Severe COVID-19 represents an undiagnosed primary immunodeficiency in a high proportion of infected individuals

Since the emergence of the COVID-19 pandemic in early 2020, a key challenge has been to define risk factors, other than age and pre-existing comorbidities, that predispose some people to severe disease, while many other SARS-CoV-2-infected individuals experience mild, if any, consequences. One explanation for intra-individual differences in susceptibility to severe COVID-19 may be that a growing percentage of otherwise healthy people have a pre-existing asymptomatic primary immunodeficiency (PID) that is unmasked by SARS-CoV-2 infection. Germline genetic defects have been identified in individuals with life-threatening COVID-19 that compromise local type I interferon (IFN)-mediated innate immune responses to SARS-CoV-2. Remarkably, these variants – which impact responses initiated through TLR3 and TLR7, as well as the response to type I IFN cytokines – may account for between 3% and 5% of severe COVID-19 in people under 70 years of age. Similarly, autoantibodies against type I IFN cytokines (IFN-α, IFN-ω) have been detected in patients' serum prior to infection with SARS-CoV-2 and were found to cause c. 20% of severe COVID-19 in the above 70s and 20% of total COVID-19 deaths. These autoantibodies, which are more common in the elderly, neutralise type I IFNs, thereby impeding innate antiviral immunity and phenocopying an inborn error of immunity. The discovery of PIDs underlying a significant percentage of severe COVID-19 may go some way to explain disease susceptibility, may allow for the application of targeted therapies such as plasma exchange, IFN-α or IFN-β, and may facilitate better management of social distancing, vaccination and early post-exposure prophylaxis

Membership function extraction from software development project managers

Software metrics are measurements of development processes, products, and resources. Once these measurements have been specified and collected they can be used as variables in empirically calibrated models for a wide range of project management purposes; including the task of predicting development effort based on some combination of size, complexity, and developer experience metrics. One difficulty encountered when using traditional algorithmic approaches to estimation has been the collection of the appropriate metrics required to use the model in its predictive capacity. Project managers are generally unable to make precise quantitative estimates for the independent variables, especially early in system development when these models are at their most valuable. The alternative of using qualitative values for the inputs, as in fuzzy logic, has been suggested but the stability and consistency of such labels has yet to be established, as well as considering the elicitation techniques available for deriving the membership functions. In this paper we examine the perceptions of data model size, functionality size, developer experience, and project effort in terms of three fuzzy membership functions from two separate surveys of project managers, each with a very different approach. The consistency of results across the two surveys is examined, and some discussion about the strengths and weaknesses of the two approaches is provided

A comparison of techniques for developing predictive models of software metrics

The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics

Fuzzy logic for software metric models throughout the development life-cycle

One problem faced by managers who are using project management models is the elicitation of numerical inputs. Obtaining these with any degree of confidence early in a project is not always feasible. Related to this difficulty is the risk of precisely specified outputs from models leading to overcommitment. These problems can be seen as the collective failure of software measurements to represent the inherent uncertainties in managers' knowledge of the development products, resources, and processes. It is proposed that fuzzy logic techniques can help to overcome some of these difficulties by representing the imprecision in inputs and outputs, as well as providing a more expert-knowledge based approach to model building. The use of fuzzy logic for project management however should not be the same throughout the development life cycle. Different levels of available information and desired precision suggest that it can be used differently depending on the current phase, although a single model can be used for consistenc

Two surveys of software development project managers' use of and attitudes towards modeling techniques

This paper describes the results from two surveys of project managers in New Zealand that asked them various questions about their use of and attitudes towards modelling techniques for supporting the management of software development projects, especially fuzzy logic. Each survey is summarized separately and then some overall conclusions are drawn. The results give some indication of how new modelling techniques, and especially fuzzy logic, can be presented to managers. The positive attitude of many managers towards the use of fuzzy logic can be used within their current software development management practices

Software forensics applied to the task of discriminating between program authors

Software forensics is here regarded as the particular field of inquiry that, by treating pieces of program source code as linguistically and stylistically analyzable entities, attempts to investigate various aspects of computer program authorship. These inquiries could be performed with any number of goals in mind, including those of intensification, discrimination and characterization of authors. In this paper we extract a set of 26 authorship-related metrics from 351 source code programs, written by 7 different authors. The use of feed-forward neural network (FFNN), multiple discriminant analysis (MDA), and case-based reasoning (CBR) models for discriminating these programs are then investigated in terms of classification accuracy for the authors on both training and testing (holdout) samples. The first two techniques (FFNN and MDA) produce remarkably similar results, with the overall best results coming from the CBR models. All of the examined modelling techniques have prediction accuracy rates of over 80% supporting the claim that it is feasible to use such techniques for the task of discriminating program authors based on source-code measurements in a majority of cases

FULSOME: fuzzy logic for software metric practitioners and researchers

There has been increasing interest in recent times for using fuzzy logic techniques to represent software metric models, especially those predicting the software development effort. The use of fuzzy logic for this application area offers several advantages when compared to other commonly-used techniques. These include the use of a single model with different levels of precision for the inputs and outputs used throughout the development life-cycle, the possibility of model development with little or no data, and its effectiveness when used as a communication tool. The use of fuzzy logic in any applied field, however, requires that suitable tools are available for both practitioners and researchers-satisfying both interface- and functionality-related requirements. After outlining some of the specific needs of the software metrics community, including results from a survey of software developers on this topic, this paper describes the use of a set of tools called FULSOME (FUzzy Logic for SOftware MEtrics). The development of a simple fuzzy logic system by a software metrician and its subsequent tuning are then discussed using a real-world set of software metric data. The automatically generated fuzzy model performs acceptably when compared to regression-based model

Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment

Estimation of project development effort is most often performed by expert judgment rather than by using an empirically derived model (although such may be used by the expert to assist their decision). One question that can be asked about these estimates is how stable are they with respect to characteristics of the development process and product? This stability can be assessed in relation to the degree to which the project has advanced over time, the type of module for which the estimate is being made, and the characteristics of that module. In this paper we examine a set of expert-derived estimates for the effort required to develop a collection of modules from a large health-care system. Statistical tests are used to identify relationships between the type (screen or report) and characteristics of modules and the likelihood of the associated development effort being underestimated, approximately correct, or over-estimated. Distinct relationships are found that suggest that the estimation process being examined was not unbiased to such characteristics. This is a potentially useful finding in that it provides an opportunity for estimators to improve their prediction performanc

FULSOME: a fuzzy logic modeling tool for software metricians

There has been a growing body of literature suggesting that some of the problems faced by software development project managers can be at least partially overcome by using fuzzy logic techniques. However, one issue that has been generally overlooked in this recommendation is the means by which these “software metricians” can collect data for, develop, and interpret fuzzy logic models in practice. We describe a freely available system that has been built with this in mind called FULSOME (FUzzy Logic for SOftware MEtrics). While there are many tools available for developing fuzzy models, it is suggested that before there will be real adoption of such techniques by project managers there will need to be suitable tools that support their particular workflows and that use appropriate terminology. Another requirement will be the development of some standard procedures and definitions for such models. Issues involved with membership function elicitation and extraction are also discussed as a first step towards this second goal

Review of health-related quality of life data in multiple myeloma patients treated with novel agents

In multiple myeloma (MM), health-related quality of life (HRQoL) data is becoming increasingly important, owing to improved survival outcomes and the impact of treatment-related toxicity on HRQoL. Researchers are more frequently including HRQoL assessments in clinical trials, but analysis and reporting of this data has not been consistent. A systematic literature review assessed the effect of novel agents (thalidomide, bortezomib and lenalidomide) on HRQoL in MM patients, and evaluated the subsequent reporting of these HRQoL results. A relatively small body of literature addresses HRQoL data in MM patients treated with novel MM therapeutic agents: 9 manuscripts and 15 conference proceedings. The literature demonstrates the complementary value of HRQoL when assessing clinical response, progression, overall survival and toxicity. However, weaknesses and inconsistencies in analysis and presentation of HRQoL data were observed, often complicating interpretation of the impact of treatment on HRQoL in MM. Further evaluation of HRQoL in MM patients treated with novel agents is required in larger cohorts, and ideally in head-to-head comparative studies. Additionally, the development of standardised MM-specific best practice guidelines in HRQoL data collection and analysis is recommended. These would ensure that future data are more useful in guiding predictive models and clinical decisions