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Problem, research strategy, and findingsCarsharing programs—subscription-based car rentals—allow users to purchase only the automobility that they need. These programs may benefit low-income travelers by increasing access at lower prices than private auto ownership. Most carshare programs, however, disproportionately serve higher-income drivers. To assess carsharing’s potential to address the accessibility needs of disadvantaged households, we interviewed members of BlueLA, an electric carsharing program in central Los Angeles (CA) that offers both subsidized and regular memberships. We found few differences in how travelers with different membership types used BlueLA. They both used the service to complement travel by other modes like public transit and ridehail. In addition, members cited the benefits of gaining car access without the financial burden of car ownership or the unpredictability of ridehail fares. Neighborhood context, including residential density and the availability of non-automobile transportation options, also increased BlueLA’s appeal. However, due to limited and unreliable vehicle availability, most users did not rely on BlueLA for time-sensitive trips. BlueLA both eased and increased access to destinations outside of the commute and complemented public transit for subsidized and regular members. Takeaway for practiceServices like BlueLA cannot meet all transportation needs. However, subsidized electric carsharing—particularly targeted to central-city neighborhoods—may address some accessibility needs of low-income households without imposing the burdens of automobile ownership.

Motivated by a holistic understanding of AI literacy, this work presents an interdisciplinary effort to make AI literacy measurable in a comprehensive way, considering generic and domain-specific AI literacy as well as AI ethics. While many AI literacy assessment tools have been developed in the last 2-3 years, mostly in the form of self-assessment scales and less frequently as knowledge-based assessments, previous approaches only accounted for one specific area of a comprehensive understanding of AI competence, namely cognitive aspects within generic AI literacy. Considering the demand for AI literacy development for different professional domains and reflecting on the concept of competence in a way that goes beyond mere cognitive aspects of conceptual knowledge, there is an urgent need for assessment methods that capture domain-specific AI literacy on each of the three competence dimensions of cognition, behavior, and attitude. In addition, competencies for AI ethics are becoming more apparent, which further calls for a comprehensive assessment of AI literacy for this very matter. This conceptual paper aims to provide a foundation upon which future AI literacy assessment instruments can be built and provides insights into what a framework for item development might look like that addresses both generic and domain-specific aspects of AI literacy as well as AI ethics literacy, and measures more than just knowledge-related aspects based on a holistic approach.

Traditional avenues of public input in the transportation planning process require residents to have the time, knowledge, and capacity to participate. Those who don’t – often lower-income and other disadvantaged groups – are left out. To encourage more broad-based participation, some states and localities have funded additional community-based, grassroots-organized transportation studies to explicitly engage with underrepresented populations. However, the effectiveness of such studies is an open question. This study analyzes the results of a pilot program in California to determine whether community studies, or community studies, include the disadvantaged groups they intended to include, and whether they offer additional benefits or costs compared to traditional public participation processes in transportation planning. The California Air Resources Board funded a series of community transportation needs assessments under the Clean Mobility Options pilot program that were intended to include historically underrepresented groups in identifying their transportation needs and potential solutions. We examine responses from those programs’ resident surveys, community engagement strategies, and findings from the needs assessments' final reports, to identify factors that predict successful outreach. We also compare public participation methods in community needs assessments with those in long-range transportation plans. Although we are unable to ascertain whether the community studies led to any changes in local transportation systems, we do identify the conditions under which additional engagement encourages traditionally hard-to-reach communities to participate in transportation planning, as well as when it does not. We conclude this paper with recommendations for when additional studies can be useful, and alternative policies to address equity gaps in the transportation planning process.

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method’s performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

This report examines transportation in California: where we are today, how we got here, and where we might be headed. We begin with facts on travel and transportation systems in California today. The vast majority of personal travel is by car across all socio-economic groups, and commercial travel is by truck. Public transit plays an important role in the biggest cities but a small one elsewhere; walking is the most popular way to get around outside of a car or truck. Californians are taking fewer trips overall, though reduced vehicle travel for personal trips is more than offset by increases in commercial travel and deliveries. We next explore the decades of public and private land development and transportation systems that have shaped the current state of play. We explore the state’s massive investments in freeways—both between and within cities—and its land use policies complementary to driving. Low-density land uses, exemplified by dispersed single-family housing, have encouraged automobile ownership and use, resulting in suburban living and universal automobile access for most of the population, at the cost of increasing travel distances, increasing isolation for those unable to drive, chronic traffic congestion, health- and environment-threatening vehicle emissions, and a housing affordability crisis. Today’s transportation problems stem, in significant part, from yesterday’s land use decisions. We then consider factors that have either recently come to the fore or are likely to emerge in the near future: the growing reliance on automobile and truck travel and declining in transit use (both before and during the pandemic), shifting patterns of public spending on transportation, changing patterns of jobs and housing locations, the travel habits of younger generations, advancements in goods movement, and the challenges of resilience in disasters. Technology, too, is reshaping Californians’ mobility, with electrification of the vehicle fleet, technology-enabled mobility services, telecommuting, and the gradual automation of driving. We conclude by reviewing possible context-specific reforms to reshape transportation in the state, in order to better manage vehicle travel and reduce chronic congestion, shift patterns of development to make them less car-dependent, and increase access

In this study, peripheral blood mononuclear cells from young and old patients with COVID-19 were examined phenotypically, transcriptionally and functionally to reveal age-, time- and severity-specific adaptations. Gene signatures within memory B cells and plasmablasts correlated with reduced frequency of antigen-specific B cells and neutralizing antibodies in older patients with severe COVID-19. Moreover, these patients exhibited exacerbated T cell lymphopenia, which correlated with lower plasma interleukin-2, and diminished antigen-specific T cell responses. Single-cell RNA sequencing revealed augmented signatures of activation, exhaustion, cytotoxicity and type I interferon signaling in memory T and natural killer cells with age. Although cytokine storm was evident in both age groups, older individuals exhibited elevated levels of myeloid cell recruiting factors. Furthermore, we observed redistribution of monocyte and dendritic cell subsets and emergence of a suppressive phenotype with severe disease, which was reversed only in young patients over time. This analysis provides new insights into the impact of aging on COVID-19.

Infection with Marburg virus (MARV), the causative agent of Marburg virus disease (MVD), results in haemorrhagic disease and high case fatality rates (>40%) in humans. Despite its public health relevance, there are no licensed vaccines or therapeutics to prevent or treat MVD. A vesicular stomatitis virus (VSV)-based vaccine expressing the MARV glycoprotein (VSV-MARV) is currently in clinical development. Previously, a single 10 million PFU dose of VSV-MARV administered 1–5 weeks before lethal MARV challenge conferred uniform protection in nonhuman primates (NHPs), demonstrating fast-acting potential. Additionally, our group recently demonstrated that even a low dose VSV-MARV (1000 PFU) protected NHPs when given 7 days before MARV challenge. In this study, we longitudinally profiled the transcriptional responses of NHPs vaccinated with this low dose of VSV-MARV either 14 or 7 days before lethal MARV challenge. NHPs vaccinated 14 days before challenge presented with transcriptional changes consistent with an antiviral response before challenge. Limited gene expression changes were observed in the group vaccinated 7 days before challenge. After challenge, genes related to lymphocyte-mediated immunity were only observed in the group vaccinated 14 days before challenge, indicating that the length of time between vaccination and challenge influenced gene expression. Our results indicate that a low dose VSV-MARV elicits distinct immune responses that correlate with protection against MVD. A low dose of VSV-MARV should be evaluated in clinical rails as it may be an option to deliver beneficial public health outcomes to more people in the event of future outbreaks.

Both age and obesity are leading risk factors for severe coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Specifically, although most infections occur in individuals under the age of 55 years, 95% of hospitalizations, admissions to the intensive care unit, and deaths occur in those over the age of 55 years. Moreover, hospitalized COVID-19 patients have a higher prevalence of obesity. It is generally believed that chronic low-grade inflammation and dysregulated innate and adaptive immune responses that are associated with aging and obesity are responsible for this elevated risk of severe disease. However, the impact of advanced age and obesity on the host response to SARS-CoV-2 infection remains poorly defined. In this study, we assessed changes in the concentration of soluble immune mediators, IgG antibody titers, frequency of circulating immune cells, and cytokine responses to mitogen stimulation as a function of BMI and age. We detected significant negative correlations between BMI and myeloid immune cell subsets that were more pronounced in aged patients. Similarly, inflammatory cytokine production by monocytes was also negatively correlated with BMI in aged patients. These data suggest that the BMI-dependent impact on host response to SARS-CoV-2 is more pronounced on innate responses of aged patients.

Fagopyrum tataricum (L.) Gaertn. is an exceptional crop known for its remarkable health benefits, high levels of beneficial polyphenols and gluten-free properties, making it highly sought-after as a functional food. Its self-fertilisation capability and adaptability to challenging environments further contribute to its potential as a sustainable agricultural option. To harness its unique traits, genetic transformation in F. tataricum is crucial. In this study, we optimised the Agrobacterium-mediated transformation protocol for F. tataricum callus, resulting in a transformation rate of regenerated plants of approximately 20%. The protocol’s effectiveness was confirmed through successful GUS staining, GFP expression, and the generation of albino plants via FtPDS gene inactivation. These results validate the feasibility of genetic manipulation and highlight the potential for trait enhancement in F. tataricum.

This report examines whether being more mobile is associated with decisions by older people to remain in the active work force after normal retirement age.  Mobility includes having access to and the capacity to drive cars and being transit accessible or being able to walk to work or work at home.  The report presents findings in three sections and concludes, based on research conducted thus far, that there are indications of causal associations between mobility and delayed retirement.  The report includes a review of the literature linking mobility, travel by the elderly, and retirement decision making. Following the review of the literature is quantitative analysis of data from the California Household Travel Survey, including mathematical models of relationships between mobility and the propensity of older Californians to remain in the active work force.  The final component of the research findings is a summary of the results of qualitative research consisting of focus groups and interviews. While the findings are not conclusive they show relationships which indicate that further research, particularly using longitudinal rather than cross sectional data, are warranted.  After presenting findings, the report concludes with recommendations for further research.

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies. [ABSTRACT FROM AUTHOR]

ABSTRACT Rotaviruses represent one of the most successful pathogens in the world, with high infectivity and efficient transmission between the young of many animal species, including humans. To overcome host defenses, rotaviruses have evolved a plethora of strategies to effectively evade the innate immune response, establish initial infection in the small intestine, produce progeny, and shed into the environment. Previously, studying the roles and relative contributions of specific rotaviral factors in innate immune evasion had been challenging without a plasmid-only reverse genetics system. Although still in its infancy, current reverse genetics technology will help address important research questions regarding rotavirus innate immune evasion, host range restriction, and viral pathogenesis. In this review, we summarize the current knowledge about the antiviral host innate immune defense mechanisms, countermeasures of rotavirus-encoded factors, and strategies to better understand these interactions using the rotavirus reverse genetics system.