Public transportation systems are often part of strategies to reduce urban environmental impacts from passenger transportation, yet comprehensive energy and environmental life-cycle measures, including upfront infrastructure effects and indirect and supply chain processes, are rarely considered. Using the new bus rapid transit and light rail lines in Los Angeles, near-term and long-term life-cycle impact assessments are developed, including consideration of reduced automobile travel. Energy consumption and emissions of greenhouse gases and criteria pollutants are assessed, as well the potential for smog and respiratory impacts.

Results show that life-cycle infrastructure, vehicle, and energy production components significantly increase the footprint of each mode (by 48–100% for energy and greenhouse gases, and up to 6200% for environmental impacts), and emerging technologies and renewable electricity standards will significantly reduce impacts. Life-cycle results are identified as either local (in Los Angeles) or remote, and show how the decision to build and operate a transit system in a city produces environmental impacts far outside of geopolitical boundaries. Ensuring shifts of between 20–30% of transit riders from automobiles will result in passenger transportation greenhouse gas reductions for the city, and the larger the shift, the quicker the payback, which should be considered for time-specific environmental goals.

Public transit systems are often accepted as energy and environmental improvements to automobile travel, however, few life cycle assessments exist to understand the effects of implementation of transit policy decisions. To better inform decision-makers, this project evaluates the decision to construct and operate public transportation systems and the expected energy and environmental benefits over continued automobile use. The public transit systems are selected based on screening criteria. Initial screening included advanced implementation (5 to 10 years so change in ridership could be observed), similar geographic regions to ensure consistency of analysis parameters, common transit agencies or authorities to ensure a consistent management culture, and modes reflecting large infrastructure investments to provide an opportunity for robust life cycle assessment of large impact components. An in-depth screening process including consideration of data availability, project age, energy consumption, infrastructure information, access and egress information, and socio-demographic characteristics was used as the second filter. The results of this selection process led to Los Angeles Metro’s Orange and Gold lines.

In this study, the life cycle assessment framework is used to evaluate energy inputs and emissions of greenhouse gases, particulate matter (10 and 2.5 microns), sulfur dioxide, nitrogen oxides, volatile organic compounds, and carbon monoxide. For the Orange line, Gold line, and competing automobile trip, an analysis system boundary that includes vehicle, infrastructure, and energy production components is specified. Life cycle energy use and emissions inventories are developed for each mode considering direct (vehicle operation), ancillary (non-vehicle operation including vehicle maintenance, infrastructure construction, infrastructure operation, etc.), and supply chain processes and services. In addition to greenhouse gas emissions, the inventories are linked to their potential for respiratory impacts and smog formation, and the time it takes to payback in the lifetime of each transit system.

Results show that for energy use and greenhouse gas emissions, the inclusion of life cycle components increases the footprint between 42% and 91% from vehicle propulsion exclusively. Conventional air emissions show much more dramatic increases highlighting the effectiveness of “tailpipe” environmental policy. Within the life cycle, vehicle operation is often small compared to other components. Particulate matter emissions increase between 270% and 5400%. Sulfur dioxide emissions increase by several orders of magnitude for the on road modes due to electricity use throughout the life cycle. NOx emissions increase between 31% and 760% due to supply chain truck and rail transport. VOC emissions increase due to infrastructure material production and placement by 420% and 1500%. CO emissions increase by between 20% and 320%. The dominating contributions from life cycle components show that the decision to build an infrastructure and operate a transportation mode in Los Angeles has impacts far outside of the city and region. Life cycle results are initially compared at each system’s average occupancy and a breakeven analysis is performed to compare the range at which modes are energy and environmentally competitive.

The results show that including a broad suite of energy and environmental indicators produces potential tradeoffs that are critical to decision makers. While the Orange and Gold line require less energy and produce fewer greenhouse gas emissions per passenger mile traveled than the automobile, this ordering is not necessarily the case for the conventional air emissions. It is possible that a policy that focuses on one pollutant may increase another, highlighting the need for a broad set of indicators and life cycle thinking when making transportation infrastructure decisions.

The goal of this working paper is to provide the methodological background for several upcoming reports and peer-reviewed journal publications. This manuscript only provides background methodology and does not show or interpret any of the results that are being generated by the research team. The methodology is consistent with the transportation LCA approach developed by the author in previous research. The discussion in this working paper provides the detailed background data and steps used by the research team for their assessment of Los Angeles Metro transit lines and a competing automobile trip.

Study Background: Researchers at ASU have determined that significant energy and environmental benefits are possible in the Phoenix metro area over the next 60 years from transit-oriented development along the current Valley Metro light rail line. The team evaluated infill densification outcomes when vacant lots and some dedicated surface parking lots are repurposed for residential development. Life cycle building (construction, use, and energy production) and transportation (manufacturing, operation, and energy production) changes were included and energy use and greenhouse gas emissions were evaluated in addition to the potential for respiratory impacts and smog formation. All light rail infill scenarios are compared against new single family home construction in outlying areas.

Overview of Results: In the most conservative scenario, the Phoenix area can place 2,200 homes near light rail and achieve 9-15% reductions in energy use and emissions. By allowing multi-family apartments to fill vacant lots, 12,000 new dwelling units can be infilled achieving a 28-42% reduction. When surface lots are developed in addition to vacant lots then multi-family apartment buildings around light rail can deliver 30-46% energy and environmental reductions. These reductions occur even after new trains are put into operation to meet the increased demand.

Better methods are necessary to fully account for anthropogenic impacts on ecosystems and the essential services provided by ecosystems that sustain human life. Current methods for assessing sustainability, such as life cycle assessment (LCA), typically focus on easily quantifiable indicators such as air emissions with no accounting for the essential ecosystem benefits that support human or industrial processes. For this reason, more comprehensive, transparent, and robust methods are necessary for holistic understanding of urban technosphere and ecosphere systems, including their interfaces. Incorporating ecosystem service indicators into LCA is an important step in spanning this knowledge gap.

For urban systems, many built environment processes have been investigated but need to be expanded with life cycle assessment for understanding ecosphere impacts. To pilot these new methods, a material inventory of the building infrastructure of Phoenix, Arizona can be coupled with LCA to gain perspective on the impacts assessment for built structures in Phoenix. This inventory will identify the origins of materials stocks, and the solid and air emissions waste associated with their raw material extraction, processing, and construction and identify key areas of future research necessary to fully account for ecosystem services in urban sustainability assessments. Based on this preliminary study, the ecosystem service impacts of metropolitan Phoenix stretch far beyond the county boundaries. A life cycle accounting of the Phoenix’s embedded building materials will inform policy and decision makers, assist with community education, and inform the urban sustainability community of consequences.

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment.

Illicit psychostimulant addiction remains a significant problem worldwide, despite decades of research into the neural underpinnings and various treatment approaches. The purpose of this review is to provide a succinct overview of the neurocircuitry involved in drug addiction, as well as the acute and chronic effects of cocaine and amphetamines within this circuitry in humans. Investigational pharmacological treatments for illicit psychostimulant addiction are also reviewed. Our current knowledge base clearly demonstrates that illicit psychostimulants produce lasting adaptive neural and behavioral changes that contribute to the progression and maintenance of addiction. However, attempts at generating pharmacological treatments for psychostimulant addiction have historically focused on intervening at the level of the acute effects of these drugs. The lack of approved pharmacological treatments for psychostimulant addiction highlights the need for new treatment strategies, especially those that prevent or ameliorate the adaptive neural, cognitive, and behavioral changes caused by chronic use of this class of illicit drugs.

In several group-living species, individuals' social preferences are thought to be influenced by cooperation. For some societies with fission–fusion dynamics, sex-specific association patterns reflect sex differences in cooperation in within- and between-group contexts. In our study, we investigated this hypothesis further by comparing sex-specific association patterns in two closely related species, chimpanzees and bonobos, which differ in the level of between-group competition and in the degree to which sex and kinship influence dyadic cooperation. Here, we used long-term party composition data collected on five chimpanzee and two bonobo communities and assessed, for each individual of 10 years and older, the sex of its top associate and of all conspecifics with whom it associated more frequently than expected by chance. We found clear species differences in association patterns. While in all chimpanzee communities males and females associated more with same-sex partners, in bonobos males and females tended to associate preferentially with females, but the female association preference for other females is lower than in chimpanzees. Our results also show that, for bonobos (but not for chimpanzees), association patterns were predominantly driven by mother–offspring relationships. These species differences in association patterns reflect the high levels of male–male cooperation in chimpanzees and of mother–son cooperation in bonobos. Finally, female chimpanzees showed intense association with a few other females, and male chimpanzees showed more uniform association across males. In bonobos, the most differentiated associations were from males towards females. Chimpanzee male association patterns mirror fundamental human male social traits and, as in humans, may have evolved as a response to strong between-group competition. The lack of such a pattern in a closely related species with a lower degree of between-group competition further supports this notion.

We recommend using backward design to develop course-based undergraduate research experiences (CUREs). The defining hallmark of CUREs is that students in a formal lab course explore research questions with unknown answers that are broadly relevant outside the course. Because CUREs lead to novel research findings, they represent a unique course design challenge, as the dual nature of these courses requires course designers to consider two distinct, but complementary, sets of goals for the CURE: 1) scientific discovery milestones (i.e., research goals) and 2) student learning in cognitive, psychomotor, and affective domains (i.e., pedagogical goals). As more undergraduate laboratory courses are re-imagined as CUREs, how do we thoughtfully design these courses to effectively meet both sets of goals? In this Perspectives article, we explore this question and outline recommendations for using backward design in CURE development.