Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Interobserver and intraobserver variability of the apparent diffusion coefficient in treated malignant hepatic lesions on a 3.0T machine: measurements in the whole lesion versus in the area with the most restricted diffusion.

PURPOSE: To assess the inter/intraobserver variability of apparent diffusion coefficient (ADC) measurements in treated hepatic lesions and to compare ADC measurements in the whole lesion and in the area with the most restricted diffusion (MRDA). MATERIALS AND METHODS: Twenty-five patients with treated malignant liver lesions were examined on a 3.0T machine. After agreeing on the best ADC image, two readers independently measured the ADC values in the whole lesion and in the MRDA. These measurements were repeated 1 month later. The Bland-Altman method, Spearman correlation coefficients, and the Wilcoxon signed-rank test were used to evaluate the measurements. RESULTS: Interobserver variability for ADC measurements in the whole lesion and in the MRDA was 0.17 x 10(-3) mm(2)/s [-0.17, +0.17] and 0.43 x 10(-3) mm(2)/s [-0.45, +0.41], respectively. Intraobserver limits of agreement could be as low as [-0.10, +0.12] 10(-3) mm(2)/s and [-0.20, +0.33] 10(-3) mm(2)/s for measurements in the whole lesion and in the MRDA, respectively. CONCLUSION: A limited variability in ADC measurements does exist, and it should be considered when interpreting ADC values of hepatic malignancies. This is especially true for the measurements of the minimal ADC.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

3D seismic facies characterization and geological patterns recognition (Australian North West Shelf)

EXECUTIVE SUMMARY This PhD research, funded by the Swiss Sciences Foundation, is principally devoted to enhance the recognition, the visualisation and the characterization of geobodies through innovative 3D seismic approaches. A series of case studies from the Australian North West Shelf ensures the development of reproducible integrated 3D workflows and gives new insight into local and regional stratigraphic as well as structural issues. This project was initiated in year 2000 at the Geology and Palaeontology Institute of the University of Lausanne (Switzerland). Several collaborations ensured the improvement of technical approaches as well as the assessment of geological models. - Investigations into the Timor Sea structural style were carried out at the Tectonics Special Research Centre of the University of Western Australia and in collaboration with Woodside Energy in Perth. - Seismic analysis and attributes classification approach were initiated with Schlumberger Oilfield Australia in Perth; assessments and enhancements of the integrated seismic approaches benefited from collaborations with scientists from Schlumberger Stavanger Research (Norway). Adapting and refining from "linear" exploration techniques, a conceptual "helical" 3D seismic approach has been developed. In order to investigate specific geological issues this approach, integrating seismic attributes and visualisation tools, has been refined and adjusted leading to the development of two specific workflows: - A stratigraphic workflow focused on the recognition of geobodies and the characterization of depositional systems. Additionally, it can support the modelling of the subsidence and incidentally the constraint of the hydrocarbon maturity of a given area. - A structural workflow used to quickly and accurately define major and secondary fault systems. The integration of the 3D structural interpretation results ensures the analysis of the fault networks kinematics which can affect hydrocarbon trapping mechanisms. The application of these integrated workflows brings new insight into two complex settings on the Australian North West Shelf and ensures the definition of astonishing stratigraphic and structural outcomes. The stratigraphic workflow ensures the 3D characterization of the Late Palaeozoic glacial depositional system on the Mermaid Nose (Dampier Subbasin, Northern Carnarvon Basin) that presents similarities with the glacial facies along the Neotethys margin up to Oman (chapter 3.1). A subsidence model reveals the Phanerozoic geodynamic evolution of this area (chapter 3.2) and emphasizes two distinct mode of regional extension for the Palaeozoic (Neotethys opening) and Mesozoic (abyssal plains opening). The structural workflow is used for the definition of the structural evolution of the Laminaria High area (Bonaparte Basin). Following a regional structural characterization of the Timor Sea (chapter 4.1), a thorough analysis of the Mesozoic fault architecture reveals a local rotation of the stress field and the development of reverse structures (flower structures) in extensional setting, that form potential hydrocarbon traps (chapter 4.2). The definition of the complex Neogene structural architecture associated with the fault kinematic analysis and a plate flexure model (chapter 4.3) suggest that the Miocene to Pleistocene reactivation phases recorded at the Laminaria High most probably result from the oblique normal reactivation of the underlying Mesozoic fault planes. This episode is associated with the deformation of the subducting Australian plate. Based on these results three papers were published in international journals and two additional publications will be submitted. Additionally this research led to several communications in international conferences. Although the different workflows presented in this research have been primarily developed and used for the analysis of specific stratigraphic and structural geobodies on the Australian North West Shelf, similar integrated 3D seismic approaches will have applications to hydrocarbon exploration and production phases; for instance increasing the recognition of potential source rocks, secondary migration pathways, additional traps or reservoir breaching mechanisms. The new elements brought by this research further highlight that 3D seismic data contains a tremendous amount of hidden geological information waiting to be revealed and that will undoubtedly bring new insight into depositional systems, structural evolution and geohistory of the areas reputed being explored and constrained and other yet to be constrained. The further development of 3D texture attributes highlighting specific features of the seismic signal, the integration of quantitative analysis for stratigraphic and structural processes, the automation of the interpretation workflow as well as the formal definition of "seismo-morphologic" characteristics of a wide range of geobodies from various environments would represent challenging examples of continuation of this present research. The 21st century will most probably represent a transition period between fossil and other alternative energies. The next generation of seismic interpreters prospecting for hydrocarbon will undoubtedly face new challenges mostly due to the shortage of obvious and easy targets. They will probably have to keep on integrating techniques and geological processes in order to further capitalise the seismic data for new potentials definition. Imagination and creativity will most certainly be among the most important quality required from such geoscientists.

Artificial Urinary Sphincter based on smart materials

Objective: There are only a few established artificial urinary sphincters for treatment of incontinence. We have developed a new device composed by three parts: the actuator, three contractile rings and a control unit. The actuator is made of Nitinol fibers, driven by microprocessor. The fibers are linked to the rings placed around the urethra. They function with alternance in their open and closed position. This concept is called piano concept. With this set-up, the constant compression on the urethra is strongly reduced. Methods: Six male sheep have been used for this study. The sphincter was open each hour for a period of 10 min., to guaranty urination. The bladder was filled with water while one cuff was closed and bladder pressure was monitored. The animals were sacrificed. Two biopsies around two cuffs of each explant and all three cuffs from each explant including urethra were analyzed. Urethra not surrounded by a cuff was taken as control. Results: The pressure exerted by the sphincter around the urethra provided continence. Simulated incontinence occurred at a pressure of 1bar measured on the bladder wall using a pressure probe. The closing force of the cuff was approx. 0·7N. No difference in tissue structure and organization of the urethra with and without artificial sphincter was observed. Conclusions: This device has several advantages compared to other urinary sphincters. It is easy to implant, has no hydraulic nature and reduces ischemic injury of the urethra by the alternance of urethral part compressed. Proof of concept in vivo has been demonstrated. Other studies are planned to determine long-term outcome.

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.

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.