Translating poetic metaphor: explorations of the processes of translating

This thesis aims to explore the processes of translating by focusing on the translating of poetic metaphor. The methodology used is the application of George Lakoff's theory of conceptual metaphor to two case studies, in which problems of translating will be identified, and a theoretical conclusion will be formulated. The Introduction sets out the author's basic assumptions on the process of translating, the cognitive approach to metaphor, and the adoption of Lakoff's cognitive models of metaphor in the following case studies. Part I deals with the translating of metaphors of sickness in Shakespeare's Hamlet. Chapter one attempts to construct cognitive models of sickness as seen in contemporary English against which concepts of sickness in the Elizabethan age are compared. Chapter two undertakes a detailed examination of selected Chinese translations of metaphors of sickness in Hamlet organized in accordance with the cognitive models identified earlier. Chapter three draws preliminary conclusions on the translatability of basic metaphors common to English and Chinese and the difficulties encountered in others, which can be traced to cosmological differences between the two cultures. Part II studies metaphors of love in Sylvia Plath's poetry. Chapter four presents Plath's model of love on the basis of Zoltán Kövecses' model, and discusses its conflicts with traditional Chinese concepts of love. Chapter five analyses problems involved in Chinese translations, mainly of the 'perverted' model of love in Plath's poetry. A preliminary conclusion reached in chapter six points to cultural incoherence as the main obstacle in the translating of her innovative metaphors. After reviewing current opinions on the translation of metaphor, the author proposes a model of the translating of poetic metaphor in the hope that the findings from the case studies may contribute towards a general theory

Early Life Relict Feature in Peptide Mass Distribution

Molecular mass of a biomolecule is characterized in mass spectroscopy by the monoisitopic mass M~mono~ and the average isotopic mass M~av~. We found that peptide masses mapped on a plane made by two parameters derived from M~mono~ and M~av~ form a peculiar global feature in form of a band-gap 5-7 ppm wide stretching across the whole peptide galaxy, with a narrow (FWHM 0.2 ppm) line in the centre. The a priori probability of such a feature to emerge by chance is less than 1:100. Peptides contributing to the central line have elemental compositions following the rules S=0; Z = (2C - N - H)/2 =0, which nine out of 20 amino acid residues satisfy. The relative abundances of amino acids in the peptides contributing to the central line correlate with the consensus order of emergence of these amino acids, with ancient amino acids being overrepresented in on-line peptides. Thus the central line is a relic of ancient life, and likely a signature of its emergence in abiotic synthesis. The linear correlation between M~av~ and M~mono~ reduces the complexity of polypeptide molecules, which may have increased the rate of their abiotic production. This, in turn may have influenced the selection of these amino acid residues for terrestrial life. Assuming the line feature is not spurious, life has emerged from elements with isotopic abundances very close to terrestrial levels, which rules out most of the Galaxy

Measurement of collective flow in heavy ion collisions using particle pair correlations

We present a new type of flow analysis, based on a particle-pair correlation function, in which there is no need for an event-by-event determination of the reaction plane. Consequently, the need to correct for dispersion in an estimated reaction plane does not arise. Our method also offers the option to avoid any influence from particle misidentification. Using this method, streamer chamber data for collisions of Ar+KCl and Ar+BaI2 at 1.2 GeV/nucleon are compared with predictions of a nuclear transport model

Dynamics of Neural Networks with Continuous Attractors

We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.Comment: 6 pages, 7 figures with 4 caption

Measurement of teicoplanin by liquid chromatography-tandem mass spectrometry:development of a novel method

Teicoplanin is an antibiotic used for the treatment of endocarditis, osteomyelitis, septic arthritis and methicillin-resistant Staphylococcus aureus. Teicoplanin is emerging as a suitable alternative antibiotic to vancomycin, where their trough serum levels are monitored by immunoassay routinely. This is the first report detailing the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for measuring teicoplanin in patients' serum

Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning, and may serve as substrates for neural systems manipulating temporal information on relevant time scales. The present study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks (CANNs) and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors-the network that is initially being stimulated to an active state decays to a silent state very slowly on the time scale of STD rather than on the time scale of neural signaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.Comment: 40 pages, 17 figure