Legacy Samples Research Articles

Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).

Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset

AbstractInfrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid‐infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components‐canonical correlation analysis [PC‐CCA], and domain‐invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC‐CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.

A soil spectral library of New Zealand

Diffuse reflectance spectroscopy, both in the visible and near-infrared (vis-NIR: 350–2500 nm) and the mid-infrared (MIR: 2500–25,000 nm) ranges, has been increasingly employed as an alternative to obtaining a multitude of soil information and data non-destructively, rapidly, and cost-effectively. Soil spectral libraries (SSLs) have been developed across the world to calibrate prediction models for rapid and non-destructive assessment of soils at local, regional, national and global scales. Several continental or national libraries exist, for example, vis-NIR SSL in Europe, Australia, Czech Republic, Denmark, France, China and Brazil, and MIR SSL in the U.S., Switzerland and Brazil. In this study, we document the development of a vis-NIR and MIR SSL of New Zealand based on legacy and modern samples that have been sieved to 2 mm and air-dried, and explore the application of such libraries for predicting a wide range of soil properties, including soil carbon (total carbon or organic carbon), total nitrogen, pH, soil texture (sand, silt and clay), phosphate retention and available water capacity (field capacity and permanent wilting point). The vis-NIR and MIR spectral features were compared between New Zealand Soil Classification (NZSC) soil orders and the performances of both vis-NIR and MIR SSLs were compared with other national SSLs. Results of the study indicate that both vis-NIR and MIR SSL demonstrated reliable modelling performance using the PLSR model. However, MIR outperforms vis-NIR for all soil attributes. Furthermore, the study found that both vis-NIR and MIR have the potential to distinguish different soil orders within the NZSC.

IgG4 antibodies to the recombinant filarial antigen Wb-Bhp-1 decrease dramatically following treatment of lymphatic filariasis.

Lymphatic filariasis (LF) is a neglected tropical disease and a major cause of chronic disability. Improved diagnostic tests are needed because of long-term persistence of anti-filarial antibodies or circulating filarial antigenemia after treatments that clear microfilaremia. Here, we assess changes in levels of antibodies to the recombinant filarial antigens Wb-Bhp-1, Wb123, and Bm14 after anti-filarial treatment. IgG4 antibodies to recombinant filarial antigens were assessed by ELISA. We tested serial plasma samples from a clinical trial in Papua New Guinea. Before treatment, 90%, 71% and 99% of participants had antibodies to Wb-Bhp-1, Wb123, and Bm14, respectively. Antibodies to Wb-Bhp-1 and Wb123, but not Bm14, were significantly higher in participants with persistent microfilaremia 24 months after treatment. Antibodies to all three antigens declined significantly by 60 months after treatment with ivermectin, diethylcarbamazine and albendazole despite circulating filarial antigen in 76% of participants. By 60 months follow up, antibodies to Wb-Bhp-1, Wb123, and Bm14 were detected in 17%, 7% and 90% of participants, respectively. Antibodies to Wb-Bhp-1 also declined more rapidly after treatment than antibodies to Bm14 in samples from a clinical trial conducted in Sri Lanka. We also tested archived serum samples from people living in filariasis-endemic communities in Egypt with different infection profiles. Antibodies to Wb-Bhp-1 were detected in 73% of microfilaremic people, 53% of amicrofilaremic people with circulating filarial antigen, and 17.5% of endemic individuals without microfilaria or circulating filarial antigen. Tests performed with legacy samples from India showed that few people with filarial lymphedema had antibodies to these recombinant antigens. Antibodies to Wb-Bhp-1 and Wb123 are more closely correlated with persistent microfilaremia than circulating filarial antigenemia or antibodies to Bm14, and they clear more rapidly after anti-filarial treatment. Additional studies are needed to assess the value of Wb-Bhp-1 serology as a tool for determining the success of LF elimination efforts.

Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data, especially where modern data is considerably different than legacy data, it is an expensive exercise. The risk associated with modernizing such legacy data lies within its uncertainty in return (e.g., the possibility of new discoveries, in primarily greenfield settings). Without any advanced knowledge of yet unanalyzed elements, the importance of re-analyses remains ambiguous. To address this uncertainty, we apply machine learning to multivariate geochemical data from different regions in Canada (i.e., the Churchill Province and the Trans-Hudson Orogen) in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses. Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data (e.g., prospectivity mapping). Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.

High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic

Detailed maps of soil properties are essential for soil protection planning and management; however, creating very high-resolution maps at the national level with sufficient accuracy is a challenging task and remains unavailable for most countries. For the Czech Republic, very high resolution (20 m·pixel−1) soil property maps (soil organic carbon—SOC, texture, pH, bulk density, soil depth) were created using digital soil mapping methods, combined with a wide database of soil legacy and current samples. The latest approaches were employed for predictive mapping: a quantile random forest model with the determination of prediction intervals, a mosaic of bare soils from Sentinel-2 satellite data, a Gaussian pyramid of terrain attributes, and a buffer distance map. These variables were found to be among the most important in the resulting models. The properties were mapped with an RMSE accuracy of 0.43% SOC, 5.56–11.14% for texture fractions, 0.70 pH, 0.13 g·cm−3 bulk density, and 20.03 cm for soil depth, thus providing detailed data on soil cover. Greater levels of inaccuracy were found in areas with extreme values, for which further investigation is necessary either through more detailed sampling based on active learning, or adapted methods for enhanced predictive ability.

Y-003. Longitudinal serum cytokine profiles in gestational hypertension and preeclampsia

Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5–210 min; Q2 = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5–210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q2 = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.

Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome

Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5–210 min; Q2 = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5–210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q2 = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.

Abstract 5466: Robust detection of sequencing batch effects in RNA through low dimensional embedding with subtype-matched reference samples

Abstract Laboratories conducting high volumes of RNA sequencing must be extremely wary of technical batch effects if samples are to be compared across extended time periods, which is imperative for the most well-powered analyses of cancer transcriptomes. Changes in reagents, protocols, or technologies used in nucleic acid extraction, library preparation, and sequencing can alter transcriptomes in ways that invalidate or complicate comparisons of samples from different batches, necessitating continuous monitoring. This monitoring can be particularly difficult when analyzing samples from distinct tissue sites as tumor type is the major biological determinant of transcriptome variance in cancer. Brain and liver cancer transcriptomes, for example, are expected to differ so drastically that their comparison is not informative for batch effect detection. Detection methods must also be robust to disparate batch effects that can manifest as minor changes in expression among many genes or major changes in a subset of genes making ad hoc detection unfeasible. To overcome these challenges, we developed MaCoBED (matched cohort batch effect detection), a novel method that evaluates technical batch effects in a set of transcriptome samples (e.g., a flow cell) by pooling them with a set of validated reference samples matched by cancer type and tissue site. This pooled set of transcriptomes is then subjected to low-dimensional embedding using Uniform Manifold Approximation and Projection (UMAP), and each component is tested for deviation from the reference set using a Wilcox test. Matching new and legacy samples by cancer type and tissue site ensures that any differences in UMAP clustering are not driven by known biological contributions. We found that UMAP was preferable to Principal Components Analysis (PCA). UMAP can capture variability in just two dimensions, accentuating modest but consistent transcriptome differences among batches that would otherwise be manifested among multiple minor principal components, making batch effects more obvious and readily detectable. This approach was able to detect a number of simulated batch effects with high specificity and sensitivity relative to randomly sampled validated legacy samples. Thus, we propose MaCoBED as a simple and rapid approach for batch effect monitoring of high-throughput RNA sequencing datasets that is versatile in detecting distinct kinds of batch effects, easily automatable, readily interpretable upon visualization, and extensible to small or large batch sizes. Citation Format: Joshua Drews, Joshua Bell, Wesley Munson, Saksham Saini, Benjamin Leibowitz, Jackson Michuda, Calvin McCarter, Lee Langer, Catherine Igartua, Kevin White. Robust detection of sequencing batch effects in RNA through low dimensional embedding with subtype-matched reference samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5466.

Legacy samples in Finnish biobanks: social and legal issues related to the transfer of old sample collections into biobanks

Biobank operations started officially in Finland in 2013 when the Biobank Act defining and regulating biobank operations came into force. Since then, ten biobanks have been established and they have started to collect new prospective samples with broad consent. The main corpus of biobank samples, however, consists of approximately 10 million “legacy samples”. These are old diagnostic or research samples that were transferred to biobanks in accordance with the Biobank Act. The focus of this article is on ambiguities concerning these legacy samples and their transfer in terms of legality, human rights, autonomy, and social sustainability. We analyse the Finnish biobank operations in the context of international regulation, such as the European Convention of Human Rights, the Oviedo Convention, European Charter of Fundamental Rights, the GDPR, and EU Clinical Trials Regulation, and show that the practice of using legacy samples is at times problematic in relation to this regulatory framework. We argue that the prevailing interpretations of these regulations as translated into the Finnish biobank practices undermine the autonomy of individuals by not giving individuals a right to consent or an actionable right to opt-out of the transfer of these legacy samples to the biobank. This is due to the fact that individuals are not given effective notification of such transfers. Thus, issues regarding the legal status of the biobank samples and the social sustainability of biobank operations remain a challenge for biobanks in Finland despite governmental efforts to create pioneering, comprehensive, and enabling legislation.

Ideating iDNA: Lessons and limitations from leeches in legacy collections.

Indirect methods for conducting faunal inventories present great promise, and genomic inventories derived from environmental sources (eDNA) are improving. Invertebrate ingested DNA (iDNA) from terrestrial leeches in the family Haemadipsidae has shown potential for surveying vertebrates and biodiversity monitoring in protected areas. Here we present an initial, and critical, evaluation of the limitations and biases of current iDNA protocols for biodiversity monitoring using both standard and NGS barcoding approaches. Key findings include the need for taxon relevant multi-locus markers and reference databases. In particular, the limitations of available reference databases have profound potential to mislead and bias eDNA and iDNA results if not critically interpreted. Nevertheless, there is great potential for recovery of amplifiable DNA from gut contents of invertebrate museum specimens which may reveal both temporal patterns and cryptic diversity in protected areas with increased efficiency. Our analyses of ingested DNA (iDNA) from both freshly stored and previously collected (legacy) samples of terrestrial leeches successfully identified vertebrates from Myanmar, Australia and Madagascar and indicate the potential to characterize microbial communities, pathogen diversity and interactions at low cost.

Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data.

MotivationBiobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.ResultsThis study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1–36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.Availability and implementationThe sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift.Supplementary information Supplementary data are available at Bioinformatics online.

Uncertainty-guided sampling to improve digital soil maps

Digital soil mapping (DSM) products represent estimates of spatially distributed soil properties. These estimations comprise an element of uncertainty that is not evenly distributed over the area covered by DSM. If we quantify the uncertainty spatially explicit, this information can be used to improve the quality of DSM by optimizing the sampling design. This study follows a DSM approach using a Random Forest regression model, legacy soil samples, and terrain covariates to estimate topsoil silt and clay contents in a small catchment of 4.2km2 in the Three Gorges Reservoir Area, Central China. We aim (i) to introduce a method to derive spatial uncertainty, and (ii) to improve the initial DSM approaches by additional sampling that is guided by the spatial uncertainty. The proposed uncertainty measure is based on multiple realizations of individual and randomized decision tree models. We used the spatial uncertainty of the initial DSM approaches to stratify the study area and thereby to identify potential sampling areas of high uncertainties. Further, we tested how precisely available legacy samples cover the variability of the covariates within each potential sampling area to define the final sampling area and to apply a purposive sampling design. For the final Random Forest model calibration, we combined the legacy sample set with the additional samples. This uncertainty-driven DSM refinement was evaluated by comparing it to a second approach. In this second approach, the additional samples were replaced by a random sample set of the same size, obtained from the entire study area. For the comparative analysis, external, bootstrap-, and cross-validation was applied. The DSM approach using the uncertainty-driven refinement performed best. The averaged spatial uncertainty was reduced by 31% for silt and by 27% for clay compared to the initial DSM approach. Using external validation, the accuracy increased by the same proportions, while showing an overall accuracy of R2=0.59 for silt and R2=0.56 for clay.

Legacy or colonization? Posteruption establishment of peregrine falcons (Falco peregrinus) on a volcanically active subarctic island

How populations and communities reassemble following disturbances are affected by a number of factors, with the arrival order of founding populations often having a profound influence on later populations and community structure. Kasatochi Island is a small volcano located in the central Aleutian archipelago that erupted violently August 8, 2008, sterilizing the island of avian biodiversity. Prior to the eruption, Kasatochi was the center of abundance for breeding seabirds in the central Aleutian Islands and supported several breeding pairs of peregrine falcons (Falco peregrinus). We examined the reestablishment of peregrine falcons on Kasatochi by evaluating the genetic relatedness among legacy samples collected in 2006 to those collected posteruption and to other falcons breeding along the archipelago. No genotypes found in posteruption samples were identical to genotypes collected from pre‐eruption samples. However, genetic analyses suggest that individuals closely related to peregrine falcons occupying pre‐eruption Kasatochi returned following the eruption and successfully fledged young; thus, a genetic legacy of pre‐eruption falcons was present on posteruption Kasatochi Island. We hypothesize that the rapid reestablishment of peregrine falcons on Kasatochi was likely facilitated by behavioral characteristics of peregrine falcons breeding in the Aleutian Islands, such as year‐round residency and breeding site fidelity, the presence of an abundant food source (seabirds), and limited vegetation requirements by seabirds and falcons.

Return of individual genomic research results: what do consent forms tell participants?

Advances in genomic technology make possible the large-scale collection of genomic data for research purposes. Many international initiatives seek to collect genomic data on large populations, often relying on existing collections to populate their databases. As these efforts progress, the debate over whether or not to return individual genetic research results to study participants remains an area of much contention. Some recommend returning results to participants only if the issue was addressed in the original study consent form. Much of the data being used in current studies, however, may have been derived from biospecimens collected years ago with consent documents that did not anticipate the possibility of returning individual level genomic results. We conducted an analysis of informed consent documents from published genome-wide association studies (GWAS) (n=40) to explore whether future research use of biospecimens or data is anticipated, and if return of results is addressed and how it is described to better understand participants' expectations for future disclosure. The majority (70%) of the GWAS consent documents we analyzed either stated explicitly that individual genomic results would not be returned or were silent on the issue. This has implications for how researchers and members of Research Ethics Committees manage the return of results from sequencing studies using legacy samples and data.

Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

AbstractMost calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHSadapt) in a small catchment (4.2 km2) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHSadapt with the original cLHS design (cLHSorig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHSadapt and cLHSorig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHSadapt by adding available legacy samples (cLHSadapt+) and compared the prediction accuracies. Based on an external validation set cLHSval (n = 20), the coefficient of determination (R2) of the cLHSadapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R2‐values of the RF predictions based on cLHSadapt+, using additional legacy samples, are marginally increased on average by 5%.

Cosmogenic nuclide data sets from the Sierra Nevada, California, for assessment of nuclide production models: II. Sample sites and evaluation

Abstract The Cosmic-Ray Produced Nuclide Systematics on Earth Project (CRONUS-Earth Project) has reused a limited number of ‘legacy’ 10Be, 26Al, and 36Cl samples of latest Pleistocene age for the assessment of cosmogenic nuclide production rates independently derived by the CRONUS-Earth Project. The studies re-evaluated here include ones from the Sierra Nevada, California, originally measured by Nishiizumi et al. (1986), Evans et al. (1997), and Phillips et al. (2009). We have also analyzed a limited number of new samples from the same area. We have reevaluated the geological context of the legacy samples, their chronological controls, and the consistency of the data. The locations and settings of the 1986 samples for 10Be and 26Al calibration can be verified to within reasonable degree of precision using comparison of published locations and field photos with Google Earth imagery. Two cosmogenic sites, Greenstone Lake and Baboon Lakes, were also the localities where key 14C samples for glacial chronology were collected, giving confidence in the application of the 14C constraints to the cosmogenic nuclide samples. A considerable amount of more distant radiocarbon dating work supports an age of 15,750 ± 500 years for the exposure of the cosmogenic calibration samples associated with the Tioga 4 glacial retreat. These samples do all appear to be on surfaces that were exposed by final Tioga glacier retreat (15,750 ± 500 years), but below the limits of the Recess Peak advance (13,550–13,050 years). The 36Cl samples reported by Phillips et al. (2009) and Evans et al. (1997) were from the terminal moraine of the last Tioga glacier advance (16,100 ± 500 years), the final Tioga glacier retreat (15,750 ± 500 years), and the Recess Peak glacial episode (13,300 ± 250 years). When ages are calculated for these samples using the final CRONUS-Earth Project production rates and scaling models, they are in good agreement with the independent radiocarbon age control, with the exception of the 36Cl ages from the Recess Peak episode, which average about 15% young. The most likely reasons for this discrepancy are either inadequate understanding of snow shielding or error in the assignment of the age of the Recess Peak glacial advance.

An heuristic uncertainty directed field sampling design for digital soil mapping

Legacy samples are a valuable data source for digital soil mapping. However, these sample sets are often small in size and ad hoc in spatial distribution. Constrained by the limited representativeness of such a sample set, the obtained soil maps are often incomplete in spatial coverage with “gaps” at the locations which cannot be well represented by these samples. The maps may also contain areas of high prediction uncertainty. In order to extend the predicted area and reduce prediction uncertainty, additional samples are needed. This paper presents a sampling design based on prediction uncertainty to select samples which will effectively complement the sparse and ad hoc samples, and maximize the spatial coverage of prediction and minimize prediction uncertainty. A case study in China shows that this sampling scheme was effective in achieving these goals. Compared with stratified random sampling scheme, when the number of additional samples is the same, the produced map using uncertainty directed samples has larger predicted area, and the accuracy of the produced map is higher than that of the maps using stratified random samples. The finding of this study suggests that prediction uncertainty is a useful indicator to aid field sample selection and to complement the legacy data. Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.

MORFOMETRYKA—A NEW WAY OF ESTABLISHING MORPHOLOGICAL CLASSIFICATION OF GALAXIES

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C1, Asymmetry A3, and Smoothness S3), and the new parameters entropy, H, and spirality σψ. The new parameters A3, S3, and H are better to discriminate galaxy classes than A1, S1, and G, respectively. The new parameter σψ captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from the Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair & Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index (Mi) and we show that it follows the human based T-type index very closely. We calculate morphometric index Mi for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how Mi correlates with stellar population parameters obtained using the spectra available from SDSS–DR7.

Integrating 40Ar/39Ar, U-Pb, and astronomical clocks in the Cretaceous Niobrara Formation, Western Interior Basin, USA

This study revises and improves the chronostratigraphic framework for late Turonian through early Campanian time based on work in the Western Interior U.S. and introduces new methods to better quantify uncertainties associated with the development of such time scales. Building on the unique attributes of the Western Interior Basin, which contains abundant volcanic ash beds and rhythmic strata interpreted to record orbital cycles, we integrate new radioisotopic data of improved accuracy with a recently published astrochronologic framework for the Niobrara Formation. New 40Ar/39Ar laser fusion ages corresponding to eight different ammonite biozones are determined by analysis of legacy samples, as well as newly collected material. These results are complemented by new U-Pb (zircon) chemical abrasion–isotope dilution–thermal ionization mass spectrometry ages from four biozones in the study interval. When combined with published radioisotopic data from the Cenomanian-Turonian boundary, paired 206Pb/238U and 40Ar/39Ar ages spanning Cenomanian to Campanian time support an astronomically calibrated Fish Canyon sanidine standard age of 28.201 Ma. Stage boundary ages are estimated via integration of new radioisotopic data with the floating astrochronology for the Niobrara Formation. The ages are determined by anchoring the long eccentricity bandpass from spectral analysis of the Niobrara Formation to radioisotopic ages with the lowest uncertainty proximal to the boundary, and adding or subtracting time by parsing the 405 k.y. cycles. The new stage boundary age determinations are: 89.75 ± 0.38 Ma for the Turonian-Coniacian, 86.49 ± 0.44 Ma for the Coniacian-Santonian, and 84.19 ± 0.38 Ma for the Santonian-Campanian boundary. The 2σ uncertainties for these estimates include systematic contributions from the radioisotopic measurements, astrochronologic methods, and geologic uncertainties (related to stratigraphic correlation and the presence of hiatuses). The latter geologic uncertainties have not been directly addressed in prior time scale studies and their determination was made possible by critical biostratigraphic observations. Each methodological approach employed in this study—new radioisotopic analysis, stratigraphic correlation, astrochronology, and ammonite and inoceramid biostratigraphy—was critical for achieving the final result.