The case of online trust

“The original publication is available at www.springerlink.com”. Copyright SpringerThis paper contributes to the debate on online trust addressing the problem of whether an online environment satisfies the necessary conditions for the emergence of trust. The paper defends the thesis that online environments can foster trust, and it does so in three steps. Firstly, the arguments proposed by the detractors of online trust are presented and analysed. Secondly, it is argued that trust can emerge in uncertain and risky environments and that it is possible to trust online identities when they are diachronic and sufficient data are available to assess their reputation. Finally, a definition of trust as a second-order property of first-order relation is endorsed in order to present a new definition of online trust. According to such a definition, online trust is an occurrence of trust that specifically qualifies the relation of communication ongoing among individuals in digital environments. On the basis of this analysis, the paper concludes by arguing that online trust promotes the emergence of social behaviours rewarding honest and transparent communications.Peer reviewe

Looking at COVID-19 from a Systems Biology Perspective

The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus\u2013human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus\u2013host interactions, and in the development of disease classifiers and tools for drug repurposing

Ligand-Based Regulation of Dynamics and Reactivity of Hemoproteins

Hemoproteins include several heme-binding proteins with distinct structure and function. The presence of the heme group confers specific reactivity and spectroscopic properties to hemoproteins. In this review, we provide an overview of five families of hemoproteins in terms of dynamics and reactivity. First, we describe how ligands modulate cooperativity and reactivity in globins, such as myoglobin and hemoglobin. Second, we move on to another family of hemoproteins devoted to electron transport, such as cytochromes. Later, we consider heme-based reactivity in hemopexin, the main heme-scavenging protein. Then, we focus on heme-albumin, a chronosteric hemoprotein with peculiar spectroscopic and enzymatic properties. Eventually, we analyze the reactivity and dynamics of the most recently discovered family of hemoproteins, i.e., nitrobindins

myTrustedCloud: Trusted cloud infrastructure for security-critical computation and data managment

Copyright @ 2012 IEEECloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

Scalable HPC & AI infrastructure for COVID-19 therapeutics

COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled

Enabling trade-offs between accuracy and computational cost: Adaptive algorithms to reduce time to clinical insight

The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. Quantifying the strength of these interactions (the so called 'binding affinity') is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient specific medicine. Recently, evidence from blind challenge predictions and retrospective validation studies has suggested that molecular dynamics (MD) can now achieve useful predictive accuracy (1 kcal/mol) This accuracy is sufficient to greatly accelerate hit to lead and lead optimization. To translate these advances in predictive accuracy so as to impact clinical and/or industrial decision making requires that binding free energy results must be turned around on reduced timescales without loss of accuracy. This demands advances in algorithms, scalable software systems, and intelligent and efficient utilization of supercomputing resources. This work is motivated by the real world problem of providing insight from drug candidate data on a time scale that is as short as possible. Specifically, we reproduce results from a collaborative project between UCL and GlaxoSmithKline to study a congeneric series of drug candidates binding to the BRD4 protein-inhibitors of which have shown promising preclinical efficacy in pathologies ranging from cancer to inflammation. We demonstrate the use of a framework called HTBAC, designed to support the aforementioned requirements of accurate and rapid drug binding affinity calculations. HTBAC facilitates the execution of the numbers of simulations while supporting the adaptive execution of algorithms. Furthermore, HTBAC enables the selection of simulation parameters during runtime which can, in principle, optimize the use of computational resources whilst producing results within a target uncertainty

Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers

The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.</p

An introduction to the Philosophy of Information

This book serves as the main reference for an undergraduate course on Philosophy of Information. The book is written to be accessible to the typical undergraduate student of Philosophy and does not require propaedeutic courses in Logic, Epistemology or Ethics. Each chapter includes a rich collection of references for the student interested in furthering her understanding of the topics reviewed in the book. The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers