365 research outputs found
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
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