Method For Composition Analysis Research Articles

Innovative solutions in easel painting through the prism of the activities of artists of the Creative Painting Workshop of the Russian Academy of Arts under the leadership of H. A. Yakupov.

The subject of the study is a set of innovative solutions for easel painting by artists of the Creative Painting Workshop of the Russian Academy of Arts under the leadership of H. A. Yakupov in Kazan from 1977 to 2008. Easel painting by artists-trainees of the Creative Painting Workshop of the Russian Academy of Arts in Kazan was chosen as the object of research. The review of the activities of the Russian Academy of Arts in the XX-XXI centuries is given, from the point of view of supporting the Russian traditions of easel painting in Russia and abroad. The characteristic of the Creative Painting Workshop of the Russian Academy of Arts under the leadership of H. A. Yakupov in Kazan as an object of research in the context of painting of the Republic of Tatarstan and Russia is given. The methodology of the analysis includes the following concepts: genre, plot, composition, color, format, and their implementation in the works of trainee artists. Comparative methods of compositional and coloristic analysis are applied, compositional solutions are considered as innovations in the composition of easel painting of a subject-themed painting. The research materials confirm the high results of the creative work of the trainee artists in creating new compositional and coloristic solutions in easel painting in the laboratory of the Creative Workshop using a combination of innovations. Сonclusions are formulated about the need to popularize the work of artists-trainees of the Creative Painting Workshop of the Russian Academy of Arts under the leadership of H. A. Yakupov in Kazan. Russian realism traditions have been preserved, and the easel painting of the trainee artists is based on the artistic experience of Russian realistic art, using a combination of innovations in author's solutions.

A review of common methods used in the analysis of human microbiome sequencing data

The past two decades have seen a rapid rise in the development and use of sequencing technologies. The advent of high-throughput sequencing (HTS) has allowed scientists sequence the genomic content of entire microbial communities rapidly and affordably. Methods for data management, visualization, and analysis of this microbiome sequencing data are now widespread in open and closed source software tools. In this review, we identify common analysis methods used in microbiome sequencing studies, including methods for normalisation of abundance table data, visualization of high-dimensional microbiome data and the analysis of longitudinal microbiome sequencing data. In particular we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data.

Non-destructive method of blood serum composition analysis in forensic science

The article is devoted to consideration of non-destructive methods of blood serum composition analysis in forensics, problem solving and their improvement. At the current stage of creating materials and devices of a new generation, the task is not only to manage their properties, but also to effectively use them in various fields, in particular, in forensics. Analysis of the structural evolution of a heteroatomic liquid (in particular, blood) from the standpoint of theoretical and experimental approaches allows us to open new horizons in the practical plane, to determine the phenomenology of the process based on modern research methods. The authors of this work conducted a qualitative and quantitative analysis of experimental data related to the kinetics and mechanisms of structure formation of a multicomponent substance in the liquid state. It should be emphasized that the structural kinetics of a liquid such as blood serum, or blood itself, is a complex multistage process.

Microbiome compositional data analysis for survival studies.

The growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.

Animalism in the Design of Clothes of the ХХ and Early XXI Centuries

The article presents an overview and systematisation of fashion design projects using animalistic motifs. The purpose of the study is to identify the manifestations of animalistic images in costume and the differences in their meanings in different historical periods. The research methodology is based on the application of methods of source, art, aesthetic, structural and compositional analysis. The scientific novelty lies in highlighting the stages of animalism development in the twentieth – early twenty-first centuries and identifying its main manifestations in costume. The results of the study are based on empirical material from designers and fashion houses that actively used and developed animalism in their work. Conclusions. As a result of the study, we conclude that there are various ways of manifesting animalism in fashion design, from copying animal images entirely to introducing their individual parts into the design or using photographs and artistic images. And while in the early twentieth century, costumes used mostly elements of animal skin, fur and feathers, and exotic animals and birds emphasised the status of the elite strata of society, with the development of technology and the spread of mass production of clothing, imitation gradually became popular. The destruction of the natural environment and the dominance of the artificial one led to the spread of fashion inspired by wildlife. Gradually, a style direction in fashion design was formed, which is now considered «fantasy classics». Over time, animalistic motifs have become visual carriers of «eco-fashion» and «eco-consciousness», reminding us of the need for significant transformations in the global consciousness of humanity. The flourishing of animalism in recent decades reflects the socio-cultural trends in society. The symbolism and interpretation of animalistic images in costume design reflects the care and desire to preserve the natural heritage in a world that is increasingly influenced by technological development.

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.

The scientific analysis of the bronze mous excavated from Wushan, Chongqing, China: new perspectives from alloy composition analysis and proteomic method

The bronze mou was an important artifact commonly used in the Ba-Shu region (now in Chongqing and Sichuan Province) during the Spring and Autumn Period and the Han Dynasty. Although extensive archaeological study of the bronze mou has been conducted, scientific analysis of the bronze mou has been limited, and no organic residue analysis has been performed on the bronze mou. In this study, four bronze mous dated to the Han dynasty (206 BC–220 AD) with organic residues unearthed in three archaeological sites in Wushan, Chongqing, were selected for analysis by portable X-ray fluorescent spectrometer (P-XRF) and liquid chromatograph-tandem mass spectrometry (LC/MS/MS). The results of the alloy composition indicated that all the samples are copper-tin-lead ternary alloys. The proteomic analysis results confirmed that the bronze mou had been used as cooking utensils or containers. At the Tuchengpo Cemetery, soybeans were found in both bronze mou M32:27 and M38:39, and vigna was discovered in M32:27. No useful protein was discovered in the bronze mou from the Shennvmiao Cemetery. Proteins of ginkgo and chicken were found in M16:12 at the Gaotangguan Cemetery. The discovery indicated that residents in Wushan, Chongqing, had access to a diverse range of food sources around 2000 years ago. Furthermore, this study demonstrates that proteomic analysis is highly effective in studying archaeological organic residues.

Impacts of physical activity, sedentary behaviour, and sleep on depression symptoms in Canadian older adults 65 years of age and above: a compositional data analysis of the Canadian Longitudinal Study on Aging

BackgroundDaily time spent in moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), sedentary behaviour (SB) and sleep (collectively known as ‘movement behaviours’) are compositional, co-dependent variables. However, most studies examining effects of these behaviours on depression outcomes in older adults do not account for this. Study objectives were to use compositional data analysis methods to (1) examine the relationship between movement behaviour composition (daily time spent in MVPA, LIPA, SB, sleep) and depression symptoms, (2) estimate the extent to which changing time spent in any given movement behaviour within the movement behaviour composition was associated with changes in depression symptoms.Methods5643 older Canadian adults ≥ 65 years of age from the Canadian Longitudinal Study on Aging were studied using a quasi-longitudinal study design. Exposure was baseline daily movement behaviours; time spent in MVPA, LIPA and SB were derived from self-reported Physical Activity Scale for the Elderly responses. Night-time sleep was self-reported separately. Outcome was depression symptoms at follow-up obtained using the ten item Center for Epidemiologic Studies Depression Scale. Compositional data analysis was used to investigate associations between movement behaviours and depression symptoms.ResultsMovement behaviour composition was significantly associated with depression symptoms. Time spent in MVPA [exp(B) = 0.97 (95% CI: 0.94, 0.99)] and sleep [exp(B) = 0.91 (95% CI: 0.85, 0.97)] relative to the remaining movement behaviours were associated with lower depression symptoms scores. Relative time spent in SB was associated with higher depression symptoms scores [exp(B) = 1.09 (95% CI: 1.04, 1.15)]. Time displacement estimates revealed that the greatest change in depression symptoms scores occurred when time spent in MVPA was decreased and replaced with LIPA, SB, sleep or combination of these behaviours (+ 0.22 to 0.26 points increase on depression symptoms scores for 30 min/day displacements).ConclusionsDaily movement behaviour composition was associated with depression symptoms in older Canadians. Replacing time in MVPA with equivalent time from any other behaviour was associated with increased depression symptoms. Preserving time spent in MVPA may play a key role in mitigating and improving mental health in this demographic.

COMMUNICATE TO ENGAGE: STORYTELLING, CONTENT CREATION, AND PROMOTING THE IDEA OF SUSTAINABILITY AND INTERCULTURAL DIALOGUE IN TRAVEL VIDEOBLOGS

In the area of digital media narratives, an important communication role is played by influencers who, thanks to original media messages, become creative gatekeepers of specific ideas. Travel videoblogs are often underestimated as pure entertainment, whereas they can be seen as important tools for public discourse creators. The research uses methods of compositional and content analysis, supplemented with narratology and netnography. In the space of popular travel videoblogs, the main ideas promoted include the ideas of sustainable development and intercultural dialogue. Bloggers create and stimulate the existence of online communities, express specific ideas, concepts and worldviews that partly come from the area of scientific discourse, particularly anthropological, cultural and ethical ones. The research highlights the serious public role played by the digital nomads as influencers and new kind of friendly authority and presents case study of successful communication strategies, narrative skills and technical competences of authors. The online travel narratives can be seen as marketing of ideas and thus used as practical tool of communication for institutions, non-governmental organizations and other actors of communication. The paper analyses new material (new genres of communication, new channels, new strategies) and topic of high importance in the context of intercultural conflicts and ecological problems. One of the most influential Polish travel vlogs, Globstory by Kaja Kraska (initiated in August, 2014), serves here as a case study. It was chosen because its content is typical for this genre and because of creative and engaging practices of the vlogger who successfully influences the public and its understanding of specific ideas and problems.

Body composition analysis via spatially resolved NIR spectroscopy with multifrequency bioimpedance precision.

Near-infrared spectroscopy (NIRS) is often criticized due to its insufficient accuracy in determining body composition compared to the gold standard methods. In this work, we show that the use of multiple source-detector distances, as well as the simultaneous use of physiological and optical features, can significantly improve the accuracy of determination of fat and lean mass percentage in the human body using NIR spectroscopy. The study performed on the n = 292 cohort revealed the mean absolute errors of 3.5% for fat content and 3.3% for soft lean mass percentage prediction (r = 0.93) using the multifrequency bioimpedance analysis (BIA) as a reference. Hence, NIRS can serve as an independent reliable method for body composition analysis with precision close to that of advanced multifrequency BIA.

Spectral fingerprinting of cellular lipid droplets using stimulated Raman scattering microscopy and chemometric analysis.

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful method for direct visualisation and compositional analysis of cellular lipid droplets. Here we report the application of spectral phasor analysis as a convenient method for the segmentation of lipid droplets using the hyperspectral SRS spectrum in the high wavenumber and fingerprint region of the spectrum. Spectral phasor analysis was shown to discriminate six fatty acids based on vibrational spectroscopic features in solution. The methodology was then applied to studying fatty acid metabolism and storage in a mammalian cancer cell model and during drug-induced steatosis in a hepatocellular carcinoma cell model. The accumulation of fatty acids into cellular lipid droplets was shown to vary as a function of the degree of unsaturation, whilst in a model of drug-induced steatosis, the detection of increased saturated fatty acid esters was observed. Taking advantage of the fingerprint and high wavenumber regions of the SRS spectrum has yielded a greater insight into lipid droplet composition in a cellular context. This approach will find application in the label-free profiling of intracellular lipids in complex disease models.

Total body water percentage and 3rd space water are novel risk factors for training-related lower extremity muscle injuries in young males

PurposeTo identify the risk factors for training-related lower extremity muscle injuries in young males by a non-invasive method of body composition analysis. MethodsA total of 282 healthy young male volunteers aged 18 – 20 years participated in this cohort study. Injury location, degree, and injury rate were adjusted by a questionnaire based on the overuse injury assessment methods used in epidemiological studies of sports injuries. The occurrence of training injuries is monitored and diagnosed by physicians and treated accordingly. The body composition was measured using the BodyStat QuadScan 4000 multifrequency Bio-impedance system at 5, 50, 100 and 200 kHz to obtain 4 impedance values. The Shapiro-Wilk test was used to check whether the data conforms to a normal distribution. Data of normal distribution were shown as mean ± SD and analyzed by t-test, while those of non-normal distribution were shown as median (Q1, Q3) and analyzed by Wilcoxon rank sum test. The receiver operator characteristic curve and logistic regression analysis were performed to investigate risk factors for developing training-related lower extremity injuries and accuracy. ResultsAmong the 282 subjects, 78 (27.7%) developed training injuries. Lower extremity training injuries revealed the highest incidence, accounting for 23.4 % (66 cases). These patients showed higher percentages of lean body mass (p = 0.001), total body water (TBW, p = 0.006), extracellular water (p = 0.020) and intracellular water (p = 0.010) as well as a larger ratio of basal metabolic rate/total weight (p = 0.006), compared with those without lower extremity muscle injuries. On the contrary, the percentage of body fat (p = 0.001) and body fat mass index (p = 0.002) was lower. Logistic regression analysis showed that TBW percentage > 65.35% (p = 0.050, OR = 3.114) and 3rd space water > 0.95% (p = 0.045, OR = 2.342) were independent risk factors for lower extremity muscle injuries. ConclusionTBW percentage and 3rd space water measured with bio-impedance method are potential risk factors for predicting the incidence of lower extremity muscle injuries in young males following training.

Effects of Hydrolysis Condition and Detection Method on the Monosaccharide Composition Analysis of Polysaccharides from Natural Sources

Monosaccharide composition analysis is essential to the structural characterization and research into the biological activity of polysaccharides. In this study, a systematic comparison was performed among commonly used monosaccharide composition analysis methods, including colorimetric and chromatographic methods. These were tested on 16 aldoses, ketoses, alditols, amino sugars, and uronic acids. Furthermore, the effect of hydrolysis methods was also investigated. The results showed that the phenol sulfuric acid method is greatly affected by the type of monosaccharide that is used as the reference substance. The determination of uronic acid using sulfuric acid carbazole is less affected by neutral sugars than that method using m-hydroxybiphenyl. The high-performance thin-layer chromatography (HPTLC) method can simultaneously analyze multiple samples and accurately determine the type of uronic acid. High-performance liquid chromatography (HPLC) can provide a good qualitative and quantitative analysis of aldose, amino sugars, and uronic acids, while gas chromatography–mass spectrometry (GC-MS) can detect aldose, ketose, and alditols. Fructose was detected in a large amount in inulin and Codonopsis pilosula after one-step hydrolysis, while it was totally destroyed in two-step hydrolysis. The release of galacturonic acid significantly increased after two-step hydrolysis in pectin and Lycium barbarum, which indicated that one-step hydrolysis is not enough for acidic polysaccharides. The results of this study are beneficial for selecting appropriate hydrolysis and analysis methods in order to accurately analyze the monosaccharide compositions of natural polysaccharides.

Costruction of the judicial defense speech: based on the material of court speeches by Y.S. Kiselev

The construction of a public speech is one of the most important components of creating a convincing oratorical performance. Despite the constant interest of researchers in the challenges of constructing a court speech, the arrangement of structural elements, the issue remains unresolved in practical aspect: modern court speakers need recommendations that would contribute to creating effective defense statements. The purpose of this research is to analyze the composition of successful court speeches of the famous Soviet lawyer Y.S. Kiselev, aimed at identifying the features of arrangement of the material, factors that condition this or that disposition, and on this basis to identify various options for the arrangement of content elements contributing to the creation of a convincing speech. Using the method of compositional analysis, as well as rhetorical analysis, descriptive and structural methods, we investigate thirteen effective judicial defense speeches by Y.S. Kiselev. Having analyzed the types of introductions used by him, we have established a high degree of viability of using hyped up or unexpected beginning, as well as conditionality of the choice of the type of introduction by the prosecutor, readiness of the audience to perceive the defense speech, and attitude of the audience to the circumstances of the case. The analysis of the main part of Kiselev’s speeches allows to identify the micro themes presented in all his defense statements, as well as conditionality of a number of micro themes by the circumstances of the case, the arguments of the prosecution, and the chosen line of defense. The conclusion reveals structural elements in all the speeches of the well-known lawyer under study: a clearly expressed position of the defense in the case, a pathetic and educational moment, and an appeal to the court for leniency or acquittal of the defendant. In general, the construction of the defense speech is conditioned by the circumstances of the case, the position of the prosecution, the chosen line of defense, the specifics of the audience, as well as the moral qualities, erudition and communicative skills of the speaker.

Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study

BackgroundAthletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels. MethodsThe Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D2O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry. ResultsIn total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water6CM/FFM6CM) had substantial variation in both males (63- 73%) and females (58- 78%). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin’s Concordance Coefficient [CCC] = 0.90-0.95) whereas the other measures had lesser agreement (CCC < 0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0%). Increasing quartiles (significant p<0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status. ConclusionThe criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations. Summary BoxThis study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes.

Optimized acid hydrolysis conditions for better characterization the structure of inulin-type fructan from Polygonatum sibiricum

Polygonatum sibiricum is an edible plant species in China known for its abundant polysaccharides. However, correlations between its analytical methods and fine structure have not been established. This is usually due to incomplete cleavage of the glycosidic linkages and instability of hydrolysis. In this study, a new optimal acid hydrolysis method for monosaccharide composition (2 M H2SO4 for 1 h) and methylation analysis (2 mol TFA hydrolysis at 100 °C for 1 h) was developed for characterization of inulin-type fructans, resulting in significantly improved monosaccharide recovery and providing more reliable methylation data. The effectiveness of this method was demonstrated through its application to the study of polysaccharide from P. sibiricum (IPS-70S). The results showed that IPS-70S with a molecular weight of 3.6 kDa is an inulin-type fructans consisting of fructose and glucose in a molar ratio of 27:1. Methylation and NMR analysis indicated that IPS-70S contains →2)-Fruf-(6 → or →2)-Fruf-(1 → with branching →1,6)-Fruf-(2 → and terminates in Glcp-(1 → or Fruf-(2→. In conclusion, optimal acid hydrolysis applicable to the specific polysaccharides contribute to its structurally characterized. The newly optimized acid hydrolysis method for monosaccharide composition and methylation analysis offers a reliable and effective approach to the structural characterization of inulin-type fructans from P. sibiricum. Providing reliable basis for the overall work of NMR analysis and structural analysis, which have potential significance in the field of polysaccharides structural characterization.

Chemical Composition Analysis and Multi-index Component Content Determination of Compounds in Goupi Plaster Based on UPLC–Q-Exactive-MS and UPLC–MS/MS

ObjectiveThis study sought to evaluate and validate a method for chemical composition analysis and content determination of Goupi plaster components, a conventional prescription preparation of traditional Chinese medicine. This is geared toward providing a basis for quality control research and future development of Goupi plaster.MethodsUPLC–Q-Exactive-MS was used to qualitatively analyze the chemical components of Goupi plaster from different manufacturers in positive and negative ion modes. UPLC–MS/MS method was used to establish the determination methods for the detection of sinomenine, osthole, and cinnamaldehyde in Goupi plaster from different manufacturers.ResultsA total of 291 chemical components were identified in Goupi plaster from four manufacturers, including 97 chemical components with known source and pharmacological activity. Further, we determined the contents of sinomenine, osthole, and cinnamaldehyde.ConclusionIn summary, the UPLC–Q-Exactive-MS method was used to analyze the chemical components of Goupi plaster from different manufacturers. We established the UPLC–MS/MS method to determine the contents of sinomenine, osthole, and cinnamaldehyde in Goupi plaster from different manufacturers. The findings indicated that the method was comprehensive, rapid, and accurate, preliminarily revealing the material basis of Goupi plaster and providing a reference for follow-up development of Goupi plaster.

Automatic Kidney Stone Composition Analysis Method Based on Dual-energy CT.

The composition of kidney stones is related to the hardness of the stones. Knowing the composition of the stones before surgery can help plan the laser power and operation time of percutaneous nephroscopic surgery. Moreover, patients can be treated with medications if the kidney stone is compounded by uric acid before treatment, which can relieve the patients of the pain of surgery. However, although the literature generally reports the kidney stone composition analysis method base on dual-energy CT images, the accuracy of these methods is not enough; they need manual delineation of the kidney stone location, and these methods cannot analyze mixed composition kidney stones. This study aimed to overcome the problem related to identifying kidney stone composition; we need an accurate method to analyze the composition of kidney stones. In this paper, we proposed the automatic kidney stone composition analysis algorithm based on a dual-energy CT image. The algorithm first segmented the kidney stone mask by deep learning model, then analyzed the composition of each stone by machine learning model. The experimental results indicate that the proposed algorithm can segment kidney stones accurately (AUC=0.96) and predict kidney stone composition accurately (mean Acc=0.86, mean Se=0.75, mean Sp=0.9, mean F1=0.75, mean AUC=0.83, MR (Exact match ratio)=0.6). The proposed method can predict the composition and location of kidney stones, which can guide its treatment. Experimental results show that the weighting strategy can improve kidney stone segmentation performance. In addition, the multi-label classification model can predict kidney stone composition precisely, including the mixed composition kidney stones.

Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet

When conducting Mineral Potential Mapping (MPM) using multiple sources of data such as geology, geochemistry, and geophysics, it often encounters the challenges of complex and diverse data distributions within these datasets. Enhancing the capability to extract nonlinear data features and further uncover metallogenic information is a crucial research objective. This study utilizes the C-A multifractal approach to extract anomalous information related to metallogenic elements, employs compositional data analysis methods for quantitatively extracting geochemical associations of mineralization, and utilizes GIS spatial analysis techniques to quantitatively extract predictive indicators from various data sources, including geology, geophysics, and remote sensing, to construct an MPM prediction dataset. Building upon the foundation of AutoEncoder (AE), this study introduces a discriminator and employs the AEGAN (Auto Encoder Generative Adversarial Network) algorithm, which combines AutoEncoder and Generative Adversarial Network (GAN), for metallogenic prospectivity prediction in the Lhasa region. Compared to AE algorithms, AEGAN combines the strengths of AE and GAN, significantly improving the model's ability to reconstruct input data through the interaction between the generator and discriminator. Additionally, this study designs comparative experiments with AE, and the results demonstrate that the AEGAN model can more accurately identify the correlation between high anomaly areas and polymetallic deposits, providing a more precise delineation of anomalous extents. The Area Under the Receiver Operating Characteristic Curve (AUC) further validates the superior performance of the AEGAN model. These findings indicate that the AEGAN model exhibits outstanding capabilities in learning the internal connections and features among multiple data sources, holding significant potential for practical applications in mineral exploration.

20 Years of polyolefin HPLC: Accomplishments and challenges

The first publication on the interaction of polyolefins with stationary phases for high performance liquid chromatography at high temperatures (HT-HPLC) appeared in 2003. After the successful application of temperature rising elution fractionation and the introduction of crystallization fractionation for the analysis of polyolefins with regard to chemical composition this was a step towards methods for chemical composition analysis that are not based on crystallizability of the polyolefin. Polyolefins as semicrystalline polymers comprise crystallizable and amorphous components, and HT-HPLC opened the way to separate all sample components irrespective of their crystallizability. The first application of this new approach was the separation of ethylene–methyl methacrylate block copolymers, presented in 2005. The development of the first commercial HT-HPLC instrument and the discovery of graphitic materials as efficient stationary phases for HT-HPLC were further important milestones in the development of advanced separation techniques for complex polyolefins. The ultimate goal of comprehensively analyzing complex polyolefins by composition and molar mass was achieved by high temperature two-dimensional chromatography, which is based on the online coupling of HT-HPLC and HT-SEC. Further important developments include HT-HPLC using temperature gradients and the combination of solvent and temperature gradients. In this review, the most important developments in the field over the last 20 years are discussed, and different techniques, experimental protocols and some major applications are presented. In conclusion, challenges for future developments are outlined.