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Background: The 24 h time-use composition of physical activity, sedentary behavior, and sleep is linked to cognitive function in adults and may contribute to future dementia risk. However, the impact of reallocating time between behaviors may differ depending on an individual's genetic dementia risk. Objective: To explore if there is an interaction between 24 h time-use composition and genetic dementia risk in relation to cognitive function, and to simulate how time-reallocations are associated with cognitive function across different levels of genetic dementia risk.Methods: Cross-sectional global cognition, executive function, genetic dementia risk (at least one apolipoprotein (APOE) epsilon 4 allele versus none) and 7 days of 24 h accelerometry (average daily time-use composition of moderate-to-vigorous physical activity (MVPA), light physical activity, sedentary behavior, sleep) were collected from 82 adults (65.6 +/- 7.5 years, 49 females). Linear regression was used to explore the relationship between time-use composition and cognitive measures, testing for interaction between APOE epsilon 4 status and time-use composition. The models were used to simulate time reallocations in both APOE epsilon 4 status groups Results: The 24 h time-use composition was associated with global cognition (F = 2.4, p = 0.02) and executive function (F = 2.6, p = 0.01). For both measures, the association differed according to genetic risk (interactions p < 0.001). In both APOE groups, reallocating time to MVPA was beneficially associated with measures of cognitive function, but associations were larger among those with at least one APOE epsilon 4 allele. Conclusion: Genetic dementia risk may impact the effectiveness of activity interventions. Increasing MVPA may provide greater benefits among those with higher genetic dementia risk. Refereed/Peer-reviewed

This study protocol describes the methodological procedures for conducting a scoping review that aims to identify and synthesize evidence about the relationship between the time composition of physical behavior and health outcomes in older adults, through studies that adopted Compositional Data Analysis (CoDA) as analytical approach. As CoDA is a relatively recent statistical approach in the field of time use epidemiology, this scoping review can provide an update overview of the topic, as well to identify as gaps in this area of knowledge.

Purpose We lack knowledge on whether the advice of “being physically active” should be the same for prevention and rehabilitation of low back pain (LBP). Sickness absence is a key outcome for LBP prevention and rehabilitation. We investigated the associations between physical activity and long-term sickness absence (LTSA) among employees with and without LBP. Methods Between 2011 and 2013, 925 Danish employees wore a Actigraph GTX3 accelerometer for 1–5 workdays to measure physical activity and reported LBP in past 7 days. Employees were followed for 4 years to determine their first register-based LTSA event (≥ 6 consecutive weeks). Results Among employees with LBP, increasing moderate-to-vigorous-intensity physical activity at work by 20 min and decreasing the remaining behaviors at work (ie., sitting, standing and light-intensity activity) by 20 min was associated with 38% (95% CI 17%; 63%) higher LTSA risk. Increasing light-intensity activity at work by 20 min and decreasing 20 min from the remaining behaviors was associated with 18% (95% CI 4%; 30%) lower risk. During leisure, increasing moderate-to-vigorous-intensity activity by 20 min or standing by 40 min was associated with 26% (95% CI 3%; 43%) lower and 37% (95% CI 0%; 87%) higher risk, respectively. Among employees without LBP, we found no such associations. Conclusions The physical activity advice ought to be different for LBP prevention and rehabilitation to reduce LTSA risk, and specified by domain and activity intensity. At work, employees with LBP should be advised to spend time on light-intensity physical activity and limit their time on moderate-to-vigorous-intensity physical activity. During leisure, employees should spend time on moderate-to-vigorous-intensity physical activity.

Refereed/Peer-reviewed Geochemical data are frequently collected from mineral exploration drill-hole samples to more accurately define and characterise the geological units intersected by the drill hole. However, large multi-element data sets are slow and challenging to interpret without using some form of automated analysis, such as mathematical, statistical or machine learning techniques. Automated analysis techniques also have the advantage in that they are repeatable and can provide consistent results, even for very large data sets. In this paper, an automated litho-geochemical interpretation workflow is demonstrated, which includes data exploration and data preparation using appropriate compositional data-analysis techniques. Multiscale analysis using a modified wavelet tessellation has been applied to the data to provide coherent geological domains. Unsupervised machine learning (clustering) has been used to provide a first-pass classification. The results are compared with the detailed geologist’s logs. The comparison shows how the integration of automated analysis of geochemical data can be used to enhance traditional geological logging and demonstrates the identification of new geological units from the automated litho-geochemical logging that were not apparent from visual logging but are geochemically distinct. Key point 1: To reduce computational complexity and facilitate interpretation, a subset of geochemical elements is selected, and then a centred log-ratio transform is applied. Key point 2: The wavelet tessellation method is used to domain the drill holes into rock units at a range of scales. Key point 3: Several clustering methods were tested to identify distinct rock units in the samples and multiscale domains for classification. Key point 4: Results are compared with geologist’s logs to assess how geochemical data analysis can inform and improve traditional geology logs.

Meal timing is suggested to influence the obesity risk in children. Our aim was to analyse the effect of energy and nutrient distributions at eating occasions (EO), including breakfast, lunch, supper, and snacks, on the BMI z-score (zBMI) during childhood in 729 healthy children. BMI and three-day dietary protocols were obtained at 3, 4, 5, 6, and 8 years of age, and dietary data were analysed as the percentage of the mean total energy intake (TEI; %E). Intakes at EOs were transformed via an isometric log–ratio transformation and added as exposure variables to linear mixed-effects models. Stratified analyses by country and recategorization of EOs by adding intake from snacks to respective meals for further analyses were performed. The exclusion of subjects with less than three observations and the exclusion of subjects who skipped one EO or consumed 5% energy or less at one EO were examined in sensitivity analyses. Around 23% of the children were overweight at a given time point. Overweight and normal-weight children showed different distributions of dietary intakes over the day; overweight children consumed higher intakes at lunch and lower intakes of snacks. However, no significant effects of timing of EOs on zBMI were found in regression analyses.

To study the effect of sleep duration, as a component of 24-hour time use, on adolescent depression, the current study adopted a statistical method called compositional data analysis (CoDA). CoDA deals with compositional data that carry relative information. The current study aims to (1) determine how different types of sedentary behaviours (reading; watching videos; playing video games; other screen usage) are associated with risk of depression while accounting for physical activity, school time and sleep; (2) estimate the effect of replacing the aforementioned sedentary behaviours and physical activity with sleep in primary, middle and high school students with either sufficient or insufficient sleep.

Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier transform infrared spectroscopy (FTIR) and magnetic susceptibility (MS), and geochemical data from X‐ray fluorescence (XRF; for total major oxides) and inductively coupled plasma‐mass spectrometry (ICP‐MS; for both total and aqua regia‐soluble trace elements) are performed on the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). Principal components (PCs) are calculated after imputation of censored data and centered log‐ratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (“heat maps”) for the various analytical techniques. The sequential provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP‐MS (geochemistry) analytical methods, offer the most precise and accurate provenance predictions. Inclusion of PCs in provenancing adds marginally to the performance. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications.

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

11 Páginas.-- 3 Figuras.-- 3 Tablas, The transformations that may suffer directly brined table olive fat during processing were studied using cracked Aloreña de Málaga olive as a model. The classical studies showed that storage increased acidity and K270, but not peroxide value, K232 and ΔK. FA profiles, nutritional fat subclasses, and TAGs suffered several significant changes along processing, although some could be spurious. Compositional data (CoDa) analysis identified C18:2n-6 and C18:1c, and their corresponding TAGs as the most affected compounds, conclusion that was particularly evident after weighted CoDa log-ratio analysis (LRA). Thus, CoDa analysis is a promising alternative statistical tool to study table olive FA, TAG profiles and fats in general. Most of the quality parameters' values and FA and TAG concentrations were compatible with Virgin Olive Oil (VOO), showing that the fat quality and nutritional characteristics of natural green olives in brine could be comparable to that of VOO., This work was supported by the “Comisión Interministerial de Ciencia y Tecnología” (CICYT-EU Spanish Government) under projects AGL2009-07436/ALI and AGL2010-15494/ALI, partially financed by European Regional Development Funds (ERDF) and Junta de Andalucía through financial support to group AGR-125. We also thank Elena Nogales Hernández for technical assistance.

14 Páginas.-- 10 Figuras, In this work, metataxonomic-compositional data analysis was applied to study the influence of inoculation, type of ecosystem (brine or fruit), and time of fermentation (0, 24, and 83 days) on the bacterial composition dynamic during industrial Manzanilla Spanish-style green table olive fermentations. Differences in the physicochemical and microbiological characteristics between inoculated and spontaneous fermentations were scarce, except for the high lactic acid bacteria population (>10 million CFU/g) in the inoculated olive biofilms. The metataxonomic analysis showed eight relevant taxa (Lactiplantibacillus, Vibrio, Halolactibacillus, Aerococcus, Catenococcus, Salinivibrio, Alkalibacterium, and Marinilactibacillus) and absence of pathogens, but the microbiota composition markedly changed throughout fermentation. At the process onset, the main genera were Vibrio, Halolactibacillus, Marinilactibacillus, and Alkalibacterium, while Lactiplantibacillus was almost absent. Regardless of inoculation, Lactiplantibacillus became the predominant genus as the fermentation progressed, albeit Vibrio, Marinilactibacillus, Alkalibacterium, and Halolactibacillus also persisted until the end of the process (but did not cause spoilage). Ferrimonas genus was detected for the first time in table olives, being found in 71% of the samples. Besides, there was more bacterial biodiversity in the brine ecosystem than olive biofilms. Overall, inoculation led to more homogenous and equilibrated bacterial populations at the end of the process. This study expands the knowledge on the bacterial population changes occurring during industrial table olive fermentation, the differences between populations in brines and biofilms, and the beneficial effects of inoculation for using table olives as a carrier of beneficial microorganisms., This research was funded through the TOBE project (RTI2018-100883-B-I00, MCIU/AEI/FEDER, UE). ELG and VRG thank the Spanish Ministry of Science and Innovation for their FPI contract (PRE2019-087812), and postdoctoral contract (FJC2019-040605-I), respectively. ABC thanks the Junta de Andalucía for his postdoctoral contract (PAIDI2020- 00162). Thanks to Mss. Virginia Martín Arranz for her technical assistant.

This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal waters are predicted to be enriched in REEs by an order of magnitude or more relative to seawater, with maximum predicted enrichments in excess of 1000-fold. Cartographic mapping of the resulting predictions indicates that maximum REE concentrations exceed values in seawater across the majority of geologic basins investigated and that REEs are typically spatially co-associated. The factors causing this co-association were not determined from ESOM analysis, but based on the information currently available, REE content in produced and geothermal waters is not directly controlled by lithology, reservoir temperature, or salinity Funding for this work was provided by U.S. Department of Energy Geothermal Technologies Office under Award DE-EE0007603 and the U.S. Geological Survey Energy Resources Program (Engle)

20 Páginas.-- 7 Figuras.-- 5 Tablas, 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., This work was supported by the Comisión Interministerial de Ciencia y Tecnología (CICYT, Spanish Government) under projects AGL2009-07436/ALI and AGL2010-15494/ALI, partially financed by European Regional Development Funds (ERDF) and Junta de Andalucía through financial support to group AGR-125.

This work was supported by FIS grants 16/609 and 19/319 (State Secretary of R + D + I and FEDER/FSE), MINECO R + D + I grant (DEP2013-47786-R), the SALAMANDER project (PCIN 2016-145), Biomedical Research Networking Center on Frailty and Healthy Aging (CIBERFES) and FEDER funds from the European Union (CB16/10/00477). VC-S is supported by the Spanish Ministry of Science, Innovation and Universities (IJC2018-038008-I). IE-C is supported by the Spanish Ministry of Economy and Competitiveness (RTI2018095284-J-100). DM-G is supported by a'Ramon y Cajal' contract (RYC-201620546). The funders played no role in the study design, the data collection or analysis, the decision to publish, or the preparation of the manuscript., Background: Most studies on the effects of sleep, sedentary behavior (SB), and physical activity (PA) on mental health did not account for the intrinsically compositional nature of the time spent in several behaviors. Thus, we examined the cross-sectional and prospective associations of device-measured compositional time in sleep, SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) with depression symptoms, loneliness, happiness, and global mental health in older people (≥ 65 years). Methods: Data were taken from the Seniors-ENRICA-2 study, with assessments in 2015–2017 (wave 0) and 2018– 2019 (wave 1). Time spent in sleep, SB, LPA and MVPA was assessed by wrist-worn accelerometers. Depression symptoms, loneliness, happiness, and global mental health were self-reported using validated questionnaires. Analyses were performed using a compositional data analysis (CoDA) paradigm and adjusted for potential confounders. Results: In cross-sectional analyses at wave 0 (n = 2489), time-use composition as a whole was associated with depression and happiness (all p < 0.01). The time spent in MVPA relative to other behaviors was beneficially associated with depression (γ = -0.397, p < 0.001), loneliness (γ = -0.124, p = 0.017) and happiness (γ = 0.243, p < 0.001). Hypothetically, replacing 30-min of Sleep, SB or LPA with MVPA was beneficially cross-sectionally related with depression (effect size [ES] ranged -0.326 to -0.246), loneliness (ES ranged -0.118 to -0.073), and happiness (ES ranged 0.152 to 0.172). In prospective analyses (n = 1679), MVPA relative to other behaviors at baseline, was associated with favorable changes in global mental health (γ = 0.892, p = 0.049). We observed a beneficial prospective effect on global mental health when 30-min of sleep (ES = 0.521), SB (ES = 0.479) or LPA (ES = 0.755) were theoretically replaced for MVPA. Conclusions: MVPA was cross-sectionally related with reduced depression symptoms and loneliness and elevated level of happiness, and prospectively related with enhanced global mental health. Compositional isotemporal analyses showed that hypothetically replacing sleep, SB or LPA with MVPA could result in modest but significantly improvements on mental health indicators. Our findings add evidence to the emerging body of research on 24-h time-use and health using CoDA and suggest an integrated role of daily behaviors on mental health in older people., Instituto de Salud Carlos III 16/609 19/319, MINECO R + D + I grant DEP2013-47786-R, SALAMANDER project PCIN 2016-145, Biomedical Research Networking Center on Frailty and Healthy Aging (CIBERFES), European Commission CB16/10/00477, Spanish Ministry of Science, Innovation and Universities IJC2018-038008-I, Spanish Ministry of Economy and Competitiveness RTI2018095284-J-100, Spanish Government RYC-201620546

Objectives Previous research has shown strong associations between occupational physical activity (OPA) and need for recovery (NFR). However this research has only utilized self-reported measures of OPA which may be biased. Thus, there is a need for investigating if the previously documented association between self-reported OPA and NFR can be found when using technical measures of OPA. There is also the need to investigate whether older workers are particularly susceptible to increased NFR, since age-related declines in physical capacity mean that it is likely these workers will have a higher NFR for a given physical activity. The aim of this study was to investigate the association between technically measured OPA and NFR, and whether this relationship is modified by age. Methods This study utilized data from the Danish Physical Activity Cohort with Objective Measurements cohort—comprising Danish workers (n = 840) from the cleaning, manufacturing, and transportation sectors. OPA was measured by accelerometers attached to the thigh and upper back for at least one work day and classified into four physical behaviour categories (sedentary, standing, light, or moderate/vigorous). NFR was measured using a shortened version of the Danish NFR scale. Analysis was conducted using linear regression and isotemporal substitution analyses for compositional data. Results The overall association between OPA and NFR was statistically significant in the unadjusted model (P < 0.001), but not when adjusted for age, sex, occupation, and shift work (P = 0.166). Isotemporal substitution showed small but significant reductions in NFR when increasing sedentary time relative to other behaviours (adjusted: ΔNFR = −0.010 [−0.019; −0.001]). There were no significant interactions between age and OPA (P = 0.409). Conclusions This study found significant associations between OPA and NFR, but the effect sizes were small. Reallocating 30 min to sedentary behaviours from other behaviours was associated with a reduced NFR, but the effect size may not be practically relevant. Moreover, no clear modifying effects of age were identified.

Monitoring energy indicators has acquired a renewed interest with the 2030 Agenda for Sustainable Development, and specifically with goal 7 (SDG7), which seeks to guarantee universal access to energy. The predominant criteria to monitor SDG7 are given in a set of individual indicators. Along this line, the UN indicators proposed in the 47th session of the UN Statistical commission are a practical starting point. A relevant characteristic of these indicators is that they can be expressed as proportions from a whole, i.e., they are compositions. Notably, directly implementing traditional multivariate models onto indicators that are proportions without an intermediate process can lead to spurious analysis. Here, we aim to assess the application of compositional data analysis(CoDa) to follow up on the temporal trend indicators of the energy sector in the context of SDG7, with a case study for the most affected areas addressing the problem of electricity access. Following CoDa methodology, we first use a log-ratio transformation to bring compositions to real space and then apply three multivariate methods: linear regression, generalized additive models and support vector machine. We also address other characteristic problems of the electricity access indicators, such as data quality, which was treated by considering models with interactions. In sum, CoDa facilitates a controlled management of the parts that make up population based indicators, suggesting that modelling evolution of compositions as individual components – even the standard splitting of country data into rural and urban “access to” indicator –should be avoided. This research has been partially funded by the Ministerio de Economía y Competitividad del Gobierno de España (MINECO/FEDER, Ref: MTM2015-65016-C2-2-R); and by the Agència de Gestió d′Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 656 and 2017 SGR 1496).

The geochemistry of the natural environment (e.g. soils, streams, sediments and groundwater) is representative of the source(s) from which these materials are derived. In fact, a geochemical dataset is essentially a breakdown of the parts that together form the composition of materials. As pointed out by Pearson (1897), spurious correlation can occur when attempting multivariate analysis on a dataset that is compositional in nature (i.e. the variables are measured as parts of some whole e.g. mg/l). Compositional Data Analysis (CoDA) was developed by John Aitchison (1982) and others to solve this issue, and is therefore the correct mathematical framework by which to statistically analyse these types of data. CoDA also allows for a unique interrogation of compositional data primarily through the use of the multi-dimensional compositional bi-plot. CoDA methods have been used extensively here, and in some cases compared to traditional multivariate statistical methods, in order to better understand the relationship between geochemistry and the natural environment on a variety of temporal and spatial scales. Specifically, two studies were undertaken; i) the geochemistry of groundwater in the area surrounding Lisheen Mine in Co. Tipperary, Ireland, and ii) the geochemistry of soils, stream sediments and stream waters in the border region of Ireland. Geochemistry datasets from Lisheen Mine offer a unique opportunity to investigate the chemical nature groundwater in an area prior to, during and following significant mining activity. Overall it would appear that the local groundwater was relatively un-impacted by the excavation of lead/zinc massive sulphides over a 16 year period (1999 – 2015). This may be in part thanks to the buffering effect of the host Carboniferous Limestone reducing the potential for acid mine drainage. Groundwater chemistry in the area appears to be more influenced by nitrogen from peat bogs, local agricultural practices and in some places redox reactions. The border region (composed of the six counties that border Northern Ireland) was the subject of an extensive geochemical examination by the Tellus Survey completed in 2013. The resulting freely available datasets were analysed here using CoDA. The primary, secondary and tertiary factors controlling soil, sediment and stream water geochemistry were determined. The dominant control on each of these materials is bedrock geology. However, soil type, landuse practices and topography also influence geochemistry.

Background Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately. Methods This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation. Results While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods’ performance in terms of reproducibility, validity, and ability to predict different outcomes. Conclusion Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.

There is a need to minimize the usage of traditional laboratory reference methods in favor of spectroscopy for routine soil carbon monitoring, with potential cost savings existing especially for labile pools. Mid-infrared spectroscopy has been associated with accurate soil carbon predictions, but the method has not been researched extensively in connection to C lability. More studies are also needed on reducing the numbers of samples and on how to account for the compositional nature of C pools. This study compares performance of two classes of partial least squares regression models to predict soil carbon in a global (models trained to data from a spectral library), local (models trained to data from a target area), and calibration-spiking (spectral library augmented with target-area spectra) scheme. Topsoil samples were+ scanned with a Fourier-transform infrared spectrometer, total and hot-water extractable carbon determined, and isometric log-ratio coordinates derived from the latter measurements. The best RMSEP was estimated as 0.38 and 0.23 percentage points TC for the district and field scale, respectively—values sufficiently low to make only qualitative predictions according to the RPD and RPIQ criteria. Models estimating soil carbon lability performed unsatisfactorily, presumably due to low labile pool concentration. Traditional weighing of spiking samples by including multiple copies thereof in training data yielded better results than canonical partial least squares regression modeling with embedded weighing. Although local modeling was associated with the most accurate predictions, calibration spiking addressed better the trade-off between data acquisition costs and model quality. Calibration spiking with compositional data analysis is, therefore, recommended for routine monitoring.

Background Little is known on how context-specific sedentary behaviors (SB) affect adiposity. This study aimed to investigate compositional associations between context-specific SB and adiposity and estimate the differences in adiposity associated with replacing school and out-of-school SB with physical activity (PA). Methods This study included 336 children and adolescents. Time spent in SB and PA was estimated using multi-day 24-hour raw accelerometer data. SB and PA were specified for school and out-of-school times. Fat mass percentage (FM%) and fat mass index (FMI) were used as adiposity indicators. A compositional isotemporal substitution model was used to estimate differences in adiposity associated with one-to-one reallocations of time from context-specific SB to PA. Results Participants spent approximately two thirds of their school and out-of-school time being sedentary. Relative to the remaining 24-h movement behaviors, significant associations between out-of-school SB and adiposity were found in both boys (βilr1 = 0.63, 95% confidence interval [CI] = 0.03–1.22 for FM%; βilr1 = 0.76, 95% CI = 0.03–1.49 for FMI) and girls (βilr1 = 0.62, 95% CI = 0.25–0.98 for FM%; βilr1 = 0.80, 95% CI = 0.28–1.32 for FMI). Replacing 30 min/day of out-of-school SB with out-of-school light PA decreased FM% by 10.1% (95% CI = 3.3–17.9) and FMI by 14% (95% CI = 2.7–24) in girls. No significant associations were found for school SB. Conclusions A reduction of out-of-school SB in favor of light PA should be advocated as an appropriate target for interventions and strategies to prevent childhood obesity.

In complex geochemical aqueous systems, chemical species are conceptually distinct but empirically related thanks to a large number of interactions taking place at different spatial and/or temporal scales. In this condition, common elements are shared, multiplicative interactions arise, and feedback mechanisms may be able to maintain the system far from the thermodynamical equilibrium, bearing wide fluctuations. Chemical species can have alternative stable states and transitions among them that could produce important consequences for the stability and the resilience of the solutions, also forced by climate changes and with impact on human health. Under the Compositional Data Analysis (CoDA) methodology, it is possible to appreciate the power of some tools able to take a look at the whole instead of the constituting parts, enhancing the understanding of the nature of mutual interactions. In this research work, the role of the perturbation operator governing addition/subtraction in the simplex geometry is explored as a way to trace compositional changes and investigate the system dynamics. The results of our approach on the chemistry of the Arno River waters (Central Italy) highlight the possibility to discover the resilience of chemical species under the pressure of the environmental drivers affecting the catchment. Geochemical mobility (e.g., ionic potential, ionic strength) can be associated with new tools that provide information on either the resistance to change, predisposing the system to critical shifts, or its adaptive capacity, which instead favors gradual changes. This information appears to be fundamental since river water chemistry enables to decipher processes at the boundaries among lithosphere, biosphere, hydrosphere, and atmosphere, all key reservoirs involved in the dynamics of the Earth. This knowledge will be particularly relevant if the pressure of the climatic changes on our planet will continue to increase.

The consequences of supplementing Lys, Met, and Thr in milk replacers (MR) for calves have been widely studied, but scarce information exists about potential roles of other AA (whether essential or not). The effects on growth performance of supplementation of 4 different AA combinations in a mixed ration (25.4% crude protein and 20.3% fat) based on skim milk powder and whey protein concentrate were evaluated in 76 Holstein male calves (3 ± 1.7 d old). The 4 MR were as follows: CTRL with no AA supplementation; PG, supplying additional 0.3% Pro and 0.1% Gly; FY, supplying additional 0.2% Phe and 0.2% Tyr; and KMT, providing additional 0.62% Lys, 0.22% Met, and 0.61% Thr. All calves were fed the same milk allowance program and were weaned at 56 d of study. Concentrate intake was limited to minimize interference of potential differences in solid feed intake among treatments. Animals were weighed weekly, intakes recorded daily, and blood samples obtained at 2, 5, and 7 wk of study to determine serum urea and plasma AA concentrations. Plasma AA concentrations were explored using compositional data analysis, and their isometric log-ratio transformations were used to analyze their potential influence on ADG and serum urea concentration using a linear mixed-effects model. We detected no differences in calf performance and feed intake. Plasma relative concentration of the AA supplemented in the KMT and PG treatments increased in their respective treatments, and, in PG calves, a slight increase in the proportion of plasma Gly, Glu, and branched-chain AA was also observed. The proportions of plasma branched-chain AA, His, and Gln increased, and those of Thr, Arg, Lys, and Glu decreased with calves' age. A specific log-contrast balance formed by Arg, Thr, and Lys was found to be the main driver for lowering serum urea concentrations and increasing calf growth. The use of compositional mixed-effects models identified a cluster formed by the combination of Arg, Thr, and Lys, as a potential AA to optimize calf growth. info:eu-repo/semantics/publishedVersion

10 Páginas.-- 4 Tablas.-- 6 Figuras, This work applies metataxonomic, standard statistics, and compositional data (CoDa) techniques to study the bacterial diversity of spoiled and normal Spanish-style table olive fermentations, analysing a total of 10-tons of industrial fermentation containers from two processing yards. Forty percent were affected by butyric, sulfidic, or putrid spoilage, while 60% followed the ordinary fermentation course. The samples were obtained at 30 days of fermentation, determining their 16S rRNA gene Amplicon Sequence Variant compositions (ASVs). The butyric containers showed a bacterial profile strongly associated with the genera Enterococcus, Leuconostoc, and Atlantibacter, but also with Lactiplantibacillus and Melissococcus, and less confident to Raoultella, Enterobacter, Serratia, and Celerinatantimonas. The sulfidic fermentation was linked to Alkalibacterium and, to a lesser extent, Marinilactibacillus and the absence of Lactiplantibacillus. Putrid spoilage was mainly related to Halolactibacillus and Alkalibacterium. Sulfidic/putrid (together) differed from butyric spoilage by the presence of Alkalibacterium/Marinilactibacillus as well as by Halomonas/Halanaerobium. Lactiplantibacillus dominated normal fermentations, but Vibrio was also frequently found (0–46%), apparently not causing any alteration. These results contribute to a better microbial characterisation of non-zapatera spoiled table olive fermentations. They also suggest using several statistical techniques to discriminate normal vs spoiled fermentations adequately., This research was funded through TOBE project (RTI2018-100883-B-I00, MCIU/AEI/FEDER, UE).

This systematic review determined if the composition of time spent in movement behaviours (i.e., sleep, sedentary behaviour (SED), light physical activity, and moderate-to-vigorous physical activity (MVPA)) is associated with health in adults. Five electronic databases were searched in August 2019. Studies were eligible for inclusion if they were peer-reviewed, examined community-dwelling adults, and used compositional data analysis to examine the associations between the composition of time spent in movement behaviours and health outcomes. Eight studies (7 cross-sectional, 1 prospective cohort) of >12 000 unique participants were included. Findings indicated that the 24-h movement behaviour composition was associated with all-cause mortality (1 of 1 analyses), adiposity (4 of 4 analyses), and cardiometabolic biomarkers (8 of 15 analyses). Reallocating time into MVPA from other movement behaviours was associated with favourable changes to most health outcomes and taking time out of SED and reallocating it into other movement behaviours was associated with favourable changes to all-cause mortality. The quality of evidence was very low for all health outcomes. In conclusion, these findings support the notion that the composition of movement across the entire 24-h day matters, and that recommendations for sleep, SED, and physical activity should be combined into a single public health guideline. (PROSPERO registration no.: CRD42019121641.) Novelty The 24-h movement behaviour composition is associated with a variety of health outcomes. Reallocating time into MVPA is favourably associated with health. Reallocating time out of SED is associated with favourable changes to mortality risk.

Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines. Sparse high-dimensional count data frequently encountered in biological applications such as DNA sequencing and proteomics are often well modeled using multinomial logistic-normal models. In many cases these datasets are also compositional, presented item-wise as fractions of a normalized total, necessitated by measurement and instrument constraints. Three key challenges prove limiting in covariance estimation using these models in compositional settings: (1) the computational complexity of inverting high-dimensional covariance matrices, (2) non-exchangability introduced from the summation constraint on multinomial parameters, (3) the irreducibility of the component multinomial logistic-normal distribution that necessitates the use of parameter augmentation, or similar techniques, during inference. We show that a variational autoencoder augmented with a fast Isometric Log-ratio (ILR) transform can address these issues and accurately estimate principal components from multinomially logistic-normal distributed data. This model can be optimized on GPUs and modified to handle mini-batching, with the ability to scale across thousands of dimensions and thousands of samples.

Covariance estimation on high-dimensional data is a central challenge across multiple scientific disciplines. Sparse high-dimensional count data, frequently encountered in biological applications such as DNA sequencing and proteomics, are often well modeled using multinomial logistic normal models. In many cases, these datasets are also compositional, presented item-wise as fractions of a normalized total, due to measurement and instrument constraints. In compositional settings, three key factors limit the ability of these models to estimate covariance: (1) the computational complexity of inverting high-dimensional covariance matrices, (2) the non-exchangeability introduced from the summation constraint on multinomial parameters, and (3) the irreducibility of the multinomial logistic normal distribution that necessitates the use of parameter augmentation, or similar techniques, during inference. Using real and synthetic data we show that a variational autoencoder augmented with a fast isometric log-ratio (ILR) transform can address these issues and accurately estimate principal components from multinomially logistic normal distributed data. This model can be optimized on GPUs and modified to handle mini-batching, with the ability to scale across thousands of dimensions and thousands of samples.

Objective We aimed to determine the association between daily activities (sleep, sedentary behavior and physical activities) and neuroplasticity in older adults by measuring motor evoked potential amplitudes (MEPs) elicited after a single and spaced continuous theta burst stimulation (cTBS) paradigm, targeting the primary motor cortex. Methods MEPs were recorded from the right first dorsal interosseous muscle of 34 older adults (66.9 ± 4.5 years) by delivering single-pulse TMS before, between and at 0, 10, 20, 40 and 60 min after the application of spaced-cTBS separated by 10 min. Habitual activity was assessed by accelerometry for 24 h/day over 7-days. Multiple linear regression models determined if the time-use composition (sleep, sedentary behavior and physical activities) was associated with neuroplasticity response. Results More physical activity at the equal expense of sleep and sedentary behaviors was associated with greater motor cortical neuroplasticity. Associations appeared to be driven by more time spent in light- but not moderate-to-vigorous- physical activities. Conclusions Engaging in light physical activity at the expense of sleep and sedentary behavior was associated with greater LTD-like motor cortex neuroplasticity (as measured with cTBS) in older adults. Significance These findings suggest the promotion of physical activity among older adults to support brain neuroplasticity.

Objective To determine whether a composite metric of arm elevation and trapezius activity (i.e. neck/shoulder load) is more strongly associated with the 2-year course of neck and shoulder pain intensity (NSPi) among construction and healthcare workers than each exposure separately. Methods Dominant arm elevation and upper trapezius muscle activity were estimated in construction and healthcare employees (n = 118) at baseline, using accelerometry and normalized surface electromyography (%MVE), respectively. At baseline and every 6 months for 2 years, workers reported NSPi (score 0–3). Compositions of working time were determined for arm elevation ( 60°), trapezius activity ( 7.0%MVE), and a composite metric “neck/shoulder load” (restitution, low, medium, and high load). Associations between each of these three compositions and the 2-year course of NSPi were determined using linear mixed models. Results Associations between exposure compositions and the course of NSPi were all weak and in general uncertain. Time spent in 0.5–7.0%MVE showed the largest and most certain association with changes in NSPi during follow-up (β = − 0.13; p = 0.037; corresponding to a −0.01 change in NPSi every 6 months). Among pain-free workers at baseline, medium (β = − 0.23; p = 0.039) and high (β = 0.15; p = 0.031) neck/shoulder load contributed the most to explaining changes in NSPi. Conclusion The composite metric of neck/shoulder load did not show a stronger association with the course of NSPi than arm elevation or trapezius activity alone in the entire population, while some indications of a stronger association were found among those who were pain-free at baseline.

The elemental composition of a sediment is set by the composition of its protolith and modified by weathering, sorting, and diagenesis. An important problem is deconvolving these contributions to a sediment's composition to arrive at information about processes that operate on the Earth's surface. We approach this problem by developing a predictive and invertible model of sedimentary major element composition. We compile a data set of sedimentary rock, river sediment, soil, and igneous rock compositions. Principal component analysis of the data set shows that most variation can be simplified to a small number of variables. We thus show that any sediment's composition can be described with just two vectors of igneous evolution and weathering. We hence define a model for sedimentary composition as a combination of these processes. A 1:1 correspondence is observed between predictions and independent data. The log ratios urn:x-wiley:ggge:media:ggge22195:ggge22195-math-0001 and urn:x-wiley:ggge:media:ggge22195:ggge22195-math-0002 are found to be simple proxies for, respectively, the model's protolith and weathering indices. Significant deviations from the model can be explained by sodium‐calcium exchange. Using this approach, we show that the major element composition of the upper continental crust has been modified by weathering, and we calculate the amount of each element that it must have lost to modify it to its present composition. By extrapolating modern weathering rates over the age of the crust, we conclude that it has not retained a significant amount of the necessarily produced weathering restite. This restite has likely been subducted into the mantle, indicating a crust‐to‐mantle recycling rate of 1.33 ± 0.89 ×1013 kg·year−1.

BackgroundResearchers applying compositional data analysis to time-use data (e.g., time spent in physical behaviors) often face the problem of zeros, that is, recordings of zero time spent in any of the studied behaviors. Zeros hinder the application of compositional data analysis because the analysis is based on log-ratios. One way to overcome this challenge is to replace the zeros with sensible small values. The aim of this study was to compare the performance of three existing replacement methods used within physical behavior time-use epidemiology: simple replacement, multiplicative replacement, and log-ratio expectation-maximization (lrEM) algorithm. Moreover, we assessed the consequence of choosing replacement values higher than the lowest observed value for a given behavior.MethodUsing a complete dataset based on accelerometer data from 1310 Danish adults as reference, multiple datasets were simulated across six scenarios of zeros (5–30% zeros in 5% increments). Moreover, four examples were produced based on real data, in which, 10 and 20% zeros were imposed and replaced using a replacement value of 0.5 min, 65% of the observation threshold, or an estimated value below the observation threshold. For the simulation study and the examples, the zeros were replaced using the three replacement methods and the degree of distortion introduced was assessed by comparison with the complete dataset.ResultsThe lrEM method outperformed the other replacement methods as it had the smallest influence on the structure of relative variation of the datasets. Both the simple and multiplicative replacements introduced higher distortion, particularly in scenarios with more than 10% zeros; although the latter, like the lrEM, does preserve the ratios between behaviors with no zeros. The examples revealed that replacing zeros with a value higher than the observation threshold severely affected the structure of relative variation.ConclusionsGiven our findings, we encourage the use of replacement methods that preserve the relative structure of physical behavior data, as achieved by the multiplicative and lrEM replacements, andto avoidsimple replacement. Moreover, we do not recommend replacing zeros with values higher than the lowest observed value for a behavior.

Modelling of topsoil geochemical data using discriminant function analysis, along with a set of corresponding geochemical maps, portray a legacy of environmental contamination in the city of Sisak. Based on centred log-ratio (clr) transformed compositional data from topsoil samples collected on Kupa-Odra and Sava River floodplain sediments and loess, underlying the Sisak urban area, and regrouped according to their land use characteristics (urban, industrial and agricultural areas), a few element clusters emerged accounting for discrimination between the defined groups. The most prominent feature of the lithological model, as suggested by the first discriminant function, is the carbonate group (Mg, Ca, Sr) versus anthropogenic sourced elements/transition metals (e.g., Sn, Zn, Ba, Mn, Pb, U, Ag, Cr, V) dichotomy separating soil developed on floodplain sediments of Sava and Kupa-Odra Rivers from those evolved on loess. The major attribute of the land use model, according to the main discriminant function, is differentiation between urban/industrial and rural (agricultural) samples based on contrast provided by the carbonate component and other anthropogenic sourced element clusters (predominantly alkaline and concentrated over the loess substrate), in contrast to fertiliser trace and transition element clusters (predominantly acid and disseminated over Kupa-Odra and Sava River floodplain sediments). In both cases, the second discriminant function provides additional information on the secondary level, i.e., in the lithology model the Kupa-Odra sediment group is distinguished from other lithological groupings owing to its predominantly transition element signature. In the land use model, urban soil samples are separated from industrial ones based on the characteristic Na-Sr-Ca-pH(KCl) element association (urban waste) against a typical ‘industrial’ element signature (Zn-Cd-Mn-Pb-Mo).

Assessment of exposure to fungi has commonly been limited to fungal spore measurements that have shown associations between fungi and development or exacerbation of different airway diseases. Because large numbers of submicronic fragments can be aerosolized from fungal cultures under laboratory conditions, it has been suggested that fungal exposure is more complex and higher than that commonly revealed by spore measurements. However, the assessment of fungal fragments in complex environmental matrix remain limited due to methodological challenges. With a recently developed immunolabeling method for field emission scanning electron microscope (FESEM), we could assess the complex composition of fungal aerosols present in personal thoracic samples collected from two Norwegian sawmills. We found that large fungal fragments (length >1 µm) dominated the fungal aerosols indicating that the traditional monitoring approach of spores severely underestimate fungal exposure. The composition of fungal aerosols comprised in average 9% submicronic fragments, 62% large fragments, and 29% spores. The average concentrations of large and submicronic fragments (0.2–1 µm) were 3 × 105 and 0.6 × 105 particles m−3, respectively, and correlated weakly with spores (1.4 × 105 particles m−3). The levels of fragments were 2.6 times higher than the average spore concentration that was close to the proposed hazardous level of 105 spores per m3. The season influenced significantly the fungal aerosol concentrations but not the composition. Furthermore, the ratio of spores in the heterogeneous fungal aerosol composition was significantly higher in saw departments as compared to sorting of green timber departments where the fungal fragments were most prevalent. Being the dominating particles of fungal aerosols in sawmills, fungal fragments should be included in exposure-response studies to elucidate their importance for health impairments. Likewise, the use of fungal aerosol composition in such studies should be considered.

8 Páginas; 3 Tablas; 2 Figuras, This article contains processed data related to the research published in “Tentative application of compositional data analysis to fatty acid profiles of green Spanish-style Gordal table olives” (Garrido-Fernández et al., 2018) [1]. It provides information on the implementation of compositional data analysis (CoDa) to the fatty acid profiles of Spanish-style Gordal table olives vs the use of conventional statistical analysis (data composition expressed in percentages). Particularly, it includes: i) the matrix of the sequential binary partition used for the balance estimation and the isometric log-ratio transformation (ilr) of the fatty acid profiles, ii) correlation among the diverse fatty acids expressed in percentages and their significances, iii) the ilr transformed values (coordinates in the Euclidean space) obtained following the sequential binary partition previously detailed, iv) the graphical presentation in the Simplex (ternary centred plot) of the treatments as a function of the four fatty acids with the higher log-ratio variances, and v) segregation of treatments based on Cluster Analysis., This work was supported by the Spanish Government under project (AGL2010-15494/ALI, partially financed by European Regional Development Funds, ERDF) and Junta de Andalucía through financial funding to group AGR-125. We also thank Elena Nogales Hernández for technical assistance.

Compositional closure, spurious negative correlations in data sets of a fixed sum (e.g., fractions and percent), is often encountered in geostatistical analyses, particularly in mineralogy, petrology, and geochemistry. Techniques to minimize the effects of closure (e.g., log-ratio transformations) can provide consistent geostatistical results. However, such approaches do not remove these effects because closure does not result from mathematical operations but is an inherent property of the physical systems under study. The natural world causes physical closure; mathematics simply describes that closure and cannot alter it by manipulations. Here, we examine the distinct types of geologic systems and samples to determine in which situations closure (physical and mathematical) does or does not ensue and the reasons therefor. We parse compositional systems based on (1) types of components under study, immutable (e.g., elements) or reactive (minerals), and (2) whether the system is open or closed to component transfer. Further, open systems can be (1) displacive in which addition of a component physically crowds out others, or (2) accommodative in which addition or subtraction of components does not affect the others. Only displacive systems are subject to compositional closure. Accommodative systems, even with components expressed as percent or fractions, are not closed physically or, therefore, mathematically.

3 Figuras.-- 3 Tablas, The work studies the effects of season, feeding type, and anatomical region on the Iberian pig fat triacylglycerol (TAG) profiles, considered as compositional data (CoDa). The analysis consisted of applying exploratory tools in the simplex and standard multivariate techniques to data transformed into the Euclidean space (ilr coordinates). Compositional biplot showed differences in TAG containing palmitic (P) and oleic (O) acids between the 2005 and 2003/2004 seasons but not within these. PPP (clr variance, 0.139), OLL (0.095), PPS (0.075), POPo + PLP (0.074), and PSS (0.629) showed high CoDa variability among treatments. The ANOVA analysis found significant (p ≤ 0.05) effects of season, feeding type, and anatomical region on pig fat TAG profile, but only that for 2005 season was well predicted (97.5%) by discriminant analysis (DA). Overall, season was more influential on the Iberian pig fat TAG profile than montanera length and sampling region, which effects were not significant for some statistical techniques.

The present thesis is a compendium of three research articles produced between 2015 and 2019. The three articles are different contributions based on compositional statistical methodology and statistical inference of genetic 2/2 relatedness. In the first work of this thesis, we review the classical graphical methods used to detect relatedness and introduce the analysis of Compositional Data for relatedness research. In the second article, we propose the analysis of identity by state genotype sharing data instead of the classical identity by state allele sharing data. The third article finishes the thesis with the development of the likelihood ratio approach to infer three-quarter siblings in genetic databases. To illustrate all the results of this doctoral thesis we use genetic markers from worldwide human population projects such as the Human Genome Diversity Project and the 1000 Genomes Project, as well as from a local prospective human cohort of the Genomes of Catalonia (GCAT) Aquesta tesi doctoral és un compendi de tres articles de recerca produïts entre el 2015-2019. Els tres articles són aportacions diferents basades en la metodologia de les dades composicionals i en la inferència estadística de relacions familiars. En el primer treball d'aquesta tesi, revisem els mètodes gràfics clàssics utilitzats per detectar relacions familiars i introduïm l'anàlisi de les dades composicionals per a la investigació de relacions familiars. En el segon, es proposa l'anàlisi de dades de genotips compartits idèntics per estat en lloc de les clàssiques dades d'al·lels compartits. El tercer article finalitza la tesi amb l'elaboració de la raó de versemblances per inferir tres quarts germans en bases de dades genètiques. Per il·lustrar els resultats, s'utilitzen marcadors genètics de projectes de població humana com el Projecte de la Diversitat del Genoma Humà, el Projecte 1000 Genomes i una cohort humana prospectiva local dels genomes de Catalunya (GCAT) Programa de Doctorat en Tecnologia

The Tethyan Himalaya is the fourth metallogenic belt discovered to date within the Tibetan Plateau and contains chromite, gold and Sb–polymetallic mineralization. The belt records a series of complex tectonic–magmatic events that include the break-up, subduction, collision, detachment, and obduction of the Eastern Gondwana continent, all of which generated ideal conditions for ore formation. However, this area remains under-explored, with only limited knowledge of the deposit types present in this area and their relationship to tectonic and magmatic events. This has hampered mineral exploration in the region. This study uses previous research andmineral exploration in this region, combined with knowledge of mineralization in adjacent regions, to suggest the presence of three main deposit types (ophiolitic chromite, orogenic Au, and hydrothermal veinSb–polymetallic deposits), and highlights the potential for this area to host as yet undiscovered island arc-type porphyry Cu–Au and leucogranite-related Sn–(Be) mineralization. This study summaries the relationship between the tectonic evolution and magmatism within the region and the mineralization, establishing a temporal and spatial framework for mineralization within the eastern Tethys Himalayan metallogenic belt.Improved knowledge of the geochemical signatures of mineralization, obtained using compositional data analysis (CoDA) combined with geological information, enabled the identification of nine compositional balances, including the presence of sedimentary units, accretionary wedge structures, leucogranites, mafic–ultramafic rocks, chromite mineralization, Au mineralization, and Sb–polymetallic mineralization. This study establishes a predictive mineral exploration model for the eastern Tethys Himalayan metallogenic belt and combines this model with geological and geochemical data to identify areas for future exploration. These targetsinclude a 50-km-long ophiolitic belt in the Nedong area that is highly prospective for chromite mineralization; a positive Au–Cu–Mo geochemical anomaly in the Qonggyai–Qusum area that is prospective for orogenic Aumineralization; and positive Au–Cu–Mo geochemical anomalies in the Nianzha, Sangri, and Gyaca areas that are prospective for exploration for Xiongcun-type island arc-associated porphyry Cu–Au mineralization, as well as deep-seated extensions to known Sb–polymetallic mineralization and the inner and outer contacts of the Lhozhag and Cuonadong leucogranite, all of which are prospective for exploration for Sn–(Be) mineralization.

36 Páginas.-- 8 Figuras.-- 1 Tabla, This work relates native lactic acid bacteria (LAB) (Lactobacillus pentosus LPG1, L. pentosus Lp13, and Lactobacillus plantarum Lpl15) and yeast (Wickerhamomyces anomalus Y12) starters to the volatile components (VOCs) produced in green Spanish-style table olives. For this aim, the VOC profile was considered as compositional data (CoDa). The CoDa analysis generated new information on the relationship among inocula and VOCs through the tetrahedral plot, CoDa-biplot, variation array matrix, and CoDa dendrogram. The ilr (which includes pivot) coordinates (Euclidean space) from VOCs produced more reliable starters’ clustering than the original data. The potential VOC markers, identified by a test based on the pairwise comparison of the logratio variation arrays from the whole data set and the individual groups, were (starters in the parenthesis): 2-phenylethyl acetate (LPG1, Y12, Y12 + LAB), methanol (Lpl15), cis-2-penten-1-ol (LPG1, Y12, Y12 + LAB), 2-methyl-3-hexanol (LPG1, Y12), U (non-identified) C (m/z 83-112-97) (Y12) and UF (m/z 95-154-110) (LPG1, Y12 + LAB). Besides, some VOCs were partial/totally inhibited by specific starters: 2-methyl-1-propanol (Lp13, Y12 + LAB), 2-phenyl ethanol (Lp13), furfuryl methyl ether (Y12 + LAB), purpurocatechol (Y12, Y12 + LAB), 4-ethyl guaiacol (Lp13, Lpl15), 4-ethyl phenol (Lpl15), 5-tert-butylpyrogallol (Lp13, Lpl15), and UE (m/z 111–198) (Lp13). A better understanding of the relationship between starters and their VOC may facilitate modelling the flavour and quality of Spanish-style green table olive fermentations., The research was funded by the Spanish Government (Project OliFilm AGL-2013-48300-R: www.olifilm.science.com.es) A-BC thanks the Spanish Ministry of Economy and Competitiveness for their FPI grant.

For the last forty-five years (from 1974 to present) ferroalloy production in Bagnolo Mella, Northern Italy, has generated particulate emissions enriched in potentially toxic metals and metalloids including arsenic (As), lead (Pb) and manganese (Mn). Of these, Mn is unique in that it has a significant background concentration and is seldom studied as a contaminant but is potentially a significant toxin derived from dusts regionally. Here we examine the distribution, redistribution, speciation and bioavailability of the Mn-contaminated top soils affected by atmospheric emissions adjacent to the ferroalloy plant. Four sites, variably located in the study area in terms of both distance and direction from the plant, were considered as representative of increasing levels of industrial influence. Soil profiles showed that metal concentrations (measured by X-ray fluorescence) varied considerably by location, i.e. higher in the sites closer to the plant and also at the surface level, although distributed throughout the top 15 cm, suggesting appreciable redistribution possibly due to soil mixing or infiltration. Most metal concentrations were correlated, except Mn which was independent and more variable across the sites than the other elements. Sequential chemical extractions indicated that Pb was primarily associated with Mn oxides, while As was most significantly associated with iron oxides. When Mn concentration significantly exceeded background levels, it was present in phases that were resistant to acid dissolution, very different from typical uncontaminated soils. X-ray Absorption Near Edge Spectroscopy (XANES) analyses suggested this recalcitrant Mn phase is likely a Mn-bearing spinel such as magnetite, that can be particularly toxic if ingested or inhaled. These first results highlight the legacy of ferroalloy production on surrounding soils, as well as the importance of Mn speciation for soil apportionment evaluation and human exposure estimation.

Thermal groundwater is currently being exploited for district-scale heating in many locations world-wide. The chemical compositions of these thermal waters reflect the provenance and circulation patterns of the groundwater, which are controlled by recharge, rock type and geological structure. Exploring the provenance of these waters using multivariate statistical analysis (MSA) techniques increases our understanding of the hydrothermal circulation systems, and provides a reliable tool for assessing these resources.Hydrochemical data from thermal springs situated in the Carboniferous Dublin Basin in east-central Ireland were explored using MSA, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), to investigate the source aquifers of the thermal groundwaters. To take into account the compositional nature of the hydrochemical data, compositional data analysis (CoDa) techniques were used to process the data prior to the MSA.The results of the MSA were examined alongside detailed time-lapse temperature measurements from several of the springs, and indicate the influence of three important hydrogeological processes on the hydrochemistry of the thermal waters: 1) salinity and increased water-rock interaction; 2) dissolution of carbonates; and 3) dissolution of sulfides, sulfates and oxides associated with mineral deposits. The use of MSA within the CoDa framework identified subtle temporal variations in the hydrochemistry of the thermal springs, which could not be identified with more traditional graphing methods, or with a standard statistical approach. The MSA was successful in distinguishing different geological settings and different annual behaviours within the group of springs. This study demonstrates the usefulness of the application of MSA within the CoDa framework in order to better understand the underlying controlling processes governing the hydrochemistry of a group of thermal springs in a low-enthalpy setting.

The management of groundwater resources has become a matter of public interest during recent years. It is a difficult and sensitive topic for all involved subjects, scientists, state agencies, public and private water supply companies and citizens. Therefore, tools for collection, storage, evaluation and public visualization of groundwater-relevant information have to be constructed and implemented. The development of network-based technologies, and the support of the Geographical Information Systems, would help the dissemination of the knowledge about groundwater chemistry, as well as the integration of the geochemical data with other type of information (geological, hydrogeological, soil use, etc.). However, dedicated repositories often contain fragmented numerical and categorical information from different sources obtained for dissimilar aims and divergent interests, thus compromising a holistic management of the resource. Given the complicated nature of the information related to groundwater and geochemical behavior, statistical methods describing and evaluating data patterns in complex regional databases have to be developed in a dedicated way. An application example is presented for the Geobasi–Tuscany (central Italy) project, a repository where all the information about the geochemistry of natural media, including groundwater, collected in the regional territory is stored. Robust methods, not sensible to anomalous cases, have been associated to the compositional approach, leading to the identification of the baseline compositional facies, related to frequent and spatially diffused geochemical phenomena. The facies have been discriminated from the other ones, related to specific environmental conditions, affecting limited portions of the territory. The methodology can be easily integrated with those based on the classical ternary or square diagrams for the identification of the hydrochemical facies. The proposed robust approach, compositional and, therefore, multivariate, appears to be an efficient way to understand how the natural and not natural processes are working on a regional scale.

The requirements for quantitative data in geological surveillance are ever increasing; traditional geological maps and 3D models are evolving into quantitative conceptual models based on a broad range of analytical measurements of surface and subsurface properties. The British Geological Survey's Geochemical Baseline Survey of the Environment (G-BASE) project provides one such source of data: national coverage of stream sediment, stream water and soil geochemistry. While this geochemical data is of obvious benefit to our understanding of the present day surface environment, the rich geological diversity of south west England provides an ideal setting in which to showcase the potential of such data to enhance our understanding of the underlying bedrock.In this paper we use compositional data analysis to extract lithostratigraphic information from G-BASE stream sediment data. We find that variations in G-BASE stream sediment geochemistry correspond very closely to mapped variations in bedrock geology. Geochemical variations between the 16 lithostratigraphic domains into which we classify the region provide insight into the distinct geological histories of the lithologies within each domain, particularly in relation to depositional environments and sediment provenance.

Single component geochemical maps are the most basic representation of spatial elemental distributions and commonly used in environmental and exploration geochemistry. However, the compositional nature of geochemical data imposes several limitations on how the data should be presented. The problems relate to the constant sum problem (closure), and the inherently multivariate relative information conveyed by compositional data. Well known is, for instance, the tendency of all heavy metals to show lower values in soils with significant contributions of diluting elements (e.g., the quartz dilution effect); or the contrary effect, apparent enrichment in many elements due to removal of potassium during weathering. The validity of classical single component maps is thus investigated, and reasonable alternatives that honour the compositional character of geochemical concentrations are presented. The first recommended such method relies on knowledge-driven log-ratios, chosen to highlight certain geochemical relations or to filter known artefacts (e.g. dilution with SiO 2 or volatiles). This is similar to the classical normalisation approach to a single element. The second approach uses the (so called) log-contrasts, that employ suitable statistical methods (such as classification techniques, regression analysis, principal component analysis, and clustering of variables) to extract potentially interesting geochemical summaries. The caution from this work is that if a compositional approach is not used, it becomes difficult to guarantee that any identified pattern, trend or anomaly is not an artefact of the constant sum constraint. In summary the authors recommend a chain of enquiry that involves searching for the appropriate statistical method that can answer the required geological or geochemical question whilst maintaining the integrity of the compositional nature of the data. The required log-ratio transformations should be applied followed by the chosen statistical method. Interpreting the results may require a closer working relationship between statisticians, data analysts and geochemists.

20 Páginas.-- 3 Tablas.-- 8 Figuras, This work aims to study the effect of the green Spanish-style table olive processing and extraction method of fat on its minor components. For this purpose, it uses standard multivariate analysis (developed for Euclidean space), Compositional Data (CoDa) analysis (for data in the simplex) and Multiple Factor analysis (MFA). Overall, processing had a scarce effect on most of the minor components except ethyl and methyl esters and diacylglycerols, which markedly increased during fermentation; however, these compounds in table olive do not have the negative connotations that those in olive oil do since they are normal metabolites from the yeast microflora habitually present during the process. The work also showed that the quantification of minor components in table olive fat was an extraction-dependent method since Soxhlet increased the concentrations of fatty alcohols, triterpene dialcohols, sterols, waxes and polar compounds. Regarding statistical methods, CoDa analysis strategies were successfully applied to produce more appropriate clustering and Principal Component Analysis (PCA) segregation than standard tools. Moreover, MFA allowed for study of the components individually and by groups; the relationships among groups led to the most appropriate clustering and PCA segregation of samples and revealed the effect of the chemical groups’ evolution on the similarity/dissimilarity between samples. Therefore, MFA was the statistical analysis that led to the most information on the effect of processing and extraction methods. Its combination with appropriate CoDa logratios could be an exciting challenge., This work was funded in part by the Ministry of Economy and Competitiveness from the Spanish government through projects AGL2009-07436/ALI and AGL2010-15494/ALI, partially financed by European regional development funds (ERDF) and Junta de Andalucía through financial assistance to group AGR-125.

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was partially funded by the ‘‘Accion Transversal del Cancer’’, approved by the Spanish Ministry Council on 11 October 2007; by the Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP); by the Instituto de Salud Carlos III-FEDER (grant nos PI08/1770, PI08/0533, PI08/1359, PS09/00773-Cantabria, PS09/01286-Leo´n, PS09/ 01903-Valencia, PS09/02078-Huelva, PS09/01662-Granada, PI11/01403, PI11/01810, PI11/02213, PI12/00488, PI14/01219, PI14/0613, PI15/00069, PI15/00914, PI15/01032, PI17/01280, PI09/0914, IJCI-2014-20900); by the Spanish Ministry of Health (grant no. CB06/02/1002); by the Spanish Ministry of Economy and Competitiveness (grant no. MTM2015-65016- C2-1-R); by the Catalan Government-Agency for Management of University and Research Grants (AGAUR) (grant nos 2014SGR551, 2017SGR656, 2017SGR733, 2017SGR723, 2017SGR1085); by the University of Girona (grant no. MPCUdG2016/069, GDRCompetUdG2017/19); by the Fundacio´n Marque´s de Valdecilla (grant no. API 10/09); by the Junta de Castilla y Leo´n (grant no. LE22A10-2); by the Consejerı´a de Salud of the Junta de Andalucı´a (grant nos PI-0571- 2009, PI-0306-2011, salud201200057018tra); by the Conselleria de Sanitat of the Generalitat Valenciana (grant no. AP_061/10); by the Regional Government of the Basque Country; by the Consejerı´a de Sanidad de la Regio´n de Murcia; by the European Commission (FOOD-CT-2006-036224-HIWATE); by the Spanish Association Against Cancer (AECC) Scientific Foundation; by the Fundacio´n Caja de Ahorros de Asturias; and by the University of Oviedo. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.

Type 1 Diabetes Mellitus (T1DM) is a chronic disease that leads to absolute insulin deficiency. People with T1DM need exogenous insulin to maintain glucose at proper levels. Achieving optimal glycemic control is a major hurdle due to the large intra-patient variability, and continuous glucose monitoring (CGM) plays an essential role for individuals with T1DM. This thesis is devoted to describe the condition assessment of patients with T1DM through the analysis of glucose data obtained from CGM. This work focuses on understanding and dissecting the CGM measures. A model of the error of a CGM sensor has been obtained and the accuracy of the CGM has been assessed during challenging conditions. Additionally, a novel approach for the categorization of daily glucose profiles based on the analysis of compositional data (CoDa) is proposed. Finally, a probabilistic model of transition between different categories of periods of glucose data obtained with CoDa analysis is presented. La diabetes mellitus tipo 1 (T1DM) es una enfermedad crónica que conduce a una deficiencia absoluta de insulina. Las personas con T1DM requieren insulina exógena para mantener los niveles de glucosa apropiados. Alcanzar un control glicémico óptimo tiene una gran dificultad debido a la variabilidad intrapaciente, y el monitoreo continuo de glucosa (CGM) desempeña un papel esencial para los individuos con T1DM. Este trabajo se enfoca en entender y diseccionar las medidas obtenidas de CGM. Se ha obtenido un modelo de error de un sensor CGM y se ha evaluado la precisión del CGM en condiciones difíciles. Además, se presenta un nuevo enfoque para la caracterización de perfiles de glucosa diarios con base en el análisis de datos composicionales (CoDa). Finalmente, se presenta un modelo de transición probabilístico entre diferentes categorías de periodos de datos de glucosa que fue obtenido usando técnicas CoDa.

[EN] The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government MINECO through Grants DPI-2016-78831-C2-1-R, DPI2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds.

Worldwide, monthly CPIs are mostly calculated as weighted averages of price relatives with fixed base weights. The main source of estimation of CPI weights are National Accounts, whose complexity in terms of data collection, estimation of aggregates and validation procedures leads to several months of delay in the release of the figures. This ends up in a non completely consistent Laspeyres formula since the weights do not refer to the same period as the base prices do, being older by one year and then corrected by the elapsed inflation. In this paper we propose to forecast CPI weights via a compositional VAR model, to obtain more updated weights and, consequently, a more updated measure of inflation through CPIs.

Soil bulk density (BD), degree of compactness (DC), maximum bulk density (MBD), and critical water content (CWC) at which MBD is reached are commonly used to characterize soil compaction, and can be predicted from soil texture and organic matter content, omitting other components such as sand sub-classes and soil cementing agents and potential biases such as data redundancy and sub-compositional incoherence. Compositional data analysis is needed to account for interactions among soil components and to avoid biases. The aim of this study was to relate soil compaction indexes to the basic components of coarse-textured soils using unbiased numerical techniques. Soil samples collected in horizons A and B at 49 sites in Quebec, Canada, were analyzed for gravimetric water content, BD, particle-size distribution, MBD, CWC, organic C, total N, Si, Fe, Al, Mn, Mg, and Ca. DC was calculated as the ratio of BD to MBD. The 14 physical-chemical soil properties were expressed as isometric log-ratios balances. We conducted principal component analysis to identify the components most correlated with compaction indexes. We used regression analysis to predict MBD and CWC, and used linear mixed-effects models to predict BD and DC. The regression models accounted for up to 83% of total variation in MBD and CWC, and the linear mixed-effects models explained 58–64% of total variation in BD and DC. BD and DC were found to decrease with clay content, and increase with larger proportion of coarser particles. Organic matter content tended to reduce BD and DC, and showed little effects on MBD. Increasing evenness of sand fractions resulted in a higher MBD value. Relationships between CWC and soil texture, and between CWC and organic C were not significant. Mineral cementing agents were the major contributors to soil compaction indexes. Si, Al, Fe, and Ca oxides increased BD, DC, and CWC, but reduced MBD. The sensitivity of coarse-textured soils to compaction could be predicted to support decisions on soil resilience after ripping and on the need to implement corrective chemical, biological and physical methods such as soil amendments, structure-building crops or textural mixtures to rebalance soil compositions.

Since the advent of modern computing, geochemists have increasingly relied on computers to garner efficiencies in calculations, data analysis, and data presentation. Entirely new fields, such as Monte Carlo-based simulation and geochemical modeling, have developed under this paradigm. With continued growth in computing power, machine learning has become an increasingly popular tool in aqueous geochemistry. However, continued reliance on algorithms to perform mathematical calculations can lead to paths of not understanding how to properly prepare information for models or not the reasons behind apparent patterns in the output. Machine learning algorithms can be heavily impacted by what variables are chosen for the model and how data are pre-processed, including handling of missing and censored values (e.g., above or below a detection limit). We propose an approach of parsimonious variable selection, based partially on the signal-to-noise ratio, and suggest and discuss strategies for handling missing and censored data. An example of unsupervised machine learning, using emergent self-organizing map analysis, is applied to water from oil and gas wells in the northern U.S. Gulf Coast Basin, whose composition is controlled by different processes and is derived from various origins. Findings from this investigation suggest five groups of water samples are present, two of which were not identified using conventional data analysis methods. One notable result is that brines derived from seawater evaporation, presumably waters from which the Jurassic Louann salt precipitated, have migrated upward into shallower reservoirs across the study area. This work demonstrates that focus on understanding data quality and exercises to better interpret the output from numerical models continue to be critical skills to further take advantage of applying machine learning to geochemistry.

Demographic indicators are an essential element in considering various problems in the social economy, such as predicting economic fluctuations and establishing policies. Although literature extensively discusses the growth of the world population or issues pertaining to its aging, it has given little to no attention to population structures and transition patterns while considering compositional data problems. This study considers the characteristics of compositional data to examine the transition of the world population structure. The Aitchison distance examines the similarity of the world population structure from 1990 to 2080 and that of countries and regions in 2015, and creates maps to illustrate the results. Accordingly, the results identified the following countries and regions as epitomes of the world&rsquo, s population structure through different periods: India, Northern Africa and South Africa, in the 1990s, South America in 2015 to 2030, Oceania and Northern America in 2040, Uruguay and Puerto Rico in 2050 to 2060, and Italy and Japan in the distant future.