«Литературные воспоминания» Д. В. Григоровича: аксиология и сюжетология

Referring to the memoirs of Dmitry Grigorovich, this article considers the general principles of axiology and plot of the memoir text. A contemporary of Leo Tolstoy and Fyodor Dostoevsky, Grigorovich became the “last chronicler” of the era of the 1840s. In this article, the author attempts to read Literary Memoirs as a work of fiction (novel), establishing typological connections between everyday sketches and essay-like sketches and plot situations that have a literary genesis.First, the author examines the phenomenon of Dmitry Grigorovich’s creative work in the axiology aspects. It is demonstrated that the symbolic capital of the writer’s name, despite the authority and significance of some of his works, was lost by the end of the nineteenth century. In this regard, the main intention of “literary recollections” is understood as an attempt to correct the past (represented by the narratives of Ivan Panaev, Avdotya Panaeva, and Pavel Annenkov), having experienced it in the present, i.e., through writing and auto-description.The main part of the article is devoted to the interpretation of plot models of memoirs and the principles of selection of historical figures. Often this selection itself (the choice of a publicist or a memoirist, what to write about and what to keep silent about) contains significant resources for auto-commentary. For example, in the description of childhood experience, referring the reader to the novels of Charles Dickens, Gustave Flaubert, and John Greenwood, one can trace “heraldic” features that frame the writer’s experience. Particular attention is paid to the figures central to the memoirs — Fyodor Dostoevsky and Alexander Dumas, embodying the specific features of the national writer and genius, with an individual portrait of Grigorovich himself built between them.На материале воспоминаний Д. В. Григоровича, ставшего «последним летописцем» эпохи «замечательного десятилетия» XIX в., рассматриваются общие принципы аксиологии и сюжетосложения мемуарного текста. Предпринята попытка прочитать «Литературные воспоминания» как художественное произведение (роман карьеры и роман воспитания), установив типологические связи между бытовыми и очерковыми зарисовками и сюжетными ситуациями, имеющими литературный генезис.В начале статьи исследуется феномен творчества Григоровича в аксиологическом аспекте. Показано, что символический капитал имени писателя, несмотря на авторитет и значение некоторых его произведений, был утрачен к концу XIX в. В связи с этим основная интенция «литературных воспоминаний» понимается как попытка скорректировать прошлое (представленное нарративами И. И. Панаева, А. Я. Панаевой, П. В. Анненкова), пережив его в настоящем — через письмо и автоописание.Основная часть статьи посвящена интерпретации сюжетных моделей воспоминаний и принципам отбора исторических фигур. Часто в самом этом отборе (выбор публициста или мемуариста, о чем писать и о чем умолчать) заключены значимые ресурсы для автокомментария. Так, например, в описании детского опыта, отсылающего читателя к романам Ч. Диккенса, Г. Флобера и Дж. Гринвуда, прослеживаются «геральдические» черты, обрамляющие опыт писателя в целом. Особое внимание уделено центральным для воспоминаний фигурам Ф. М. Достоевского и А. Дюма, в которых воплотились специфические черты национального писателя и гения, между которыми выстраивается индивидуальный портрет самого Григоровича

The Exhaustive Particle =ok in Hill Mari and Beyond

The paper examines the semantics and distribution of the polyfunctional Hill Mari focus particle =ok. We describe two interpretations of =ok ­possible on a wide range of hosts: the exhaustive use and the counteradditive use; besides, we consider several uses that are only possible with a lexically or semantically conditioned set of entities. We argue that =ok falls into a class of devices with not-at-issue exhaustive inferences, along with the English it-cleft and some other cross-linguistic counterparts. We discuss the implications that the Hill Mari data have for the typology of this class of constructions: Hill Mari =ok suggests that discourse givenness of the denotation of the focus constituent is an important dimension along which such elements vary across languages. Besides, in this paper we draw an areal comparison of the Hill Mari =ok with its counterparts in the Volga-Kama languages: Meadow Mari, Chuvash, Tatar, Bashkir, and Udmurt. Although the origin and the general set of readings are the same, the ­syntactic behavior of =okâs counterparts varies significantly

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available a

The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference

Maximum likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103, respectively, results in an average tree inference speedup of 1.9 ± 0.6 on Data collection 1, 1.8 ± 1.1 on Data collection 2, and 1.9 ± 0.8 on Data collection 2 for the RAxML-NG bootstrap compared to the runtime under the current default setting. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed.European Research Council | Ref. ERC-617457- PHYLOCANCERAgencia Estatal de Investigación | Ref. PID2019-106247GB-I00Fundação para a Ciência e a Tecnologia | Ref. PTDC/BIA-EVL/32030/2017Xunta de Galici

Exploring parallel MPI fault tolerance mechanisms for phylogenetic inference with RAxML-NG

Motivation Phylgenetic trees are now routinely inferred on large scale high performance computing systems with thousands of cores as the parallel scalability of phylogenetic inference tools has improved over the past years to cope with the molecular data avalanche. Thus, the parallel fault tolerance of phylogenetic inference tools has become a relevant challenge. To this end, we explore parallel fault tolerance mechanisms and algorithms, the software modifications required and the performance penalties induced via enabling parallel fault tolerance by example of RAxML-NG, the successor of the widely used RAxML tool for maximum likelihood-based phylogenetic tree inference. Results We find that the slowdown induced by the necessary additional recovery mechanisms in RAxML-NG is on average 1.00 ± 0.04. The overall slowdown by using these recovery mechanisms in conjunction with a fault-tolerant Message Passing Interface implementation amounts to on average 1.7 ± 0.6 for large empirical datasets. Via failure simulations, we show that RAxML-NG can successfully recover from multiple simultaneous failures, subsequent failures, failures during recovery and failures during checkpointing. Recoveries are automatic and transparent to the user

Phylogeny-aware identification and correction of taxonomically mislabeled sequences

Molecular sequences in public databases are mostly annotated by the submitting authors without further validation. This procedure can generate erroneous taxonomic sequence labels. Mislabeled sequences are hard to identify, and they can induce downstream errors because new sequences are typically annotated using existing ones. Furthermore, taxonomic mislabelings in reference sequence databases can bias metagenetic studies which rely on the taxonomy. Despite significant efforts to improve the quality of taxonomic annotations, the curation rate is low because of the labor-intensive manual curation process. Here, we present SATIVA, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution. We use the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal, and automatically propose a corrected taxonomic classification for those. Using simulated data, we show that our method attains high accuracy for identification (96.9% sensitivity/91.7% precision) as well as correction (94.9% sensitivity/89.9% precision) of mislabels. Furthermore, an analysis of four widely used microbial 16S reference databases (Greengenes, LTP, RDP and SILVA) indicates that they currently contain between 0.2% and 2.5% mislabels. Finally, we use SATIVA to perform an in-depth evaluation of alternative taxonomies for Cyanobacteria. SATIVA is freely available at https://github.com/amkozlov/sativa