Circulating proteomic biomarkers for diagnosing sporadic amyotrophic lateral sclerosis:a cross-sectional study

Biomarke rs are required for the early detection,prognosis prediction,and monitoring of amyotrophic lateral sclerosis,a progressive disease.Proteomics is an unbiased and quantitative method that can be used to detect neurochemical signatures to aid in the identification of candidate biomarke rs.In this study,we used a label-free quantitative proteomics approach to screen for substantially differentially regulated proteins in ten patients with sporadic amyotrophic lateral scle rosis compared with five healthy controls.Su bstantial upregulation of serum proteins related to multiple functional clusters was observed in patients with spo radic amyotrophic lateral sclerosis.Potential biomarke rs were selected based on functionality and expression specificity.To validate the proteomics profiles,blood samples from an additional cohort comprising 100 patients with sporadic amyotrophic lateral sclerosis and 100 healthy controls were subjected to enzyme-linked immunosorbent assay.Eight substantially upregulated serum proteins in patients with spora dic amyotrophic lateral sclerosis were selected,of which the cathelicidin-related antimicrobial peptide demonstrated the best discriminative ability between patients with sporadic amyotrophic lateral sclerosis and healthy controls(area under the curve[AUC]=0.713,P<0.0001).To further enhance diagnostic accuracy,a multi-protein combined discriminant algorithm was developed incorporating five proteins(hemoglobin beta,cathelicidin-related antimicrobial peptide,talin-1,zyxin,and translationally-controlled tumor protein).The algo rithm achieved an AUC of 0.811 and a P-value of<0.0001,resulting in 79%sensitivity and 71%specificity for the diagnosis of sporadic amyotrophic lateral scle rosis.Subsequently,the ability of candidate biomarkers to discriminate between early-stage amyotrophic lateral sclerosis patients and controls,as well as patients with different disease severities,was examined.A two-protein panel comprising talin-1 and translationally-controlled tumor protein effectively distinguished early-stage amyotrophic lateral sclerosis patients from controls(AUC=0.766,P<0.0001).Moreove r,the expression of three proteins(FK506 binding protein 1A,cathelicidin-related antimicrobial peptide,and hemoglobin beta-1)was found to increase with disease progression.The proteomic signatures developed in this study may help facilitate early diagnosis and monitor the progression of sporadic amyotrophic lateral sclerosis when used in co mbination with curre nt clinical-based parameters.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Mining elite loci and candidate genes for root morphology-related traits at the seedling stage by genome-wide association studies in upland cotton(Gossypium hirsutum L.)

Root system architecture plays an essential role in water and nutrient acquisition in plants,and it is significantly involved in plant adaptations to various environmental stresses.In this study,a panel of 242 cotton accessions was collected to investigate six root morphological traits at the seedling stage,including main root length(MRL),root fresh weight(RFW),total root length(TRL),root surface area(RSA),root volume(RV),and root average diameter(AvgD).The correlation analysis of the six root morphological traits revealed strong positive correlations of TRL with RSA,as well as RV with RSA and AvgD,whereas a significant negative correlation was found between TRL and AvgD.Subsequently,a genome-wide association study(GWAS)was performed using the root phenotypic and genotypic data reported previously for the 242 accessions using 56,010 single nucleotide polymorphisms(SNPs)from the CottonSNP80K array.A total of 41 quantitative trait loci(QTLs)were identified,including nine for MRL,six for RFW,nine for TRL,12 for RSA,12 for RV and two for AvgD.Among them,eight QTLs were repeatedly detected in two or more traits.Integrating these results with a transcriptome analysis,we identified 17 candidate genes with high transcript values of transcripts per million(TPM)≥30 in the roots.Furthermore,we functionally verified the candidate gene GH_D05G2106,which encodes a WPP domain protein 2in root development.A virus-induced gene silencing(VIGS)assay showed that knocking down GH_D05G2106significantly inhibited root development in cotton,indicating its positive role in root system architecture formation.Collectively,these results provide a theoretical basis and candidate genes for future studies on cotton root developmental biology and root-related cotton breeding.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

Highly Aligned Graphene Aerogels for Multifunctional Composites

Stemming from the unique in-plane honeycomb lattice structure and the sp^(2)hybridized carbon atoms bonded by exceptionally strong carbon–carbon bonds,graphene exhibits remarkable anisotropic electrical,mechanical,and thermal properties.To maximize the utilization of graphene’s in-plane properties,pre-constructed and aligned structures,such as oriented aerogels,films,and fibers,have been designed.The unique combination of aligned structure,high surface area,excellent electrical conductivity,mechanical stability,thermal conductivity,and porous nature of highly aligned graphene aerogels allows for tailored and enhanced performance in specific directions,enabling advancements in diverse fields.This review provides a comprehensive overview of recent advances in highly aligned graphene aerogels and their composites.It highlights the fabrication methods of aligned graphene aerogels and the optimization of alignment which can be estimated both qualitatively and quantitatively.The oriented scaffolds endow graphene aerogels and their composites with anisotropic properties,showing enhanced electrical,mechanical,and thermal properties along the alignment at the sacrifice of the perpendicular direction.This review showcases remarkable properties and applications of aligned graphene aerogels and their composites,such as their suitability for electronics,environmental applications,thermal management,and energy storage.Challenges and potential opportunities are proposed to offer new insights into prospects of this material.

Mangrove wetlands distribution status identification, changing trend analyzation and carbon storage assessment of China

This research investigates the ecological importance,changes,and status of mangrove wetlands along China’s coastline.Visual interpretation,geological surveys,and ISO clustering unsupervised classification methods are employed to interpret mangrove distribution from remote sensing images from 2021,utilizing ArcGIS software platform.Furthermore,the carbon storage capacity of mangrove wetlands is quantified using the carbon storage module of InVEST model.Results show that the mangrove wetlands in China covered an area of 278.85 km2 in 2021,predominantly distributed in Hainan,Guangxi,Guangdong,Fujian,Zhejiang,Taiwan,Hong Kong,and Macao.The total carbon storage is assessed at 2.11×10^(6) t,with specific regional data provided.Trends since the 1950s reveal periods of increase,decrease,sharp decrease,and slight-steady increases in mangrove areas in China.An important finding is the predominant replacement of natural coastlines adjacent to mangrove wetlands by artificial ones,highlighting the need for creating suitable spaces for mangrove restoration.This study is poised to guide future mangroverelated investigations and conservation strategies.

Method for evaluation of geological strength index of carbonate cliff rocks:Coupled hyperspectral-digital borehole image technique

The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Photoacoustic/ultrasound dual-modal imaging of human nails: A pilot study

Traditional diagnostic techniques including visual examination,ultrasound(US),and magnetic resonance imaging(MRI)have limitations of in-depth information for the detection of nail disorders,resolution,and practicality.This pilot study,for thefirst time,evaluates a dualmodality imaging system that combines photoacoustic tomography(PAT)with the US for the multiparametric quantitative assessment of human nail.The study involved a small cohort offive healthy volunteers who underwent PAT/US imaging for acquiring the nail unit data.The PAT/US dual-modality imaging successfully revealed thefine anatomical structures and microvascular distribution within the nail and nail bed.Moreover,this system utilized multispectral PAT to analyze functional tissue parameters,including oxygenated hemoglobin,deoxyhemoglobin,oxygen saturation,and collagen under tourniquet and cold stimulus tests to evaluate changes in the microcirculation of the nail bed.The quantitative analysis of multispectral PAT reconstructed images demonstrated heightened sensitivity in detecting alterations in blood oxygenation levels and collagen content within the nail bed,under simulated different physiological conditions.This pilot study highlights the potential of PAT/US dual-modality imaging as a real-time,noninvasive diagnostic modality for evaluating human nail health and for early detection of nail bed pathologies.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Atomic-level quantitative analysis of electronic functional materials by aberration-corrected STEM

The stable sub-angstrom resolution of the aberration-corrected scanning transmission electron microscope(ACSTEM)makes it an advanced and practical characterization technique for all materials.Owing to the prosperous advancement in computational technology,specialized software and programs have emerged as potent facilitators across the entirety of electron microscopy characterization process.Utilizing advanced image processing algorithms promotes the rectification of image distortions,concurrently elevating the overall image quality to superior standards.Extracting high-resolution,pixel-level discrete information and converting it into atomic-scale,followed by performing statistical calculations on the physical matters of interest through quantitative analysis,represent an effective strategy to maximize the value of electron microscope images.The efficacious utilization of quantitative analysis of electron microscope images has become a progressively prominent consideration for materials scientists and electron microscopy researchers.This article offers a concise overview of the pivotal procedures in quantitative analysis and summarizes the computational methodologies involved from three perspectives:contrast,lattice and strain,as well as atomic displacements and polarization.It further elaborates on practical applications of these methods in electronic functional materials,notably in piezoelectrics/ferroelectrics and thermoelectrics.It emphasizes the indispensable role of quantitative analysis in fundamental theoretical research,elucidating the structure–property correlations in high-performance systems,and guiding synthesis strategies.

Characterization and quantification of multi-field coupling in lithium-ion batteries under mechanical constraints

The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.

Novel damage constitutive models and new quantitative identification method for stress thresholds of rocks under uniaxial compression

Four key stress thresholds exist in the compression process of rocks,i.e.,crack closure stress(σ_(cc)),crack initiation stress(σ_(ci)),crack damage stress(σ_(cd))and compressive strength(σ_(c)).The quantitative identifications of the first three stress thresholds are of great significance for characterizing the microcrack growth and damage evolution of rocks under compression.In this paper,a new method based on damage constitutive model is proposed to quantitatively measure the stress thresholds of rocks.Firstly,two different damage constitutive models were constructed based on acoustic emission(AE)counts and Weibull distribution function considering the compaction stages of the rock and the bearing capacity of the damage element.Then,the accumulative AE counts method(ACLM),AE count rate method(CRM)and constitutive model method(CMM)were introduced to determine the stress thresholds of rocks.Finally,the stress thresholds of 9 different rocks were identified by ACLM,CRM,and CMM.The results show that the theoretical stress−strain curves obtained from the two damage constitutive models are in good agreement with that of the experimental data,and the differences between the two damage constitutive models mainly come from the evolutionary differences of the damage variables.The results of the stress thresholds identified by the CMM are in good agreement with those identified by the AE methods,i.e.,ACLM and CRM.Therefore,the proposed CMM can be used to determine the stress thresholds of rocks.

Recent advances in quantifying the inactive lithium and failure mechanism of Li anodes in rechargeable lithium metal batteries

Lithium metal is considered as the ultimate anode material for the next generation of high-energy density batteries.However,non-uniform lithium dendrite growth,serious electrolyte consumption,and significant volume changes during lithium deposition/stripping processes lead to sustained accumulation of inactive lithium and poor cycling reversibility.Quantifying the formation and evolution of inactive lithium under different conditions and fully evaluating the complex failure modes are the key issues in this challenging field.This article comprehensively reviews recent research progress on the quantification of formation and evolution of inactive lithium detected by different quantitative techniques in rechargeable lithium metal batteries.The key research challenges such as failure mechanism,modification strategies and operando characterization of lithium metal anodes are systematically summarized and prospected.This review provides a new angle of view to understand failure mechanism of lithium metal anodes and inspiration and guidance for the future development of rechargeable lithium metal batteries.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Wood anatomy chronologies of Scots pine in the foothills of the Western Sayan(Siberia)

Recent methodological advances in quantitative wood anatomy have provided new insights into the climatic responses of radial growth at the scale of cell structure of tree rings. This study considered long-term chronologies of tracheid measurements, indexed by a novel approach to separate their specific climatic responses from signal recorded in cell production(closely reflected in tree-ring width). To fill gaps in understanding the impact of climate on conifer xylem structure, Scots pine(Pinus sylvestris L.)trees > 200 years old were selected within the forest-steppe zone in southern Siberia. Such habitats undergo mild moisture deficits and the resulting climatic regulation of growth processes. Mean and maximum values of cell radial diameter and cell wall thickness were recorded for each tree ring.Despite a low level of climatogenic stress, components of cell chronologies independent of cambial activity were separated to obtain significant climatic signals revealing the timing of the specific stages of tracheid differentiation. Cell expansion lasted from mid-April to July and was impacted similarly to tree-ring width(stimulated by precipitation and stressed by heat), maximum cell size formed late June. A switch in the climatic responses of mean anatomical traits indicated transition to latewood in mid-July. Secondary wall deposition lasted until mid-September, suppressed by end of season temperatures. Generally, anatomical climatic responses were modulated by a less dry May and September compared with summer months.