High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Training medical students to conduct motivational interviewing: a randomized controlled trial.

OBJECTIVE: To examine the effectiveness of motivational interviewing (MI) training among medical students. METHODS: All students (n=131) (year 5) at Lausanne Medical School, Switzerland were randomized into an experimental or a control group. After a training in basic communication skills (control condition), an 8-h MI training was completed by 84.8% students in the exprimental group. One week later, students in both groups were invited to meet with two standardized patients. MI skills were coded by blinded research assistants using the Motivational Interviewing Treatment Integrity 3.0. RESULTS: Superior MI performance was shown for trained versus control students, as demonstrated by higher scores for "Empathy" [p<0.001] and "MI Spirit" [p<0.001]. Scores were similar between groups for "Direction", indicating that students in both groups invited the patient to talk about behavior change. Behavior counts assessment demonstrated better performance in MI in trained versus untrained students regarding occurences of MI-adherent behavior [p<0.001], MI non-adherent behavior [p<0.001], Closed questions [p<0.001], Open questions [p=0.001], simple reflections [p=0.03], and Complex reflections [p<0.001]. Occurrences were similar between groups regarding "Giving information". CONCLUSION: An 8-h training workshop was associated with improved MI performance. PRACTICE IMPLICATIONS: These findings lend support for the implementation of MI training in medical schools.

Effect of intermittent hypoxic training on HIF gene expression in human skeletal muscle and leukocytes

Intermittent hypoxic exposure with exercise training is based on the assumption that brief exposure to hypoxia is sufficient to induce beneficial muscular adaptations mediated via hypoxia-inducible transcription factors (HIF). We previously demonstrated (Mounier et al. Med Sci Sports Exerc 38:1410-1417, 2006) that leukocytes respond to hypoxia with a marked inter-individual variability in HIF-1alpha mRNA. This study compared the effects of 3 weeks of intermittent hypoxic training on hif gene expression in both skeletal muscle and leukocytes. Male endurance athletes (n = 19) were divided into an Intermittent Hypoxic Exposure group (IHE) and a Normoxic Training group (NT) with each group following a similar 3-week exercise training program. After training, the amount of HIF-1alpha mRNA in muscle decreased only in IHE group (-24.7%, P < 0.05) whereas it remained unchanged in leukocytes in both groups. The levels of vEGF(121) and vEGF(165) mRNA in skeletal muscle increased significantly after training only in the NT group (+82.5%, P < 0.05 for vEGF(121); +41.2%, P < 0.05 for vEGF(165)). In leukocytes, only the IHE group showed a significant change in vEGF(165) (-28.2%, P < 0.05). The significant decrease in HIF-1alpha mRNA in skeletal muscle after hypoxic training suggests that transcriptional and post-transcriptional regulations of the hif-1alpha gene are different in muscle and leukocytes.

Effects of Intermittent Training on Anaerobic Performance and MCT Transporters in Athletes.

This study examined the effects of intermittent hypoxic training (IHT) on skeletal muscle monocarboxylate lactate transporter (MCT) expression and anaerobic performance in trained athletes. Cyclists were assigned to two interventions, either normoxic (N; n = 8; 150 mmHg PIO2) or hypoxic (H; n = 10; ∼3000 m, 100 mmHg PIO2) over a three week training (5×1 h-1h30.week-1) period. Prior to and after training, an incremental exercise test to exhaustion (EXT) was performed in normoxia together with a 2 min time trial (TT). Biopsy samples from the vastus lateralis were analyzed for MCT1 and MCT4 using immuno-blotting techniques. The peak power output (PPO) increased (p<0.05) after training (7.2% and 6.6% for N and H, respectively), but VO2max showed no significant change. The average power output in the TT improved significantly (7.3% and 6.4% for N and H, respectively). No differences were found in MCT1 and MCT4 protein content, before and after the training in either the N or H group. These results indicate there are no additional benefits of IHT when compared to similar normoxic training. Hence, the addition of the hypoxic stimulus on anaerobic performance or MCT expression after a three-week training period is ineffective.