Разновидности Текстовой Динамики В Свете Феномена Признаковости В Повести Ф. М. Достоевского «Двойник» (Статья Первая)

В настоящей статье изучаются поэтические принципы и функции двух типов структурно-семантической логики, влияющей на возникновение текстовой динамики в «Двойнике». Парадоксально, что главная линия развития сводится к структуре высокой повторяемости рекуррентных / рекурсивных фигурирований стабильных нарративных единиц, мотивов, лексических элементов текста. Их постоянное возвращение (см. события, характерные черты персонажа, семантические признаки, компоненты устойчивых фразеологических выражений) создает впечатление модели неизменного мира, в котором не происходит никаких смен. Такой феномен можно условно назвать динамикой рекуррентности под знаком статичности. Транспозиционная динамика, с другой стороны, определяется в качестве второго направления текстового развертывания, формирующего механизм порождения смысла. Транспозиция в данной работе понимается в широком смысле, со специальным сосредоточением на 1) трансфигурации и проек\xD1\x86ии смысла путем сдвига от однородности семантических признаков к наделению означающих или означаемых новыми признаками (проблема референтности); 2) процессах семантической интеграции, касающейся включения мелких смысловых единиц в более крупные формации (семантический признак → мотив → литературный персонаж). Цель статьи состоит в выяснении взаимоотношения (своеобразной интеракции) двух типов динамики. Рассматриваемое смысловое образование, воплощаемое в минимальной единице моторной (моторно-словесной) «экспрессии» Голядкина, обладает двойной ориентацией, основанной на корреляции движения и остановки, которую В. B. Виноградов интерпретировал в своей знаменитой работе. С точки зрения объекта изучения в настоящей статье указанная нарративная и лингвистическая единица подвергается толкованию в качестве рекурсивной пары мотивов, обладающей особенной способностью перевоплощаться путем транспозиции. Применением метода «close reading» в пятой главе повести, где появляется двойник господина Голядкина, подробно продемонстрировано, как транспозиция действует в рамках процесса смысловых интеграций. Значение мотива врага претерпевает сдвиг от смысла человеческого врага к смыслу петербургской погоды и человеческой судьбы как новых субъектов. Они ассоциируются с мифологией и литературной культурой (интертексты), подводящими к толкованию личности Голядкина. Образ главного героя проецируется на его двойника многими семантическими признаками. «Тоскливая побежка» в смысле столкновения Голядкина с самим собой представляет его в качестве прозревающего героя, который как л\xD0\xB8тературный персонаж развивается постепенно, включением в этот процесс и образ двойника как транспозицию фигуры, также представляющей собой динамическое смысловое образование. Соотношение Голядкина и его двойника в аспекте проблемы первичности и вторичности поставлено также в контекст семиотики Пирса. The paper examines the poetic principles and functions of two sorts of structural-semantic logic influencing the emergence of textual dynamics in The Double. Paradoxically, the main developmental line consists in a highly repetitive structure of permanent reiteration of fixed narrative units, motifs, and lexical items in the text. Their constant recurrence (cf. events, character traits, semantic attributes, and elements of stable idiomatic phrases) creates the impression of a static world model in which no changes occur. This may be called recursive dynamics under the sign of a static state. Transpositional dynamics, on the other hand, can be regarded as the second direction of the textual developmental movement, producing the mechanism for meaning-generation. Transposition is interpreted broadly, in the paper, with a special accent on 1) meaning transfigurations and projections through the shift from the uniformity of semantic attributes to the acquiring of new attributes by the signifier or the signified (the problem of reference); 2) processes of semantic integration concerning the inclusion of smaller semantic units into larger ones (semantic attribute → motif → character figure). The purpose of the paper is to clarify the reciprocity (the special interaction) of the two types of dynamics. The semantic formation under scrutiny embodied by the smallest unit of Golyadkin’s motoric-verbal expression has a double orientation based on the correlation of motion and stop interpreted by V. Vinogradov in his famous study. From the perspective of the paper’s research object, this narrative and linguistic unit is examined as a recursive motif pair with a special capacity of transformation through transposition. It is demonstrated in detail, within the close reading of the fift h chapter, where Golyadkin’s double appears, how transposition works through a process of semantic integrations. The meaning of enemy is shift ed from human rivals to Petersburg weather and human fate as new subjects, associated with mythology and literary culture (intertexts), leading up to the interpretation of Golyadkin’s personality. The protagonist’s figure is projected upon his double through many semantic attributes. The “melancholy flight” as his encounter with himself presents him as an awakening hero, who as a character is developed gradually, including his double as his figure-transposition which also embodies a dynamic semantic pattern. The correlation of Golyadkin and his double in the context of firstness and secondness is also put into the context of Peircean semiotics.

Eukaryotic Box C/D methylation machinery has two non-symmetric protein assembly sites

Box C/D ribonucleoprotein complexes are RNA-guided methyltransferases that methylate the ribose 2’-OH of RNA. The central ‘guide RNA’ has box C and D motifs at its ends, which are crucial for activity. Archaeal guide RNAs have a second box C’/D’ motif pair that is also essential for function. This second motif is poorly conserved in eukaryotes and its function is uncertain. Conflicting literature data report that eukaryotic box C’/D’ motifs do or do not bind proteins specialized to recognize box C/D-motifs and are or are not important for function. Despite this uncertainty, the architecture of eukaryotic 2’-O-methylation enzymes is thought to be similar to that of their archaeal counterpart. Here, we use biochemistry, X-ray crystallography and mutant analysis to demonstrate the absence of functional box C’/D’ motifs in more than 80% of yeast guide RNAs. We conclude that eukaryotic Box C/D RNPs have two non-symmetric protein assembly sites and that their three-dimensional architecture differs from that of archaeal 2’-O-methylation enzymes.

Transcriptomics: Quantifying Non-Uniform Read Distribution Using MapReduce

RNA-seq is a high-throughput Next-sequencing technique for estimating the concentration of all transcripts in a transcriptome. The method involves complex preparatory and post-processing steps which can introduce bias, and the technique produces a large amount of data [7, 19]. Two important challenges in processing RNA-seq data are therefore the ability to process a vast amount of data, and methods to quantify the bias in public RNA-seq datasets. We describe a novel analysis method, based on analysing sequence motif correlations, that employs MapReduce on Apache Spark to quantify bias in Next-generation sequencing (NGS) data at the deep exon level. Our implementation is designed specifically for processing large datasets and allows for scalability and deployment on cloud service providers offering MapReduce. In investigating the wild and mutant organism types in the species D. melanogaster we have found that motifs with runs of Gs (or their complement) exhibit low motif-pair correlations in comparison with other motif-pairs. This is independent of the mean exon GC content in the wild type data, but there is a mild dependence in the mutant data. Hence, whilst both datasets show the same trends, there is however significant variation between the two samples.

Predicting CTCF-mediated chromatin loops using CTCF-MP

The three dimensional organization of chromosomes within the cell nucleus is highly regulated. It is known that CTCF is an important architectural protein to mediate long-range chromatin loops. Recent studies have shown that the majority of CTCF binding motif pairs at chromatin loop anchor regions are in convergent orientation. However, it remains unknown whether the genomic context at the sequence level can determine if a convergent CTCF motif pair is able to form chromatin loop. In this paper, we directly ask whether and what sequence-based features (other than the motif itself) may be important to establish CTCF-mediated chromatin loops. We found that motif conservation measured by “branch-of-origin” that accounts for motif turn-over in evolution is an important feature. We developed a new machine learning algorithm called CTCF-MP based on word2vec to demonstrate that sequence-based features alone have the capability to predict if a pair of convergent CTCF motifs would form a loop. Together with functional genomic signals from CTCF ChIP-seq and DNase-seq, CTCF-MP is able to make highly accurate predictions on whether a convergent CTCF motif pair would form a loop in a single cell type and also across different cell types. Our work represents an important step further to understand the sequence determinants that may guide the formation of complex chromatin architectures.

CLUSTERING-BASED APPROACH FOR PREDICTING MOTIF PAIRS FROM PROTEIN INTERACTION DATA

Predicting motif pairs from a set of protein sequences based on the protein–protein interaction data is an important, but difficult computational problem. Tan et al. proposed a solution to this problem. However, the scoring function (using χ2 testing) used in their approach is not adequate and their approach is also not scalable. It may take days to process a set of 5000 protein sequences with about 20,000 interactions. Later, Leung et al. proposed an improved scoring function and faster algorithms for solving the same problem. But, the model used in Leung et al. is complicated. The exact value of the scoring function is not easy to compute and an estimated value is used in practice. In this paper, we derive a better model to capture the significance of a given motif pair based on a clustering notion. We develop a fast heuristic algorithm to solve the problem. The algorithm is able to locate the correct motif pair in the yeast data set in about 45 minutes for 5000 protein sequences and 20,000 interactions. Moreover, we derive a lower bound result for the p-value of a motif pair in order for it to be distinguishable from random motif pairs. The lower bound result has been verified using simulated data sets. Availability: