scholarly journals sandbox – Creating and Analysing Synthetic Sediment Sections with R

2021 ◽  
Author(s):  
Michael Dietze ◽  
Sebastian Kreutzer ◽  
Margret C. Fuchs ◽  
Sascha Meszner

Abstract. The majority of palaeoenvironmental information is inferred from proxy data contained in accretionary sediments, called geo-archives. The validity of proxy data and analysis workflows are usually assumed implicitly, with systematic tests and uncertainty estimates restricted to modern analogue studies or reduced-complexity case studies. However, a more generic and consistent approach to exploring the validity and variability of proxy functions would be to translate a given geo-archive into a model scenario: a "virtual twin". Here, we introduce a conceptual framework and numerical toolset that allows the definition and analysis of synthetic sediment sections. The R package sandbox describes arbitrary stratigraphically consistent deposits by depth-dependent rules and grain-specific parameters, allowing full scalability and flexibility. Virtual samples can be taken, resulting in discrete grain-mixtures with well-defined parameters. These samples can then be virtually prepared and analysed, for example to test hypotheses. We illustrate the concept of sandbox, explain how a sediment section can be mapped into the model and, by focusing on an exemplary field of application, we explore universal geochronological research questions related to the effects of sample geometry and grain-size specific age inheritance. We summarise further application scenarios of the model framework, relevant for but not restricted to the broader geochronological community.

Oecologia ◽  
2021 ◽  
Author(s):  
Peng He ◽  
Pierre-Olivier Montiglio ◽  
Marius Somveille ◽  
Mauricio Cantor ◽  
Damien R. Farine

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.


2021 ◽  
pp. 104649642110124
Author(s):  
Joseph A. Bonito

The Group Actor-Partner Interdependence Model (GAPIM) conceptualizes group composition as a relational construct and provides methods for estimating the effects of compositional characteristics on outcomes of interest. This paper extends the GAPIM to a multilevel structural equation model framework, which expands the range of research questions the GAPIM might address, including those based on input-process-outcome models. Simulations, based on group size, number of groups, effect size, and compositional skewness, provide guidance for designing studies to maximize power to detect compositional effects. Discussion addresses composition in general, especially how “deep” characteristics become manifest and meaningful during interaction.


2020 ◽  
Author(s):  
Daniel Lakens ◽  
Lisa Marie DeBruine

Making scientific information machine-readable greatly facilitates its re-use. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine readable. We believe there are two benefits to specifying a hypothesis test in a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis test will become more transparent, falsifiable, and rigorous. Second, scientists will benefit if information related to hypothesis tests in scientific articles is easily findable and re-usable, for example when performing meta-analyses, during peer review, and when examining meta-scientific research questions. We examine what a machine readable hypothesis test should look like, and demonstrate the feasibility of machine readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.


2019 ◽  
Vol 35 (17) ◽  
pp. 2916-2923 ◽  
Author(s):  
John C Stansfield ◽  
Kellen G Cresswell ◽  
Mikhail G Dozmorov

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.


2013 ◽  
Vol 142 ◽  
pp. 37-46 ◽  
Author(s):  
Ian J. Stewart ◽  
Allan C. Hicks ◽  
Ian G. Taylor ◽  
James T. Thorson ◽  
Chantell Wetzel ◽  
...  

2018 ◽  
Author(s):  
Zhe Sun ◽  
Li Chen ◽  
Hongyi Xin ◽  
Qianhui Huang ◽  
Anthony R Cillo ◽  
...  

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.


2017 ◽  
Author(s):  
Roland Eichinger ◽  
Gary Shaffer ◽  
Nelson Albarrán ◽  
Maisa Rojas ◽  
Fabrice Lambert

Abstract. Interactions between the land biosphere and the atmosphere play an important role for the Earth's carbon cycle and thus should be considered in studies of global carbon cycling and climate. Simple approaches are a useful first step in this direction but may not be applicable for certain climatic conditions. To improve the ability of the reduced-complexity Danish Center for Earth System Science (DCESS) Earth System Model DCESS to address cold climate conditions, we reformulated the model's land biosphere module by extending it to include three dynamically varying vegetation zones as well as a permafrost component. The vegetation zones are formulated by emulating the behavior of a complex land biosphere model. We show that with the new module, the size and timing of carbon exchanges between atmosphere and land are represented more realistically in cooling and warming experiments. In particular, we use the new module to address carbon cycling and climate change across the last glacial transition. Within the constraints provided by various proxy data records, we tune the DCESS model to a Last Glacial Maximum state and then conduct transient sensitivity experiments across the transition under the application of explicit transition functions for high latitude ocean exchange, atmospheric dust, and the land ice sheet extent. We compare simulated time evolutions of global mean temperature, pCO2, atmospheric and oceanic carbon isotopes as well as ocean dissolved oxygen concentrations with proxy data records. In this way we estimate the importance of different processes across the transition with emphasis on the role of land biosphere variations.


2016 ◽  
Author(s):  
Lara Stas ◽  
Felix D. Schönbrodt ◽  
Tom Loeys

Family research aims to explore family processes, but is often limited to the examination of unidirectional processes. As the behavior of one person has consequences that go beyond that one individual, Family functioning should be investigated in its full complexity. The Social Relations Model (SRM; Kenny & La Voie, 1984) is a conceptual and analytical model which can disentangle family data from a round-robin design at three different levels: the individual level (actor and partner effects), the dyadic level (relationship effects) and the family level (family effect). Its statistical complexity may however be a hurdle for family researchers. The user-friendly R package fSRM performs almost automatically those rather complex SRM analyses and introduces new possibilities for assessing differences between SRM-means and between SRM-variances, both within and between groups of families. Using family data on negative processes, different type of research questions are formulated and corresponding analyses with fSRM are presented.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruixue Hou ◽  
Lewis E. Tomalin ◽  
Mayte Suárez-Fariñas

Abstract Background Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates. Results We developed the cosinoRmixedeffects R package for modelling longitudinal periodic data using mixed-effects cosinor models. The model allows for covariates and interactions with the non-linear parameters MESOR, amplitude, and acrophase. To facilitate ease of use, the package utilizes the syntax and functions of the widely used emmeans package to obtain estimated marginal means and contrasts. Estimation and hypothesis testing involving the non-linear circadian parameters are carried out using bootstrapping. We illustrate the package functionality by modelling daily measurements of heart rate variability (HRV) collected among health care workers over several months. Differences in circadian patterns of HRV between genders, BMI, and during infection with SARS-CoV2 are evaluated to illustrate how to perform hypothesis testing. Conclusion cosinoRmixedeffects package provides the model fitting, estimation and hypothesis testing for the mixed-effects COSINOR model, for the linear and non-linear circadian parameters MESOR, amplitude and acrophase. The model accommodates factors with any number of categories, as well as complex interactions with circadian parameters and categorical factors.


2021 ◽  
Author(s):  
Odin Marc ◽  
Jens Turowski ◽  
Patrick Meunier

<div>The size of grains delivered to rivers by hillslopes processes is thought to be a key factor to better understand sediment transport, long-term erosion as well as sedimentary archives. Recently, models have been developed for the grain size distribution produced in soils, but they may be irrelevant to active orogens where high erosion rates on hillslopes are driven by landsliding. Still, until now relatively few studies have focused on measuring and explaining the variability of landslide grain size distributions.</div><div>Here we present grain size distribution obtained by the grid-by-number method on 17 recent landslide deposits in Taiwan, and we compare it to the geometrical and physical properties of the landslides, such as their width, area, rock-type and strength, drop height and estimated depth. All landslides occurred in slightly metamorphosed sedimentary units, except two which occurred in younger unmetamorphosed shales, with rock strength expected to be 3 to 10 times weaker from their metamorphosed counterparts. We found that 4 deposits displayed a strong grain size segregation on their deposit with grains at the toe (downslope) of the deposit 3 to 10 times coarser than the one at the apex. In 3 cases, we could also measure the grain size distribution inside the landslides that presented percentiles 3 to 10 times finer than the surface of their deposits. Both observations could be due to either kinetic sieving or deposit reworking after the landslide failure but we could not explain why only some deposits had a strong segregation.</div><div>Averaging this spatial variability we found the median grain size (D50) of the deposits to be strongly negatively correlated to drop height, scar width and depth. However, previous work suggests that regolith particlesvand bedrock blocks should become coarser with increasing depth (Cohen et al., 2010; Clarke and Burbank, 2011), opposite to our observation. Accounting for a model of regolith coarsening with depth, we found that the ratio of the original bedrock blocksize and the D50 was proportional to the potential energy of the landslide normalized to its bedrock strength. Thus the studied landslides agree well with the simple fragmentation model from Locat et al. (2006), even if it was calibrated on much larger and much stronger rock avalanches. This scaling may thus serve for future model of grain size transfer from hillslopes to river, trying to better understand landslide sediment evacuation and the coupling between hillslopes and river erosional dynamic.</div><div> </div><div>References:</div><div> <div> <div>Clarke, B. A. and Burbank, D. W.: Quantifying bedrock-fracture patterns within the shallow subsurface: Implications for rock mass strength, bedrock landslides, and erodibility, Journal of Geophysical Research: Earth Surface, 116(F4), F04009, , 2011.</div> <div>Cohen, S., Willgoose, G. and Hancock, G.: The mARM3D spatially distributed soil evolution model: Three-dimensional model framework and analysis of hillslope and landform responses, Journal of Geophysical Research: Earth Surface, 115(F4), , 2010.</div> <div>Locat, P., Couture, R., Leroueil, S., Locat, J. and Jaboyedoff, M.: Fragmentation energy in rock avalanches, Canadian Geotechnical Journal, 43(8), 830–851, , 2006.</div> </div> </div><div> </div>


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