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This study adopts a resilience perspective to explain how companies managed to contribute innovative solutions to fight the COVID-19 crisis. We studied how five companies operating in different industries (three in automotive, one in printing, and one in rubber and plastic products manufacturing) managed to reorganize activities and employ their R&D and innovation capabilities to enhance their resilience. Simultaneously, they increased the health system’s capacity to cope with the outbreak. Through a qualitative inductive study, based on interviews with company managers, we found that the firms mobilized their resources and capabilities to expand their ability to adapt and cope with adversity at the organizational level. In addition, moved by the sensitivity to the extreme context and a perceived sense of urgency, the firms deployed the same endowments to strengthen the community’s response to a crisis. Our study shows that an organization can directly and positively foster the broader social system’s resilience. This study contributes to the innovation literature by identifying innovation capabilities as fundamental antecedents of resilience building for organizational response, paving the way for strengthening the link between resilience and innovation.

Background Adherence to ADHD medication is a complex phenomenon as the decision to adhere is influenced by a range of factors. To design tailored interventions to promote adherence, it is important to understand the factors that influence adherence in the context of its three phases: initiation, implementation and discontinuation. Objective The objective of this study was to explore the phase‐specific factors that influence adherence to medication in adults who have a diagnosis of ADHD. Methods Three focus groups (FGs) were conducted with twenty adults with ADHD in different metropolitan areas of Sydney, Australia. FGs were transcribed verbatim and thematically analysed. Results Participants’ decision to initiate medication (the initiation phase) was influenced by their perceived needs (desire to improve academic and social functioning) and concerns (fear of side‐effects) about medication following a similar process as defined by the Necessity‐Concerns Framework (NCF). The balance between benefits of medication (needs) and side‐effects (concerns) continued to determine participants’ daily medication‐taking (the implementation phase) and persistence (or discontinuation) with their medication. Forgetfulness and stigma were reported as concerns negatively impacting the implementation phase, while medication cost and dependence influenced the discontinuation phase of adherence. Conclusions Adults’ decision to initiate, continue or discontinue medication is influenced by a range of factors; some are unique to each phase while some are common across the phases. Participants balanced the needs for the medication against their concerns in determining whether to adhere to medication at each phase. It appears that the NCF has applicability when decision making about medication is explored at the three phases of adherence.

This paper explores the methodological aspects of a user‐led study investigating mental health service user experiences of targeted violence and abuse (often called 'hate crime'). 'Keeping Control' was a 16‐month qualitative study, undertaken in the context of adult safeguarding reforms in England. By collecting data on service user concepts and experiences, the research sought to address a gap in research and practice knowledge relating to targeted violence, abuse and hostility against people with mental health problems. In this paper, we discuss the significance of the design and methodology used for this study, with a particular focus on the interviews with service users. The research was both user‐led and carried out in collaboration with practitioners and academics, a form of research co‐production. Our aim is to inform researchers, practitioners and policymakers about the value of user leadership in co‐productive research with practitioners, particularly for a highly sensitive and potentially distressing topic.

Innovation systems have seen diverse actors attempting to tame the Covid‐19 crisis, under varying degrees of government direction. Largely neglected in scholarly and public attention, however, are ‘bottom‐up’ solutions arising from the periphery of innovation systems. Drawing on inductive case research on a fringe doctor who invented the idea of the drive‐through testing system, and two university student teams that developed coronavirus applications, this study examines how peripheral actors generate innovative, bottom‐up solutions at speed in a time of crisis. Our findings reveal that, in a crisis situation, bottom‐up solutions transpire on the basis of three innovation drivers: (a) peripheral status, expediting the commence of innovation activities; (b) interdisciplinary collaboration, enabling access to a greater spectrum of knowledge and perspectives; and (c) prior knowledge, prescribing the direction of solution generation. We also identify that system intermediaries support the innovation activities of peripheral actors, thereby helping bottom‐up solutions to become more customer facing. Such functions of intermediaries include demand articulation, technical assistance, and promulgation of generated solutions. Our findings offer theoretical implications for the literature on innovation in a time of crisis and practical implications for governments and organizations preparing themselves for the potential second wave of coronavirus emergencies, or even a completely new form of future crisis.

Background Individual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD-MA. Objectives To create a web-based tool which integrates recommended statistical analyses for DTA IPD-MA, and allows researchers to analyse the data and visualize the results with interactive plots. Methods A systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD-MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package. Results IPDmada allows users to upload their own data, perform the meta-analysis with both continuous and dichotomized tests, and incorporate individual level covariate-adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada. Conclusions IPDmada will be very helpful to researchers doing DTA IPD-MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD-MA and ensure the quality of such analyses. Highlights IPDmada is a newly developed web-based tool for performing statistical analysis of individual patient data meta-analysis of diagnostic accuracy and visualizing the results. The tool is freely available to all the researchers, and requiring no installation of statistical software/packages. The tool has an user-friendly interface, and allows meta-analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy. The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD-MA and increase the quality of such studies.

Evidence-based decision-making is predicated on the ability of users to find and comprehend results from systematic review. Evidence producers have an obligation to support evidence users in this process. The Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) program-a producer of rigorous and comprehensive systematic reviews for two decades-has set a gold standard for reliability in health evidence reviews in the United States. It has recently begun a program of active support for evidence dissemination and uptake beyond mere publication of lengthy reports. This Brief Methods Note critiques the current paper-based format for systematic reviews and describes the development of a next generation (NxGen) AHRQ EPC Effective Health Care website. This redesigned platform will allow end-users of all types to find and share the evidence they need through data visualizations and other interactive displays. Several design principles guided the development of NxGen to make systematic review findings more accessible, customizable, adaptable, interactive, and shareable. NxGen will include visualizations for report results that are expressed as meta-analyses as well as those with narrative syntheses, through forest or bubble plots, respectively. Visual and interactive evidence heat maps are also planned. The NxGen version of the Effective Health Care website is planned to go live in the latter half of 2020 or early 2021.

Introduction Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short‐ and long‐term impact of an interruption. Further, well‐constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta‐analyses. Aim We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013‐2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re‐use of the data in systematic reviews and meta‐analyses., Highlights Application of data visualization recommendations can improve quality of interrupted time series graphs.Well‐designed graphs accurately depict time series data, any impact of the interruption, and the results of the analysis.Well‐designed graphs facilitate data extraction for use in systematic reviews and reproducibility.An assessment of graphs included in interrupted time series studies (published between 2013 and 2017) found that graphs often do not meet core graphing recommendations.

Aim The aim of the study was to assess the influence of the number of hours of daily nursing care for NHPPD in medical departments on missed care and the correlation between NHPPD and in-hospital mortality. Background Patient mortality can be a consequence of missed care as it correlates with the nurse-patient ratio. One of the methods to measure missed care is the Nursing Hours per Patient Day rating. Methods The study sample included 44,809 patients including 971 deaths in 8 wards. The influence of nursing hours, nursing education, and the percentage of patients' classification on in-hospital mortality were evaluated with backward stepwise linear regression. Results One hour added to the average NHPPD in medical departments was related to a decrease in mortality rate by 6.8 per 1,000 patient days and a lower chance for the emergence of unplanned death by 36%. Conclusions The number of NHPPD and the percentage of professional nurses are factors influencing missed care and in-hospital mortality. Implications for nursing management The severe consequences of missed care, that is mortality, and the correlation between in-hospital mortality, nursing education and nursing-patient ratio, which are indicators of care quality, are arguments for maintaining adequate staffing levels to avoid missed care.

Being a grand challenge of global scale, the COVID‐19 pandemic requires collective and collaborative efforts from a variety of actors to enable the expected scientific advancement and technological progress. To achieve such an open innovation approach, several initiatives have been launched in order to leverage potential distributed knowledge sources that go beyond those available to any single organization. A particular tool that has gained some momentum during COVID‐19 times is hackathons, which have been used to unleash the innovation potential of individuals who voluntarily came together, for a relatively short period of time, with the aim to solve specific problems. In this paper, we describe and analyze the case of the hackathon EUvsVirus, led by the European Innovation Council. EUvs Virus was a 3‐day online hackathon to connect civil society, innovators, partners, and investors across Europe and beyond in order to develop innovative solutions to coronavirus‐related challenges. We have identified four dimensions to explore hackathons as a crowdsourcing tool for practicing effective open innovation in the face of COVID‐19: broad scope, participatory architecture, online setting, and community creation. We discuss how these four elements can play a strategic role in the face of grand challenges, which require, as in the case of the COVID‐19 pandemic, both urgent action and long‐term thinking. Our case analysis also suggests the need to look beyond the ‘usual suspects’, through knowledge recombination with atypical resources (e.g., retired experts, graduate students, and the general public). On this basis, we call for a broader perspective on open innovation, to be extended beyond openness across organizational boundaries, and to explore the role of openness at societal level.

Latinos in the United States represent a disproportionate burden of illness and disease and face barriers to accessing health care and related resources. Culturally tailored, evidence‐based interventions hold promise in addressing many of these challenges. Yet, ensuring patient voice is vital in the successful development and implementation of such interventions. Thus, this paper examines the application of analytic hierarchy process (AHP) to inform the augmentation and implementation of an evidence‐based chronic disease self‐management programme for underserved Latinos living with both minor depression and chronic illness. The process of AHP allows for direct input from the individuals that would utilize such a programme, including afflicted individuals, their family members and the health educators/promotores that would be responsible for implementation. Specifically, 45 participants, including 15 individuals with chronic disease, 15 family members/caregivers and 15 promotores, partook in the Stakeholder Values Questionnaire, which elicited preferences and values regarding major goals, processes and content for the intervention. AHP was employed to analyse pairwise comparison ratings and to determine differences and similarities across stakeholder groups. This analytical technique allowed for the adaptation of the EBI to stakeholders' specific priorities and preferences and facilitated complex decision‐making. Findings not only shed light on similarities and differences between stakeholder groups, but also the magnitude of these priorities and preferences and allowed the intervention to be driven by the participants, themselves. Applying AHP was a unique opportunity to optimize the decision‐making process to inform cultural adaptation of an EBI while considering multiple viewpoints systematically.

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

This study contributes to our understanding of how the emergence of the COVID‐19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID‐19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID‐19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID‐19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real‐world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID‐19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

Summary Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H‐channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country‐specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid‐19 related parameters in the long‐term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well‐suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long‐term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real‐time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Summary COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

The goal of this study is to examine knowledge sharing as a boundary condition under which employee innovation can be enhanced in response to the job stress induced by the COVID-19 pandemic. We argue that when stressed employees share knowledge, they can expand their knowledge base and thereby enhance their innovative potential. Consistent with our hypothesis, multiple regression analysis results based on a sample of 61 R&D employees of UK and US technology-based firms show that knowledge sharing moderated the relationship between COVID-19-induced job stress and employee innovation, such that the relationship was negative when knowledge sharing was lower but became positive when knowledge sharing was higher. These findings highlight the importance of investing in knowledge-based resources to promote innovation behavior at work during a pandemic.

COVID-19 has been the most important pandemic that hit the world in the last century An overwhelming number of initiatives aimed to save lives and protect humanity came out especially at the beginning of the health crisis, following an outside the box approach to address relevant R&D problems Digital makers, not surprisingly, have been part of the game Being masters in using digital technologies for customized products, and being strategically fitted to cope with the imperative to be fast, many of the players on the front line to fight the pandemic have looked at them as the ‘providers next to the door’ able to offer fast solutions to critical problems Based on these premises and adopting frugal innovation as theoretical base, the aim of the study is to analyze how digital makers have taken part in R&D processes to find solutions to the problems created by an exogenous and unexpected sanitary problem Through an in-depth case study, we provide insights on if and how frugal approach to innovation is adapt to cope with the extraordinary pressure coming from event like the COVID-19 The results of the study highlight the power of connecting frugal innovation principles, agile mindset of makers and open innovation strategies in providing effective solutions on a local scale and eventually exploit their potential global impact Furthermore, we highlight implications and pathways for future researches © 2021 RADMA and John Wiley & Sons Ltd

The COVID‐19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID‐19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re‐establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain‐based privacy‐preserving platform for COVID‐19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self‐sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID‐19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self‐sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof‐of‐concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer‐indistinguishability, and revocation.

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

Purpose: The implementation of photon-counting detectors is widely expected to be the next breakthrough in X-ray computed tomography (CT) instrumentation. A small number of prototype scanners equipped with direct-conversion detectors based on room-temperature semiconductors, such as CdTe and CdZnTe (CZT), are currently installed at medical centers. Here, we investigate the feasibility of using silicon photomultiplier (SiPM)-based scintillation detectors in photon-counting computed tomography (PCCT) scanners, as a potential alternative to CdTe and CZT detectors. Methods: We introduce a model that allows us to compute the expected energy resolution as well as the expected pulse shape and associated rate capability of SiPM-based PCCT detectors. The model takes into account SiPM saturation and optical crosstalk, because these phenomena may substantially affect the performance of SiPM-based PCCT detectors with sub-mm pixels. We present model validation experiments using a single-pixel detector consisting of a 0.9 × 0.9 × 1.0 mm3 LuAP:Ce scintillation crystal coupled to a 1 × 1 mm2 SiPM. We subsequently use the validated model to compute the expected performance of the fast scintillators LYSO:Ce, LuAP:Ce, and LaBr3:Ce, coupled to currently available SiPMs, as well as to a more advanced SiPM prototype with improved dynamic range, for sub-mm pixel sizes. Results: The model was found to be in good agreement with the validation experiments, both with respect to energy resolution and pulse shape. It shows how saturation progressively degrades the energy resolution of detectors equipped with currently available SiPMs as the pixel size decreases. Moreover, the expected pulse duration is relatively long (~200 ns) with these SiPMs. However, when LuAP:Ce and LaBr3:Ce are coupled to the more advanced SiPM prototype, the pulse duration improves to less than 60 ns, which is in the same order of magnitude as pulses from CdTe and CZT detectors. It follows that sufficient rate capability can be achieved with pixel sizes of 400 μm or smaller. Moreover, LaBr3:Ce detectors can provide an energy resolution of 11.5%-13.5% at 60 keV, comparable to CdTe and CZT detectors. Conclusions: This work provides first evidence that it may be feasible to develop SiPM-based scintillation detectors for PCCT that can compete with CdTe and CZT detectors in terms of energy resolution and rate capability.

Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot require updating in light of new Cochrane guidance on alternative synthesis methods. To update the ED plot, statistical significance was removed from the algorithm for within-study synthesis and use of a sign test was considered to examine whether patterns of ED across studies could be due to chance alone. The revised methods were applied to an existing Cochrane review of the health impacts of housing improvements. The revised ED plot provides a method of data visualization in synthesis without meta-analysis that incorporates information about study characteristics and study quality, using ED as a common metric, without relying on statistical significance to combine outcomes of single studies. The results of sign tests, when appropriate, suggest caution in over-interpreting apparent patterns in effect direction, especially when the number of included studies is small. The revised ED plot meets the need for alternative methods of synthesis and data visualization when meta-analysis is not possible, enabling a transparent link between the data and conclusions of a systematic review. ED plots may be particularly useful in reviews that incorporate nonrandomized studies, complex systems approaches, and diverse sources of evidence, due to the variety of study designs and outcomes in such reviews.

The COVID-19 pandemic has caused unprecedented public health and economic crises. As a response to face the current emergency, science and innovation communities are realizing a fundamental contribution to tackle the crisis. During the past few months, we have witnessed an impressive number of initiatives to encourage networking opportunities, to foster interactions between the different stakeholders involved (health care, industry, governments, academics, ordinary people), and to develop innovative solutions and collaborative infrastructures in support of the health sector. Adopting an open and collaborative approach and joining forces is essential in the fight against the COVID-19 crisis. Also, the involvement of crowds as innovation partners can be of great support. Therefore, our work aims to review and classify those initiatives, based on the crowdsourcing model, that have been put into place to face the emergency generated by the novel coronavirus pandemic. We illustrate the 16 crowdsourcing initiatives devoted to the SARS-CoV-2 outbreak that we identified, detailing their development and implementation. Then, we propose a classification of them, along two dimensions: type of crowdsourcing configuration and kind of tasks, being able to find a relationship between these two aspects. Evidence from the analyzed projects suggests that across disparate domains, crowdsourcing can be an effective strategy in the response to the COVID-19 pandemic. To conclude, we suggest some important implications for innovation best practices and lessons that can be learned for the future: crowdsourcing, harnessing the power of crowds and online communities, can help tackle the COVID-19 pandemic, by providing original, actionable, quick, and low-cost solutions to the challenges of the current health and economic crisis.

Background The prevalence of neuropsychiatric symptoms (NPS) diminishes the quality of life and increases the care burden in patients with dementia. Despite the clinical importance of dementia-associated NPS, no protocols for treating NPS are already well established. Attention has turned to the effectiveness of nonpharmacological treatments for NPS since their potential safe alternative to pharmacotherapy. Objective This study is aimed to compare the effects in older individuals with dementia living in a residential care, of two intervention programs, the gesture-verbal treatment (GVT), a treatment implemented by us on a previous method for word retrieval in individuals with aphasia, and the better-known doll therapy (DT). The GVT would act on both receptive and expressive language skills, the DT on attachment and emotional connections. Methods We evaluated NPS by the neuropsychiatric inventory in a total of 30 patients divided into 3 groups, the GVT, the DT, and control groups, using a pre-post design. The treatment groups completed 12-week nonpharmacological interventions in addition to standard rehabilitative therapies, while the control group participated only in standard rehabilitative therapies. Results The DT group showed significant improvements in agitation, irritability, apathy, depression, and delusions relative to controls. The GVT group showed significant improvements in apathy and depression with respect to controls. The DT intervention ameliorated symptoms of agitation compared to the GVT intervention whereas the GVT intervention improved apathy compared to the DT intervention. Conclusion Improved understanding of the potential therapeutic benefits of different treatments for neuropsychiatric symptoms is crucial for establishing nonpharmacological interventions in dementia.

Aim To compare the patient acuity, nurse staffing and workforce, missed nursing care and patient outcomes among hospital unit-clusters. Background Relationships among acuity, nurse staffing and workforce, missed nursing care and patient outcomes are not completely understood. Method Descriptive design with data from four unit-clusters: medical, surgical, combined and step-down units. Descriptive statistics were used to compare acuity, nurse staffing coverage, education and expertise, missed nursing care and selected nurse-sensitive outcomes. Results Patient acuity in general (medical, surgical and combined) floors is similar to step-down units, with an average of 5.6 required RN hours per patient day. In general wards, available RN hours per patient day reach only 50% of required RN hours to meet patient needs. Workforce measures are comparable among unit-clusters, and average missed nursing care is 21%. Patient outcomes vary among unit-clusters. Conclusion Patient acuity is similar among unit-clusters, while nurse staffing coverage is halved in general wards. While RN education, expertise and missed care are comparable among unit-clusters, mortality, skin injuries and risk of family compassion fatigue rates are higher in general wards. Implications for nursing management Nurse managers play a pivotal role in hustling policymakers to address structural understaffing in general wards, to maximize patient safety outcomes.

The analysis of adverse events plays an important role in the benefit assessment of drugs. Consequently, results on adverse events are an integral part of reimbursement dossiers submitted by pharmaceutical companies to health policy decision-makers. Methods applied in the analysis of adverse events commonly include simple standard methods for contingency tables. However, the results produced may be misleading if observations are censored at the time of discontinuation due to treatment switching or noncompliance, resulting in unequal follow-up periods. In this paper, we present examples to show that the application of inadequate methods for the analysis of adverse events in the reimbursement dossier can lead to a downgrading of the evidence on a drug's benefit in the subsequent assessment, as greater harm from the drug cannot be excluded with sufficient certainty. Legal regulations on the benefit assessment of drugs in Germany are presented, in particular, with regard to the analysis of adverse events. Differences in safety considerations between the drug approval process and the benefit assessment are discussed. We show that the naive application of simple proportions in reimbursement dossiers frequently leads to uninterpretable results if observations are censored and the average follow-up periods differ between treatment groups. Likewise, the application of incidence rates may be misleading in the case of recurrent events and unequal follow-up periods. To allow for an appropriate benefit assessment of drugs, adequate survival time methods accounting for time dependencies and duration of follow-up are required, not only for time-to-event efficacy endpoints but also for adverse events. © 2016 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.

Marine pollution is ubiquitous, and is one of the key factors influencing contemporary marine biodiversity worldwide. To protect marine biodiversity, how do we surveil, document and predict the short- and long-term impacts of pollutants on at-risk species? Modern genomics tools offer high-throughput, information-rich and increasingly cost-effective approaches for characterizing biological responses to environmental stress, and are important tools within an increasing sophisticated kit for surveiling and assessing impacts of pollutants on marine species. Through the lens of recent research in marine killifish, we illustrate how genomics tools may be useful for screening chemicals and pollutants for biological activity and to reveal specific mechanisms of action. The high dimensionality of transcriptomic responses enables their usage as highly specific fingerprints of exposure, and these fingerprints can be used to diagnose environmental problems. We also emphasize that molecular pathways recruited to respond at physiological timescales are the same pathways that may be targets for natural selection during chronic exposure to pollutants. Gene complement and sequence variation in those pathways can be related to variation in sensitivity to environmental pollutants within and among species. Furthermore, allelic variation associated with evolved tolerance in those pathways could be tracked to estimate the pace of environmental health decline and recovery. We finish by integrating these paradigms into a vision of how genomics approaches could anchor a modernized framework for advancing the predictive capacity of environmental and ecotoxicological science.

Risk prediction models are used to predict a clinical outcome for patients using a set of predictors. We focus on predicting low‐dimensional binary outcomes typically arising in epidemiology, health services and public health research where logistic regression is commonly used. When the number of events is small compared with the number of regression coefficients, model overfitting can be a serious problem. An overfitted model tends to demonstrate poor predictive accuracy when applied to new data. We review frequentist and Bayesian shrinkage methods that may alleviate overfitting by shrinking the regression coefficients towards zero (some methods can also provide more parsimonious models by omitting some predictors). We evaluated their predictive performance in comparison with maximum likelihood estimation using real and simulated data. The simulation study showed that maximum likelihood estimation tends to produce overfitted models with poor predictive performance in scenarios with few events, and penalised methods can offer improvement. Ridge regression performed well, except in scenarios with many noise predictors. Lasso performed better than ridge in scenarios with many noise predictors and worse in the presence of correlated predictors. Elastic net, a hybrid of the two, performed well in all scenarios. Adaptive lasso and smoothly clipped absolute deviation performed best in scenarios with many noise predictors; in other scenarios, their performance was inferior to that of ridge and lasso. Bayesian approaches performed well when the hyperparameters for the priors were chosen carefully. Their use may aid variable selection, and they can be easily extended to clustered‐data settings and to incorporate external information. © 2015 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.

Various sources of variation in observed response in clinical trials and clinical practice are considered, and ways in which the corresponding components of variation might be estimated are discussed. Although the issues have been generally well‐covered in the statistical literature, they seem to be poorly understood in the medical literature and even the statistical literature occasionally shows some confusion. To increase understanding and communication, some simple graphical approaches to illustrating issues are proposed. It is also suggested that reducing variation in medical practice might make as big a contribution to improving health outcome as personalising its delivery according to the patient. It is concluded that the common belief that there is a strong personal element in response to treatment is not based on sound statistical evidence. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Summary For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose–response function assumes correct specification of both the GPS and the outcome‐treatment relationship. This paper introduces a machine learning method, the ‘Super Learner’, to address model selection in this context. In the two‐stage estimation approach proposed, the Super Learner selects a GPS and then a dose–response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose–response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.

This article is about the recognition of personhood when death occurs in early life. Drawing from anthropological perspectives on personhood at the beginnings and ends of life, it examines the implications of competing religious and customary definitions of personhood for a small sample of young British Pakistani Muslim women who experienced miscarriage and stillbirth. It suggests that these women's concerns about the lack of recognition given to the personhood of their fetus or baby constitute a challenge to customary practices surrounding burial as a Muslim. The article suggests that these women's concerns cannot be adequately glossed as a clash of Islamic belief versus Western medicine. Rather, they represent a renegotiation of Islamic opinion and customary practices within the broader context of changes in the medical and social norms surrounding pregnancy loss and infant death in multi-ethnic British society.

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of ‘big data’ research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European ‘Network of Excellence’ (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.

Network meta-analysis (multiple treatments meta-analysis, mixed treatment comparisons) attempts to make the best use of a set of studies comparing more than two treatments. However, it is important to assess whether a body of evidence is consistent or inconsistent. Previous work on models for network meta-analysis that allow for heterogeneity between studies has either been restricted to two-arm trials or followed a Bayesian framework. We propose two new frequentist ways to estimate consistency and inconsistency models by expressing them as multivariate random-effects meta-regressions, which can be implemented in some standard software packages. We illustrate the approach using the mvmeta package in Stata. Copyright © 2012 John Wiley & Sons, Ltd.

More than 70 years after the first ex situ genebanks have been established, major efforts in this field are still concerned with issues related to further completion of individual collections and securing of their storage. Attempts regarding valorization of ex situ collections for plant breeders have been hampered by the limited availability of phenotypic and genotypic information. With the advent of molecular marker technologies first efforts were made to fingerprint genebank accessions, albeit on a very small scale and mostly based on inadequate DNA marker systems. Advances in DNA sequencing technology and the development of high-throughput systems for multiparallel interrogation of thousands of single nucleotide polymorphisms (SNPs) now provide a suite of technological platforms facilitating the analysis of several hundred of Gigabases per day using state-of-the-art sequencing technology or, at the same time, of thousands of SNPs. The present review summarizes recent developments regarding the deployment of these technologies for the analysis of plant genetic resources, in order to identify patterns of genetic diversity, map quantitative traits and mine novel alleles from the vast amount of genetic resources maintained in genebanks around the world. It also refers to the various shortcomings and bottlenecks that need to be overcome to leverage the full potential of high-throughput DNA analysis for the targeted utilization of plant genetic resources.

Life expectancy has dramatically increased in industrialized nations over the last 200 hundred years. The aging of populations carries over to clinical research and leads to an increasing representation of elderly and multimorbid individuals in study populations. Clinical research in these populations is complicated by the fact that individuals are likely to experience several potential disease endpoints that prevent some disease-specific endpoint of interest from occurrence. Large developments in competing risks methodology have been achieved over the last decades, but we assume that recognition of competing risks in the clinical community is still marginal. It is the aim of this article to address translational aspects of competing risks to the clinical community. We describe clinical populations where competing risks issues may arise. We then discuss the importance of agreement between the competing risks methodology and the study aim, in particular the distinction between etiologic and prognostic research questions. In a review of 50 clinical studies performed in individuals susceptible to competing risks published in high-impact clinical journals, we found competing risks issues in 70% of all articles. Better recognition of issues related to competing risks and of statistical methods that deal with competing risks in accordance with the aim of the study is needed. Copyright (C) 2011 John Wiley & Sons, Ltd.

As time goes by, it becomes more and more apparent that the puzzles of life involve more and more molecular pieces that fit together in increasingly complex ways. Genomics and Proteomics technologies nowadays, produce reliable and quantitative data that could potentially reveal all the molecular pieces of a particular puzzle. However, this is akin to the opening of Pandora's box; and we are now facing the problem of integrating this vast amount of data with its incredible complexity into some coherent whole. With the aid of engineering methods designed to build and analyze computerized man-made systems, a new emerging field called 'Executable Biology' aims to create computer programmes that put together the pieces in ways that allows capturing their dynamicity and ultimately elucidating how molecular function generates cellular function. This review aspires to highlight the main features characterizing these kinds of executable models and what makes them uniquely qualified to reason about and analyze biological networks.

In standard survival analysis, it is generally assumed that every individual will experience someday the event of interest. However, this is not always the case, as some individuals may not be susceptible to this event. Also, in medical studies, it is frequent that patients come to scheduled interviews and that the time to the event is only known to occur between two visits. That is, the data are interval‐censored with a cure fraction. Variable selection in such a setting is of outstanding interest. Covariates impacting the survival are not necessarily the same as those impacting the probability to experience the event. The objective of this paper is to develop a parametric but flexible statistical model to analyze data that are interval‐censored and include a fraction of cured individuals when the number of potential covariates may be large. We use the parametric mixture cure model with an accelerated failure time regression model for the survival, along with the extended generalized gamma for the error term. To overcome the issue of non‐stable and non‐continuous variable selection procedures, we extend the adaptive LASSO to our model. By means of simulation studies, we show good performance of our method and discuss the behavior of estimates with varying cure and censoring proportion. Lastly, our proposed method is illustrated with a real dataset studying the time until conversion to mild cognitive impairment, a possible precursor of Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even 'standard' analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests. In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved.

Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies.

OBJECTIVES: Hearing, vision, and cognitive impairment commonly co-occur in older adults. Improving sensory function may positively impact outcomes in people with dementia (PwD). We developed a "sensory intervention" (SI) to support hearing and vision in PwD. Here, we report the findings of an international open-label field trial, and nested case series, to explore the impact of the SI on dementia-related outcomes.METHODS: This was a home-based trial conducted in France, England, and Cyprus. Participants were people with mild-to-moderate dementia and hearing and/or vision impairment (n = 19) and their study partners (unpaid carers; n = 19). The "basic" SI included a hearing and vision assessment and provision of glasses and/or hearing aids. A subsample received the "extended" SI with additional weekly visits from a sensory support therapist (SST). Exploratory analyses of dementia-related, health utility and resource utilisation outcomes were performed.RESULTS: Quality of life (QoL) and sensory functional ability improved. Change in QoL exceeded the threshold for a minimum clinically important difference. There was a modest improvement (in absolute terms) post intervention in behavioural disturbance, self-efficacy, and relationship satisfaction. Study partner time assisting instrumental activities of daily living (iADL) and supervision decreased by about 22 and 38 hours per month, respectively, although time for personal ADL support increased. Qualitative data supported effectiveness of the intervention: PwD were more socially engaged, less isolated, less dependent on study partners, and had improved functional ability and communication.CONCLUSIONS: These findings support the need for a definitive randomised controlled trial (RCT) to evaluate the effectiveness of the intervention.