Original Sample Research Articles

Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions for luminescence dating

Abstract. During ionizing irradiation, potassium (K)-rich feldspar grains emit infrared (IR) light, which is used for infrared radiofluorescence (IR-RF) dating. The late-saturating IR-RF emission centred at ∼880 nm represents a promising tool to date Quaternary sediments. In the present work, we report the presence of individual grains in the K-feldspar density fraction displaying an aberrant IR-RF signal shape, whose combined intensity contaminates the sum signal of an aliquot composed of dozens of grains. Our experiments were carried out at the National Synchrotron Light Source (NSLS-II) at the submicron-resolution X-ray spectroscopy (SRX) beamline. We analysed coarse (>90 µm) K-feldspar-bearing grains of five samples of different ages and origin in order to characterize the composition of grains yielding the desired or contaminated IR-RF emission. Using micro-X-ray fluorescence (μ-XRF), we successfully acquired element distribution maps of up to 15 elements (<1 µm resolution) of sections of full grains previously used for IR-RF dating. In keeping with current theories of IR-RF signal production, we observed a trend between the relative proportions of Pb and Fe and the shape of the IR-RF signal, namely that most grains with the desired IR-RF signal shape had high Pb and low Fe contents. Interestingly, these grains were also defined by high Ba and low Ca contents. Our study also represents a proof of concept for mapping the oxidation states of Fe using micro-X-ray absorption near-edge structure spectroscopy (μ-XANES) on individual grains. The high spatial resolution enabled by synchrotron X-ray spectroscopy makes it a powerful tool for future experiments to elucidate long-standing issues concerning the nature and type of defect(s) associated with the main dosimetric trap in feldspar.

The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder–Fine-Grained Meta-Analyses

Background. While the brain correlates of major depressive disorder (MDD) have been extensively studied, there is no consensus conclusion so far. Various meta-analyses tried to determine the most consistent findings, but the results are often discordant for grey matter volume (GMV) atrophy and hypertrophy. Applying rigorous and stringent inclusion criteria and controlling for confounding factors, such as the presence of anxiety comorbidity, we carried out two novel meta-analyses on the existing literature to unveil MDD signatures. Methods. A systematic literature search was performed up to January 2023. Seventy-three studies on MDD patients reporting GMV abnormalities were included in the first meta-analysis, for a total of 6167 patients and 6237 healthy controls (HC). To test the effects of anxiety comorbidity, we conducted a second meta-analysis, by adding to the original pure MDD sample a new cohort of MDD patients with comorbid anxiety disorders (308 patients and 342 HC). An activation likelihood estimation (ALE) analysis and a coordinate-based mapping approach separate for atrophy and hypertrophy were used to identify common brain structural alterations among patients. Results. The pure MDD sample exhibited atrophy in the left insula, as well as hypertrophy in the bilateral amygdala and parahippocampal gyri. When we added patients with comorbid anxiety to the original sample, bilateral insula atrophy emerged, whereas the hypertrophy results were not replicated. Conclusions. Our findings revealed important structural alterations in pure MDD patients, particularly in the insula and amygdala, which play key roles in sensory input integration and in emotional processing, respectively. Additionally, the amygdala and parahippocampal gyrus hypertrophy may be related to MDD functional overactivation to emotional stimuli, rumination, and overactive self-referential thinking. Conversely, the presence of anxiety comorbidity revealed separate effects which were not seen in the pure MDD sample, underscoring the importance of strict inclusion criteria for investigations of disorder-specific effects.

Augmented Sparse Representation for Incomplete Multiview Clustering.

Incomplete multiview data are collected from multiple sources or characterized by multiple modalities, where the features of some samples or some views may be missing. Incomplete multiview clustering (IMVC) aims to partition the data into different groups by taking full advantage of the complementary information from multiple incomplete views. Most existing methods based on matrix factorization or subspace learning attempt to recover the missing views or perform imputation of the missing features to improve clustering performance. However, this problem is intractable due to a lack of prior knowledge, e.g., label information or data distribution, especially when the missing views or features are completely damaged. In this article, we proposed an augmented sparse representation (ASR) method for IMVC. We first introduce a discriminative sparse representation learning (DSRL) model, which learns the sparse representations of multiple views as applied to measure the similarity of the existing features. The DSRL model explores complementary and consistent information by integrating the sparse regularization item and a consensus regularization item, respectively. Simultaneously, it learns a discriminative dictionary from the original samples. The sparsity constrained optimization problem in the DSRL model can be efficiently solved by the alternating direction method of multipliers (ADMM). Then, we present a similarity fusion scheme, namely, a sparsity augmented fusion of sparse representations, to obtain a sparsity augmented similarity matrix across different views for spectral clustering. Experimental results on several datasets demonstrate the effectiveness of the proposed ASR method for IMVC.

Decreasing Photoreactivity and Concurrent Change in Dissolved Organic Matter Composition With Increasing Inland Water Residence Time

AbstractPhotochemical degradation of dissolved organic matter (DOM) has been the subject of numerous studies; however, its regulation along the inland water continuum is still unclear. We aimed to unravel the DOM photoreactivity and concurrent DOM compositional changes across 30 boreal aquatic ecosystems including peat waters, streams, rivers, and lakes distributed along a water residence time (WRT) gradient. Samples were subjected to a standardized exposure of simulated sunlight. We measured the apparent quantum yield (AQY), which corresponds to DOM photomineralization per photon absorbed, and the compositional change in DOM at bulk and individual compound levels in the original samples and after irradiation. AQY increased with the abundance of terrestrially derived DOM and decreased at higher WRT. Additionally, the photochemical changes in both DOM optical properties and molecular composition resembled changes along the natural boreal WRT gradient at low WRT (<3 years). Accordingly, mass spectrometry revealed that the abundance of photolabile and photoproduced molecules decreased with WRT along the boreal aquatic continuum. Our study highlights the tight link between DOM composition and DOM photodegradation. We suggest that photodegradation is an important driver of DOM composition change in waters with low WRT, where DOM is highly photoreactive.

Preliminary study on the enhanced bioremediation of PAH-contaminated soil in Beijing and assessment of remediation effects based on toxicity tests.

In this study, we focused on soil contaminated by polycyclic aromatic hydrocarbons (PAHs) at typical coking-polluted sites in Beijing, conducted research on enhanced PAH bioremediation and methods to evaluate remediation effects based on toxicity testing, and examined changes in pollutant concentrations during ozone preoxidation coupled with biodegradation in test soil samples. The toxicity of mixed PAHs in soil was directly evaluated using the Ames test, and the correlation between mixed PAH mutagenicity and benzo(a)pyrene (BaP) toxicity was investigated in an effort to establish a carcinogenic risk assessment model based on biological toxicity tests to evaluate remediation effects on PAH-contaminated soil. This study provides a theoretical and methodological foundation for evaluating the effect of bioremediation on PAH-contaminated soil at industrially contaminated sites. The results revealed that the removal rate of PAHs after 5min of O3 preoxidation and 4weeks of soil reaction with saponin surfactants and medium was 83.22%. The soil PAH extract obtained after remediation had a positive effect on the TA98 strain at a dose of 2000μg·dish-1, and the carcinogenic risk based on the Ames toxicity test was 8.98 times greater than that calculated by conventional carcinogenic PAH toxicity parameters. The total carcinogenic risk of the remediated soil samples was approximately one order of magnitude less than that of the original soil samples.

First report of the maize cyst nematode Heterodera zeae in Sichuan Province of Southwest China.

Maize is the largest crop planted in China. Nine species of cyst nematodes have been reported to affect maize production. Heterodera zeae, H. avenae and Punctodera chalcoensis can cause significant maize yield losses annually (Luc et al. 2005). In 1971, the maize cyst nematode H. zeae was first detected in Rajasthan, India (Koshy et al. 1971). Subsequently, it has been reported in many other countries such as the United States, Greece, Pakistan, and Egypt. In China, H. zeae was first identified in the maize fields of Laibin City, Guangxi Zhuang Autonomous Region (Wu et al., 2017). Cui et al. (2020) identified H. zeae in a maize field of Yuzhou City, Henan Province of Central China in 2018. From 2018 to 2022, a survey of cyst-forming nematodes was conducted in Southwest China. Fifteen soil samples of about 500 g each were collected from Luding County, Ganzi Prefecture of Sichuan Province. No major aboveground symptoms were shown on maize, but a few females were observed on the roots of maize in one field. The cysts and second-stage juveniles (J2s) were collected from each soil sample using Cobb's screening gravity method. A total of 8.50±2.0 cysts per 100 ml of soil on the average were observed in the field. A thin subcrystalline layer was discernible only in young cysts. Morphological and molecular studies of cysts and J2s indicated that the nematodes were identified to be H. zeae in a maize-field. Morphologically, the cysts were in a lemon shape, light brown or pearly white in color. The vulval cone was prominent. Fenestra ambifenestrate, and semifenestra were separated by a fairly wide vulval bridge, fenestral length and width were variable, and the cyst wall was shown in a zigzag pattern. The J2s' body was in a vermiform, tapering at both ends, with a hyaline tail. Stylet was strongly developed with round or slightly anteriorly directed knobs. Morphological measurements of the cysts (n = 9) determined that the mean body length was 417.2 μm (403.6 to 439.4 μm), body width was 429.7 μm (397.6 to 456.9μm); length-width ratio was 1.4 (0.75 to 3); fenestra length was 525.3 μm (498.5 to 570.7 μm); and the mean semifenestra width was 458.6 μm (403.6 to 546.3 μm). Morphometric measurements of second-stage juveniles (n = 20) showed a body length of 419.7μm (355.8 to 492.5 μm); a stylet length of 20.8 μm (19.51 to 23.3μm); a tail length of 41.5 μm (20 to 49.4 μm); and a hyaline tail length of 20.7 μm (16.6 to 24 μm). The main morphological characteristics and measured values were basically consistent with those described by Cui et al. (2022), and all of which were similar to those of H. zeae. Amplification of DNA from random single cysts (n = 5) was conducted using the protocol described by Cui et al. (2022). The rDNA-internal transcribed spacer (ITS) was amplified and sequenced using a pair of universal primers TW81 (5'-GTTTCCGTAGGTGAA CCTGC-3') and AB28 (5'-ATATGCTTAAGTTCAGCGGGT-3'). The ITS sequences were deposited at GenBank with the accession number OR811029.1. Alignments of sequences showed an identity of 98% with H. zeae sequences from China (OP692769.2, MW785772.1) and the USA (GU145616.1), which were confirmed using a pair of species-specific primers HzF1 (5'-GGGGAGGTGAATGTGGG-3') and HzR1 (5'-CCTTTGGCAATCGGTGA-3') of H. zeae with a targeted PCR fragment of 393 bp (Cui et al. 2022). Pathogenicity was conducted and confirmed by infection and reproduction on maize. Seeds (cv. Zhengda 619) were sown in three pots that contained 150 ml of a sterile soil mixture (loamy soil: sand=1:1), and 5 cysts (103 eggs/cyst on the average) were inoculated in each pot at 25/30°C, under a 12-h dark/12-h light condition (Cui et al. 2023). Fifteen days after sowing, third- and fourth-stage juveniles were observed in the rootstained with acid fuchsin, and a total of 32 cysts per maize plant on the average were collected at 40 days after sowing. The new cysts' morphological and molecular characteristics were identical to the cysts from the original soil samples. To the best of our knowledge, this is the first report of H. zeae as a pathogen on maize in Sichuan Province, Southwest China. Our findings will be useful for management and further research of maize cyst nematodes.

Heat 'n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour.

Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.

Mean Square Error Estimation of Small Area Predictors by Use of Parametric and Nonparametric Bootstrap

In this article, we propose and compare some old and new parametric and nonparametric bootstrap methods for MSE estimation in small area estimation, restricting to the case of the widely used Fay-Herriot model. The parametric method consists of generating parametrically a large number of area bootstrap samples from the model fitted to the original data, re-estimating the model parameters for each bootstrap sample and then estimating the separate components of the MSE. The use of double-bootstrap is also considered. The nonparametric method generates the samples by bootstrapping standardized residuals, estimated from the original sample data. The bootstrap procedures are compared to other methods proposed in the literature in a simulation study, which also examines the robustness of the various methods to non-normality of the model error terms. A design-based MSE estimator for the Fay-Herriot model-dependent predictor is also described and its performance is investigated in a separate simulation study. AMS subject classification: 62F10, 62F40

Anchor-based scalable multi-view subspace clustering

Multi-view clustering is a major topic in pattern recognition and machine learning. Common multi-view clustering algorithms construct similarity graphs from original samples and use them to perform spectral clustering. The time complexity of the singular value decomposition process in graph construction and spectral clustering is high, leading to high computational and memory costs. In addition, subsequent K-means clustering is sensitive to the initial points, yielding unstable clustering results. To address these issues, this study proposes a novel approach to reduce time overhead and memory space from two perspectives. First, a new anchor selection method is proposed to reduce the dimension of the original data by lowering the cost. Second, the self-representation matrix of multi-views is fused into a consistent graph matrix using the post-fusion technique, and the fused graph is directly processed in postprocessing. Furthermore, the proposed method directly obtains clustering results based on the connectivity of the fusion graph, eliminating the need for K-means postprocessing, which avoids the issue of unstable clustering results. Experimental results on artificial and real multi-view datasets indicate that the proposed algorithm is superior to existing algorithms.

A fault diagnosis method based on label-wise density-domain space learning

Abstract Nonlinear space learning of fault samples is a category of common fault diagnosis methods, which usually use Euclidean distances to describe manifold structures among fault samples. However, in nonlinear space, Euclidean distances lead to a potential manifold loss problem. Aiming these issues, we propose a novel fault diagnosis method based on label-wise density-domain space learning. The label-wise density-domain space learns more intrinsic manifold structures from four density-constrained order graphs. Density-constrained order graphs constructed by our method integrate different discriminative relationships from original fault samples with the help of density-domain information, and the density-domain information can effectively capture potential density information and global structure between fault samples. By density Laplacian of the graphs, we further construct a label-wise density-domain manifold space learning model, and the analytical solutions of space projections can be obtained by solving the model. Fault features directly obtained by the space projections possess good class separability. Extensive experiments on the Case Western Reserve University fault dataset and a roll-bearing fault dataset from our roll-bearing test platform show the effectiveness and robustness of our method.

Bioinspired stormwater control measure for the enhanced removal of truly dissolved polycyclic aromatic hydrocarbons and heavy metals from urban runoff

Stormwater harvesting (SWH) addresses the UN's Sustainable Development Goals (SDGs). Conventional stormwater control measures (SCMs) effectively remove particulate and colloidal contaminants from urban runoff; however, they fail to retain dissolved contaminants, particularly substances of concern like polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs), thereby hindering the SWH applicability. Here, inspired by protein folding in nature, we reported a novel biomimetic SCM for the efficient removal of dissolved PAHs and HMs from urban runoff. Lab-scale tests were conducted together with a more mechanistic investigation on how the contaminants were removed. By integrating hydrophobic organic chains with low-cost hydrophilic flocculant matrixes, our biomimetic flocculants achieved a 1.4–9.5 times removal of all detected dissolved PAHs and HMs, while enhancing the removal of a wide-spectrum of particulate and colloidal contaminants, compared to existing SCMs. Ecotoxicity, as indicated by newborn Daphnia magna as experimental organisms, was reduced from “acute toxicity” of the original runoff sample (toxic unit of ∼2.6) to “non-toxicity” (toxic unit < 0.4) of the treated water. The improved performance is attributed to the protein-folding-like features of the bioinspired flocculants providing: (i) stronger binding to PAHs (via hydrophobic association) and HMs (via coordination), and (ii) the ability of spontaneous aggregation. The bio-inspired approach in this work holds strong promise as an alternative or supplementary component in SCM systems, and is expected to contribute to sustainable water management practices in relation to SDGs.

A new xG model for football analytics

The aim of this exploratory study is to refine and improve, in terms of prediction performance, the expected goal (xG) model, one of the emerging tools in the field of football analytics. With this final goal, we merged data from different sources: tracking data, match event data and some players’ performance composite indicators. Using an original sample of match data relying on the 2019/2020 season of the Italian Serie A, composed of 660 shots, one outcome (i.e. the GOAL) and 22 regressors, a supervised machine learning approach (logistic regression model with imbalanced training sample adjustment) was applied to different scenarios for sample balanced techniques. Results are interesting in terms of sensitivity and F1 metrics, compared with a benchmark (Understat). Other results concerning the classic imbalance framework significantly outperform the benchmark in terms of the AUC metric. In addition, some performance composite indicators and one original tracking variable are significant for the classification model, contributing to increasing the goal prediction probability compared with the benchmark.

Further enhancement of surface mechanical properties of carburized 9310 steel by electropulsing-assisted ultrasonic surface rolling process

9310 carburized steel experiences significant wear and spalling problems when operating under high friction and heavy loads, which severely restricts its applications in the aviation industry. Therefore, it holds great significance to further enhance the surface mechanical properties of 9310 carburized steel through suitable surface strengthening technologies. The Electropulsing-Assisted Ultrasonic Surface Rolling Process (EP-USRP) has been successfully applied to carburized 9310 steel, yielding significant improvements in surface quality, hardness, friction and wear properties, and microstructure. Systematic investigations were conducted under varying EP-USRP parameters. Regarding the electric pulse parameters, it is observed that with the increase in output voltage, surface properties such as surface roughness and surface hardness tend to first optimize and then deteriorate. After applying EP-USRP with optimal parameters, the surface roughness of carburized 9310 steel was reduced to Ra 0.154 μm, a mere 5 % of its initial value. The surface hardness experienced a substantial increase, reaching 909.2 HV, representing a notable improvement of 29.9 % compared to the original sample, with a corresponding hardening depth of 1400 μm. Moreover, the wear amount of carburized 9310 steel witnessed a remarkable reduction to 11.8 % of its initial value, accompanied by a decrease in the coefficient of friction to 43.0 %. The enhancement of carburized 9310 steel through EP-USRP is attributed to two key mechanisms: deformation hardening induced by severe plastic deformation and microstructure strengthening resulting from strain-induced martensitic transformation. Among them, the severe plastic deformation mechanism is dominant, Through these mechanisms, the proportion of small-angle boundaries in carburized 9310 steel strengthened by EP-USRP increased. Additionally, the grain size of the surface layer decreased, and the content of retained austenite in the surface layer decreased, collectively contributing to significant improvements in surface properties, as well as friction and wear performance.

Adaptive weighted dictionary representation using anchor graph for subspace clustering

Samples are commonly represented as sparse vectors in many dictionary representation algorithms. However, this method may result in loss of discriminatory information. Moreover, a redundant dictionary can increase the computational complexity of the algorithm. To tackle these challenges, we propose a novel method named Adaptive Weighted Dictionary Representation using Anchor Graph for Subspace Clustering (AWDR). First, AWDR constructs an anchor graph that encodes the classification information and establishes accurate connectivity components between anchors and clusters, thereby fully utilizing the discriminative information of the original samples. In addition, AWDR learns a complete-dictionary in the subspace to eliminate the noise and out-of-sample effects of the original sample space, while also improving computational efficiency. Finally, AWDR computes the coefficients for the samples in an adaptively weighted manner to find discriminative representation of the samples from the dictionary. Extensive experiments on real-world datasets demonstrate that our method is effective and efficient compared to the state-of-the-art methods.

Molecular Characterization Analysis and Adaptive Responses of Spodoptera frugiperda (Lepidoptera: Noctuidae) to Nutritional and Enzymatic Variabilities in Various Maize Cultivars.

The fall armyworm, Spodoptera frugiperda Smith (Lepidoptera: Noctuidae), a common agricultural pest known for its extensive migration and wide host ranges, causes considerable harm to maize (Zea mays L.). In this study, we utilized two molecular marker genes, COI and Tpi, to compare the genetic characteristics of the collected original samples. Additionally, through an interactive study between S. frugiperda larvae and six maize varieties aiming to understand the insect's adaptability and resistance mechanisms, our analysis revealed that both the COI and Tpi genes identified S. frugiperda as the corn strain. Further examination of the larvae showed significant differences in nutritional indices, digestive, and detoxification enzyme activities. Special maize varieties were found to offer higher efficiency in nutrient conversion and assimilation compared with common varieties. This study revealed adaptations in S. frugiperda's digestive and detoxification processes in response to the different maize varieties. For instance, larvae reared on common maize exhibited elevated amylase and lipase activities. Interestingly, detoxification enzyme activities exhibited different patterns of variation in different maize varieties. The Pearson correlation analysis between nutritional indices, enzyme activities, and the nutritional content and secondary metabolites of maize leaves provided deeper insights into the pest's adaptability. The results highlighted significant relationships between specific nutritional components in maize and the physiological responses of S. frugiperda. Overall, our findings contribute substantially to the understanding of S. frugiperda's host plant adaptability, offering critical insights for the development of sustainable pest management strategies.

Effective time-series Data Augmentation with Analytic Wavelets for bearing fault diagnosis

In the realm of rotary machine maintenance, rolling bearings emerge as crucial yet frequently vulnerable components. Ensuring their operational integrity is pivotal for the overall safety and performance of the machinery. Traditional diagnostic methods have utilized a variety of signal processing techniques, ranging from blind source separation to wavelet analysis, and more recently, deep neural networks (DNNs). However, a significant challenge in this domain is the scarcity of labeled failure data in real-world applications, leading to a reliance on data augmentation (DA) techniques to enhance the quality and quantity of the training data. Compounding this problem is the use of DA techniques tailored for images, which are not suitable for the time–frequency representations. This study proposes an innovative Data Augmentation with Analytic Wavelets (DAAW) approach, tailored specifically for time-series (TS) data classification in bearing fault diagnosis. The essence of DAAW lies in its ability to generate synthetic scalogram samples that closely mirror the properties of original samples. The proposed DAAW is directly applied at the input stage of a learning model by successively adjusting predefined parameters to generate scalograms, without the need for auxiliary DA algorithms. This technique is versatile in generating synthetic time–frequency data samples, and the parameterization enables it to be tailored to different time series datasets. Through experimentation with publicly available datasets, we show that combining a simple Convolutional Neural Network (CNN) with the proposed approach results in improved performance. It outperforms existing methods, achieving a mean classification accuracy of 99.49%. The critical distance for average accuracy, as determined by the Nemenyi post-hoc test, is 0.4619. Our results also show enhanced performance with accuracy rates of 90.14%, 78.94%, and 62.86% even under low signal-to-noise ratio (SNR) conditions, using 80%, 60%, and 50% of the dataset, respectively, for model training. Notably, its efficacy remains robust even under challenging low SNR conditions. The proposed DAAW significantly improves classification accuracy in bearing fault diagnosis, simultaneously obviating the need for proxy DA tasks.

Superior high-efficiency and high-throughput volatile flavor extraction of Japanese fermented seasonings by solvent-assisted stir bar solid extraction with reverse extraction

Fermented seasonings have pleasant flavors that stimulate our appetite. Their flavoring properties change depending on factors such as their materials and fermented conditions. Therefore, a comparative analysis of their flavor is important when evaluating their quality. However, seasonings contain high levels of various matrices such as sugars, proteins, lipids, and ethanol, making it difficult to extract aroma compounds efficiently from them. In this study, we verified a high-efficient and high-throughput volatile flavor analysis of fermented seasonings by solvent-assisted stir bar solid extraction (SA-SBSE) with reverse extraction. We applied SA-SBSE to Japanese fermented seasonings, soy sauce, miso (fermented beans), and mirin (sweet rice wine) and compared their profiles with those from other common extraction methods, headspace gas-solid-phase microextraction (HS-SPME), liquid extraction with solvent-assisted flavor evaporation (LE-SAFE), and conventional SBSE (C-SBSE). The aroma properties and profiles of extracts from SA-SBSE were close to those of the original sample, being similar to that of LE-SAFE. In addition, potent aroma compounds in each sample were extracted by SA-SBSE and LE-SAFE, which were far superior to those by C-SBSE. For quantification, SA-SBSE extracts showed a good standard curve by the standard addition method. We could quantify maltol, one of the most common potent aroma compounds in all samples, for various commercial samples by such high-throughput analysis.

Cd(II) and Pb(II) adsorption in karst soils amended with litter extract from Ficus virens

In this study, different proportions of Le were used to amend purple, yellow, and yellow–brown soils of karst areas to investigate the effect of litter extract (Le) from Ficus virens on Cd(II) and Pb(II) adsorption in karst soils. The basic physicochemical properties of Le-amended karst soils were determined, and the microscopic morphology of Le-amended karst soils was detected. The batch method was used to study the isothermal adsorption characteristics of Cd(II) and Pb(II) adsorption on different Le-amended karst soils. Physicochemical properties and microscopic morphology results showed that Le was amended on the karst soil surface and changed the surface properties of the karst soil. The maximum adsorption capacity range of Cd(II) and Pb(II) by Le-amended karst soils was 92.10–241.61 and 108.91–262.12 mmol/kg, respectively, and the peak value was reached when the karst soils were amended with 20 % of Le. Increasing the pH and temperature were beneficial to Cd(II) and Pb(II) adsorption in each Le-amended karst soil. The adsorption amount of Cd(II) and Pb(II) initially increased and thendecreased with the increase in ionic strength. Cd(II) and Pb(II) adsorption was a spontaneous, endothermic, and entropy-increasing process, and electrostatic attraction, precipitation, complexation, and ion exchange were the main adsorption mechanisms. The adsorption amount of Cd(II) and Pb(II) by Le-amended yellow–brown soil maintained approximately 85 % of original sample after three rounds of regeneration.

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

BackgroundSurveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys.ObjectiveTo develop a new reweighting method for overlapping panel surveys affected by non-response.MethodsWe chose the Healthcare and Social Survey which has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement.ResultsOur reweighting methodological approach is the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. It is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health.ConclusionThe proposed method produces suitable estimators for both cross-sectional and longitudinal samples. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study.

A direct PCR approach with low-biomass insert opens new horizons for molecular sciences on cryptogam communities.

Molecular sequence data have transformed research on cryptogams (e.g., lichens, microalgae, fungi, and symbionts thereof) but methods are still strongly hampered by the small size and intermingled growth of the target organisms, poor cultivability and detrimental effects of their secondary metabolites. Here, we aim to showcase examples on which a modified direct PCR approach for diverse aspects of molecular work on environmental samples concerning biocrusts, biofilms, and cryptogams gives new options for the research community. Unlike traditional approaches, this methodology only requires biomass equivalent to colonies and fragments of 0.2 mm in diameter, which can be picked directly from the environmental sample, and includes a quick DNA lysis followed by a standardized PCR cycle that allows co-cycling of various organisms/target regions in the same run. We demonstrate that this modified method can (i) amplify the most widely used taxonomic gene regions and those used for applied and environmental sciences from single colonies and filaments of free-living cyanobacteria, bryophytes, fungi, and lichens, including their mycobionts, chlorobionts, and cyanobionts from both isolates and in situ material during co-cycling; (ii) act as a tool to confirm that the dominant lichen photobiont was isolated from the original sample; and (iii) optionally remove inhibitory secondary lichen substances. Our results represent examples which highlight the method's potential for future applications covering mycology, phycology, biocrusts, and lichenology, in particular.IMPORTANCECyanobacteria, green algae, lichens, and other cryptogams play crucial roles in complex microbial systems such as biological soil crusts of arid biomes or biofilms in caves. Molecular investigations on environmental samples or isolates of these microorganisms are often hampered by their dense aggregation, small size, or metabolism products which complicate DNA extraction and subsequent PCRs. Our work presents various examples of how a direct DNA extraction and PCR method relying on low biomass inserts can overcome these common problems and discusses additional applications of the workflow including adaptations.