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.

A Transportation Safety Planning Tool for the City of Ames, August 2011

The historically-reactive approach to identifying safety problems and mitigating them involves selecting black spots or hot spots by ranking locations based on crash frequency and severity. The approach focuses mainly on the corridor level without taking the exposure rate (vehicle miles traveled) and socio-demographics information of the study area, which are very important in the transportation planning process, into consideration. A larger study analysis unit at the Transportation Analysis Zone (TAZ) level or the network planning level should be used to address the needs of development of the community in the future and incorporate safety into the long-range transportation planning process. In this study, existing planning tools (such as the PLANSAFE models presented in NCHRP Report 546) were evaluated for forecasting safety in small and medium-sized communities, particularly as related to changes in socio-demographics characteristics, traffic demand, road network, and countermeasures. The research also evaluated the applicability of the Empirical Bayes (EB) method to network-level analysis. In addition, application of the United States Road Assessment Program (usRAP) protocols at the local urban road network level was investigated. This research evaluated the applicability of these three methods for the City of Ames, Iowa. The outcome of this research is a systematic process and framework for considering road safety issues explicitly in the small and medium-sized community transportation planning process and for quantifying the safety impacts of new developments and policy programs. More specifically, quantitative safety may be incorporated into the planning process, through effective visualization and increased awareness of safety issues (usRAP), the identification of high-risk locations with potential for improvement, (usRAP maps and EB), countermeasures for high-risk locations (EB before and after study and PLANSAFE), and socio-economic and demographic induced changes at the planning-level (PLANSAFE).

Controlled expression of recombinant proteins in Physcomitrella patens by a conditional heat-shock promoter: a tool for plant research and biotechnology.

The ability to express tightly controlled amounts of endogenous and recombinant proteins in plant cells is an essential tool for research and biotechnology. Here, the inducibility of the soybean heat-shock Gmhsp17.3B promoter was addressed in the moss Physcomitrella patens, using beta-glucuronidase (GUS) and an F-actin marker (GFP-talin) as reporter proteins. In stably transformed moss lines, Gmhsp17.3B-driven GUS expression was extremely low at 25 degrees C. In contrast, a short non-damaging heat-treatment at 38 degrees C rapidly induced reporter expression over three orders of magnitude, enabling GUS accumulation and the labelling of F-actin cytoskeleton in all cell types and tissues. Induction levels were tightly proportional to the temperature and duration of the heat treatment, allowing fine-tuning of protein expression. Repeated heating/cooling cycles led to the massive GUS accumulation, up to 2.3% of the total soluble proteins. The anti-inflammatory drug acetyl salicylic acid (ASA) and the membrane-fluidiser benzyl alcohol (BA) also induced GUS expression at 25 degrees C, allowing the production of recombinant proteins without heat-treatment. The Gmhsp17.3B promoter thus provides a reliable versatile conditional promoter for the controlled expression of recombinant proteins in the moss P. patens.

MADAP, a flexible clustering tool for the interpretation of one-dimensional genome annotation data.

A recurring task in the analysis of mass genome annotation data from high-throughput technologies is the identification of peaks or clusters in a noisy signal profile. Examples of such applications are the definition of promoters on the basis of transcription start site profiles, the mapping of transcription factor binding sites based on ChIP-chip data and the identification of quantitative trait loci (QTL) from whole genome SNP profiles. Input to such an analysis is a set of genome coordinates associated with counts or intensities. The output consists of a discrete number of peaks with respective volumes, extensions and center positions. We have developed for this purpose a flexible one-dimensional clustering tool, called MADAP, which we make available as a web server and as standalone program. A set of parameters enables the user to customize the procedure to a specific problem. The web server, which returns results in textual and graphical form, is useful for small to medium-scale applications, as well as for evaluation and parameter tuning in view of large-scale applications, requiring a local installation. The program written in C++ can be freely downloaded from ftp://ftp.epd.unil.ch/pub/software/unix/madap. The MADAP web server can be accessed at http://www.isrec.isb-sib.ch/madap/.

2 Iowa Safety Efforts: Analyst Tool & Location Aggregation Project, July 8, 2003

Report produced by the Iowa Department of Transportation about Iowa Safety with Tools and Aggregation.

High-precision satellite positioning system as a new tool to study the biomechanics of human locomotion.

New Global Positioning System (GPS) receivers allow now to measure a location on earth at high frequency (5Hz) with a centimetric precision using phase differential positioning method. We studied whether such technique was accurate enough to retrieve basic parameters of human locomotion. Eight subjects walked on an athletics track at four different imposed step frequencies (70-130steps/min) plus a run at free pace. Differential carrier phase localization between a fixed base station and the mobile antenna mounted on the walking person was calculated. In parallel, a triaxial accelerometer, attached to the low back, recorded body accelerations. The different parameters were averaged for 150 consecutive steps of each run for each subject (total of 6000 steps analyzed). We observed a perfect correlation between average step duration measured by accelerometer and by GPS (r=0.9998, N=40). Two important parameters for the calculation of the external work of walking were also analyzed, namely the vertical lift of the trunk and the velocity variation per step. For an average walking speed of 4.0km/h, average vertical lift and velocity variation were, respectively, 4.8cm and 0.60km/h. The average intra-individual step-to-step variability at a constant speed, which includes GPS errors and the biological gait style variation, were found to be 24. 5% (coefficient of variation) for vertical lift and 44.5% for velocity variation. It is concluded that GPS technique can provide useful biomechanical parameters for the analysis of an unlimited number of strides in an unconstrained free-living environment.