable to conclude that they are lacking in the glabrous skin. A full understanding of the behavioural and affective consequences of the differential innervation of CT afferents awaits a fuller understanding of their function. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Electromagnetic MEMS microspeaker for portable electronic devices

This work presents the conception, the microfabrication, and the electroacoustic characterization of a new electromagnetic microspeaker based on silicon. The objectives are to get improved sound quality compared to that of conventional microspeakers, while keeping the electroacoustic efficiency as high as possible. An optimized stiffening silicon microstructure let the sound radiator be extremely light and rigid. The mobile part is suspended to the fixed part by silicon suspension springs, which enable large out-of-plane displacement. The acoustic radiator is actuated by an electromagnetic motor, composed of a fixed permanent magnet and a planar coil located on top of the silicon radiator. The piston-like motion of the radiator favored by this structure is very beneficial for the sound quality. Electro–mechano–acoustic characterization of the microfabricated microspeaker showed that the radiator surface could run out-of-plane with displacements higher than ±400 μm, with no mechanical and electrical failure. For an electrical power of 0.5 W, the microspeaker was capable to generate a sound pressure level of 80 dB at 10 cm, from 330 Hz up to 20 kHz frequency. The efficiency reaches 3 × 10^?5, that is to say three times more than typical efficiency of conventional microspeakers. Moreover, as characterization results showed, the existence of very few structural modes and the low electroacoustic distortions evidence the high sound quality of the microspeaker.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis

We present a simple image transform that optimally combines four image channels into a greyscale image for threshold-based cloud detection. These image channels, namely blue, green, red and near infrared, are present on many low Earth-orbit resource satellites. Applying a single threshold to a greyscale image is a computationally efficient method suitable for onboard implementation. We used heteroscedastic discriminant analysis (HDA), which is a generalization of the popular dimension-reducing linear discriminant analysis, to transform the image. Comparative tests between HDA, existing transforms from the remote-sensing literature (the haze optimized and D transforms), as well as the single red and blue image channels were conducted. Although thin clouds remain challenging for global threshold-based techniques, the HDA transform consistently gave the best average segmentation errors across the test dataset. This dataset consisted of 32 1 megapixel Quickbird and Landsat images. HDA has not previously been applied to remote-sensing data.     

Errors-in-variables identification of dynamic systems excited by arbitrary non-white input

This work deals with the identification of dynamic systems from noisy input–output observations, where the noise-free input is not parameterized. The basic assumptions made are (1) the dynamic system can be modeled by a (discrete- or continuous-time) rational transfer function model, (2) the temporal input–output disturbances are mutually independent, identically distributed noises, and (3) the input power spectrum is non-white (not necessarily rational) and is modeled nonparametrically. The system identifiability is guaranteed by exploiting the non-white spectrum property of the noise-free input. A frequency domain identification strategy is developed to estimate consistently the plant model parameters and the input–output noise variances. The uncertainty bound of the estimates is calculated and compared to the Cramér–Rao lower bound. The efficiency of the proposed algorithm is illustrated on numerical examples.     

On using available environmental data in service life estimations

In the process of service life prediction or estimation of building and construction components and materials, data of the prevailing exposure environment, (the conditions at and around a building or construction) is required. However, most environmental data is measured by and available from meteorological and air quality research communities. This data is collected at macro and meso levels, some distance from the object studied, and raises the need to transform data in order to describe the specific, local conditions adjacent to that object. To estimate levels of degradation agents in the exposure environment, especially those close to the building or construction at local and micro levels have to be considered. This paper will show and discuss useful environmental data sources, and how to transform such data by means of available distribution models.

Lorsqu'il s'agit de faire des prévisions ou des estimations de durée de vie de bâtiments ainsi que de composants et matériaux de construction, il faut disposer des données sur les conditions prévalantes d'exposition et d'environnement (conditions qui règnent dans un bâtiment ou à proximité). Or, la plupart des données environnementales sont mesurées et fournies par des instituts de recherche en météorologie et qualité de l'air. Ces données sont collectées à grande et à moyenne échelle, à une certaine distance de l'objet étudié, et elles doivent donc être transformées si l'on veut décrire les conditions spécifiques locales à proximité dudit objet. Pour estimer le niveau de dégradation dans l'environnement d'exposition, notamment à proximité des bâtiments ou constructions, il faut considérer les agents au micro-niveau et au niveau local. Cet article présente et examine des sources de données environnementales utiles et montre comment transformer ces données en utilisant les modèles de distribution existants.