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Understanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often‐overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (<7 mm), while other years show more substantial offsets (>9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region. [ABSTRACT FROM AUTHOR]

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible. [ABSTRACT FROM AUTHOR]

Electrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‐rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted. [ABSTRACT FROM AUTHOR]

Aim: When studying the effects of global change on biodiversity, it is far more common for the effects of climate change and land‐use changes to be assessed separately rather than jointly. However, the effects of land‐use changes in recent decades on species richness in areas affected by climate change have been less studied. We assess the temporal turnover in species richness of an avian community between a historical period and a modern one as a consequence of global change. Location: Semiarid Mediterranean ecosystem (southeastern Spain). Method: We fitted a hierarchical multispecies occupancy model for each period (1991–1992, and 2012–2017), obtaining avian species‐specific estimates of occupancy probability in relation to environmental covariates (elevation and forest cover). We analyse the relationships between changes in the bird community and environmental variables, analysing the temporal turnover of the species richness and the richness‐based species‐exchange ratio. Results: The estimated species richness accounting for detectability was higher than observed species richness, and decreased in the more recent period. Following our hypotheses, we observed a dual pattern of species richness increase associated with different elevations, showing different species turnover rates due to the joint effects of climate change and land‐use change. There is a trend towards greater species richness with higher elevations that is associated with climate change, where the species turnover rate is low. Also, species richness increased towards lower elevations, but with a high turnover rate. The latter can be due to species expansions throughout new habitat configurations in bordering forest systems associated with anthropic land‐use changes. Conclusions: Our study is of great interest to understand the temporal turnover of avian species richness associated with areas experiencing both climate and land‐use change. [ABSTRACT FROM AUTHOR]

Power priors are used for incorporating historical data in Bayesian analyses by taking the likelihood of the historical data raised to the power α as the prior distribution for the model parameters. The power parameter α is typically unknown and assigned a prior distribution, most commonly a beta distribution. Here, we give a novel theoretical result on the resulting marginal posterior distribution of α in case of the normal and binomial model. Counterintuitively, when the current data perfectly mirror the historical data and the sample sizes from both data sets become arbitrarily large, the marginal posterior of α does not converge to a point mass at α=1 but approaches a distribution that hardly differs from the prior. The result implies that a complete pooling of historical and current data is impossible if a power prior with beta prior for α is used. [ABSTRACT FROM AUTHOR]

Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof‐of‐concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof‐of‐concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno‐inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint. [ABSTRACT FROM AUTHOR]

An ongoing body of research documents that women empowerment is associated with improved outcomes for children. However, little is known about the long-run effects on health outcomes. This paper adds to this literature and studies the association between maternal exposure to suffrage reforms and children's old-age longevity. We utilize changes in suffrage laws across US states and over time as a source of incentivizing maternal investment in children's health and education. Using the universe of death records in the US over the years 1979-2020 and implementing a difference-in-difference econometric framework, we find that cohorts exposed to suffrage throughout their childhood live 0.6 years longer than unexposed cohorts. Furthermore, we show that these effects are not driven by preexisting trends in longevity, endogenous migration, selective fertility, and changes in the demographic composition of the sample. Additional analysis reveals that improvements in education and income are candidate mechanisms. Moreover, we find substantial improvements in early-adulthood socioeconomic standing, height, and height-for-age outcomes due to childhood exposure to suffrage movements. A series of state-level analyses suggest reductions in infant and child mortality following suffrage law change. We also find evidence that counties in states that passed the law experienced new openings of County Health Departments and increases in physicians per capita. [ABSTRACT FROM AUTHOR]

Air temperatures in Europe in 2022 had been the highest on record for the meteorological summer season [June, July and August (JJA)], with +1.3°C above the 1991–2020 average. We studied the effects of recent warming on permafrost and periglacial conditions at a historical mountain pass in the Eastern Alps (Hochtor, 2,576 m asl, 47.08°N, 12.84°E). We used ground temperature data (2010–2022), repeated electrical resistivity tomography measurements (2019, 2022) and auxiliary data dating back to Roman times. We quantified permafrost conditions, evaluated frost‐related weathering and slope processes and assessed the impact of atmospheric warming on it. Results show that summer ground surface temperatures increased by 2.5°C between 1891–1920 and 1991–2020, whereas frost‐related weathering and periglacial processes decreased. The summers of 2003, 2015, 2019 and 2022 were the four warmest ones in 1887–2022. Hochtor changed in 2010–2022 from an active permafrost site to an inactive one with supra‐permafrost talik. A general three‐layer structure was quantified for all three ERT profiles measured. The middle, 5–10 m thick layer is ice‐poor permafrost detected in 2019, whose existence, although smaller, was confirmed in 2022. Resistivity decreased at the three profiles by 3.9% to 5.2% per year, suggesting permafrost degradation. We interpret the resistivity changes between the summers of 2019 and 2022 as a long‐term signal of permafrost degradation and not as the single effect of the summer heatwave in 2022. As our data show how rapidly permafrost degrades and as we face an even warmer climate for the remaining part of the 21st century, we expect that near‐surface permafrost at the Hochtor site will soon be history. [ABSTRACT FROM AUTHOR]

Previous studies explore the role of birth order in children's and adults' outcomes. This literature usually provides evidence of disadvantage of children with higher birth order. A narrow strand of this literature explores the association between birth order and old‐age mortality. This study re‐visits the birth‐order‐longevity relationship using US data. We employ Social Security Administration death records between the years 1988 and 2005 linked to the 1940 full‐count census and implement family fixed effect strategy to compare within‐sibling differences in the outcome. The findings suggest that later‐born children live, on average, 1–3 fewer months of life. The observed associations are exclusively concentrated among whites. However, the results do not point to significant heterogeneity based on family socioeconomic status, maternal education, and gender. Additional analyses suggest that higher birth order is associated with negative early educational outcomes. [ABSTRACT FROM AUTHOR]

Using models to predict future changes in species distributions in response to projected climate change is a common tool to aid management and species conservation. However, the assumption underlying this approach, that ecological processes remain stationary through time, can be unreliable, and more empirical tests are needed to validate predictions of biotic outcomes of global change. The scarcity of reliable historical and long‐term datasets can make these tests difficult. Moreover, incorporating abundance and multiple sampling methods can improve model predictions and usability for management. Our study 1) provides insight into how well models can predict environmental change under a warming climate, 2) incorporates multiple sampling gears and abundance data in modeling to better capture changes in populations and 3) shows the value of historical datasets for improving predictive models of population change. We used contemporary (2003–2019) and historical (1936–1964) abundance datasets of the North American fish largemouth bass Micropterus salmoides in lakes across the state of Michigan, USA. We developed Bayesian hierarchical models that leverage the use of multiple gears in contemporary lake surveys to estimate the relative catchabilities of largemouth bass for each gear and hindcast the models to predict historical abundance. Our estimates of relative density change over time were correlated with temperature change over time; increasing surface water temperature led to increasing largemouth bass density. Hindcasting models to historical lake temperatures performed similarly in predicting historical density to models predicting contemporary density. Our results suggest that models built using spatial environmental gradients can reliably predict population changes through time. Understanding the sampling methods and the environmental context of observational datasets can help researchers test for potential sampling biases and identify confounding factors that will improve predictions of future impacts of environmental change. [ABSTRACT FROM AUTHOR]

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time‐consuming, and not always practicable. At the same time, huge quantities of available patient‐level control condition data in analyzable format of (former) RCTs or real‐world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single‐arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single‐arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user‐friendly R Shiny application (available at: https://web.imbi.uni‐heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real‐world application example illustrates them. [ABSTRACT FROM AUTHOR]

Previous literature suggests that empowering women is associated with children's improved outcomes. However, little is known about its effects on children's later‐life crime and incarceration. We argue that women empowerment through suffrage law changes during the late nineteenth and early twentieth centuries in the US generated incentives for women to invest in their children's human capital. The accumulated human capital then has the potential to reduce future incarceration of children. We use full‐count censuses 1920–1930, implement a difference‐in‐difference framework, and empirically show that childhood exposure to suffrage laws is associated with considerable reductions in incarceration. The effects appear to be primarily driven by decreases in male and Black incarceration. The balancing tests rule out the concern that the effects are driven by demographic compositional changes or endogenous changes in other state‐level characteristics. Furthermore, an event‐study analysis rejects the concern that the effects are driven by preexisting trends in incarceration among exposed cohorts. The findings of this research note offer informative implications for overlooked externalities of women empowerment in a historical setting. [ABSTRACT FROM AUTHOR]

Predictable aggregations of large marine mammals are valuable conservation targets, but aggregated populations can also be exposed to site‐level threats.The globally vulnerable Dugong dugon has a wide distribution but is found in large numbers mainly in Australia and the Arabian Gulf. Though Australian dugong populations are well studied, much less is known of the dugongs in the Arabian Gulf.The spatial and temporal persistence of dugongs around Bahrain, with a focus on large dugong groups (>50 dugongs), was determined using an occupancy modelling framework supported by historical records, structured interviews, citizen science network reports, and small‐scale boat and unmanned aerial vehicle surveys.Historical records and current distributional studies confirmed that large dugong groups have been reliably sighted around Hawar Island (Bahrain) since at least 1986, forming large, clumped groups that persist almost year round. The largest recorded so far in the world, these fluid groups (maximum of ~700 dugongs) account for ~60% of the dugongs found in Bahrain and ~12% of all dugongs in the Arabian Gulf.The delineated occupancy core area of large dugong groups (~145 km2) straddles the Bahrain–Qatar border, reflecting the transboundary nature of these groups.Careful management of human‐induced stressors (in particular, fishing, boating, and coastal development) combined with regular monitoring of Hawar Island's large dugong groups and their seagrass habitat is critical to safeguard this globally important population.The effectiveness of any conservation management is predicated on strengthening cooperation among all range states in the Arabian Gulf. A key recommendation of this study is to establish a regional network of marine protected areas encompassing core aggregation sites for dugongs, particularly the Hawar Islands in Bahrain, the north‐western waters of Qatar, Marawah Island in the United Arab Emirates in addition to the shallow waters between Saudi Arabia, Qatar, and United Arab Emirates. [ABSTRACT FROM AUTHOR]

Documenting climate‐induced range shifts of freshwater fish is difficult because there are few spatially representative, long‐term data sets with re‐surveys of the same location over time. While such standardised data sets would allow for the identification of range shifts, most data sets are confounded by changing sampling priorities and methodologies through time. Finding a robust framework for detecting climate‐induced range shifts thus remains a research priority. Using a database from stream surveys conducted during the period 1957–2019, we employed a multi‐method framework for determining whether 10 stream fish species (nine warm‐water, one cold‐water) have exhibited elevational range shifts within the North Platte River watershed, Wyoming, USA. By employing a combination of different approaches (predictive reference models, quantile regressions, spatial aggregations), we produced a consensus framework for determining whether observed range expansions and range contractions were consistent across species. The weight of evidence from our consensus framework (>50% of all modelling approaches) indicated strong support for upstream range expansions of three (33%) warm‐water species, including a long‐established nonnative species (common carp [Cyprinus carpio]). The consensus framework revealed no evidence for range contractions of one cold‐water trout species (brown trout [Salmo trutta]). As global climate change continues, the value of historical datasets for evaluating pre‐warming, baseline conditions will increase. Given the methodological limitations of different analytical approaches, our findings illustrate that consensus frameworks based on the weight of evidence can improve confidence in the detection of climate‐induced range shifts when robust historical data sets are lacking. [ABSTRACT FROM AUTHOR]

This paper studies the effects of the enactment of birth registration laws, as the official universal and uniform method of recording births, across US states in the first decades of the 20th century on old‐age longevity for children affected by these laws. We show that establishing birth registration laws has long‐term benefits for old‐age health. The benefits are primarily driven by states with an effective child labor policy, suggesting that registering births helps the enforcement of child labor laws which in turn operate as the mechanism channel to improve old‐age longevity. A treatment‐on‐treated calculation suggests an increase of 0.6 years of longevity from not working during childhood due to the birth registration law. [ABSTRACT FROM AUTHOR]

We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data. This situation arises often in clinical trials, for example, when historical data involve binary responses and the current data involve some other type of continuous or discrete outcome. The power prior, proposed by Ibrahim and Chen, does not address the issue of different data types. Herein, we develop a new type of power prior, which we call the scale transformed power prior (straPP). The straPP is constructed by transforming the power prior for the historical data by rescaling the parameter using a function of the Fisher information matrices for the historical and current data models, thereby shifting the scale of the parameter vector from that of the historical to that of the current data. Examples are presented to motivate the need for such a transformation, and simulation studies are presented to illustrate the performance advantages of the straPP over the power prior and other informative and noninformative priors. A real dataset from a clinical trial undertaken to study a novel transitional care model for stroke survivors is used to illustrate the methodology. [ABSTRACT FROM AUTHOR]

Magnetic observations at the Clementinum Observatory were started in July 1839 and data up to 1917 were published in yearbooks. In addition to regular daily observations with a typical 2-hr time step, two categories of data with a higher cadence were provided in the first decade. The first category captures 73 magnetic storms with their magnitude being of at least a moderate level. The second category is the dense observations during the days that had been agreed for joint measurements by the observatories organized in the Göttingen Magnetic Union (GMU), these measurements being known as term-day observations. Whereas four terms per year were set by GMU, many observatories agreed to carry out additional observations in the eight remaining months. The term-day observations also continued being performed for several years after the end of the GMU activities in 1841. Data of 120 term days from January 1840 to December 1849 were published. The declination was observed using the magnetic compass principle, while a bifilar apparatus was used to measure the horizontal intensity. When converting the data to the physical units of the SI, we have achieved the consistency of these high cadence data with previously published regular hourly observations of the geomagnetic field. This requirement was also met by considering the temperature dependence of measurements by the bifilar apparatus in determining the absolute values of horizontal intensity. Revealing the historical observations of the geomagnetic field can serve as a valuable material for studying the space weather in the past and may also contribute to the refinement of global field modelling. [ABSTRACT FROM AUTHOR]

As the temporal, financial, and ethical cost of randomized clinical trials (RCTs) continues to rise, researchers and regulators in drug discovery and development face increasing pressure to make better use of existing data sources. This pressure is especially high in rare disease, where traditionally designed RCTs are often infeasible due to the inability to recruit enough patients or the unwillingness of patients or trial leaders to randomly assign anyone to placebo. Bayesian statistical methods have recently been recommended in such settings for their ability to combine disparate data sources, increasing overall study power. The use of these methods has received a boost in the United States thanks to a new willingness by regulators at the Food and Drug Administration to consider complex innovative trial designs. These designs allow trialists to change the nature of the trial (eg, stop early for success or futility, drop an underperforming trial arm, incorporate data on historical controls, etc) while it is still running. In this article, we review a broad collection of Bayesian techniques useful in rare disease research, indicating the benefits and risks associated with each. We begin with relatively innocuous methods for combining information from RCTs and proceed on through increasingly innovative approaches that borrow strength from increasingly heterogeneous and less carefully curated data sources. We also offer 2 examples from the very recent literature illustrating how clinical pharmacology principles can make important contributions to such designs, confirming the interdisciplinary nature of this work. [ABSTRACT FROM AUTHOR]

In this work, we provide instrumental meteorological data recovered for the Extremadura region (interior SW Iberia), from 1826 to mid‐20th century. Meteorological variables such as air temperature, atmospheric pressure, precipitation, wind direction and humidity, among others, were retrieved. In total, more than 750 000 instrumental data in 157 meteorological series belonging to 131 different locations throughout Extremadura were rescued. It must be noted that daily resolution data constitutes 80% of the database. This great effort of digitization and data collection has been carried out with the aim of contributing to a significant expansion of the length of the databases with meteorological information in this region. Therefore, this database will provide a better understanding of climate variability, trends and extreme events of the Extremadura region. [ABSTRACT FROM AUTHOR]

Understanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from >100 monitoring stations collected over 60 years (1958–2018) across the Colorado River Watershed in Texas (103,000 km2). We paired this river chemistry data with complementary lithology, land use, climate, and stream discharge information. Machine learning techniques were used to produce new insights on controls of stream water chemical behavior, which were validated using traditional multivariate analyses. Studies on stream flow and chemistry in the American west and globally have shown strong relationships between major ion chemical composition, climate, and lithology which hold true for the Colorado River basin in this study. Reactive minerals, including carbonates and evaporites, dominate major ion chemistry across the upper, low‐precipitation regions of the watershed. Upstream and middle reaches of the Colorado River showed shifts from Na‐Cl‐SO4 dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, and agricultural activities, to Ca‐HCO3 water types controlled by carbonate dissolution. In the lower portion of the watershed multiple analyses demonstrate that stream chemistry is more influenced by greater precipitation and the presence of silicate minerals than the middle and upstream reaches. This study demonstrates the power of applying machine learning approaches to publicly available long term water chemistry data sets to improve the understanding of watershed interactions with surficial lithology, salinity sources, and anthropogenic influences of water quality. Plain Language Summary: Across the United States public and private users rely on large rivers for access to water, making water quality of crucial concern. Water quality measurements are widely available but require intensive pre‐processing due to their irregular collection across space and time. Here, publicly available water quality measurements from the Colorado River basin were analyzed using machine learning techniques to understand the influence of land use, geologic, and climate factors on water quality. This is especially important for the Colorado River as it runs across a range of rock types, land uses, and precipitation regimes and therefore displays complex interactions with the land surface that produce changes in water quality. We found that the upper Colorado River is dominated by Na+, Cl−, and SO42− which are derived from multiple sources including agriculture, and salt dissolution in shallow groundwater. The middle reaches are dominated by Ca2+ and HCO3− which are mainly contributed by the large areas of limestone bedrock. Downstream reaches of the river show more inputs from precipitation as well as potential seawater mixing in coastal areas. Overall, these machine learning techniques were effective in demonstrating large scale trends across this watershed and could be improved with more detailed data sets. Key Points: The distribution of reactive minerals (e.g., evaporites and carbonates) dictates the chemical behavior of the Colorado RiverClimate factors act as a secondary control, with increasing precipitation leading to decreased overall concentrations and dilution behaviorAgriculture, urban development, reservoirs, and oil and gas wells lead to noise in chemical measurements and obscures natural signals [ABSTRACT FROM AUTHOR]

The Clean Water Act requires states to develop methods for assessing water quality. Assessment methods serve as decision‐making procedures for including waterbodies on the Section 303(d) List of Impaired Waters. We used 17 years of ambient water quality data to explore statistical analyses for assessment methods that represent New York's waterbodies. Power analyses were performed to determine how many samples are needed to evaluate exceedances of water quality criteria using one‐sample t‐tests in lakes and flowing waters. Results suggest six samples for lakes and eight samples for flowing waters are needed to obtain at least 80% power, which is fewer samples than most other types of statistical assessment methodologies. This smaller number was possible because the power analysis was applied to the actual variability found in monitoring data to calculate the effect size as opposed to more conservative statistical estimates based on random data. Water quality criteria can have different analysis requirements such as single samples or means above the threshold, so we compared how many impairments would occur in the dataset if the six or eight samples were assessed as two single exceedances or a mean above the water quality criteria. Because the power analysis gives no indication of the time frame of when samples should be collected, the intra‐ and interannual variability of the data was assessed to determine whether sampling over a growing season in one year or sampling over multiple years is more representative of the water quality status. Results demonstrated that data collected over the growing season captured more variability in water quality data than data collected over multiple years in both waterbody types. With the prevalence of regulatory agencies having large, historical datasets rising, it would be possible for other agencies to apply these types of analyses to their assessment methodologies. Integr Environ Assess Manag 2022;18:1621–1628. © 2021 SETAC KEY POINTS: Use of power analysis on historical water quality datasets was a successful method for statistically determining how many samples are needed to perform regulatory assessments for impairment of lakes and flowing waters.In New York State, six samples for lakes and eight samples for flowing waters were found to be sufficient for performing regulatory assessments for impairment of waterbodies. [ABSTRACT FROM AUTHOR]

Anthropogenic and climatic factors affect the survival of animal species. Chinese pangolin is a critically endangered species, and identifying which variables lead to local extinction events is essential for conservation management. Local chronicles in China serve as long‐term monitoring data, providing a perspective to disentangle the roles of human impacts and climate changes in local extinctions. Therefore, we established generalized additive models to identify factors leading to local extinction with historical data from 1700–2000 AD in mainland China. Then we decreased the time scale and constructed extinction risk models using MaxEnt in a 30‐year transect (1970–2000 AD) to further assess extinction probability of extant Chinese pangolin populations. Lastly, we used principal component analysis to assess variation of related anthropogenic and climatic variables. Our results showed that the extinction probability increased with global warming and human population growth. An extinction risk assessment indicated that the population and distribution range of Chinese pangolins had been persistently shrinking in response to highly intensive human activities (main cause) and climate change. PCA results indicated that variability of climatic variables is greater than anthropogenic variables. Overall, the factors causing local extinctions are intensive human interference and drastic climatic fluctuations which induced by the effect of global warming. Approximately 28.10% of extant Chinese pangolins populations are confronted with a notable extinction risk (0.37 ≤ extinction probability≤0.93), specifically those in Southeast China, including Guangdong, Jiangxi, Zhejiang, Hunan and Fujian Provinces. To rescue this critically endangered species, we suggest strengthening field investigations, identifying the exact distribution range and population density of Chinese pangolins and further optimizing the network of nature reserves to improve conservation coverage on the landscape scale and alleviate human interference. Conservation practices that concentrate on the viability assessment of scattered populations could help to improve restoration strategies of the Chinese pangolin. [ABSTRACT FROM AUTHOR]

Understanding the breadth and complexity of changes in phenology is limited by the availability of long‐term historical data sets with broad geographic range.We compare a recently discovered historical data set of plant phenology observations collected across the state of New York (1826–1872) to contemporary volunteer‐contributed observations (2009–2017) to evaluate changes in plant phenology between time periods. These multi‐site, multi‐taxa phenology data matched with temperature data uniquely extend historical observations back in time prior to the major atmospheric effects of the Industrial Revolution.The majority of the 36 trees, shrubs and forbs that comprised our analysable data set flowered and leafed out earlier in contemporary years than in the early to mid‐19th century. This shift is associated with a warming trend in mean January‐to‐April temperatures, with flowering and leafing advancing on average 3 days/°C earlier. On average, plants flowered 10.5 days earlier and leafed out 19 days earlier in the contemporary period. Urban areas exhibit more advanced phenology than their rural counterparts overall, and insect‐pollinated trees show more advanced phenology than wind‐pollinated trees and seasonality and growth form explain significant variation in flowering phenology. The greatest rates of temperature sensitivity and change between time periods for flowering are seen in early‐season species, particularly trees. Changes in the timing of leaf out are the most advanced for trees and shrubs in urban areas.Synthesis. Citizen science observations across two centuries reveal a dramatic, climate‐driven shift to earlier leaf out and flowering. The magnitude of advancement varies across settings, species and functional groups, and illustrates how long‐term monitoring and citizen science efforts are invaluable for ecological forecasting and discovery. [ABSTRACT FROM AUTHOR]

We investigate whether US government spending multipliers are higher during periods of heightened uncertainty or economic slumps as opposed to normal times. Using quarterly data from 1890 onward and local projections, we estimate a cumulative 1‐year multiplier of 2 during uncertain periods. In contrast, the multiplier is about 1 in times of high unemployment and about 0.4–0.8 during normal times. While we find positive employment effects in slumps as well as in uncertain times, two transmission channels can explain the higher multipliers in the latter: greater price flexibility leading to short‐term inflation (lowering the real interest rate) and diminishing risk premiums. [ABSTRACT FROM AUTHOR]

Historical data from plant breeding programs provide valuable resources to study the response of genotypes to the changing environment (i.e. genotype‐by‐environment interaction). Such data have been used to evaluate the pattern of genotype performance across regions or locations, but its use to evaluate the long‐term pattern of genotype performance across environments (i.e. locations‐by‐years) has been hampered by the lack of common genotypes across years. This lack of common genotypes is due to the structure of the breeding program, especially for annual crops, where only a proportion of selected genotypes are tested in subsequent years. This has resulted in a sparse prediction of the performance of genotypes across years (i.e. a genotype‐by‐year table). A genomic prediction method that fitted both a relationship matrix among genotypes and a relationship matrix among environments (i.e. years) could overcome this limitation and produce a dense genotype‐by‐year table, thereby enabling some evaluation of long‐term genotype performance. In this paper, we applied the genomic prediction model to the yield data from CIMMYT's Elite Spring Wheat Yield Trials (ESWYT) to visualise the pattern of genotype performance over 25 years. [ABSTRACT FROM AUTHOR]

Climate change affects the life cycle of many species. Yet, responses to yearly variation of weather can either help species track optimal conditions or be maladaptive.We analysed phenological data of 46,479 pine processionary moths (Thaumetopoea pityocampa) during 15 years along an altitudinal gradient in southern France. These larvae were sampled in situ and allowed to pupate in a common garden at lower elevation.Individuals originating from higher elevation emerged earlier than those sampled at low elevation, which suggests local adaptation. Yearly variations in temperature also affected phenology. Warm springs caused an earlier adult emergence, while autumn temperatures had an opposite effect. Environmental cues could thus induce contradictory plastic responses.Synchronization mechanisms were identified. Variability in the duration of the pupal phase is a key parameter to synchronize adult emergence in spite of different larval development rates that only marginally influenced emergence dynamics. Semivoltine individuals experiencing prolonged diapause were synchronized with univoltine individuals emerging the same year.These data highlight some contradiction in the effect of spatial versus temporal variations of the temperature on adult emergence. This suggests that phenological responses to the current climate change cannot easily be anticipated by space‐for‐time substitution designs. [ABSTRACT FROM AUTHOR]

Spruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated. [ABSTRACT FROM AUTHOR]

Climate warming is predicted to result in changes to phenology, behaviour and abundance as well as poleward shifts or upslope displacements of the distribution of species. However, climate‐warming induced changes in distribution patterns have rarely been studied in ground‐dwelling arthropods.We investigated changes in the upper elevational limit and relative abundance of 11 millipede species by repeating historical surveys from 1917 to 1919 in five valleys in the Swiss National Park (Eastern Alps) in 2018–2019.We found that the upper elevational limit for 10 out of 11 millipede species has risen, on average, by 161 m in 100 years, accompanying a 1.5 °C rise in mean annual temperature in the investigation area. The 10 millipede species differed in upslope expansion of their distribution with the shift ranging from 50 m to 363 m.The relative abundances of species (percentage of individuals sampled for each of the 11 millipede species) were very similar in 1917–1919 and 2018–2019. Only three species showed a change in frequency >6% over 100 years: two species had reduced abundances by 13.7% and 12.5% in the recent survey, while one species increased in abundance by 12.2%.We provide evidence showing that soil‐dwelling millipede species with low dispersal ability have raised their upper distribution limit to higher elevation between the surveys done in 1917–1919 and 2018–2019. This upslope shift in distribution is most probably due to the significant increase in temperature recorded in the investigation area in past decades. [ABSTRACT FROM AUTHOR]

Background: The long-tailed macaque (Macaca fascicularis fascicularis) is an Old World species, which is one among the most commonly used monkeys for pharmaceutical research. However, most of the available background data are not suitable for good laboratory practice (GLP)-regulated drug safety tests because the current reverence value covers fewer indices than necessary. Therefore, in this study, historical data for preclinical safety test were collected and managed. Methods: Twenty-five hematology, 20 clinical chemistry, 19 urine analysis, and 16 organ weights were evaluated in a drug safety test of 228 male and 140 female 2-to 4-year-old long-tailed macaques at the Korea Institute of Toxicology under GLP regulations. Results: The absolute and relative count of lymphocyte, basophil, and large unstained cell were higher, whereas neutrophil was lower in male than in female monkeys. In serum biochemistry, IP, GGT, ALP, and TCHO of male were higher than female. Conclusion: Historical data suitable for preclinical safety analysis were determined. [ABSTRACT FROM AUTHOR]

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In clinical trials, there often exist multiple historical studies for the same or related treatment investigated in the current trial. Incorporating historical data in the analysis of the current study is of great importance, as it can help to gain more information, improve efficiency, and provide a more comprehensive evaluation of treatment. Enlightened by the unit information prior (UIP) concept in the reference Bayesian test, we propose a new informative prior called UIP from an information perspective that can adaptively borrow information from multiple historical datasets. We consider both binary and continuous data and also extend the new UIP to linear regression settings. Extensive simulation studies demonstrate that our method is comparable to other commonly used informative priors, while the interpretation of UIP is intuitive and its implementation is relatively easy. One distinctive feature of UIP is that its construction only requires summary statistics commonly reported in the literature rather than the patient‐level data. By applying our UIP to phase III clinical trials for investigating the efficacy of memantine in Alzheimer's disease, we illustrate its ability to adaptively borrow information from multiple historical datasets. The Python codes for simulation studies and the real data application are available at https://github.com/JINhuaqing/UIP. [ABSTRACT FROM AUTHOR]

Incorporating historical data has a great potential to improve the efficiency of phase I clinical trials and to accelerate drug development. For model‐based designs, such as the continuous reassessment method (CRM), this can be conveniently carried out by specifying a "skeleton," that is, the prior estimate of dose limiting toxicity (DLT) probability at each dose. In contrast, little work has been done to incorporate historical data into model‐assisted designs, such as the Bayesian optimal interval (BOIN), Keyboard, and modified toxicity probability interval (mTPI) designs. This has led to the misconception that model‐assisted designs cannot incorporate prior information. In this paper, we propose a unified framework that allows for incorporating historical data into model‐assisted designs. The proposed approach uses the well‐established "skeleton" approach, combined with the concept of prior effective sample size, thus it is easy to understand and use. More importantly, our approach maintains the hallmark of model‐assisted designs: simplicity—the dose escalation/de‐escalation rule can be tabulated prior to the trial conduct. Extensive simulation studies show that the proposed method can effectively incorporate prior information to improve the operating characteristics of model‐assisted designs, similarly to model‐based designs. [ABSTRACT FROM AUTHOR]

The power prior is a popular tool for constructing informative prior distributions based on historical data. The method consists of raising the likelihood to a discounting factor in order to control the amount of information borrowed from the historical data. However, one often wishes to assign this discounting factor a prior distribution and estimate it jointly with the parameters, which in turn necessitates the computation of a normalizing constant. In this article, we are concerned with how to approximately sample from joint posterior of the parameters and the discounting factor. We first show a few important properties of the normalizing constant and then use these results to motivate a bisection‐type algorithm for computing it on a fixed budget of evaluations. We give a large array of illustrations and discuss cases where the normalizing constant is known in closed‐form and where it is not. We show that the proposed method produces approximate posteriors that are very close to the exact distributions and also produces posteriors that cover the data‐generating parameters with higher probability in the intractable case. Our results suggest that the proposed method is an accurate and easy to implement technique to include this normalization, being applicable to a large class of models. They also reinforce the notion that proper inclusion of the normalizing constant is crucial to the drawing of correct inferences and appropriate quantification of uncertainty. [ABSTRACT FROM AUTHOR]

Arctic plants are adapted to climatic variability, but their long‐term responses to warming remain unclear. Responses may occur by range shifts, phenological adjustments in growth and reproduction, or both. Here, we compare distribution and phenology of 83 arctic and boreal mountain species, sampled identically in the early 20th (1917–1919) and 21st centuries (2017–2018) from a region of northern Sweden that has warmed significantly. We test two compensatory hypotheses to high‐latitude warming—upward shifts in distribution, and earlier or extended growth and reproduction. For distribution, we show dramatic upward migration by 69% of species, averaging 6.1 m per decade, especially boreal woodland taxa whose upward expansion has reduced arctic montane habitat by 30%. Twenty percent of summit species showed distributional shifts but downward, especially moisture‐associated snowbed flora. For phenology, we detected wide inter‐annual variability in the onset of leafing and flowering in both eras. However, there was no detectable change in growing‐season length, relating to two mechanisms. First, plot‐level snow melt data starting in 1917 demonstrated that melt date, rather than vernal temperatures, better predicts plant emergence, with snow melt influenced by warmer years having greater snowfall—warmer springs did not always result in earlier emergence because snowbeds can persist longer. Second, the onset of reproductive senescence between eras was similar, even when plant emergence was earlier by a month, possibly due to intensified summer heat stress or hard‐wired 'canalization' where senescence occurs regardless of summer temperature. Migrations in this system have possibly buffered arctic species against displacement by boreal expansion and warming, but ongoing temperature increases, woody plant invasion, and a potential lack of flexibility in timing of senescence may foreshadow challenges. [ABSTRACT FROM AUTHOR]

Climate‐driven changes in the distribution of species are a pervasive and accelerating impact of climate change, and despite increasing research effort in this rapidly emerging field, much remains unknown or poorly understood. We lack a holistic understanding of patterns and processes at local, regional and global scales, with detailed explorations of range shifts in the southern hemisphere particularly under‐represented. Australian waters encompass the world's third largest marine jurisdiction, extending from tropical to sub‐Antarctic climate zones, and have waters warming at rates twice the global average in the north and two to four times in the south. Here, we report the results of a multi‐taxon continent‐wide review describing observed and predicted species redistribution around the Australian coastline, and highlight critical gaps in knowledge impeding our understanding of, and response to, these considerable changes. Since range shifts were first reported in the region in 2003, 198 species from nine Phyla have been documented shifting their distribution, 87.3% of which are shifting poleward. However, there is little standardization of methods or metrics reported in observed or predicted shifts, and both are hindered by a lack of baseline data. Our results demonstrate the importance of historical data sets and underwater visual surveys, and also highlight that approximately one‐fifth of studies incorporated citizen science. These findings emphasize the important role the public has had, and can continue to play, in understanding the impact of climate change. Most documented shifts are of coastal fish species in sub‐tropical and temperate systems, while tropical systems in general were poorly explored. Moreover, most distributional changes are only described at the poleward boundary, with few studies considering changes at the warmer, equatorward range limit. Through identifying knowledge gaps and research limitations, this review highlights future opportunities for strategic research effort to improve the representation of Australian marine species and systems in climate‐impact research. [ABSTRACT FROM AUTHOR]

When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof-of-concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta-analytic-predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials. [ABSTRACT FROM AUTHOR]

This article presents daily rainfall data and metadata for Ireland transcribed from historical manuscript and printed copies of rainfall registers located in Met Éireann's archives. To facilitate the transcription of rainfall observations from paper records, the historical manuscripts were scanned and integrated into Met Éireann's digital archives. The transcription from digital image to data format was undertaken in collaboration with students at Maynooth University as part of a novel crowdsourcing initiative to integrate data rescue activities into the classroom. In total, 3,616 station years of rainfall data (~1.32 million daily values) were transcribed. The data, which was double keyed, have undergone basic quality assurance to check for transcription errors and the resultant raw data and associated metadata are presented here. Ongoing work involves the application of further quality assurance and homogenization techniques to develop a long‐term, quality assured daily rainfall network for Ireland. [ABSTRACT FROM AUTHOR]

PREMISE: Shifting phenology in response to climate is one mechanism that can promote population persistence and geographic spread; therefore, species with limited ability to phenologically track changing environmental conditions may be more susceptible to population declines. Alternatively, apparently nonresponding species may demonstrate divergent responses to multiple environmental conditions experienced across seasons. METHODS: Capitalizing on herbarium records from across the midwestern United States and on detailed botanical surveys documenting local extinctions over the past century, we investigated whether extirpated and extant taxa differ in their phenological responses to temperature and precipitation during winter and spring (during flowering and the growing season before flowering) or in the magnitude of their flowering time shift over the past century. RESULTS: Although warmer temperatures across seasons advanced flowering, extirpated and extant species differed in the magnitude of their phenological responses to winter and spring warming. Extirpated species demonstrated inconsistent phenological responses to warmer spring temperatures, whereas extant species consistently advanced flowering in response to warmer spring temperatures. In contrast, extirpated species advanced flowering more than extant species in response to warmer winter temperatures. Greater spring precipitation tended to delay flowering for both extirpated and extant taxa. Finally, both extirpated and extant taxa delayed flowering over time. CONCLUSIONS: This study highlights the importance of understanding phenological responses to seasonal warming and indicates that extirpated species may demonstrate more variable phenological responses to temperature than extant congeners, a finding consistent with the hypothesis that appropriate phenological responses may reduce species' likelihood of extinction. [ABSTRACT FROM AUTHOR]

Comparison of historical data with recent surveys of grasslands across Victoria's basalt plain reveals a substantial decline in native annual forbs. Eleven of the 35 species once common in this ecosystem have not been recorded for decades. The near loss of a lifeform from an ecosystem should ring alarm bells, but it seems the warning has passed largely unnoticed. Is this due to 'shifting baseline syndrome'? What has caused this quiet decline in biodiversity, and what does it teach us about management and restoration? [ABSTRACT FROM AUTHOR]

A standard two‐arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down‐weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error. [ABSTRACT FROM AUTHOR]

A compilation is made of the largest and second‐largest magnetic‐storm‐maximum intensities, −Dst1 and −Dst2, for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published −Dstm values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm‐maximum −Dstm values (reported here) for cycles 14–18. Three different models are fitted to the cycle‐ranked −Dst1 and −Dst2 values using a maximum‐likelihood algorithm: A Gumbel model, an unconstrained Generalized‐Extreme‐Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of −Dstm = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm‐maximum −Dstm values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once‐per‐century storm intensity is estimated to be −Dst1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, −Dstm > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once‐per‐century intensity and a higher probability for a −Dstm > 565 nT storm. Plain Language Summary: Past and possible future magnetic storm intensities are investigated. As part of this work, a dataset is developed of the most intense and second most intense storms for each of the past 11 solar cycles (1902–2016)—augmenting a traditional dataset that only covers the past 6 solar cycles (1957–2016) with recently published intensities for several magnetic superstorms and with new storm intensity estimates, reported here and derived from historical magnetic observatory records. These data are analyzed using statistical methods that provide estimates of the probability of future magnetic superstorms. A storm as intense as that of March 1989, which caused widespread disruption of technological systems and an electricity blackout in Québec, Canada, is predicted to occur, on average, about every four solar cycles. This is twice as often as estimated using only the traditional shorter dataset. A once‐per‐century storm is estimated to be substantially more intense than that of March 1989. Key Points: Magnetic‐storm‐maximum −Dstm, ranked per solar cycle, are well represented by a Weibull modelBased on geomagnetic data for solar cycles 14–24 yields, a 100‐year storm has an intensity of at least −Dstm = 663 nTBased on geomagnetic data for solar cycles 14–24, the average return rate for a storm as intense as March 1989 (−Dstm = 565 nT) is less than 4.1 solar cycles [ABSTRACT FROM AUTHOR]

This study proposes a method to determine optimal load demand changes of power generation units, in order to complete a total load demand change for power grids in the minimum response time. The main feature is to exploit the information of actual ramp rates, which are the maximum changing speeds of generated powers in practice from power generation units. The proposed method is composed of two steps. First, actual ramp rates are estimated from historical data based on the piece-wise linear representations of generated powers. Second, optimal values of load demand changes are determined based on the solutions to an optimisation problem minimising the response time. A main challenge is on the uncertainties of estimated actual ramp rates and their effects on the response time. This challenge is resolved by exploiting Bayesian estimators to yield posterior probability distributions of the estimated actual ramp rates, from which the optimal response time and its confidence interval are obtained. Numerical examples are provided to support the proposed method and compare with existing methods. [ABSTRACT FROM AUTHOR]

Despite growing pressure on biodiversity deriving from increasing anthropogenic disturbances, some species successfully persist in altered ecosystems. However, these species' characteristics and thresholds, as well as the environmental frame behind that process are usually unknown. We collected data on body size, fluctuating asymmetry (FA), as well as nitrogen stable isotopes (δ15N) from museum specimens of the European Common Frog, Rana temporaria, all originating from the Berlin–Brandenburg area, Germany, in order to test: (a) if specimens have changed over the last 150 years (1868–2018); and (b) if changes could be attributed to increasing urbanization and agricultural intensity. We detected that after the Second World War, frogs were larger than in pre‐war Berlin. In rural Brandenburg, we observed no such size change. FA analysis revealed a similar tendency with lower levels in Berlin after the war and higher levels in Brandenburg. Enrichment of δ15N decreased over time in both regions but was generally higher and less variable in sites with agricultural land use. Frogs thus seem to encounter favorable habitat conditions after pollution in postwar Berlin improved, but no such tendencies were observable in the predominantly agricultural landscape of Brandenburg. Urbanization, characterized by the proportion of built‐up area, was not the main associated factor for the observed trait changes. However, we detected a relationship with the amount of urban greenspace. Our study exemplifies that increasing urbanization must not necessarily worsen conditions for species living in urban habitats. The Berlin example demonstrates that public parks and other urban greenspaces have the potential to serve as suitable refuges for some species. These findings underline the urgency of establishing, maintaining, and connecting such habitats, and generally consider their importance for future urban planning. [ABSTRACT FROM AUTHOR]

The synchronised phasor measurement units (PMUs), serving as 'GridEye', provide data that improve the observability and controllability of the power grid due to its high accuracy and high‐upload rate. However, missing or abnormal data may seriously affect its applications. This study proposes a method to recover the missing or abnormal amplitude data in PMU measurements (i.e. the active power, reactive power, positive sequence current, and voltage amplitude), based on the historical PMU data obtained from both ends of the line, which is independent of the transmission line parameters and the phase angle that may be influenced by synchronisation. First, the issues in the quality of measured PMU data are analysed, and the motivation of the proposed method is stated. Then, a method to screen out the bad data from the historical data is proposed based on the density‐based spatial clustering of applications with noise. Furthermore, the model of data recovery is established, and the recovery method, which recovers the voltage amplitude, active power, reactive power, and current amplitude in sequence, using historical amplitude data to calculate related recovery coefficients is proposed. Finally, the effectiveness and practicability of the proposed method are verified by examples of simulation and measured data. [ABSTRACT FROM AUTHOR]

Understanding extreme space weather events is of paramount importance in efforts to protect technological systems in space and on the ground. Particularly in the thermosphere, the subsequent extreme magnetic storms can pose serious threats to low Earth orbit (LEO) spacecraft by intensifying errors in orbit predictions. Extreme magnetic storms (minimum Dst ≤ −250 nT) are extremely rare: Only seven events occurred during the era of spacecraft with high‐level accelerometers such as CHAMP (CHAllenge Minisatellite Payload) and GRACE (Gravity Recovery And Climate experiment) and none with minimum Dst ≤ −500 nT, here termed magnetic superstorms. Therefore, current knowledge of thermospheric mass density response to superstorms is very limited. Thus, in order to advance this knowledge, four known magnetic superstorms in history, that is, events occurring before CHAMP's and GRACE's commission times, with complete data sets, are used to empirically estimate density enhancements and subsequent orbital drag. The November 2003 magnetic storm (minimum Dst = −422 nT), the most extreme event observed by both satellites, is used as the benchmark event. Results show that, as expected, orbital degradation is more severe for the most intense storms. Additionally, results clearly point out that the time duration of the storm is strongly associated with storm time orbital drag effects, being as important as or even more important than storm intensity itself. The most extreme storm time decays during CHAMP/GRACE‐like sample satellite orbits estimated for the March 1989 magnetic superstorm show that long‐lasting superstorms can have highly detrimental consequences for the orbital dynamics of satellites in LEO. Plain Language Summary: We investigate drag effects on satellites orbiting Earth in its upper atmosphere during magnetic storms caused by the impacts of solar superstorms. During magnetic storms, the upper atmosphere is heated and expands upward, resulting in increased drag forces on satellites flying in those regions. Enhanced drag effects directly impact operations of such spacecraft, for instance, orbital tracking and predictions, maneuvers, and lifetime maintenance. The U.S. Federal Government has recognized space weather phenomena as natural hazards, and the understanding of their consequences, particularly during extreme circumstances, is of paramount importance. The very extreme events, here termed magnetic superstorms, occurred before the space era when no in situ observations of the atmospheric density are available. Therefore, we use an empirical model to estimate drag from these historical events. Results generally show that the most extreme events drive the most severe effects. Additionally, we point out that another storm feature, its time duration, can play a significant role in enhancing drag. Therefore, we argue that space weather forecasters should be aware of events with long duration, particularly the ones caused by sequential impacts of solar disturbances on the Earth's magnetic field, when predicting and forecasting the subsequent drag effects on satellites in the upper atmosphere. Key Points: Satellite orbital drag during magnetic superstorms (standard/equivalent Dst < −500 nT) has been quantitatively estimatedThe November 2003 extreme magnetic storm is used as the benchmark event and for model performance assessment when predicting drag effectsInterplay between storm time duration and minimum Dst and Dst‐like values determine the severity of satellite drag effects in low Earth orbit [ABSTRACT FROM AUTHOR]

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision‐making in a model‐based dose‐escalation procedure. We make a proposal for how to measure and address a prior‐data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two‐component mixture prior for the parameters of the human dose–toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision‐theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose–toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study. [ABSTRACT FROM AUTHOR]

The contamination of outliers severely damages the data quality, resulting in the inaccurate data‐driven optimisation model. This study proposes a data quality aware chance‐constrained model for the direct current optimal power flow (DC‐OPF) problem under uncertainties. Under the framework of Bayesian statistics, the variational Bayesian Gaussian mixture model (VBGMM) is employed to extract the probabilistic information from the available historical data, i.e. realisations of random variables. VBGMM can identify the outliers by capturing their probability characteristics, in which way improving the data quality. Notably, VBGMM automatically determines the number of components, which is a remarkable difference from the conventional Gaussian mixture model. In addition, based on the affine policy, a method integrating VBGMM with chance‐constrained programming is proposed to make VBGMM scalable. The proposed method is firstly tested on a 6‐bus system for an illustrative purpose, and then on a 118‐bus system for validating the potential practical application. Comparative studies verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

SUMMARY: The feasibility of a new clinical trial may be increased by incorporating historical data of previous trials. In the particular case where only data from a single historical trial are available, there exists no clear recommendation in the literature regarding the most favorable approach. A main problem of the incorporation of historical data is the possible inflation of the type I error rate. A way to control this type of error is the so‐called power prior approach. This Bayesian method does not "borrow" the full historical information but uses a parameter 0 ≤ δ ≤ 1 to determine the amount of borrowed data. Based on the methodology of the power prior, we propose a frequentist framework that allows incorporation of historical data from both arms of two‐armed trials with binary outcome, while simultaneously controlling the type I error rate. It is shown that for any specific trial scenario a value δ > 0 can be determined such that the type I error rate falls below the prespecified significance level. The magnitude of this value of δ depends on the characteristics of the data observed in the historical trial. Conditionally on these characteristics, an increase in power as compared to a trial without borrowing may result. Similarly, we propose methods how the required sample size can be reduced. The results are discussed and compared to those obtained in a Bayesian framework. Application is illustrated by a clinical trial example. [ABSTRACT FROM AUTHOR]

SUMMARY: Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient‐level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors. [ABSTRACT FROM AUTHOR]

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials. [ABSTRACT FROM AUTHOR]