Composition Data Research Articles

A Multivariate Mixture Regression Model for Constrained Responses

Compositional data are vectors typically representing proportions of a whole, that is, those whose elements are strictly positive and subject to a unit-sum constraint. The increasing number of fields where this type of data arises makes the development of proper statistical tools an important issue. From a regression perspective, whenever the multivariate response is a compositional vector, a proper model that accounts for the unit-sum constraint is the well-established Dirichlet regression model. However, there are significant drawbacks mainly due to the limited flexibility of the Dirichlet distribution. The aim of this contribution is to introduce a new multivariate regression model for constrained responses, that is based on the extended flexible Dirichlet distribution (which is a structured mixture with Dirichlet distributed components). The new model is obtained by adopting a novel reparameterization which allows for, among other things, the presence of suitably designed cluster-specific regression patterns. It is shown to provide considerably greater flexibility and better performance than the standard Dirichlet regression model. In particular, from theoretical analysis, intensive simulation studies in many challenging scenarios, as well as from a real data application, it emerges that the new regression model can handle several issues affecting the Dirichlet regression, such as the presence of outliers, latent groups, multi-modality, and positive correlations.

Niche structure and habitat shifts for coastal sharks of the US Southeast Atlantic and Gulf of Mexico

AbstractUnderstanding spatial ecology and predicting animal movements in response to environmental changes, such as anthropogenic climate change and multidecadal variability, is critical for effective conservation strategies. Niche structuring is key to some coastal shark species and size classes coexisting in the US Atlantic and Gulf of Mexico to limit interspecific and intraspecific interaction. Data from four fishery‐independent bottom longline surveys were used to evaluate the abiotic ecological niches of eight species of small and large coastal sharks. Gaussian mixture models separated length composition data into 14 size categories for ecological niche analysis. Generalized additive mixed effect models were fit and coupled with output from dynamic high‐resolution ocean models to predict suitable abiotic habitats, evaluate potential shifts in distribution, and explore the impacts of large‐scale climatological trends on abiotic habitats from 1994 to 2019. The abiotic niche for small coastal sharks generally tended toward warmer, high salinity, shallow bottom waters close to shore. No overarching niche was found for large coastal sharks, but appreciable ontogenetic differences were seen. Most taxa analyzed exhibited declining annual trends in higher quality habitats, particularly during fall months. The analysis provided evidence of shifts north along the Atlantic, to deeper offshore waters in the Atlantic and Gulf of Mexico, and the potential to redistribute in response to multidecadal climate variability for multiple species. The analytical framework described could aid in developing various spatiotemporal management measures, and results provide insight into the habitat characteristics of several species over broad spatiotemporal ranges and through ontogeny.

Application of Composite Frequency Data Analysis Model to Forecast the Growth of Vietnam's Coffee Exports

In recent years, mixed-frequency data analysis models have received much attention and application in the fields of economics and finance, especially in forecasting national macroeconomic indicators. However, in Vietnam, there are almost no studies applying these models. Vietnam is a country with a long-standing agricultural production background and the workforce mainly makes a living from farming. There are many types of Vietnamese agricultural products with high export value, bringing a significant source of foreign currency to the country. Coffee is Vietnam's main export product, a product that earns a lot of foreign currency, contributing to the country's socio-economic development. Therefore, applying models with mixed frequency data such as MIDAS to forecast coffee export growth is a promising new research direction.

Effects of Different Storage Times on the Stability of 12 Traditional Chinese Medicine Decoction Pieces.

As storage time increases, the quality of traditional Chinese medicines (TCMs) may change, and stability is an essential aspect of ensuring the safety and efficacy of TCMs. In this study, the effects of different storage times on the stability of 12 decoction pieces were evaluated. High-performance liquid chromatography was used to determine the contents of the active components in the 12 decoction pieces. The chemical composition data were analyzed using fingerprinting and clustering heatmap (CH). Results showed that during storage, significant variations (relative standard deviation > 10%) were observed in the levels of paeoniflorin in Paeoniae Radix Alba and Paeoniae Radix Rubra, hesperidin in Citri Reticulatae Pericarpium and Citri Reticulatae Pericarpium Viride, bufothionine in Siccus Bufo and chlorogenic acid in White Chrysanthemi Flos and Lonice Raejaponicae Caulis. However, calycosin-7-glucoside and calycosin in Astragali Radix Praeparata Cum Melle and chlorogenic acid in Lonicerae Japonicae Flos, Yellow Chrysanthemi Flos and Mori Folium were all <10%, which is consistent with the CH. Decoction pieces can be stored for up to sixmonths, but it is recommended that volatile oil-containing and animal-based decoction pieces should not be stored for more than one month. This study provides new perspectives for the stability and quality control studies of TCM.

Application of handheld/portable spectroscopic tools to the identification, inner stratigraphy and mapping of archaeological metal artefacts

Field handheld/portable instrumentations, such as in-situ geochemical analyzers, have the potential to assist efficiently targeted geochemical archaeometry campaigns in detecting and quantifying specific elements. Non-destructive portable energy dispersive x-ray fluorescence and micro-destructive handheld laser-induced breakdown spectroscopy (LIBS) instrumentation were utilized to investigate the elemental composition, internal stratigraphy by depth profiling and microscale compositional mapping of five copper and two iron alloy artefacts collected from various ancient graves in the Minervino Murge area, Apulia, Italy. The primary elements identified by both techniques included Cu, Sn and Pb in copper alloys, and Fe with minor amounts of Cu and Pb in iron alloys. Furthermore, the elements Al, Ca, Si, Mg, Na and K, mostly originated from soil contamination, and the trace elements Sb, Ni and Zn were detected. The satisfactory performance of both techniques was assessed by their capacity to provide reproducible elemental composition data. Finally, the depth profile and mapping achieved by LIBS contributed to understanding the metal processing and history of the objects studied, so confirming both techniques to be robust analytical tools in outdoor archaeology and archaeometry campaigns.

Integrating CoDA and PCA for enhanced characterization of fluvial depositional processes: a case study of the Shendi formation, Sudan

Traditional approaches to fluvial sedimentary analysis often face challenges in deciphering complex, multivariate datasets. This study combines compositional data analysis (CoDA) with principal component analysis (PCA) to enhance the characterization of depositional processes and sub-environments within the Shendi Formation. The PCA applied to centered log ratio (clr)-transformed grain size distributions, reveals three principal components with ~ 91.86% of the data variance explained, representing distinct processes: bedload-dominated channel-bar dynamics, overbank deposition, and high-energy flood events. Specific lithofacies associations strongly correlate to each principal component. This integrated approach enables the identification of subtle yet significant patterns within the complex sedimentological record. The Shendi Formation exhibits characteristics of a dynamic fluvial setting with variations in flow energy, channel migration, and periodic flooding. Our findings demonstrate the power of CoDA-PCA in refining the understanding of fluvial depositional systems and highlight its potential for broader applications.

Social network dynamics, infant loss, and gut microbiota composition in female Colobus vellerosus during time periods with alpha male challenges.

The gut microbiota of group-living animals is strongly influenced by their social interactions, but it is unclear how it responds to social instability. We investigated whether social instability associated with the arrival of new males and challenges to the alpha male position could explain differences in the gut microbiota in adult female Colobus vellerosus at Boabeng-Fiema, Ghana. First, we used a data set collected during May-August 2007 and May 2008-2009 that consisted of (i) 50 fecal samples from adult females in eight social groups for V4 16S rRNA sequencing to determine gut microbiota composition, and (ii) demographic and behavioral data ad libitum to determine male immigration, challenges to the alpha male position, and infant births and deaths. Sørensen and Bray-Curtis beta diversity indices (i.e., between-sample microbiota variation) were predicted by year, alpha male stability, group identity, and age. Next, we used a more detailed behavioral data set collected during focal observations of adult females in one group with a prolonged alpha male takeover and three cases of infant loss, to create 12-month versus 3-month 1-m proximity networks that preceded and overlapped the gut microbiome sampling period in that group. The long versus short-term networks were not correlated, suggesting temporal variation in proximity networks. In this group, beta diversity among the five adult females was predicted by similarity in infant loss status and short-term (rather than yearly) 1-m proximity ties. Although the mechanism driving this association needs to be further investigated in future studies, our findings indicate that alpha male takeovers are associated with gut microbiota variation and highlight the importance of taking demographic and social network dynamics into account.

FastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.

The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies. We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data. FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

The Late Holocene deglaciation of James Ross Island, Antarctic Peninsula: OSL and 14C-dated multi-proxy sedimentary record from Monolith Lake

Lentic waterbodies provide terrestrial sedimentary archives of palaeoenvironmental change in deglaciated areas of the Antarctic. Knowledge of the long-term evolution of Antarctic palaeoenvironments affords important context to the current marked impacts of climate change in the Polar regions. Here, we present a comprehensively dated, multi-proxy sedimentary record from Monolith Lake, a distal proglacial lake in one of the largest ice-free areas of the Antarctic Peninsula region. Of the two defined sedimentary units in the cores studied, the lower Unit 1 exhibits a homogeneous composition and unvarying proxy data profiles, suggesting rapid clastic deposition under uniform, ice-proximal conditions with a sedimentation rate of ∼1 mm yr−1. 14C and optically stimulated luminescence (OSL) dating bracket the deposition interval to 1.5–2.5 ka BP, with the older age being more probable when compared to independent dating of the local deglaciation. The uppermost 11 cm of the record spans the last ∼2.2 ka BP (maximum age), suggesting a markedly decreased sedimentation rate of ∼0.05 mm yr−1 within Unit 2. Whereas Unit 1 shows only scarce evidence of biological activity, Unit 2 provides an uninterrupted record of diatoms (with 29 species recorded) and faunal subfossils, including the fairy shrimp Branchinecta gaini. Concentrations of organically-derived elements, as well as diatoms and faunal remains, are consistent, implying a gradual increase in lake productivity. These results provide an example of long-term Antarctic ‘greening’ (i.e. increasing organic productivity in terrestrial habitats) from a palaeolimnological perspective. The boundary between Units 1 and 2, therefore, marks the timing of local deglaciation at the final stages of a period of negative glacier mass balance, i.e. the Mid-Late Holocene Hypsithermal. Subsequent Neoglacial cooling is evidenced by the abated influence of glacial meltwater streams and turbidity decline linked to reduced glacier runoff, although most proxy responses mirror the natural proglacial lake ontogeny.

How Indonesia’s Cities Are Grappling with Plastic Waste: An Integrated Approach towards Sustainable Plastic Waste Management

In Indonesia, plastic constitutes the second largest component of municipal solid waste; however, 58% remains uncollected. Most plastic recycling depends on informal sectors, accounting for only 10% of the total, leaving the remainder to potentially harm the environment. This paper analyzes how cities in Indonesia have tackled their plastic waste problems from the perspective of the integrated sustainable waste management framework. This study focuses on plastic waste management (PWM) in three cities: Bandung, Yogyakarta, and Magelang. Data were collected from 41 semi-structured interviews, a plastic waste composition data analysis, and site visits. The research outcomes include a comprehensive analysis of stakeholder roles, plastic waste flow, plastic waste composition data, and various challenges in PWM. This study identifies three main stakeholders in collecting plastic wastes: the Department of Environment as the main local government body, community-based waste banks, and private waste management companies. Most recyclable plastic waste is recovered through waste banks and private collection services. In 2022, the predominant types of plastic waste in Bandung, Yogyakarta, and Magelang City were soft plastics, thick plastics, and hard plastics, respectively. The challenges for the PWM stem from various aspects in political, legal, and institutional; environmental; financial and technical; and socio-cultural spheres. Positive trends towards an integrated system are observed, although full integration has not yet been achieved. The research recommends a multi-stakeholder cooperative approach involving municipal authorities, waste banks, and private collectors, responsive to local conditions and emphasizing sustainability aspects throughout waste management stages for a sustainable resource recycling in developing countries.

ARTIFICIAL INTELLIGENCE IN NUTRITION: REVOLUTIONARY DIETARY RESEARCH

Artificial intelligence has emerged as a transformative technology with the potential to revolutionize various aspects of society, including the field of nutrition. This research paper aims to explore the impact of artificial intelligence on nutrition and how it can revolutionize dietary research. With the rapid advancements in AI technology, it holds great promise for improving our understanding of nutrition and enhancing personalized dietary recommendations. By utilizing machine learning algorithms and big data analysis, AI can analyse vast amounts of nutritional information, including food composition, dietary patterns, and individual health data, to identify patterns and associations that can help inform dietary recommendations. Furthermore, AI can also assist in the development of novel food products and formulations that cater to individual dietary needs and preferences. By leveraging AI capabilities, researchers can gain deeper insights into the complex interactions between diet, genes, and health outcomes. This research paper aims to provide an overview of the current applications of AI in the field of nutrition, including its use in dietary assessment, personalized nutrition recommendations, and food product development. Keywords—Nutrition, Artificial Intelligence, Diet.

Evaluating the effects of hormone therapy termination on skeletal muscle and physical independence in postmenopausal women.

In women, the age-related decline in skeletal muscle structure and function is accelerated after menopause, which implicates the role of decreased circulating estrogen levels. Indeed, boosting estrogen, by means of postmenopausal hormone therapy (HT), generally proves beneficial to skeletal muscle. The evidence regarding whether these benefits persist even after cessation of HT is limited, nor is it clear how physical behavior (PB) impacts on benefits. Hence, this exploratory study focused on the interplay between HT administration/cessation, PB and in vivo skeletal muscle structure and function. Fifty healthy women (≥60 y) were included; 19 had an HT administration history (≥9 mo, with now ~8-y hiatus in treatment) and 31 no such history. On seven continuous days, PB data were collected using triaxial accelerometry and analyzed using compositional data analysis. Gastrocnemius medialis muscle volume, architecture, and function were determined using ultrasonography, electromyography, dual x-ray absorptiometry, and dynamometry. Current serum estradiol levels were measured using ELISA. Only fascicle length and duration of HT administration were positively associated. With respect to PB levels, we found a pattern suggesting greater vitality (higher physical activity and lower sedentarism) in previous HT users, compared with nonusers, despite the two groups currently no longer exhibiting significantly different levels of circulating estradiol. After an 8-year hiatus in treatment, HT provides limited advantages in gastrocnemius medialis muscle properties. Interestingly, it perhaps enhances vitality despite prolonged cessation, which in the longer term would facilitate greater physical independence, especially considering the association of sedentary behavior with greater frailty.

Effect of crustal stress state on magmatic stalling and ascent: case study from Puyehue-Cordón Caulle, Chile

The Southern Volcanic Zone (SVZ) in Chile is an active continental arc with a complex history of volcanism, where a range of magmatic compositions have been erupted in a variety of styles. In the Central SVZ, both monogenetic and polygenetic volcanoes exist, in close proximity to the Liquiñe-Ofqui Fault System (LOFS), but with variable local stress states. Previous studies have inferred varying crustal storage timescales, controlled by the orientation of volcanic centres relative to the N-S striking LOFS and σHMax in this region. To assess the relationship between volcanism and crustal stress states affected by large-scale tectonic structures and edifice controls, we present whole rock geochemical data, to ensure consistency in source dynamics and crustal processing, mineral-specific compositional data, thermobarometry, and Fe–Mg diffusion modelling in olivine crystals from mafic lavas, to assess ascent timescales, from the stratovolcanic edifice of Puyehue-Cordón Caulle and proximal small eruptive centres. Textural observations highlight differences in crystal maturation timescales between centres in inferred compression, transpression, and extension, yet source melting dynamics remain constant. Only samples from the stratovolcanic edifice (in regional compression) preserve extensive zonation in olivine macrocrysts; these textures are generally absent from proximal small eruptive centres in transtension or extension. The zonation in olivines from stratovolcanic lavas yields timescales on the order of a few days to a few weeks, suggesting that even in environments which inhibit ascent, timescales between unrest and eruption of mafic magmas may be short. Significantly, high-resolution compositional profiles from olivine grains in the studied samples record evidence for post-eruptive growth and diffusion, highlighting the importance of careful interpretation of diffusion timescales from zoned minerals in more slowly cooled lavas when compared with tephra samples.

Nutritional profiles of Middle Eastern dishes and composition

The study examined 27 traditional Middle Eastern/Kuwaiti dishes, primarily meat, chicken, and fish-based, to assess their taste and nutritional content. The evaluation included protein, moisture, carbohydrates, ash, fat, energy, dietary fiber, 11 vitamins, 8 minerals, and 3 sugars. Certain dishes displayed high levels of protein, fat, energy, water, carbohydrates, and dietary fiber. For instance, Marag laham and Majbous dajaj stood out for their high protein, fat, and energy content. Additionally, Marag laham and Marag dajaj exhibited high water content of 63.55% and 69.42% respectively for the meat and chicken section, while Majbous laham and Majbous dajaj had elevated carbohydrate levels due to the rice (27.52%). Notably, Turkey and Arabic meat shawarma had the highest fat energy with Arabic meat shawerma energy calculated at 400.91 kcal due to its total fat content (31.74%). The study concludes that using standard recipes for common meals can facilitate the collection of global and regional food composition data, which can be integrated into global food composition databases for diverse ethnic communities. Overall, the research highlights the importance of considering both taste and nutrition when making food choices and supports making informed dietary decisions based on comprehensive nutritional information

Building a socially accountable medical school: A layered analysis of the Hackensack Meridian School of Medicine.

If medicine fundamentally exists to care for the wellbeing of individuals and societies, there should be a direct and comprehensive link between a medical school's social mission and its educational programme. We have not found a description of development or reform that utilises social mission to guide the comprehensive development of the educational programme. As a new school, we utilised a systematic mission-driven approach to develop the curriculum, pedagogical methods and structure of the programme. Using layered analysis, this paper demonstrates how a school's mission can drive all aspects of the educational programme. This supports the transferability of this work to other schools so that they can achieve their unique missions. Layered analysis is used for reporting an intervention through three tiered lenses: philosophies, principles and techniques. This provides a structure to guide implementation and evaluation. It can also be used to transfer the innovation to other contexts. Each principle guiding the school's development is linked to context specific techniques and drives the focus of programme evaluation. Evaluation approaches using these principles are described, including an example of composite student performance data in a core area of focus. Through layered analysis of a medical school that developed and implemented a mission-driven curriculum, this can enable other schools to transfer this approach to achieve their missions through the design and implementation of their programmes.

Multivariate mapping of seabed grain size parameters in the Bay of Fundy using convolutional neural networks

High-resolution seabed sediment information is critical for a range of marine spatial planning applications in multi-use shelf environments. To establish this information for the Bay of Fundy, Canada, legacy seabed sediment measurements were obtained from regional data compilations, and eight parameters describing the grain size were modelled across the extent of the bay using high resolution acoustic seafloor mapping and oceanographic datasets. This was achieved using a purpose-made convolutional neural network configured for geospatial modelling of multivariate grain size parameters. Shared information between the response parameters enabled model training with partially complete observations from the varied legacy data sources, and an explicit multiscale model architecture ensured that environmental predictors were implemented at appropriate scales for modelling each parameter. This avoids typical exhaustive exploration and selection of scale-specific predictor sets that often precede model building. Compositional grain size parameters were additionally accommodated using appropriate output activation functions, providing an efficient alternative to compositional data transformation and imputation. Results agreed well with our current understanding of the surficial geology of the bay, and cross-validation was used to quantitatively evaluate map predictions. Of the eight predicted parameters, the mean grain size and mud (clay and silt) fractions were predicted with high accuracy (> 50% variance explained); the accuracy of grain size skewness was comparatively low (24% variance explained). Exploration of variable importance suggested that compiled acoustic backscatter was the most important environmental variable for predicting the grain size, but that geographic information describing the latitude and longitude within the bay was also highly useful. We hypothesize an interaction between these variables that enables location-specific prediction. Data layers of predicted grain size parameter values are made available for further sedimentological and ecological exploration, and for marine spatial planning activities within the bay.

Abstract PO1-11-09: Body composition correlates with BMI and menopausal status in patients with breast cancer

Abstract Background: Obesity is associated with higher risk of breast cancer (BC)-related death, all-cause mortality, and recurrence. While used to estimate body fat, body mass index (BMI) is insensitive to body fat distribution and lean muscle mass. We hypothesized that BMI categories (BMI 18.5-24.9 [healthy], 25-29.9 [overweight], and ≥30 kg/m2 [obesity]) would not correlate with CT-derived body composition. Methods: We retrospectively identified 180 patients diagnosed with new or recurrent Stage I-IV BC who presented to Johns Hopkins from 2015-2018 and were part of an institutional database. We extracted demographics, diagnosis date, cancer characteristics, BMI, and CT abdomen pelvis scans from the medical record. Baseline BMI was defined as the closest measurement to diagnosis between 1 year prior and 1 month after diagnosis. Baseline CT was within 6 months of and closest to the baseline BMI date. Fully automated deep-learning algorithms identified L1 and performed muscle and adipose tissue segmentation and quantification. Body composition data included: cross-sectional areas (CSA) of subcutaneous, visceral, and total adipose tissue; the mean, median, and standard deviation of the average of the muscle attenuation (density); and the body wall musculature CSA. ANOVA tests assessed associations between body composition, BMI, and menopausal status. Results: Among 180 patients, 136 (76%) had early-stage BC, and 44 (24%) had metastatic BC. Most patients were women (98%) and non-Hispanic (94%). Patients identified as White (57%), Black (29%), Asian (6%), and Other Race (8%). Sixty percent were post-menopausal. Hormone receptor-positive, HER2-positive, and triple negative subtypes were 56%, 19% and 22%, respectively. At diagnosis, 24% had a healthy BMI while 36% and 41% had overweight and obesity, respectively. All body composition measures were significantly associated with BMI (Table 1). As BMI increased, the average body wall musculature and subcutaneous, visceral, and total adipose tissue CSAs increased while the average muscle attenuation decreased. Specifically, the average visceral adipose tissue CSAs were 2.7 and 4.0-fold larger for overweight and obesity compared to that of the healthy BMI group, respectively, while their average muscle attenuations were 62.4% and 40.6% of the healthy BMI group. Postmenopausal women had 1.5-fold higher visceral adipose tissue CSA and 44.7% less average muscle attenuation compared to premenopausal women. Conclusion: Body composition correlates with all BMI categories in primarily white, non-Hispanic women with breast cancer. As BMI increases, body composition changes reliably, with increased subcutaneous and visceral adipose tissue, increased muscle area, and decreased muscle density. Muscle density, not size/area, is more associated with muscle performance, which can relate to functional status and outcomes. Despite validity concerns, BMI is an accessible and economical surrogate for body adiposity in patients with breast cancer. Postmenopausal status was also associated with greater visceral and total adiposity and lower muscle density. Further investigation is needed to confirm these findings. Table 1: Correlation between body composition, BMI, and menopausal status in patients with breast cancer All measures of body composition were significantly associated with BMI. Visceral adipose tissue increased and muscle density decreased as BMI increased and with post-menopausal status. Citation Format: Terrence Tsou, Tingchang Wang, Amanda Blackford, Ronald Summers, Vered Stearns, Jennifer Sheng. Body composition correlates with BMI and menopausal status in patients with breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-09.

Association between long-term exposure to fine particulate matter and its chemical constituents and premature death in individuals living with HIV/AIDS

Long-term exposure to fine particulate matter (PM2.5) is associated with an increased total mortality. However, the association of PM2.5 with mortality in people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS, PLWHA) and the relationship between its constituents and adverse outcomes remain unknown. In this cohort study, 28,140 PLWHA were recruited from the HIV/AIDS Comprehensive Response Information Management System of the Hubei Provincial Centre for Disease Control and Prevention in China between 2001 and 2020. The annual PM2.5 chemical composition data, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), black carbon (BC), and organic matter (OM), was extracted from the Tracking Air Pollution (TAP) dataset in China. A Cox proportional hazard model with time-varying exposure and time-to-event quantile-based generalized (g) computation was used to assess the associations between PM2.5 chemical constituents, and mortality in PLWHA. A multivariate Cox proportional hazard model estimated an excess hazard ratio (eHR) of 0.32% [95% confidence interval (CI): (0.01%, 0.64%)] for AIDS-related death (ARD), associated with 1 μg/m3 rise in PM2.5 exposure. An increase of 1 μg/m3 in NH4+ was associated with 5.13% [95% CI: (2.89%, 7.43%)] and 2.97% [95% CI: (1.52%, 4.44%)] increase in the risk of ARD and all-cause deaths (ACD), respectively. When estimated using survival-based quantile g-computation, the eHR for ARD with a joint change in a decile increase in all five components was 6.10% [95% CI: 3.77%, 8.48%)]. Long-term exposure to PM2.5 chemical composition, particularly NH4+ increased the risk of death in PLWHA. This study provides epidemiological evidence that SO42− and NH4+ increased the risk of ARD and that NH4+ increased the risk of ACD in PLWHA. Multi-constituent analyses further suggested that NH4+ may be a key component in increasing the risk of premature death in patients with HIV/AIDS. Individuals aged ≥65 with HIV/AIDS are more vulnerable to SO42−, and consequent ACD.

Compositional-geochemical characterization of lead (Pb) anomalies and Pb-induced human health risk in urban topsoil.

Urban areas are characterized by a constant anthropogenic input, which is manifested in the chemical composition of the surface layer of urban soil. The consequence is the formation of intense anomalies of chemical elements, including lead (Pb), that are atypical for this landscape. Therefore, this study aims to explore the compositional-geochemical characteristics of soil Pb anomalies in the urban areas of Yerevan, Gyumri, and Vanadzor, and to identify the geochemical associations of Pb that emerge under prevalent anthropogenic influences in these urban areas. The results obtained through the combined use of compositional data analysis and geospatial mapping showed that the investigated Pb anomalies in different cities form source-specific geochemical associations influenced by historical and ongoing activities, as well as the natural geochemical behavior of chemical elements occurring in these areas. Specifically, in Yerevan, Pb was closely linked with Cu and Zn, forming a group of persistent anthropogenic tracers of urban areas. In contrast, in Gyumri and Vanadzor, Pb was linked with Ca, suggesting that over decades, complexation of Pb by Ca carbonates occurred. These patterns of compositional-geochemical characteristics of Pb anomalies are directly linked to the socio-economic development of cities and the various emission sources present in their environments during different periods. The human health risk assessment showed that children are under the Pb-induced non-carcinogenic risk by a certainty of 63.59% in Yerevan and 50% both in Gyumri and Vanadzor.

Secular Changes in the Occurrence of Subduction During the Archean

AbstractSubduction processes play a pivotal role in facilitating material exchange between the crust and mantle, contributing to the growth of continents. However, the onset and evolution of subduction remain hotly debated. Here, we developed a high‐dimensional machine learning (ML) model to use multiple compositional data (e.g., Nb/La, Nb, Ti, Nb/U, Pb/Nd, and Nb/Th) to distinguish arc‐type from non‐arc basalts worldwide, then applied this well‐trained ML model to identify and delineate the secular occurrence of Archean arc‐type basalts. Our findings indicate that basaltic arc magmatism can be traced back to only a few early Archean cratons. A noticeable increase in the prevalence of arc‐type basalts during the Mesoarchean and Neoarchean suggests the synchronous formation of Archean subduction across different cratons. This observation hints at transitioning from the predominantly stagnant geodynamic regime to the early, more mobile tectonic regime.