Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries

Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

Moving from sustainable management to sustainable governance of natural resources: The role of social learning processes in rural India, Bolivia and Mali

The present paper discusses a conceptual, methodological and practical framework within which the limitations of the conventional notion of natural resource management (NRM) can be overcome. NRM is understood as the application of scientific ecological knowledge to resource management. By including a consideration of the normative imperatives that arise from scientific ecological knowledge and submitting them to public scrutiny, ‘sustainable management of natural resources’ can be recontextualised as ‘sustainable governance of natural resources’. This in turn makes it possible to place the politically neutralising discourse of ‘management’ in a space for wider societal debate, in which the different actors involved can deliberate and negotiate the norms, rules and power relations related to natural resource use and sustainable development. The transformation of sustainable management into sustainable governance of natural resources can be conceptualised as a social learning process involving scientists, experts, politicians and local actors, and their corresponding scientific and non-scientific knowledges. The social learning process is the result of what Habermas has described as ‘communicative action’, in contrast to ‘strategic action’. Sustainable governance of natural resources thus requires a new space for communicative action aiming at shared, intersubjectively validated definitions of actual situations and the goals and means required for transforming current norms, rules and power relations in order to achieve sustainable development. Case studies from rural India, Bolivia and Mali explore the potentials and limitations for broadening communicative action through an intensification of social learning processes at the interface of local and external knowledge. Key factors that enable or hinder the transformation of sustainable management into sustainable governance of natural resources through social learning processes and communicative action are discussed.

Human urinary metabolomic profile of PPARalpha induced fatty acid beta-oxidation

Activation of the peroxisome proliferator-activated receptor alpha (PPARalpha) is associated with increased fatty acid catabolism and is commonly targeted for the treatment of hyperlipidemia. To identify latent, endogenous biomarkers of PPARalpha activation and hence increased fatty acid beta-oxidation, healthy human volunteers were given fenofibrate orally for 2 weeks and their urine was profiled by UPLC-QTOFMS. Biomarkers identified by the machine learning algorithm random forests included significant depletion by day 14 of both pantothenic acid (>5-fold) and acetylcarnitine (>20-fold), observations that are consistent with known targets of PPARalpha including pantothenate kinase and genes encoding proteins involved in the transport and synthesis of acylcarnitines. It was also concluded that serum cholesterol (-12.7%), triglycerides (-25.6%), uric acid (-34.7%), together with urinary propylcarnitine (>10-fold), isobutyrylcarnitine (>2.5-fold), (S)-(+)-2-methylbutyrylcarnitine (5-fold), and isovalerylcarnitine (>5-fold) were all reduced by day 14. Specificity of these biomarkers as indicators of PPARalpha activation was demonstrated using the Ppara-null mouse. Urinary pantothenic acid and acylcarnitines may prove useful indicators of PPARalpha-induced fatty acid beta-oxidation in humans. This study illustrates the utility of a pharmacometabolomic approach to understand drug effects on lipid metabolism in both human populations and in inbred mouse models.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

'Saying it without words': a qualitative study of oncology staff's experiences with speaking up about safety concerns.

OBJECTIVES To explore the experiences of oncology staff with communicating safety concerns and to examine situational factors and motivations surrounding the decision whether and how to speak up using semistructured interviews. SETTING 7 oncology departments of six hospitals in Switzerland. PARTICIPANTS Diverse sample of 32 experienced oncology healthcare professionals. RESULTS Nurses and doctors commonly experience situations which raise their concerns and require questioning, clarifying and correcting. Participants often used non-verbal communication to signal safety concerns. Speaking-up behaviour was strongly related to a clinical safety issue. Most episodes of 'silence' were connected to hygiene, isolation and invasive procedures. In contrast, there seemed to exist a strong culture to communicate questions, doubts and concerns relating to medication. Nearly all interviewees were concerned with 'how' to say it and in particular those of lower hierarchical status reflected on deliberate 'voicing tactics'. CONCLUSIONS Our results indicate a widely accepted culture to discuss any concerns relating to medication safety while other issues are more difficult to voice. Clinicians devote considerable efforts to evaluate the situation and sensitively decide whether and how to speak up. Our results can serve as a starting point to develop a shared understanding of risks and appropriate communication of safety concerns among staff in oncology.

The relation between cognitive and motor performance and their relevance for children’s transition to school: A latent variable approach

Both theoretically and empirically there is a continuous interest in understanding the specific relation between cognitive and motor development in childhood. In the present longitudinal study including three measurement points, this relation was targeted. At the beginning of the study, the participating children were 5-6-year-olds. By assessing participants' fine motor skills, their executive functioning, and their non-verbal intelligence, their cross-sectional and cross-lagged interrelations were examined. Additionally, performance in these three areas was used to predict early school achievement (in terms of mathematics, reading, and spelling) at the end of participants' first grade. Correlational analyses and structural equation modeling revealed that fine motor skills, non-verbal intelligence and executive functioning were significantly interrelated. Both fine motor skills and intelligence had significant links to later school achievement. However, when executive functioning was additionally included into the prediction of early academic achievement, fine motor skills and non-verbal intelligence were no longer significantly associated with later school performance suggesting that executive functioning plays an important role for the motor-cognitive performance link.

Quality Control for SP Performance Regarding Patient Portrayal in a High-Stakes OSCE

Introduction Since the quality of patient portrayal of standardized patients (SPs) during an Objective Structured Clinical Exam (OSCE) has a major impact on the reliability and validity of the exam, quality control should be initiated. Literature about quality control of SP’s performance focuses on feedback [1, 2] or completion of checklists [3, 4]. Since we did not find a published instrument meeting our needs for the assessment of patient portrayal, we developed such an instrument after being inspired by others [5] and used it in our high-stakes exam. Methods SP trainers from all five Swiss medical faculties collected and prioritized quality criteria for patient portrayal. Items were revised with the partners twice, based on experiences during OSCEs. The final instrument contains 14 criteria for acting (i.e. adequate verbal and non-verbal expression) and standardization (i.e. verbatim delivery of the first sentence). All partners used the instrument during a high-stakes OSCE. Both, SPs and trainers were introduced to the instrument. The tool was used in training (more than 100 observations) and during the exam (more than 250 observations). FAIR_OSCE The list of items to assess the quality of the simulation by SPs was primarily developed and used to provide formative feedback to the SPs in order to help them to improve their performance. It was therefore named “Feedbackstruckture for the Assessment of Interactive Role play in Objective Structured Clinical Exams (FAIR_OSCE). It was also used to assess the quality of patient portrayal during the exam. The results were calculated for each of the five faculties individually. Formative evaluation was given to the five faculties with individual feedback without revealing results of other faculties other than overall results. Results High quality of patient portrayal during the exam was documented. More than 90% of SP performances were rated to be completely correct or sufficient. An increase in quality of performance between training and exam was noted. In example the rate of completely correct reaction in medical tests increased from 88% to 95%. 95% completely correct reactions together with 4% sufficient reactions add up to 99% of the reactions meeting the requirements of the exam. SP educators using the instrument reported an augmentation of SPs performance induced by the use of the instrument. Disadvantages mentioned were high concentration needed to explicitly observe all criteria and cumbersome handling of the paper-based forms. Conclusion We were able to document a very high quality of SP performance in our exam. The data also indicate that our training is effective. We believe that the high concentration needed using the instrument is well invested, considering the observed augmentation of performance. The development of an iPad based application for the form is planned to address the cumbersome handling of the paper.

FAIR_OSCE – Feedback structure for assessment of interactive roleplay in Objective Structured Clinical Exams

Introduction Since the quality of patient portrayal of standardized patients (SPs) during an Objective Structured Clinical Exam (OSCE) has a major impact on the reliability and validity of the exam, quality control should be initiated. Literature about quality control of SPs’ performance focuses on feedback [1, 2] or completion of checklists [3, 4]. Since we did not find a published instrument meeting our needs for the assessment of patient portrayal, we developed such an instrument after being inspired by others [5] and used it in our high-stakes exam. Project description SP trainers from five medical faculties collected and prioritized quality criteria for patient portrayal. Items were revised twice, based on experiences during OSCEs. The final instrument contains 14 criteria for acting (i.e. adequate verbal and non-verbal expression) and standardization (i.e. verbatim delivery of the first sentence). All partners used the instrument during a high-stakes OSCE. SPs and trainers were introduced to the instrument. The tool was used in training (more than 100 observations) and during the exam (more than 250 observations). Outcome High quality of SPs’ patient portrayal during the exam was documented. More than 90% of SP performances were rated to be completely correct or sufficient. An increase in quality of performance between training and exam was noted. For example, the rate of completely correct reaction in medical tests increased from 88% to 95%. Together with 4% of sufficient performances these 95% add up to 99% of the reactions in medical tests meeting the standards of the exam. SP educators using the instrument reported an augmentation of SPs’ performance induced by the use of the instrument. Disadvantages mentioned were the high concentration needed to observe all criteria and the cumbersome handling of the paper-based forms. Discussion We were able to document a very high quality of SP performance in our exam. The data also indicates that our training is effective. We believe that the high concentration needed using the instrument is well invested, considering the observed enhancement of performance. The development of an iPad-based application for the form is planned to address the cumbersome handling of the paper.

VIRTOPSY--scientific documentation, reconstruction and animation in forensic: individual and real 3D data based geo-metric approach including optical body/object surface and radiological CT/MRI scanning.

Until today, most of the documentation of forensic relevant medical findings is limited to traditional 2D photography, 2D conventional radiographs, sketches and verbal description. There are still some limitations of the classic documentation in forensic science especially if a 3D documentation is necessary. The goal of this paper is to demonstrate new 3D real data based geo-metric technology approaches. This paper present approaches to a 3D geo-metric documentation of injuries on the body surface and internal injuries in the living and deceased cases. Using modern imaging methods such as photogrammetry, optical surface and radiological CT/MRI scanning in combination it could be demonstrated that a real, full 3D data based individual documentation of the body surface and internal structures is possible in a non-invasive and non-destructive manner. Using the data merging/fusing and animation possibilities, it is possible to answer reconstructive questions of the dynamic development of patterned injuries (morphologic imprints) and to evaluate the possibility, that they are matchable or linkable to suspected injury-causing instruments. For the first time, to our knowledge, the method of optical and radiological 3D scanning was used to document the forensic relevant injuries of human body in combination with vehicle damages. By this complementary documentation approach, individual forensic real data based analysis and animation were possible linking body injuries to vehicle deformations or damages. These data allow conclusions to be drawn for automobile accident research, optimization of vehicle safety (pedestrian and passenger) and for further development of crash dummies. Real 3D data based documentation opens a new horizon for scientific reconstruction and animation by bringing added value and a real quality improvement in forensic science.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.

Automatic assessment of time-resolved OCT images for selective retina therapy

Purpose In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications. Methods We leverage time-resolved optical coherence tomography (OCT) to provide informative feedback to clinicians on outcomes of location-specific treatment. By coupling an OCT system to SRT treatment laser, we visualize structural changes in the retinal layers as they occur via time-resolved depth images. We then propose a novel strategy for automatic assessment of such time-resolved OCT images. To achieve this, we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities. Results Our approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting, yielding performances above 95 % accuracy for predicting patient treatment outcomes. In addition, we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data. Conclusion The proposed technique presents a much needed strategy toward noninvasive, safe, reliable, and repeatable SRT applications. These results are encouraging for the broader use of new treatment options for neovascularization-based retinal pathologies.