Managing the Ethical Dimensions of Brain-Computer Interfaces in eHealth: An SDLC-based Approach

A growing range of brain-computer interface (BCI) technologies is being employed for purposes of therapy and human augmentation. While much thought has been given to the ethical implications of such technologies at the ‘macro’ level of social policy and ‘micro’ level of individual users, little attention has been given to the unique ethical issues that arise during the process of incorporating BCIs into eHealth ecosystems. In this text a conceptual framework is developed that enables the operators of eHealth ecosystems to manage the ethical components of such processes in a more comprehensive and systematic way than has previously been possible. The framework’s first axis defines five ethical dimensions that must be successfully addressed by eHealth ecosystems: 1) beneficence; 2) consent; 3) privacy; 4) equity; and 5) liability. The second axis describes five stages of the systems development life cycle (SDLC) process whereby new technology is incorporated into an eHealth ecosystem: 1) analysis and planning; 2) design, development, and acquisition; 3) integration and activation; 4) operation and maintenance; and 5) disposal. Known ethical issues relating to the deployment of BCIs are mapped onto this matrix in order to demonstrate how it can be employed by the managers of eHealth ecosystems as a tool for fulfilling ethical requirements established by regulatory standards or stakeholders’ expectations. Beyond its immediate application in the case of BCIs, we suggest that this framework may also be utilized beneficially when incorporating other innovative forms of information and communications technology (ICT) into eHealth ecosystems

Bioinformatics tools in predictive ecology: Applications to fisheries

This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

Alien Registration- Duplisea, John E. (Woodville, Penobscot County)

https://digitalmaine.com/alien_docs/10333/thumbnail.jp

Recognition, Reassessment, and Remediation: A small scale approach to improving harmful or exclusionary descriptive practices in the archives

Archives and special collections repositories rely on descriptive practices to provide researchers with entry points to locate and guides to navigate the materials that we steward. The efforts to remediate harmful and missing information, even in incremental steps can make a huge difference. To repair and improve our collections access and descriptions is an iterative process, and one that is scalable for any size collection or staff. This presentation will demonstrate the steps we are taking to develop a scalable project to review and assess or collections and implement changes. It will also outline the future plans to expand to diversify our reviewers to include researchers, faculty, students, and other community stakeholders

Response of benthic fauna to experimental bottom fishing: A global meta-analysis

Bottom‐contact fishing gears are globally the most widespread anthropogenic sources of direct disturbance to the seabed and associated biota. Managing these fishing disturbances requires quantification of gear impacts on biota and the rate of recovery following disturbance. We undertook a systematic review and meta‐analysis of 122 experiments on the effects‐of‐bottom fishing to quantify the removal of benthos in the path of the fishing gear and to estimate rates of recovery following disturbance. A gear pass reduced benthic invertebrate abundance by 26% and species richness by 19%. The effect was strongly gear‐specific, with gears that penetrate deeper into the sediment having a significantly larger impact than those that penetrate less. Sediment composition (% mud and presence of biogenic habitat) and the history of fishing disturbance prior to an experimental fishing event were also important predictors of depletion, with communities in areas that were not previously fished, predominantly muddy or biogenic habitats being more strongly affected by fishing. Sessile and low mobility biota with longer life‐spans such as sponges, soft corals and bivalves took much longer to recover after fishing (>3 year) than mobile biota with shorter life‐spans such as polychaetes and malacostracans (<1 year). This meta‐analysis provides insights into the dynamics of recovery. Our estimates of depletion along with estimates of recovery rates and large‐scale, high‐resolution maps of fishing frequency and habitat will support more rigorous assessment of the environmental impacts of bottom‐contact gears, thus supporting better informed choices in trade‐offs between environmental impacts and fish production