Annalisa Occhipinti

• Doctor of Philosophy
• Lecturer at Teesside University

The aggregation of α-synuclein is a central neuropathological hallmark in neurodegenerative disorders known as Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. In the aggregation process, α-synuclein transitions from its native disordered/α-helical form to a β-sheet-rich structure, forming oligomers and protofibrils...

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration and neuropsychiatric abnormalities. Loss of full-length dystrophins is both necessary and sufficient to initiate DMD. These isoforms are expressed in the hippocampus, cerebral cortex (Dp427c), and cerebellar Purkinje cells (Dp427p). However, our understanding of t...

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson’s disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and...

A large body of research has been developed with the aim of assisting policymakers in setting ambitious and achievable environmental targets for the retrofit of current and future building types for energy-efficiency and in creating effective retrofit strategies to meet these targets. The aim of this research is to conduct a comprehensive study to...

Background Neonatal resuscitation airway training can be difficult as there is no feedback on the face mask technique. “JUNO” is a training respiratory function monitor that provides feedback on mask leak, ventilatory rate, and tidal volume. Objective To evaluate whether the use of the JUNO improves face mask ventilation techniques in manikin mode...

For structural engineers, existing surrogate models of buildings present challenges due to inadequate datasets, exclusion of significant input variables impacting nonlinear building response, and failure to consider uncertainties associated with input parameters. Moreover, there are no surrogate models for the prediction of both pushover and nonlin...

Motivation Multimodal deep-learning models can be used to obtain personalized survival predictions. However, the small size of most matched omics-imaging-clinical studies currently poses significant challenges to the development and application of such tools. Furthermore, the lack of interpretability makes it difficult to understand the biological...

Alterations in Dp71 expression, the most ubiquitous dystrophin isoform, have been associated with patient survival across tumours. Intriguingly, in certain malignancies, Dp71 acts as a tumour suppressor, while manifesting oncogenic properties in others. This diversity could be explained by the expression of two Dp71 splice variants encoding protein...

Traditionally, nonlinear time history analysis (NLTHA) is used to assess the performance of structures under future hazards which is necessary to develop effective disaster risk management strategies. However, this method is computationally intensive and not suitable for analyzing a large number of structures on a city-wide scale. Surrogate models...

Within the overarching theme of “Managing the Digital Transformation of Construction Industry” the 23rd International Conference on Construction Applications of Virtual Reality (CONVR 2023) presented 123 high-quality contributions on the topics of: Virtual and Augmented Reality (VR/AR), Building Information Modeling (BIM), Simulation and Automation...

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and ma...

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts—the effector cells of muscle growth and regeneration—are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the...

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded...

Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods fo...

Text-based communication is highly favoured as a communication mean, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam s, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text...

Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample s...

Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic chang...

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important...

Background Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa . However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applicat...

Background The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different grow...

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved ou...

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Co...

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression...

Author Summary Breast cancer is caused by genetic mutations leading to uncontrollable cell reproduction. During successive proliferations, the progenies of tumour cells acquire further mutations increasing their heterogeneity. Among the tumoural mutated cells, there are some which present specific markers of increased aggressiveness and resistance....

Recent studies in theoretical computer science have exploited new algorithms and methodologies based on statistical physics for investigating the structure and the properties of the Satisfiability (SAT) problem. We propose a characterization of the SAT problem as a physical system, using both quantum and classical statistical-physical models. We as...

This paper is concerned with the complex behavior arising in satisfiability problems. We present a new statistical physics-based characterization of the satisfiability problem. Specifically, we design an algorithm that is able to produce graphs starting from a k-SAT instance, in order to analyze them and show whether a Bose-Einstein condensation oc...

This paper is concerned with the complex behavior arising in satisfiability problems. We present a new statistical physics-based characterization of the satisfiability problem. Specifically, we design an algorithm that is able to produce graphs starting from a k-SAT instance, in order to analyze them and show whether a Bose–Einstein condensation oc...