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This study aims to predict financial distress in pulp and paper companies in Indonesia. The data used are the financial statements of each pul and paper company listed on the Indonesia Stock Exchange in 2012-2017. Data analysis techniques used descriptive analysis with three methods of financial distress prediction, namely the Altman Z-Score, Springate, and Zmijewski methods. The results showed that the Zmijewski method is a prediction method with the highest accuracy rate of 100%, with an error type of 0%. The Altman Z-Score method has an accuracy rate of 28.6%, with an error type of 71.4%. While the Springate method has an accuracy rate of 14.3%, with an error type of 85.7%. Therefore an accurate prediction method to predict the potential for financial distress is the Zmijewski method.

This study analyzed the current status, hotspots, and emerging trends of global research on cognitive frailty, in order to provide new research ideas for researchers. Articles and reviews related to cognitive frailty, published from 2013 to 2021, were retrieved from the Web of Science Core Collection (WoSCC) database on 26 November 2021. CiteSpace 5.8.R3 was employed for data analyses. A total of 2077 publications were included. There has been a rapid growth of publications on cognitive frailty research since 2016. The United States, Italy, England, and Australia have been the leading research centers of cognitive frailty; however, China has also recently focused on this topic. The National Center for Geriatrics and Gerontology, and Shimada H. were found to be the most prolific institution and author, respectively. Co-citation analysis identified 16 clusters, of which the largest was cognitive frailty. The keywords which occurred most frequently were “older adult”, followed by “cognitive impairment”, “frailty”, “risk”, “dementia”, “prevalence”, “mortality”, “health”, and “Alzheimer’s disease”. Burst keyword detection revealed a rising interest in cognitive frailty models. By analyzing these publications from recent years, this study provides a comprehensive analysis of cognitive frailty research.

A sensitive and simple nanomaterial based colorimetric sensor array (NBCSA) was developed for discrimination of monofloral honey from various European countries based on three botanical origins as Acacia, Canola and Honeydew. The NBCSA was designed by spotting gold (AuNPs) and silver (AgNPs) nanoparticles synthesized using six different reducing and/or capping agents. The colour intensity of AuNPs represented differential changes when interacting with volatile organic compounds appeared in the headspace of the honey samples. The color difference maps, which are calculated as the difference between color intensity of the sensor before and after exposing to the sample vapors, were used as a fingerprint to discriminate the honey samples based on botanical origin. Classification was achieved utilizing data pre-processing and chemometrics data analysis. Fitting accuracies of 88% and 86% were obtained by partial least squares discriminant analysis and linear discriminant analysis whereas 100% was achieved using support vector machine.

Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods. Data quality control and validation are prerequisites for the deep learning application in order to provide reliable further analysis, classification, interpretation, probabilistic and predictive modelling from the vast heterogeneous big data. Challenges in practical data analytics relate to both horizontal and longitudinal analysis aspects. Quantitative aspects of data validation, quality control, physically meaningful measures, parameter connections and system modelling for the future artificial intelligence (AI) methods are positioned firmly in the field of Medical Physics profession. It is our interest to ensure that our professional education, continuous training and competence will follow this significant global development.

Accurate and up-to-date biodiversity forecasts enable robust planning for environmental management and conservation of landscapes under a wide range of uses. Future predictions of the species composition of ecological communities complement more frequently reported species richness estimates to better characterize the different dimensions of biodiversity. The models that make community composition forecasts are calibrated with data on species' geographic patterns for the present, which may not be good proxies for future patterns. The future establishment of novel communities represents data on species interactions unaccounted for by these models. However, detecting them in a systematic way presents challenges due to the lack of monitoring data for landscapes with high environmental turnover, where such communities are likely to establish. Here, we propose lightweight monitoring over both ecological and anthropogenic disturbance gradients using passive sensors (i.e., those that operate continuously without much human input) to detect novel communities with the aim of updating models that make community composition forecasts. Monitoring over these two gradients should maximize detection of novel communities and improve understanding of relationships between community composition and environmental change. Further, barriers regarding cost and effort are reduced by using relatively few sensors requiring minimal upkeep. Ongoing updates to community composition forecasts based on novel community data and better understanding of the associated uncertainty should improve future decision-making for both resource management and conservation efforts.

Currently, anomaly detection is receiving a great deal of attention due to its importance in many real-world applications especially in data analytics. Anomaly detection has two important purposes in data pre-processing stages: one is to detect and attempts to eliminate them if and only if it is really outlier, while the other requires attention be paid to valid outlier (extreme value) because extreme value themselves carry the significant and critical information, such as extreme weather events. It will lead to significant harm if not detected and treated properly. This paper begins with the summary of anomaly detection, challenges, type of anomalies, extreme value followed by machine learning algorithms approach that deal with anomaly detection. Finally, we provide a conclusion that discusses about the important of detecting clearly between outlier or extreme value in data pre-processing stage, so that an effective and efficient prediction can be achieved later in modelling stage.

At present, Data remains as the most important part of human life. The future of data generation is manipulated through different data analysis techniques. But every day it is becoming much more difficult. Due to current growth of technology, People are generating huge amount of uncontrollable data. Because of that text summarization became important to reduce the volume of the data and extracts the required useful information. This review based paper discusses about different text summarization techniques and algorithms along with their accuracy and efficiency. So that the researchers can easily understand the concept of text summarization and find their expected information in a fast pace.

With the new era in information technology, Educational Informationization not only profoundly affects the modern education paradigm, but also provides strong technological feasibility to facilitate and realize education reform. However, the ubiquitous accessibility brought about by today's internet and wireless technologies creates explosive increase in educational related data (big data) from a diversity of resources. It also becomes much more challenging to analyze and understand the underlying meaning of the big data. It is high time that the modern education systems should embed data mining into education paradigm and apply decision analysis to guide teaching strategy. It is our belief that Education Data Mining (EDM) is vital to achieve such educational innovation. This paper proposes a novel Online System for Correcting and Analyzing Exam Paper (OSCAEP) based on a combination of EDM and Network Informatization, which is aimed to provide in-depth analysis of exam papers, to construct an innovative EDM model and to study the interrelationship between educational factors. (1) First, we introduce the background, significance, and purpose of our research. We illustrate OSCAEP from three perspectives: $\pmb{a}$. Educators: For educators, they can get an insight into potential problems from the quantitative results by data mining; $\pmb{b}$. Learners: For learners, they can understand their learning progress from various angles; $\pmb{c}$. Administrators: For administrators, they can timely redesign the education strategies based on such quantitative feedbacks. (2) Second, we present the innovations of OSCAEP from theoretical part and technological part. OSCAEP also include some advanced teaching theories such as Personalized Learning, Precision Teaching and Customized Teaching. Our main technological innovation hinges upon integrating several machine learning data analysis techniques (e.g., K-means, DNN) to train scores and obtain the classification rules. (3) Third, Our presentation of OSCAEP contains two major parts: $\pmb{a}$. build a distributed multi-layer hardware architecture, which includes web server, application server cluster and management workstation, and $\pmb{b}$. provide a multi-stage pipeline software model, which expands the traditional process of data mining into six stages (Data Preparation, Data Filtration, Data Pre-processing, Data Conversion, Model Building and Data Training). From the application perspective, we develop an effective model for examination analysis and learning behavior prediction based on education big data. Examples include statistical score data of different types of questions (Blank, True/False, Choice and Coding). (4) Forth, OSCAEP also offers educators some potentially very valuable suggestions and advices. It is found that the learning performance has no direct relation with the learning time, but it has much to do with the learning habits. With the EDM analysis results, educators can better customize teaching and design efficient rules and regulations in accordance with learner's features. This leads to precision teaching and plays a vital role for a more desirable atmosphere for learning and teaching. In short, OSCAEP processes information in a digital, automatic, intelligent and networked way. As such, it provides more precise and timely information than the traditional approach.

Employing mobile devices to perform data analytics is a typical fog computing application that utilizes the intelligence at the edge of networks. Such an application relies on the knowledge of the mobility of mobile devices and their users, e.g., to deploy computation tasks efficiently at the edge. This paper surveys the literature on the mobility-related utilization of operator-collected CDR (charging data records) – the most sig- nificant proxy of large-scale human mobility studies. We provide an innovative introductory guide to the CDR data preliminary. It reveals original issues regarding CDR-based mobility feature computation and applications at the edge. Our survey plays an important role in utilizing mobile devices in terms of both human mobility investigation and fog computing.

In the last few years, all companies have been interested in the analysis of data related to Human Resources and have focused on human capital, which is considered as the major factor influencing the company’s development and all its activities at all levels of human resource policies. Data analysis (HR analytics) will significantly improve business profitability over the next years.We started with an extensive survey of different human resources problems and risks reported by HR specialists, then a comprehensive review of recent research efforts on computer science techniques proposed to solve these problems and finally focusing on suggested artificial intelligence methods. This review article will be an archive and a reference for computer scientists working on HR by summarizing the IT solutions already made in human resources for the period between 2008 and 2018. It aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions. It summarizes at the same time the recent and different methods, IT approaches and tools already used by highlighting those using artificial intelligence.

Background The GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) working group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation. CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations; (2) coherence; (3) adequacy of data; and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on CERQual’s adequacy of data component. Methods We developed the adequacy of data component by searching the literature for definitions, gathering feedback from relevant research communities and developing consensus through project group meetings. We tested the CERQual adequacy of data component within several qualitative evidence syntheses before agreeing on the current definition and principles for application. Results When applying CERQual, we define adequacy of data as an overall determination of the degree of richness and the quantity of data supporting a review finding. In this paper, we describe the adequacy component and its rationale and offer guidance on how to assess data adequacy in the context of a review finding as part of the CERQual approach. This guidance outlines the information required to assess data adequacy, the steps that need to be taken to assess data adequacy, and examples of adequacy assessments. Conclusions This paper provides guidance for review authors and others on undertaking an assessment of adequacy in the context of the CERQual approach. We approach assessments of data adequacy in terms of the richness and quantity of the data supporting each review finding, but do not offer fixed rules regarding what constitutes sufficiently rich data or an adequate quantity of data. Instead, we recommend that this assessment is made in relation to the nature of the finding. We expect the CERQual approach, and its individual components, to develop further as our experiences with the practical implementation of the approach increase. Electronic supplementary material The online version of this article (10.1186/s13012-017-0692-7) contains supplementary material, which is available to authorized users.

Nowadays, manufacturers are increasingly able to collect and analyze data from sensors on manufacturing equipment, and also from other types of machinery, such as smart meters, pipelines, delivery trucks, etc. Nevertheless, many manufacturers are not yet ready to use analytics beyond a tool to track historical performance. However, just knowing what happened and why it happened does not use the full potential of the data and is not sufficient anymore. Manufacturers need to know what happens next and what actions to take in order to get optimal results. It is a challenge to develop advanced analytics techniques including machine learning and predictive algorithms to transform data into relevant information for gaining useful insights to take appropriate action. In the proposed research we target new analytic methods and tools that make insights not only more understandable but also actionable by decision makers. The latter requires that the results of data analytics have an immediate effect on the processes of the manufacturer. Thereby, data analytics has the potential to improve industrial processes by reducing maintenance costs, avoiding equipment failures and improving business operations. Accordingly, the overall objective of this project is to develop a set of tools — including algorithms, analytic machinery, planning approaches and visualizations — for industrial process improvements based on data analytics.

CONTEXT: There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and poorly understood underlying phenomena are causing some concern as to the reliability of studies. OBJECTIVE: Our goal is to provide guidance for producers and consumers of software analytics studies (computational experiments and correlation studies). METHOD: We propose using "bad smells", i.e., surface indications of deeper problems and popular in the agile software community and consider how they may be manifest in software analytics studies. RESULTS: We list 12 "bad smells" in software analytics papers (and show their impact by examples). CONCLUSIONS: We believe the metaphor of bad smell is a useful device. Therefore we encourage more debate on what contributes to the validty of software analytics studies (so we expect our list will mature over time)., Comment: Accepted April 2019. To appear

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for &ldquo, food and nutrition&rdquo, (n = 37), &ldquo, disease and specific population&rdquo, (n = 100), &ldquo, data description&rdquo, (n = 21), &ldquo, research description&rdquo, (n = 35), and &ldquo, supplementary (meta) data description&rdquo, (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

The purpose of this study was to carry out a content analysis on the papers published in high-impact educational journals between 2009 and 2014 and to identify the trends over the selected years. The criteria for the analysis were; number of authors, time between the submission and publication of the papers, keywords, the field and rationale of the study, sample size, descriptions of participants, data collection tools and analysis, and software. The current study was designed as a descriptive content analysis study and utilized a purposive sampling technique. A total of 789 papers were selected from the “Journal of Educational Psychology”, “Educational Psychologist”, “Educational Researcher” and “American Education Research Journal”. Content analysis was employed to analyze the collected data. The results of the analyses showed that the most commonly studied fields were educational psychology, linguistic properties and mathematics in the four journals. In terms of the content, the rationales for writing the papers were generally related to gaps in the literature and theoretical discussion. It was found that generally, studies involving research were conducted with elementary/high school students and data was collected from large samples (larger than 10000) using achievement tests and questionnaires. It was revealed that concerning the trends in data analysis methods, there was a similar pattern from 1970s to date, and generally, multilevel modelling was used when appropriate to the data sets. This situation indicates standard data analyses are essential for researchers. From the findings of the current study, it is recommended for researchers to work with heterogenous sample and various types of participants together (family, teacher, peer, etc.)., Bu araştırmacının amacı, 2009-2014 yılları arasında en yüksek beş yıllık etki faktörüne sahip eğitim bilimleri alanındaki uluslararası dergilerde yayımlanan makaleleri, belirlenen ölçütlere göre inceleyerek yıllara göre eğilimlerini belirlemektir. Bu amaçla; makalelerdeki yazar sayısı, yayımlanma süresi, anahtar kelimeler, konu alanı, araştırmanın yapılma ihtiyacı, evren-örneklem/çalışma grubunun büyüklüğü ve özellikleri, veri toplama tekniklerinin/araçlarının özellikleri, verilerin analizinde kullanılan yöntemler/teknikler ve paket programlar ölçüt olarak seçilmiştir. Betimsel içerik analizi modelinde olan araştırmada, amaçlı örnekleme yöntemiyle seçilen “Journal of Educational Psychology”, “Educational Psychologist”, “Educational Researcher” ve “American Education Research Journal” dergilerinde yayımlanan 789 makale incelenmiştir. Verilerin analizinde, içerik analizi kullanılmıştır. Analizler sonucu, en yüksek etki faktörüne sahip eğitim dergilerinde en fazla çalışan konu alanlarının, eğitim psikolojisi, dil becerileri ve matematik olduğu görülmüştür. Bununla birlikte, bu çalışmaların yapılma ihtiyaçları genellikle alan yazındaki eksiklikler ve kuramsal tartışmadır. Ayrıca, çalışmalar daha çok ilköğretim ve lise öğrencileri ve büyük örneklemler (10000 den büyük) üzerinde yürütülürken; çalışmalarda en fazla başarı testi ve anketlerle veriler toplanmaktadır. Yapılan incelemeler sonucu, verilerin doğasına uygun olarak çok düzeyli modelleme çalışmaları yapıldığı görülmekle birlikte sık kullanılan veri analiz yöntemlerindeki eğilimlerin 1970’lerden günümüze benzer olduğu tespit edilmiştir. Bu durum, standart veri analiz yöntemlerinden vazgeçilemediğini göstermektedir. İncelenen özellikler çerçevesinde araştırmacılara, çalışılan örneklemlerde heterojenliğin (aile, öğretmen, akran vb.) sağlanmasına dikkat etmeleri önerilmektedir.

The use of digital tools in algebra education is expected to not only contribute to master skill, but also to acquire conceptual understanding. The question is how digital tools affect students” thinking and understanding. This paper presents an analysis of data of one group of three grade seventh students (12-13 year-old) on the use of a digital tool for algebra, the Cover-up applet for solving equations in particular. This case study was part of a larger teaching experiment on initial algebra enriched with digital technology which aimed to improve students” conceptual understanding and skills in solving equations in one variable. The qualitative analysis of a video observation, digital and written work showed that the use of the applet affects student thinking in terms of strategies used by students while dealing with the equations. We conclude that the effects of the use of the digital tool can be traced from student problem solving strategies on paper-and-pencil environment which are similar to strategies while working with the digital tool. In future research, we recommend to use specific theoretical lenses, such as the theory of instrumental genesis and the onto-semiotic approach, to reveal more explicit relationships between students” conceptual understanding and the use of a digital tool.

We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliche, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.

Motivation Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods. Existing deconvolutional analysis of bulk transcriptomics relies on predefined marker genes in the transcriptomics data, which are unavailable for novel cell types and does not utilize the FCM data that provide canonical phenotypic definitions of the cell types. Results We developed a novel analytics pipeline—FastMix—for computational immunology, which integrates flow cytometry, bulk transcriptomics and clinical covariates for identifying cell type-specific gene expression signatures and biomarker genes. FastMix addresses the ‘large p, small n’ problem in the gene expression and flow cytometry integration analysis via a linear mixed effects model (LMER) for both cross-sectional and longitudinal studies. Its novel moment-based estimator not only reduces bias in parameter estimation but also is more efficient than iterative optimization. The FastMix pipeline also includes a cutting-edge flow cytometry data analysis method—DAFi—for identifying cell populations of interest and their characteristics. Simulation studies showed that FastMix produced smaller type I/II errors than competing methods. Validation using real data of two vaccine studies showed that FastMix identified a consistent set of signature genes as in independent single-cell RNA-seq analysis, producing additional interesting findings. Availability and implementation Source code of FastMix is publicly available at https://github.com/terrysun0302/FastMix. Supplementary information Supplementary data are available at Bioinformatics online.

Background People with severe mental illness (SMI) often have poorer physical health than the general population. A coproduced physical activity intervention to improve physical activity for people with SMI in Northern Ireland was evaluated by co‐researchers (researchers with lived experience of SMI) and academic researchers using a new approach to participatory data analysis called participatory theme elicitation (PTE). Objective Co‐researchers and academic researchers analysed the data from the pilot study using PTE. This paper aimed to compare these analyses to validate the findings of the study and explore the validity of the PTE method in the context of the evaluation of a physical activity intervention for individuals with SMI. Results There was alignment and congruence of some themes across groups. Important differences in the analyses across groups included the use of language, with the co‐researchers employing less academic and clinical language, and structure of themes generated, with the academic researchers including subthemes under some umbrella themes. Conclusions The comparison of analyses supports the validity of the PTE approach, which is a meaningful way of involving people with lived experience in research. PTE addresses the power imbalances that are often present in the analysis process and was found to be acceptable by co‐researchers and academic researchers alike.

Using a deconvolution algorithm, it is shown that resonant inelastic X-ray scattering (RIXS) spectra can be reliably estimated from measurements of photoelectrons created through absorption of RIXS photons in a chosen material. The method works on previously reported data with ∼0.5 eV resolution, and simulations show its potential for accurately estimating spectral features with hundreds of meV or smaller width., Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers, which limits measurement accuracy. Here, the performance of a different method for measuring RIXS, i.e. photoelectron spectrometry for analysis of X-rays (PAX), is computationally investigated. This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of high-throughput, compact electron spectrometers. X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. A deconvolution algorithm for analysis of such PAX data is proposed. It is shown that the deconvolution algorithm works well on data recorded with ∼0.5 eV resolution. Additional simulations show the potential of PAX for estimation of RIXS features with smaller widths. For simulations using the 3d levels of Ag as a converter material, and with 105 simulated detected electrons, it is estimated that features with a few hundred meV width can be accurately estimated in a model RIXS spectrum. For simulations using a sharp Fermi edge to encode RIXS spectra, it is estimated that one can accurately distinguish 100 meV FWHM peaks separated by 45 meV with 105 simulated detected electrons that were photoemitted from within 0.4 eV of the Fermi level.

In this short paper, we discuss the impact of data analytics in services and delineate future research directions for the field. After illustrating how data analytics are transforming different service sectors, we consider the provision of data analysis as a service in its own right. We discuss how the very nature of data and certain features of the machine learning method give rise to new issues and pitfalls for the management of these services, which delineates as many future research directions. We also discuss the coproduction of services by humans and machines and call for more research on responsible data analytics services to tackle some of the most pressing ethical issues in our societies.

Objective:During COVID-19, the Internet was a prime source for getting relevant updates on guidelines and desirable information. The objective of the present study was to determine the nutritional immunity information-seeking behaviour during COVID-19 in India.Design:Google Trends (GTs) data on relevant COVID-19 and nutritional topics were systematically selected and retrieved. Data on newly reported COVID-19 cases were also examined on a daily basis. The cross-correlation method was used to determine the correlation coefficient between the selected terms and daily new COVID-19 cases, and the joinpoint regression models were utilised to measure monthly percent change (MPC) in relative search volumes (RSV).Setting:Online.Participants:People using Google search during the period 1 January 2020–31 August 2020 in India.Results:The date of peak searches can be attributed to the COVID-19 guidelines announcement dates. All the nutritional terms showed a significant increase in average monthly percentage change. The higher than the average daily rise in COVID-19 cases leads to a higher than average increase in RSV of nutritional terms with the greatest association after 14–27 d. The highest mean relative search volume for nutritional terms was from Southern India (49·34 ± 7·43), and the lowest was from Western India (31·10 ± 6·30).Conclusion:There was a significant rise in the Google searches of nutritional immunity topics during COVID-19 in India. The local/regional terms can be considered for better outreach of public health guidelines or recommendations. Further automation of Google Trends using programming languages can help in real-time monitoring and planning various health/nutritional events.

Advances in sensing, communications and data analytics have made it possible to construct virtual replicas of Cyber-Physical Systems (CPSs). Such replicas, known as digital twins, can in principle inform decision making during operation and evolution of the systems they model. This short paper introduces the ISoLA 2020/21 series of papers on the technology and practice of engineering digital twins for CPSs. The focus is on the relationship between model-based design, machine learning, digital twins and CPSs.

Background COVID-19 is the most widely discussed topic worldwide in 2020, and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objective In this paper, a data analytics study on the diffusion of COVID-19 in Italy and the Lombardy Region is developed to define a predictive model tailored to forecast the evolution of the diffusion over time. Methods Starting with all available official data collected worldwide about the diffusion of COVID-19, we defined a predictive model at the beginning of March 2020 for the Italian country. Results This paper aims at showing how this predictive model was able to forecast the behavior of the COVID-19 diffusion and how it predicted the total number of positive cases in Italy over time. The predictive model forecasted, for the Italian country, the end of the COVID-19 first wave by the beginning of June. Conclusions This paper shows that big data and data analytics can help medical experts and epidemiologists in promptly designing accurate and generalized models to predict the different COVID-19 evolutionary phases in other countries and regions, and for second and third possible epidemic waves.

For current steganalysis of the image content-adaptive steganography, there are multiple problems to be improved, such as high difficulty and low accuracy, when detecting images with various contents and textures. For this problem, an improved steganalytic method is proposed in this paper based on the pre-classification and feature selection. First, using the features extracted based on the dependency analysis of image adjacent data, the images with various content and texture complexities are pre-classified as multiple clusters by the K-means algorithms. Then, the performance of existing various steganalytic features are analyzed for different clusters of images, and the optimal features for each cluster are selected for final classification. The experimental results show that the detection accuracy could be improved by the proposed method, and the rationality and availability are also verified. At the same time, the analysis and experimental results in this paper also show that the images with rich content and complex texture should be paid more attention both in steganography and in steganalysis.

Motivated by the current COVID-19 health crisis, we consider data analysis for quantitative polymerase chain-reaction (qPCR) measurements. We derive a theoretical result specifying the conditions under which all qPCR amplification curves (including their plateau phases) are identical up to an affine transformation, i.e. a multiplicative factor and horizontal shift. We use this result to develop a data analysis procedure for determining when an amplification curve exhibits characteristics of a true signal. The main idea behind this approach is to invoke a criterion based on constrained optimization that assesses when a measurement signal can be mapped to a master reference curve. We demonstrate that this approach: (i) can decrease the fluorescence detection threshold by up to a decade; and (ii) simultaneously improve confidence in interpretations of late-cycle amplification curves. Moreover, we demonstrate that the master curve is transferable reference data that can harmonize analyses between different labs and across several years. Application to reverse-transcriptase qPCR measurements of a SARS-CoV-2 RNA construct points to the usefulness of this approach for improving confidence and reducing limits of detection in diagnostic testing of emerging diseases. Graphical Abstract Left: a collection of qPCR amplification curves. Right: Example of data collapse after affine transformation.

Objective:To analyse the determinants of exclusive breast-feeding (EBF) discontinuation in southeastern Brazil between 2008 and 2013.Design:Secondary cross-sectional data were analysed from three waves of child feeding surveys conducted in the city of Marília, São Paulo, Brazil, in 2008, 2011 and 2013 (n 1645 children under 6 months). Multivariable Poisson regression models were used to test the association between EBF discontinuation and socio-economic, demographic and biomedical factors in a pooled sample and within each survey wave.Setting:Regionally representative cross-sectional survey from Brazil.Participants:The analytical sample included 1645 infants under 6 months old.Results:In the pooled sample, 40·7 % of the infants were exclusively breastfed. Between 2008 and 2013, there was a significant increase in C-section (35·1–42·7 %) and pacifier use (41·4–48·8 %). The determinants of EBF discontinuation in the pooled analysis were mothers working outside the home (adjusted prevalence ratio (APR) = 1·10; 95 % CI 1·00, 1·21), first-time mothers (APR = 1·10; 95 % CI 1·01, 1·20), pacifier use (APR = 1·48; 95 % CI 1·36, 1·61) and low birth weight (APR = 1·17; 95 % CI 1·05, 1·32).Conclusions:Mothers working outside the home, first-time moms, pacifier use and low birth weight were the factors associated with EBF discontinuation. Evidence-based counselling strategies during antenatal and early postpartum care in primary healthcare are needed to address the modifiable determinants of EBF discontinuation and ultimately to improve its rates in southeastern Brazil.

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.

High-Performance Computing (HPC) is at an inflection point in its evolution. General-purpose architectures approach limits in terms of speed and power/energy, requiring the development of specialized architectures to deliver accelerated performance. Additionally, the arrival of new user communities and workloads---including machine learning, data analytics, and quantum simulation---increases the breadth of application characteristics we need to support, putting pressure on the complexity of the architectural portfolio. At the same time, data movement has been identified as a main culprit of energy waste, pushing hardware designers towards a tighter integration of the different technologies. The resulting integrated systems offer great opportunities in terms of power/performance tradeoffs, but also lead to challenges on the software side. In this position paper, we highlight the trends leading us to integrated systems and describe their substantial advantages over simpler, single accelerated designs. Further, we highlight its impact on the corresponding software stack and its challenges and impact on the user. This introduces a different way to design, program and operate HPC systems, and ultimately the need to drop some long-held dogmas or believes in HPC systems.

Background The fatality rate of coronavirus disease (COVID-19) in Italy is controversial and is greatly affecting discussion on the impact of containment measures that are straining the world’s social and economic fabric, such as instigating large-scale isolation and quarantine, closing borders, imposing limits on public gatherings, and implementing nationwide lockdowns. Objective The scientific community, citizens, politicians, and mass media are expressing concerns regarding data suggesting that the number of COVID-19–related deaths in Italy is significantly higher than in the rest of the world. Moreover, Italian citizens have misleading perceptions related to the number of swab tests that have actually been performed. Citizens and mass media are denouncing the coverage of COVID-19 swab testing in Italy, claiming that it is not in line with that in other countries worldwide. Methods In this paper, we attempt to clarify the aspects of COVID-19 fatalities and testing in Italy by performing a set of statistical analyses that highlight the actual numbers in Italy and compare them with official worldwide data. Results The analysis clearly shows that the Italian COVID-19 fatality and mortality rates are in line with the official world scenario, as are the numbers of COVID-19 tests performed in Italy and in the Lombardy region. Conclusions This up-to-date analysis may elucidate the evolution of the COVID-19 pandemic in Italy.

Different modes of data collection are explored, and the effect of flux and multiplicity on the resulting quality of the data set is discussed. Advice is offered on how to collect data in the absence of prior knowledge of the sample., Strategies for collecting X-ray diffraction data have evolved alongside beamline hardware and detector developments. The traditional approaches for diffraction data collection have emphasised collecting data from noisy integrating detectors (i.e. film, image plates and CCD detectors). With fast pixel array detectors on stable beamlines, the limiting factor becomes the sample lifetime, and the question becomes one of how to expend the photons that your sample can diffract, i.e. as a smaller number of stronger measurements or a larger number of weaker data. This parameter space is explored via experiment and synthetic data treatment and advice is derived on how best to use the equipment on a modern beamline. Suggestions are also made on how to acquire data in a conservative manner if very little is known about the sample lifetime.

Background The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. Objective The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. Methods We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. Results The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. Conclusions The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.

This study aimed to identify an appropriate simple mathematical model to fit the number of coronavirus disease 2019 (COVID-19) cases at the national level for the early portion of the pandemic, before significant public health interventions could be enacted. The total number of cases for the COVID-19 epidemic over time in 28 countries was analysed and fit to several simple rate models. The resulting model parameters were used to extrapolate projections for more recent data. While the Gompertz growth model (mean R2 = 0.998) best fit the current data, uncertainties in the eventual case limit introduced significant model errors. However, the quadratic rate model (mean R2 = 0.992) fit the current data best for 25 (89%) countries as determined by R2 values of the remaining models. Projection to the future using the simple quadratic model accurately forecast the number of future total number of cases 50% of the time up to 10 days in advance. Extrapolation to the future with the simple exponential model significantly overpredicted the total number of future cases. These results demonstrate that accurate future predictions of the case load in a given country can be made using this very simple model.

BACKGROUND The number of organ donations from hepatitis B virus (HBV) or hepatitis C virus (HCV)-positive donors is gradually increasing; however, the current status of organ donation from brain-dead donors with hepatitis in South Korea has not been analyzed. This study aimed to analyze this. MATERIAL AND METHODS In total, 9210 potential brain deaths were reported in South Korea from January 2013 to December 2017, of which 333 were hepatitis carriers (HBV, n=246; HCV, n=87). Based on the data from the Korean Network for Organ Sharing and Korea Organ Donation Agency, 2460 completion of transplantations from brain-dead donors have been performed, of which 71 were hepatitis carriers (HBV, n=60; HCV, n=11). RESULTS There were 60 and 11 transplantations from HBV- and HCV-positive brain-dead donors, respectively. The main reasons for organ transplantation failure were recipient's refusal (n=90), unsuitability as donors (n=80), non-brain death (n=45), and cardiac death (n=20). There were 71 and 31 kidney and liver donations, respectively; the average number of organs donated by HBV-positive donors was higher than that donated by HCV-positive donors. HBV-positive donors donated more hearts and livers than HCV-positive donors. CONCLUSIONS There are few organ donations from brain-dead donors with hepatitis B or C which led to transplantation completion in South Korea, and the main reasons for failure are recipient's refusal to receive organs from donors with hepatitis and unsuitability for donation due to active viral conditions. To promote organ transplantations from donors with hepatitis B and C virus, we could consider 3 strategies: 1) reducing recipient's refusal rates by educating recipients and their families on the outcomes of organ donation from hepatitis carriers, 2) establishing treatment protocols for infection management after organ transplantations from HBV/HCV brain-dead donors, and 3) increasing the relevant experience of medical staff.

Background Computationally derived (“synthetic”) data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. Objective We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. Methods We used the National COVID Cohort Collaborative’s instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19–positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19–related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. Results For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. Conclusions This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.

Big data is an appealing source and often perceived to bear all sorts of hidden information. Filtering out the gemstones of information besides the rubbish that is equally easy to “deduce” is, however, a nontrivial issue. This position paper will open with the motivating problem of risk estimation for an enterprise, using big data. Our illustrative context here is the synERGY project (“security for cyber-physical value networks Exploiting smaRt Grid sYstems”), which serves as a case study to show the (unexplored) potential, application and difficulties of using big data in practice. The paper first goes into a list of a few general do’s and don’ts about data analytics, and then digs deeper into (semi-) automated risk evaluation via a statistical trust model. Ideally, the trust and hence risk assessment should be interpretable, justified, up-to-date and comprehensible in order to provide a maximum level of information with minimal additional manual effort. The ultimate goal of projects like synERGY is to establish trust in a system, based on observed behavior and its resilience to anomalies. This calls for a distinction of “normal” (in the sense of behavior under expected working conditions) from “abnormal” behavior, and trust can intuitively be understood as the (statistical) expectation of “normal” behavior.

A new scaling program is presented with new features to support multi-sweep workflows and analysis within the DIALS software package., In processing X-ray diffraction data, the intensities obtained from integration of the diffraction images must be corrected for experimental effects in order to place all intensities on a common scale both within and between data collections. Scaling corrects for effects such as changes in sample illumination, absorption and, to some extent, global radiation damage that cause the measured intensities of symmetry-equivalent observations to differ throughout a data set. This necessarily requires a prior evaluation of the point-group symmetry of the crystal. This paper describes and evaluates the scaling algorithms implemented within the DIALS data-processing package and demonstrates the effectiveness and key features of the implementation on example macromolecular crystallographic rotation data. In particular, the scaling algorithms enable new workflows for the scaling of multi-crystal or multi-sweep data sets, providing the analysis required to support current trends towards collecting data from ever-smaller samples. In addition, the implementation of a free-set validation method is discussed, which allows the quantification of the suitability of scaling-model and algorithm choices.

Digital enterprises and their networks increasingly rely on advanced decision-making capabilities, however, development of decision-making models requires significant effort and is often performed independently of other digitalization activities. Additionally, dynamic nature of many decision-making problems requires rapid ramp-up of decision-making capabilities. To addresses these challenges, this position paper proposes to elaborate a method for integrated on-demand decision modeling. The method combines mathematical programming and data analytics models to create case specific models on the basis of generic decision-making models. The integrated model and its data supply pipelines are configured using enterprise models allowing for consistent and rapid model deployment. The integrated model is intended for the trusted ICT supply chain configuration problem though it can be used for solving various types of decision-making problems. The main expected results are formulation of the new type decision-making model and the method for on-demand configuration of such models.

Agriculture in India is prone to a lot of uncertainties and different types of risks. Crop insurance is a mechanism hedge against such contingencies. Though many governments in the past had introduced various crop insurance schemes, the penetration rate was shallow, and there were no private players. Thus, the current Govt introduced PMFBM, which can be a game-changer. Karnataka went a step ahead and implemented an end to end technology platform – Samrakshane - to promote and manage the crop insurance. Samrakshane, being an innovative technology platform enabling the complete automation of crop insurance adoption. The objective of this study is to understand and measure the innovation adoption impact of the crop insurance scheme. In this short paper, we present a conceptual outline of our ongoing research.

The amount of data generated during crystallographic fragment-screening projects requires sophisticated automated methods to analyse the data and to identify binders. FragMAXapp is the MAX IV Laboratory approach to managing fragment-screening campaigns: a web application that provides scientists with access to the MAX IV computing cluster and visualization tools., Crystallographic fragment screening (CFS) has become one of the major techniques for screening compounds in the early stages of drug-discovery projects. Following the advances in automation and throughput at modern macromolecular crystallography beamlines, the bottleneck for CFS has shifted from collecting data to organizing and handling the analysis of such projects. The complexity that emerges from the use of multiple methods for processing and refinement and to search for ligands requires an equally sophisticated solution to summarize the output, allowing researchers to focus on the scientific questions instead of on software technicalities. FragMAXapp is the fragment-screening project-management tool designed to handle CFS projects at MAX IV Laboratory. It benefits from the powerful computing infrastructure of large-scale facilities and, as a web application, it is accessible from everywhere.

A new web platform is presented for the pair distribution function (PDF) community to use and share advanced PDF analysis software in the cloud., A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data (PDFitc) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarity­Mapping, are described. In the first and second the user uploads a single PDF and the application returns a list of best-fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy-to-use workflow on PDFitc. In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform.

Background The knowledge of cancer burden in the population, its time trends, and the possibility of international comparison is an important starting point for cancer programs. A reliable interactive tool describing cancer epidemiology in children and adolescents has been nonexistent in the Czech Republic until recently. Objective The goal of this study is to develop a new web portal entitled the Czech Childhood Cancer Information System (CCCIS), which would provide information on childhood cancer epidemiology in the Czech Republic. Methods Data on childhood cancers have been obtained from the Czech National Cancer Registry. These data were validated using the clinical database of childhood cancer patients and subsequently combined with data from the National Register of Hospitalised Patients and with data from death certificates. These validated data were then used to determine the incidence and survival rates of childhood cancer patients aged 0 to 19 years who were diagnosed in the period 1994 to 2016 (N=9435). Data from death certificates were used to monitor long-term mortality trends. The technical solution is based on the robust PHP development Symfony framework, with the PostgreSQL system used to accommodate the data basis. Results The web portal has been available for anyone since November 2019, providing basic information for experts (ie, analyses and publications) on individual diagnostic groups of childhood cancers. It involves an interactive tool for analytical reporting, which provides information on the following basic topics in the form of graphs or tables: incidence, mortality, and overall survival. Feedback was obtained and the accuracy of outputs published on the CCCIS portal was verified using the following methods: the validation of the theoretical background and the user testing. Conclusions We developed software capable of processing data from multiple sources, which is freely available to all users and makes it possible to carry out automated analyses even for users without mathematical background; a simple selection of a topic to be analyzed is required from the user.

Background Cervical cancer is a growing health concern, especially in resource-limited settings. Objective The objective of this study was to assess the burden of cervical cancer mortality and disability-adjusted life years (DALYs) in the Eastern Mediterranean Region (EMR) and globally between the years 2000 and 2017 by using a pooled data analysis approach. Methods We used an ecological approach at the country level. This included extracting data from publicly available databases and linking them together in the following 3 steps: (1) extraction of data from the Global Burden of Disease (GBD) study in the years 2000 and 2017, (2) categorization of EMR countries according to the World Bank gross domestic product per capita, and (3) linking age-specific population data from the Population Statistics Division of the United Nations (20-29 years, 30-49 years, and >50 years) and GBD’s data with gross national income per capita and globally extracted data, including cervical cancer mortality and DALY numbers and rates per country. The cervical cancer mortality rate was provided by the GBD study using the following formula: number of cervical cancer deaths × 100,000/female population in the respective age group. Results The absolute number of deaths due to cervical cancer increased from the year 2000 (n=6326) to the year 2017 (n=8537) in the EMR; however, the mortality rate due to this disease decreased from the year 2000 (2.7 per 100,000) to the year 2017 (2.5 per 100,000). According to age-specific data, the age group ≥50 years showed the highest mortality rate in both EMR countries and globally, and the age group of 20-29 years showed the lowest mortality rate both globally and in the EMR countries. Further, the rates of cervical cancer DALYs in the EMR were lower compared to the global rates (2.7 vs 6.8 in 2000 and 2.5 vs 6.8 in 2017 for mortality rate per 100,000; 95.8 vs 222.2 in 2000 and 86.3 vs 211.8 in 2017 for DALY rate per 100,000; respectively). However, the relative difference in the number of DALYs due to cervical cancer between the year 2000 and year 2017 in the EMR was higher than that reported globally (34.9 vs 24.0 for the number of deaths and 23.5 vs 18.1 for the number of DALYs, respectively). Conclusions We found an increase in the burden of cervical cancer in the EMR as per the data on the absolute number of deaths and DALYs. Further, we found that the health care system has an increased number of cases to deal with, despite the decrease in the absolute number of deaths and DALYs. Cervical cancer is preventable if human papilloma vaccination is taken and early screening is performed. Therefore, we recommend identifying effective vaccination programs and interventions to reduce the burden of this disease.

Background A growing number of health care practices are adopting software systems that link with their existing electronic medical records to generate outgoing phone calls, emails, or text notifications to patients for appointment reminders or practice updates. While practices are adopting this software technology for service notifications to patients, its use for collection of patient-reported measures is still nascent. Objective This study assessed the mode preferences, response rates, and mode effect for a practice-based automated patient survey using phone and email modalities to patients of primary care practices. Methods This cross-sectional study analyzed responses and respondent demographics for a short, fully automated, telephone or email patient survey sent to individuals within 72 hours of a visit to their regular primary care practice. Each survey consisted of 5 questions drawn from a larger study’s patient survey that all respondents completed in the waiting room at the time of their visit. Automated patient survey responses were linked to self-reported sociodemographic information provided on the waiting room survey including age, sex, reported income, and health status. Results A total of 871 patients from 87 primary care practices in British Columbia, Ontario, and Nova Scotia, Canada, agreed to the automated patient survey and 470 patients (45.2%) completed all 5 questions on the automated survey. Email administration of the follow-up survey was preferred over phone-based administration, except among patients aged 75 years and older (P Conclusions An automated practice-based patient experience survey achieved significantly different response rates between phone and email and increased response rates for email as income group rose. Potential mode effects for the different survey modalities may limit multimodal survey approaches. An automated minimal burden patient survey could facilitate the integration of patient-reported outcomes into care planning and service organization, supporting the move of our primary care practices toward a more responsive, patient-centered, continual learning system. However, practices must be attentive to furthering inequities in health care by underrepresenting the experience of certain groups in decision making based on the reach of different survey modes.

Motivation Many methods for testing association between the microbiome and covariates of interest (e.g. clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence–absence associations face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence–absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction. Results We have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here, we present an extension of the LDM for testing presence–absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM to all rarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms an F-statistic based on these average RSS terms. We show that this approach compares favorably to averaging the F-statistic from R rarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for. Our simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which cases have systematically smaller library sizes than controls. Availabilityand implementation The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. Supplementary information Supplementary data are available at Bioinformatics online.

Background COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. Objective The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. Methods We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. Results We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. Conclusions Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations.

The method of quantum refinement has been extended to systems with multiple conformations in the quantum system. It has been used to improve the description of the P-cluster in three different oxidation states in two crystal structures of the enzyme nitrogenase., X-ray crystallography is the main source of atomistic information on the structure of proteins. Normal crystal structures are obtained as a compromise between the X-ray scattering data and a set of empirical restraints that ensure chemically reasonable bond lengths and angles. However, such restraints are not always available or accurate for nonstandard parts of the structure, for example substrates, inhibitors and metal sites. The method of quantum refinement, in which these empirical restraints are replaced by quantum-mechanical (QM) calculations, has previously been suggested for small but interesting parts of the protein. Here, this approach is extended to allow for multiple conformations in the QM region by performing separate QM calculations for each conformation. This approach is shown to work properly and leads to improved structures in terms of electron-density maps and real-space difference density Z-scores. It is also shown that the quality of the structures can be gauged using QM strain energies. The approach, called ComQumX-2QM, is applied to the P-cluster in two different crystal structures of the enzyme nitrogenase, i.e. an Fe8S7Cys6 cluster, used for electron transfer. One structure is at a very high resolution (1.0 Å) and shows a mixture of two different oxidation states, the fully reduced PN state (Fe8 2+, 20%) and the doubly oxidized P2+ state (80%). In the original crystal structure the coordinates differed for only two iron ions, but here it is shown that the two states also show differences in other atoms of up to 0.7 Å. The second structure is at a more modest resolution, 2.1 Å, and was originally suggested to show only the one-electron oxidized state, P1+. Here, it is shown that it is rather a 50/50% mixture of the P1+ and P2+ states and that many of the Fe—Fe and Fe—S distances in the original structure were quite inaccurate (by up to 0.8 Å). This shows that the new ComQumX-2QM approach can be used to sort out what is actually seen in crystal structures with dual conformations and to give locally improved coordinates.

Motivation Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction. Results We propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms. Availability and implementation CytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads. Supplementary information Supplementary data are available at Bioinformatics online.