Composite Dataset Research Articles

Carbon isotopic composition of methane and its origin in natural gas from the Petišovci-Dolina oil and gas field (Pannonian Basin System, NE Slovenia) – a preliminary study

The carbon isotopic composition of methane (δ13CCH4) in natural gas from the Petišovci-Dolina oil and gas field (NE Slovenia) was measured for the first time in August and September 2021. The gas samples from different depths were taken from three wells: Dolina-deep (Pg-6) from the depth interval 3102–3104 m, Petišovci-deep (Pg-5) from the depth interval 2772–2795 m, and Petišovci-shallow (D-5) from the depth interval 1212–1250 m. According to the available composition dataset of gas, available from the Petrol Geo d.o.o. documentation, the “deep” gases sampled from the Pg-6 and Pg-5 wells consist of 85 % methane (C1), 11 % hydrocarbons heavier than methane (C2–C6) and 4 % CO2. The “shallow” gas from well D-5 contains more than 89 % methane, up to 11 % C2–C6 gases, while the CO2 content is negligible. The “deep« gas from the Pg-6 and Pg-5 wells has δ13CCH4 -36.7 ‰ and -36.6 ‰, respectively, while the “shallow” gas from the D-5 well has the δ13CCH4 of -38.6 ‰. The methane from the “shallow” gas is slightly enriched in the lighter 12C isotope. δ13CCH4 in the range from -38.6 to -36.6 ‰ clearly indicates the thermogenic origin of methane formed during the catagenesis phase of gas formation.

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

Extending the National Burned Area Composite Time Series of Wildfires in Canada

Wildfires are a major natural disturbance in Canada that are postulated to increase under a warming climate. To derive accurate trends in burned area and to quantify the effects of fire frequency, duration, and extent, a sufficiently long time series of reliable burned area maps is required. With that in mind, we extended Canada’s National Burned Area Composite (NBAC) dataset from its previous start year (2004) back to 1986. NBAC consists of annual maps in polygon format where the area burned in each fire event is represented by the best available delineation among various mapping methods and data sources of varying quality. Ordered from more to less reliability, in the new 35-year time series (1986 to 2020), 10% of the total burned area was derived from airborne and high-resolution (<5 m) satellite imagery, 81% from change detection methods using 30 m Landsat satellite imagery, and the remaining 9% was largely from aerial surveys. Total (Canada-wide) annual burned area estimates ranged from 215,797 ha in 2020 to 6.7 million ha (Mha) in 1989. We computed 95% confidence intervals for the estimate of each year from 1986 to 2020 based on the accuracy and relative contribution in that year of the different data sources, for both the new NBAC time series and the polygon version of the Canadian National Fire Database (CNFDB), a commonly used source of spatially explicit data on burned area in Canada. NBAC confidence intervals were on average ±9.7% of the annual figure, about one-third the width of the confidence intervals derived for CNFDB. The NBAC time series also included nearly 5000 fire events (totalling 4 Mha, with the largest event being 120,661 ha in size) that are missing in the CNFDB. In a regional analysis for the Northwest Territories, retroactive fire mapping from Landsat imagery reduced historical estimates by 3 Mha (16%), which would result in a 1.6 Mha increase in the reported undisturbed critical habitat for threatened woodland caribou. The NBAC dataset is freely downloadable from the Canadian Wildland Fire Information System.

Abstract 312: Redirecting T cells to tumor targets with functionally diverse CD3-binding antibodies

Abstract Bispecific antibodies that redirect cancer-killing T cells towards tumors are promising next-generation cancer therapies. While there are hundreds of T cell engagers (TCEs) in development, there is only one approved and marketed CD3-binding TCE. The high rate of attrition is largely attributable to dose-limiting toxicities, including cytokine release syndrome, due in part to the small pool of high affinity CD3-binding antibodies that are commonly used. The discovery of safe and effective TCEs is limited because diverse panels of parental CD3 antibodies are hard to produce, the pairing of parentals is hard to perfect, and the sheer complexity and volume of data is hard to action. In this study, we will present a panel of functionally diverse, fully human CD3-binding parental antibodies. We will present data characterizing the diversity of our panel across multiple parameters, including sequence diversity, CD3 affinity, epitope binding, T cell activation, cytokine release, and tumor cell killing. Using OrthoMab࣪, our clinically-validated bispecific engineering platform, allows this panel to be tested with a series of tumor-associated antigens (TAAs) in a matrix format. Results from high-throughput production and characterization of bispecific antibodies will be presented. These multidimensional datasets of TCE composition and function allow for the identification of pairs that are optimal candidates for clinical development. This work will show how the diversity of our CD3-binding panel, combined with a robust bispecific protein engineering technology, can be used to quickly assess large and diverse TAA-binding panels discovered through our technology stack. An integrated workflow that doubles the data with diverse panels of parentals, assembles stable, safe, and manufacturable TCEs, and visualizes multidimensional datasets are critical to successfully identifying lead therapeutic candidates to bring the next generation of cancer therapies to patients sooner. Citation Format: Lindsay DeVorkin, Tim M. Jacobs, Raffi Tonikian, Karine Hervé, Kate Caldwell, Yuri Hwang, Cristina Faralla, Wei Wei, Katherine J. Lam, Harveer Dhupar, Tran NT Tran, Melissa Cid, Lena M. Bolten, Tova Pinsky, Kush Dalal, Kevin A. Heyries, Bryan C. Barnhart. Redirecting T cells to tumor targets with functionally diverse CD3-binding antibodies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 312.

Abstract 5015: Precision profile simulation study for a next generation sequencing bTMB assay

Abstract Background: Precision profile simulations (PPS) can be used to assess variability of biomarker profiles and provide valuable insight into assay performance, especially when reliable precision estimates can not be obtained empirically due to scarcity of representative samples or insufficient materials per sample. A PPS was conducted for the GuardantOMNI assay to characterize the expected variability in blood tumor mutational burden (bTMB) score across a representative range of expected bTMB scores in clinical samples. The simulations were aligned to, but not completely prescribed by, the PPS guidance provided in Guidance for Industry and and Food and Drug Administration Staff Class II Special Controls Guidance Document: Ovarian Adnexal Mass Assessment Score Test System. A sample’s bTMB score is a real valued quantity (e.g., bTMB = 21.04 mut/Mb) that is derived by multiplying the number of qualified mutations observed within a targeted panel by a scaling factor. Variability in observed bTMB scores for a given blood sample is governed primarily by sample coverage, tumor shedding level, and the assay somatic variant detection probabilities (a function of underlying variant allele frequencies, VAFs). Methods: The relationship between site-specific total molecule counts and coverage was modeled utilizing a composite dataset consisting of both clinical and contrived samples. Sample coverage was modeled using variance component estimates from Precision Study data (18 cancer samples each with 6 to 18 replicates). The reference, single-strand mutant, and double-strand mutant molecule counts for a somatic variant site detected in at least one sample replicate were modeled utilizing a bias corrected Dirichlet Multinomial model. The variants with the simulated VAF and coverage levels were processed with the GuardantOMNI germline/somatic classifier to account for the uncertainty in germline/somatic classification at lower coverage values. Results: Precision profiles consisting of simulation derived %CV estimates for 18 clinical samples with a representative set of mean bTMB scores were generated. The PPS bTMB score distributions were consistent with the bTMB scores observed in the Precision Study, supported by visualization and confidence intervals at level 0.05 margins of equivalence for the empirical mean bTMB and standard deviation estimates. The sample specific %CV estimates were observed, in most instances, to decrease with increasing input levels for matched targeted LoD (Limit of Detection) simulation results. Precision profile %CV estimates were observed to be inversely related to mean bTMB scores. Conclusions: The results provide proof of principle that estimation of GuardantOMNI bTMB score precision via an intuitive and interpretable simulation model is viable. The simulation results were consistent with empirical data and general expectations regarding the precision of the bTMB scores. Citation Format: Kevin Doubleday, Daniel Gaile, Ravi Vijaya-Satya, Xianxian Liu, Kevin D'Auria, Soni Shukla, Han-Yu Chuang, Katie Quinn, Darya Chudova. Precision profile simulation study for a next generation sequencing bTMB assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5015.

Modelling the laminar connectome of the human brain.

The human connectome is the complete structural description of the network of connections and elements that form the 'wiring diagram' of the brain. Due to the current scarcity of information regarding laminar end points of white matter tracts inside cortical grey matter, tractography remains focused on cortical partitioning into regions, while ignoring radial partitioning into laminar components. To overcome this biased representation of the cortex as a single homogenous unit, we use a recent data-derived model of cortical laminar connectivity, which has been further explored and corroborated in the macaque brain by comparison to published studies. The model integrates multimodal MRI imaging datasets of both white matter connectivity and grey matter laminar composition into a laminar-level connectome. In this study, we model the laminar connectome of healthy human brains (N = 30) and explore them via a set of complex network measures. Our analysis demonstrates a subdivision of network hubs that appear in the standard connectome into each individual component of the laminar connectome, giving a fresh look into the role of laminar components in cortical connectivity and offering new prospects in the fields of both structural and functional connectivity.

ROCker Models for Reliable Detection and Typing of Short-Read Sequences Carrying β-Lactamase Genes.

ABSTRACTIdentification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla. Application of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice.IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings.

Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (fstarfm8); (2) the estimation accuracy based on 8d composite datasets (RMSE¯ = 11.1d) is better than daily fusion datasets (RMSE¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The RMSE¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.

Robust differential abundance test in compositional data

Summary Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data’s compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with covariate-balancing techniques to remove potential confounding effects and draw reliable conclusions. The proposed test is applied to several numerical examples, and its merits are demonstrated using both simulated and real datasets.

Hyperspectral indices developed from the low order fractional derivative spectra can capture leaf dry matter content across a variety of species better

Leaf mass per area (LMA) is an important indicator of plant functioning and photosynthetic capacity and is critical for understanding plant physiology and ecosystem function. Despite detailed and continuous spectral information offered in hyperspectral reflectance, LMA remains a difficult leaf characteristic to be retrieved due to its complex constituents and overlapping absorptions with leaf water. Traditional derivative analysis is commonly used to extract the absorption band positions and to resolve overlapping spectral features, but most cases are only limited to an integral derivative that ignores the asymptotic information between spectral curves. Recent advances in fractional-order derivative (FOD) based analyses, however, have shown their advantages in eliminating background noise as well as in extracting effective information from spectral information. We have thus investigated the potentials of using the fractional derivative indices to retrieve LMA based on a composite dataset consisting of 842 leaf samples from various species. The results demonstrated that the 0.3-order FOD indices provided the highest accuracies to trace LMA and, meanwhile, had the least sensitivity to random noise. Among the ten different index types examined in this study, the SR(1320, 1715) calculated from the 0.3-order derivative spectra had the best performance with an R2 of 0.79. Furthermore, the band around 1715 nm was confirmed to be the wavelength with the highest relative absorption of LMA, while the band around 1320 nm was the non-absorbing wavelength for LMA, which could be applied as a base to describe the effects of other leaf constituents. The results of this study revealed the potential of low-order FOD indices to capture LMA and we foresee their wide applications in the future.

CHARACTERIZATION OF TRACE ELEMENTS AND LIGHT ABSORBING PARTICLES (LAPs) AT SNOW OF HUAYTAPALLANA GLACIER (PERU)

This study reports for the first time the content of trace elements and light-absorbing particles (LAPs) in snow samples collected from a Peruvian glacier (Huaytapallana). The sampling campaign was carried out monthly from November 2015 to March 2019. The trace elements content was quantified by inductively coupled plasma mass spectrometry, while LAPs were analyzed using the light absorption heating method. The chemical composition dataset was assessed by descriptive statistics and t-test for assessing dry season and wet season differences. In addition, enrichment factor (EF) and hierarchical cluster analysis (HCA) were employed to identify possible emission sources. The snow, ice, and aerosol radiative (SNICAR) model was used to measure the effect of LAPs on snow albedo and radiative forcing (RF). Based on analysis of EF and HCA, it was shown that Al, Ti, Si, Co, Ce, Sr, Mn, Mg, Ba and Na have mainly natural sources; K, Ca, Fe, Cu, Pb and As have a mixture of natural and anthropogenic sources, and Zn has anthropogenic source. SNICAR model results indicated that LAPs reduced the snow albedo by up 4.5 % in the dry season with RF values as high as 33 W/m2. Therefore, we conclude that the presence of these particles substantially increases melt or sublimation rates of Peruvian glaciers.

The nature, origin, and predictors of porosity in the Middle to Late Devonian Horn River Group of the Central Mackenzie Valley, Northwest Territories, Canada

The characterization of porosity is an essential step in the evaluation of resource-bearing porous media. Here, we focus on the Devonian Hare Indian and Canol Formations, two potential unconventional mudstone reservoirs, in a core from the Horn River Group of the Central Mackenzie Valley, Northwest Territories, Canada. By combining bulk porosity, low-pressure N2 adsorption, and scanning electron microscopy (SEM) results with composition and lithofacies datasets, we assess the porosity in these successions to understand pore types, size, distribution, degree of connectivity, and controls and predictors of porosity. Mineral matrix pores (interparticle and intraparticle), organic matter pores, and lithofacies-dependant natural fractures are present. All pore types display limited connectivity in two dimensions. Mineralogy is the most significant control on porosity with trends in porosity present among lithofacies. No relationship is observed between porosity and total organic carbon (TOC), suggesting that mineral matrix pores, rather than organic matter pores, are dominant in this unit. We compare these results to other mudstone reservoirs in North America and show that the Bluefish Member (Hare Indian Formation) and the Canol Formation are characterized by comparable bulk porosity, lower N2 mesopore volume, and higher quartz content relative to the other units considered. In contrast, compared to the other unconventional reservoir examples, the Bell Creek Member of the Hare Indian Formation exhibits lower quartz and higher clay content, average bulk porosity, and lower N2 pore volume. The results collectively suggest that high quartz and low clay content are the best predictors of porosity in the Horn River Group. Natural fractures may serve as flow pathways to induced fractures; however, these units lack the network of interconnected organic matter pores that can be present in other successions.

A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials

Machine learning (ML) is widely used in the field of material informatics. However, limitations on the size of available datasets are a key bottleneck in the use of machine learning methods to predict material properties or reverse-design high-performance materials. To solve this problem, we propose a virtual sample generation algorithm based on a Gaussian mixture model (GMM-VSG) to address the lack of training samples in machine learning. The core idea of the algorithm is to generate virtual samples by fitting the distribution of the original samples. We used an open rubber composite dataset (24 samples) to establish a machine learning model to predict the wear resistance of rubber materials through mechanical properties to verify the performance of the GMM-VSG algorithm. The results show that after using our algorithm, the R2 of the prediction model reached 0.95, and the prediction accuracy increased by 41%. This shows that the proposed algorithm can effectively promote the prediction accuracy of data model with small sample size.

Investigating the Impacts of Feedstock Variability on a Carbon-Negative Autothermal Pyrolysis System Using Machine Learning

Feedstock properties impact the economic feasibility and sustainability of biorefinery systems. Scientists have developed pyrolysis kinetics, process, and assessment models that estimate the costs and greenhouse gas (GHG) emissions of various biorefineries. Previous studies demonstrate that feedstock properties have a significant influence on product costs and lifecycle emissions. However, feedstock variability remains a challenge due to a large number of possible feedstock property combinations and limited public availability of feedstock composition data. Here, we demonstrate the use of machine learning (ML) models to generate large feedstock sample data from a smaller sample set for sustainability assessment of biorefinery systems. This study predicts the impact of feedstock properties on the profitability and sustainability of a lignocellulosic biomass autothermal pyrolysis (ATP) biorefinery producing sugar, phenolic oil, and biochar. Generative Adversarial Networks and Kernel Density Estimation machine learning models are used to generate 3,000 feedstock samples of diverse biochemical compositions. Techno-economic and lifecycle assessments estimated that the ATP minimum sugar selling price (MSSP) ranges between $66/metric ton (MT) and $280/MT, and the greenhouse gas (GHG) range from a net negative GHG emission(s) of −0.56 to −0.74 kg CO2e/kg lignocellulosic biomass processed. These results show the potential of ML to enhance sustainability analyses by replacing Monte Carlo-type approaches to generate large feedstock composition datasets that are representative of empirical data.

Evolution and spread of a highly drug resistant strain of Mycobacterium tuberculosis in Papua New Guinea.

BackgroundMolecular mechanisms determining the transmission and prevalence of drug resistant tuberculosis (DR-TB) in Papua New Guinea (PNG) are poorly understood. We used genomic and drug susceptibility data to explore the evolutionary history, temporal acquisition of resistance and transmission dynamics of DR-TB across PNG.MethodsWe performed whole genome sequencing on isolates from Central Public Health Laboratory, PNG, collected 2017–2019. Data analysis was done on a composite dataset that also included 100 genomes previously sequenced from Daru, PNG (2012–2015).ResultsSampled isolates represented 14 of the 22 PNG provinces, the majority (66/94; 70%) came from the National Capital District (NCD). In the composite dataset, 91% of strains were Beijing 2.2.1.1, identified in 13 provinces. Phylogenetic tree of Beijing strains revealed two clades, Daru dominant clade (A) and NCD dominant clade (B). Multi-drug resistance (MDR) was repeatedly and independently acquired, with the first MDR cases in both clades noted to have emerged in the early 1990s, while fluoroquinolone resistance emerged in 2009 (95% highest posterior density 2000–2016). We identified the presence of a frameshift mutation within Rv0678 (p.Asp47fs) which has been suggested to confer resistance to bedaquiline, despite no known exposure to the drug. Overall genomic clustering was significantly associated with rpoC compensatory and inhA promoter mutations (p < 0.001), with high percentage of most genomic clusters (12/14) identified in NCD, reflecting its role as a potential national amplifier.ConclusionsThe acquisition and evolution of drug resistance among the major clades of Beijing strain threaten the success of DR-TB treatment in PNG. With continued transmission of this strain in PNG, genotypic drug resistance surveillance using whole genome sequencing is essential for improved public health response to outbreaks. With occurrence of resistance to newer drugs such as bedaquiline, knowledge of full drug resistance profiles will be important for optimal treatment selection.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12879-022-07414-2.

Detrital Carbonate Minerals in Earth's Element Cycles.

We investigate if the commonly neglected riverine detrital carbonate fluxes might reconciliate several chemical mass balances of the global ocean. Particulate inorganic carbon (PIC) concentrations in riverine suspended sediments, that is, carbon contained by these detrital carbonate minerals, were quantified at the basin and global scale. Our approach is based on globally representative data sets of riverine suspended sediment composition, catchment properties, and a two‐step regression procedure. The present‐day global riverine PIC flux is estimated at 3.1 ± 0.3 Tmol C/y (13% of total inorganic carbon export and 4% of total carbon export) with a flux‐weighted mean concentration of 0.26 ± 0.03 wt%. The flux prior to damming was 4.1 ± 0.5 Tmol C/y. PIC fluxes are concentrated in limestone‐rich, rather dry and mountainous catchments of large rivers near Arabia, South East Asia, and Europe with 2.2 Tmol C/y (67.6%) discharged between 15°N and 45°N. Greenlandic and Antarctic meltwater discharge and ice‐rafting additionally contribute 0.8 ± 0.3 Tmol C/y. This amount of detrital carbonate minerals annually discharged into the ocean implies a significant contribution of calcium (∼4.75 Tmol Ca/y) and alkalinity fluxes (∼10 Tmol (eq)/y) to marine mass balances and moderate inputs of strontium (∼5 Gmol Sr/y) based on undisturbed riverine and cryospheric inputs and a dolomite/calcite ratio of 0.1. Magnesium fluxes (∼0.25 Tmol Mg/y), mostly hosted by less‐soluble dolomite, are rather negligible. These unaccounted fluxes help in elucidating respective marine mass balances and potentially alter conclusions based on these budgets.

Community dynamics of estuarine forage fishes are associated with a latitudinal basal resource regime

AbstractForage fishes are an important component of marine, estuarine, and aquatic food webs that facilitate the transfer of energy and nutrients from primary producers to upper trophic levels. Previous studies of forage fishes have focused primarily on pelagic planktivorous species in pelagic environments. However, benthically associated taxa can be just as important as planktivorous species, particularly in highly productive estuarine environments that provide critical habitat for many predators. In this study, we analyzed a 20‐year forage fish community composition and abundance dataset across four eastern Gulf of Mexico estuaries spanning a broad latitudinal gradient to investigate spatiotemporal variability in community structure and quantify associations with habitat. Our analyses revealed significant regional structuring of forage fish communities, coupled with a strong association with habitat characteristics related to latitudinal effects and basal resource regime. Communities in the two northern estuaries and two southern estuaries were associated primarily with planktonically reliant and benthically reliant taxa, respectively. Despite regional differences, we uncovered a coherent annual cycle in forage fish communities across all estuaries related to seasonal shifts in abundances of several abundant and ubiquitous species. We additionally revealed significant subdecadal periodicity potentially associated with bottom‐up effects of global climatic cycles. The significant association of forage fish communities with habitat regime shown in this study underlies the importance of continued monitoring of these communities. This study represents a novel approach to assess this critical ecosystem component in diverse estuarine systems globally.

Standardization procedure to provide a unified multi-method elemental compositional dataset, application to ferruginous colouring matters from Namibia

Curation of archaeological materials often leads to carrying out multi-analytical methodologies that combine non-invasive and invasive elemental analyses. Such materials are often analysed with different techniques. It results in the production of complementary but apparently non-compatible compositional datasets that cannot be easily compared. In the present paper, we propose to compare results acquired on geological ferruginous colouring matters from Namibia with analytical techniques (X-Ray Fluorescence spectrometry (XRF), Proton-Induced-X-ray Emission spectrometry (PIXE), Inductively Coupled Plasma coupled to Optical Emission spectrometry (ICP-OES) and Inductively Coupled Plasma coupled to Mass Spectrometry (ICP-MS)). We aim to provide a unified elemental dataset about these ferruginous colouring matters, usually referred to as “ochre” in archaeology. Analysed geological samples come from three distinct tectonostratigraphic zones of Namibia surveyed in the frame of rock art research. When compared directly, three of the four datasets obtained from these measurements appear as non-compatible because of the inter-equipment variability. However, through a simple standardization procedure, we demonstrate that it is possible to unify these datasets. This procedure minimizes the inter-equipment variability, making the inter-zones of provenance predominant and allowing distinctions of the samples according to their sole origin. Beyond shedding new light on the possibility to compare different elemental analytical techniques, this procedure paves the way for complementary multimodal studies of ferruginous colouring matters.

Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction

Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination errors and produce suboptimal solutions. SOS was used to optimize hyperparameter tuning in LSTM for stock prediction. The stock prices were time-series data, and LSTM has proven to be a popular method for time-series forecasting. This research employed the Indonesia composite index dataset and assessed it using root mean square error (RMSE) as a key indicator and the fitness function for the metaheuristic approach. Genetic algorithm (GA) and particle swarm optimization (PSO) were used as benchmarking algorithms in this research. The hybrid SOS-LSTM model outperformed GA-LSTM and PSO-LSTM with an RMSE of 78.799, compared to the GA-LSTM model with an RMSE of 142.663 and the PSO-LSTM model with an RMSE of 529.170.

Scalability of the Positive and Negative Syndrome Scale in first-episode schizophrenia assessed by Rasch models.

ObjectiveHistorically, assessment of the psychometric properties of the Positive and Negative Syndrome Scale (PANSS) has had several foci: (1) calculation of reliability indexes, (2) extraction of subdimensions from the scale, and (3) assessment of the validity of the total score. In this study, we aimed to examine the scalability and to assess the clinical performance of the 30‐item PANSS total score as well as the scalability of a shorter version (PANSS‐6) of the scale.MethodsA composite data set of 1073 patients with first‐episode schizophrenia or schizophrenia spectrum disorder was subjected to Rasch analysis of PANSS data from baseline and 4–6 weeks follow‐up.ResultsThe central tests of fit of the Rasch model failed to satisfy the statistical requirements behind item homogeneity for the PANSS‐30 as well as the PANSS‐6 total score. For the PANSS‐30, Differential Item Functioning was pronounced both for the 7‐point Likert scale rating categories and when dichotomizing the rating categories. Subsequently, the Rasch structure analysis in the context of dichotomized items was used to isolate and estimate a systematic error because of item inhomogeneity, as well as a random error. The size of the combined sources of error for the PANSS‐30 total score approximated 20% which is often regarded as clinical cut‐off between response versus no‐response.ConclusionThe results demonstrate the operational consequences of a lack of statistical fit of the Rasch model and suggest that the calculated measure of uncertainty needs to be considered when using the PANSS‐30 total score.