Product lifespan is a fundamental variable in understanding the environmental impacts associated with the life cycle of products. Existing life cycle and materials flow studies of products, almost without exception, consider lifespan to be constant over time. To determine the validity of this assumption, this study provides an empirical documentation of the long-term evolution of personal computer lifespan, using a major U.S. university as a case study. Results indicate that over the period 1985-2000, computer lifespan (purchase to "disposal") decreased steadily from a mean of 10.7 years in 1985 to 5.5 years in 2000. The distribution of lifespan also evolved, becoming narrower over time. Overall, however, lifespan distribution was broader than normally considered in life cycle assessments or materials flow forecasts of electronic waste management for policy. We argue that these results suggest that at least for computers, the assumption of constant lifespan is problematic and that it is important to work toward understanding the dynamics of use patterns. We modify an age-structured model of population dynamics from biology as a modeling approach to describe product life cycles. Lastly, the purchase share and generation of obsolete computers from the higher education sector is estimated using different scenarios for the dynamics of product lifespan.

The COVID-19 pandemic caused unprecedented disruptions to food systems, leading to both food shortages and food waste across the supply chain. These disruptions have, in turn, altered how people consume and then ultimately discard food. To better understand these impacts, their underlying drivers, and their sustainability implications, this study surveyed U.S. consumers about food purchasing, use, and waste behaviors during the pandemic. Survey respondents reported an increase in overall food purchases and a slight decrease in food waste generation due to the pandemic, but the linkages between these outcomes and underlying behaviors were complex. For instance, reduced household food waste was significantly correlated with an increase in behaviors such as meal planning, preserving foods, and using leftovers and shelf-stable items. On the other hand, behaviors aimed at self-sufficiency, including bulk purchasing and stockpiling, were significantly correlated with increased food purchase, which in turn led to increased waste. Results may offer insight for future resource and waste management strategies. For example, over 60% of respondents who started or increased efficient food use behaviors stated an intent to continue these activities after the pandemic. In contrast, less than 10% of respondents reported that they began or increased separating or composting food waste during the pandemic, and many stopped altogether due to suspension of local curbside composting services. Findings suggest that it may be easier to shift food consumption and use behaviors but more challenging to alter food waste separation behaviors, particularly those influenced by external factors, such as infrastructure that may be vulnerable to disruption. Identifying ways to facilitate ongoing behavior change and foster robust food waste management systems can contribute to resilience of food systems now and once the immediate threat of the pandemic has subsided.

The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.

Traditional informatics in comparative genomics work only with static representations of biomolecules (i.e., sequence and structure), thereby ignoring the molecular dynamics (MD) of proteins that define function in the cell. A comparative approach applied to MD would connect this very short timescale process, defined in femtoseconds, to one of the longest in the universe: molecular evolution measured in millions of years. Here, we leverage advances in graphics-processing-unit-accelerated MD simulation software to develop a comparative method of MD analysis and visualization that can be applied to any two homologous Protein Data Bank structures. Our open-source pipeline, DROIDS (Detecting Relative Outlier Impacts in Dynamic Simulations), works in conjunction with existing molecular modeling software to convert any Linux gaming personal computer into a "comparative computational microscope" for observing the biophysical effects of mutations and other chemical changes in proteins. DROIDS implements structural alignment and Benjamini-Hochberg-corrected Kolmogorov-Smirnov statistics to compare nanosecond-scale atom bond fluctuations on the protein backbone, color mapping the significant differences identified in protein MD with single-amino-acid resolution. DROIDS is simple to use, incorporating graphical user interface control for Amber16 MD simulations, cpptraj analysis, and the final statistical and visual representations in R graphics and UCSF Chimera. We demonstrate that DROIDS can be utilized to visually investigate molecular evolution and disease-related functional changes in MD due to genetic mutation and epigenetic modification. DROIDS can also be used to potentially investigate binding interactions of pharmaceuticals, toxins, or other biomolecules in a functional evolutionary context as well.

Summary This article describes how biological ecology models are adapted to analyze the dynamic structure and function of a consumer electronic product “community.” Treating an entire group of interdependent and continually evolving electronic devices as an ecological community provides a basis for more comprehensive analyses of the energy, material, and waste flows associated with household consumption than would be possible using conventional per product approaches. Results show that, similar to a maturing natural community, the average U.S. household electronic product community evolved from a low‐diversity structure dominated by a few products to a highly diverse, evenly distributed community of products between 1990 and 2010. The maturing community of household electronics experienced increased functionality at a community and product level resulting, in part, from introduction of new products, but primarily as a result of increasing ownership of multifunctional products. Multifunctional mobile products are driving increased functionality in a manner similar to a broadly adaptive invasive species, but the community's high functional redundancy, as the result of device convergence, resembles a stable natural community. These results suggest that future strategies to encourage green design, production, and consumption of consumer electronics should focus on minimizing the total number of products owned by maximizing multifunctionality with convergent device design.