Sustainable Engineering Design in Education: A Pilot Study of Teaching Right-to-Repair Principles through Project-Based Learning.

Over 60 million tons of E-waste is expected to be generated in 2023, with associated significant impacts on health and the environment. To reduce the number of products sent to landfills, "Right to Repair" (RtR) movements are gaining momentum in many countries, including the UK, USA, and EU member states. While Universities are seen as important stakeholders to drive forward sustainable design practices, there is currently little work looking at training undergraduate design engineers in the principles of designing household products in support of RtR. In particular, the project-based learning (PBL) pedagogy shows promise in engaging and training students with the skills and knowledge required to successfully design products for RtR. In this paper, a pilot-study of teaching engineers is presented to design products compatible with RtR principles, alongside many technical skills, in a first-year PBL course. The key outputs of this paper are the design of the module, which can be used to help inform first-year engineering education, the high engagement of students, with 100% of respondents agreeing that they intend to try to implement sustainable design practices in future, and some of the innovative features that students implement in their projects.

Diagnostics for personalized medicine: what will change in the era of large-scale genomics studies?

The era of personalized medicine is upon us and it is being fueled by large available data sets of many types that are setting the foundation for the development of more precise diagnostic tools and targeted therapies, which are improving patient outcomes. Technology innovation and concomitant price decreases in molecular scanning technologies are at the heart of this change, both accelerating at a rate that has exceeded Moore's law. This technology trend is enabling the research community to generate, and make publicly available, massive amounts of genomic data. These data come in the form not only of contextual information about the structure and function of the genome, but also in the form of variants that are correlated with human disease. Coupled with this molecular information, we are making dramatic inroads into capturing and making available high-resolution phenotypic and environmental exposure data through both incentives to physicians to migrate electronic medical records and to adoption of consumer-facing data collection and aggregation technologies. These large-scale genomic, environmental and phenotypic data together allow us to provide a multitude of new diagnostic correlations across the spectrum of possible clinical indications. To fully leverage the data foundation that will lead us to precise diagnostics and truly move the needle in outcome improvement, we need to achieve a culture shift as to how to apply this new personalized and probabilistic diagnostic information to better practice the art of medicine.

Ontology-based meta-analysis of global collections of high-throughput public data.

BACKGROUND: The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. METHODOLOGY/RESULTS: We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. CONCLUSIONS: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Analysis of expression data: an overview.

After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. Data analysis from microarray experiments generally involves two parts: acquiring and normalizing the data, and interpreting it. This unit focuses mostly on the latter, as it is less technology-specific.

Analysis of expression data: an overview.

After providing a brief introduction to microarray chips and experimental details, this overview discusses analysis techniques. Data analysis from microarray experiments generally involves two parts: acquiring and normalizing the data, and interpreting it. This unit focuses mostly on the latter, as it is less technology-specific.

Tools for discovery: gene expression enterprise solutions.

Expression profiling in drug discovery is a highly collaborative process requiring robust, centralized databases and exhaustive exploration of expanding libraries of expression experiments. In this review, state-of-the-art data analysis tools that identify relationships between gene expression and biological activities are described. Informatics workflow, system scalability and regulatory compliance issues are discussed in the context of expression data management.