Biosecurity in the age of Big Data: a conversation with the FBI

New scientific frontiers and emerging technologies within the life sciences pose many global challenges to society. Big Data is a premier example, especially with respect to individual, national, and international security. Here a Special Agent of the Federal Bureau of Investigation discusses the security implications of Big Data and the need for security in the life sciences.     

Biosecurity in Scandinavia

This article investigates the extent to which biosecurity measures are recognized and have been implemented in the Nordic countries, in the absence of formalized security standards and legislation. Two trials were undertaken: first, a broad combined biosafety and biosecurity questionnaire survey of the Nordic countries, and, second, a focused on-site audit of 22 facilities, with 94 laboratories, in Denmark. Both trials indicated that external security had been partially implemented but that little attention had been paid to internal security and the establishment of biosecurity. It was demonstrated that the backgrounds and identities of insiders were rarely checked and that they could have gained access to both pathogen inventory lists and freezers in many facilities. In 81% of pathogen-containing facilities, pathogens were not routinely and centrally accounted for. The authors recommend the establishment of a legal framework congruent with international standards and obligations; novel governmental national biosecurity authorities, requiring a fusion of both microbiological and technical expertise and legislative powers; and the formulation of a new code of conduct termed "Good Biosecurity Practice."     

Marine science in the age of sail

Contrary to commonly expressed opinion, scientific interest in the deep ocean did not begin in the second half of the nineteenth century with the famous cruise of HMS Challenger . However, before the widespread use of steam power, any would-be deep sea scientist had a number of difficulties to overcome. This paper explores a few of the more obvious ones that probably explain why, prior to the Challenger voyage, attempts at deep ocean science were generally small scale, uncoordinated — and largely unsuccessful!     

Do-it-yourself biology: challenges and promises for an open science and technology movement

The do-it-yourself biology (DIYbio) community is emerging as a movement that fosters open access to resources permitting modern molecular biology, and synthetic biology among others. It promises in particular to be a source of cheaper and simpler solutions for environmental monitoring, personal diagnostic and the use of biomaterials. The successful growth of a global community of DIYbio practitioners will depend largely on enabling safe access to state-of-the-art molecular biology tools and resources. In this paper we analyze the rise of DIYbio, its community, its material resources and its applications. We look at the current projects developed for the international genetically engineered machine competition in order to get a sense of what amateur biologists can potentially create in their community laboratories over the coming years. We also show why and how the DIYbio community, in the context of a global governance development, is putting in place a safety/ethical framework for guarantying the pursuit of its activity. And finally we argue that the global spread of DIY biology potentially reconfigures and opens up access to biological information and laboratory equipment and that, therefore, it can foster new practices and transversal collaborations between professional scientists and amateurs.     

Swine genome science comes of age

Pigs were among the first animals to be domesticated and pork is one of the most widely eaten meats in the world today. The pig has also been an excellent biomedical model for understanding a variety of human health issues such as obesity, diabetes, cancer, female reproductive health, cardiovascular disease, and infectious diseases. Genome sequencing, mapping, expression and functional analyses have significantly advanced our ability to unravel the secrets of the pig. Therefore, this edition, with six reviews from leading scientists, offers the opportunity for all interested researchers and readers to see the big picture of porcine genomics.     

Vegetation science in the age of big data

The age of big data is poised to revolutionize vegetation science. As online resources continue to grow, vegetation ecologists will need a growing set of computational skills to advance vegetation science in the digital age. Two papers in this issue of the Journal of Vegetation Science (Wiser 2016, Sandel et al. 2016) illustrate the resources available and use of big data to explore challenging ecological questions.     

生物安全时代:新生物科技变革与国家安全治理*

科技进步改进了人类对天然生物危害因子的操控能力,在诱发新的生物安全危害形态的同时,也赋予了生物安全客体的源头难以追溯性、生物安全主体的多元性、生物安全危害演变机理的复杂性等特点。生物安全在很大程度上体现了非传统安全的非传统特点。随着生物科技与生物安全在推动人类社会发展进程的作用日益显著,21世纪或将成为生物安全的时代。新一轮生物科技变革及其与人类社会互动衍生的生物安全问题,已经逐渐触及人类安全观念和现代文明的内源性危机或挑战。全面提升国家生物安全能力、优化国家生物安全治理,不仅是世界各国的战略选择,也是对人类科技文明与政治文明的新探索。     

生物安全时代:新生物科技变革与国家安全治理*

科技进步改进了人类对天然生物危害因子的操控能力,在诱发新的生物安全危害形态的同时,也赋予了生物安全客体的源头难以追溯性、生物安全主体的多元性、生物安全危害演变机理的复杂性等特点。生物安全在很大程度上体现了非传统安全的非传统特点。随着生物科技与生物安全在推动人类社会发展进程的作用日益显著,21世纪或将成为生物安全的时代。新一轮生物科技变革及其与人类社会互动衍生的生物安全问题,已经逐渐触及人类安全观念和现代文明的内源性危机或挑战。全面提升国家生物安全能力、优化国家生物安全治理,不仅是世界各国的战略选择,也是对人类科技文明与政治文明的新探索。     

When will ‘open science’ become simply ‘science’?

Open science describes the practice of carrying out scientific research in a completely transparent manner, and making the results of that research available to everyone. Isn’t that just ‘science’?     

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.