Compositional Data Analysis Approach Research Articles

A nose for trouble: ecotoxicological implications for climate change and disease in Saiga antelope (S. t. tatarica)

In recent decades, Saiga antelope (Saiga t. tatarica) mass die-offs have become more common. The mass die-off of 2015 in central Kazakhstan, recorded 140,000 individual deaths across multiple herds. Previously, research has shown atmospheric humidity, the bacterium Pasteurella multocida serotype B, and resultant haemorrhagic septicaemia, were the primary cause. However, other synergistic factors may have impacted this process. Here we use a multivariate compositional data analysis (CoDA) approach to assess what other factors may have been involved. We show a pollutant linkage mechanism where relative humidity and dewpoint temperature combine with environmental pollutants, potentially toxic elements (e.g., Hg, As), complex carbon compounds (e.g., Acetone, Toluene), and inorganic compounds (e.g., CHx, SO2) which affected the Saiga during the calving season (start and peak) and at the onset of the mass die-off. We suggest a mechanism for this process. Upon arrival at their carving grounds, the Saiga experienced a sudden precipitation event, a spike in temperatures, and resultant high humidity occurs. The infectious bacterium P. multocida serotype B then spreads. Further, environmental pollutants contained within steppe soils are released to the air, forming localised smog events, these synergistically combine, and mass die-off occurs.

Beyond the bell: exploring the link between time allocation on extracurricular activities and academic performance in Chinese adolescents

ABSTRACT Academic performance could have an impact on job trajectories and socioeconomic status for societies in the future. However, only a few studies have focused on the impact of time allocation on extracurricular activity strategies. Based on that observation, we recruited 91,161 school students from 451 primary, middle, and high schools in China, and used a cross-sectional compositional data analysis approach to investigate the linkage between extracurricular activities and time allocation strategies and academic performance. Our study found that, in general, a suitable daily sleep routine is of primary importance in improving students’ academic performance. Additionally, daily exercise appears to be able to significantly facilitate higher academic performance. it’s important to note that exercising while sleep-restricted may have adverse effects. The most effective way to improve student academic performance proves to be reducing the time spent playing video games and watching short videos and replacing it with hours of sleep or exercise.

Resilience and high compositional variability reflect the complex response of river waters to global drivers: The Eastern Siberian River Chemistry database

The chemical composition of river waters represents an important matter of investigation to understand environment modifications in response to climate changes and global warming. Prolonged dry periods, heavy flood events, degradation of the lands and ice thawing, modify the chemical composition of river waters influencing the drivers governing the complex dynamics of river catchments where everything comes together. In this framework, Compositional Data Analysis (CoDA) offers methods in which the complex structure of the river water composition and the interrelationships among the various components are put into the proper context for their statistical analysis. In this research, we propose a new CoDA approach combining the robust Mahalanobis distance (D) calculus of ilr-transformed chemical variables and the perturbation difference, both with respect to a pristine compositional benchmark. The aim was to trace the change in the chemical composition of the Eastern Siberian River Chemistry Database where degradation of the permafrost for global warming produces important effects on natural waters. The findings indicate complex multiplicative laws and feedback mechanisms governing solutes in Eastern Siberian rivers, with high values of D found where permafrost is more discontinuous. Perturbations clearly discriminate chemical components more resilient to stresses induced by global changes (Ca2+, Mg2+ and HCO3−) from those whose variability is not maintained under control (Cl−, Na+, SO42−). These outcomes open up a new scenario in searching for spatiotemporal resilience metrics to reveal rivers response to environmental changes.

Characterization of the Airborne Microbiome in Different Indoor and Outdoor Locations of a University Building Using an Innovative Compositional Data Analysis Approach

In this work, we investigated the structure of the airborne bacterial community obtained by 16S rRNA gene sequencing performed on aerosol samples from different indoor and outdoor locations. The 48-h aerosol samples were collected in two laboratories, in the corridors, and on the roof of the Mathematics and Physics Department of the University of Salento (Italy). The investigation was carried out through the application of an innovative compositional data analysis approach, mainly based on a centered log-ratio transformation as a standardization procedure, the Aitchison distance for data ordination, and the principal component analysis via singular value decomposition for data clustering. This methodology allowed us to explore the main relationships among samples, identifying different results between indoor and outdoor samples both at the genus level and at the species level. Bacillus and Pseudomonas represented the most abundant genera identified in the analyzed samples. Out of the 21 identified bacterial species with the highest abundances in the collected aerosol samples, Acinetobacter lwoffii, Propionibacterium acnes, Diplorickettsia massiliensis, and Corynebacterium tuberculostearicum were the only four commonly classified as human opportunistic pathogens. Among the genera mostly associated with indoor environments, Hymenobacter and Arthrobacter could be noted as including many species that are unique in being radiation resistant.

Prospective association between 24-hour movement behaviors and mental health among overweight/obese college students: a compositional data analysis approach.

24-h movement behaviors, including light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), screen-based sedentary behavior (SSB), non-screen-based sedentary behavior (NSB) and sleep are crucial components affecting mental health. This study aimed to examine the associations of movement behaviors with mental health outcomes among overweight/obese college students using a compositional data analysis approach. Using a prospective design, 437 Chinese college students (20.1 ± 1.7 years, 51.7% female) completed a two-wave online data collection, where demographics and movement behaviors (LPA, MVPA, SSB, NSB, sleep) were collected at baseline, while depression, anxiety and stress were measured at the 2-month follow-up (Apr-Jul 2022). Compositional data analyses were implemented using R. Depression, anxiety, and stress were negatively associated with time spent in MVPA and sleep (p < 0.001) and positively associated with time spent in SSB and NSB (p < 0.001). Reallocating 15 min to MVPA from LPA, SSB and NSB predicted improvements in depression (LPA: -0.234 unit; SSB: -0.375 unit; NSB: -0.249 unit), anxiety (LPA: -0.092 unit; SSB: -0.284 unit; NSB: -0.165 unit), and stress (LPA: -0.083 unit; SSB: -0.312 unit; NSB: -0.191 unit). For dose-response relationships of 5-55 min isotemporal substitution, when time was reallocated to MVPA from LPA, NSB, and SSB, the estimated detriments to mental health were larger in magnitude than the estimated benefits of time reallocation from MVPA to LPA, NSB, and SSB. The findings emphasize the importance of participating in MVPA to improve mental health in overweight/obese college students during the post-COVID-19 era. The compositional analysis produced clear targets for the time allocation of these behaviors for future interventions and policymaking.

Visualizing high dimensional structures in geochemical datasets using a combined compositional data analysis and Databionic swarm approach

Classical tools for exploratory analysis of large geochemical datasets (e.g., cluster analysis, principal component analysis, etc.) have been successfully utilized for decades to understand complex structures and identify natural, multi-dimensional data clusters. More recent development of machine learning algorithms, both supervised and unsupervised, have increasingly been shown to derive deeper insights from complex datasets than older methods. Moreover, geochemists have long recognized that concentration data are compositional and require special mathematical treatment, such as application of Compositional Data Analysis (CoDA) techniques. Proper approaches for linking CoDA and machine learning is an area of active research. Here, we investigate the behavior of trace elements in coal and coal combustion products from a power plant burning Appalachian Basin coals using a combination of CoDa methods and a relatively new tool for visualizing complex multivariate structures, Databionic swarm (DBS). Databionic swarm is an unsupervised method which combines concepts from emergence, self-organization, game theory, and swarm intelligence, and allows for mapping of higher-order structures onto a lower-dimensional output space (2-dimensions in this case). A suggested approach for converting the raw geochemical data to isometric log-ratios is developed for the system and results from DBS and robust PCA are compared and contrasted.Both PCA and DBS results show similar clustering of feed coal (FC) and pulverized coal (PC) samples and bottom ash (BA) with economizer fly ash (EFA) samples. However, within group variation of the samples was relatively difficult to identify in the PCA analysis. Results from DBS show that the BA and EFA samples are geochemically distinguishable based on the relative abundances of As, Cd, and Pb vs. Cr and Ni and As and Cd vs. Pb. Similarly, the PC and FC samples exhibit statistically different Cr vs. Ni ratios, which was not obvious from the PCA scores. Abnormally low Hg to Cl ratios in the fly ash (FA) samples relative to the rest of the samples, provided insight into the preferential removal of Hg vs. Cl, possibly in response to the generation of more gaseous oxidized mercury in the flue gas, owing to elevated Cl concentrations in the coal. Such findings demonstrate that DBS and other machine learning techniques allow for quick visualization of complex geochemical data structures in a way that is both consistent with the principals of CoDA and an improvement over classical methods.

Cross‐sectional associations between use of time and cognition in healthy older adults

AbstractBackgroundPhysical activity, sedentary behaviour and sleep are independently associated with cognition in older adults. These time‐use behaviours are not independent, rather, co‐dependent parts of the 24‐hour day. As such, spending more time in one leaves less time for the others. The balance of the three time‐use behaviours in the 24‐hour day (or time‐use composition) should be considered to inform 24‐hour movement guidelines for cognition and dementia risk reduction in older populations. Using a compositional data analysis approach, we investigated the associations between time use and cognition in a sample of healthy older adults.Method347 participants (age 60‐70 years; 68% female) took part in this study. 7 days of 24‐hour time use (time spent in sleep, sedentary behaviour, light and moderate‐vigorous physical activity) were captured using accelerometry. Cognition was measured using Addenbrooke’s Cognitive Examination III (global cognition) and computerized CANTAB tests measuring memory, executive function, fluid reasoning and processing speed.Cognitive outcomes were regressed on isometric log ratios (ilrs) of the time‐use compositions, while adjusting for age and sex demographics, to explore potential associations between cognition and time‐use behaviours. For each outcome, four sets of ilr‐coordinate systems were constructed, each with a different time‐use behaviour as the numerator of a log ratio to the mean of remaining time‐use behaviours. The regression coefficient of the first ilr‐coordinate in each of the four ilr systems therefore represented the association between increasing time in the first behaviour relative to all remaining behaviours.ResultAge was negatively associated with performance on memory, executive function, fluid reasoning and processing speed tests. More time in SB at the equal expense of all other behaviours was associated with better memory (β = 0.65, p&lt;.01), whilst spending more time in sleep was associated with poorer memory (β = ‐0.63, p = .02). Spending more time in moderate‐vigorous physical activity was associated with better processing speed (β = 0.25, p = .01).ConclusionHow older adults use their time is associated with cognition. Findings suggest that moving towards a holistic understanding of time use is important to inform future dementia prevention strategies.

Characterization and chemical fractionation of potentially toxic elements in soils of a pre-mining mineralized area; an evaluation of mobility and environmental risk.

The environmental geochemical characterization of mineralized areas prior to mining does not receive adequate attention. This study shows trace element distribution in soils of two unexploited porphyry copper deposits located in Darreh-Zereshk and Ali-Abad in central Iran. The study was carried out using a compositional data analysis (CoDa) approach and combination of multivariate statistics and clustering techniques, which made it possible to identify the geochemical associations representing the different areas of the mineral deposits. The results of the chemical analyses, performed by ICP-MS, revealed high concentrations of those elements typically associated with porphyry deposits (As, Co, Cu, Mo, Ni, Pb, and Zn). The typical zonal pattern with an anomaly of Cu in central parts of the system and the prevalence of epithermal elements (Ag, Cd, Pb, and Zn) toward the peripheral propylitic alteration zone were recognized. The XRD analysis of selected soil samples allowed us to determine the distribution of elements within the different carrier minerals. Afterward, geochemical speciation patterns were investigated by a four-step sequential extraction procedure based on BCR protocol. The residual fraction consisting of primary resistant minerals was found to be the main host for As (73-93.4%), Cr (65.1-79.6%), Cu (54.3-81.4%), Ni (58.9-80.6%), V (75.9-88%), and Zn (56.5-60.5%) in the studied soils. Even though these elements are not readily leachable, their behavior and distribution could be largely affected by the mining operation and consequent changes in the physicochemical properties of the soil. The soluble-exchangeable phase was only less than 15% of the total extractions for all elements, except for Cd. With respect to the mobility factor (MF), Cd was the most mobile element followed by Sb and Pb. The measured risk assessment code (RAC) presented the following risk order: Cd > Sb > Ni > Co > Pb > Cr > As > Zn > Cu > V. This study reveals that the acquisition of pre-mining geo-environmental data of trace elements is very important to establish pre-mining backgrounds and baselines for evaluating post-mining or post-reclamation geochemical signatures.

The relationship between dissolved radon and other geochemical parameters in Campi Flegrei volcanic aquifer (Southern Italy): A follow-up study

Campi Flegrei is one of the most active volcanic areas in the world and assessing the potential proxies of volcanic-related phenomena is critical. Therefore, the spatial distribution of radon and carbon dioxide in groundwater and the statistical relationships between the dissolved gases and other variables deserve further attention. Compositional data analysis (CoDA) was proposed at the end of the last century and further developed in the last decades for reliable data mining, but its potential has not been fully explored for characterization of the groundwater aquifers affected by hydrothermal activity. Based on a prospecting campaign mainly aimed at the determination of both radon and carbon dioxide in Campi Flegrei groundwater, this article explores the spatial patterns of these gases in the local aquifer system and uses a CoDA approach to extract the relevant information and to determine the meaningful geochemical associations. The results show that the spatial distribution of both dissolved gases corresponds to the hydrothermal system. The logratio transformed CO2 (aq) distinguishes bicarbonate-rich groundwater better than the raw values. Principal component analysis reveals two associations: A1) Ca2+, Mg2+, K+, SO42−, HCO3− + CO2 and pH; and A2) Na+, Cl−, As, B, Li, Rn, TDS and T. It highlights that the groundwater composition is generally influenced by two main factors: (1) meteoric water, which is modified by CO2‒rich magmatic gases in some cases; and (2) hydrothermal fluid and/or seawater. The results are in agreement with the literature and application of CoDA is recommended in future investigations because the study area is highly populated and considering the compositional nature of geochemical data might help mitigate the volcanic hazard at Campi Flegrei.

Effect of Spanish-Style Table Olive Processing on Fatty Acid Profile: A Compositional Data Analysis (CoDA) Approach.

This manuscript considers that the composition of Manzanilla and Hojiblanca fats are compositional data (CoDa). Thus, the work applies CoDa analysis (CoDA) to investigate the effect of processing and packaging on the fatty acid profiles of these cultivars. To this aim, the values of the fat components in percentages were successively subjected to exploratory CoDA tools and, later, transformed into ilr (isometric log-ratio) coordinates in the Euclidean space, where they were subjected to the standard multivariate techniques. The results from the first approach (bar plots of geometric means, tetrahedral plots, compositional biplots, and balance dendrograms) showed that the effect of processing was limited while most of the variability among the fatty acid (FA) profiles was due to cultivars. The application of the standard multivariate methods (i.e., Canonical variates, Linear Discriminant Analysis (LDA), ANOVA/MANOVA with bootstrapping and n = 1000, and nested General Linear Model (GLM)) to the ilr coordinates transformed data, following Ward's clustering or descending order of variances criteria, showed similar effects to the exploratory analysis but also showed that Hojiblanca was more sensitive to fat modifications than Manzanilla. On the contrary, associating GLM changes in ilr with fatty acids was not straightforward because of the complex deduction of some coordinates. Therefore, according to the CoDA, table olive fatty acid profiles are scarcely affected by Spanish-style processing compared with the differences between cultivars. This work has demonstrated that CoDA could be successfully applied to study the fatty acid profiles of olive fat and olive oils and may represent a model for the statistical analysis of other fats, with the advantage of applying appropriate statistical techniques and preventing misinterpretations.

Within-Person Associations Between Physical and Social Contexts With Movement Behavior Compositions in Adolescents: An Ecological Momentary Assessment Study Using a Compositional Data Analysis Approach

External contexts, including the social and physical contexts, are independent predictors of momentary physical activity and sedentary behaviors. However, no studies to date have examined how external contexts are related to overall momentary movement behavior compositions using compositional data analysis. Therefore, this study aimed to determine differences in momentary movement behavior compositions between different social and physical contexts in adolescents. Overall, 119 adolescents (mean age 14.7y, SD = 1.44) provided details about their momentary physical and social contexts over 4days using ecological momentary assessment. Sedentary behaviors, light-intensity physical activity, and moderate to vigorous physical activity were assessed using ActiGraph GT3X+ accelerometers. Compositional multivariate multilevel models were estimated to determine if movement behavior compositions differed between contexts. Participants engaged in significantly less sedentary behaviors when outdoors compared with indoors and replaced it with moderate to vigorous physical activity. Participants also engaged in significantly less sedentary behaviors when with friends or friends and family and replaced it with light-intensity physical activity. These results highlight the potential of targeting external contexts to increase physical activity and to reduce sedentary behavior in adolescents' daily lives. These factors could be targeted in mobile health and just-in-time adaptive interventions to improve young people's movement behavior compositions.

P10-11 The effect of a pedometer-based intervention across two years, in people with type 2 diabetes and prediabetes - a compositional data analysis

BackgroundFor people with prediabetes and type 2 diabetes it is important to be regularly physical active. Increasing and maintaining physical activity (PA) can be challenging. The aim of this study was to evaluate the effects of a pedometer-based intervention on PA, with a compositional data analysis (CoDA) approach, in individuals with prediabetes or type 2 diabetes.MethodsLongitudinal data on 188 participants with prediabetes and type 2 diabetes (40% female, mean age = 64.1 years) from a three-armed randomized controlled trail, the Sophia Step Study, was used. The three groups were a multi?component group (self?monitoring of steps with a pedometer, together with group (12 occasions) and individual counselling (10 occasions)), a single?component group (self?monitoring of steps with a pedometer, without counselling) and a standard care group. PA (moderate-to-vigorous PA (MVPA), light-intensity PA (LIPA) and sedentary behaviour (SB)) during the awake time were measured with ActiGraph GT1M accelerometer at baseline, 6, 12, 18 and 24 months. Relative time in MVPA, LIPA and SB for each participant at each measurement point was calculated by using the CoDA approach. Linear mixed models were used to evaluate the intervention effect between the three groups on the relative time in MVPA, LIPA and SB over the two-year period.ResultsIn total, 41% had ≤30 min MVPA at baseline. Significant group by time interactions were found for the multi-component group and the standard care group for the relative time in MVPA, at 6, 18 and 24 months. Differences in predicted group means between the groups were 1.2% at 6 months, 1.3% at 18 months and 0.9% at 24 months. Significant group by time interactions were also found for the single-component group and the standard care group for the relative time in MVPA at 24 months, with a difference in predicted group means of 0.8%. No significant interactions for LIPA or SB were found.ConclusionsThe multi-component group and the single-component component group maintained their relative time in MVPA, while the standard care group decreased their relative time in MVPA over the two-year period. This indicate that the Sophia Step Study intervention can prevent a decrease in MVPA in people with prediabetes and type 2 diabetes.

Impacts of the COVID-19 lockdown measures on coarse and fine atmospheric aerosol particles (PM) in the city of Rome (Italy): compositional data analysis approach.

In the year 2020, Italy faced a pandemic due to the virus SARS-CoV-2 for short COVID-19. Following this pandemic, a national lockdown period was imposed and throughout the year 2020 various measures were taken by the government to limit the mobility of people and contain the mortality associated with COVID-19. In Italy, pandemic measures led to a reduction in anthropogenic activities and provided an unprecedented opportunity to evaluate the possible effects that restrictions on anthropogenic activities may have on the air quality. Two background site (i.e., Cipro and Cinecittà) and a traffic sites (i.e., Corso Francia) were studied in the city of Rome. PM10 and PM2.5 were considered for the years 2019 and 2020. Moreover, the vehicular mobility, the emission classes of the vehicles, and the people mobility were taken into consideration along with meteorological variables. A compositional data analysis was used to evaluate the effect of pandemic measures on the fine- and coarse-size fractions of PM in the three considered sites. The results showed that in the traffic site (i.e., Corso Francia site) in 2020, there was a reduction of fine-size fraction of PM of about 10% when compared to the data of 2019, whereas in the background site (i.e., Cinecittà site) in 2020 there was an increase of fine-size fraction of PM of about 14% when compared to the data of 2019. No variation in the coarse- and fine-size fractions of PM were observed at the background site Cipro. This study showed how, in an urban context, PM can be influenced by strong changes in people’s habits and in vehicular mobility such as those recorded during the investigated period and due to pandemic lockdown measures.

Compositional data analysis of regional geochemical data in the Lhasa area of Tibet, China

It is now widely recognized that geochemical survey data are compositional in nature, with each component having relative importance as part of a whole. Compositional data analysis (CoDA) based on log-ratio transformations can be used to deal with the ‘sum to one’ data constraint. This study explored the use of two data-driven CoDA approaches, centered log-ratio (clr)-biplot analysis and a compositional-balance approach, to investigate associations between elements for potential mineral exploration in the Lhasa area of Tibet, China. Results revealed that (1) the compositional-balance approach, based on a hierarchical cluster and sequential binary partition (SBP) technique, well reflects the range of rocks and metal deposits in the area; (2) the clr-biplot indicates associated relationships between elements, and a consistency is found between principal component (PC1 and PC2) and key compositional balances (Balance 1 and 4); and (3) comparison with traditional integrated geochemical mapping, a knowledge-driven method, proves the validity of the compositional-balance and clr-biplot methods. These results provided metallogenic and petrogenetic information and crucial evidence supporting further geological and geochemical exploration. The improved knowledge demonstrates the importance of using a CoDA approach for geochemical data before performing further statistical analysis.

Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

It remains unclear whether the time-use composition of 24-h movement behaviours (sleep, sedentary time (ST), physical activity (PA)) and recreational screen use are independently associated with psychosocial health. This study examined the cross-sectional and longitudinal associations between 24-h movement behaviour composition, recreational screen use and psychosocial health outcomes in children. Measures completed at baseline (n = 127; 11.7 years) and follow-up (n = 88; 12.8 years) included accelerometer-based 24-h movement behaviours, self-reported recreational screen use and psychosocial health (Strengths and Difficulties Questionnaire, Kessler’s Psychological Distress Scale). Linear mixed models were used to examine the cross-sectional and longitudinal associations between the 24-h movement behaviour composition and recreational screen use levels with psychosocial health outcomes. Overall, the movement behaviour composition (p < 0.05) and recreational screen use levels (p < 0.01) were both cross-sectionally but not longitudinally associated with psychosocial health outcomes. Relative to other behaviours, sleep was negatively associated, while light-intensity PA was positively associated with internalising problems and total difficulties scores. ST was positively associated with internalising problems. High levels of recreational screen use (>2 h/day) were associated with greater externalising problems, total difficulties scores and psychological distress. These findings reinforce the importance of achieving a balance between different types of movement behaviours over a 24-h period for psychosocial health.

Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach.

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.

Objectively Measured Intensity-specific Physical Activity and Hippocampal Volume Among Community-dwelling Older Adults.

The hippocampus is a brain structure important for memory and cognitive function. Physical activity may help prevent hippocampal atrophy. However, few studies have measured sedentary behavior (SB) and intensity-specific physical activity using an accelerometer. This study aimed to examine the cross-sectional associations of objectively-determined SB, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) measured by an accelerometer with hippocampal volume among community-dwelling older adults using compositional data analysis (CoDa) approach. This cross-sectional study was part of the Neuron to Environmental Impact across Generations (NEIGE) study. A randomly recruited sample of 485 Japanese older adults (47% male; aged 65-84 years) wore tri-axial accelerometers (Omron Healthcare) for 7 consecutive days in 2017. Hippocampal volume was measured with magnetic resonance imaging and the left and right hippocampal volumes were automatically segmented using FreeSurfer software. Associations of sedentary and physically active behaviors with hippocampal volume were examined with compositional linear regression analysis based on isometric log-ratio transformations of time use adjusted for potential confounding factors. The relative proportion of time spent in MVPA, compared to the other two activities, was significantly positively associated with right hippocampal volume (β: 57.1, P-value = 0.027). However, no association existed between higher proportions of MVPA and left hippocampal volume, or between proportions of SB or LPA with either left or right hippocampal volumes. The proportion of time spent in MVPA, relative to the other two activities, was significantly positively associated with right hippocampal volume. MVPA may be beneficial for maintaining hippocampal volume.

The Relation between Domain-Specific Physical Behaviour and Cardiorespiratory Fitness: A Cross-Sectional Compositional Data Analysis on the Physical Activity Health Paradox Using Accelerometer-Assessed Data.

In contrast to leisure time physical activity (LTPA), occupational physical activity (OPA) does not have similar beneficial health effects. These differential health effects might be explained by dissimilar effects of LTPA and OPA on cardiorespiratory fitness (CRF). This study investigated cross-sectional associations between different physical behaviours during both work and leisure time and CRF by using a Compositional Data Analysis approach. Physical behaviours were assessed by two accelerometers among 309 workers with various manual jobs. During work time, more sedentary behaviour (SB) was associated with higher CRF when compared relatively to time spent on other work behaviours, while more SB during leisure time was associated with lower CRF when compared to other leisure time behaviours. Reallocating more time to moderate-to-vigorous physical activity (MVPA) from the other behaviours within leisure time was positively associated with CRF, which was not the case for MVPA during work. The results of our study are in line with the physical activity health paradox and we call for further study on the interaction between LTPA and OPA by implementing device-worn measures in a longitudinal design. Our results highlight the need for recommendations to take into account the different effects of OPA and LTPA on CRF.

Associations between 24 h Movement Behavior and Mental Health in Office Workers.

The associations between 24 h movement behavior, i.e., the way people distribute their time in different movement-related behaviors, on mental health are not well understood. This study applied a compositional data analysis approach to explore cross-sectional associations between device-measured moderate to vigorous physical activity (MVPA), light intensity physical activity (LIPA), sedentary behavior (SED), self-reported time in bed and mental health outcomes, i.e., depression or anxiety symptoms, burnout, mental wellbeing and stress, in office workers. ActiGraph accelerometers were worn for 24 h for at least 4 days to assess MVPA, LIPA, and SED. Sleep diaries were used in addition to identify time in bed. Analytic sample sizes for the different outcomes ranged from N = 345–370 participants. In this population of office workers with high levels of MVPA, the entire movement behavior composition was not associated to any of the mental health outcomes, but MVPA relative to all other behaviors was positively associated with mental wellbeing. This confirms the importance of MVPA for health relative to other movement-related behaviors.

Transfer processes of potential toxic elements (PTE) between rock-soil systems and soil risk evaluation in the Baoshan area, Yunnan Province, Southwest China

Geochemical fluxes in rock-soil systems are important tools in the evaluation of soil environmental risk, especially where the geological background (parent materials) are enriched in potential toxic elements (PTE). In the Baoshan area, the presence of mineral resources along with carbonate and basalt parent materials often leads to PTE being elevated in soils. In this study, we compared rock and soil geochemical compositions by collecting rock samples from the host formations and ore samples from two mineral deposits (Cu–Pb–Zn and Fe–Cu–Pb–Zn) and their overlying soils. This research demonstrates enrichment of PTE (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) in soils relative to their parent materials. The enrichment in soils may be due to the removal of mobile elements (e.g., Ca, Na, Mg, and Sr), and coprecipitation (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) and/or adsorption by Fe-oxy-hydroxides (Cr and Ni) under the influence of high pH values during soil formation. The results point to a parent lithological origin and natural enrichment of PTE in Baoshan soils. However, anthropogenic contamination of soils near mining areas is also possible. Soils forming from carbonate rocks, basalts, and especially from mineralized formations have higher PTE concentrations, and in places, levels can exceed the risk screening values for soil contamination of agricultural land of China. Where the latter is the case, it is suggested that ecological monitoring be implemented in the study area. The conventional use of the geo-accumulation index (Igeo) and enrichment factor (EF) methods is challenged in this research and found to underestimate the PTE contamination. A compositional data analysis approach (using clr biplots) is presented which shows that comparison with regional-scale background datasets is essential to provide a more informative approach to evaluate the risk of soil contamination, especially in regions with elevated levels of PTE in parent materials.