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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
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