Localised Dosing and Nanodetection Using a Novel Scanning Ion Conductance Microscope and Its Application to Alzheimer's Disease

Scanning ion conductance microscopy (SICM) is a technique for non-contact topographic imaging. In this thesis, a biophysical investigation into Alzheimer's Disease (AD) was carried, with toxic oligomers dosed locally and quantitatively on to single astrocytes using SICM and simultaneously monitoring the response of the target cell. Examination of the effectiveness of antibodies that bind to Abeta or alpha-synuclein (Asyn)peptides depends on the measurement of oligomer-induced abnormal calcium homeostasis in single astrocytes. The method was shown to work at physiological concentrations of oligomers. A series of experiments measuring the reduction in calcium inux in mixtures of antibodies and cerebrospinal fluid (CSF) of AD patients suggested that the binding to co-oligomers composed of Abeta and Asyn may be crucial in the treatment of AD. Furthermore, it may be beneficial to test antibodies before the clinical trial using this assay. The mechanism of this entry of calcium is hypothesised to be the result of the formation of oligomer-induced transient pores in the cell membrane. To verify this hypothesis, a new SICM instrument was built with two nanopipettes; one for dosing and one for detection of the adenosine triphosphate (ATP) release from these pores. A variety of different ATP sensors were made. The best had a sensitivity of 10 micro molar and works as a hexokinase-cofunctioned electrolyte-gated organic field-effect-transistor. However no statistically significant results for ATP release have been obtained in the experiments performed to date. Overall this thesis describes new biophysical methods to study the effect of protein aggregates on live cells and the effectiveness of potential therapies, such as antibodies and nanobodies, to reduce these aggregate induced effects. It can be applied to synthetic aggregates of Abeta or the aggregates present in human CSF.Taiwan-Cambridge Scholarshi

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

Large language models (LMs) have exhibited superior in-context learning (ICL) ability to adopt to target tasks by prompting with a few input-output demonstrations. Towards better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such a setting is not aligned with real-world practices, as end-users usually query LMs without accesses to demonstration pools. Inspired by evidence suggesting LMs' zero-shot capabilities are underrated, and the role of demonstrations are primarily for exposing models' intrinsic functionalities, we introduce Self-ICL, a simple framework for zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we construct pseudo-demonstrations from pseudo-input-label pairs, and perform ICL for the test input. Evaluation on BIG-Bench Hard shows Self-ICL steadily surpasses zero-shot and zero-shot chain-of-thought baselines on head-to-head and all-task average performance. Our findings suggest the possibility to bootstrap LMs' intrinsic capabilities towards better zero-shot performance.Comment: Work in progres

Large Language Models Perform Diagnostic Reasoning

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures

Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation

In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fidelity-Enriched Contrastive Search (FECS), which augments the contrastive search framework with context-aware regularization terms. FECS promotes tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. We demonstrate its effectiveness across two tasks prone to hallucination: abstractive summarization and dialogue generation. Results show that FECS consistently enhances faithfulness across various language model sizes while maintaining output diversity comparable to well-performing decoding algorithms.Comment: Accepted as a short paper at EMNLP 202

Biomass Processing for Biofuels, Bioenergy and Chemicals

Biomass can be used to produce renewable electricity, thermal energy, transportation fuels (biofuels), and high-value functional chemicals. As an energy source, biomass can be used either directly via combustion to produce heat or indirectly after it is converted to one of many forms of bioenergy and biofuel via thermochemical or biochemical pathways. The conversion of biomass can be achieved using various advanced methods, which are broadly classified into thermochemical conversion, biochemical conversion, electrochemical conversion, and so on. Advanced development technologies and processes are able to convert biomass into alternative energy sources in solid (e.g., charcoal, biochar, and RDF), liquid (biodiesel, algae biofuel, bioethanol, and pyrolysis and liquefaction bio-oils), and gaseous (e.g., biogas, syngas, and biohydrogen) forms. Because of the merits of biomass energy for environmental sustainability, biofuel and bioenergy technologies play a crucial role in renewable energy development and the replacement of chemicals by highly functional biomass. This book provides a comprehensive overview and in-depth technical research addressing recent progress in biomass conversion processes. It also covers studies on advanced techniques and methods for bioenergy and biofuel production