Thesis

Interaction, Identity & Social Class

September 2019

Thesis for: PhD in Sociology - University of York
Advisor: Dr Merran Toerien; Dr Steph Lawler.

Authors:

Edward J. B. Holmes

Categories in Social Interaction

Book

Dec 2024

‘Looking after the least fortunate in our society’; Shared membership, commonsense, and morality as resources for identification between politicians and voters

Article

Apr 2024

The conversational rollercoaster: Conversation analysis and the public science of talk

Article

Full-text available

Jun 2018
DISCOURSE STUD

How does talk work, and can we engage the public in a dialogue about the scientific study of talk? This article presents a history, critical evaluation and empirical illustration of the public science of talk. We chart the public ethos of conversation analysis that treats talk as an inherently public phenomenon and its transcribed recordings as public data. We examine the inherent contradictions that conversation analysis is simultaneously obscure yet highly cited; it studies an object that people understand intuitively, yet routinely produces counter-intuitive findings about talk. We describe a novel methodology for engaging the public in a science exhibition event and show how our ‘conversational rollercoaster’ used live recording, transcription and public-led analysis to address the challenge of demonstrating how talk can become an informative object of scientific research. We conclude by encouraging researchers not only to engage in a public dialogue but also to find ways to actively engage people in taking a scientific approach to talk as a pervasive, structural feature of their everyday lives.

ResearchGate has not been able to resolve any references for this publication.

Article

Full-text available

Promising Drugs Targeting Specific Mechanisms of Deregulation in T Cell Lineage Acute Lymphoblastic...

April 2025 · Oncology and Therapy

T cell acute lymphoblastic leukemia (T-ALL) is a class of hematological malignancies predominantly affecting children, adolescents, and young adults, marked by aggressive behavior and poor clinical response, especially in relapsing cases. Advances in molecular biology for the last decade have led to the identification of potential targetable alterations, which should lead to the development of ... [Show full abstract] new therapies. T oncogenesis involves not only overexpression of some oncogenes, but also deregulation of some signaling pathways, epigenetic mechanisms, and apoptosis. The present review presents the promising therapeutic agents targeting these specific alterations involved in the development of T-ALL.

Preprint

Full-text available

Efficient identification of linear, parameter-varying, and nonlinear systems with noise models

April 2025

We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parameter-varying} (LPV), and nonlinear (NL) dynamics, along with rather general classes of noise models. Similar to the LTI case, we show that for this general class of model structures, including the NL case, the model ... [Show full abstract] dynamics can be separated into a deterministic process and a stochastic noise part, allowing to seamlessly tune the complexity of the combined model both in terms of nonlinearity and noise modeling. We parameterize the involved nonlinear functional relations by means of artificial neural-networks (ANNs), although alternative parametric nonlinear mappings can also be used. To estimate the resulting model structures, we optimize a prediction-error-based criterion using an efficient combination of a constrained quasi-Newton approach and automatic differentiation, achieving training times in the order of seconds compared to existing state-of-the-art ANN methods which may require hours for models of similar complexity. We formally establish the consistency guarantees for the proposed approach and demonstrate its superior estimation accuracy and computational efficiency on several benchmark LTI, LPV, and NL system identification problems.

Article

Full-text available

Disease candidate genes prediction using positive labeled and unlabeled instances

April 2025 · BMC Medical Genomics

Identifying disease genes and understanding their performance is critical in producing drugs for genetic diseases. Nowadays, laboratory approaches are not only used for disease gene identification but also using computational approaches like machine learning are becoming considerable for this purpose. In machine learning methods, researchers can only use two data types (disease genes and unknown ... [Show full abstract] genes) to predict disease candidate genes. Notably, there is no source for the negative data set. The proposed method is a two-step process: The first step is the extraction of reliable negative genes from a set of unlabeled genes by one-class learning and a filter based on distance indicators from known disease genes; this step is performed separately for each disease. The second step is the learning of a binary model using causing genes of each disease as a positive learning set and the reliable negative genes extracted from that disease. Each gene in the unlabeled gene's production and ranking step is assigned a normalized score using two filters and a learned model. Consequently, disease genes are predicted and ranked. The proposed method evaluation of various six diseases and Cancer class indicates better results than other studies.

Article

Full-text available

Development of an Automated Workflow for Screening the Assembly and Host–Guest Behavior of Metal‐Org...

April 2025 · Angewandte Chemie International Edition

Metal‐organic cages (MOCs) are a class of self‐assembled materials with promising applications in chemical purifications, sensing, and catalysis. Their potential is, however, hampered by challenges in the targeted design of MOCs with desirable properties. MOC discovery is thus often reliant on trial‐and‐error approaches and brute‐force manual screening, which are time‐consuming, costly, and ... [Show full abstract] material‐intensive. Translating the synthesis and property screening of MOCs to an automated workflow is therefore attractive, to both accelerate discovery and provide the datasets crucial for data‐led approaches to accelerate MOC discovery and to realize their targeted properties for specific applications. Here, an automated workflow for the streamlined assembly and property screening of MOCs was developed, incorporating automated high‐throughput screening of variables pertinent to MOC synthesis, data curation and automated analysis, and development of a host–guest assay to rapidly assess binding behavior. Computational modelling supplemented this automated experimental workflow for post priori rationalization of experimental outcomes. This study lays the groundwork for future large‐scale MOC screening: from a relatively modest screen of 24 precursor combinations under one set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host–guest behavior highlighted trends in binding and the identification of potential applications in molecular separations.