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The genetic load hypothesis of Hermann Muller raised the profound question of possible species extinction, even for humans, following a prolonged accumulation of recessive genes due to ionizing radiation exposure within the population. Two major mouse radiation research teams in the United States provided the most extensive tests of Muller's hypothesis. One group continued its study for more than two decades, over 82 consecutive generations, approximating 2500 human years. Even though Muller had stressed for decades his fear of species-threatening effects, no significant effects were observed for related factors such as reproductive fitness and longevity. Yet, the paper presenting the data of the 82-generation negative study has only been cited five times in 45 years. Altogether numerous laboratories worldwide collected vast amounts of data on mice, rats, and swine in an unsuccessful attempt to see if there was convincing evidence to support the genetic load theory and claims that species might deteriorate or be rendered extinct. This paper re-examines Muller's genetic load hypothesis with a new evaluation of how that hypothesis was tested and the significance of the findings, with most of those studies being completed before the BEIR I Committee Report in 1972. That committee briefly discussed the available evidence, mostly ignoring those results as they proceeded to make hereditary risk estimates both for (1) the first generation after a radiation exposure and (2) for the time, in the distant future, when a hypothetical genetic equilibrium would be reached. Their estimates assumed accumulation of harmful mutations and a linear no-threshold dose response extending all of the way down to a single ionization. More recent data on induction by ionizing radiation of dominant mutations that affect the skeletons of mice provide further robust supporting evidence that the generationally cumulative and LNT-based assumptions underpinning Muller's genetic load hypothesis are not correct. • Accumulation of recessive mutation were hypothesized to cause species extinction. • Muller referred to this as his genetic load hypothesis. • This hypothesis was based upon the LNT model for mutation. • Multiple large-scale experimental studies failed to support this hypothesis. • An 82-generation mouse study failed to support this hypothesis. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing is an increasingly common method of oil and gas extraction across the United States. Many of the chemicals used in hydraulic fracturing processes have been proven detrimental to human and environmental health. While disclosure frameworks have advanced significantly in the last 20 years, the practice of withholding chemical identities as "trade secrets" or "proprietary claims" continues to represent a major absence in the data available on hydraulic fracturing. Here, we analyze rates of trade secret claims using FracFocus, a nationwide database of hydraulic fracturing data, from January 1, 2014 to December 31, 2022. We use the open-source tool Open-FF, which collates FracFocus data, makes it accessible for systematic analysis, and performs several quality-control measures. We found that the use by mass of chemicals designated as trade secrets has increased over the study time period, from 728 million pounds in 2014 to 2.96 billion pounds in 2022 (or a 43.7% average yearly increase). A total of 10.4 billion pounds of chemicals were withheld as trade secrets in this time period. The water volume used (and therefore total mass of fracturing fluid) per fracturing job has shown a large increase from 2014 to 2022, which partly explains the increase in mass of chemicals withheld as trade secrets over this time period, even as total fracturing jobs and individual counts of proprietary records have decreased. Our analysis also shows increasing rates of claiming proppants (which can include small grains of sand, ceramic, or other mineral substances used to prop open fractures) as proprietary. However, the mean and median masses of non-proppant constituents designated as trade secrets have also increased over the study period. We also find that the total proportion of all disclosures including proprietary designations has increased by 1.1% per year, from 79.3% in 2014 to 87.5% in 2022. In addition, most disclosures designate more than one chemical record as proprietary: trade secret withholding is most likely to apply to 10–25% of all records in an individual disclosure. We also show the top ten reported purposes that most commonly include proprietary designations, after removing vague or multiple entries, the first three of which are corrosion inhibitors, friction reducers, and surfactants. Finally, we report the top ten operators and suppliers using and supplying proprietary chemicals, ranked by mass used or supplied, over our study period. These results suggest the importance of revisiting the role of proprietary designations within state and federal disclosure mechanisms. • Proprietary claims allow chemical identities used in hydraulic fracturing to be withheld as trade secrets. • Proprietary claims have increased from 2014 to 2022 in mass, frequency, and proportion. • a total of 10.4 billion pounds of chemicals were withheld as trade secrets from 2014 to 2022. • These results suggest the importance of revisiting the role of proprietary claims within disclosure mechanisms. This paper investigates rates of trade secrets, or chemical records designated as proprietary, in hydraulic fracturing chemical disclosure data. This research is necessary to close information gaps in environmental health and toxicological research on hydraulic fracturing. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the responsiveness of renewable energy production (REP) to fluctuations in geopolitical risks, oil prices and economic policy uncertainty (EPU). It applies a cross-quantilogram framework to examine monthly data of the US economy for the period of 1986–2022. The findings illustrate the asymmetric effect of historical geopolitical risk (GPRH) on REP under long memory. The findings also hold after different subcategories of GPRH, including geopolitical threats and geopolitical acts, are considered. A positive shock in GPRH has the most decisive positive impact on REP when the policies are driven by both energy security and environmental commitments. A positive shock in GPRH can negatively impact REP when policies are driven by energy security causes only. EPU exerts strong negative effects on REP in bearish and bullish states of the market under medium and long memory across different measures of EPU. Dynamic connectedness analysis applying TVP-VAR method between pairwise variables indicates that net REP is a volatility receiver to the changes in GPRH, its subcomponents, oil prices and different measures of EPU. [Display omitted] • Application of cross-quantilogram approach to US monthly data during 1986–2022. • Responsiveness of renewable energy production to historical geopolitical risk. • A positive shock in GPRH has asymmetric effects on REP in long memory. • This nexus is sensitive to the motivation of energy policies in US. [ABSTRACT FROM AUTHOR]

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD. [Display omitted] [ABSTRACT FROM AUTHOR]

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the 'Automated Interactive Monitoring System (AIMS) for Wildlife'. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations. [Display omitted] • Our Automated Interactive Monitoring System (AIMS) for Wildlife utilizes animal GPS data. • We developed proof-of-concept Customized Wildlife Reports (CWRs) with 11 million locations. • CWRs inform wildlife managers' decision-making in near real-time. • We conceptualize a multi-tool AIMS for Wildlife incorporating remote-sensing data. • Automated delivery of animal movement data will allow for true adaptive management. [ABSTRACT FROM AUTHOR]

The topic of algorithmic fairness is of increasing importance to the Human–Computer Interaction research community following accumulating concerns regarding the use and deployment of Artificial Intelligence-based systems. How we conduct research on algorithmic fairness directly influences our inferences and conclusions regarding algorithmic fairness. To better understand the methodological decisions of studies focused on people's perceptions of algorithmic fairness, we systematic analysed relevant papers from the CHI and FAccT conferences. We identified 200 relevant papers published between 1993 and 2022 and assessed their study design, participant sample, and geographical location of participants and authors. Our results highlight that studies are predominantly cross-sectional, cover a wide range of participant roles, and that both authors and participants are primarily from the United States. Based on these findings, we reflect on the potential pitfalls and shortcomings in how the community studies algorithmic fairness. • Mapping of CHI and FAccT papers on algorithmic fairness published 1993–2022. • Over one-third of papers miss details on participant locality and compensation. • Algorithmic fairness research largely restricted to US and other Western countries. • Limited cross-country collaboration, most study samples from a single country. • Remote studies are most common, longitudinal studies are relatively rare. [ABSTRACT FROM AUTHOR]

The impact of international trade on greenhouse gas (GHG) emissions has primarily been viewed through the lens of emissions embodied in goods, which flow from lower-income to higher-income countries. This trade has been interpreted as allowing the high-income countries to shirk their responsibilities to address the global climate challenge by offshoring emissions. However, international trade may also have an impact on climate through flows from higher-income to lower-income countries, particularly long-lived capital and durable goods that have the potential to avoid future emissions. Given the innovation resources concentrated in higher-income countries and their historic domination of trade in such long-lived goods, this concept may balance the scales to some degree. This paper briefly develops the concept of future avoided emissions and illustrates it with the empirical case of the United States' trade in battery electric vehicles (BEVs) from 2017 to 2020. • Trade in capital goods may reduce future emissions in importing countries. • Such trade may balance trade in goods that embody emissions. • Trade in BEVs is an example, reducing future emissions from conventional cars. • A case study of US BEV trade reveals a surplus in future avoided emissions. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted on the concept of jobless recoveries and their potential causes, primarily focused on the United States from the 1990s. This paper finds that the prolonged employment downturn following the brief 1980–1981 recession in Britain qualifies as a jobless recovery and then investigates possible contributing factors: labor reallocation across industries, regional employment changes, and job polarization. The United States, which did not have a jobless recovery from the early 1980s recession, is taken as a comparison case. I find that the leading candidate explanation for this jobless recovery is the reallocation of labor across industries. This suggests an important role for structural change in the early 1980s recession and in jobless recoveries more generally. [ABSTRACT FROM AUTHOR]

Elongate mineral particles, including asbestos, have long been screened in talc and other mineral powders. In recent years, there has been a renewed scrutiny of talc containing asbestos due to allegations in civil litigation in the United States as well as reports, proposals, and white papers by international laboratories and government bodies related to this subject. This study demonstrates the importance of the fundamental understanding of both mineralogy and its application, using microscopy with empirical examples from conflicting analyses of the same talc powders by two independent laboratories in civil litigation in the United States. Methods include polarized light microscopy (PLM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) in the accurate measurement of morphological, optical, compositional, and structural data to characterize mineral-based samples. Discussions in this study include: 1) contrasting the interlaboratory findings of amphibole and amphibole asbestos by PLM and TEM using various preparation techniques, 2) the use of multiple analytical tools on a singular particle for identification, 3) the misidentification of anthophyllite asbestos by inexpert use of electron diffraction using TEM, and 4) the misidentification of chrysotile in talc by PLM. These examples emphasize the importance of not only maintaining the existing requirements, but of the need for even more rigorous analytical requirements in routine monitoring of elongate mineral particles that may occur in mineral-based powders. • Amphibole, biopyribole, talc, chrysotile, asbestos, amphibole asbestos, talcum powder, TEM, PLM, SEM, EBSD, SAED, zone axis index, asbestos testing, asbestiform, non-asbestiform. [ABSTRACT FROM AUTHOR]

This investigation explores whether sustainable finance and renewable energy could facilitate U.S. carbon neutrality. We perform the time-varying parameter-stochastic volatility-vector auto-regression (TVP-SV-VAR) model to obtain the changing relations among U.S. sustainable finance (SF), renewable energy (RE) and carbon dioxide emission (CO2). The empirical outcomes reveal a short-term negative effect from RE to CO2, indicating that renewable energy consumption could promote U.S. carbon neutrality. This effect is asymmetrical, and it could be observed that RE increase has a greater effect on CO2 than RE reduction. Also, the development of sustainable finance could facilitate U.S. carbon neutrality, and the direct impact is longer and more significant than RE, but the indirect effect of SF on CO2 by influencing RE is hysteretic. Besides, the asymmetric effect reveals that the negative direct impact of SF increase on CO2 is smaller than SF reduction, and the latter's indirect effect is more rapid than the former. Against the backdrop of global warming and frequent extreme weather, the above conclusions have meaningful practical applications for the U.S. to achieve carbon neutrality targets through developing sustainable finance and renewable energy. [Display omitted] • This paper explores the effects of SF and RE on U.S. carbon neutrality. • The facilitation of SF on U.S. carbon neutrality is more influential than that of RE. • There is a hysteretic indirect effect of SF on CO2 by influencing RE. • The negative effect of RE reduction on CO2 is smaller than that of RE increase. • The direct and indirect effects of SF on CO2 are asymmetric. [ABSTRACT FROM AUTHOR]

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected. • This paper develops a temporal model to estimate daily weather fluctuation's effects on growth rate of COVID-19 infection. • Marginal increases in temperature, relative humidity and wind speed reduces COVID-19 growth rate. • The effect of weather fluctuations on COVID-19 growth rate is non-linear. • The impact of weather changes differ by geography. [ABSTRACT FROM AUTHOR]

This paper based on quantile stochastic frontier framework constructs quantile eco-efficiency measures. Using the estimates from the quantile stochastic frontier, the eco-performance of the U.S. states for nitrogen oxides (NO X), carbon dioxide (CO 2), and sulfur dioxide (SO 2) emissions is evaluated. A decoupling analysis involving the evaluation of the nonsynchronous change among states' economic output and environmental degradation levels is also performed. The findings suggest that U.S. states have followed a decoupling process among their GDP and emission levels over the period 1990–2017. In addition, a quantile eco-productivity change estimator is presented alongside with its main components (i.e. quantile eco-technical change and quantile eco-efficiency change). Our findings suggest that over the examined period states' eco-productivity levels have been improved driven both from their eco-technical and eco-efficiency change levels. • We examine regional eco-efficiency across the US states. • We examine regional eco-productivity across the US states. • We employ a quantile stochastic frontier analysis. • We employ a decoupling analysis among U.S. states' GDP and emissions levels. • We find that eco-productivity levels was driven by eco-technical change levels. [ABSTRACT FROM AUTHOR]

• Describes the ABCD study aims and design. • Covers issues surrounding estimation of meaningful associations, including population inferences, effect sizes, and control of covariates. • Outlines best practices for reproducible research and reporting of results. • Provides worked examples that illustrate the main points of the paper. The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9–10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results. [ABSTRACT FROM AUTHOR]

Estimated labor force participation rates among free women in the pre-Civil War period were exceedingly low. This is due, in part, to cultural or societal expectations of the role of women and the lack of thorough enumeration by Census takers. This paper develops an augmented labor force participation rate for free women in 1860 and compares it with the augmented rate for 1920 and today. Our methodology identifies women who are likely providing informal and unenumerated labor for market production in support of a family business, that is, unreported family workers. These individuals are not coded in the original data as formally working, but are likely to be engaged in the labor force on the basis of the self-employment of other relatives in their household. Unreported family workers are classified into four categories: farm, merchant, craft, and boardinghouse keepers. Using microdata, the inclusion of these workers more than triples the free female labor force participation rate in the 1860 Census from 16 percent to 57 percent, more than doubles the participation rate in the 1920 Census from 24 percent to 50 percent, and has a small effect on the currently measured rate of 56 percent (2015-2019 American Community Survey). This suggests that rather than a steep rise from a very low level in the female labor force participation rate since 1860, it has in fact always been high and fairly stable over time. In contrast, the effect of including unreported family workers in the male augmented labor force participation rate is relatively small. [ABSTRACT FROM AUTHOR]