Method For Composition Analysis Research Articles

Effect of Cr on nano-indentation Young's modulus and hardness of the β-Ti phase in equilibrium with α2-Ti3Al and γ-TiAl phases in Ti-Al-Cr alloys

Young's modulus and hardness of the constituent phases of β-Ti (bcc or B2), in equilibrium with α2-Ti3Al (D019) and γ-TiAl (L10) phases in Ti-Al-Cr ternary alloys were quantitatively evaluated in terms of the chemical composition analysis and nano-indentation method, in order to identify the Cr concentration dependence on the mechanical properties of the β phase. An alloy with a nominal composition of Ti-43Al-3Cr (at.%) decomposes into the three phases of β, α2, and γ by equilibrium heat treatment at 1373 K, with chemical composition of Ti-37.2Al-5.2Cr, Ti-38.9Al-2.2Cr, and Ti-47.4Al-1.4Cr, respectively. The β phase shows a Young's modulus of 141 ± 5 GPa, smaller and less orientation dependent than the other two phases of α2 (174 ± 9 GPa) and γ phases (164 ± 15 GPa). In contrast, the β phase has a hardness of 5.9 ± 0.3 GPa, slightly softer than the α2 phase (6.4 ± 0.8 GPa) but much harder than the γ phase (3.7 ± 0.6 GPa). The alloys Ti-44Al-4Cr and Ti-45Al-6Cr consist of two phases of β and γ phases, and the chemical composition of the β phase in these two alloys is Ti-36.5 Al-8.5 Cr and Ti-36.3 Al-13.9 Cr, respectively, with nearly the same Al concentration. The Young's modulus and hardness of the β phase with the latter composition become 158 GPa and 6.7 GPa, respectively, with increasing Cr content. These results found that the effect of solid solution Cr on the Young's modulus for β phase is relatively weak, and that on the hardness is strong, in comparison with that for the other two phases.

Biomass chemical exergy estimation using novel intelligent tool: application to biorefinery sustainability

ABSTRACT In response to the growing importance of sustainable energy and environmental considerations in biorefinery technologies, this study introduces a novel hybrid artificial intelligence approach for predicting biomass chemical exergy. The proposed least-squares support vector machine method based on an imperialist competitive algorithm (ICA-LSSVM) combines composition and energy analysis methods to model the chemical energy of biomass accurately. Extensive data were collected to train the model, resulting in a high-precision prediction with a coefficient of determination (R2) of 0.999 and a root mean square error (RMSE) of 30.3. The main point of this study is the development of this accurate model to determine the chemical energy, and also different forms of comparison are used to prove this issue. The sensitivity analysis highlights the direct impact of carbon (C%), hydrogen (H%), and oxygen (O%) percentages on biomass chemical exergy, while nitrogen (N%) and sulfur (S%) exhibit an inverse relationship. The model outperforms existing correlations in the literature, emphasizing its accuracy and practicality for biomass chemical exergy prediction, and contributing to both laboratory and industrial applications. The study emphasizes the model’s real-data reliance and suggests further exploration with an expanded dataset for enhanced industrial applicability.

Development of a rapid and fruit-saving method for fatty acid composition analysis in olive: a comparative study on 27 cultivars.

Fatty acid composition is an essential aspect of the qualitative assessment of olive oil. A method for evaluating and trending fatty acid composition of olive varieties directly from a limited amount of drupes, has been proven reliable in comparison with traditional oil analysis. No significant difference was detected between the two methods for the 27 cultivars tested, despite presenting decidedly different acid compositions. The results obtained, crossed with those of oil yield, can represent a useful resource to set the harvest calendars by choosing the most suitable time for the production of superior quality oils and for reducing the risk of pathogen infections or pest attack. For three cultivars, the acid composition was evaluated during three ripening phases (green, veraisoned and veraisoned to black). The different behaviors suggest it is the genotype that determines this -still little known- physiological trait in olive. An interesting finding was that the oils from drupes harvested in August showed linolenic acid values higher than the limit (1.00%) set in the international standards for the classification of olive oils, requesting further investigation.

Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.

A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA). 2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (AllE), or from the 1st echo only (1stE) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the AllE U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation. The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (AllE/1stE) and 0.88 ± 0.03/0.88 ± 0.03 (AllE/1stE) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6ms/ + 4.1 ± 10.2ms (AllE/1stE). The AllE U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen's D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the AllE U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41). The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis. Clinicaltrials.gov identification: NCT00080171.

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO2, CH4, H2, C6H6O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images

Background & AimsSeveral automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation. MethodsDiagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement. ResultsA total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900-0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement. ConclusionsAutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.

Modes of occurrence of tungsten in coals: a review

Relevance. The necessity to know the conditions and forms of W concentration in coals for solving a number of scientific and engineering problems at complex development of coal deposits. Aim. Complex estimation of W modes of occurrence in coal for development of measures for rational ecologically safe use of coal. Methods. Correlation analysis, scanning electron microscopy, infrared spectroscopy, coal group analysis, inductively coupled plasma mass spectrometry, instrumental neutron activation analysis. Results and conclusions. The modes of occurrence of W in coal were studied by a complex of methods. In the majority of W-enriched coal deposits, a negative significant correlation of its content in coal and coal ash with ash yield was found, indicating its association with organic matter. Using the method of coal group composition analysis, it was found that the main carrier and concentrator of W in the studied lignite deposits is organic matter. The contribution of the mineral phase in general in W-rich coals and in coals with its normal content does not exceed 20%, usually less than 5%. These conclusions are also confirmed by infrared spectroscopy data, according to which no more than 15% of the metal in the samples studied is associated with mineral phases. The association of W with high molecular humic acids predominates. In anomalously W-enriched lignites, the humic acid phase represents 76 to 88% of the gross metal content. The role of bitumen and low-molecular-weight humic acids in the balance of W is marginal at their different levels in the coals. Mineral phases of W are not characteristic of coal. Nano-microinclusions of scheelite, wolframite, hubnerite and ferberite were recorded as isolated occurrences. The main mineral phases are associated with Fe and Mn hydroxides, in which W is presented as a trace element in the amount of 1–5% together with other elements (Ge, As, etc.). In more metamorphosed bituminous and anthracite coals, authigenic mineral formations were found, represented by tungstite, phyllotungstite, native W and complex Fe-Mn-Ca-W-O mineral phases.

Intrauterine hyperglycaemia during late gestation caused mitochondrial dysfunction in skeletal muscle of male offspring through CREB/PGC1A signaling

BackgroundMaternal diabetes mellitus can influence the development of offspring. Gestational diabetes mellitus (GDM) creates a short-term intrauterine hyperglycaemic environment in offspring, leading to glucose intolerance in later life, but the long-term effects and specific mechanism involved in skeletal muscle dysfunction in offspring remain to be clarified.MethodsPregnant mice were divided into two groups: The GDM group was intraperitoneally injected with 100 mg/kg streptozotocin on gestational days (GDs) 6.5 and 12.5, while the control (CTR) group was treated with vehicle buffer. Only pregnant mice whose random blood glucose level was higher than 16.8 mmol/L beginning on GD13.5 were regarded as the GDM group. The growth of the offspring was monitored, and the glucose tolerance test was performed at different time points. Body composition analysis and immunohistochemical methods were used to evaluate the development of lean mass at 8 weeks. The exercise capacity and grip strength of the male mouse offspring were assessed at the same period. Transmission electron microscopy was used to observe the morphology inside skeletal muscle at 8 weeks and as a foetus. The genes and proteins associated with mitochondrial biogenesis and oxidative metabolism were investigated. We also coanalyzed RNA sequencing and proteomics data to explore the underlying mechanism. Chromatin immunoprecipitation and bisulfite-converted DNA methylation detection were performed to evaluate this phenomenon.ResultsShort-term intrauterine hyperglycaemia inhibited the growth and reduced the lean mass of male offspring, leading to decreased endurance exercise capacity. The myofiber composition of the tibialis anterior muscle of GDM male offspring became more glycolytic and less oxidative. The morphology and function of mitochondria in the skeletal muscle of GDM male offspring were destroyed, and coanalysis of RNA sequencing and proteomics of foetal skeletal muscle showed that mitochondrial elements and lipid oxidation were consistently impaired. In vivo and in vitro myoblast experiments also demonstrated that high glucose concentrations impeded mitochondrial organisation and function. Importantly, the transcription of genes associated with mitochondrial biogenesis and oxidative metabolism decreased at 8 weeks and during the foetal period. We predicted Ppargc1α as a key upstream regulator with the help of IPA software. The proteins and mRNA levels of Ppargc1α in the skeletal muscle of GDM male offspring were decreased as a foetus (CTR vs. GDM, 1.004 vs. 0.665, p = 0.002), at 6 weeks (1.018 vs. 0.511, p = 0.023) and 8 weeks (1.006 vs. 0.596, p = 0.018). In addition, CREB phosphorylation was inhibited in GDM group, with fewer activated pCREB proteins binding to the CRE element of Ppargc1α (1.042 vs. 0.681, p = 0.037), Pck1 (1.091 vs. 0.432, p = 0.014) and G6pc (1.118 vs. 0.472, p = 0.027), resulting in their decreased transcription. Interestingly, we found that sarcopenia and mitochondrial dysfunction could even be inherited by the next generation.ConclusionsShort-term intrauterine hyperglycaemia significantly reduced lean mass in male offspring at 8 weeks, resulting in decreased exercise endurance and metabolic disorders. Disrupted organisation and function of the mitochondria in skeletal muscle were also observed among them. Foetal exposure to hyperglycaemia decreased the ratio of phosphorylated CREB and reduced the transcription of Ppargc1α, which inhibited the transcription of downstream genes involving in mitochondrial biogenesis and oxidative metabolism. Abnormal mitochondria, which might be transmitted through aberrant gametes, were also observed in the F2 generation.

Understanding arsenic and manganese enrichment in the aquifers of the Ghaghara river basin, Middle Gangetic Plain (MGP), India: A multivariate statistical, compositional data analysis (CoDA), and receptor model approach

The focus of this study was to comprehend the spatial distribution, source apportionment, evaluation of natural background level (NBL), and mobilization mechanisms of arsenic (As) and manganese (Mn) in the Ghaghara river basin, located in the middle Gangetic plain (MGP) of India. A combination of analytical tools was employed, including multivariate statistical analysis (MSA), correlation analysis, and receptor models such as positive matrix factorization (PMF) and principal component analysis-multi linear regression (PCA-MLR). Taking into consideration the compositional constraints of the geochemical data, compositional data analysis (CoDA) methods were applied on the raw data prior to the correlation analysis and MSA. The results of MSA, correlation analysis and receptor models showed that major ion chemistry and As, Mn enrichment in groundwater were largely controlled by carbonate weathering, Fe and Mn oxyhydroxides dissolution. Anthropogenic activities, such as the infiltration of dissolved organic matter-rich water from local surface water bodies used as dumping sites and the infiltration of fertilizer-rich water from agricultural lands, were found to notably impact the groundwater geochemistry and release of As in the research area. The lower NBL of As (5.92 μg/L) compared to its mean concentrations (17.9 μg/L) in the study area also indicated that the rate of As release in groundwater through natural processes was comparatively low but various anthropogenic activities operating in the study region possibly acted as a trigger for the mobility of As from the mineral phases of the subsurface sediments. This study also highlighted the significance of applying CoDA techniques on geochemical data prior to statistical analysis, and the importance of receptor models, to better understand the nature and contribution of various natural and anthropogenic processes governing the hydrochemistry of groundwater in the alluvial aquifers.

Quantitative composition analysis of natural gas by high-pressure proton low-field NMR spectroscopy: Development of an improved experimental methodology

Natural gas (NG) has been proposed as a bridging fuel between fossil and renewable energy sources, playing a significant role in the decarbonization of the energy sector. Its commercial value and the integrity of the NG infrastructure depend on the NG physical properties, which are strongly related to its composition. Thus, the development of low-cost and reliable analytical methods for composition analysis of NG can enable a more efficient operation. Recently, we showed that high-pressure (HP) benchtop 1H NMR spectroscopy combined with indirect hard modelling (IHM) is an efficient and low-cost method for composition quantification of NG. The current work extends this methodology by introducing and testing novel experimental strategies for increasing the reliability and accuracy of the method. This new approach includes the use of a recently published high-pressure tube design and single-scan acquisition combined with novel quality control parameters to obtain highly reproducible peak shape 1H NMR spectra over long periods. The proposed methodology tested on binary, ternary, and more complex mixtures at 200 bar and 160 bar gives compositions in excellent agreement with the vendor certificates.

Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm.

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with Random Forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-Random Forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450; coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

Ion Mobility Mass Spectrometry-Based Disaccharide Analysis of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are linear and acidic polysaccharides. They are ubiquitous molecules, which are involved in a wide range of biological processes. Despite being structurally simple at first glance, with a repeating backbone of alternating hexuronic acid and hexosamine dimers, GAGs display a highly complex structure, which predominantly results from their heterogeneous sulfation patterns. The commonly applied method for compositional analysis of all GAGs is "disaccharide analysis." In this process, GAGs are enzymatically depolymerized into disaccharides, derivatized with a fluorescent label, and then analysed through liquid chromatography. The limiting factor in the high throughput analysis of GAG disaccharides is the time-consuming liquid chromatography. To address this limitation, we here utilized trapped ion mobility-mass spectrometry (TIM-MS) for the separation of isomeric GAG disaccharides, which reduces the measurement time from hours to a few minutes. A full set of disaccharides comprises twelve structures, with eight possessing isomers. Most disaccharides cannot be differentiated by TIM-MS in underivatized form. Therefore, we developed chemical modifications to reduce sample complexity and enhance differentiability. Quantification is performed using stable isotope labelled standards, which are easily available due to the nature of the performed modifications.

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.