Search

Significance: Coherent Raman scattering (CRS) microscopy is an optical imaging technique with capabilities that could benefit a broad range of biomedical research studies. Aim: We reflect on the birth, rapid rise, and inescapable growing pains of the technique and look back on nearly four decades of developments to examine where the CRS imaging approach might be headed in the next decade to come. Approach: We provide a brief historical account of CRS microscopy, followed by a discussion of the challenges to disseminate the technique to a larger audience. We then highlight recent progress in expanding the capabilities of the CRS microscope and assess its current appeal as a practical imaging tool. Results: New developments in Raman tagging have improved the specificity and sensitivity of the CRS technique. In addition, technical advances have led to CRS microscopes that can capture hyperspectral data cubes at practical acquisition times. These improvements have broadened the application space of the technique. Conclusion: The technical performance of the CRS microscope has improved dramatically since its inception, but these advances have not yet translated into a substantial user base beyond a strong core of enthusiasts. Nonetheless, new developments are poised to move the unique capabilities of the technique into the hands of more users.

Electronic data capture is fast becoming the preferred method of collecting patient-reported outcome (PRO) data in clinical trials. Data collection can be site-based (clinical study site), and typically collected on a tablet, or field-based (subject's typical environment such as home, school, or workplace), and most often accomplished with handheld devices, such as a smartphone. While site and study subject compliance with protocol-specific data collection procedures using these devices is critical to trial success, so is the robustness of the device hardware and the software these devices use to capture the trial data. Technology failures and/or site or subject resistance to the electronic data capture protocol may lead a subject to record data on paper, which can result in undesirable data challenges. As such, both site and subject compliance issues and technology-related factors must be anticipated to adhere to the ePRO data collection plan. The objective of this paper is to provide the technology industry's best practice recommendations for optimizing ePRO data collection in clinical trials by proposing the inclusion of a planned approach to data collection that includes viable electronic backup strategies so that defaulting to a paper-based backup becomes unnecessary.

International audience; Ionizing radiation is a peculiar perturbation when it comes to damage to biological systems it proceeds through discrete energy depositions, over a short temporal scale and a spatial scale critical for subcellular targets as DNA, whose damage complexity determines the outcome of the exposure. This lies at the basis of the success of track structure (and nanodosimetry) and microdosimetry in radiation biology. However, such reductionist approaches cannot account for the complex network of interactions regulating the overall response of the system to radiation, particularly when effects are manifest at the supracellular level and involve long times. Systems radiation biology is increasingly gaining ground, but the gap between reductionist and holistic approaches is becoming larger. This paper presents considerations on what roles track structure and microdosimetry can have in the attempt to fill this gap, and on how they can be further exploited to interpret radiobiological data and inform systemic approaches. © The Author(s) 2018. Published by Oxford University Press.

International audience; The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Multivariate information decompositions hold promise to yield insight into complex systems, and stand out for their ability to identify synergistic phenomena. However, the adoption of these approaches has been hindered by there being multiple possible decompositions, and no precise guidance for preferring one over the others. At the heart of this disagreement lies the absence of a clear operational interpretation of what synergistic information is. Here we fill this gap by proposing a new information decomposition based on a novel operationalisation of informational synergy, which leverages recent developments in the literature of data privacy. Our decomposition is defined for any number of information sources, and its atoms can be calculated using elementary optimisation techniques. The decomposition provides a natural coarse-graining that scales gracefully with the system's size, and is applicable in a wide range of scenarios of practical interest., Comment: 14 pages, 8 figures

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Genomic data hold salient information about the characteristics of a living organism. Throughout the past decade, pinnacle developments have given us more accurate and inexpensive methods to retrieve genome sequences of humans. However, with the advancement of genomic research, there is a growing privacy concern regarding the collection, storage and analysis of such sensitive human data. Recent results show that given some background information, it is possible for an adversary to reidentify an individual from a specific genomic data set. This can reveal the current association or future susceptibility of some diseases for that individual (and sometimes the kinship between individuals) resulting in a privacy violation. Regardless of these risks, our genomic data hold much importance in analyzing the well-being of us and the future generation. Thus, in this article, we discuss the different privacy and security-related problems revolving around human genomic data. In addition, we will explore some of the cardinal cryptographic concepts, which can bring efficacy in secure and private genomic data computation. This article will relate the gaps between these two research areas-Cryptography and Genomics.

The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated—to say it another way, there are no ‘first principals’ from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

© 2020 Federation of European Biochemical Societies, Bibliometric quantification is frequently used as metrics for the evaluation of the scientific performance of researchers and institutions. The researchers’ merit is usually assessed by the analysis of quantitative parameters such as the number of publications, the impact factor of journals, the total number of citations, or the h-index, although the limitations in translating these indicators into the impact of the outcome of scientific production are a matter of harsh criticism. To assess, based on factual evidences, the validity of traditional bibliometric analyses to conclude on the impact of papers to advance the state of the art, we carried out an innovative methodology on selected publications (test set). This methodology is based on identifying those citations of the test set papers that truly embed the methods, concepts, or hypotheses to build new knowledge and formulate conclusions. The results show that the percentage of citations that reflect the real impact of the papers of the test set has an average value of 12.4% of total citations and is not related to the impact factor of the journal where the test set papers were published. In conclusion, our analysis demonstrates factually, using experimental data, the total failure of using quantitative bulk citation analyses to conclude on the scientific impact of publications. Only a careful analysis of how the work described in papers was embedded on the subsequent work and/or conclusions of others can tell about the real contribution of a published work to the development of new knowledge and advancement of science., This work was supported by ‘La Caixa’ Foundation (grant reference: IMM/BPD/107-2018).

Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

International audience; The diagnosis of infectious diseases is entering a new and interesting phase. Technologies based on paper microfluidics, coupled to developments in isothermal amplification of Nucleic Acids (NAs) raise opportunities for bringing the methods of molecular biology in the field, in a low setting environment. A lot of work has been performed in the domain over the last few years and the landscape of contributions is rich and diverse. Most often, the level of sample preparation differs, along with the sample nature, the amplification and detection methods, and the design of the device, among other features. In this review, we attempt to offer a structured description of the state of the art. The domain is not mature and there exist bottlenecks that hamper the realization of Nucleic Acid Amplification Tests (NAATs) complying with the constraints of the field in low and middle income countries. In this domain however, the pace of progress is impressively fast. This review is written for a broad Lab on a Chip audience.

Bioinformatics is now intrinsic to life science research, but the past decade has witnessed a continuing deficiency in this essential expertise. Basic data stewardship is still taught relatively rarely in life science education programmes, creating a chasm between theory and practice, and fuelling demand for bioinformatics training across all educational levels and career roles. Concerned by this, surveys have been conducted in recent years to monitor bioinformatics and computational training needs worldwide. This article briefly reviews the principal findings of a number of these studies. We see that there is still a strong appetite for short courses to improve expertise and confidence in data analysis and interpretation; strikingly, however, the most urgent appeal is for bioinformatics to be woven into the fabric of life science degree programmes. Satisfying the relentless training needs of current and future generations of life scientists will require a concerted response from stakeholders across the globe, who need to deliver sustainable solutions capable of both transforming education curricula and cultivating a new cadre of trainer scientists.

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

The paper electrocardiogram (ECG) has been widely used for cardiac assessment for well over a century. ECGs can be obtained quickly and cheaply. For this reason, an ever-growing amount of paper ECG records continue to accumulate, some of which are stored into a paper-only format. Converting paper ECGs into digital form has been proposed as the most efficient means to store and analyze an otherwise cumbersome paper archive. In this article, a literature review was conducted for conversion algorithms, criticisms of said algorithms, applications, and standardization efforts. The algorithms were compared in tabulated form. Key functions that have advanced the conversion algorithms as well as remaining challenges are discussed.

Computer simulation methods are under-used tools in genetic analysis because simulation approaches have been portrayed as inferior to analytic methods. Even when simulation is used, its advantages are not fully exploited. Here, I present SHIMSHON, our package of genetic simulation programs that have been developed, tested, used for research, and used to generated data for Genetic Analysis Workshops (GAW). These simulation programs, now web-accessible, can be used by anyone to answer questions about designing and analyzing genetic disease studies for locus identification. This work has three foci: (1) the historical context of SHIMSHON’s development, suggesting why simulation has not been more widely used so far. (2) Advantages of simulation: computer simulation helps us to understand how genetic analysis methods work. It has advantages for understanding disease inheritance and methods for gene searches. Furthermore, simulation methods can be used to answer fundamental questions that either cannot be answered by analytical approaches or cannot even be defined until the problems are identified and studied, using simulation. (3) I argue that, because simulation was not accepted, there was a failure to grasp the meaning of some simulation-based studies of linkage. This may have contributed to perceived weaknesses in linkage analysis; weaknesses that did not, in fact, exist.

Scholarly research faces threats to its sustainability on multiple domains (access, incentives, reproducibility, inclusivity). We argue that “after-the-fact” research papers do not help and actually cause some of these threats because the chronology of the research cycle is lost in a research paper. We propose to give up the academic paper and propose a digitally native “as-you-go” alternative. In this design, modules of research outputs are communicated along the way and are directly linked to each other to form a network of outputs that can facilitate research evaluation. This embeds chronology in the design of scholarly communication and facilitates the recognition of more diverse outputs that go beyond the paper (e.g., code, materials). Moreover, using network analysis to investigate the relations between linked outputs could help align evaluation tools with evaluation questions. We illustrate how such a modular “as-you-go” design of scholarly communication could be structured and how network indicators could be computed to assist in the evaluation process, with specific use cases for funders, universities, and individual researchers.

Instruments for assessing visual function are valuable tools for optometry, ophthalmology, vision science, education, and public health. Inspired by my observations in the Teller lab, with Dobson, on the process of developing a useful clinical tool from laboratory work, I present four examples of functional vision tests that are made of paper and currently used in the field: the Amsler Grid, the Pelli-Robson Contrast Sensitivity Chart, the Teller Acuity Cards, and the Developmental Eye Movement Test. All are characterized by ease of use and rigorous design. All are either being used with children or have the potential to be so. Each tool is reviewed in terms of its development, with a view toward similarities in the steps or process taken. The goal is to encourage the further development of the functional vision assessments already in existence, and to urge scientists and clinicians alike to consider ways in which their own work can be translated into clinically useful, simple paper tools.

Quantifying and comparing the scientific output of researchers has become critical for governments, funding agencies and universities. Comparison by reputation and direct assessment of contributions to the field is no longer possible, as the number of scientists increases and traditional definitions about scientific fields become blurred. The h-index is often used for comparing scientists, but has several well-documented shortcomings. In this paper, we introduce a new index for measuring and comparing the publication records of scientists: the pagerank-index (symbolised as π). The index uses a version of pagerank algorithm and the citation networks of papers in its computation, and is fundamentally different from the existing variants of h-index because it considers not only the number of citations but also the actual impact of each citation. We adapt two approaches to demonstrate the utility of the new index. Firstly, we use a simulation model of a community of authors, whereby we create various ‘groups’ of authors which are different from each other in inherent publication habits, to show that the pagerank-index is fairer than the existing indices in three distinct scenarios: (i) when authors try to ‘massage’ their index by publishing papers in low-quality outlets primarily to self-cite other papers (ii) when authors collaborate in large groups in order to obtain more authorships (iii) when authors spend most of their time in producing genuine but low quality publications that would massage their index. Secondly, we undertake two real world case studies: (i) the evolving author community of quantum game theory, as defined by Google Scholar (ii) a snapshot of the high energy physics (HEP) theory research community in arXiv. In both case studies, we find that the list of top authors vary very significantly when h-index and pagerank-index are used for comparison. We show that in both cases, authors who have collaborated in large groups and/or published less impactful papers tend to be comparatively favoured by the h-index, whereas the pagerank-index highlights authors who have made a relatively small number of definitive contributions, or written papers which served to highlight the link between diverse disciplines, or typically worked in smaller groups. Thus, we argue that the pagerank-index is an inherently fairer and more nuanced metric to quantify the publication records of scientists compared to existing measures.

The emergence of Zika virus (ZIKV) infections in Latin America and Southeast Asia has created an urgent need for new, simple, yet sensitive, diagnostic tests. We highlight recent work using paper-based sensors coupled with CRISPR/Cas9 to detect ZIKV RNA as a new approach to achieve rapid development and deployment of field-ready diagnostics for emerging infectious diseases.

Screen designs in computerized clinical information systems (CIS) have been modeled on their paper predecessors. However, limited understanding about how paper forms support clinical work means that we risk repeating old mistakes and creating new opportunities for error and inefficiency as illustrated by problems associated with computerized provider order entry systems.This study was designed to elucidate principles underlying a successful ICU paper-based CIS. The research was guided by two exploratory hypotheses: (1) paper-based artefacts (charts, notes, equipment, order forms) are used differently by nurses, doctors and other healthcare professionals in different (formal and informal) conversation contexts and (2) different artefacts support different decision processes that are distributed across role-based conversations.All conversations undertaken at the bedsides of five patients were recorded with any supporting artefacts for five days per patient. Data was coded according to conversational role-holders, clinical decision process, conversational context and artefacts. 2133 data points were analyzed using Poisson logistic regression analyses.Results show significant interactions between artefacts used during different professional conversations in different contexts (chi(2)((df=16))=55.8, p0.0001). The interaction between artefacts used during different professional conversations for different clinical decision processes was not statistically significant although all two-way interactions were statistically significant.Paper-based CIS have evolved to support complex interdisciplinary decision processes. The translation of two design principles - support interdisciplinary perspectives and integrate decision processes - from paper to computerized CIS may minimize the risks associated with computerization.

Structural computing represents a form of hypertext, in which structure is foregrounded in the analysis, design and implementation of systems. Until now, the vast majority of structural computing work has focused on the implementation of frameworks. The applications implemented in these environments are often reimplementations of existing hypertext applications, intended to show the utility of the provided environments in terms of implementation, interoperability, or other metrics. Often, spatial hypertext applications are reimplemented, since they are seen as particularly good tests of structural computing infrastructures. This is because the demonds they pose are quite different from many other types of hypertext applications. However, structural computing researchers have failed to reanalyse and redesign these applications in a structurally-aware way. There are substantial improvements that result from rethinking such applications from a structure-first perspective. In this paper, we present such a reanalysis, and describe its implications for spatial hypertext within structural computing environments.

CONFLICT OF INTEREST: NONE DECLARED The paper presents an analysis of how EFMI disseminates new knowledge and of the active medical informatics journals in EFMI member countries, which was carried out as an outcome of the EFMI Council meetings in London in 2008, Sarajevo in 2009 and Antalya in 2009 . The analysis identifies eight active major informatics journals and several other publications. Most are subscription-based and published at least quarterly. There is a possibility for the editors to meet regularly and form a community of practice with the aim of further improving their effectiveness in disseminating new knowledge and best practice in medical informatics. It is feasible to share expertise and it may be possible to harmonise several aspects of preparation and submission of manuscripts so that some of the identified barriers in publishing are reduced.

The intent of this paper is to make artists aware of the fragility of papers and other art materials, of the precautions to take in the selection of materials, and the on-going conservation methods that are necessary to preserve works of art on paper.