Scholarly communication between health informatics and information systems: A bibliometric study.

The challenges of IT adoption in the healthcare sector have generated much interest across a range of research communities, including Information Systems (IS) and Health Informatics (HI). Given their long-standing interest in IT design, development, implementation, and adoption to improve productivity and support organisational transformation, the IS and HI fields are highly correlated in their research interests. Nevertheless, the two fields serve different academic audiences, have different research foci, and theorise IT artifacts differently. We investigate the dyadic relationship between health information systems (HIS) research in IS and HI through the communication patterns between the two fields. We present the citation analysis results of HIS research published in IS and HI journals between 2000 and 2020. The results revealed that despite the two fields sharing a common interest, communication between them is limited and only about specific topics. Potentially relevant ideas and theories generated in IS have not yet been sufficiently recognised by HI scholars and incorporated into the HI literature. However, the upward trend of HIS publications in IS indicates that IS has the potential to contribute more to HI.

A bibliometric analysis of the Journal of Molecular Graphics and Modelling: An update.

This paper provides a bibliometric review of the articles published in the Journal of Molecular Graphics and Modelling (formerly the Journal of Molecular Graphics). The journal has grown rapidly since its establishment in 1983, with articles coming from countries throughout the world. It now primarily contains articles describing applications of molecular graphics and modelling in chemical and biological systems, rather than the underlying technology that was the focus of many of the early papers in the journal; however, it is these early, system-based papers that continue to attract by far the largest numbers of citations.

Commentary: the first twelve years of the Journal of chemoinformatics.

This commentary provides an overview of the publications in, and the citations to, the first twelve volumes of the Journal of Cheminformatics, covering the period 2009-2020. The analysis is based on the 622 articles that have appeared in the journal during that time and that have been indexed in the Clarivate Web of Science Core Collection database. It is clear that the journal has established itself as one of the most important publications in the field of cheminformatics: it attracts citations not only from other journals in its specialist field but also from biological and chemical journals more widely, and moreover from journals that are far removed in focus from it but that are still able to benefit from the articles that it publishes.

COVID-19 impact on global maritime mobility.

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62 and -13.77% for container ships, between +2.28 and -3.32% for dry bulk, between -0.22 and -9.27% for wet bulk, and between -19.57 and -42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

Decision support for the quickest detection of critical COVID-19 phases.

During the course of an epidemic, one of the most challenging tasks for authorities is to decide what kind of restrictive measures to introduce and when these should be enforced. In order to take informed decisions in a fully rational manner, the onset of a critical regime, characterized by an exponential growth of the contagion, must be identified as quickly as possible. Providing rigorous quantitative tools to detect such an onset represents an important contribution from the scientific community to proactively support the political decision makers. In this paper, leveraging the quickest detection theory, we propose a mathematical model of the COVID-19 pandemic evolution and develop decision tools to rapidly detect the passage from a controlled regime to a critical one. A new sequential test-referred to as MAST (mean-agnostic sequential test)-is presented, and demonstrated on publicly available COVID-19 infection data from different countries. Then, the performance of MAST is investigated for the second pandemic wave, showing an effective trade-off between average decision delay [Formula: see text] and risk [Formula: see text], where [Formula: see text] is inversely proportional to the time required to declare the need to take unnecessary restrictive measures. To quantify risk, in this paper we adopt as its proxy the average occurrence rate of false alarms, in that a false alarm risks unnecessary social and economic disruption. Ideally, the decision mechanism should react as quick as possible for a given level of risk. We find that all the countries share the same behaviour in terms of quickest detection, specifically the risk scales exponentially with the delay, [Formula: see text], where [Formula: see text] depends on the specific nation. For a reasonably small risk level, say, one possibility in ten thousand (i.e., unmotivated implementation of countermeasures every 27 years, on the average), the proposed algorithm detects the onset of the critical regime with delay between a few days to 3 weeks, much earlier than when the exponential growth becomes evident. Strictly from the quickest-detection perspective adopted in this paper, it turns out that countermeasures against the second epidemic wave have not always been taken in a timely manner. The developed tool can be used to support decisions at different geographic scales (regions, cities, local areas, etc.), levels of risk, instantiations of controlled/critical regime, and is general enough to be applied to different pandemic time-series. Additional analysis and applications of MAST are made available on a dedicated website.

The Literature of Chemoinformatics: 1978-2018.

This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry.

Adaptive Bayesian Learning and Forecasting of Epidemic Evolution-Data Analysis of the COVID-19 Outbreak.

Since the beginning of 2020, the outbreak of a new strain of Coronavirus has caused hundreds of thousands of deaths and put under heavy pressure the world's most advanced healthcare systems. In order to slow down the spread of the disease, known as COVID-19, and reduce the stress on healthcare structures and intensive care units, many governments have taken drastic and unprecedented measures, such as closure of schools, shops and entire industries, and enforced drastic social distancing regulations, including local and national lockdowns. To effectively address such pandemics in a systematic and informed manner in the future, it is of fundamental importance to develop mathematical models and algorithms to predict the evolution of the spread of the disease to support policy and decision making at the governmental level. There is a strong literature describing the application of Bayesian sequential and adaptive dynamic estimation to surveillance (tracking and prediction) of objects such as missiles and ships; and in this article, we transfer some of its key lessons to epidemiology. We show that we can reliably estimate and forecast the evolution of the infections from daily - and possibly uncertain - publicly available information provided by authorities, e.g., daily numbers of infected and recovered individuals. The proposed method is able to estimate infection and recovery parameters, and to track and predict the epidemiological curve with good accuracy when applied to real data from Lombardia region in Italy, and from the USA. In these scenarios, the mean absolute percentage error computed after the lockdown is on average below 5% when the forecast is at 7 days, and below 10% when the forecast horizon is 14 days.

Motivations, understandings, and experiences of open-access mega-journal authors: Results of a large-scale survey.

Open-access mega-journals (OAMJs) are characterized by their large scale, wide scope, open-access (OA) business model, and "soundness-only" peer review. The last of these controversially discounts the novelty, significance, and relevance of submitted articles and assesses only their "soundness." This article reports the results of an international survey of authors (n = 11,883), comparing the responses of OAMJ authors with those of other OA and subscription journals, and drawing comparisons between different OAMJs. Strikingly, OAMJ authors showed a low understanding of soundness-only peer review: two-thirds believed OAMJs took into account novelty, significance, and relevance, although there were marked geographical variations. Author satisfaction with OAMJs, however, was high, with more than 80% of OAMJ authors saying they would publish again in the same journal, although there were variations by title, and levels were slightly lower than subscription journals (over 90%). Their reasons for choosing to publish in OAMJs included a wide variety of factors, not significantly different from reasons given by authors of other journals, with the most important including the quality of the journal and quality of peer review. About half of OAMJ articles had been submitted elsewhere before submission to the OAMJ with some evidence of a "cascade" of articles between journals from the same publisher.

Comparison of Maximum Common Subgraph Isomorphism Algorithms for the Alignment of 2D Chemical Structures.

The identification of the largest substructure in common when two (or more) molecules are overlaid is important for several applications in chemoinformatics, and can be implemented using a maximum common subgraph (MCS) algorithm. Many such algorithms have been reported, and it is important to know which are likely to be the useful in operation. A detailed comparison was hence conducted of the efficiency (in terms of CPU time) and the effectiveness (in terms of the size of the MCS identified) of eleven MCS algorithms, some of which were exact and some of which were approximate in character. The algorithms were used to identify both connected and disconnected MCSs on a range of pairs of molecules. The fastest exact algorithms for the connected and disconnected problems were found to be the fMCS and MaxCliqueSeq algorithms, respectively, while the ChemAxon_MCS algorithm was the fastest approximate algorithm for both types of problem.

Cross-Classified Multilevel Modelling of the Effectiveness of Similarity-Based Virtual Screening.

The screening effectiveness of a chemical similarity search depends on a range of factors, including the bioactivity of interest, the types of similarity coefficient and fingerprint that comprise the similarity measure, and the nature of the reference structure that is being searched against a database. This study introduces the use of cross-classified multilevel modelling as a way to investigate the relative importance of these four factors when carrying out similarity searches on the ChEMBL database. Two principal conclusions can be drawn from the analyses: that the fingerprint plays a more important role than the similarity coefficient in determining the effectiveness of a similarity search, and that comparative studies of similarity measures should involve many more reference structures than has been the case in much previous work.

Molecular similarity considerations in the licensing of orphan drugs.

The large costs associated with modern drug discovery mean that governments and regulatory bodies need to provide economic incentives to promote the development of orphan drugs (i.e., medicinal products that are designed to treat rare disease that affect only small numbers of patients). Under European Union (EU) legislation, a medicine can only be authorised for treating a specific rare disease if it is not similar to other orphan drugs already authorised for that particular disease. Here, we discuss the use of 2D fingerprints to calculate the Tanimoto similarity between potential and existing orphan drugs for the same disease, and present logistic regression models correlating these computed similarities with the judgements of human experts.

Open-Access Mega-Journals: A Bibliometric Profile.

In this paper we present the first comprehensive bibliometric analysis of eleven open-access mega-journals (OAMJs). OAMJs are a relatively recent phenomenon, and have been characterised as having four key characteristics: large size; broad disciplinary scope; a Gold-OA business model; and a peer-review policy that seeks to determine only the scientific soundness of the research rather than evaluate the novelty or significance of the work. Our investigation focuses on four key modes of analysis: journal outputs (the number of articles published and changes in output over time); OAMJ author characteristics (nationalities and institutional affiliations); subject areas (the disciplinary scope of OAMJs, and variations in sub-disciplinary output); and citation profiles (the citation distributions of each OAMJ, and the impact of citing journals). We found that while the total output of the eleven mega-journals grew by 14.9% between 2014 and 2015, this growth is largely attributable to the increased output of Scientific Reports and Medicine. We also found substantial variation in the geographical distribution of authors. Several journals have a relatively high proportion of Chinese authors, and we suggest this may be linked to these journals' high Journal Impact Factors (JIFs). The mega-journals were also found to vary in subject scope, with several journals publishing disproportionately high numbers of articles in certain sub-disciplines. Our citation analsysis offers support for Björk & Catani's suggestion that OAMJs's citation distributions can be similar to those of traditional journals, while noting considerable variation in citation rates across the eleven titles. We conclude that while the OAMJ term is useful as a means of grouping journals which share a set of key characteristics, there is no such thing as a "typical" mega-journal, and we suggest several areas for additional research that might help us better understand the current and future role of OAMJs in scholarly communication.

Special Issue: Chemoinformatics.

Chemoinformatics techniques were originally developed for the construction and searching of large archives of chemical structures but they were soon applied to problems in drug discovery and are now playing an increasingly important role in many additional areas of chemistry. This Special Issue contains seven original research articles and four review articles that provide an introduction to several aspects of this rapidly developing field.

Warm fresh whole blood and thoracic traumain iraq and afghanistan.

BACKGROUND: Thoracic trauma occurred in 10% of the patients seen at US military treatment facilities in Iraq and Afghanistan and 52% of those patients were transfused. Among those transfused, 281 patients received warm fresh whole blood. A previous report documented improved survival with warm fresh whole blood in patients injured in combat without stratification by injury pattern. A later report described an increase in acute lung injuries after its administration. Survivorship and warm fresh whole blood have never been analyzed in a subpopulation at highest risk for lung injuries, such as patients with thoracic trauma. There may be a heterogeneous relationship between whole blood and survival based on likelihood of a concomitant pulmonary injury. In this report, the relationship between warm fresh whole blood and survivorship was analyzed among patients at highest risk for concomitant pulmonary injuries. MATERIALS AND METHODS: Patients with thoracic trauma who received a transfusion were identified in the Joint Theater Trauma Registry. Gross mortality rates were compared between whole blood recipients and patients transfused with component therapy only. The association between each blood component and mortality was determined in a regression model. The overall mortality risk was compared between warm fresh whole blood recipients and non-recipients. RESULTS: Patients transfused with warm fresh whole blood in addition to component therapy had a higher mortality rate than patients transfused only separated blood components (21.3% vs. 12.8%, P < 0.001). When controlling for covariates, transfusion of warm fresh whole blood in addition to component therapy was not associated with increased mortality risk compared with the transfusion of component therapy only (OR 1.247 [95% CI 0.760-2.048], P = 0.382). CONCLUSION: Patients with combat related thoracic trauma transfused with warm fresh whole blood were not at increased risk for mortality compared to those who received component therapy alone when controlling for covariates.

Maximum common substructure-based data fusion in similarity searching.

Data fusion has been shown to work very well when applied to fingerprint-based similarity searching, yet little is known of its application to maximum common substructure (MCS)-based similarity searching. Two similarity search applications of the MCS will be focused on here. Typically, the number of bonds in the MCS, as well as the bonds in the two molecules being compared, are used in a similarity coefficient. The power of this technique can be extended using data fusion, where the MCS similarities of a set of reference molecules against one database molecule are fused. This "group fusion" technique forms the first application of the MCS in this work. The other application is that of the chemical hyperstructure. The hyperstructure concept is an alternative form of data fusion, being a hypothetical molecule that is constructed from the overlap of a set of existing molecules. This paper compares fingerprint group fusion (extended-connectivity fingerprints), MCS similarity group fusion, and hyperstructure similarity searching, and describes their relative merits and complementarity in virtual screening. It is concluded that the hyperstructure approach as implemented here is less generally effective than conventional fingerprint approaches.

Calculation of substructural analysis weights using a genetic algorithm.

This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier.

Chemoinformatics at the University of Sheffield 2002-2014.

This paper summarises work in chemoinformatics carried out in the Information School of the University of Sheffield during the period 2002-2014. Research studies are described on fingerprint-based similarity searching, data fusion, applications of reduced graphs and pharmacophore mapping, and on the School's teaching in chemoinformatics.

COGNAC: a web server for searching and annotating hydrogen-bonded base interactions in RNA three-dimensional structures.

Hydrogen bonds are crucial factors that stabilize a complex ribonucleic acid (RNA) molecule's three-dimensional (3D) structure. Minute conformational changes can result in variations in the hydrogen bond interactions in a particular structure. Furthermore, networks of hydrogen bonds, especially those found in tight clusters, may be important elements in structure stabilization or function and can therefore be regarded as potential tertiary motifs. In this paper, we describe a graph theoretical algorithm implemented as a web server that is able to search for unbroken networks of hydrogen-bonded base interactions and thus provide an accounting of such interactions in RNA 3D structures. This server, COGNAC (COnnection tables Graphs for Nucleic ACids), is also able to compare the hydrogen bond networks between two structures and from such annotations enable the mapping of atomic level differences that may have resulted from conformational changes due to mutations or binding events. The COGNAC server can be accessed at http://mfrlab.org/grafss/cognac.