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\"This book develops a philosophical structure for historical explanation that resolves disputes about the scientific status of history that have persisted since the nineteenth century. It does this by showing why historical explanations must take the form of a narrative and by making their logic explicit. The books formulates a unique positive account of the logic of narrative explanations. This logic reveals how the rational evaluation of narrative explanation becomes possible. The book also develops a nonrealist (irrealist) metaphysics and epistemology of history--that is, it argues that there exists no one fixed past but many pasts. It includes a novel reading of Kuhn's The Structure of Scientific Revolutions, displaying how Kuhn offers a narrative explanation of theory change in science. The book situates narrative explanations within a naturalistic framework. Naturalism regards all canons of rational inquiry as evolving and contingent. The book also argues for the general philosophical significance of historical explanation. The first four chapters defuse methodological and the metaphysical objections to narrative explanations. The final three chapters explore how narrative explanations relate to other sciences. It will be of interest to researchers in historiography, theory of history, philosophy of history, philosophy of science, philosophy of social science, and epistemology\"-- Provided by publisher.

Background At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi .

\"In the midst of Trump's attacks on the media, comes this look at the rigorous, independent reporting of the year's most underreported news stories\"-- Provided by publisher.

Deep-genome and single-cell sequencing analyses of patient-derived breast cancer xenografts reveal extensive, dynamic and reproducible changes in intra-tumoral mutational clonal composition on engraftment and serial propagation. Clonal evolution in xenograft tumours Xenograft transplantation of primary human cancer cells into mice provides valuable models in which to study mechanisms underlying tumorigenesis, drug response and resistance. This study demonstrates that clonal evolution resembling that seen in human tumours also occurs on engraftment and during subsequent passaging of breast tumours in immunodeficient mice. In addition, similar clonal expansion patterns emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. These findings suggest that patient-derived xenografts may be useful for studying patient-specific tumour characteristics such as the response to drugs tailored to specific genomic alterations. Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution 1 , 2 , underpinning important emergent features such as drug resistance and metastasis 3 , 4 , 5 , 6 , 7 . Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours 8 , 9 , 10 . However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

The hierarchical Bayesian model identifies and quantifies clonal populations in tumors from deep-sequenced somatic mutations. We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

For decades, investigators have studied the interaction of Mycobacterium tuberculosis (Mtb) with macrophages, which serve as a major cellular niche for the bacilli. Because Mtb are prone to aggregation, investigators rely on varied methods to disaggregate the bacteria for these studies. Here, we examined the impact of routinely used preparation methods on bacterial cell envelope integrity, macrophage inflammatory responses, and intracellular Mtb survival. We found that both gentle sonication and filtering damaged the mycobacterial cell envelope and markedly impacted the outcome of infections in mouse bone marrow-derived macrophages. Unexpectedly, sonicated bacilli were hyperinflammatory, eliciting dramatically higher TLR2-dependent gene expression and elevated secretion of IL-1β and TNF-α. Despite evoking enhanced inflammatory responses, sonicated bacilli replicated normally in macrophages. In contrast, Mtb that had been passed through a filter induced little inflammatory response, and they were attenuated in macrophages. Previous work suggests that the mycobacterial cell envelope lipid, phthiocerol dimycocerosate (PDIM), dampens macrophage inflammatory responses to Mtb. However, we found that the impact of PDIM depended on the method used to prepare Mtb. In conclusion, widely used methodologies to disaggregate Mtb may introduce experimental artifacts in Mtb-host interaction studies, including alteration of host inflammatory signaling, intracellular bacterial survival, and interpretation of bacterial mutants.

Sohrab Shah, Samuel Aparicio and colleagues analyze whole genomes and single cells from ovarian cancers in the peritoneal cavity to establish patterns of disease spread. They determine the clonal relationships between multiple tumor sites and characterize the migratory potential of genomically diverse clones. We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.