Integration of biplanar X-ray, three-dimensional animation and particle simulation reveals details of human ‘track ontogeny’

The emergence of bipedalism had profound effects on human evolutionary history, but the evolution of locomotor patterns within the hominin clade remains poorly understood. Fossil tracks record in vivo behaviours of extinct hominins, and they offer great potential to reveal locomotor patterns at various times and places across the human fossil record. However, there is no consensus on how to interpret anatomical or biomechanical patterns from tracks due to limited knowledge of the complex foot–substrate interactions through which they are produced. Here, we implement engineering-based methods to understand human track formation with the ultimate goal of unlocking invaluable information on hominin locomotion from fossil tracks. We first developed biplanar X-ray and three-dimensional animation techniques that permit visualization of subsurface foot motion as tracks are produced, and that allow for direct comparisons of foot kinematics to final track morphology. We then applied the discrete element method to accurately simulate the process of human track formation, allowing for direct study of human track ontogeny. This window lets us observe how specific anatomical and/or kinematic variables shape human track morphology, and it offers a new avenue for robust hypothesis testing in order to infer patterns of foot anatomy and motion from fossil hominin tracks.

A framework to measure collaboration in a construction project

Collaboration amongst stakeholders in a construction project plays a significant role in managing and completing a project successfully. It specifically helps in interface management amongst the stakeholders. Among the various aspects of collaboration, there are two key factors that predominant. Firstly, the psychological factors that define a person as a natural collaborator, and secondly, the project-level enablers that determine a collaborative project. Therefore, in this study, two inductive theories are developed- one for psychological factors and another for project-level enablers of collaboration. This study aims to identify the key psychological factors and project enablers associated with collaboration and develop a conceptual framework to measure collaboration in a construction project. The workflow of the conceptual framework is developed in the first part of the research, and the input requirements are quantified. Robust hypothesis testing methodology is adopted to identify the key psychological factors and project enablers. Hypotheses testing yields three specific psychological factors for defining a person as a natural collaborator, and six enablers are essential for facilitating project collaboration. These results are used as input parameters in the derived conceptual framework to measure the level of collaboration in a construction project.

Value of multi-source dataset for hydrological catchment modeling

<p>Global meteorological and hydrological datasets have become increasingly available in the past few decades, marked by an increase in the number of large datasets, often including hundreds of catchments. These data sets bring two main advantages: the ability to perform hydrological modeling over a large number of catchments located in different hydroclimate characteristics, - which leads us to more robust hypothesis testing, and the ability to address the uncertainties related to the hydrological model input data. However, the full added value to hydrological modeling is not yet fully understood. The main questions surrounding the use of multi-source and large-scale datasets are related to how much value these datasets add to the performance of hydrological models. How different are these datasets, how accurate are they, and whether their use results in similar or rather different hydrological simulations? Other questions are how can we better combine them for improved predictions, and what is the average uncertainty of the input datasets in hydrological modeling? We aimed here to investigate better those issues using Brazilian catchments as study cases. The Brazilian hydrometeorological network has several issues to overcome, such as an undistributed spatial network resulting in data-scarce areas, a large amount of missing data, and the lack of standardized and transparent quality analysis. In this study, we used a national hydrometeorological dataset (CAMELS-BR) along with other several global forecast and reanalysis meteorological datasets, such as the CFSv2 and ECMWF, for the streamflow prediction using the data-driven model Long-Short Term Memory (LSTM). Initial results indicate that calibrating a recurrent neural network is clearly depending on the data source. Moreover, the tested global meteorological products are found to be suitable for hydrological modeling. The combination of different data sources in the hydrological model seems to be beneficial, especially in those areas where ground-level gauge stations are scarce.</p>

Detection of haplotype-dependent allele-specific DNA methylation in WGBS data

In heterozygous genomes, allele-specific measurements can reveal biologically significant differences in DNA methylation between homologous alleles associated with local changes in genetic sequence. Current approaches for detecting such events from whole-genome bisulfite sequencing (WGBS) data perform statistically independent marginal analysis at individual cytosine-phosphate-guanine (CpG) sites, thus ignoring correlations in the methylation state, or carry-out a joint statistical analysis of methylation patterns at four CpG sites producing unreliable statistical evidence. Here, we employ the one-dimensional Ising model of statistical physics and develop a method for detecting allele-specific methylation (ASM) events within segments of DNA containing clusters of linked single-nucleotide polymorphisms (SNPs), called haplotypes. Comparisons with existing approaches using simulated and real WGBS data show that our method provides an improved fit to data, especially when considering large haplotypes. Importantly, the method employs robust hypothesis testing for detecting statistically significant imbalances in mean methylation level and methylation entropy, as well as for identifying haplotypes for which the genetic variant carries significant information about the methylation state. As such, our ASM analysis approach can potentially lead to biological discoveries with important implications for the genetics of complex human diseases.