A prospective observational study of machine translation software to overcome the challenge of including ethnic diversity in healthcare research

Aim This study investigates whether machine translation could help with the challenge of enabling the inclusion of ethnic diversity in healthcare research. Design A two phase, prospective observational study. Methods Two machine translators, Google Translate and Babylon 9, were tested. Translation of the Strengths and Difficulties Questionnaire (SDQ) from 24 languages into English and translation of an English information sheet into Spanish and Chinese were quality scored. Quality was assessed using the Translation Assessment Quality Tool. Results Only six of the 48 translations of the SDQ were rated as acceptable, all from Google Translate. The mean number of acceptably translated sentences was higher (P = 0·001) for Google Translate 17·1 (sd 7·2) than for Babylon 9 11 (sd 7·9). Translation by Google Translate was better for Spanish and Chinese, although no score was in the acceptable range. Machine translation is not currently sufficiently accurate without editing to provide translation of materials for use in healthcare research

Environmental Effects on TPB Wavelength-Shifting Coatings

The scintillation detection systems of liquid argon time projection chambers (LArTPCs) require wavelength shifters to detect the 128 nm scintillation light produced in liquid argon. Tetraphenyl butadiene (TPB) is a fluorescent material that can shift this light to a wavelength of 425 nm, lending itself well to use in these detectors. We can coat the glass of photomultiplier tubes (PMTs) with TPB or place TPB-coated plates in front of the PMTs. In this paper, we investigate the degradation of a chemical TPB coating in a laboratory or factory environment to assess the viability of long-term TPB film storage prior to its initial installation in an LArTPC. We present evidence for severe degradation due to common fluorescent lights and ambient sunlight in laboratories, with potential losses at the 40% level in the first day and eventual losses at the 80% level after a month of exposure. We determine the degradation is due to wavelengths in the UV spectrum, and we demonstrate mitigating methods for retrofitting lab and factory environments

Tradeoffs in jet inlet design: a historical perspective

The design of the inlet(s) is one of the most demanding tasks of the development process of any gas turbine-powered aircraft. This is mainly due to the multi-objective and multidisciplinary nature of the exercise. The solution is generally a compromise between a number of conflicting goals and these conflicts are the subject of the present paper. We look into how these design tradeoffs have been reflected in the actual inlet designs over the years and how the emphasis has shifted from one driver to another. We also review some of the relevant developments of the jet age in aerodynamics and design and manufacturing technology and we examine how they have influenced and informed inlet design decision

10 simple rules to create a serious game, illustrated with examples from structural biology

Serious scientific games are games whose purpose is not only fun. In the field of science, the serious goals include crucial activities for scientists: outreach, teaching and research. The number of serious games is increasing rapidly, in particular citizen science games, games that allow people to produce and/or analyze scientific data. Interestingly, it is possible to build a set of rules providing a guideline to create or improve serious games. We present arguments gathered from our own experience ( Phylo , DocMolecules , HiRE-RNA contest and Pangu) as well as examples from the growing literature on scientific serious games

GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction

We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs or window-based data using data structures to efficiently represent input–output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF

Improved residue contact prediction using support vector machines and a large feature set

BACKGROUND: Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved. RESULTS: Here we develop a new contact map predictor (SVMcon) that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7) experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence separation >= 12 on 13 de novo domains. CONCLUSION: We describe SVMcon, a new contact map predictor that uses SVMs and a large set of informative features. SVMcon yields good performance on medium- to long-range contact predictions and can be modularly incorporated into a structure prediction pipeline

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures