Composite Dataset Research Articles

Unusual sources of fossil micrometeorites deduced from relict chromite in the small size fraction in ~467 Ma old limestone

ABSTRACTExtraterrestrial chrome spinel and chromite extracted from the sedimentary rock record are relicts from coarse micrometeorites and rarely meteorites. They are studied to reconstruct the paleoflux of meteorites to the Earth and the collisional history of the asteroid belt. Minor element concentrations of Ti and V, and oxygen isotopic compositions of these relict minerals were used to classify the meteorite type they stem from, and thus to determine the relative meteorite group abundances through time. While coarse sediment‐dispersed extraterrestrial chrome‐spinel (SEC) grains from ordinary chondrites dominate through the studied time windows in the Phanerozoic, there are exceptions: We have shown that ~467 Ma ago, 1 Ma before the breakup of the L chondrite parent body (LCPB), more than half of the largest (>63 μm diameter) grains were achondritic and originated from differentiated asteroids in contrast to ordinary chondrites which dominated the meteorite flux throughout most of the past 500 Ma. Here, we present a new data set of oxygen isotopic compositions and elemental compositions of 136 grains of a smaller size fraction (32–63 μm) in ~467 Ma old pre‐LCPB limestone from the Lynna River section in western Russia, that was previously studied by elemental analysis. Our study constitutes the most comprehensive oxygen isotopic data set of sediment‐dispersed extraterrestrial chrome spinel to date. We also introduce a Raman spectroscopy‐based method to identify SEC grains and distinguish them from terrestrial chrome spinel with ~97% reliability. We calibrated the Raman method with the established approach using titanium and vanadium concentrations and oxygen isotopic compositions. We find that ordinary chondrites are approximately three times more abundant in the 32–63 μm fraction than achondrites. While abundances of achondrites compared to ordinary chondrites are lower in the 32–63 μm size fraction than in the >63 μm one, achondrites are approximately three times more abundant in the 32–62 μm fraction than they are in the present flux. We find that the sources of SEC grains vary for different grain sizes, mainly as a result of parent body thermal metamorphism. We conclude that the meteorite flux composition ~467 Ma ago ~1 Ma before the breakup of the LCPB was fundamentally different from today and from other time windows studied in the Phanerozoic, but that in contrast to the large size fraction ordinary chondrites dominated the flux in the small size fraction. The high abundance of ordinary chondrites in the studied samples is consistent with the findings based on coarse extraterrestrial chrome‐spinel from other time windows.

Spatial Analysis Using Geographically Weighted Ordinary Logistic Regression (GWOLR) Method for Prediction of Particle-Size Fraction in Soil Surface

Spatial analysis is a method used to understand the spatial variation of geospatial data. In this study, the Geographically Weighted Ordinary Logistic Regression (GWOLR) method was used in spatial analysis to predict the particle size fraction of the surface soil. The particle size fraction of the surface soil is an important parameter in determining soil productivity and environmental quality. However, the particle size fraction in surface soils can vary spatially and is influenced by geographical factors such as elevation, rainfall, and soil texture. This study will be carried out by collecting particle size fraction data and geospatial data at randomly selected locations. Accurate modelling of soil texture is necessary because it‘s a crucial factor in determining how soil management will go. However, because soil texture is a compositional data set, it is one of the soil attributes that is more challenging to model. The challenge presented by this compositional data set is the imposition of constant quantities, specifically the requirement that the total of the fractions of clay, silt, and sand be 100%. Topographical variability can be derived from DEM data, making it an independent variable or predictor for soil texture prediction. The data will then be analyzed using the GWOLR method to predict the particle size fraction at locations that have not been observed before. The resulting prediction model will then be evaluated using cross-validation to check the accuracy of the model. This study will provide benefits for land management and natural resource management and can improve understanding of the spatial variation of particle size fractions in surface soils and the spatial and geographical factors that influence them. The GWOLR model for predicting particle size fractions in surface soils was carried out with a fixed bi-square weight and a bandwidth of 0.28895. The GWOLR model classification accuracy value is 94 percent, this shows that the GWOLR model for predicting soil particle size is more suitable than the ordinal logistic regression model with a classification accuracy of 90 percent. The aims of this study are to: (1) Establish a soil texture prediction model using the GWOLR method; and (2) Test the reliability of the model in predicting surface soil texture.

Ambient PM2.5 components and prevalence of chronic kidney disease: a nationwide cross-sectional survey in China.

Chronic kidney disease (CKD) is a global public health concern, and accumulating evidence has indicated that air pollution increases the odds of CKD. However, a limited number of studies have examined the long-term effects of ambient fine particulate matter (PM2.5) components on the risk of CKD among general population; thus, major knowledge gaps remain. Using data from a nationwide representative cross-sectional survey in China and a validated PM2.5 composition dataset, we established generalized linear models to quantify the association between five major components of PM2.5 and CKD prevalence. There were significant associations between long-term exposure to three PM2.5 components [including black carbon (BC), sulfate (SO42-), organic matter (OM)] and increased odds of CKD prevalence. Along with an interquartile range (IQR) increment in BC (3.3 μg/m3), SO42- (9.7 μg/m3), and OM (16.2 μg/m3) at a 4-year moving average, the odds ratios (ORs) for CKD prevalence were 1.28 (95% CI 1.07, 1.54), 1.23 (95% CI 1.03, 1.45), and 1.23 (95% CI 1.02, 1.47), respectively. We did not detect any significant association of the other two PM2.5 components [nitrate (NO3-) or ammonium (NH4+)] with CKD prevalence. Stratified analyses revealed no differences (P ≥ 0.05) in the effect estimates of subgroups based on administrative region, sex, age, and other demographic characteristics. For instance, along with an IQR increment in BC at a 4-year moving average, the ORs of CKD prevalence among males and females were 1.30 (95% CI 0.98, 1.73) and 1.29 (95% CI 1.01, 1.65), respectively. The odds of CKD were generally higher with increasing PM2.5 composition concentration. Our study demonstrated that long-term exposure to specific PM2.5 components including BC, SO42-, and OM increased CKD risk in the general population. This study could provide new insights into source-directed PM2.5 control and CKD prevention.

Combining compositional data sets introduces error in covariance network reconstruction

Abstract Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

Estimating tree species composition from airborne laser scanning data using point-based deep learning models

Airborne laser scanning (ALS) data is increasingly being used for accurate estimations of forest inventory attributes, such as tree height and volume. However, determining tree species information, an important attribute required by inventories for a range of forest management applications, is challenging to extract from ALS data. This is due to complex species assemblages and vertical structures in certain forest environments, and a lack of spectral information for most ALS sensors. Our objective was to estimate tree species compositions in a Canadian boreal forest environment using ALS data and point-based deep learning techniques. To accomplish this, we utilised existing polygonal forest resource information to develop a large comprehensive dataset of tree species compositions across a 630,000 ha forest management area. We then used an adapted PointAugment generative adversarial network (GAN) coupled with the dynamic graph convolutional neural network to estimate tree species proportions. This innovative deep learning approach resulted in an overall R2adj of 0.61 for all tree species proportions. A weighted F1 score of 63% was observed when classifying the leading species of a plot, 66% for leading genus, and 85% when distinguishing between coniferous and deciduous dominated plots. Furthermore, the PointAugment GAN successfully generated augmented point clouds of forest plots which can alleviate issues associated with limited training samples for use in deep learning. This approach demonstrates the capability of point-based deep learning techniques to accurately estimate tree species compositions from ALS point clouds within an expansive forested region, characterized by a complex management history and a diversity of tree species. The source code along with the pretrained models are available at https://github.com/Brent-Murray/point-dl/tree/main/Pytorch/models/PointAugment.

Mapping emission heterogeneity in layered halide perovskites using cathodoluminescence

Recent advancements in the fabrication of layered halide perovskites and their subsequent modification for optoelectronic applications have ushered in a need for innovative characterisation techniques. In particular, heterostructures containing multiple phases and consequently featuring spatially defined optoelectronic properties are very challenging to study. Here, we adopt an approach centered on cathodoluminescence, complemented by scanning electron microscopy coupled with energy-dispersive x-ray spectroscopy analysis. Cathodoluminescence enables assessment of local emission variations by injecting charges with a nanometer-scale electron probe, which we use to investigate emission changes in three different systems: PEA2PbBr4, PEA2PbI4 and lateral heterostructures of the two, fabricated via halide substitution. We identify and map different emission bands that can be correlated with local chemical composition and geometry. One emission band is characteristic of bromine-based halide perovskite, while the other originates from iodine-based perovskite. The coexistence of these emissions bands in the halide-substituted sample confirms the formation of lateral heterostructures. To improve the signal quality of the acquired data, we employed multivariate analysis, specifically the non-negative matrix factorization algorithm, on both cathodoluminescence and compositional datasets. The resulting understanding of the halide replacement process and identification of potential synergies in the optical properties will lead to optimised architectures for optoelectronic applications.

Deciphering the microbial landscapes in the early life stages of a high-value marine fish, cobia (Rachycentron canadum, Rachycentridae) through high-resolution profiling by PacBio SMRT sequencing

Microbial dynamics across multiple ontogenetic stages represent one confounding feature in teleost microbiome research. The present study recorded the dynamics in the whole microbiome of early life stages and the juvenile gut microbiome of a high-value mariculture species, cobia (Rachycentron canadum), during standard culturing practices. This is the first report on using the PacBio system targeting full-length 16S rRNA sequences, for deciphering fish microbiomes. The results highlighted a temporal shift in the whole microbiome compositions along the host's ontogenetic development. There was a significant decrease in Cyanobacteria and Firmicutes to Bacteroidetes ratio with a corresponding increase in Plactomycetacea and Bacteriodata in the whole microbiome profiles of the post-metamorphosis samples. The gut microbiome data suggested an adaptive mechanism toward establishing beneficial strains (Bacilli and Actinobacteria) alongside the ontogenetic progression. Nevertheless, a small, abundant core bacterial community occupied ∼50% and ∼ 64% of total abundance in the whole microbiome and gut microbiome, respectively. The results revealed high-dimensional microbial biomarkers for various ontogenetic stages of cobia during standard culturing practices. The composite dataset generated offers several possible sustainable means of microbiome manipulations to improve the low survival encountered in the early life stages of cobia.

Machine learning prediction of mafic–ultramafic rock-related Cr-spinel formation environments and its application to the tectonic settings of magmatic sulfide deposits

This study uses the random forest machine learning algorithm to classify and predict Cr-spinel formation environments in mafic–ultramafic rocks. Cr-spinel is an early-crystallized oxide phase in these rocks and exhibits significant compositional variations that provide valuable insights into their formation environments. Traditionally, empirical diagrams have been used to classify the origins of Cr-spinel based on major and trace element compositions; however, overlap exists among spinel from different formation environments. To overcome this limitation, we develop a random forest model using a compiled dataset of Cr-spinel compositions from various tectonic settings, including small intrusions from continental collisional zones, layered intrusions and small intrusions from intra-continental plates (including large igneous provinces and intra-continental rifts), Alaskan-type complexes from oceanic subduction zones, and ophiolites from oceanic spreading centers. The model is trained, validated, and optimized using cross-validation and hyperparameter tuning. The results show high accuracy in predicting the Cr-spinel formation environments, as demonstrated by high precision, recall, and F1 scores. Feature importance analyses reveal that elements, such as Ti, Cr, and Al, play a more significant role in the prediction. Visualization techniques, i.e., the t-distributed stochastic neighbor embedding (TSNE) algorithm, are applied to reduce the dimensionality of the dataset and create decision boundaries for better interpretation. The model is validated using an independent Cr-spinel composition dataset obtained from an area with known geological settings, to confirm the model’s reliability. The model is applied successfully to reveal the heterogeneous sources of the rifting-related Daxueshan magmatic Ni-Cu sulfide deposit in the Baoshan block in the eastern part of the Tethyan orogenic belt. The machine learning algorithm can accurately distinguish different Cr-spinel types using large and diverse datasets to provide valuable insights into geological formation conditions. This approach is a powerful tool for understanding Earth’s geological processes, improving exploration models of magmatic sulfide deposit and reducing mineral exploration costs.

A dataset of formulation compositions for self-emulsifying drug delivery systems

Self-emulsifying drug delivery systems (SEDDS) are a well-established formulation strategy for improving the oral bioavailability of poorly water-soluble drugs. Traditional development of these formulations relies heavily on empirical observation to assess drug and excipient compatibility, as well as to select and optimize the formulation compositions. The aim of this work was to leverage previously developed SEDDS in the literature to construct a comprehensive SEDDS dataset that can be used to gain insights and advance data-driven approaches to formulation development. A dataset comprised of 668 unique SEDDS formulations encompassing 20 poorly water-soluble drugs was curated. While there are still opportunities to enhance the quality and quantity of data on SEDDS, this research lays the groundwork to potentially simplify the SEDDS formulation development process.

Continuous Time-Series Land Cover Maps for South Korea using Google Earth Engine

In this study, we utilized Google Earth Engine to construct, for the first time in South Korea, a long-term (1986-2021) continuous time series of land cover maps with a spatial resolution of 30 meters. Derived from the surface reflectance data of the Landsat satellite series, a total of 44 input variables were generated, including various spectral bands and indices related to land cover. For accuracy verification of the maps, 4,824 reference data were established using areas where land cover remained unchanged, identified by comparing the most recent (2018) and historical (1988) land cover maps from the Ministry of Environment. The Random Forest model was employed to classify seven land cover types (settlements, cropland, forest land, grassland, wetlands, bare land, and water bodies), with an overall accuracy of 0.97 and a Macro F1-score of 0.91, indicating a generally high performance of the model. However, considering the annual variability, potentially due to unidentified or untraceable errors, a composite land cover map dataset, integrated in five-year intervals, was suggested to ensure the generation of stable data.

Com-STAL: Compositional Spatio-Temporal Action Localization

Spatio-temporal action localization aims to locate the spatial and temporal positions of actors and classify their actions. However, prior research overlooks the fact that human actions often interact with novel objects in real-world scenarios, which neglects the various combinations of action-object, and considerably limits the generalization of the developed models. In this paper, we study the action-object combinations by researching multi-modal vision information of them. To this end, we propose a novel compositional spatio-temporal action localization (Com-STAL) task, which features non-overlapping action-object combinations in their training and test sets. Based on this, we construct a compositional action localization dataset (Com-AD). Beyond that, we propose a simple yet effective framework, Instance-Centric Interaction Network (ICIN), to reduce invalid induction biases within the visual modality and alleviate the combined distribution bias issue by leveraging additional modal information. The extensive experiment results on Com-AD demonstrate superior action localization performance of ICIN.

Rare Germline Loss-of-Function Variants in HSP47 ( SERPINH1) Are Associated with an Intermediate Osteogenesis Imperfecta Phenotype Characterized By Atopic Inflammation and Increased Risk of Thrombosis

Background: There has been considerable interest in the collagen-specific chaperone HSP47 ( SERPINH1) as a potential drug target for the treatment of cirrhosis, fibrotic disease, and more recently, thrombosis (Thienel et al., Science 380, 178-187, 2023). While homozygous or compound heterozygous loss of function in SERPINH1 is known to cause a rare form of osteogenesis imperfecta (OI) in humans, little is known about the clinical effects of moderately decreased HSP47 activity. In order to assess the potential safety and efficacy of an antithrombotic strategy based on HSP47 blockade, we evaluated the clinical impacts of heterozygous loss-of-function variants in SERPINH1 in a dataset of over 400,000 individuals. Aims: Determine the clinical effects of SERPINH1 loss of function in a large-scale whole exome sequencing dataset. Methods: The UK Biobank (UKBB) contains paired whole exome sequencing and clinical data on 415,921 subjects in addition to plasma proteomics data for a subset of participants (N= 48,892). We identified all rare (MAF<0.1%)variants in SERPINH1 that were predicted in silico to alter protein activity (functional impact score ≥0.7). Using Firth's logistic regression, we controlled for age, sex, ancestry, and other risk factors and assessed the association between the presence of qualifying SERPINH1 variants and four thrombotic disorders: venous thromboembolism (VTE), non-cardioembolic ischemic stroke (NCEIS), myocardial infarction (MI), and peripheral arterial disease (PAD). Replication was performed in a composite dataset from the NIH All of Us program and the Mass General Brigham (MGB) Biobank (N=150,017). We also evaluated differences in the plasma levels of 1,472 proteins (Olink Explore 1536 proteomics panel) between carriers and non-carriers of qualifying SERPINH1 variants. Results: Rare qualifying variants in SERPINH1 were identified in 382 UKBB participants (100% heterozygous). On average, SERPINH1 variant carriers were of significantly shorter stature (P=3.97 x 10-5) and lower weight (P=0.006) than individuals without such variants, and this effect was more pronounced in subjects with higher functional impact scores ( Figure 1). By contrast, bone mineral density did not differ significantly as measured by calcaneal quantitative ultrasound and femur shaft dual x-ray absorptiometry. The presence of SERPINH1 variants was associated with significantly increased risk of VTE (OR=1.87, 95% CI: 1.21-2.76, P=0.006), MI (OR=1.98, 95% CI: 1.27-2.96, P=0.003), PAD (OR=2.37, 95% CI: 1.29-3.98, P=0.007), and NCEIS (OR=2.67, 95% CI: 1.24-4.98, P=0.015). Restricting the analysis to high-confidence loss-of-function variants (i.e., nonsense, frameshift, and essential splice site mutations) markedly boosted effect size estimates for MI, PAD, and NCEIS. The VTE and MI disease associations replicated in the composite dataset (VTE OR=3.29, 95% CI: 1.46-7.39, P=0.004; MI OR=2.31, 95% CI: 1.05-5.08, P=0.038). Additionally, the association with NCEIS demonstrated a strong trend towards significance in the replication dataset (OR=2.08, 95% CI: 0.90-4.83, P=0.088). Plasma proteome analysis showed a significant increase in several proteins related to asthma, atopic inflammation, and/or eosinophil activation among individuals with qualifying SERPINH1 variants, including RNASE3, DPP10, TSPAN1, CCL20, and IL10RA ( Figure 2). In order to further investigate these differences in circulating protein profile, we assessed the risk of obstructive asthma in a meta-analysis of the UKBB, All of Us, and MGB datasets (total N=565,455) and found that SERPINH1 variant carriers were significantly more likely to have disease (OR=1.32, 95% CI: 1.03-1.69, P=0.026). Conclusions: Loss of function in SERPINH1 is associated with a significantly increased risk of both venous and arterial thrombosis, raising concerns that long-term therapeutic inhibition of HSP47 may not be a safe or effective antithrombotic strategy. Our data indicate that SERPINH1 variant carriers experience higher levels of eosinophil-driven inflammation, which has consistently been linked to cardiovascular disease. Further, heterozygous SERPINH1 variant carriers appear to have an intermediate OI phenotype characterized by shorter stature but without significant differences in bone mineral density compared to non-carriers.

Trial in Progress: Investigating the Prognostic Significance of Malnutrition and Sarcopenia in Older Adults with Acute Myeloid Leukemia

Background and Significance: For older adults (≥60 years) newly diagnosed with acute myeloid leukemia (AML), tumor-specific and patient-specific factors have been used to predict treatment-related mortality (TRM) and determine suitability for intensive antileukemic therapy. Comprehensive Geriatric Assessment (CGA) is a tool designed to comprehensively evaluate health among older adults. CGA assesses multiple health domains including physical function, cognition, nutrition, mental health, polypharmacy, comorbidities, and social support but does not currently incorporate body imaging. Sarcopenia is defined as loss of muscle mass and strength. It can be measured objectively by CT, DEXA or Bioimpedance Analysis (BIA) in combination with tests of muscle strength. It has been proposed as a more accurate nutritional marker compared to BMI or serum markers. Nearly all older adults with AML receive a CT scan during their cancer evaluation. Therefore, there is an opportunity to leverage CT sarcopenia measures to improve risk prediction. Our study aims to (1) assess the burden of malnutrition and sarcopenia in older adults with newly diagnosed AML undergoing induction and (2) determine the prognostic impact of traditional markers of nutrition and novel sarcopenia measures on TRM. Study Design and Methods: This is a prospective, observational study of 82 newly diagnosed, older adult patients with AML undergoing induction treatment at the University of Chicago Comprehensive Cancer Center (NCT05458258). Key inclusion criteria include age ≥60 years and receipt of induction therapy for newly diagnosed AML. Key exclusion criteria include presence of a pacemaker or defibrillator. To assess Aim 1, newly diagnosed older adult patients with AML will be undergo a subjective global nutrition assessment and serum nutritional markers [prealbumin, albumin, CRP, ferritin]. Body composition (fat-mass, fat-free mass, lean mass) will be assessed using BIA and CT. Sarcopenia will be defined by the CT Skeletal Muscle Index (SMI) at L3 plus impairment on either maximal hand grip strength, 6-minute walk, Timed Up and Go test (TUG), the Short Physical Performance Battery (SPPB). Patients will also undergo an assessment of disability (instrumental activities of daily living (IADL) and activities of daily living (ADL) surveys), Short Physical Performance Battery [SPPB], and Montreal cognitive assessment [MOCA]) prior to starting induction. All measures will be repeated at the start of post-remission therapy. Results from both timepoints will be compared against healthy controls matched by age, sex and Charlson Comorbidity Index. Healthy control data will come from the Frailty, Activity, Body Composition, and Energy Expenditure (FACE) Aging dataset housed by the Department of Geriatrics at the University of Chicago, a 1-year longitudinal, observation study of frailty in older adults residing around the university. We will match 1 AML case to 1 control. In order to ensure that all subjects are matched, 2:1 propensity score matching will be used to generate matching controls for each case. To assess Aim 2, multivariable Cox proportional hazards regression models will be performed for each of those nutrition status and sarcopenia markers significant for TRM in univariate analysis. In multivariable analyses, we will control for age, European Leukemia Net 2022 risk stratification, ECOG PS, and CGA measures as covariates. A sample size of 82 patients will give us 80% power to detect a hazard ratio of 3.0 for TRM, the primary end point, for CT diagnosed sarcopenia using a Cox proportional hazards model with a 0.05 significance level and a 60-day mortality rate of 18%. We anticipate completion of enrollment within two years of study initiation. To date, 17 patients have been approached for consent with 11 patients enrolled. When complete, this trial will provide initial evidence necessary to recommend nutritional assessment as a part of the Comprehensive Geriatric Assessment in all older adult AML patients. Furthermore, it may provide evidence to support a future interventional study assessing the impact of improved nutritional status on TRM. Figure 1. NCT05458258 study schema.

Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method

ABSTRACTRemote sensing-based vegetation index (VI) data are significantly impacted by cloud contamination. Spatiotemporal reconstruction methods demonstrate higher accuracy than temporal reconstruction methods. However, the computing time and random access memory (RAM) consumption of these spatiotemporal reconstruction methods for large-scale reconstruction remains unclear. In this study, a method called spatial-interannual reconstruction (SIR) was proposed to reconstruct cloud-contaminated pixels in MODIS normalized difference VI (NDVI) and enhanced VI (EVI) data. SIR has four major advantages: (1) High accuracy. The average mean absolute error of SIR was 0.0338, which was 20.2% and 23.4% lower than that of two state-of-the-art spatiotemporal reconstruction methods (i.e. interpolation of the mean anomalies (IMA) and Gapfill). (2) High computing speed. The average computing time of SIR was 99.7% and 98.8% lower than IMA and Gapfill, respectively. (3) Low RAM consumption. (4) Simultaneous reconstruction of all invalid values. Reconstructed 250 m spatial resolution and 16-day composite NDVI and EVI datasets in China from 2000 to 2022 (written as ReVIChina) were developed based on the SIR method and MODIS MOD13Q1 data. Spatiotemporal analyses revealed that the reconstructed datasets were more reliable than the original product and a similar dataset.

Porewater composition in clay rocks explored by advective displacement and squeezing experiments

Argillaceous rocks are foreseen in many countries as the potential hosts for nuclear waste repositories. The knowledge of the chemical composition of the free porewater in these formations is required for the understanding of the paleo-hydrogeological evolution, for the assessment of radionuclide solubilities and migration parameters and for assessing the long-term stability of the technical barrier system. High pressure squeezing and advective displacement are two methods that aim at direct sampling of this porewater fraction while minimizing experimental artefacts. Within the framework of a recent deep drilling campaign in Switzerland, targeting the Opalinus Clay as the designated host rock, a substantial dataset of porewater compositions was obtained by these two methods. It included 51 squeezing and 30 advective displacement experiments on drillcore samples from the Opalinus Clay and confining units distributed over 8 boreholes in 3 study areas. Porewater compositions obtained by either method reflect the geochemical characteristics of each siting region, such as different salinities and water types as well as depth gradients informing on the diffusive exchange with bounding aquifers. An in depth comparison of the Opalinus Clay porewater compositions obtained by both methods shows a high degree of consistency with regard to the ion ratios or mineral equilibria. The pH/pCO2 system was found to be prone to experimental artefacts, but applying a correction, a fairly consistent dataset was obtained. Porewaters acquired by squeezing exhibit systematically lower salinities by 10–40% when compared to those from advective displacement. It is concluded that this is due to the mobilization of a higher fraction of an anion depleted porewater, either due to the higher mobilization of water from the diffuse layer or due to the expulsion of water from interlayer (-like) pores. The comparison with a geochemical model indicates that the experimental data from both methods can be considered as proxies for in-situ major-ion porewater compositions. It also confirms the robustness of the geochemical model predicting porewaters of the Opalinus Clay and confining units. Differences between in-situ, sample storage and extraction temperatures need to be taken into account when interpreting the porewaters obtained by either method and modelling in-situ porewater compositions. Combining different laboratory and analytical methods for porewater investigations in clay rocks provides an added value as it enables a detailed assessment of natural heterogeneities and experimental uncertainties.

VTwins: inferring causative microbial features from metagenomic data of limited samples

It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method—virtual twins (VTwins)—to eliminate the confounder effects by transforming the original cohort into a paired cohort of “Twin” samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.

Prediction of ash fusion temperatures of municipal solid waste incinerator ash based on support vector regression

The ash fusion temperatures (AFTs) of the municipal solid waste incinerator (MSWI) ash and slags is the main indicator to judge its slagging characteristics. Support vector regression (SVR) algorithm is more appropriate for creating an AFT prediction model than the classic regression method because it can adjust to a wider range of ash sample types in a wider range of areas by avoiding underfitting and overfitting. In this paper, SVR prediction models for the deformation temperature (IDT), softening temperature (ST), hemisphere temperature (HT), and flow temperature (FT) of ash are constructed using the ash chemical composition and AFTs dataset of coal ash and biomass ash and other ash. The AFTs of fly ash, bottom slag, and other ash in the municipal solid waste incinerator are predicted. With average relative errors of 1.98 % and 3.56 % and maximum relative errors of 5.21 % and 5.2 %, respectively, the results demonstrate that ST and FT have greater prediction accuracy. IDT and HT both provide poor predictions, with maximum relative errors of 14.08 % and 9.36 %, respectively, and average relative errors of 10.06 % and 5.95 %, respectively. Additionally, the impact of removing various numbers of outlier samples from training data on prediction accuracy, and the impact of test sample measures, such as ash composition analysis and AFTs determination, on prediction accuracy are analyzed and examined.

Image Composition Method Based on a Spatial Position Analysis Network

Realistic image composition aims to composite new images by fusing a source object into a target image. It is a challenging problem due to the complex multi-task framework, including sensible object placement, appearance consistency, shadow generation, etc. Most existing researchers attempt to address one of the issues. Especially before compositing, there is no matching assignment between the source object and target image, which often leads to unreasonable results. To address the issues above, we consider image composition as an image generation problem and propose a deep adversarial learning network via spatial position analysis. We target the analysis network segment and classify the objects in target images. A spatial alignment network matches the segmented objects with the source objects, and predicts a sensible placement position, and an adversarial network generates a realistic composite image with the shadow and reflection of the source object. Furthermore, we use the classification information of target objects to filter out unreasonable image compositing. Moreover, we introduce a new test set to evaluate the network generalization for our multi-task image composition dataset. Extensive experimental results of the SHU (Shanghai University) dataset demonstrate that our deep spatial position analysis network remarkably enhances the compositing performance in realistic, shadow, and reflection generations.

Polynomial Regression Model Utilization to Determine Potential Refuse-Derived Fuel (RDF) Calories in Indonesia

Waste-to-energy (WTE) is one of the Indonesian government’s programs aiming to meet the target of achieving a new and renewable energy (NRE) mix, as well as one of the solutions proposed to overcome the problem of waste. One of the products of WTE is energy derived from raw material waste (refuse-derived fuel/RDF). Using the formula y = 0.00003 x5 − 0.0069 x4 + 0.6298 x3 − 24.3245 x2 + 432.8401 x + 55.7448 with R2 = 0.9963, which was obtained by comparing a scatter plot diagram from the RDF calorie test dataset produced through a bio-drying process, the potential RDF calories produced using the waste composition dataset taken from each region in Indonesia can be calculated. The results of the calculations using the determined equations produce a list of provinces with RDF calorie potential, ordered from the largest to the smallest, using which the government can determine which areas are the main priority for processing waste into energy. Thus, through this method, the target of 5.1% renewable energy sourced from waste can be achieved by 2025.

Measurements of aerosol microphysical and chemical properties in the central Arctic atmosphere during MOSAiC

The Arctic environment is transforming rapidly due to climate change. Aerosols’ abundance and physicochemical characteristics play a crucial, yet uncertain, role in these changes due to their influence on the surface energy budget through direct interaction with solar radiation and indirectly via cloud formation. Importantly, Arctic aerosol properties are also changing in response to climate change. Despite their importance, year-round measurements of their characteristics are sparse in the Arctic and often confined to lower latitudes at Arctic land-based stations and/or short high-latitude summertime campaigns. Here, we present unique aerosol microphysics and chemical composition datasets collected during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in the central Arctic. These datasets, which include aerosol particle number concentrations, size distributions, cloud condensation nuclei concentrations, fluorescent aerosol concentrations and properties, and aerosol bulk chemical composition (black carbon, sulfate, nitrate, ammonium, chloride, and organics) will serve to improve our understanding of high-Arctic aerosol processes, with relevance towards improved modelling of the future Arctic (and global) climate.