Automation in clinical bacteriology: what system to choose?

With increased activity and reduced financial and human resources, there is a need for automation in clinical bacteriology. Initial processing of clinical samples includes repetitive and fastidious steps. These tasks are suitable for automation, and several instruments are now available on the market, including the WASP (Copan), Previ-Isola (BioMerieux), Innova (Becton-Dickinson) and Inoqula (KIESTRA) systems. These new instruments allow efficient and accurate inoculation of samples, including four main steps: (i) selecting the appropriate Petri dish; (ii) inoculating the sample; (iii) spreading the inoculum on agar plates to obtain, upon incubation, well-separated bacterial colonies; and (iv) accurate labelling and sorting of each inoculated media. The challenge for clinical bacteriologists is to determine what is the ideal automated system for their own laboratory. Indeed, different solutions will be preferred, according to the number and variety of samples, and to the types of sample that will be processed with the automated system. The final choice is troublesome, because audits proposed by industrials risk being biased towards the solution proposed by their company, and because these automated systems may not be easily tested on site prior to the final decision, owing to the complexity of computer connections between the laboratory information system and the instrument. This article thus summarizes the main parameters that need to be taken into account for choosing the optimal system, and provides some clues to help clinical bacteriologists to make their choice.

Regulador de llum controlat per ordinador

RESUM El llum electric és un tipus d’energia amb la que s’il•lumina tot el món i s’utilitza tant per a il•luminar la nit com per a disposar de llum addicional durant el dia. L’energia es pren directament de la xarxa de subministrament elèctric i permet encendre tot tipus de focus i bombetes. Actualment la necessitat de controlar la intensitat lumínica de focus és de gran utilitat i es poden veure exemples en escenaris de teatres, concerts musicals, domòtica bàsica a vivendes, botigues, restaurants, etc. on s’incorporen aparells òptims per aquest control. Aspectes com la programació d’encesa, apagat i intensitat desitjada de focus a una hora convinguda facilita el fet de fer-ho manualment i disposar de més temps propi. L’objectiu principal d’aquest treball és dissenyar i construir un regulador de llum controlat per ordinador capac de regular la intensitat lumínica de 8 focus independentment l’un de l’altre. El control de regulació s’efectua mitjancant un programa informàtic compatible amb ordinadors que incorporin el sistema operatiu Windows i és programable en el temps permetent seleccionar la intensitat desitjada a diferents hores del dia seleccionat. Com a conclusions es pot destacar un estalvi energètic al regular la intensitat dels focus evitant així la permanent connexio a una tensió màxima de 230 VAC i la oportunitat de construir un regulador de llum amb els documents subministrats.

Reliure de : Le Diverse et artificiose machine del capitano Agostino Ramelli dal ponte della Tresia... Nelle quali si contengono varii et industriosi movimenti... composte in lingua italiana et francese

Numérisation partielle de reliure

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Improvement of a testing apparatus for dynamometry: procedures for penetrometry and influence of strain rate to quantify the tensile strength of soil aggregates

Soil penetration resistance (PR) and the tensile strength of aggregates (TS) are commonly used to characterize the physical and structural conditions of agricultural soils. This study aimed to assess the functionality of a dynamometry apparatus by linear speed and position control automation of its mobile base to measure PR and TS. The proposed equipment was used for PR measurement in undisturbed samples of a clayey "Nitossolo Vermelho eutroférrico" (Kandiudalfic Eutrudox) under rubber trees sampled in two positions (within and between rows). These samples were also used to measure the volumetric soil water content and bulk density, and determine the soil resistance to penetration curve (SRPC). The TS was measured in a sandy loam "Latossolo Vermelho distrófico" (LVd) - Typic Haplustox - and in a very clayey "Nitossolo Vermelho distroférrico" (NVdf) - Typic Paleudalf - under different uses: LVd under "annual crops" and "native forest", NVdf under "annual crops" and "eucalyptus plantation" (> 30 years old). To measure TS, different strain rates were applied using two dynamometry testing devices: a reference machine (0.03 mm s-1), which has been widely used in other studies, and the proposed equipment (1.55 mm s-1). The determination coefficient values of the SRPC were high (R² > 0.9), regardless of the sampling position. Mean TS values in LVd and NVdf obtained with the proposed equipment did not differ (p > 0.05) from those of the reference testing apparatus, regardless of land use and soil type. Results indicate that PR and TS can be measured faster and accurately by the proposed procedure.