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The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveraging a machine learning (ML) pipeline to perform a granular quantification of Aβ deposits and assess their distribution in the temporal lobe. Utilizing 131 whole-slide-images from consecutive autopsied cases at the University of California Davis Alzheimer Disease Research Center, our objectives were threefold: (1) Validate an automatic workflow for Aβ deposit quantification in white matter (WM) and gray matter (GM); (2) define the distributions of different Aβ deposit types in GM and WM, and (3) investigate correlates of Aβ deposits with dementia status and the presence of mixed pathology. Our methodology highlights the robustness and efficacy of the ML pipeline, demonstrating proficiency akin to experts' evaluations. We provide comprehensive insights into the quantification and distribution of Aβ deposits in the temporal GM and WM revealing a progressive increase in tandem with the severity of established diagnostic criteria (NIA-AA). We also present correlations of Aβ load with clinical diagnosis as well as presence/absence of mixed pathology. This study introduces a reproducible workflow, showcasing the practical use of ML approaches in the field of neuropathology, and use of the output data for correlative analyses. Acknowledging limitations, such as potential biases in the ML model and current ML classifications, we propose avenues for future research to refine and expand the methodology. We hope to contribute to the broader landscape of neuropathology advancements, ML applications, and precision medicine, paving the way for deep phenotyping of AD brain cases and establishing a foundation for further advancements in neuropathological research.

The primary pathological feature of Membranous Nephropathy (MN) is the presence of tiny spike-like projections on the glomerular basement membrane. Early detection and efficient treatment of spike-like projections are essential in halting disease progression. Renal pathology biopsy stands as the gold standard for diagnosing MN, and the accurate identification of glomerular spike-like projections plays a vital role in aiding diagnosis. Nevertheless, the tiny spike-like projection lesions and constraints in data quantity pose considerable challenges for supervised learning-based glomerular classification and quantification. We develop a Multi-Scale Annotation-based Multiple Instance Learning (MSA-MIL) model to address the issues. MSA-MIL utilizes image labels and box-level labels to jointly enhance the classification performance of the MIL model. Specifically, we first employ U-Net for glomerular image edge segmentation and subsequently train the MIL model on the dataset with image-level labels. Then, to overcome the limitations arising from the scarcity of positive instances and the relatively small size of spike-like projection features, we manually augment the number of instances with spike-like projections via using box-level annotation to further enhance the MIL model's classification performance. The designed MSA-MIL model enables the classification, visualization, and quantitative analysis of glomeruli with spike-like projections in renal pathology images. We validated and evaluated the designed MSA-MIL model. The model performed exceptionally well, achieving a high accuracy of 0.9847 and demonstrating a high recall rate, effectively preventing misdiagnosis. Additionally, we utilized heatmaps to visualize the locations of spike-like projections within glomeruli, enhancing the model's interpretability. Furthermore, through an analysis of the correlation between the stages of membranous nephropathy and the proportion of spike-like projections, we observed that as the disease advances, the proportion of spike-like projections increases. This finding serves to further validate the results obtained by the model. The MSA-MIL model is the first one specifically designed for classifying glomerular spike-like projections. It not only enhances classification performance but also proves to be more suitable for categorizing minute lesions compared to conventional Convolutional Neural Network (CNN) models. The visualization of glomerular lesions and the proportion of spike-like projections provide doctors with insights into the model's inference process, offering intuitive assistance for accurate diagnoses. This model brings significant hope for advancing research and diagnosis in the field of kidney diseases. [ABSTRACT FROM AUTHOR]

The significance of small molecule metabolites as biomarkers for disease diagnosis and prognosis is growing increasingly evident, necessitating the development of highly sensitive qualitative and quantitative methods. Herein, multi‐chemoselective probes are synthesized and applied for profiling metabolites, including carboxyl, phosphate, hydroxyl, amino, thiol, and carbonyl compounds. This approach seamlessly integrates magnetic solid‐phase materials, orthogonal cleavage sites, isotopic tags, and selective coupling sites, minimizes matrix interference, and enhances quantitative accuracy. Meanwhile, a homemade program, High‐Resolution Isotope‐Assisted Identification and Quantitative (HRIAIQuant) is developed to process the data, which adeptly filters through 33,874 ion pairs present in human serum, leading to the identification of 701 known metabolites and a remarkable 1,062 potential novel ones. This method is successfully applied to analyze metabolites in multiple brain regions of SAMP8 and SAMR1 models, offering a novel tool for Alzheimer's disease research. [ABSTRACT FROM AUTHOR]

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests. Methods: We retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls. Results: ROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85–0.96). Additionally, with F% = 4.05% as the threshold, the Youden's J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = − 0.571, − 0.520, − 0.521, − 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = − 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001). Conclusions: Quantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression. [ABSTRACT FROM AUTHOR]

Variable infiltration capacity model is used to quantitatively analyse the runoff reduction benefits of three basins on the Loess Plateau since the implementation of the 'Grain for Green' programme. The programme played an important role, contributing to >60% of the runoff variation in the different basins. Furthermore, for every 1000 km2 increase in cumulative forest area in basins, the rate of runoff coefficient reduction changed by −30%, −37.3% and 169% in the late stage of the programme compared to the early stage, respectively ('-' represents a decrease in reduction rate). This was closely related to the restoration of vegetation. [ABSTRACT FROM AUTHOR]

Tumor is a complex and aggressive type of disease that poses significant health challenges. Understanding the cellular mechanisms underlying its progression is crucial for developing effective treatments. In this study, we develop a novel mathematical framework to investigate the role of cellular plasticity and heterogeneity in tumor progression. By leveraging temporal single-cell data, we propose a reaction–convection–diffusion model that effectively captures the spatiotemporal dynamics of tumor cells and macrophages within the tumor microenvironment. Through theoretical analysis, we obtain the estimate of the pulse wave speed and analyze the stability of the homogeneous steady state solutions. Notably, we employe the AddModuleScore function to quantify cellular plasticity. One of the highlights of our approach is the introduction of pulse wave speed as a quantitative measure to precisely gauge the rate of cell phenotype transitions, as well as the novel implementation of the high-plasticity cell state/low-plasticity cell state ratio as an indicator of tumor malignancy. Furthermore, the bifurcation analysis reveals the complex dynamics of tumor cell populations. Our extensive analysis demonstrates that an increased rate of phenotype transition is associated with heightened malignancy, attributable to the tumor’s ability to explore a wider phenotypic space. The study also investigates how the proliferation rate and the death rate of tumor cells, phenotypic convection velocity, and the midpoint of the phenotype transition stage affect the speed of tumor cell phenotype transitions and the progression to adenocarcinoma. These insights and quantitative measures can help guide the development of targeted therapeutic strategies to regulate cellular plasticity and control tumor progression effectively. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problem that the traditional detection method for litchi sugar content is time-consuming and laborious and will destroy the tested sample, this paper proposed a non-destructive detection method for litchi sugar content based on near-infrared spectroscopy (NIR) and artificial intelligence algorithm. Firstly, to remove noise and other interference, the preprocessing methods for spectral data are studied. Nine preprocessing methods, such as moving average smoothing (MA), standard normal variate transform (SNV), and multiplicative scatter correction (MSC), are adopted to preprocess the spectral data. Then, to reduce the input dimension of the model and overcome the interference of redundant bands, the feature extraction methods for spectral data are examined. Two feature extraction methods, including Monte-Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighted sampling (CARS), are utilized to extract the features of spectral data. Finally, partial least squares regression (PLSR) and stochastic configuration network (SCN) are adopted to establish the prediction model of litchi sugar content. The experimental results show that the SNV-CARS-SCN prediction model has the highest accuracy. The coefficient of determination ( R 2 ), RMSE, and MAE of the training dataset are 0.9996, 0.1145, and 0.1154, respectively. R 2 , RMSE, and MAE of the test dataset are 0.9740, 0.4962, and 0.3818, respectively. The NIR detection system and SCN prediction model designed in this paper are of great significance for the design of litchi automatic sorting system. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Local governments increasingly create and use corporate forms of organization to provide public services. While there is growing evidence on the factors influencing such corporatization, less is known about what leads some local governments to bring corporatized services back in-house. This paper investigates financial, socioeconomic, political and organizational drivers of reverse corporatization among 62 Arms-Length Management Organizations (ALMOs), wholly owned nonprofit organizations created by local governments in England to provide social housing services. Survival analysis for the period 2003–2019 suggests that reverse corporatization is more likely to occur in left-wing local governments and in those with more financial reserves, but is less likely if ALMOs have operated for a longer time and have a large board of directors. Points for practitioners: Understanding of the drivers of reverse corporatization can help local politicians and public managers improve their decisions about the appropriate mode of delivery for local public services. This paper highlights that good fiscal health may be essential for efforts to bring corporatized service back in-house, but that well-established municipal corporations may be better left to continue operating. It is also important for policymakers to recognize the role that political ideology (and socioeconomic circumstances) can play in shaping a commitment to reversing corporatization. [ABSTRACT FROM AUTHOR]

Elicitors have a substantial impact on the accumulation of phytochemical components in wild plants. The present study aimed to evaluate the impact of salicylic acid (SA) and methyl jasmonate (MJ) on the enrichment of carbazole alkaloids (CAs) in the leaves of Murraya koenigii under controlled environment. Effect of elicitors at 1.0, 1.5 and 2.0 mM and their time of exposition 6 h, 12 h, 24 h, 2 days and 5 days were evaluated. Methanolic extracts of the treated samples were subjected to UPLC-ESI-QTRAP LC-MS/MS analysis for the separation and subsequent quantification of seven identified CAs, viz. murrayamine A, koenigine, koenimbidine, koenimbine, mahanine, 8,8′-biskoenigine and mahanimbine. Accumulation of all the CAs was found to be enhanced with both the elicitors, but it was dependent on the type of elicitor, its concentration, and time of exposition. The data were analyzed through multivariate statistical models, like PCA, HCA and PLS-DA. The analysis revealed that 1.5 mM dose of MJ and SA had a significant influence on CAs accumulation at 24 and 12 h, respectively. Five marker CAs from both MJ and SA-treated samples were identified using VIP scoring. These findings suggest that MJ and SA with their specific concentration and exposition time could be used for the accumulation of particular CA to a substantial limit in this plant. [ABSTRACT FROM AUTHOR]

The educational outcomes of the descendants of migrants are important indicators of migrants' incorporation into host societies and an indicator of intergenerational social im/mobility. This paper examines this relationship using data from a survey that follows a cohort of young adults, born between 1988 and 1997, who grew up in Switzerland. It looks at the relationship between the educational output of respondents and their parental migratory background, with the theoretical consideration that the family's social capital is a starting point in the descendants' trajectories. The paper is based on secondary data and exploratory cross-sectional quantitative analyses. The results highlight first a correspondence between migrant parents' national origins and their socio-economic status—in other words, an 'ethno-class'. Second, they show differences in educational outcomes between migrants' descendants and native Swiss as well as between the migrants' descendants themselves—which indicates a segmented incorporation process for both the first and the second generation, in confirmation of previous research. Third, results show that parental background and language region of residence are statistically significant in determining the level of education achieved by the migrants' descendants, especially those with a low socio-economic status. Their social mobility is 'limited', and they remain mostly in vocational education. The paper concludes that the Swiss school system still fails to include the most unprivileged and that a glass ceiling remains for them. [ABSTRACT FROM AUTHOR]

Today, the concept of the circular economy (CE) has different meanings for various scholars. There are numerous definitions that focus on highlighting different aspects and principles of CE. However, there has been little effort made regarding the focus of CE definitions on various environmental aspects, such as waste management, wastewater treatment, biodiversity, carbon emissions and climate change. To clarify this complex environment, this paper examines the research endeavors surrounding the CE by analyzing 136 descriptions sourced from various entities and researchers. In particular, a more thorough investigation of CE is warranted to explore the possible ways of adopting its principles. To achieve this, Microsoft Excel and Leximancer™ software are employed to thematically and content-wise analyze the descriptions. The results reveal that "waste" and "recycling" are central concepts in CE descriptions, which align with the goals set forth in Union legislative acts, the "Roadmap for a Resource-Efficient Europe," and the European Green Deal aimed at increasing recycling rates and eliminating waste. [ABSTRACT FROM AUTHOR]

AbstractThe present study aims to document and analyze information related to significant ethnomedicinal plants in rural communities of Northeastern Madhya Pradesh, India. Data collection involved the use of open-ended and semi-structured questionnaires to gather information on ethnomedicinal plant usage, including specific plants, applications, and traditional healing practices. Quantitative indices such as Relative frequency of citation (RFC), Use value (UV), Family use value (FUV), Informant consensus factor (ICF), and Jaccard’s coefficient of similarity (JCS) were employed to quantify and analyze the gathered information. A total of 201 medicinal plants, belonging to 170 genera and 76 families, were recorded for treating various diseases, with trees (43%), herbs (33%), and climbers (13%) being predominantly used. Plants such as Hedychium coronarium and Cordia macleodii exhibited high RFC and high UV, particularly for treating eye and blood pressure problems. Vanda tessellata showed a high informant consensus factor for treating ear-related problems. Positive correlations were found between RFC and UV (0.631) and RFC and FUV (0.159). The study highlights the prevalence of traditional healing practices using medicinal plants in the area. Documentation of ethnomedicinal species and their therapeutic applications could stimulate further exploration into phytochemical and pharmacological investigations, potentially leading to the development of novel pharmaceuticals. [ABSTRACT FROM AUTHOR]

Surface enhanced Raman spectroscopy (SERS) offers ultrasensitive and label-free detection of specific molecules with broad applications in chemistry, life sciences, security, environmental sciences, and sensors. Among environmental contaminants, Hg2+ poses a significant threat due to its high toxicity, necessitating precise and high-throughput detection methods. Here, we introduce a novel SERS chip that integrates a multiwell plate with a plasmonic array, boasting strong electromagnetic enhancement and exceptional uniformity. Through surface modification of the chip with I–, we achieve high throughput, selectivity, and low limit of detection (LOD) for Raman-inactive Hg2+. Notably, the chip serves as a convenient platform for real-time Hg2+ detection and offers versatility for various surface modifications, paving the way for a next-generation quantitative and high-throughput sensor for a Raman-inactive analyte. [ABSTRACT FROM AUTHOR]

This study investigates the ventilation performance driven by height differences in residential buildings within the Shanghai region, utilizing a quantitative experiment framework based on the Virtualwind (VW) platform. The research emphasizes the integration of a "wind performance-oriented design" (WPOD) paradigm, aimed at promoting sustainable architectural practices. A core objective of this research is to develop a robust set of quantitative control methodologies for height-difference ventilation, designed to streamline the traditionally complex and time-consuming process of case-by-case analysis into a series of precise and reliable wind performance metrics. These metrics are intended to be incorporated into the early stages of architectural design, functioning similarly to the control of sunlight spacing, and serving as critical guiding parameters that align architectural design with natural environmental factors. The WPOD approach is anticipated to optimize ventilation efficiency in residential environments, thereby enhancing the living conditions in densely populated urban areas like Shanghai. Additionally, the paper explores the application of the WPOD design paradigm in higher education, with a focus on its integration into architectural teaching curricula, aiming to equip future architects with the skills necessary to incorporate sustainable design principles into their practice. The results of this study are expected to provide a scientific basis for the development of architectural designs that harmonize with the natural environment, contributing to both the field of architecture and the advancement of sustainable living environments. [ABSTRACT FROM AUTHOR]

Background: Due to insufficient near‐field resolution and artifacts, it is challenging to evaluate the left ventricular apical perfusion with phased‐array probes. By combining high‐frequency linear probe and contrast‐enhanced ultrasound (CEUS), imaging of apical myocardial perfusion could be improved. The study aims to evaluate the preliminary application of CEUS by high‐frequency linear probes to assess the apical perfusion. Methods: The study enrolled retrospectively 91 patients to test the feasibility of the novel method. In protocol 1, patients were stratified into a group with left anterior descending artery (LAD) stenosis (N = 40) and a group without LAD stenosis or coronary artery disease (N = 41) based on the degree of coronary artery narrowing, quantified by >50% stenosis in coronary angiography. Receiver operating characteristics (ROC) analysis was performed to test the diagnostic value of perfusion parameters. In protocol 2, the reproducibility of high‐frequency linear probe in apical perfusion analysis was compared with the conventional phased‐array probe in 30 patients. Results: (1) The novel method is feasible in 81(89.01%) patients. (2) In protocol 1, to detect LAD stenosis, the best cut‐off of β, T, A, and MBF were 10.32, 3.28, 9.39, and 4.99, respectively. Area under the curve of β, T, A, and MBF were.880,.881,.761, and.880, respectively. (3) In protocol 2, compared with phased‐array probe, the quantitative analysis of high‐frequency linear probe is of high reproducibility and could get good curve fitting (R2 =.29 vs. R2 =.71, P <.01). Conclusion: Observation of apical perfusion using this method is feasible and quantitative analysis allows an accurate and convenient identification of LAD stenosis. This method provides an alternative for patients who have difficulties in visualizing the apical region with a phased‐array probe. [ABSTRACT FROM AUTHOR]

Polystyrene plastic pollution poses a pressing environmental concern and represents a significant risk factor for infertility. Despite this, a comprehensive overview of the field remains scarce, with future trends largely unknown. Bibliometrics, an applied mathematical and statistical method, offers a means to analyze textual information across various levels, facilitating quantitative assessments of all knowledge carriers and unveiling the nature and developmental trajectories of a discipline. This study aimed to employ bibliometric methods to scrutinize the current status and research hotspots within the realm of polystyrene and infertility. Literature spanning from 1980 to 2023 pertaining to polystyrene and infertility was retrieved from the core database of Web of Science. Quantitative analyses were conducted utilizing CiteSpace (version 5.7.R7), VOSviewer (version 1.6.18.0), and an online literature analysis website (https://bibliometric.com/). The analysis visually represented countries, institutions, authors, journals, and keywords within the field. This study delved into the development history, knowledge structure, research hotspots, and potential trends in the field, furnishing a macro perspective for researchers. The investigation encompassed 267 articles published across 120 journals by 1,352 authors affiliated with 417 institutions in 51 countries, with these articles garnering 10,310 citations across 2,811 journals. The top three countries contributing the most articles were China, the United States, and Germany. In essence, the research hotspots primarily revolved around metabolism, endocrinology, and immunity. Despite China's relatively recent entry into this field, its rapid development is evident. However, the low citation frequency suggests a need for improved article quality. [ABSTRACT FROM AUTHOR]

This scientometric analysis significantly advances the understanding of landslide risk assessment and economic losses, focusing on scientometric insights. This study aims at analyzing the global trends and structures of landslide risk and economic loss research from 2002 to 2023 using scientometric techniques such as co-authorship, co-word, co-citation, cluster analysis, and trend topics, among others. Thus, analysis of 92 studies gathered from Scopus and Web of Science databases reveals a continuous growth in environmental, social, and quantitative research topics. Predominant contributions hail mainly from China and Italy. The research identifies critical themes, including risk analysis, vulnerability, fragility, and economic losses. The current identified research combines advanced statistical methods, including logistic regression, with climate change scenarios and susceptibility assessments to reveal intricate connections between climatic shifts, hydrogeological hazards, and their economic and environmental impacts. This study provides researchers and practitioners with a comprehensive understanding of the status quo and research trends of ontology research landslide risk and its economic losses. It also promotes further studies in this domain. [ABSTRACT FROM AUTHOR]

Raman spectroscopy, a non-destructive analytical technique, provides information about the structure and composition of substances. It has been widely applied in the fields of chemistry, biology, and materials science for analysis and research. Through three years of pedagogical development, our instructional design for Raman scattering spectroscopy experiment has accumulated considerable practical experience. This experimental course is structured around three segments: qualitative, quantitative, and trace analysis, initiating with the intriguing task of distinguishing between authentic and counterfeit jadeite—a familiar challenge that ignites students’ curiosity and eagerness to delve into Raman spectroscopy. Utilizing portable Raman spectrometer enables students to gain an understanding of the instrument's construction and functionality, while mastering the fundamental concepts of Raman spectroscopy and its surface-enhanced variant. This educational venture equips students with the proficiency to adeptly conduct Raman spectroscopic analyses on solid and liquid samples, as well as trace substances, thus establishing a solid foundation for their mastery of related spectroscopy analysis courses. [ABSTRACT FROM AUTHOR]

Lindera erythrocarpa M., a medicinal plant commonly found in China, Japan, and Korea, is well known for its antioxidant, anti-inflammatory, and potential anti-cancer effects. However, data on the quantification of different marker compounds found in this species across plant parts and geographical regions remain limited. To address this gap in the literature, the marker compounds methyl lucidone (1), methyl linderone (2), and kanakugiol (3) in leaves and stems of L. erythrocarpa collected from five different regions in the Republic of Korea were analyzed using high-performance liquid chromatography (HPLC/UV). Among the three compounds analyzed, kanakugiol (3) was the most abundant and was predominantly found in the stem samples. Overall, stems contained higher concentrations of methyl linderone (2) and kanakugiol (3) than the leaves. These findings highlight the importance of considering regional factors and plant part selection to maximize the yield of bioactive compounds. The results support the potential of L. erythrocarpa as a medicinal source and contribute to the standardization and quality improvement of herbal goods, thereby enhancing consumer protection and product efficacy. [ABSTRACT FROM AUTHOR]

Eupatorium lindleyanum DC. (EL) is a traditional Chinese herb known for its phlegm-reducing, cough-relieving and asthma-calming properties. It is widely used for treating cough and bronchitis. However, preliminary experiments have revealed wide variations in the composition of its different medicinal parts (flowers, leaves and stems), and the composition and efficacy of its different medicinal parts remain largely underexplored at present. In this study, non-targeted rapid resolution liquid chromatography coupled with a quadruple time-of-flight mass spectrometry (RRLC-Q-TOF-MS)-based metabolomics approach was developed to investigate the differences in the chemical composition of different medicinal parts of EL. We identified or tentatively identified 9 alkaloids, 11 flavonoids, 14 sesquiterpene lactones, 3 diterpenoids and 24 phenolic acids. In addition, heatmap visualization, quantitative analysis by high-performance liquid chromatography (HPLC-PDA) and ultra-high-performance liquid chromatography–triple quadrupole tandem mass spectrometry (UPLC-MS/MS) showed particularly high levels of sesquiterpene lactones, flavonoids and phenolic acids in the flowers, such as eupalinolide A and B and chlorogenic acid, among others. The leaves also contained some flavonoid sesquiterpene lactones and phenolic acids, while the stems were almost absent. The findings of in vitro activity studies indicated that the flowers exhibited a notable inhibitory effect on the release of the inflammatory factors TNF-α and IL-6, surpassing the anti-inflammatory efficacy observed in the leaves. Conversely, the stems demonstrated negligible anti-inflammatory activity. The variations in anti-inflammatory activity among the flowers, leaves and stems of EL can primarily be attributed to the presence of flavonoids, phenolic acids and sesquiterpene lactones in both the flowers and leaves. Additionally, the flowers contain a higher concentration of these active components compared to the leaves. These compounds mediate their anti-inflammatory effects through distinct biochemical pathways. The results of this study are anticipated to provide a scientific basis for the rational and effective utilization of EL resources. [ABSTRACT FROM AUTHOR]

Traditional Jiangnan garden architecture in China offers distinct spatial impressions that hold significance in contemporary architectural expression. Yet, the understanding and analysis of these spaces have historically relied on subjective, sensory experiences, often lacking precise, quantitative research. Consequently, establishing clear logical connections between visual cognition and emotional perception within these spatial experiences has been a challenge. This study introduces virtual reality spatial simulation and quantification techniques, summarizing key Jiangnan garden spatial characteristics and prototypes. It includes a series of virtual reality experiments focusing on cognitive experiences within Jiangnan garden spaces. A comprehensive analysis of isovisit quantitative data, questionnaires, and behavioral information explores the logical relationships between emotional perceptions (calmness, surprise, interest, confusion) and visual cognition qualities (openness, complexity, theatricality) in garden space impressions. This research aims to reveal deeper connections between garden space qualities, visual cognition, and emotional experiences, offering valuable insights for the application of historical traditional spatial features in contemporary architecture. It bridges the gap between sensory experiences and rational analysis, enhancing our understanding of the intricate spatial narratives within Jiangnan gardens. [ABSTRACT FROM AUTHOR]

The quantification microstructural parameters has gained significant attention to estimate the strength of rock materials. Some researchers have qualitatively explained the influence of microstructural parameters on rock mechanical properties and many developed empirical relations between various microstructural indices and engineering properties of different rock types. A comprehensive review on the relation between microstructure and strength of rock materials has been carried out in this study based on the available literature and the tests performed on granite and sandstone from Malanjkhand and Kothagudem respectively. It has been observed that the correlation of rock strength with mineral content, textural parameters and micro-crack patterns does not signifies any perceptible correlation or any definite trend. This is attributed to the fact that rock strength is a manifestation of cumulative responses of a number of microstructural parameters and thus, correlating rock strength with any single parameter would overlook the impact of others. The study also discusses the challenges and potential benefits of both quantitative and qualitative analysis of microstructure in rock mechanics, providing a way forward for future research in the field. It is ascertained that microstructural studies are time consuming approach and involve significant limitations when quantifying the parameters from the microscopical images and relating them individually with strength of the rocks. However, they might be an important aspect in comprehending the variation in strength and deformation of rock materials and in this respect the implication of modern technological setups would be efficient over the conventional methods. [ABSTRACT FROM AUTHOR]

Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of "field data collection—data processing—data analysis and application", which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields. [ABSTRACT FROM AUTHOR]

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography–mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea. [ABSTRACT FROM AUTHOR]

Introduction: Croton crassifolius Geisel. (CCG) is a traditional Chinese medicine widely used in South China. It has various pharmacological effects and is often used in treating rheumatoid arthritis and gastric and duodenal ulcers. However, the chemical characteristics and its effective constituents are still scarcely studied. Objective: To determine the phytochemical profile of the CCG extract and to investigate the chemical characteristics of terpenoids extracted from rat plasma following oral administration of CCG extract based on UPLC‐Q/TOF‐MS. Moreover, six terpenoids in CCG were quantified, and in vivo pharmacokinetic behavior after oral CCG extract was further explored. Results: In total, 56 terpenoids were tentatively identified in the CCG extract and 16 terpenoids were detected in rat plasma after oral CCG extract. In addition, the contents of six terpenoids in CCG were clarified. The plasma quantification method of six terpenoids was further established, validated, and confirmed to have good sensitivity and specificity. The six analytes exhibited excellent linearity in respective concentration ranges (r ≥ 0.998). The intra‐day and inter‐day precisions relative standard deviation (RSD, %) were less than 11.27%, and the accuracies ranged from −7.06% to 9.91%. Stability, extraction recovery, and matrix effect in plasma were within the required limits (RSD < 15%). Conclusion: A total of 56 terpenoids were identified in CCG and 16 prototype components in plasma after oral CCG. The validated quantitative method was successfully applied to the simultaneous determination of six major terpenoids in plasma. The pharmacokinetic parameters are clarified, which can guide the clinical application of CCG. A total of 56 compounds in Croton crassifolius Geisel. (CCG) and 16 prototype components in plasma after oral CCG extract were identified by UPLC‐Q/TOF‐MS. In addition, the contents of six terpenoids in CCG were determined. The UPLC Q/TOF‐MS method was further developed and successfully applied to elucidate the pharmacokinetic characteristics of the six components in plasma after oral CCG extract. According to that, it can better guide the clinical application of CCG. [ABSTRACT FROM AUTHOR]

A reagent of phenol − hypochlorite was developed for the spot test and smartphone-based digital image colorimetric for the detection and determination of 3-nitro-1, 2, 4-triazole-5-one (NTO) explosive. The green color was produced in the presence of NTO and the proposed reagent. Different color spaces of red, green, and blue (RGB) or hue, saturation and value (HSV) of captured images have been extracted and processed by the written program in MATLAB. The B code color of RGB color space was altered with NTO concentration, and the related numerical data of B code color were utilized as an analytical signal to plot the calibration curve. Amine functional groups of NTO molecules take part in the reaction with phenol-hypochlorite reagent and create green color. The experimental conditions including the amounts of the used reagents were optimized by the method of one variable at a time. Limit of detection and dynamic range of the proposed method was obtained 3.1 and 6.0 − 150.0 mg⋅L−1, respectively. The color of reagent in presence of NTO explosive was a specific color in spot test and colorimetric method in comparison to famous explosives including hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX), tetra methylene tetra nitramine (HMX), trinitrotoluene (TNT), 2,4- dinitroanisole (DNAN), ammonium perchlorate (AP), ammonium nitrate (AN), and 1,2,4-triazol-3-one (TO) as the precursor of NTO. The accuracy and precision of the used procedure and written program were investigated by comparing them to UV–Vis spectrophotometric method and GNU Image Manipulation Program (GIMP). Simple reactants beside the smartphone as an available device are the advantages of proposed method in determination of NTO explosive. [ABSTRACT FROM AUTHOR]

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This review paper provides an in-depth analysis of the significance of lipid nanocarriers in drug delivery and the crucial role of characterization techniques. It explores various types of lipid nanocarriers and their applications, emphasizing the importance of microscopy-based characterization methods such as light microscopy, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The paper also delves into sample preparation, quantitative analysis, challenges, and future directions in the field. The review concludes by underlining the pivotal role of microscopy-based characterization in advancing lipid nanocarrier research and drug delivery technologies. [ABSTRACT FROM AUTHOR]

Self‐assembled peptides have been among the important biomaterials due to its excellent biocompatibility and diverse functions. Over the past decades, substantial progress and breakthroughs have been made in designing self‐assembled peptides with multifaceted biomedical applications. The techniques for quantitative analysis, including imaging‐based quantitative techniques, chromatographic technique and computational approach (molecular dynamics simulation), are becoming powerful tools for exploring the structure, properties, biomedical applications, and even supramolecular assembly processes of self‐assembled peptides. However, a comprehensive review concerning these quantitative techniques remains scarce. In this review, recent progress in techniques for quantitative investigation of biostability, cellular uptake, biodistribution, self‐assembly behaviors of self‐assembled peptide etc., are summarized. Specific applications and roles of these techniques are highlighted in detail. Finally, challenges and outlook in this field are concluded. It is believed that this review will provide technical guidance for researchers in the field of peptide‐based materials and pharmaceuticals, and facilitate related research for newcomers in this field. [ABSTRACT FROM AUTHOR]

Background: Endometriosis is a gynecological disease characterized by the presence of endometrial tissue in abnormal locations, leading to severe symptoms, inflammation, pain, organ dysfunction, and infertility. Surgical removal of endometriosis lesions is crucial for improving pain and fertility outcomes, with the goal of complete lesion removal. This study aimed to analyze the location and expression patterns of poly (ADP-ribose) polymerase 1 (PARP-1), epithelial cell adhesion molecule (EpCAM), and folate receptor alpha (FRα) in endometriosis lesions and evaluate their potential for targeted imaging. Methods: Gene expression analysis was performed using the Turku endometriosis database (EndometDB). By immunohistochemistry, we investigated the presence and distribution of PARP-1, EpCAM, and FRα in endometriosis foci and adjacent tissue. We also applied an ad hoc platform for the analysis of images to perform a quantitative immunolocalization analysis. Double immunofluorescence analysis was carried out for PARP-1 and EpCAM, as well as for PARP-1 and FRα, to explore the expression of these combined markers within endometriosis foci and their potential simultaneous utilization in surgical treatment. Results: Gene expression analysis revealed that PARP-1, EpCAM, and FOLR1 (FRα gene) are more highly expressed in endometriotic lesions than in the peritoneum, which served as the control tissue. The results of the immunohistochemical study revealed a significant increase in the expression levels of all three biomarkers inside the endometriosis foci compared to the adjacent tissues. Additionally, the double immunofluorescence analysis consistently demonstrated the presence of PARP-1 in the nucleus and the expression of EpCAM and FRα in the cell membrane and cytoplasm. Conclusion: Overall, these three markers demonstrate significant potential for effective imaging of endometriosis. In particular, the results emphasize the importance of PARP-1 expression as a possible indicator for distinguishing endometriotic lesions from adjacent tissue. PARP-1, as a potential biomarker for endometriosis, offers promising avenues for further investigation in terms of both pathophysiology and diagnostic-therapeutic approaches. [ABSTRACT FROM AUTHOR]

Objective: White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective. Methods: The resting-state functional magnetic resonance imaging (rsfMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system. Results: The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch." Conclusion: Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education. [ABSTRACT FROM AUTHOR]

In response to global compound risks, resilience has been incorporated into China’s national governance discourse. This paper presents a quantitative analysis of 88 central-level policies building resilience capacity in China, to refine policy design to address complex risks more effectively. A comprehensive two-dimensional “policy tool-application field” analysis framework is developed based on policy tool theory and employs bibliometrics and content analysis methods. By extracting 210 policy analysis units corresponding to 289 coding reference points, we unveil the structural characteristics, policy themes, and distribution of policy tools on resilience building at the central level. Results show a notable upward trend in policy publication, forming a multi-agency policy-making network. We observe an imbalance in the structure of policy tools, with a strong reliance on environment-based policy tools. Policy tools are configured across all application fields. Supply-based policy tools are primarily applied for environmental and technological resilience, while demand-based tools find greater utilization in economic and organizational resilience dimensions. However, there is a neglect of social resilience. This paper recommends integrating policy tools to bolster resilience in diverse fields, and how to assess risks and their dynamic adjustments in resilience building as a significant direction in the future. [ABSTRACT FROM AUTHOR]

Companies have been experiencing increasing pressure from NGOs to overcome unethical and unsustainable behaviours. The purpose of this research was to study the interplay between supply chain transparency and NGO pressure. The analysis builds on the literature on supply chain transparency and institutional pressures. We conducted a time-lagged, multi-level regression analysis that included data from 270 fashion companies over a 5-year period to investigate the effect of NGO pressure on transparency and vice versa. The results revealed that companies with higher levels of transparency are more likely to be pressured by NGOs than less transparent companies. Furthermore, our findings show that, depending on the number of campaigns, NGO power and communication about campaigns, NGO pressure can either stimulate corporate transparency or turn into a negative effect that leads to lower levels of transparency. [ABSTRACT FROM AUTHOR]

Traditional medicine, 'LuRu', is a commonly used Tibetan medicine for clearing away heat and detoxifying. Dried products of Pedicularis flava and Pedicularis muscicola are often used as 'LuRu' in the market. This study aims to compare the chemical constituents of P. flava and P. muscicola using GC-MS and UPLC-TOF-MS, and confirm which plant species is more suitable to be used as 'LuRu'. A total of 46 and 68 compounds were identified from the volatile and non-volatile components, respectively. Out of these, 17 and 37 volatile and non-volatile components, respectively, had pharmacological activities. P. flava showed a higher content of the same active components than P. muscicola. Good biological activities are only observed in the unique components in P. flava, and not in P. muscicola. The two herbs should not be mixed in clinical medication. Our study shows that P. flava is better suited as a high-quality herb for the Tibetan medicine, 'LuRu'. Volatile components of Tibetan Pedicularis flava and Pedicularis muscicola were analysed for the first timewiooi. Non-volatile components of Tibetan Pedicularis flava and Pedicularis muscicola were analysed for the first time. Differences in chemical composition and content between Pedicularis flava and Pedicularis muscicola were studied. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the utility of qualitative and quantitative analysis employing contrast-enhanced ultrasound (CEUS) in predicting the WHO/ISUP grade of small (≤4 cm) clear cell renal cell carcinoma (ccRCCs).Patients with small ccRCCs, confirmed by histological examination, underwent preoperative CEUS and were classified into low- (grade I/II) and high-grade (grade III/IV) groups. Qualitative and quantitative assessments of CEUS were conducted and compared between the two groups. Diagnostic performance was assessed using receiver operating characteristic curves.A total of 72 patients were diagnosed with small ccRCCs, comprising 23 individuals in the high-grade group and 49 in the low-grade group. The low-grade group exhibited a significantly greater percentage of hyper-enhancement compared to the high-grade group (79.6% VS 39.1%, P < 0.05). The low-grade group showed significantly higher relative index values for peak enhancement, wash-in area under the curve, wash-in rate, wash-in perfusion index, and wash-out rate compared to the high-grade group (all P < 0.05). The AUC values for qualitative and quantitative parameters in predicting the WHO/ISUP grade of small ccRCCs ranged from 0.676 to 0.756.Both qualitative and quantitative CEUS analysis could help to distinguish the high- from low-grade small ccRCCs. [ABSTRACT FROM AUTHOR]

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend. [ABSTRACT FROM AUTHOR]

This review comprehensively explores fluoride removal from phosphogypsum, focusing on its composition, fluorine-containing compounds, characterization methods, and defluorination techniques. It initially outlines the elemental composition of phosphogypsum prevalent in major production regions and infers the presence of fluorine compounds based on these constituents. The study highlights X-ray photoelectron spectroscopy (XPS) as a pivotal method for characterizing fluorine compounds, emphasizing its capability to determine precise binding energies essential for identifying various fluorine species. Additionally, the first-principle density functional theory (DFT) is employed to estimate binding energies of different fluorine-containing compounds. Significant correlations are observed between the total atomic energy of binary fluorides (e.g., of alkali metals, earth metals, and boron group metals) and XPS binding energies. However, for complex compounds like calcium fluorophosphate, correlations with the calculated average atomic total energy are less direct. The review categorizes defluorination methods applied to phosphogypsum as physical, chemical, thermal, and thermal-combined processes, respectively. It introduces neural network machine learning (ML) technology to quantitatively analyze and optimize reported defluorination strategies. Simulation results indicate potential optimizations based on quantitative analyses of process conditions reported in the literature. This review provides a systematic approach to understanding the phosphogypsum composition, fluorine speciation, analytical methodologies, and effective defluorination strategies. The attempts of adopting DFT simulation and quantitative analysis using ML in optimization underscore its potential and feasibility in advancing the industrial phosphogypsum defluorination process. [ABSTRACT FROM AUTHOR]

The excessive use of pesticides has detrimental effects on the ecosystem, leading to soil contamination and the spread of pollution beyond the targeted areas. Concerns arise regarding the permissible limits of pesticides, typically around 10−8 mol/L. In this study, we employed surface‐enhanced Raman spectroscopy (SERS), a highly sensitive and selective technique, to investigate the behavior of the pesticide ametrine (AMT) on silver colloids. The Ag nanoparticles (AgNPs) exhibited an average size of (26 ± 2) nm and a zeta potential of (−30 ± 1) mV in the absence of AMT, which decreased to (−24 ± 1) mV in the presence of AMT, resulting in mild AgNPs aggregation, with the average diameter of AgNPs increasing to approximately 300 nm. This aggregation is advantageous, as they provide active sites for pesticide detection. Besides, the purification method employed ensured that AMT remained undegraded, and various conformations of AMT were simulated using Ag clusters to study the SERS effect. Comparison with the experimental spectra indicated that the SERS‐4 conformer closely resembled the experimental spectrum, suggesting simultaneous interaction between the Ag surface and the sulfur (S) and nitrogen (N) atoms of the AMT triazine ring. Notably, changes in the AMT molecule were observed with pH variations: at pH below 5, hydroxylation occurred, resulting in an Ag–Cl stretching at 241 cm−1 in the SERS spectra. Conversely, at pH above 5 (pH 6–13), the presence of bands at 230 and 219 cm−1 in the SERS spectra indicate the formation of Ag–N and Ag–S bonds, respectively, between the AMT and the AgNPs. Furthermore, the study successfully detected AMT at pH 7, establishing a limit of detection (LOD) of 1.36 × 10−8 mol/L (3 ppb) based on the SERS spectra. These findings underscore the applicability of the SERS technique in the sensitive and selective detection of AMT, offering a promising approach for monitoring the presence of this pesticide in environmental samples. [ABSTRACT FROM AUTHOR]

An X-ray fluorescence handheld spectrometer (hh-XRF) is adapted for real-time qualitative and quantitative elemental analysis in scanning mode for applications in cultural heritage. Specifically, the Tracer-5i (Bruker) is coupled with a low-cost constructed computer-controlled x–y target stage that enables the remote control of the target's movement under the ionizing X-ray beam. Open-source software synchronizes the spectrometer's measuring functions and handles data acquisition and data analysis. The spectrometer's analytical capabilities, such as sensitivity, energy resolution, beam spot size, and characteristic transition intensity as a function of the distance between the spectrometer and the target, are evaluated. The XRF scanner's potential in real-time imaging, object classification, and quantitative analysis in cultural heritage-related applications is explored and the imaging capabilities are tested by scanning a 19th-century religious icon. The elemental maps provide information on used pigments and reveal an underlying icon. The scanner's capability to classify metallic objects was verified by analyzing the measured raw spectra of a coin collection using Principal Components Analysis. Finally, the handheld's capability to perform quantitative analysis in scanning mode is demonstrated in the case of precious metals, applying a pre-installed quantification routine. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization necessitates effective analytical methods to address its complexities. Peri-urbanization, the expansion of settlement boundaries and urban spread, is a critical aspect of this phenomenon. This study introduces a quantitative method to analyze peri-urbanization, providing a peri-urban index (PUI) for medium-sized cities based on peri-urban dynamics. Utilizing the analytical hierarchy process (AHP), the weight values of influencing dynamics are calculated, establishing a peri-urban scale (PUS) ranging from one to five based on rural and urban characteristics. Applied to a medium-sized case study city, the method assesses the peri-urbanization from 2007 to 2022. Four main dynamics—socio-demographics, economic-employment, land use-accessibility, and building-texture patterns—and fourteen sub-dynamics were identified and weighted using AHP. The city's PUI values over different years reveal a 41.6% increase, indicating significant peri-urbanization. This quantitative approach, which innovatively integrates multiple numerical analysis methods, not only highlights the peri-urbanization trends of the city but also provides a comparative analysis framework for other cities. The method's ability to track changes over time and compare different urban areas supports the development of sustainable urbanization strategies, ensuring balanced growth and resource allocation. This method offers urban planners, policymakers, and architects a powerful tool to manage and guide future urban expansion effectively through interdisciplinary collaboration. [ABSTRACT FROM AUTHOR]

Simple Summary: Double-strand breaks (DSBs) in a single nucleus are usually measured using the sperm chromatin structure assay (SCSA), sperm chromatin dispersion (SCD) test, and comet assay (CA). Mono-dimensional single-cell pulsed-field gel electrophoresis (1D-SCPFGE) and angle-modulated two-dimensional single-cell pulsed-field gel electrophoresis (2D-SCPFGE) were developed to observe DNA fragmentation in separated motile sperm. Comparative standards, calibration curves, required sensitivity levels, and eligibility criteria for test sperm were set up to validate the measurement principles of these tests. The conventional methods overlooked the interference of nucleoproteins in their measurements. In-gel proteolysis improves the measurement accuracy levels of 1D- and 2D-SCPFGE. Naked DNA is suitable for comparative standards and test specimens. Moreover, several dysfunctions that might induce DNA damage are observed in the separated motile sperm. Overall, the discussion highlights the need to revisit the conventional univariable analyses based on the SCSA, SCD test, and CA. Human infertility is a complex syndrome, and the aim of quality control in intracytoplasmic sperm injection is to identify the underlying dysfunctions remaining in the separated motile sperm that render them ineligible for injection. Multivariable analyses with special consideration to confounding factors are necessary in future cohort studies. (1) Background: Double-strand breaks (DSBs) in a single nucleus are usually measured using the sperm chromatin structure assay (SCSA), sperm chromatin dispersion (SCD) test, and comet assay (CA). Mono-dimensional single-cell pulsed-field gel electrophoresis (1D-SCPFGE) and angle-modulated two- dimensional single-cell pulsed-field gel electrophoresis (2D-SCPFGE) were developed to observe DNA fragmentation in separated motile sperm. (2) Methods: Comparative standards, calibration curves, required sensitivity levels, and eligibility criteria for test sperm were set up to validate the measurement principles of these tests. (3) Results: The conventional methods overlooked the interference of nucleoproteins in their measurements. In-gel proteolysis improves the measurement accuracies of 1D- and 2D-SCPFGE. Naked DNA is suitable for comparative standards and test specimens. Moreover, several dysfunctions that might induce DNA damage are observed in the separated motile sperm. Overall, the discussion highlights the need to revisit the conventional univariable analyses based on the SCSA, SCD test, and CA. (4) Conclusions: Human infertility is a complex syndrome, and the aim of quality control in intracytoplasmic sperm injection is to identify the underlying dysfunctions remaining in the separated motile sperm that render them ineligible for injection. Multivariable analyses with special consideration to confounding factors are necessary in future cohort studies. [ABSTRACT FROM AUTHOR]

The medicinal nature of traditional Chinese medicine (TCM) mainly plays a role in regulating the energy and material metabolism of the body. The energy metabolism of the body is also regulated by the nervous and endocrine systems, and the hormones are an important component of the endocrine system to realize the coordinated action. Therefore, the medicinal nature of TCM is inextricably linked with the metabolism of hormones. Hyperthyroidism, qi-yin deficiency syndrome is regarded as the classic heat syndrome category in Chinese medicine, which can be used as model to reflect the mechanism and difference of qi tonifying of ginseng herbs with different medicinal natures. Steroid hormones are synthesized in the adrenal cortex, mitochondria of placental cells, smooth endoplasmic reticulum, and gonads of the human body by the participation of various metabolic enzymes. Meanwhile, most of the patients with hyperthyroidism have adrenocortical dysfunction in the clinic, and the corresponding clinical symptoms are closely related to the hypothalamic-pituitary-thyroid axis (HPT), the hypothalamic-pituitary-adrenal axis (HPA), and the hypothalamic-pituitary-gonadal axis (HPG). When hyperthyroidism occurs, the production and release of steroid hormones will be affected to a certain extent. To explore the correlation between the level of steroid hormones and hyperthyroidism, and the ameliorative effects of red ginseng and ginseng leaves of different types of the genus Ginseng on hypermetabolism of hyperthyroidism and the changes of related indexes, a method of ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry (UPLC-QqQ-MS/MS) was established for rapid identification and quantification of chemical components, including adrenaline, androstenedione, testosterone, dehydroepiandrosterone, estriol, progesterone, 17α-hydroxypregnenolone, corticosterone, and cortisol in rat serum. Then, multivariate analysis method, principal component analysis (PCA), and Spearman test were used to analyze the correlation among adrenaline, steroid hormones, and thyroid function. The results showed that the contents of steroid hormone in the serum of the model group of rats decrease to different degrees, compared with the control group, and increase after the administration of different types of ginsengs. Meanwhile, it was proved that the established method has high sensitivity, good reproducibility, high accuracy, and fast analysis, which can screen a large number of steroid hormones in complex samples in a short time, for accurately investigation of the effects of ginsengs on the content of 9 types of steroid hormones in hyperthyroidism rats. The abnormal levels of steroid hormones are related to thyroid hormones. Corticosterone and adrenaline can serve as key indicators for regulating steroid hormone metabolism of TCMs like ginsengs. Testosterone can be used as the biomarker for regulating steroid hormones of the cold nature herbs. Adrenaline and androstenedione can be used as biomarkers for regulating steroid hormones of the warm nature herbs. This method can conveniently, quickly, accurately, and efficiently complete the analysis of target components in samples. The average recoveries of steroid hormones in rat serum range from 88.75% to 106.94%, and the relative standard deviation is less than 1.99%. This can be used for the determination of steroid hormones in rat serum systematically, rapidly, accurately and efficiently, and provides a methodological reference for the analysis and determination of steroid hormones. [ABSTRACT FROM AUTHOR]

Protein content in food is an important indicator of the nutritional value of food, and it is also related to food safety and many other issues, therefore it is significant to accurately determine the protein content for the valuation of food nutrition and safety. The main elements in food include carbon, nitrogen, hydrogen and sulphur, of which nitrogen mainly comes from protein. The average nitrogen content in protein is 16%, so the conversion factor for “nitrogen-to-protein” is 6.25. Therefore, the protein content in food can be obtained by measuring the nitrogen content of food combined with “nitrogen-to-protein” conversion factor. The traditional methods of protein detection are Kjeldahl method, spectrophotometry, and Dumas combustion. The Kjeldahl method is timeconsuming and consumes a large amount of reagents, which can cause environmental pollution. Spectrophotometry is susceptible to interference and the quantitative curve is not easy to draw. Dumas combustion is expensive in terms of consumables. In this paper, a platform of tube furnacequadrupole mass spectrometer was set up to analyze the protein content in food. Food samples were heated at high temperatures in a tube furnace and combusted in an oxygen-rich environment. After the gas products were ionized, they were directly introduced into a quadrupole mass analyzer for detection, which eliminated the need for processes such as oxidation-reduction, adsorption-desorption by using the selection ion scan function of the mass spectrometer. Five kinds of food samples with different protein contents were selected as research object. Each food sample was weighed at 1g. The food samples were fully combusted in tube furnace at high temperature (1 300 ℃). The gas products were infused through a drying and filtering tube to remove water and smoke, and then entered the EI ion source through a capillary for ionization. By electronic ionization, nitrogen element characteristic ions, N+, NO+, and NO2+ are generated. The peak intensity of the NO2+ ions were detected by the selection ion scan function of the quadrupole mass analyzer. A standard curve for nitrogen quantification is plotted to be y=115.64x+1 896.9 with the correlation coefficient (R²) of 0.999 92 and the relative standard deviation (RSD) of 2.1%-6.1%. The nitrogen content can be calculated by using the peak intensity of NO2+ ions according to the standard curve. The experimental platform of tube furnace-mass spectrometer provides a green, rapid, accurate, and low-cost detection method for quantitative analysis of protein content in food. [ABSTRACT FROM AUTHOR]

This article spotlights the interest in using co-localized infrared (IR)–Raman spectroscopy as an innovative approach for the in situ monitoring of complex gas mixtures, e.g., hydrogen (H2), nitrogen (N2), carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4), at elevated pressures. Thus, by combining the IR and Raman spectra of CH4, we proposed a new methodology for the calibration of the Raman spectra to circumvent the fact that Raman intensities are arbitrary (laser power, instrument response, integration time, and fluorescence). Applying our methodology to scale several consecutive experiments, the concentrations of all gases were determined with a relative uncertainty lower than 10%. These original results highlight the interest in co-localized IR–Raman spectroscopy analysis in a single cell for the quantitative analysis of solutes by Raman spectroscopy without the use of an internal standard. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are typically used in the spatial domain. Recently, deep learning models have been increasingly applied in the field of medical imaging. In this study, we evaluated the effectiveness of a convolutional neural network-based residual network (ResNet) deep learning model for noise reduction when Gaussian and speckle noises were present. We compared the results with those obtained from conventional filtering techniques. A dataset of 500 images was prepared, and Gaussian and speckle noises were added to create noisy input images. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The ResNet deep learning model, comprising 16 residual blocks, was trained using optimized hyperparameters, including the learning rate, optimization function, and loss function. For quantitative analysis, we calculated the normalized noise power spectrum, peak signal-to-noise ratio, and root mean square error. Our findings showed that the ResNet deep learning model exhibited superior noise reduction performance to median, Wiener, and median-modified Wiener filter algorithms. [ABSTRACT FROM AUTHOR]