Evolution of communities of software: using tensor decompositions to compare software ecosystems

© 2019 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s41109-019-0193-5Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software “ecosystems” are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as , so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers—for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted—may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.OAB’s work was supported as part of an Engineering and Physical Sciences Research Council (EPSRC) grant, project reference EP/I028099/1.Published versio

Protein structure and evolution: are they constrained globally by a principle derived from information theory?

That the physicochemical properties of amino acids constrain the structure, function and evolution of proteins is not in doubt. However, principles derived from information theory may also set bounds on the structure (and thus also the evolution) of proteins. Here we analyze the global properties of the full set of proteins in release 13-11 of the SwissProt database, showing by experimental test of predictions from information theory that their collective structure exhibits properties that are consistent with their being guided by a conservation principle. This principle (Conservation of Information) defines the global properties of systems composed of discrete components each of which is in turn assembled from discrete smaller pieces. In the system of proteins, each protein is a component, and each protein is assembled from amino acids. Central to this principle is the inter-relationship of the unique amino acid count and total length of a protein and its implications for both average protein length and occurrence of proteins with specific unique amino acid counts. The unique amino acid count is simply the number of distinct amino acids (including those that are post-translationally modified) that occur in a protein, and is independent of the number of times that the particular amino acid occurs in the sequence. Conservation of Information does not operate at the local level (it is independent of the physicochemical properties of the amino acids) where the influences of natural selection are manifest in the variety of protein structure and function that is well understood. Rather, this analysis implies that Conservation of Information would define the global bounds within which the whole system of proteins is constrained; thus it appears to be acting to constrain evolution at a level different from natural selection, a conclusion that appears counter-intuitive but is supported by the studies described herein

Impact of Chaos in the Progression of Heart Failure

Abstract Purpose of review: Cardiologists and researchers are well informed of the advances in chaos theor

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased

Reduced baroreceptor sensitivity in patients with chronic obstructive pulmonary disease.

Baroreceptor sensitivity, reflected by the slope of the linear regression of the electrocardiographic R-R interval on the rise of systolic blood pressure after injection of phenylephrine, was significantly lower in 27 patients with chronic obstructive pulmonary disease (4.67 +/- 2.67) than in 10 normal subjects (12.07 +/- 3.3) of comparable age (p less than 0.001). In 20 patients in whom right heart catheterisation was performed, pulmonary artery pressure was inversely related to baroreflex sensitivity (r = - 0.603, p less than 0.01). Independent variables such as arterial Po2, Pco2, and mean pulmonary artery pressure were examined in order to assess their ability to predict baroreflex sensitivity. The independent variable that made the most significant contribution was mean pulmonary artery pressure. It seems that the attenuation of baroreflex response in patients with chronic obstructive pulmonary disease is caused mainly by pulmonary hypertension and partly by the central effects of hypoxia and hypercapnia

Progressive nature of heart failure and systems biology

The progressive nature of heart failure (HF) is the predominant cause for the clinical course that the HF syndrome is taking. Systems biology methodology is of the utmost importance to explain and comprehend the built-in mechanisms of adverse clinical progression. Various heart diseases produce myocardial damage with subsequent left ventricular remodeling which is the principal underlying pathophysiological mechanism for the clinical progression of HF. The self-organized positive feedback stabilization mechanisms of left ventricular remodeling, adrenergic stimulation and activation of the renin-angiotensin-aldosterone system and natriuretic peptide systems, are hierarchical adaptive processes. These adaptive processes are responsible for further left ventricular remodeling with subsequent clinical deterioration and for the emergence of clinical phenotypes. These mechanisms are counteracted with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and β-blockers in an attempt to improve the adverse clinical phenomena of HF progression in a new but clinically worse stabilization level. In this review our intention is to underline the progressive nature of the HF syndrome and to demonstrate the significance of ventricular remodeling and the role of self-organized positive feedback adaptive processes

Conceptual Foundations of Systems Biology Explaining Complex Cardiac Diseases

Systems biology is an important concept that connects molecular biology and genomics with computing science, mathematics and engineering. An endeavor is made in this paper to associate basic conceptual ideas of systems biology with clinical medicine. Complex cardiac diseases are clinical phenotypes generated by integration of genetic, molecular and environmental factors. Basic concepts of systems biology like network construction, modular thinking, biological constraints (downward biological direction) and emergence (upward biological direction) could be applied to clinical medicine. Especially, in the field of cardiology, these concepts can be used to explain complex clinical cardiac phenotypes like chronic heart failure and coronary artery disease. Cardiac diseases are biological complex entities which like other biological phenomena can be explained by a systems biology approach. The above powerful biological tools of systems biology can explain robustness growth and stability during disease process from modulation to phenotype. The purpose of the present review paper is to implement systems biology strategy and incorporate some conceptual issues raised by this approach into the clinical field of complex cardiac diseases. Cardiac disease process and progression can be addressed by the holistic realistic approach of systems biology in order to define in better terms earlier diagnosis and more effective therapy

Heart Failure in Patients with Preserved Ejection Fraction: Questions Concerning Clinical Progression

Over the last two decades, important advances have been made in explaining some pathophysiological aspects of heart failure with preserved ejection fraction (HFpEF) with repercussions for the successful clinical management of the syndrome. Despite these gains, our knowledge for the natural history of clinical progression from the pre-clinical diastolic dysfunction (PDD) until the final clinical stages is significantly limited. The subclinical progression of PDD to the clinical phenotype of HFpEF and the further clinical progression to some more complex clinical models with multi-organ involvement, similar to heart failure with reduced ejection fraction (HFrEF), continue to be poorly understood. Prospective studies are needed to elucidate the natural history of clinical progression in patients with HFpEF and to identify the exact left ventricular remodeling mechanism that underlies this progression