Prospective Head and Neck Cancer Research: A Four‐Decade Bibliometric Perspective

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139977/1/onco0584.pd

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes

Benchmarking and Compliance in the UK Offshore Decommissioning Hazardous Waste Stream

The decommissioning sector of the United Kingdom offshore oil and gas industry is growing rapidly due to the number of ageing installations within United Kingdom waters. In line with current United Kingdom requirements, installations must be decontaminated from hazardous waste before any part can be reused or recycled. This hazardous waste must be handled, transported, and disposed of in a way that does not impact safety or the environment. This research project analyses the key issues associated with the handling of hazardous waste during the decommissioning of offshore installations with the United Kingdom Continental Shelf. A comprehensive literature review and analysis of decommissioning close-out reports was conducted to allow for the key issues to be identified. Expert judgements were sought and analysed using an analytical hierarchy process. This study emphases the need to improve the handling of hazardous materials during the decommissioning process. The clarity of legislative requirements, identification of hazardous materials and sharing of knowledge and experience are areas that require improvement to meet the increasingly stringent environmental and sustainability requirements

Sequential dependency for affective appraisal of food images

Abstract How we perceive the world is not solely determined by our experiences at a given moment in time, but also by what we have experienced in our immediate past. Here, we investigated whether such sequential effects influence the affective appraisal of food images. Participants from 16 different countries (N = 1278) watched a randomly presented sequence of 60 different food images and reported their affective appraisal of each image in terms of valence and arousal. For both measures, we conducted an inter-trial analysis, based on whether the rating on the preceding trial(s) was low or high. The analyses showed that valence and arousal ratings for a given food image are both assimilated towards the ratings on the previous trial (i.e., a positive serial dependence). For a given trial, the arousal rating depends on the arousal ratings up to three trials back. For valence, we observed a positive dependence for the immediately preceding trial only, while a negative (repulsive) dependence was present up to four trials back. These inter-trial effects were larger for males than for females, but independent of the participants’ BMI, age, and cultural background. The results of this exploratory study may be relevant for the design of websites of food delivery services and restaurant menus

Oldest known pantherine skull and evolution of the tiger

The tiger is one of the most iconic extant animals, and its origin and evolution have been intensely debated. Fossils attributable to extant pantherine species-lineages are less than 2 MYA and the earliest tiger fossils are from the Calabrian, Lower Pleistocene. Molecular studies predict a much younger age for the divergence of modern tiger subspecies at <100 KYA, although their cranial morphology is readily distinguishable, indicating that early Pleistocene tigers would likely have differed markedly anatomically from extant tigers. Such inferences are hampered by the fact that well-known fossil tiger material is middle to late Pleistocene in age. Here we describe a new species of pantherine cat from Longdan, Gansu Province, China, Panthera zdanskyi sp. nov. With an estimated age of 2.55–2.16 MYA it represents the oldest complete skull of a pantherine cat hitherto found. Although smaller, it appears morphologically to be surprisingly similar to modern tigers considering its age. Morphological, morphometric, and cladistic analyses are congruent in confirming its very close affinity to the tiger, and it may be regarded as the most primitive species of the tiger lineage, demonstrating the first unequivocal presence of a modern pantherine species-lineage in the basal stage of the Pleistocene (Gelasian; traditionally considered to be Late Pliocene). This find supports a north-central Chinese origin of the tiger lineage, and demonstrates that various parts of the cranium, mandible, and dentition evolved at different rates. An increase in size and a reduction in the relative size of parts of the dentition appear to have been prominent features of tiger evolution, whereas the distinctive cranial morphology of modern tigers was established very early in their evolutionary history. The evolutionary trend of increasing size in the tiger lineage is likely coupled to the evolution of its primary prey species

Dense sampling of ethnic groups within African countries reveals fine-scale genetic structure and extensive historical admixture

Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data have only been obtained from a small proportion of present-day ethnolinguistic groups. By analyzing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria, and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example, correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relate to the expansions of Bantu language-speaking peoples

Origins of cattle on Chirikof Island, Alaska, elucidated from genome-wide SNP genotypes

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Survival regression models with dependent Bayesian nonparametric priors

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum (1974) and on the Cox proportional hazards model of Kim and Lee (2003). The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates whilst allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data