Marginal production technologies for life cycle inventories

Marginal technologies are defined as the technologies actually affected by the small changes in demand typically studied in prospective, comparative life cycle assessments. Using data on marginal technologies thus give the best reflection of the actual consequences of a decision. Furthermore, data on marginal technologies are easier to collect, more precise, and more stable in time than data on average technologies. A 5-step procedure is suggested to identify the marginal technologies. The step-wise procedure first clarifies the situation in which the marginal should apply, and then identifies what specific technology is marginal in this situation. The procedure is illustrated in two examples: European electricity production and pulp and paper production.     

Turning quantity into quality: novel quality assurance strategies for data produced by high-throughput genomics technologies

The pharmaceutical industry is facing the challenge of managing the exponential increase in volume, diversity and complexity of data generated by high-throughput technologies such as genome sequencing, gene-expression profiling, protein-expression profiling, metabolic profiling and high-throughput screening. These novel ‘genomics’ technologies are expected to reshape the approach of life science companies to research. Unfortunately, in many cases genomics technologies have been used uncritically, and some preliminary results have been disappointing. The lack of standardized data validation and quality assurance processes is recognized as one of the major hurdles for successfully implementing genomics technologies. This is particularly important for industrialized drug discovery processes, because more and more key conclusions and far-reaching decisions in the pharmaceutical industry are based on data that is generated automatically. Therefore, automated, specialized quality-control systems that can spot erroneous data that might obscure important biological effects are needed urgently. In this article, special emphasis is placed on DNA microarray technologies, a key genomics technology that suffers from severe problems with data quality. A generic, automatable data-quality-assurance workflow is discussed that will ultimately improve the quality of the drug candidates and, at the same time, reduce overall drug-development costs.     

Biological data integration using Semantic Web technologies

Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling.     

Technologies for the study of gene and protein expression in Plasmodium

With the imminent completion of the genome sequences of several species of Plasmodium, attention is now turning to the exploitation of these genomic sequence data for vaccine, drug and diagnostic development. Several technologies have been developed over the past decade to assist in the determination of gene and protein expression on a global scale. Of these, DNA microarrays, novel high-throughput proteomic technologies and recombinational cloning technologies are lowering the barrier to functional genomic studies in Plasmodium. Of equal importance is the capacity to manipulate, store, retrieve and analyse the tremendous quantity of data generated from these genomic studies. This paper will address the use of these technologies as well as some of the computational tools that will be ultimately required to adequately study gene and protein expression in Plasmodium.     

Medical student fitness to practise committees at UK medical schools

The fabric of science is changing, driven by a revolution in digital technologies that facilitate the acquisition and communication of massive amounts of data. This is changing the nature of collaboration and expanding opportunities to participate in science. If digital technologies are the engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in short supply. The publication of primary data is not a universal or mandatory part of science, and despite policies and proclamations to the contrary, calls to make data publicly available have largely gone unheeded. In this short essay I consider why, and explore some of the challenges that lie ahead, as we work toward a database of everything.     

LiSET: A Framework for Early‐Stage Life Cycle Screening of Emerging Technologies

While life cycle assessment (LCA) is a tool often used to evaluate the environmental impacts of products and technologies, the amount of data required to perform such studies make the evaluation of emerging technologies using the conventional LCA approach challenging. The development paradox is such that the inputs from a comprehensive environmental assessment has the greatest effect early in the development phase, and yet the data required to perform such an assessment are generally lacking until it is too late. Previous attempts to formalize strategies for performing streamlined or screening LCAs were made in the late 1990s and early 2000s, mostly to rapidly compare the environmental performance of product design candidates. These strategies lack the transparency and consistency required for the environmental screening of large numbers of early‐development candidates, for which data are even sparser. We propose the Lifecycle Screening of Emerging Technologies method (LiSET). LiSET is an adaptable screening‐to‐LCA method that uses the available data to systematically and transparently evaluate the environmental performance of technologies at low readiness levels. Iterations follow technological development and allow a progression to a full LCA if desired. In early iterations, LiSET presents results in a matrix structure combined with a “traffic light” color grading system. This format inherently communicates the high uncertainty of analysis at this stage and presents numerous environmental aspects assessed. LiSET takes advantage of a decomposition analysis and data not traditionally used in LCAs to gain insight to the life cycle impacts and ensure that the most environmentally sustainable technologies are adopted.     

A hybrid approach for the automated finishing of bacterial genomes

Advances in DNA sequencing technology have improved our ability to characterize most genomic diversity. However, accurate resolution of large structural events is challenging because of the short read lengths of second-generation technologies. Third-generation sequencing technologies, which can yield longer multikilobase reads, have the potential to address limitations associated with genome assembly. Here we combine sequencing data from second- and third-generation DNA sequencing technologies to assemble the two-chromosome genome of a recent Haitian cholera outbreak strain into two nearly finished contigs at >99.9% accuracy. Complex regions with clinically relevant structure were completely resolved. In separate control assemblies on experimental and simulated data for the canonical N16961 cholera reference strain, we obtained 14 scaffolds of greater than 1 kb for the experimental data and 8 scaffolds of greater than 1 kb for the simulated data, which allowed us to correct several errors in contigs assembled from the short-read data alone. This work provides a blueprint for the next generation of rapid microbial identification and full-genome assembly.     

Using next-generation sequencing approaches to isolate simple sequence repeat (SSR) loci in the plant sciences

The application of next-generation sequencing (NGS) technologies for the development of simple sequence repeat (SSR) or microsatellite loci for genetic research in the botanical sciences is described. Microsatellite markers are one of the most informative and versatile DNA-based markers used in plant genetic research, but their development has traditionally been a difficult and costly process. NGS technologies allow the efficient identification of large numbers of microsatellites at a fraction of the cost and effort of traditional approaches. The major advantage of NGS methods is their ability to produce large amounts of sequence data from which to isolate and develop numerous genome-wide and gene-based microsatellite loci. The two major NGS technologies with emergent application in SSR isolation are 454 and Illumina. A review is provided of several recent studies demonstrating the efficient use of 454 and Illumina technologies for the discovery of microsatellites in plants. Additionally, important aspects during NGS isolation and development of microsatellites are discussed, including the use of computational tools and high-throughput genotyping methods. A data set of microsatellite loci in the plastome and mitochondriome of cranberry (Vaccinium macrocarpon Ait.) is provided to illustrate a successful application of 454 sequencing for SSR discovery. In the future, NGS technologies will massively increase the number of SSRs and other genetic markers available to conduct genetic research in understudied but economically important crops such as cranberry.     

The impact of next-generation sequencing on genomics

This article reviews basic concepts,general applications,and the potential impact of next-generation sequencing(NGS)technologies on genomics,with particular reference to currently available and possible future platforms and bioinformatics.NGS technologies have demonstrated the capacity to sequence DNA at unprecedented speed,thereby enabling previously unimaginable scientific achievements and novel biological applications.But,the massive data produced by NGS also presents a significant challenge for data storage,analyses,and management solutions.Advanced bioinformatic tools are essential for the successful application of NGS technology.As evidenced throughout this review,NGS technologies will have a striking impact on genomic research and the entire biological field.With its ability to tackle the unsolved challenges unconquered by previous genomic technologies,NGS is likely to unravel the complexity of the human genome in terms of genetic variations,some of which may be confined to susceptible loci for some common human conditions.The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.     

Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development—A review

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.     

大数据时代的整合生物信息学

随着生物数据测量技术的不断发展,生物数据的类型、内容、复杂度不断增加,生物信息学已迈入大数据时代。面对大数据时代多模态、多层次、高维度、非线性的复杂生物数据,生物信息学需要发展相应的方法和技术进行有效整合生物信息学研究与应用。本文对大数据时代整合生物信息学所涉及的数据整合、方法整合、系统整合及相关问题进行梳理和探讨。     

From Data to Decisions: Helping Crop Producers Build Their Actionable Knowledge

Recent advances in distributed information technologies are providing the means to capture and process abundant data, and to reveal associations between variables describing the crop-environment-management interaction. This review describes the determinants and moderating factors influencing how much value a crop producer and his or her advisor can derive from data, and information derived from data. We describe the social, technological, and entrepreneurial processes needed to progress the nonlinear pathway from data to an on-farm decision, and explore the meaning of actionable knowledge; that is, knowledge that can be acted upon and applied to solve a real-world problem. We argue that effective decision support is also a system that supports the learning needs of crop producers and their transactions with trusted advisors. Crop protection, the sub-set of crop management used to mitigate crop loss, is used to illustrate current approaches and technologies to support farmers' decisions. We describe how situational awareness and actionable knowledge could be improved through use of emerging platform technologies, advances in artificial intelligence, consideration of farmer decision style, knowledge capture and maintenance, and embedding technology in human-centered services. Implications for the conduct of research and development are discussed.     

Next-generation sequencing technologies for environmental DNA research

Since 2005, advances in next-generation sequencing technologies have revolutionized biological science. The analysis of environmental DNA through the use of specific gene markers such as species-specific DNA barcodes has been a key application of next-generation sequencing technologies in ecological and environmental research. Access to parallel, massive amounts of sequencing data, as well as subsequent improvements in read length and throughput of different sequencing platforms, is leading to a better representation of sample diversity at a reasonable cost. New technologies are being developed rapidly and have the potential to dramatically accelerate ecological and environmental research. The fast pace of development and improvements in next-generation sequencing technologies can reflect on broader and more robust applications in environmental DNA research. Here, we review the advantages and limitations of current next-generation sequencing technologies in regard to their application for environmental DNA analysis.     

The use of information technology in improving medical performance. Part II. Physician-support tools

Increasing data from a few sites demonstrate that information technologies can improve physician decision making and clinical effectiveness. For example, computer-based physician order entry systems, automated laboratory alert systems, and artificial neural networks have demonstrated significant reductions in medical errors. In addition, Internet services to disseminate new knowledge and safety alerts to physicians more rationally and effectively are rapidly developing, and telemedicine to improve rural access to specialty services is undergoing substantial growth. However, even technologies demonstrated to yield beneficial effects have not yet achieved widespread adoption, though the pace of change appears to be increasing as the Internet takes hold. Scientific evaluation of many technologies is also lacking, and the dangers of some of these technologies may be underappreciated. Research on the effects of specific technologies should be a priority. Policies should be developed to press information technology companies, such as pharmaceutical and medical device manufacturers, to recognize the importance of clinical evaluation. Research could also analyze the characteristics of effective technologies and of physicians and organizations who implement these technologies effectively.     

A closer look at reproductive technology and postmenopausal motherhood.

Although reproductive technologies have been aimed at young, infertile women, evidence suggests that postmenopausal women are also taking advantage of them. Dr. Eike-Henner Kluge asserts in an article in CMAJ (1994; 151; 353-355) that there are ethical reasons to deny older women access to these technologies. Kluge''s comparison of postmenopausal women to prepubescent girls is fallacious. His assertion that older parents harm children by denying them a "normal" childhood is not supported by any empiric data. Kluge''s distinction between medical intervention, in offering reproductive technologies to a woman in her reproductive years, and "improving on nature", by offering these technologies to postmenopausal a woman is spurious. Unless technologies that are expensive and minimally successful, such as in-vitro fertilization, are denied to everyone, there are no grounds for denying them to postmenopausal women.     

MIRG Survey 2011: Snapshot of Rapidly Evolving Label-Free Technologies Used for Characterizing Molecular Interactions

The field of label-free biophysical technologies used to quantitatively characterize macromolecular interactions with each other and with small molecules has grown enormously in the last 10 years. The most widely used analytical technologies for characterizing biomolecular interactions are surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), biolayer interferometry (BLI), and analytical ultracentrifugation (AUC). Measuring interaction parameters accurately and quantitatively is challenging, as it requires specialized expertise, training, and instrumentation. The Molecular Interaction Research Group (MIRG) conducted an online survey designed to capture the current profile of label-free technologies, including ITC, SPR, and other biosensors used in academia and the pharmaceutical industry sector. The main goal of the survey was to take a snapshot of laboratory, instrumentation, applications for measuring various biophysical parameters, confidence in data interpretation, data validation and acceptability, and limitations of using various technologies. Through this survey, we anticipate that the participating laboratories will be able to gauge their own capabilities and gain insights into the relative success of the different technologies that they use for characterizing molecular interactions.