Zoom In, Class Out: An Event Study on Publicly Traded Ed Tech Firm Valuations During COVID-19

This paper examines how publicly traded Ed Tech firms reacted to negative announcements regarding COVID-19. Using an event study method, I document how an international portfolio of Ed Tech firms react across multiple event windows. The results show that Ed Tech firms reacted positively to the announcement of the first US death and negatively to the World Health Organization’s declaration that COVID-19 was a pandemic. Additionally, differences in geographical location did not impact cumulative abnormal returns across event windows. Finally, firm-specific characteristics such as volatility and financial leverage had little or no significance on stock returns

Peptide Location Fingerprinting Reveals Tissue Region-Specific Differences in Protein Structures in an Ageing Human Organ

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-14, pub-electronic 2021-09-27Publication status: PublishedFunder: Manchester Institute for Collaborative Research on Ageing; Grant(s): n/aFunder: Walgreens Boots Alliance; Grant(s): n/aIn ageing tissues, long-lived extracellular matrix (ECM) proteins are susceptible to the accumulation of structural damage due to diverse mechanisms including glycation, oxidation and protease cleavage. Peptide location fingerprinting (PLF) is a new mass spectrometry (MS) analysis technique capable of identifying proteins exhibiting structural differences in complex proteomes. PLF applied to published young and aged intervertebral disc (IVD) MS datasets (posterior, lateral and anterior regions of the annulus fibrosus) identified 268 proteins with age-associated structural differences. For several ECM assemblies (collagens I, II and V and aggrecan), these differences were markedly conserved between degeneration-prone (posterior and lateral) and -resistant (anterior) regions. Significant differences in peptide yields, observed within collagen I α2, collagen II α1 and collagen V α1, were located within their triple-helical regions and/or cleaved C-terminal propeptides, indicating potential accumulation of damage and impaired maintenance. Several proteins (collagen V α1, collagen II α1 and aggrecan) also exhibited tissue region (lateral)-specific differences in structure between aged and young samples, suggesting that some ageing mechanisms may act locally within tissues. This study not only reveals possible age-associated differences in ECM protein structures which are tissue-region specific, but also highlights the ability of PLF as a proteomic tool to aid in biomarker discovery

Peptide location fingerprinting reveals modification‐associated biomarker candidates of ageing in human tissue proteomes

From Wiley via Jisc Publications RouterHistory: received 2020-10-08, rev-recd 2021-02-18, accepted 2021-03-15, pub-electronic 2021-04-08, pub-print 2021-05Article version: VoRPublication status: PublishedFunder: Walgreens Boots AllianceAbstract: Although dysfunctional protein homeostasis (proteostasis) is a key factor in many age‐related diseases, the untargeted identification of structurally modified proteins remains challenging. Peptide location fingerprinting is a proteomic analysis technique capable of identifying structural modification‐associated differences in mass spectrometry (MS) data sets of complex biological samples. A new webtool (Manchester Peptide Location Fingerprinter), applied to photoaged and intrinsically aged skin proteomes, can relatively quantify peptides and map statistically significant differences to regions within protein structures. New photoageing biomarker candidates were identified in multiple pathways including extracellular matrix organisation (collagens and proteoglycans), protein synthesis and folding (ribosomal proteins and TRiC complex subunits), cornification (keratins) and hemidesmosome assembly (plectin and integrin α6β4). Crucially, peptide location fingerprinting uniquely identified 120 protein biomarker candidates in the dermis and 71 in the epidermis which were modified as a consequence of photoageing but did not differ significantly in relative abundance (measured by MS1 ion intensity). By applying peptide location fingerprinting to published MS data sets, (identifying biomarker candidates including collagen V and versican in ageing tendon) we demonstrate the potential of the MPLF webtool for biomarker discovery

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.</jats:p

Predicting and characterising protein damage in the extracellular matrix

Chronic UVR exposure of human skin can result in photo-ageing which manifests both externally and internally (as remodelling of skin layers including the extracellular matrix-rich dermis). However, the intermittent nature of UVR exposure over a timescale of decades combined with the longevity of many structural dermal proteins makes the identification of dermal photo-ageing targets and mechanisms challenging. Over the past ten years work in our group has demonstrated that: (i) proteins which are rich in amino acid chromophores are susceptible to physiologically relevant doses of solar simulated radiation, (ii) this protein degradation is mediated primarily by the photodynamic production of reaction oxygen species and (iii) UVR-chromophore rich proteins are located in tissue regions where they may act as endogenous sunscreens. We have also shown that ECM proteases selectively degrade UVR-damaged assemblies in vitro and have developed new machine learning-based models to predict protease cleavage sites and hence relative protease susceptibilities in the human skin proteome. The recent development of peptide location fingerprinting applied to conventional mass spectrometry datasets has allowed the identification of novel candidate biomarkers of UVR-induced damage as a consequence of photo-ageing. For example, this approach is able to identify structure-associated modifications in collagen VI alpha chains, although collagen VI remodeling is not evident by conventional histological analysis. These methods and resources (skin proteome database, protein susceptibility prediction and peptide location fingerprinting analysis) are available for use at www.manchesterproteome.manchester.ac.uk. Key areas for future research include using peptide location fingerprinting to: (i) characterise the structural hallmarks of ageing and photo-ageing mechanisms across different phototypes and (ii) evaluate the efficacy of rejuvenating treatments against novel protein biomarkers