The systemic functional grammar of Chinese nominal groups: a text-based approach

For the past two decades, great efforts have been made in the systemic functional description of English and many other languages including Chinese at the clause level. However, very few descriptive work has been done at the group/phrase level. As an attempt to fill in the gap, this thesis extends the lexicogrammatical study to the level of nominal groups, focusing on Mandarin Chinese in particular. Drawing upon a corpus of 180 quality texts of different text types, the investigation is heavily text-based: the nominal groups are observed in the environment of clauses, sentences, paragraphs and texts in contexts. The description of Chinese nominal groups is unfolded on the basis of Halliday’s metafunctional model, exploring the metafunctional resources that Chinese nominal groups provide in realising the three strands of meaning: ideational (including logical and experiential), interpersonal and textual meanings. Through the investigation of each metafunction, some important systems within the nominal group are presented, which provide selections in realising the metafunctional meanings. In terms of logical metafunction, the system of MODIFICATION is presented. In terms of experiential metafunction, the systems presented include THING TYPE, CLASSIFICATION, EPITHESIS, QUALIFICATION, and MEASURE. In terms of interpersonal metafunction, the system of PERSON is introduced. And finally, in terms of textual metafunction, the exploration includes the system of NAMING, the system of IDENTIFICATION, and the system of NUMERATION. Major selections on each of these systems are discussed in detail, with examples demonstrating the potential lexicogrammatical choices. Apart from the exploration of the systems, another focus of the study is on the potential application of the findings to other areas. For this purpose, at the end of each metafunctional exploration, a case study involving different types of texts is presented, demonstrating the significance of nominal groups in their contributions to the metafunctional meanings. The case studies are designed to explore different issues in relation to the use of nominal groups, including the modification structure of the nominal groups and its relation with text types (in the logical exploration), the experiential environment of Thing type choices in different types of texts (in the experiential exploration), the choice of nominal groups in enacting different attitudes (in the interpersonal exploration), and challenges of cohesion and coherence in machine translation presented by nominal groups (in the textual exploration). Through in-depth metafunctional exploration of the nominal gorups, as well as illustrative case studies, this research is expected to contribute to the development of language typology in Systemic Functional Linguistics, as well as other areas including delicate discourse analysis, the study of Chinese language, and machine translation research

Sequential memory retrieval in asymptomatic and depressive states.

A: Retrieval performance for the three cases A|A, A|D, D|D (indicated by color) at different levels of pattern separation σa (left to right) and retrieval noise σn (top to bottom). B: The duration of depressive episode affects the retrieval performance of A|D and D|D. Duration is measured by the fraction of memories stored in the depressive episode k (Eq 12). C: Increasing the number of stored sequences negatively impacts the retrieval performance in all cases, while the difference are preserved. D: Increasing the dimensionality of the pattern separation vector, up to a certain point, increases the difference between the A|A and the other cases. Values in B, C and D are calculated based on the 30th element in the sequence (σa = 1, σn = 0.1). For A,B,C: a:2-D; for A,C,D: k = 0.9; for A,B,D: 200 stored sequences in both asymptomatic and depressive state respectively.</p

Probability of incorrect jumps and sequence divergence.

A:left, probability of incorrect jumps between sequences (pb); right, probability of incorrect jumps within sequences (pw). B: Sequence divergence. For A, B: a:2-D, k = 0.2, 200 stored sequences.</p

LncRNA OSER1-AS1 regulates the inflammation and apoptosis of rheumatoid arthritis fibroblast like synoviocytes via regulating miR-1298-5p/E2F1 axis

It has been reported that long noncoding RNAs (LncRNAs) take part in the progression and occurrence of rheumatoid arthritis (RA). The current work aimed to dig the effect of lncRNA OSER1-AS1 on RA and the associated mechanism. Quantitative real-time polymerase chain reaction (qRT-PCR) was made to decide that OSER1-AS1 was significantly lowly expressed in synovial tissue and serum of RA patients, which was consistent in RA-FLSs cell lines. The result of ROC curve indicated that OSER1-AS1 could be a diagnostic biomarker for RA patients. Cell Counting Kit-8 assay (CCK-8), EdU staining and flow cytometry were performed to explore the effect of OSER1-AS1 on RA-FLSs in vitro. Relative levels of interleukin-1 (IL-1), interleukin-6 (IL-6), matrix metalloproteinases-3 (MMP-3) were detected by ELISA and the result displayed that overexpression of OSER1-AS1 inhibited RA-induced inflammatory production of IL-1, IL-6 and MMP3. Bioinformatics analysis, luciferase reporter, RNA immunoprecipitation assays (RIP) and RNA pull-down assay were conducted to confirm the binding between microRNA-1298-5p (miR-1298-5p) and OSER1-AS1 or E2F transcription factor 1 (E2F1). Mechanistically, OSER1-AS1 serves as a competing endogenous (ceRNA) in RA-FLSs through the sponge of miR-1298-5p and increase in the expression of E2F1. Further restoration experiments revealed that miR-1298-5p mimics and E2F1 silencing could partially reverse the inhibiting effect of OSER1-AS1 overexpression on propagation and apoptosis in RA-FLSs. The results illustrated the biological mechanism of OSER1-AS1/miR-1298-59/E2F1 axis in RA progression. The outcomes indicated that OSER1-AS1 might be adopted as a hopeful diagnostic and therapeutic objective for RA.</p

Illustration of the role of adult neurogenesis in the dentate gyrus.

<p>Top: Schematic of three stored sequences in the memory model, where the first elements in sequences 2 and <i>i</i> are similiar to each other. A: Without adult neurogenesis, the memory patterns are located in close proximity to each other in the memory space. B: In the asymptomatic state with a normal rate of adult neurogenesis, the augmentation with distinct pattern separation vectors distributes the sequences along an additional dimension in memory space. C: In the depressive state, the new sequence (<i>i</i>) is stored by re-using a pattern separation vector that had been assigned to a memory stored in a preceding asymptomatic state, based on the similarity of their first patterns. As a result, the two memory sequences, 2 and <i>i</i>, are more likely to interfere during retrieval.</p

Schematic of the episodic memory model.

<p>A. The relationship between systems involved episodic memory. B. Example of the input stimuli. Top: 300 × 300 black-and-white pixels; bottom: pattern scaled down to 30 × 30 greyscale pixels. C. Hierarchical network of slow feature analysis (SFA) as a model of the semantic system. The dots in each layer symbolize SFA nodes. The grey patches indicate the receptive field of each node, partially overlapping with the neighbouring nodes’ receptive fields. As an ensemble nodes in a given layer cover the full input space. Each node performs a number of processing steps as visualized on the right hand side. The activity in the top layer is taken as the output of the semantic system in our memory model. D. Example of the output of the semantic representation layer. The object in the input sequence <i>i</i> moves along the trajectory (yellow arrow) and rotates by 360 degrees (indicated by black arrows). Shown on the right are the four slowest features calculated by the SFA-network. The feature values at time <i>t</i>, <b>y</b>_{<i>i</i>,<i>t</i>} (dashed line), form a semantic (more abstract) representation of the input. E. Sequence storage network (see main text in Methods for details).</p

Pattern separation improves episodic memory retrieval.

A: Example performance of single-pattern retrieval across different level of retrieval noise (raw data). B: Distribution of the distance between cued and retrieved patterns at different levels of pattern separation σa (only for the data within the dashed rectangle in A) as indicated by different colors. The legend is given in panel C, the reference σa = 0 is shown in dark blue. C: Average performance of single-pattern retrieval as a function of retrieval noise. D: Retrieval error for retrieval of sequences at different levels of σa (100 stored sequences, σn = 0.1, a: 2-D).</p

Three scenarios of memory storage and retrieval in the model.

<p>The rate of adult neurogenesis (AN) is normal in the asymptomatic state and reduced in the depressive state. The origin of the arrow indicates during which state the memory was stored, and the termination of the arrow indicates during which state the memory is retrieved. A|A: memories stored and retrieved in the asymptomatic state; A|D: memories stored in the asymptomatic state and retrieved in the depressive state; D|D: memories stored and retrieved in the depressive state.</p

Retrieval performance with “pattern separation” in the sensory system.

<p>A: Example of the manipulated input patterns. Top: same pattern as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198406#pone.0198406.g001" target="_blank">Fig 1B</a>, but with 5% pixels flipped (300 × 300 pixels); Bottom: the scaled version. B: Retrieval error as a function of the fraction of randomly flipped pixels in the input image (<i>σ</i>_<i>n</i> = 0.2, 200 stored sequences). Dashed curve: retrieval performance of the model with neurogenesis (<i>σ</i>_<i>a</i> = 1) for comparison. C: With the same amount of noise in the input (1% flipped pixels), retrieval error increases monotonically with increasing retrieval noise <i>σ</i>_<i>n</i>. D: The difference between the retrieval error for original patterns and that for noisy input pattern gradually increases with input noise (<i>σ</i>_<i>n</i> = 0.2). Values in C and D are drawn from the 30^th element in the sequence.</p

Augmentation with pattern separation vector leads to pattern separation.

<p>Left: The distance between pairs of augmented patterns (<i>D</i>_<i>a</i>), i.e., containing the pattern separation vectors, against the distance between pairs of original patterns (<i>D</i>_<i>t</i>). A curve above the diagonal means that the augmented vectors and more dissimilar than the original vectors, indicative of pattern separation. Right: Same data as left panel, but plotted to emphasize pattern separation (= <i>D</i>_<i>a</i> − <i>D</i>_<i>t</i>).</p