Natural Variation Research Articles

Job insecurity and vote for radical parties: A four-country study

Job insecurity is a pervasive phenomenon whose effects on support for different parties have attracted increasing attention in the literature. A growing body of research has assessed the relationship between job insecurity and the success of radical parties in Western democracies, but results are still inconclusive due to the complex nature of this independent variable. This article contributes new evidence to the current debates on this topic by analyzing how both objective labour market status and perceived job insecurity are associated with the vote for radical right and radical left parties. Our findings, based on four original surveys conducted in France, Germany, Greece and Spain, suggest that perceived job insecurity is generally associated with greater support for the radical left, but not for the radical right. In contrast, we find the relationship between objective labour market status and support for radical parties to be more ambiguous (and, in most cases, statistically non-significant).

Regression to the mean for overdispersed count data

In repeated measurements, regression to the mean (RTM) is a tendency of subjects with observed extreme values to move closer to the mean when measured a second time. Not accounting for RTM could lead to incorrect decisions such as when observed natural variation is incorrectly attributed to the effect of a treatment/intervention. A strategy for addressing RTM is to decompose the total effect, the expected difference in paired random variables conditional on the first being in the tail of its distribution, into regression to the mean and unbiased treatment effects. The unbiased treatment effect can then be estimated by subtraction. Formulae are available in the literature to quantify RTM for Poisson distributed data which are constrained by mean–variance equivalence, although there are many real life examples of overdispersed count data that are not well approximated by the Poisson. The negative binomial can be considered an explicit overdispersed Poisson process where the Poisson intensity is chosen from a gamma distribution. In this study, the truncated bivariate negative binomial distribution is used to decompose the total effect formulae into RTM and treatment effects. Maximum likelihood estimators (MLE) and method of moments estimators are developed for the total, RTM, and treatment effects. A simulation study is carried out to investigate the properties of the estimators and compare them with those developed under the assumption of the Poisson process. Data on the incidence of dengue cases reported from 2007 to 2017 are used to estimate the total, RTM, and treatment effects.

Natural variation in yeast reveals multiple paths for acquiring higher stress resistance

BackgroundOrganisms frequently experience environmental stresses that occur in predictable patterns and combinations. For wild Saccharomyces cerevisiae yeast growing in natural environments, cells may experience high osmotic stress when they first enter broken fruit, followed by high ethanol levels during fermentation, and then finally high levels of oxidative stress resulting from respiration of ethanol. Yeast have adapted to these patterns by evolving sophisticated “cross protection” mechanisms, where mild ‘primary’ doses of one stress can enhance tolerance to severe doses of a different ‘secondary’ stress. For example, in many yeast strains, mild osmotic or mild ethanol stresses cross protect against severe oxidative stress, which likely reflects an anticipatory response important for high fitness in nature.ResultsDuring the course of genetic mapping studies aimed at understanding the mechanisms underlying natural variation in ethanol-induced cross protection against H2O2, we found that a key H2O2 scavenging enzyme, cytosolic catalase T (Ctt1p), was absolutely essential for cross protection in a wild oak strain. This suggested the absence of other compensatory mechanisms for acquiring H2O2 resistance in that strain background under those conditions. In this study, we found surprising heterogeneity across diverse yeast strains in whether CTT1 function was fully necessary for acquired H2O2 resistance. Some strains exhibited partial dispensability of CTT1 when ethanol and/or salt were used as mild stressors, suggesting that compensatory peroxidases may play a role in acquired stress resistance in certain genetic backgrounds. We leveraged global transcriptional responses to ethanol and salt stresses in strains with different levels of CTT1 dispensability, allowing us to identify possible regulators of these alternative peroxidases and acquired stress resistance in general.ConclusionsUltimately, this study highlights how superficially similar traits can have different underlying molecular foundations and provides a framework for understanding the diversity and regulation of stress defense mechanisms.

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts.

Spatially varying impacts of pacific and southern ocean climate modes on tidal residuals in New South Wales, Australia

Still water levels along the New South Wales (NSW) coast often vary from those predicted by harmonic analysis based purely on astronomical forcing. This variation can lead to unexpected inundation episodes along the coast and in estuaries. This study investigates the large-scale ocean-atmosphere drivers that can contribute to these unexpected water levels. Time series regressions and cross- and coherence-wavelet transforms were used to investigate relationships between six tidal residual datasets from NSW ocean gauges and three indices of large-scale climate modes: canonical El Niño Southern Oscillation (ENSO), ENSO Modoki, and Southern Annular Mode (SAM). Significant relationships were identified between canonical ENSO and SAM and the tidal residual datasets; however, ENSO Modoki did not have a significant impact. Collectively, SAM and canonical ENSO account for up to 45% of the variance in tidal residuals along the NSW coast, with impacts increasing southwards. Wavelet coherences showed that tidal residuals covary significantly with SAM and ENSO, with the largest peaks in the two- and eight-year frequency bands. These results demonstrate a strong dependence of tidal residuals on the phasing of SAM and canonical ENSO. With continued improvements in seasonal forecasts of climate modes, including canonical ENSO and SAM, this research presents important new insights that may contribute to improved management of inundation along the NSW coast. The research also provides a baseline of natural variability on which climate change projections can be considered, given that inundation events are projected to become more frequent.

Glial expression of a steroidogenic enzyme underlies natural variation in hitchhiking behavior

Phoresy is an interspecies interaction that facilitates spatial dispersal by attaching to a more mobile species. Hitchhiking species have evolved specific traits for physical contact and successful phoresy, but the regulatory mechanisms involved in such traits and their evolution are largely unexplored. The nematode Caenorhabditis elegans displays a hitchhiking behavior known as nictation during its stress-induced developmental stage. Dauer-specific nictation behavior has an important role in natural C. elegans populations, which experience boom-and-bust population dynamics. In this study, we investigated the nictation behavior of 137 wild C. elegans strains sampled throughout the world. We identified species-wide natural variation in nictation and performed a genome-wide association mapping. We show that the variants in the promoter of nta-1, encoding a putative steroidogenic enzyme, underlie differences in nictation. This difference is due to the changes in nta-1 expression in glial cells, which implies that glial steroid metabolism regulates phoretic behavior. Population genetic analysis and geographic distribution patterns suggest that balancing selection maintained two nta-1 haplotypes that existed in ancestral C. elegans populations. Our findings contribute to further understanding of the molecular mechanism of species interaction and the maintenance of genetic diversity within natural populations.

Perceptions of El Niño-Southern Oscillation (ENSO) and La Niña Shape Fishers’ Adaptive Capacity and Resilience

Much research has raised concerns about how a warming planet will interact with natural cyclical climatic variations, and the implications for the resilience and vulnerability of coastal communities. As the anticipated effects of climate change will continue to intensify, it is necessary to understand the response and adaptive capacity of individuals and communities. Coastal communities in Ecuador have evolved in an environment of such cyclical climatic variations referred to as El Niño-Southern Oscillation (ENSO) and La Niña. These climatic events are frequently characterized by extreme variations in precipitation, violent storms, and coastal flooding during El Niño and lowered sea water temperatures and drought during La Niña. This paper draws on survey data and long-term ethnographic research in Ecuadorian coastal communities to explore how fishers understand the impacts of ENSO and implications for their livelihood decisions and resilience to climate variability. The results suggest that fishers along the coast of Ecuador understand and respond differentially to the impacts of ENSO depending on social, cultural, environmental, and geographical factors. These differential levels of response suggest that livelihood diversification may uphold social resilience, which has implications for how coastal communities may adapt to the increasingly harsh weather conditions predicted by many climate models. Our findings further suggest that the impacts of El Niño are more salient than the impacts of La Niña; these findings have significant implications for fisheries management and science communication.

Corticosterone exposure is associated with long-term changes in DNA methylation, physiology and breeding decisions in a wild bird.

When facing challenges, vertebrates activate a hormonal stress response that can dramatically alter behaviour and physiology. Although this response can be costly, conceptual models suggest that it can also recalibrate the stress response system, priming more effective responses to future challenges. Little is known about whether this process occurs in wild animals, particularly in adulthood, and if so, how information about prior experience with stressors is encoded. One potential mechanism is hormonally mediated changes in DNA methylation. We simulated the spikes in corticosterone that accompany a stress response using non-invasive dosing in tree swallows (Tachycineta bicolor) and monitored the phenotypic effects 1 year later. In a subset of individuals, we characterized DNA methylation using reduced representation bisulfite sequencing shortly after treatment and a year later. The year after treatment, experimental females had stronger negative feedback and initiated breeding earlier-traits that are associated with stress resilience and reproductive performance in our population-and higher baseline corticosterone. We also found that natural variation in corticosterone predicted patterns of DNA methylation. Finally, corticosterone treatment influenced methylation on short (1-2 weeks) and long (1 year) time scales; however, these changes did not have clear links to functional regulation of the stress response. Taken together, our results are consistent with corticosterone-induced priming of future stress resilience and support DNA methylation as a potential mechanism, but more work is needed to demonstrate functional consequences. Uncovering the mechanisms linking experience with the response to future challenges has implications for understanding the drivers of stress resilience.

Competitive Analysis at the Cournot-Nash Equilibrium of an Interconnected Network

The electric power network has long been subject to monopolization, encompassing production, transportation, and distribution sectors. However, recent liberalization efforts have introduced competition into the electricity market. To understand and manage this competition, game theory, a prominent tool in economics, is frequently employed. Specifically, competition within the electricity market has been analyzed through various game-theoretical models, including Bertrand's atomicity, Cournot's homogeneity, and Nash's research competition. These models aim to achieve the Cournot-Nash equilibrium, where each participant in the market makes optimal decisions given the strategies of others. To effectively allocate production and ensure a balance between supply and demand, as well as to maintain the stability of the interconnected network, one has adopted a method that combines Load Flow techniques with game theory principles. This hybrid approach enables a strategic distribution of power production, taking into account the competitive dynamics of the market. By integrating these methodologies, one can address the complexities of competition while ensuring efficient and stable operation of the power grid. This innovative approach not only enhances the management of electricity production and distribution but also fosters a more competitive and resilient power network. Moreover, the application of game theory in this context allows for a deeper understanding of strategic interactions among market participants. It helps in predicting behaviors, formulating strategies, and anticipating market changes, thus providing a robust framework for decision-making. This is particularly crucial in a liberalized market where multiple entities vie for market share and profitability. By employing game-theoretical insights, one can simulate various market scenarios, optimize resource allocation, and enhance overall market efficiency. Furthermore, this approach supports the integration of renewable energy sources by ensuring that their variable nature is accommodated within the grid's operational dynamics. In summary, the intersection of game theory and load flow methods offers a comprehensive solution to the challenges posed by a competitive electricity market, paving the way for a sustainable and efficient energy future.

It's a Trap! Part I: Exploring the Applications of Rotating-Arm Impaction Samplers in Plant Pathology.

Although improved knowledge on the movement of airborne plant pathogens is likely to benefit plant health management, generating this knowledge is often far more complicated than anticipated. This complexity is driven by the dynamic nature of environmental variables, diversity among pathosystems that are targeted, and the unique needs of each research group. When using a rotating-arm impaction sampler, particle collection is dependent on the pathogen, environment, research objectives, and limitations (monetary, environmental, or labor). Consequently, no design will result in 100% collection efficiency. Fortunately, it is likely that multiple approaches can succeed despite these constraints. Choices made during design and implementation of samplers can influence the results, and recognizing this influence is crucial for researchers. This article is for beginners in the art and science of using rotating-arm impaction samplers; it provides a foundation for designing a project, from planning the experiment to processing samples. We present a relatively nontechnical discussion of the factors influencing pathogen dispersal and how placement of the rotating-arm air samplers alters propagule capture. We include a discussion of applications of rotating-arm air samplers to demonstrate their versatility and potential in plant pathology research as well as their limitations.

On the Nature of the Radio Calibrator and Gamma-Ray Emitting NLS1 Galaxy 3C 286 and Its Multiwavelength Variability

The quasar 3C 286, a well-known calibrator source in radio astronomy, was found to exhibit exceptional multiwavelength properties. Its rich and complex optical emission-line spectrum revealed its narrow-line Seyfert 1 (NLS1) nature. Given its strong radio emission, this makes 3C 286 one of the radio-loudest NLS1 galaxies known to date. 3C 286 is also one of very few known compact steep-spectrum (CSS) sources detected in the gamma-ray regime. Observations in the X-ray regime, rarely carried out so far, revealed evidence for variability, raising the question whether it is driven by the accretion disk or jet. 3C 286 is also well known for its damped Lyman alpha system from an intervening absorber at z = 0.692, triggering a search for the corresponding X-ray absorption along the line-of-sight. Here, we present new observations in the radio, X-ray, optical, and UV bands. The nature of the X-ray variability is addressed. Spectral evidence suggests that it is primarily driven by the accretion disk (not the jet), and the X-ray spectrum is well fit by a powerlaw plus soft excess model. The radio flux density and polarization remain constant at the Effelsberg telescope resolution, reconfirming the use of 3C 286 as radio calibrator. The amount of reddening/absorption along the line-of-sight intrinsic to 3C 286 is rigorously assessed. None is found, validating the derivation of a high Eddington ratio (L/LEdd∼ 1) and of the very high radio-loudness index of 3C 286. Based on the first deep Chandra image of 3C 286, tentative evidence for hard X-ray emission from the SW radio lobe is reported. A large variety of models for the gamma-ray emission of 3C 286 are briefly discussed.

Age at natural menopause and its associated characteristics among Brazilian women: cross-sectional results from ELSI-Brazil.

This study aimed to determine the median age at natural menopause and analyze lifestyle, anthropometric, and dietary characteristics associated with the age at natural menopause among Brazilian women. This cross-sectional study involved 2,731 women 50 years and over, drawn from the baseline of the Brazilian Longitudinal Study of Aging (ELSI-Brazil, 2015/16). Nonparametric Kaplan-Meier cumulative survivorship estimates were used to assess the median timing of natural menopause. Cox proportional hazards regression models were employed to estimate the associations between age at natural menopause and exposure variables. The overall median age at natural menopause was 50 years. In the adjusted Cox model, current smoking (hazard ratio [HR], 1.11; 95% CI, 1.01-1.23) and underweight (HR, 1.34; 95% CI, 1.12-1.61) were associated with earlier natural menopause. Conversely, performing recommended levels of physical activity in the last week (HR, 0.88; 95% CI, 0.80-0.97), being overweight (HR 0.86; 95% CI, 0.74-0.99), and adhering to two or three healthy eating markers (HR, 0.80; 95% CI, 0.66-0.97; HR, 0.76; 95% CI, 0.61-0.94, respectively) were associated with later age at natural menopause. Binge drinking and waist circumference were not associated with age at natural menopause. Lifestyle, anthropometric, and dietary characteristics are significant factors that affect the age at natural menopause. Our findings can contribute to public policies targeted at Brazilian women's health.

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

REGENERACIÓN DE PLAYAS Y SU IMPACTO AMBIENTAL

The marine coastline is a dynamic environment, with a complex natural variation dependent on its biological, physical and geological interactions. In the last years, local and regional environmental managers have modified our coasts for socioeconomic interests, eliminating the natural supply of sediments to our coastline. As a consequence, the authorities were forced to carry out beach regeneration techniques to protect and conserve this natural space, and also, to promote economic activity in the sector, such as tourism. The purpose of this work is to study the environmental impact of beach regeneration. To meet these objectives, the natural morphology of the coast and its natural sediment deposits, the impact on the Posidonia oceánica meadows and the legal framework covering coastal management are defined.

Chemical fingerprint analysis for quality assessment and control of Curcuma longa L. rhizomes from Vietnam using a high-performance liquid chromatography-diode array detector (HPLC-DAD)

Turmeric, extensively cultivated across Southeast Asia, especially in Vietnam, harbors active polyphenols, primarily curcumin (2–5%), renowned for its diverse health benefits. Pharmacopoeias recognize turmeric, yet it lacks standardized quality assessments and encounters challenges in extraction and identification due to natural variations and adulteration. This analytical method is vital for verifying the authenticity, purity, and quality of turmeric products in both the pharmaceutical and nutraceutical industries. This study successfully developed an efficient extraction process for curcumin (CUR), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) from Curcuma longa L. rhizomes. The herbal powder was extracted with methanol (1:30, w/v) by the ultrasound-assisted method for 10 minutes, and this process was repeated three times. A high-performance liquid chromatography-diode array detector (HPLC-DAD) method was validated for the simultaneous quantification of three analytes, following the AOAC guideline and achieving a correlation coefficient (R2) value greater than 0.9950. Utilizing the HPLC-DAD method, the study developed a chemical fingerprint analysis for three analytes to identify the characteristic chemical components distinguishing turmeric from each region. Nineteen samples collected from various provinces across Vietnam were subjected to analysis. In all analyzed samples, the concentrations of CUR, DMC, and BDMC ranged from 0.77–10.30%, 0.33–6.92%, and 0.03–3.23%, respectively. CUR was determined to be the dominant compound in most samples, while BDMC consistently exhibited the lowest levels of content. Utilizing the findings derived from the analysis of RRT and RPA metrics, the research assessed variances across sample batches. It is suggested that this newly established approach can be applied to construct and develop raw material areas to serve the needs of each field.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Exploring the Factors Controlling the Annual Range of Amazon Precipitation

Abstract The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model, version 2 (CESM2), were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contributed to the trend changes, ranging from 25% to 35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land-use changes may potentially influence the precipitation AR in the future. Significance Statement The annual range (AR) in precipitation, the difference between wet- and dry-season precipitation, has increased from 1979 to 2014 in the Amazon. This increase may have resulted from global warming, deforestation, and sea surface temperature variability in North Atlantic and Pacific. To explore the role of each of these factors in altering the Amazon precipitation AR, five experiments were designed in the climate model (CESM). Among these experiment results, the effect of North Atlantic SSTs was the strongest. In the future, deforestation, global warming, and different ocean temperature states in the North Atlantic and Pacific may become increasingly influential on the changes in precipitation. Further investigation is needed to ascertain how the AR of precipitation in the Amazon will change.

Active learning for adaptive surrogate model improvement in high-dimensional problems

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.

Active learning for adaptive surrogate model improvement in high-dimensional problems

This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is used to identify a corresponding low-dimensional subspace of the input domain; then a surrogate model is built for each feature in its corresponding active subspace. A low-dimensional adaptive learning strategy is proposed to identify training samples to improve the surrogate model. In contrast to existing adaptive learning methods that focus on a scalar output or a small number of outputs, this paper addresses adaptive learning with high-dimensional input and output, with a novel learning function that balances exploration and exploitation, i.e., considering unexplored regions and high-error regions, respectively. The adaptive learning is in terms of the active variables in the low-dimensional space, and the newly added training samples can be easily mapped back to the original space for running the expensive physics model. The proposed method is demonstrated for the numerical simulation of an additive manufacturing part, with a high-dimensional field output quantity of interest (residual stress) in the component that has spatial variability due to the stochastic nature of multiple input variables (including process variables and material properties). Various factors in the adaptive learning process are investigated, including the number of training samples, range and distribution of the adaptive training samples, contributions of various errors, and the importance of exploration versus exploitation in the learning function.

20th century climate warming and human disturbance triggered high aquatic production and strong water-column mixing in maar Lake Xiaolongwan, northeastern China

Lake ecosystems in northeastern (NE) China are sensitive to global environmental change, and are currently under threats of eutrophication and hypolimnetic anoxia. However, the lack of long-term records of lake production and anoxia in this region makes it difficult to discriminate the impacts of recent anthropogenic activity on lake ecosystems from natural ecosystem variability. This study investigates varved sediments from remote maar Lake Xiaolongwan, NE China and reconstructs high-resolution changes in lake primary production, anoxia, nutrient cycling and catchment processes over the past 1500 years using hyperspectral imaging inferred sedimentary total chlorophyll-a and bacteriopheophytin-a (Bphe-a), combined with sedimentary iron and phosphorus fractions data. Results show that, prior to discernible human impacts in this area from ~600 CE to 1900 CE, lake primary production was higher during warm periods and reduced during cold periods. Bphe-a records show that hypolimnetic anoxia persisted regardless of temperature variability. Cluster analysis suggests that lake algal communities and biogeochemical conditions in the twentieth-century warm period are unprecedented and significantly different from any other time over the past 1500 years. This pheonmenon mostly results from global warming and stronger local catchment disturbance in the early 1900s, combined with atmospheric pollution after the 1950s. Human-driven climate warming has caused stronger seasonal mixing (due to shortened ice-cover duration) and overall better oxygenation in the lake. This study demonstrates clear anthropogenic impacts to lake ecosytems in a relatively pristine region in NE China. We anticipate that our findings will have implications for understanding the status of aquatic ecosystems in NE China under future interacting stressors of anthropogenic climate warming and pollution.