Objective Ultrasonic Characterization of Welding Defects Using Physically Based Pattern Recognition Techniques

Computer-based methods for analysing ultrasonic data to distinguish between different defect types have been based on a variety of techniques such as adaptive learning [1], artificial intelligence [2] and statistical pattern recognition [3]. The uncertain classification reliability of these techniques when applied to a range of realistic defect types has, however, often been a significant practical limitation to their use

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts

De facto exchange rate regime classifications: an evaluation

There exist several statistically-based exchange rate regime classifications that disagree with one another to a disappointing degree. To what extent is this a matter of the quality of the design of these schemes, and to what extent does it reflect the need to supplement statistics with other information (as is done in the IMF’s de facto classification)? It is shown that statistical methods are good at the basics (distinguishing some type of peg from some type of float), but less helpful in other respects, such as determining whether a float is managed, particularly for countries that are not very remote from their main trading partners. Different measures of exchange rate volatility have been used but are not primarily responsible for differences between classifications. The theoretical underpinning of particular classification schemes needs to be more explicit

Enhanced fluorescence from X-Ray line coincidence pumping

Many resonant photo-pumped X-ray laser schemes that use a strong pump line such as Ly-α or He-α to populate the upper laser state of a separate lasing material have been proposed over the last four decades but none have been demonstrated. As a first step to creating a photo-pumped X-ray laser we have decided to reinvestigate some of these schemes at the Orion laser facility with the goal to show enhanced fluorescence. In particular we look at using the Ly-α or He-α K lines to pump the 1s–3p and 4p transitions in H-like Cl and see fluorescence on the 4f–3d line at 65 Å and the 3d–2p line at 23 Å. Preliminary experiments are presented that show a modest enhancement. As an alternative we also look at enhancing the 2p–2s line in Ne-like Ge at 65 Å using the Ly-α Mg line to photo-pump the 2s–3p line of Ne-like Ge. Calculations are presented that suggest modest enhancements of 2.5

Do health systems delay the treatment of poor children? A qualitative study of child deaths in rural Tanzania.

Child mortality remains one of the major public-health problems in Tanzania. Delays in receiving and accessing adequate care contribute to these high rates. The literature on public health often focuses on the role of mothers in delaying treatment, suggesting that they contact the health system too late and that they prefer to treat their children at home, a perspective often echoed by health workers. Using the three-delay methodology, this study focus on the third phase of the model, exploring the delays experienced in receiving adequate care when mothers with a sick child contact a health-care facility. The overall objective is to analyse specific structural factors embedded in everyday practices at health facilities in a district in Tanzania which cause delays in the treatment of poor children and to discuss possible changes to institutions and social technologies. The study is based on qualitative fieldwork, including in-depth interviews with sixteen mothers who have lost a child, case studies in which patients were followed through the health system, and observations of more than a hundred consultations at all three levels of the health-care system. Data analysis took the form of thematic analysis. Focusing on the third phase of the three-delay model, four main obstacles have been identified: confusions over payment, inadequate referral systems, the inefficient organization of health services and the culture of communication. These impediments strike the poorest segment of the mothers particularly hard. It is argued that these delaying factors function as 'technologies of social exclusion', as they are embedded in the everyday practices of the health facilities in systematic ways. The interviews, case studies and observations show that it is especially families with low social and cultural capital that experience delays after having contacted the health-care system. Reductions of the various types of uncertainty concerning payment, improved referral practices and improved communication between health staff and patients would reduce some of the delays within health facilities, which might feedback positively into the other two phases of delay

The atm-1 gene is required for genome stability in Caenorhabditis elegans

The Ataxia-telangiectasia-mutated (ATM) gene in humans was identified as the basis of a rare autosomal disorder leading to cancer susceptibility and is now well known as an important signal transducer in response to DNA damage. An approach to understanding the conserved functions of this gene is provided by the model system, Caenorhabditis elegans. In this paper we describe the structure and loss of function phenotype of the ortholog atm-1. Using bioinformatic and molecular analysis we show that the atm-1 gene was previously misannotated. We find that the transcript is in fact a product of three gene predictions, Y48G1BL.2 (atm-1), K10E9.1, and F56C11.4 that together make up the complete coding region of ATM-1. We also characterize animals that are mutant for two available knockout alleles, gk186 and tm5027. As expected, atm-1 mutant animals are sensitive to ionizing radiation. In addition, however, atm-1 mutants also display phenotypes associated with genomic instability, including low brood size, reduced viability and sterility. We document several chromosomal fusions arising from atm-1 mutant animals. This is the first time a mutator phenotype has been described for atm-1 in C. elegans. Finally we demonstrate the use of a balancer system to screen for and capture atm-1-derived mutational events. Our study establishes C. elegans as a model for the study of ATM as a mutator potentially leading to the development of screens to identify therapeutic targets in humans

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Molecular Characterization of the Mouse Superior Lateral Parabrachial Nucleus through Expression of the Transcription Factor Runx1

The ability to precisely identify separate neuronal populations is essential to the understanding of the development and function of different brain structures. This necessity is particularly evident in regions such as the brainstem, where the anatomy is quite complex and little is known about the identity, origin, and function of a number of distinct nuclei due to the lack of specific cellular markers. In this regard, the gene encoding the transcription factor Runx1 has emerged as a specific marker of restricted neuronal populations in the murine central and peripheral nervous systems. The aim of this study was to precisely characterize the expression of Runx1 in the developing and postnatal mouse brainstem.Anatomical and immunohistochemical studies were used to characterize mouse Runx1 expression in the brainstem. It is shown here that Runx1 is expressed in a restricted population of neurons located in the dorsolateral rostral hindbrain. These neurons define a structure that is ventromedial to the dorsal nucleus of the lateral lemniscus, dorsocaudal to the medial paralemniscal nucleus and rostral to the cerebellum. Runx1 expression in these cells is first observed at approximately gestational day 12.5, persists into the adult brain, and is lost in knockout mice lacking the transcription factor Atoh1, an important regulator of the development of neuronal lineages of the rhombic lip. Runx1-expressing neurons in the rostral hindbrain produce cholecystokinin and also co-express members of the Groucho/Transducin-like Enhancer of split protein family.Based on the anatomical and molecular characteristics of the Runx1-expressing cells in the rostral hindbrain, we propose that Runx1 expression in this region of the mouse brain defines the superior lateral parabrachial nucleus