Variation in tooth crown morphology plays a crucial role in species diagnoses, phylogenetic inference, and the reconstruction of the evolutionary history of the primate clade. While a growing number ...of studies have identified developmental mechanisms linked to tooth size and cusp patterning in mammalian crown morphology, it is unclear (1) to what degree these are applicable across primates and (2) which additional developmental mechanisms should be recognized as playing important roles in odontogenesis. From detailed observations of lower molar enamel-dentine junction morphology from taxa representing the major primate clades, we outline multiple phylogenetic and developmental components responsible for crown patterning, and formulate a tooth crown morphology framework for the holistic interpretation of primate crown morphology. We suggest that adopting this framework is crucial for the characterization of tooth morphology in studies of dental development, discrete trait analysis, and systematics.
Molar crown configuration plays an important role in systematics, and functional and comparative morphology. In particular, the number of cusps on primate molars is often used to identify fossil ...species and infer their phylogenetic relationships. However, this variability deserves renewed consideration as a number of studies now highlight important developmental mechanisms that may be responsible for the presence of molar cusps in some mammalian taxa. Experimental studies of rodent molars suggest that cusps form under a morphodynamic, patterning cascade model of development (PCM) that involve the iterative formation of enamel knots. This model posits that the size, shape and location of the first-forming cusps determines the presence and positioning of later-forming cusps.
Here we test whether variation in accessory cusp presence in 13 Macaca fascicularis mandibular second molars (M2s) is consistent with predictions of the PCM. Using micro-CT, we imaged these M2s and employed geometric morphometrics to examine whether shape variation in the enamel-dentine junction (EDJ) correlates with accessory cusp presence.
We find that accessory cusp patterning in macaque M2s is broadly consistent with the PCM. Molars with accessory cusps were larger in size and possessed shorter relative cusp heights compared to molars without accessory cusps. Peripheral cusp formation was also associated with more centrally positioned primary cusps, as predicted by the PCM.
While these results demonstrate that a patterning cascade model is broadly appropriate for interpreting cusp variation in Macaca fascicularis molars, it does not explain all manifestations of accessory cusp expression in this sample.
•Accessory cusp patterning in macaque M2s is broadly consistent with a patterning cascade model of cusp development.•Molars with accessory cusps were larger in size and possessed shorter relative cusp heights.•Peripheral cusp formation was associated with more centrally positioned primary cusps.•The pattering cascade model does not, however, appear to explain all manifestations of accessory cusp expression.
Cusp patterning on living and extinct primate molar teeth plays a crucial role in species diagnoses, phylogenetic inference, and the reconstruction of the evolutionary history of the primate clade. ...These studies rely on a system of nomenclature that can accurately identify and distinguish between the various structures of the crown surface. However, studies at the enamel-dentine junction (EDJ) of some primate taxa have demonstrated a greater degree of cusp variation and expression at the crown surface than current systems of nomenclature allow. In this study, we review the current nomenclature and its applicability across all the major primate clades based on investigations of mandibular crown morphology at the enamel-dentine junction revealed through microtomography. From these observations, we reveal numerous new patterns of lower molar accessory cusp expression in primates. We highlight numerous discrepancies between the expected patterns of variation inferred from the current academic literature, and the new patterns of expected variation seen in this study. Based on the current issues associated with the crown nomenclature, and an incomplete understanding of the precise developmental processes associated with each individual crown feature, we introduce these structures within a conservative, non-homologous naming scheme that focuses on simple location-based categorisations. Until there is a better insight into the developmental and phylogenetic origin of these crown features, these categorisations are the most practical way of addressing these structures. Until then, we also suggest the cautious use of accessory cusps for studies of taxonomy and phylogeny.
Vital rates and cause of death for Māori on the island of Ruapuke are examined for the period 1844 to 1885. Natural decline is evident over the period, but is lower for later years. Infant mortality ...is higher for females. Cause of death data suggests the importance of both tuberculosis and periodic childhood epidemics for general mortality, as well as drowning for adult males.
Was sex-selective infanticide of girls prevalent amongst Māori in the first half of the 19th century? If so, why? Why might the practice have evolved and why did it disappear? What impact did it have ...on depopulation? A body of contemporary evidence by reliable observers suggests considerable sex-selective infanticide. Recorded motivations for female infanticide relate primarily to warfare, although other reasons are also advanced. Contemporary census data showing unbalanced Māori sex ratios to the detriment of females, especially children, provide corroborative quantitative evidence. Sex-selective infanticide probably arose concomitantly with war and resource pressures in the mid 16th century. While it contributed significantly to overall Māori death rates, there is no empirical evidence suggesting a musket-war related rise in sex-selective infanticide. Thus, it made no causal contribution to post-contact depopulation. However, its elimination from the mid-1830s contributed to ending depopulation by the 1890s.
Master the robust features of R parallel programming to accelerate your data science computations About This Book * Create R programs that exploit the computational capability of your cloud platforms ...and computers to the fullest * Become an expert in writing the most efficient and highest performance parallel algorithms in R * Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single- threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn * Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package * Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) * Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code * Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages * Build and extend a parallel R package (SPRINT) with your own MPI-based routines * Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL * Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them * Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.
Master the robust features of R parallel programming to accelerate your data science computations About This Book • Create R programs that exploit the computational capability of your cloud platforms ...and computers to the fullest • Become an expert in writing the most efficient and highest performance parallel algorithms in R • Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn • Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package • Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) • Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code • Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages • Build and extend a parallel R package (SPRINT) with your own MPI-based routines • Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL • Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them • Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.
The Covid-19 Level 4 lockdown represented an unprecedented and sudden shock to the New Zealand labour market. Using unique data collected during lockdown (n = 2002), this study examined the work ...circumstances of individuals and the economic shock in terms of income and job loss to both individuals and households. We found that the unemployment effectively doubled rising from 5.2% just prior to lockdown to 10.5% by week 3 of lockdown. Close to 44% of individuals lived in a household where members experienced job and/or income loss. While economic loss was widespread, some groups were harder hit, particularly those with lower incomes.
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