A Mathematical Calculation Model Using Biomarkers to Quantitatively Determine the Relative Source Proportion of Mixed Oils

It is difficult to identify the source（s） of mixed oils from multiple source rocks, and in particular the relative contribution of each source rock. Artificial mixing experiments using typical crude oils and ratios of different biomarkers show that the relative contribution changes are non-linear when two oils with different concentrations of biomarkers mix with each other. This may result in an incorrect conclusion if ratios of biomarkers and a simple binary linear equation are used to calculate the contribution proportion of each end-member to the mixed oil. The changes of biomarker ratios with the mixing proportion of end-member oils in the trinal mixing model are more complex than in the binary mixing model. When four or more oils mix, the contribution proportion of each end-member oil to the mixed oil cannot be calculated using biomarker ratios and a simple formula. Artificial mixing experiments on typical oils reveal that the absolute concentrations of biomarkers in the mixed oil cause a linear change with mixing proportion of each end-member. Mathematical inferences verify such linear changes. Some of the mathematical calculation methods using the absolute concentrations or ratios of biomarkers to quantitatively determine the proportion of each end-member in the mixed oils are deduced from the results of artificial experiments and by theoretical inference. Ratio of two biomarker compounds changes as a hyperbola with the mixing proportion in the binary mixing model, as a hyperboloid in the trinal mixing model, and as a hypersurface when mixing more than three end- members. The mixing proportion of each end-member can be quantitatively determined with these mathematical models, using the absolute concentrations and the ratios of biomarkers. The mathematical calculation model is more economical, convenient, accurate and reliable than conventional artificial mixing methods.

A Method for Directly Counting and Quantitatively Comparing Aggregated Structures during Cluster Formation

Assembling of a few particles into a cluster commonly occurs in many systems.However,it is still challenging to precisely control particle assembling,due to the various amorphous structures induced by thermal fluctuations during cluster formation.Although these structures may have very different degrees of aggregation,a quantitative method is lacking to describe them,and how these structures evolve remains unclear.Therefore a significant step towards precise control of particle self-assembly is to describe and analyze various aggregation structures during cluster formation quantitatively.In this work,we are motivated to propose a method to directly count and quantitatively compare different aggregated structures.We also present several case studies to evaluate how the aggregated structures during cluster formation are affected by external controlling factors,e.g.,different interaction ranges,interaction strengths,or anisotropy of attraction.

Soil Moisture Retrieval Quantitatively with Remotely Sensed Data and Its Crucial Factors Analysis

The Ts/NDVI method was adopted to retrieve soil moisture with multi-temporal and multi-sensor remotely sensed data f ETM+ and ASTER in study area. The retrieved soil moisture maps were consistent with the soil type and vegetation, which were also the two main factors determining the distribution of soil moisture.

A MODEL FOR QUANTITATIVELY ESTIMATING SHORTRANGE PRECIPITATION BASED ON GMS DIGITALIZED CLOUD MAPS-PART Ⅰ:ANALYSIS OF QUANTITATIVE CLOUD-PRECIPITATION RELATIONS AND MODEL DESIGN

Some typical samples are used to explore the quantitative correlation with their features between a convective cloud and its rainfall field,with which to develop two morphological functions for the correlation and by singling out their most suitable groups of parameters we propose a model for quantitatively estimating precipitation in the context o{ the in-advance recognition of meso-α convective system properties and its precipitating center.From the model fitting precision and forecasting accuracy we find that it is feasible to utilize geostationary meteorological satellite (GMS) digitalized imagery for estimating short-term rainfall in a quantitative manner.Also,evidence suggests that the model is supposed to be restricted in its applicability due to the fact that the employed samples are from rather typical rainfall events that are large-scale,slow-moving and have well-defined genesis and dissipative stages.

A new method for quantitatively characterizing atmospheric oxidation capacity

Based on atmospheric chemical kinetics, the rate constant of overall pseudo-first order oxidation re-moval of gaseous pollutants (Kpor,T) is proposed to characterize the atmospheric oxidation capacity in troposphere. Being a quantitative parameter, Kpor,T can be used to address the issues related to at-mospheric oxidation capacity. By applying this method, the regional oxidation capacity of the atmos-phere in Pearl River Delta (PRD) is numerically simulated based on CBM-IV chemical mechanism. Re-sults show the significant spatio-temporal variation of the atmospheric oxidation capacity in PRD. It is found that OH initiated oxidations, heterogeneous oxidation of SO2, and photolysis of aldehydes are the three most important oxidation processes influencing the atmospheric oxidation capacity in PRD.

A MODEL FOR QUANTITATIVELY ESTIMATING SHORTRANGE PRECIPITATION BASED ON GMS DIGITALIZED CLOUD MAPS-PART Ⅱ:ASSESSMENT OF RESULTS FROM THE MODEL FOR SATELLITE-IMAGE QUANTITATIVELY ESTIMATING PRECIPITATION

This is Part Ⅱ of this series.It introduces the technique for recognizing MαCS phased properties and its precipitation center or centers by means of dynamic digitalized cloud maps and presents the assessment of the effectiveness of the model proposed in Part Ⅰ as to its fitting and forecasting accuracy.

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.

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.

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.

Establishment of quantitative evaluation system for cochlear implant effectiveness for hearing-impaired children

Introduction:Cochlear implant is currently the most widely proven interventions for auditory rehabilitation for children with severe sensorineural hearing impairment.However,there are obvious limitations in these current evaluation methods.This study aims to develop an evaluation system for quantitatively evaluating the effectiveness of cochlear implants for hearing-impaired children.Methods:A correspondence questionnaire was developed based on an initial indicator system that was developed based on the literature focused on the evaluation of cochlear implant outcomes in children.Twenty-five experts in otology,clinical audiology,rehabilitation audiology,and mental health from nine provinces in China were consulted.The degree of authority and coordination of experts and the indicators and weights of the quantitative evaluation system were analyzed.Seventy-eight children aged 3–11 years after cochlear implantation were recruited from two centers in Hubei province to evaluate the reliability and validity of the quantitative evaluation system.Results:The opinions of experts converged after the second round of correspondence,and the coordination and authority of the expert consensus were met.The recall rate of the questionnaire was 100%for both rounds.Five secondary indicators,including auditory ability,verbal ability,behavioral assessment,learning capabilities,and quality of life,and 13 tertiary indicators were reserved for the evaluation of cochlear implant effectiveness.The weight of each indicator was calculated.The Cronbach’sαcoefficient of the quantitative evaluation system based on the standardized items was 0.930,and the three extracted common factors could explain 78.86%of the total variance.Conclusions:An expert consensus-based evaluation system that can quantitatively evaluate the effectiveness of cochlear implants in children has been developed with good reliability and validity.

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.