Co-Management of COVID-19 and heart failure during the COVID-19 pandemic. lessons learned

The COVID pandemic has brought many new challenges worldwide, which has impacted on patients with chronic conditions. There is an increasing evidence base suggesting an interaction between chronic heart failure (HF) and COVID-19, and in turn the prognostic impact of co-existence of the two conditions. Patients with existing HF appear more prone to develop severe complications on contracting COVID-19, but the exact prevalence in patients with mild symptoms of COVID-19 not requiring hospital admission is poorly investigated. In addition, hospitalization rates for acute HF over the pandemic period appear reduced compared to previous periods. Several key issues remain rather unaddressed and, importantly, a specific algorithm focused on diagnostic differentiation between HF and acute respiratory distress syndrome, a severe complication of COVID-19, is still lacking. Furthermore, recent data suggests potential interaction existing between HF treatment and some anti-viral anti-inflammatory drugs prescribed during the infection, raising some doubts about a universal treatment strategy for all patients with COVID-19. With this manuscript, we aim to review the current literature in this field in light of growing understanding of COVID-19 in the setting of the HF population, its associated morbidity and mortality burden, and the impact on healthcare systems. We hope that this may stimulate a discussion to guarantee a better, more tailored delivery of care for patients with HF in the setting of concomitant COVID-19 infection

Higher-order nonlinear modes and bifurcation phenomena due to degenerate parametric four-wave mixing

We demonstrate that weak parametric interaction of a fundamental beam with its third harmonic field in Kerr media gives rise to a rich variety of families of non-fundamental (multi-humped) solitary waves. Making a comprehensive comparison between bifurcation phenomena for these families in bulk media and planar waveguides, we discover two novel types of soliton bifurcations and other interesting findings. The later includes (i) multi-humped solitary waves without even or odd symmetry and (ii) multi-humped solitary waves with large separation between their humps which, however, may not be viewed as bound states of several distinct one-humped solitons.Comment: 9 pages, 17 figures, submitted to Phys. Rev.

Gravitational waves from Sco X-1: A comparison of search methods and prospects for detection with advanced detectors

The low-mass X-ray binary Scorpius X-1 (Sco X-1) is potentially the most luminous source of continuous gravitational-wave radiation for interferometers such as LIGO and Virgo. For low-mass X-ray binaries this radiation would be sustained by active accretion of matter from its binary companion. With the Advanced Detector Era fast approaching, work is underway to develop an array of robust tools for maximizing the science and detection potential of Sco X-1. We describe the plans and progress of a project designed to compare the numerous independent search algorithms currently available. We employ a mock-data challenge in which the search pipelines are tested for their relative proficiencies in parameter estimation, computational efficiency, robust- ness, and most importantly, search sensitivity. The mock-data challenge data contains an ensemble of 50 Scorpius X-1 (Sco X-1) type signals, simulated within a frequency band of 50-1500 Hz. Simulated detector noise was generated assuming the expected best strain sensitivity of Advanced LIGO and Advanced VIRGO ($4 \times 10^{-24}$ Hz$^{-1/2}$). A distribution of signal amplitudes was then chosen so as to allow a useful comparison of search methodologies. A factor of 2 in strain separates the quietest detected signal, at $6.8 \times 10^{-26}$ strain, from the torque-balance limit at a spin frequency of 300 Hz, although this limit could range from $1.2 \times 10^{-25}$ (25 Hz) to $2.2 \times 10^{-26}$ (750 Hz) depending on the unknown frequency of Sco X-1. With future improvements to the search algorithms and using advanced detector data, our expectations for probing below the theoretical torque-balance strain limit are optimistic.Comment: 33 pages, 11 figure

TDR-based water content estimation on globigerina limestone through permittivity measurements

Most monuments and historical buildings in the Maltese Islands are made of the local Globigerina Limestone (GL). This type of stone, however, is very delicate and prone to degradation caused by the environmental conditions of the islands. Hence, for the preservation of the Cultural Heritage monuments, it is necessary to promptly assess the health status of these structures and, in particular, their water content (which represents one of the major causes of degradation). Starting from these considerations, in this work, a time domain reflectometry (TDR)-based method for estimating water content of GL is presented. More specifically, the proposed method relies on estimating the water content value of the GL structure from TDR-based dielectric permittivity measurements. To verify the suitability of this system, experimental tests were carried out on a GL sample. The results anticipate the strong potential of the proposed method for practical applications in the Cultural Heritage diagnostics

Coupling of ventricular action potential duration and local strain patterns during reverse remodeling in responders and non-responders to cardiac resynchronization therapy

BACKGROUND: The high risk of ventricular arrhythmias in heart failure patients remains despite the benefit of cardiac resynchronization therapy (CRT). An electromechanical interaction between regional myocardial strain patterns and the electrophysiological substrate is thought to be important. OBJECTIVE: We investigated the in-vivo relation between left ventricular (LV) activation recovery interval (ARI), as a surrogate measure of activation potential duration (APD), and local myocardial strain patterns in responders and non-responders to CRT. METHODS: ARI were recorded from the left ventricular epicardium in 20 CRT patients 6 weeks and 6 months post implant. Two-dimensional speckle tracking echocardiography was performed at the same time to assess myocardial strains. Patients with ≥15% reduction in end-systolic volume at 6-months were classified as responders. RESULTS: ARI reduced in responders, 263±46ms vs. 246±47ms, p145ms and QRS shortening with biventricular pacing was associated with ARI shortening during CRT. CONCLUSIONS: Changes in ventricular wall mechanics predict local APD lengthening or shortening during CRT. Non-responders have a worsening of myocardial strain and local APD. Baseline QRS >145ms and QRS shortening on biventricular pacing identified patients who exhibited improvement in APD

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform

Social representations and the politics of participation

Recent work has called for the integration of different perspectives into the field of political psychology (Haste, 2012). This chapter suggests that one possible direction that such efforts can take is studying the role that social representations theory (SRT) can play in understanding political participation and social change. Social representations are systems of common-sense knowledge and social practice; they provide the lens through which to view and create social and political realities, mediate people's relations with these sociopolitical worlds and defend cultural and political identities. Social representations are therefore key for conceptualising participation as the activity that locates individuals and social groups in their sociopolitical world. Political participation is generally seen as conditional to membership of sociopolitical groups and therefore is often linked to citizenship. To be a citizen of a society or a member of any social group one has to participate as such. Often political participation is defined as the ability to communicate one's views to the political elite or to the political establishment (Uhlaner, 2001), or simply explicit involvement in politics and electoral processes (Milbrath, 1965). However, following scholars on ideology (Eagleton, 1991; Thompson, 1990) and social knowledge (Jovchelovitch, 2007), we extend our understanding of political participation to all social relations and also develop a more agentic model where individuals and groups construct, develop and resist their own views, ideas and beliefs. We thus adopt a broader approach to participation in comparison to other political-psychological approaches, such as personality approaches (e.g. Mondak and Halperin, 2008) and cognitive approaches or, more recently, neuropsychological approaches (Hatemi and McDermott, 2012). We move away from a focus on the individual's political behaviour and its antecedents and outline an approach that focuses on the interaction between psychological and political phenomena (Deutsch and Kinnvall, 2002) through examining the politics of social knowledge

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

The Pfam protein families database

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metagenomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/), as well as from mirror sites in France (http://pfam.jouy.inra.fr/) and South Korea (http://pfam.ccbb.re.kr/)