A focal point in the safety evaluation of cosmetic ingredients includes oral repeated dose toxicity testing, which is intended to address the most complex human endpoints. Seven years after the full implementation of the animal testing ban for cosmetic ingredients in the EU, there are still no alternative methods available capable of fully replacing oral repeated dose toxicity testing. Until this issue is resolved, the development of new cosmetic ingredients remains seriously hampered. The present paper describes a thorough screening of the oral repeated dose toxicity data included in safety evaluation reports of cosmetic ingredients addressed in the Annexes of the Cosmetics Regulation (EC) No 1223/2009, issued by the Scientific Committee on Consumer Safety between 2009 and 2019. The liver and the haematological system were identified as the potentially most frequently affected organs upon oral administration of cosmetic ingredients to animals. Evaluation of altered biochemical, morphological, and histopathological parameters related to hepatotoxicity indicated that the most recurrent events are liver weight changes, elevated liver enzymes, and alterations in serum cholesterol and bilirubin levels. Combined listing of affected parameters associated with steatosis and cholestasis indicated the possible occurrence of cholestasis, provoked by a limited number of cosmetic ingredients. The most frequently affected parameters related to the haematological system were indicative of anaemia. An in-depth analysis allowed characterisation of both regenerative and non-regenerative anaemia, pointing to direct and indirect haematotoxicity, respectively. The results presented in this study call for prioritisation of research targeted towards the development of new approach methodologies fit for animal-free repeated dose toxicity evaluation of cosmetic ingredients.The online version of this article (10.1007/s00204-020-02868-2) contains supplementary material, which is available to authorized users. The procedure for safety evaluation of cosmetic products and their ingredients has undergone major changes in Europe during the past decade. Since 11 September 2004, the 7th Amendment to Directive 76/768/EEC prohibits animal experiments of finished cosmetic products, while such testing ban on cosmetic ingredients, or combinations thereof, entered into force from 11 March 2009 onwards EU , 2003. AToday, validated non-animal methods are available for testing short-term and local toxicological endpoints, including skin irritation/corrosion (sys (PODsys) over systemic exposure dose (SED). PODsys denotes the point of departure for systemic toxicity of the substance under consideration. Typically, the no observed adverse effect level (NOAEL) in the most sensitive organ, historically derived through in vivo RDT testing, is used as POD and consequently is a key point in the overall safety assessment of the cosmetic ingredient. The SED is a function of the concentration present in the cosmetic product, usually topically applied, and its dermal absorption. When the MoS is equal to, or greater than, 100, the substance is generally considered safe under the intended use conditions (SCCS According to Cosmetics Regulation (EC) No 1223/2009 EU . The MoSons SCCS .Several EU and industry-funded projects, both completed and ongoing, such as the Safety Evaluation Ultimately Replacing Animal Testing (SEURAT),The study material consisted of 114 SCCS opinions issued between 22 January 2009 and 31 December 2019 dealing with 101 unique cosmetic ingredients in total. All information used in this study was downloaded from the SCCS website.® Office Excel spreadsheet based on the organ in which the critical effect(s) occurred and from which a NOAEL could be derived. In cases of multiple target organs, the sorting was based on what appeared to be the most relevant and sensitive effect. Further analysis of the data included the listing of all changes in morphological, histopathological, and blood biochemical parameters relevant for the top 2 affected organs and/or organ systems described in 88 opinions containing RDT data.For identification of target organ(s), data were manually collected from oral RDT studies described in the SCCS opinions, thus without access to the raw data submitted to the SCCS. The information was sorted into a MicrosoftAmong the 101 substances covered in the SCCS opinions included in the study, no adequate oral RDT data for determination of an NOAEL were available for 13 cosmetic ingredients. For 1 of the remaining 89 substances, a read-across approach from other routes of exposure was applied. Hence, 88 opinions contained oral RDT data for which an NOAEL determination and subsequent MoS calculation was feasible  and through read-across from 28-day studies (5) or from chronic studies (2). A total of 120 teratogenicity studies were provided for 79 cosmetic ingredients, again suggesting that a specific ingredient may have been repeatedly subjected to similar tests. Because teratogenicity studies are specifically designed to assess the effects on maternal health, foetal abnormalities and/or altered growth of the foetus, they were not further considered in the present study. Chronic, carcinogenicity, and two-generation reproductive toxicity studies were represented to a lesser extent in the SCCS opinions, albeit displaying a similar pattern of studies repeated for a certain ingredient.As the 90-day oral RDT studies are specifically designed to provide information on toxic effects and indicate target organs upon repeated exposure OECD , informaCritical effects and/or target organs used to set an NOAEL were identified through RDT studies in 75 opinions. Among these 75 opinions, the liver was identified as the target organ for 20 cosmetic ingredients, based on alterations in blood biochemistry, including enzyme activities, and on histopathological changes , making it one of the most frequent targets of toxicity. Due to its anatomical proximity to the digestive tract and high blood flow rate, together with its key role in the biotransformation of xenobiotics, it is not surprising that the liver appears as a main target of toxicity upon repeated oral exposure , will comprise the next generation risk assessment (NGRA). Although scientific challenges remain to be tackled before NAMs are used by default in safety evaluations and advantages with their use, such as an increased relevance to human health, becomes evident. Indeed, NAMs have shown great promise to provide a large amount of data to fill information gaps in both hazard and exposure assessments Below is the link to the electronic supplementary material.


Preterm birth (PTB) and small for gestational age (SGA) are increasingly prevalent, with major consequences for health and development into later life. There is emerging evidence that some risk processes begin before pregnancy. We report on associations between maternal and paternal common mental disorders (CMD) before and during pregnancy and offspring PTB and SGA.398 women with 609 infants and 267 men with 421 infants were assessed repeatedly for CMD symptoms before pregnancy between age 14 and 29 and during pregnancy. Associations between preconception and antenatal CMD symptoms and offspring gestational age/PTB and size for gestational age/SGA were estimated using linear and Poisson regression.In men, persistent preconception CMD across adolescence and young adulthood predicted offspring PTB after adjustment for ethnicity, education, BMI and adolescent substance use , corresponding to a population attributable fraction of 31% of preterm births. In women, antenatal CMD symptoms predicted offspring PTB . There was little evidence of associations with SGA.This first report of an association between paternal preconception mental health and offspring gestational age, while requiring replication in larger samples, complements earlier work on stress in animals, and further strengthens the case for expanding preconception mental health care to both men and women.National Health and Medical Research Council , Victorian Health Promotion Foundation, Australian Rotary Health, Colonial Foundation, Perpetual Trustees, Financial Markets Foundation for Children , Royal Children's Hospital Foundation, Murdoch Children's Research Institute, Australian Research Council. Consistent evidence links maternal antenatal symptoms of common mental disorders (CMD) with offspring preterm birth and small for gestational age. Although findings from animal studies implicate both maternal and paternal preconception stress in offspring birth outcomes, the extent to which maternal and paternal history of preconception CMD predict offspring preterm birth and small for gestational age in humans is unknown.In this population-based preconception cohort study, we prospectively assessed maternal and paternal CMD over 15 years from adolescence to young adulthood and in the third trimester of subsequent pregnancies. Our findings that up to 31% of preterm births may be attributable to processes relating to persistent preconception CMD symptoms in men heralds a paradigm shift for the developmental origins of health and disease. It highlights a need to further explore paternal CMD and other preconception risk factors as determinants of early life health, growth and development.Paternal mental health represents a neglected and potentially novel risk factor for preterm birth. While they require replication in other populations and a greater understanding of potential mechanisms, our findings support expansion of the focus of preconception care to include both men and women. Therefore new and potentially innovative approaches to preconception interventions will be necessary to optimise the health of future generations.Alt-text: Unlabelled box1Preterm birth (PTB) and small for gestational age (SGA) are leading causes of neonatal morbidity and mortality One explanation may be that some risks emerge well before pregnancy Mental health problems commonly become prominent in adolescence, persisting into adulthood and the transition to parenthood ,12. InveHere, we used a two-generation cohort study with prospective data on maternal and paternal mental disorder over 15 years from adolescence to young adulthood, and again during pregnancy. We aimed to examine the association between maternal and paternal symptoms of common mental disorders (CMD) from adolescence to parenthood and offspring birth outcomes of PTB or SGA and whether these associations differ by timing of parental CMD symptoms and parent exposed.22.1The Victorian Intergenerational Health Cohort Study (VIHCS) is an ongoing prospective intergenerational study of preconception predictors of infant and child health, described elsewhere 2.22.2.1Preconception common mental disorder (CMD) symptoms were assessed during VAHCS Waves 2–7 (participant ages 14–21 years) using the Revised Clinical Interview Schedule (CIS-R) Antenatal CMD symptoms were assessed in trimester three of pregnancy. Women were assessed using the Edinburgh Postnatal Depression Scale (EPDS) 2.2.2Offspring sex, gestational age at birth, and birthweight were maternally reported  either at the first postpartum assessment at two months postpartum (86% of participants), at one year postpartum (3%), or during VAHCS wave 10  2.2.3Our conceptual model included factors that were potential confounders of the associations between parental CMD symptoms and infant birth outcomes, excluding those potentially on the causal pathway from exposure to outcome 2.3Data were analysed separately for women and men using Stata version 15 All analyses included participants who responded in at least one preconception wave and at least one perinatal wave. Multiple imputation by chained equations was implemented to handle missing data 3Funding sources had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all data in the study and had responsibility for the final decision to submit for publication.4Data collection protocols were approved by the human research ethics committee at the Royal Children's Hospital, Melbourne, Australia.5Our sample included 398 women with 609 children and 267 men with 421 children. The flow of participants through VIHCS is presented in The unadjusted and adjusted associations between the two exposures of preconception and antenatal CMD symptoms and birth outcomes for women, using imputed data are shown in In men , there wAvailable case analyses yielded a similar pattern of results for both women and men (see Supplementary Table 1 and Supplementary Table 2).6Infants of men with persistent preconception CMD symptoms were more than six times more likely to be born preterm than infants of men without preconception CMD symptoms. Similarly, mean gestational age at birth among infants exposed to persistent paternal preconception CMD symptoms was almost one week earlier than among unexposed infants. If our estimates reflect the magnitude of the true causal effects, up to 31% of PTBs could be attributable to processes relating to persistent paternal preconception CMD symptoms. We also found an increased risk of PTB among infants exposed to maternal antenatal CMD symptoms, corresponding to a population attributable fraction of 13% of PTBs; these findings are consistent with earlier reports The prevalence of preconception CMD symptoms in our study was similar to that previously reported in other prospective cohorts ,41. LikeSeveral mechanisms may explain the association between maternal antenatal CMD symptoms and offspring PTB. We accounted for a number of background factors and maternal characteristics that might increase risk of both maternal CMD symptoms and PTB, including family of origin demographics such as education and ethnicity, substance use, prior preconception CMD symptoms, and BMI. There remains potential for residual confounding, including lifetime experience of interpersonal violence or unplanned pregnancy ,47. TherWe did not find evidence of preconception maternal effects. Prior findings are mixed; those studies reporting preconception associations have mostly assessed preconception stress exposure closer to the time of conception There are similarly a range of processes by which paternal preconception CMD symptoms may be associated with offspring PTB. One possibility is residual confounding by early life characteristics, such as childhood maltreatment Associations between persistent preconception CMD symptoms in men and infant PTB may also be mediated by paternal processes occurring in the months immediately before conception. In the testes, the production of new sperm cells from germ cells begins at puberty and continues throughout life. Production of each sperm cell takes around three months and is affected by environmental exposures, including substance use, poor nutrition, and exposure to environmental toxins, which are associated with both paternal CMD and offspring development Given the dose-response relationship between preconception CMD symptoms and offspring PTB, a further possibility is that chronic or recurrent CMD in the preconception years may have direct, cumulative and enduring effects on the male reproductive tract. There is some precedent for this proposal in the animal literature We did not find evidence of an association between preconception or antenatal CMD symptoms and offspring SGA in either women or men. These findings are consistent with results of prior meta-analyses ,60. One Strengths of this study include the repeated assessment of CMD symptoms across 15 years from adolescence to young adulthood and during subsequent pregnancies. Some limitations should also be noted. While we attempted to control for baseline confounders associated with both CMD and adverse birth outcomes, it is also possible that there is some residual confounding from unmeasured socio-environmental risks such as early life trauma, interpersonal violence, and socioeconomic adversity. Nonetheless, our finding of different maternal and paternal associations also suggest that results are not entirely attributable to familial confounding, under the assumption that maternal and paternal CMD symptoms are similarly confounded As with all cohort studies, the possibility of differential recruitment, attrition and non-response, and changing population demographics over time may have led to underrepresentation of some population groups. VAHCS maintains a high retention rate, and 85% of those with live births during screening participated in VIHCS. The retained and participating samples were broadly representative of the baseline VAHCS and eligible VIHCS samples respectively on measured baseline characteristics, but may differ on unmeasured confounders. Similarly, we were unable to examine associations within specific population groups known to have higher rates of preterm births, such as Aboriginal and Torres Strait Islander peoples ,64, and Life course epidemiology and public health policy have long been grounded in assumptions around the causal primacy of maternal exposures and those occurring during pregnancy and early life 710.13039/501100000923Australian Research Council (DP180102447). Data collection for VIHCS was supported by the National Health and Medical Research Council , Australian Rotary Health, Colonial Foundation, Perpetual Trustees, Financial Markets Foundation for Children , Royal Children's Hospital Foundation and the Murdoch Children's Research Institute. CAW is supported by a 10.13039/501100000265Medical Research Council (UK) Clinical Research Training Fellowship (MR/P019293/1). GCP is supported by an NHMRC Senior Principal Research Fellowship (APP1117873) and Career Development Fellowship to JLYC (1141354). SB is supported by an NHMRC Senior Research Fellowship (APP1103976) and CAO is supported by an NHMRC Investigator Grant (APP1175086). MMB is the recipient of an Australian Research Council Discovery Early Career Award (project number DE190101326) funded by the Australian Government. LMH receives salary support from the South London and Maudsley NHS Foundation Trust and King's College London Biomedical Research Centre. AJH is supported by an NHMRC Principal Research Fellowship (APP1117148). LAH is supported by the Wellcome Trust (UK). Research at the Murdoch Children's Research Institute is supported by the Victorian Government's Operational Infrastructure Program.This work was supported by the 8https://www.mcri.edu.au/research/projects/2000-stories.Ethics approvals for this study do not permit the data to be made publicly available, due to limitations of participant consent and concerns regarding potential re-identifiability. Upon request, following publication of this article, the dataset subset used for these analyses can be made available to a named individual for the purpose of replication of research findings. The study protocol and variable documentation will also be provided. Data requestors will need to sign a data access agreement. Contact details for data requests can be found at LMH has received funding from NIHR HS&DR Programme for research into the effectiveness of perinatal mental health services in England.


For pain management after surgery, inconsistency of effectiveness assessment is still observed. A subproject of IMI-PainCare , COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN)). In a first step, outcome domains will be identified via systematic literature review and consented on during a 1-day consensus meeting by 10 stakeholder groups, including patient representatives, forming an IMI PROMPT consensus panel. In a second step, outcome measurement instruments regarding the beforehand consented core outcome domains and their psychometric properties will be searched for via systematic literature review and approved by COSMIN checklist for study quality and scale quality separately. In a three-step online survey, the IMI PROMPT consensus panel will vote for most suitable measurement instruments. The process is planned to be conducted between 11/2017 (systematic literature review on common outcome domains) and 3/2022 . More than 300 million patients receive surgery each year worldwide, where pain is one of the most common and devastating symptoms thereafter . Acute pPain management options need to be evaluated regarding their effectiveness in preventing and managing acute postoperative pain . There aCore outcome sets (COS) are considered legitimate approaches to overcome irrelevant and inconsistent outcome assessment in clinical trials . They arCore Outcome Measures in Effectiveness Trials [The development of such a COS, comprising of both outcome domains and measurement instruments, is a multifaceted process, containing systematic research and consensus processes. Standards for their development have been set by COMET . Four kacute postoperative pain has not been considered as a separate health condition so far. Postoperative pain has been acknowledged as one domain (out of many) in perioperative medicine [The a priori defined health condition medicine  but was medicine , 16–28, medicine , 22–28 omedicine , 21). Remedicine , 30. Sommedicine –34, but www.imi-paincare.eu, 30th March 2020) funded by the European Union and European Federation of Pharmaceutical Industries and Associations (EFPIA). Within PROMPT , one initiative  seeks to improve postoperative pain by effective perioperative pain management in terms of developing a COS of patient-reported outcome measures assessing efficacy and effectiveness in any clinical and observational studies as well as in clinical practice. Due to the assumable fact, that outcome domains and measurement instruments might differ dependently to specific treatment effects of certain surgical procedures , four surgical procedures are addressed: sternotomy (St), breast cancer surgery (BS), total knee arthroplasty (TKA), and surgery for endometriosis (EM). Those surgeries were chosen for (1) the differences in type and extent of tissue injury , (2) the differences in patient populations , (3) the frequency of practice and the concomitant moderate to severe postoperative pain, thus being relevant both for new treatment approaches and for many patients and health care practitioners worldwide. These procedures represent a broad spectrum of requirements of perioperative pain management, with the option to address the question of developing either separate COS for perioperative pain management after each surgical procedure or an overarching COS comprising all.PROMPT (Patient-Reported Outcome Measures in Pain Treatment) is one of three subprojects within the IMI-2 JU project IMI-PainCare , reliability, and sensitivity to change [For estimating and evaluating psychometric properties for future measurement instruments  and other processes will be basing on PROMPT future results, the IMI PROMPT consensus panel was extended by corresponding working group members of the IMI-PainCare project  and Translational Research in Pelvic Pain (TriPP)), announced by the IMI-PainCare leaders. In preparation of the process, international scientific organizations associated with pain research and management (EFIC) or anesthesia and postoperative pain management (ESA) have been approached and were invited to nominate at least 4 relevant experts from their field of interest. In Table Eligible for the IMI PROMPT consensus panel are individuals experienced in perioperative pain management  after breast surgery, thoracotomy/sternotomy, total knee arthroplasty, and endometrioses or having experienced such procedure or other painful surgeries themselves (patient representatives). For endometriosis, inclusion was extended to individuals experienced in unspecified treatment of endometriosis. Further, pharma representatives, representatives of health technology assessment agencies, and regulators (experienced in drug development) have been identified as important stakeholders in the field of perioperative pain management. Since PROMPT is embedded into the large EU-funded project (please compare n = 10). It was therefore intended that each stakeholder group should consists of 4 representatives, in sum a number of n = 40 was expected to participate.Group size considerations referred to the consensus meeting because of financial, timely, and administrative resources on one side and requirements for equal distribution of stakeholder representatives on the other. During the consensus, four separate breakout groups have been planned  with at least one stakeholder representative from each group  and to the best of our knowledge, no COS initiative is engaged in perioperative pain management in general or for the chosen procedures total knee arthroplasty, breast surgery, and sternotomy so far.According to the COMET data source  until 2018, searching for all forms of clinical prospective observational and randomized controlled trials regarding effectiveness of perioperative pain management after TKA, BS, and St. No quality assessment  is intended since the sole frequency of outcomes or outcome domains will be of interest. Screening of title/abstract and full text will be performed by two independent reviewers. Extraction will contain, besides study characteristics, types and frequencies of the applied outcomes or outcome domains forming a descriptive synthesis.For endometriosis, previous results –34, 40 oBased on the a priori defined and recommended outcome domains for perioperative pain management after surgery , relevant measurement instruments will be identified via systematic review in two steps following the COSMIN guidance. The first step will comprise a scoping review and hand search for measurement instruments mapping outcome domains and definitions. Search will be performed in Embase, MEDLINE, and CENTRAL, in case of psychological domains additionally in PsychINFO and PsychArticle and, if needed, in common data bases for PROMs. Two independent reviewers will screen the results for title and abstract and for full text. Extraction will contain, besides study characteristics, definition of construct, description of the developmental process (in terms of identifying patient-reported outcome measures), scale construction, description of scale, and preliminary results in case of first validation. Primary search terms will refer to inauguration articles  and the specific domain (including synonyms or related terms).COSMIN and COMET suggest searching also in their database of SLRs regarding measurement instruments. In case of old or low-quality SLRs, they advise to conduct an update or to perform a new SLR; otherwise, it is considered sufficient to rely on existing results.Measurement instruments corresponding to IMI PROMPT outcome domain definitions, developed as patient-reported outcome and with a similar target population  will be chosen for further investigation of psychometric properties regarding the COSMIN quality criteria for creating a list of potential PROMs.COSMIN search strings will be applied  for subsThe consensus process (a 1-day face to face consensus meeting) will be facilitated by two members of the steering committee . A structured schedule (provided as a handout), consisting of plenary discussions, breakout groups  shall support consistent and transparent discussion and approaching a stable consensus.Enhancing respectful discussion, the IMI PROMPT consensus panel will be advised to discuss from the perspective of their stakeholder group, not as unique person, wherefore a basic understanding of representative of a stakeholder group will be formed by a specific, introductory part. Brainstorming and aligning on therapy aims and finally choosing relevant corresponding outcome domains for perioperative pain management will be performed within breakout groups referring to each surgery separately. All results of the breakout groups will be discussed subsequently after completing each step  and overarching results, comprising perioperative pain management in all surgery groups in general, are appreciated. Members of the steering committee  will facilitate the breakout groups. The variety of steps and groups enables equal chances to contribute for each participant.The first section of the meeting aims for the aligning on relevant and most critical therapy aims of perioperative pain management. Starting with a brainstorming on relevant therapy aims for each surgery , all participants will be invited to leave as much information to the breakout groups as they feel is relevant. A prioritization within the breakout groups on most critical therapy aims will complete this section. The breakout groups prioritize therapy aims according to the OMERACT 2.0 filter  will be matched to the a priori aligned most critical therapy aims ), added by official definitions of those outcome domains if available. Regarding alignment on relevant and important therapy aims, existing and via SLR-identified outcome domains can be included, excluded, or merged into each other, always ensuring transparent documentation of the process. Reasons for excluding, merging, or otherwise amending outcome domains will be reported in the future meeting report. The group also will have the opportunity to decide either to choose generic  or specific (for perioperative pain management after the specific surgery) outcome domains. In case of competing outcome domains within one therapy aim, the panel will approve the outcome domain matching most and drop the less important outcome domain regarding the therapy aim.It will be suggested to recommend at least one domain for each core area; otherwise, it will be explicitly explained why a core area has not been considered for COS .In a final plenary section (complete IMI PROMPT consensus panel), all breakout groups present and discuss their results. When discussion is completed, the voting will be performed for all outcome domains ranging from 7 to 9 as most critical, starting with generic suggestions. Four outcome domains are expected. The outcome domain with the highest rating will be preferred in case of competing outcome domains.Patient representatives are able to veto in case of complete disagreement with a single outcome domain. Patient representatives need to be unanimously against a decision of other stakeholder groups to set a veto. If another stakeholder group consistently disagrees with a panel decision, it is able to advice the panel to discuss the issue again, but the panel needs to approve the necessity of that action. In case of disapproval to discuss an outcome domain again, further discussion will be dismissed and the group will move forward to the next outcome domain.Consensus on measurement instruments shall be achieved via a final online Delphi survey (Surveymonkey), planned as a 3-step online survey. Information resulting from a second set of systematic literature reviews, now on psychometric properties of corresponding measurement instruments (PROMs) and search for construct definition of the relevant measurement instruments will be provided to the IMI PROMPT consensus panel see “”, alongsConsidering the quality of reporting outcome domains in intervention and effectiveness studies in terms of perioperative pain management after BS, TKA, St, and EM, only few studies have reported outcome domains explicitly and clearly defined. During the consensus meeting, outcome domains will be classified into 1–3 not important, 4–6 important but not critical, 7–9 critical, as recommended by COMET . InclusiThe same scoring will be applied for measurement instruments during an online Delphi survey. In case of competing measurement instruments for a single outcome domain, the measurement instrument with the highest rating will be included into future COS.Consensus on outcome domains (consensus face to face meeting) and measurement instruments (online Delphi exercise) will be defined as at least ≥70% voting for 7–9 and ≤ 20% voting for 1–3 rating (means that most of the panel feels that the specific outcome domain is important to include) by the IMI PROMPT consensus panel in order to include an outcome domain and a measurement instrument into future COS Consensus meeting on outcome domains 06/2018 Systematic reviews and research between 08/2018 and 08/2020 .Preparation of results of systematic reviews for psychometric properties of corresponding measurement instruments and preparation of the online Delphi exercise between 09/2020 and 09/2021Final consensus on outcome measurement instruments between 09/2021 and 03/2022As summarized in previous publications , there aMajor limitations of both approaches as forms of qualitative processes comprise the affection of the final results by types of stakeholders involved, the existing knowledge of the participants , the queFor Delphi methods, additionally attrition during the iterative process is highly relevant, challenging process completion and interpretation of results, especially when missing specific stakeholder groups . The comBased on these experiences and the situation of heterogeneity in designing COS developmental studies, the steering committee of IMI PROMPT decided to apply a mixed method approach for establishing the COS for perioperative pain management in four surgeries for clinical trials. One main consideration started with the observation that bringing together different stakeholders with different backgrounds in an online survey does not support understanding and acceptance since backgrounds and opinions cannot be exchanged easily. There is a considerable risk that results of such consensus repeat what has been there already, not assuming or critically reflecting on still missing aspects. Solely relying on outcome domains already published also creates biases, based on individual routines or preferences. Besides the limits of face to face meetings in timely and financial resources, it indeed provides all participants with much more information by discussion and exchange. The application of a facilitating schedule  was intended to reduce the danger of potential biases due to opinion leading by single individuals or strong stakeholder groups enhancing acceptance, transparency, and presence of multiple perspectives. Since developing COS domains refers much to personal backgrounds, needs, and preferences, a face to face meeting was considered most appropriate and, at the end, time saving. For defining COS on measurement instruments, performing an online based Delphi survey will suffice, since there are several distinct criteria , facilitating preparation of online Delphi survey and decision by the panel.There are possible limitations to our schedule and design. One refers to the representativeness of the panel. The announcement by organizations might help to reduce the selection bias induced by possible preferences of the steering committee, but which representatives will be chosen depends much on the organization. The steering committee has no control of real expertise, motivation, and background of the participants. Patients will be announced also by an international patient advocacy organization in order to send patients experienced in consensus processes and sufficiently speaking English. Since international networking of patient advocacy organizations is still a work in progress, selection bias for patients cannot be ruled out, such as coming from countries with a high amount of well-educated inhabitants, culturally open and experienced in scientific discussions.Complexity of consensus processes and the preparation of such time- and resource-consuming multifaceted approaches for developing COS might be a discouragement. Yet, there is no alternative referring to the major aim of finding best ways for improved care for our patients.


The goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security, telecommunications, and manufacturing. In response to the problems of analyzing large-scale data, different tools, techniques, and technologies have bee developed and are available for experimentation. In our analysis, we focused on literature (review articles) accessible via the Elsevier ScienceDirect service and the Springer Link service from more recent years, mainly from the last two decades. For the selected industries, this chapter also discusses challenges that can be addressed and overcome using the semantic processing approaches and knowledge reasoning approaches discussed in this book. Big Data paradigm has gain momentum and is generally employed by businesses on a large scale to create value that surpasses the investment and maintenance costs of data. Novel applications have been created for different industries allowing (1) storing as much data as possible in a cost-effective manner ; (2) rapid analysis capabilities ; (3) structured and unstructured data to be harvested, stored, and used simultaneously ; (4) accuracy of data processing ; etc. In the next decade, the amount of data will continue to grow and is expected to reach 175 zetabytes in 2025 [85]. This will fundamentally affect worldwide enterprises. This chapter is interested in identifying:RQ1: What are the main application areas of big data analytics and the specific data processing aspects that drive value for a selected industry domain?RQ2: Which are the main tools, techniques, and technologies available for experimentation in the field of big data analytics?In the last decade, the Big Data is a very active area of research nowadays, we are also involved in analysis of different industry cases studies, as is presented in the research methodology depicted in Fig. In December 2018, within the LAMBDA project framework, a literature review was initiated that included an extensive and comprehensive analysis of journal articles from available sources such as (1) the Elsevier ScienceDirect serviceBig Data Analytics returns:180,736 results in ScienceDirect , 10,042 of them review articles, where the oldest 2 papers are from 1989 and discuss the challenges to computational science and use of supercomputers for conducting experiments in key scientific and engineering areas such as atmospheric science, astronomy, materials science, molecular biology, aerodynamics, and elementary particle physics [467];40,317 results in SpringerLink , where the oldest publications dating from 1950s are related to mathematics.Big Data Analytics is a broad topic that, depending on the objectives of the research, can be linked on the one hand to data science and machine learning, and on the other to data and software engineering. Being interested in the role that analytics plays in business strategy, we limited our search to articles in the domain of business intelligence. Business intelligence entails the analysis of past and present data to create actionable insights for informed decision-making. Thus, the search for review articles linked to Big Data Analytics and Business Intelligence leads to 615 articles. The number is even smaller if we are looking for Business Intelligence (BI) and NoSQL solutions– see Table Business Intelligence still prevails in the scientific literature but is based on relational database-driven applications. Further on, looking for the year of publication, the authors have found that there are articles from the 1930s also linked to the topic Big Data albeit mainly related to medical studies. In our analysis, we focused on review articles from more recent years, mainly from the last two decades.This section presents the literature review approach that was adopted in order to identify the relevant application areas of big data technologies. In April 2020, a simple keyword based query on term Big Data Analytics, Business Intelligence, and Applications in Industry. ThetTable below points to a number of articles published in international journals between 2015 and 2019, as well as the journals relevant for the search criteria. What can be noticed is that there are three times more articles related to Big Data and Applications, then to Big Data Analytics and Applications. The number of retrieved results is drastically smaller if we introduce the topic ‘Business Intelligence’.Hence, in order to identify the main application area, we first identified journals (using ScienceDirect service) that most frequently publish research articles about Some of the journals listed in Table Trends: Detailed analysis of the retrieved surveys on BDA and Apps and NoSQL (54 papers) showed that there is a shift of focus from operational data management systems, data-warehouses and business intelligent solutions (present for instance in Finance and Insurance domain in 1990s) [336] to parallel and distributed computing [478], as well as scalable architectures [187] for storing and processing data in the cloud . Emerging paradigms such as the Internet of Things  and blockchain additionally influence cloud computing systems [157]. Interconnected technologies like RFID (Radio Frequency IDentification) and WSAN (Wireless Sensor and Actor Networks) enabled development of smart environments [122] that will be explored further in subsequent sections. Wide availability of cheap processing power and vast amounts of data in recent years have enabled impressive breakthroughs in machine learning , semantic computing , artificial neural networks and multimodal affective analytics [400].The analysis presented in this section examines the BDA-driven applications in sectors spanning healthcare, transport, telecommunications, energy production and smart grids, energy consumption and home automation, finance, media, e-Government [220] and other public utilities. The research was motivated by the needs of the Mihajlo Pupin Institute to innovate the existing product portfolio that is currently mainly focused on building advanced analytical services for control, monitoring and management of large facilities, for instance from the transport and the energy sector.Healthcare and PharmaHealthcare and Data Engineering. Advances in Internet of Things (IoT) and sensor devices have enabled integrated data processing from diverse healthcare data sources in a real-time manner [339]. In addition to existing sources , healthcare providers can use new data sources such as social media platforms, telematics, and wearable devices in order to personalize treatment plans. However, healthcare organizations face unique challenges when it comes to developing and implementing the smart health concept [11] based on using a remote cloud server with powerful computing capabilities. Besides taking into account the 3Vs  that raise issues related to scalability, efficiency, speed, transparency, availability, reliability, security, and others, the veracity dimension is very important because the value of health information is directly dependent on the ability to determine the quality of the data in question . Hence, fog-enabled smart health solutions are proposed where fog nodes create a heterogeneous fog network layer and complement a portion of computation and storage of the centralized cloud server [421].Personalized medicine is an approach to the practice of medicine that uses information about a patient’s unique genetic makeup and environment to customize their medical care to fit their individual requirements. Recently, epigenetics has grown in popularity as a new type of science that refers to the collection of chemical modifications to the DNA and chromatin in the nucleus of a cell, which profoundly influence the functional output of the genome. The identification of novel individual epigenetic-sensitive trajectories at the single cell level might provide additional opportunities to establish predictive, diagnostic and prognostic biomarkers as well as drug targets [386]. Based on emerging trends, patient care can be improved in many ways including using:modern healthcare applications that almost every smartphone possesses like Apple Healththe data obtained by wireless body area networks, implemented with adequate permissions by the user (WBANs) can be integrated  and analysed in order to improve the effectiveness of medical institutions and to aid doctors in their decision making;advanced data management and processing  for better prescription recommendations and optimizations of the drug supply chain, which results in cutting losses and increasing efficiency.Over the years, the role of Artificial Intelligence in medicine has become increasingly important, for instance for image processing and diagnosis purposes. Also deep-learning neural networks have proved very useful for extracting associations between a patient’s condition and possible causes. To summarize opportunities and challenges of using innovative big data tools in healthcare, we point in Table Pharma. New trends in pharmaceutical research (such as genomic computing [370]) make the process of discovering disease patterns, early epidemic and pandemic detection and forecasting much easier. Das, Rautaray and Pandey [96] outline the general potential uses of big data in medicine like heart attack prediction, brain disease prediction, diagnosis of chronic kidney disease, analysing specific disease data, tuberculosis prediction, early hearth stage detection, HIV/AIDS prediction and some general aspects like disease outbreak and disease outcome prediction. Lee and Yoon [275] discuss some technical aspects of big data applications in medicine like missing values, the effects of high dimensionality, and bias control. Ristevski and Chen [374] mention privacy and security on the topic of big data in healthcare, while Tafti [420] offers an open source toolkit for biomedical sentence classification. Modern concepts relating to mobile health are discussed in [214] with Bayne [32] exploring big data in neonatal health care.Transportation and Smart CitiesAs suggested in Chap. 10.1007/978-3-030-53199-7_1, Smart Transportation is one of the key big data vertical applications besides Healthcare, Government, Energy and Utilities, Manufacturing and Natural Resources, Banking and Insurance, the Financial industry, Communications and Media, Environment and Education. The collection of related articles to this topic is possibly the largest of all applications. Zhang [483] offers a methodology for fare reduction in modern traffic congested cities, Liu [285] discusses the Internet of Vehicles, Grant-Muller [165] talks about the impacts that the data extracted from the transport domain has on other spheres, Torre-Bastida [429] talks about recent advances and challenges of modern big data applications in the transportation domain, while Imawan [211] analyses the important concept of visualization in road traffic applications. Also related, Ghofrani [154] surveys big data applications for railways, Gohar [158] discusses data-driven modelling in intelligent transportation systems, and Wang [454] attempts fuzzy control applications in this domain. Herein, we will discuss route planning applications and future challenges related to self-driving cars and user behaviour analysis.Route Planning Applications. Using Global Positioning System (GPS) data, for instance, a large number of smartphone users benefit from the routing system by receiving information about the shortest or fastest route between two desired points. Some applications like Waze rely on direct user inputs in order to locate closed-off streets, speed traps etc. but at its most rudimentary level, this approach can work with just raw GPS data, calculating average travel times per street segments, and thus forming a live congestion map. Of course, such a system would be of no benefit to end users if it were not precise, but since the aggregated results that are finally presented are obtained based on many different sources, classifying this as a big data processing task, the data uncertainty is averaged out, an accurate results tend to be presented. In order to provide a quick response, geo-distributed edge devices also known as edge servers are used that can form an edge cloud for providing computation, storage and networking resources to facilitate big data analytics around the point of capture [91].Self-driving cars rely on vast amounts of data that are constantly being provided by its users and used for training the algorithms governing the vehicle in auto-pilot mode. Holding on to the automation aspect, big data processing in the transportation domain could even be used to govern traffic light scheduling, which would have a significant impact on this sector, at least until all vehicles become autonomous and traffic lights are no longer required.User Behaviour Analysis. Furthermore, the transportation domain can be optimized using adequate planning obtained from models with data originating from user behaviour analysis. Ticketing systems in countries with high population density or frequent travellers where reservations have to be made, sometimes, a few months in advance, rely on machine learning algorithms for predictions governing prices and availability. Patterns discovered from toll collecting stations and border crossings can be of huge importance when planning the duration of one’s trip and optimizing the selected route.Energy Production and Smart GridsEnergy Production. The energy sector has been dealing with big data for decades, as tremendous amounts of data are collected from numerous sensors, which are generally attached to different plant subsystems. Recently, modern big data technologies have also been applied to plant industry such as oil and gas plants, hydro, thermal and nuclear power plants, especially in the context of improving operational performance. Thus, some of the applications of big data in the oil and gas industry [311] are analyzing seismic and micro-seismic data, improving reservoir characterization and simulation, reducing drilling time and increasing drilling safety, optimization of the performance of production pumps, improved petrochemical asset management, improved shipping and transportation, and improved occupational safety. Promising applications of big data technology in future nuclear fusion power plants are (1) data/plasma modeling in general [88], (2) real-time emergency planning [276], (3) early detection of accidents in reactors [290], etc. Related to hydro-power plants, many authors have discussed the use of IoT applications for measuring water supply . Zohrevand [490] talks about the application of Hidden Markov models for problem detection in systems for water supply.Smart Grids. The smart grid (SG) is the next-generation power grid, which uses two-way flows of electricity and information to create a widely distributed automated energy delivery network [155]. The goal is to optimize the generation, distribution and consumption of electricity. In general, there are three main areas where data analytics have been applied:Ensuring smart grid stability, load forecast and prediction of energy demand for planning and managing energy network resources;Improving malfunction diagnosis, either on the production side (in plant facilities) or health state estimation, and identifying locations and forecasting future line outages in order to decrease the outage costs and improve system reliability;Profiling user behaviours to adjust individual consumption patterns and to design policies for specific users.lack of comprehensive and general standard, specifically concentrated on big data management in SGs;interoperability of smart devices dealing with massive data used in the SGs;the constraint to work with approximate analytics and data uncertainty due to the increasing size of datasets and real-time necessity of processing [354];security and privacy issues and the balance between easier data processing and data access control for big data analytics, etc.Smart metering equipment and sensors provide key insights into load distribution and profiles required by plant operators to sustain system stability. Predictive maintenance also plays a key role in smart grid upkeep since all segments are both critical and expensive, and any unplanned action cuts users from the electricity supply upon which almost all modern devices rely to function. Analytics methodologies or algorithms used in these cases are: 1) statistical methods; 2) signal processing methodologies; 3) supervised regression forecasting (short and long-term forecasts); 4) clustering algorithms; 4) dimensionality reduction techniques; and 5) feature selection and extraction. Tu [431] and Ghorbanian [155] present a long list of various open issues and challenges in the future for smart grids such asMore insight into potential applications of big data-oriented tools and analytical technologies in the energy domain are given in Chap. 10.1007/978-3-030-53199-7_10.Energy Consumption and Home AutomationAn unavoidable topic when discussing big data applications, in general, is home automation. One of the challenges that the world is facing nowadays is reducing our energy consumption and improving energy efficiency. The Internet of Things, as a network of modern sensing equipment, plays a crucial role in home automation solutions that based on this data are capable of processing and providing accurate predictions, and energy saving recommendations. Home automation solutions provide optimal device scheduling to maximize comfort and minimize costs, and can even be extended from the operation aspect to planning and offering possible home adjustments or suggesting investments in renewable sources if the location being considered is deemed fit. Having smart appliances initially presented the concept of human-to-machine communication but, governed by big data processing, this concept has been further popularized with machine-to-machine communication where the human input is removed, resulting in less interference. Predictive maintenance and automatic fault detection can also be obtained from sensor data for both basic household appliances and larger mechanical systems like cars, motors, generators, etc. IoT applications require proper cloud frameworks [456]. Ge [151] presents a comprehensive survey of big data applications in the IoT sphere, Martis [300] introduce machine learning to the mix. Kumari [270] gives a survey but with the main focus on multimedia, and Kobusińska [248] talks about current trends and issues.Banking and InsuranceBusiness intelligence tools have been used to drive profitability, reduce risk, and create competitive advantage since the 1990s. In the late 1990s, many banks and insurance companies started using machine learning techniques for categorizing and prioritizing clients, assessing the credit risk of individual clients or companies, and survival analysis, etc. As this industry generally adopts new technologies early on, thanks to advances in cognitive computing and artificial intelligence, companies can now use sophisticated algorithms to gain insights into consumer behavior. Performing inference on integrated data from internal and external sources is nowadays the key for detecting fraud and security vulnerabilities. Furthermore, novel approaches state that the applied machine learning can be supplemented with semantic knowledge, thus improving the requested predictions and classifications and enriching them with reasoning explanations that pure machine learning based deduction lacks [40]. Regarding other financial institutions, stock markets, for instance, are also a considerable use case for big data as the sheer volume and frequency of transactions slowly renders traditional processing solutions and computation methods obsolete. Finding patterns and surveilling this fast-paced process is key for proper optimization and scam prevention. Hasan [186] and Huang [204] offer concrete approaches like predicting market conditions by deep learning and applying market profile theory with Tian [427] discussing latency critical applications, Begenau [36] looking at the link between Big Data and corporate growth, and  in this domain are not only related to marketing and sales but are also used for proper display of information relating to the user. Some search engines companies have even publicly stated that their infrastructure relies on big data architecture, which is not surprising considering the amount of data that needs to be processed.Environment MonitoringEnvironmental monitoring involves the collection of one or more measurements that are used to assess the status of an environment. Advances in remote sensing using satellite and radar technologies have created new possibilities in oceanography, meteorology, forestry, agriculture and construction (urban planning). Environmental remote sensing can be subdivided into three major categories based on the distance between the sensor and the area being monitored 139]. The first category, satellite-based measurement systems, is primarily employed to study the Earth and its changing environment. The most valuable source of data from this category is the Landsat, a joint satellite program of the USGS and NASA, that has been observing the Earth continuously from 1972 through to the present day. More than 8 million images [207] are available via the NASA website. The firThe second major category of remote sensing encompasses aircraft-borne instruments, for instance, the light detection and ranging (LIDAR) systems that permit better monitoring of important atmospheric species such as ozone, carbon monoxide, water vapor, hydrocarbons, and nitrous oxide as well as meteorological parameters such as atmospheric density, pressure, and temperature [139].Ground-based instruments (e.g. aerosols measurement instruments) and Wireless Sensor Networks (WSN) [397] are the third major category for outdoor monitoring technologies that create new opportunities to monitor farms and rain forests, cattle, agricultural (soil moisture), water quality, volcanic eruptions and earth-quakes, etc.The table below points to some social-economic and natural environment applications enabled by big data, IoT and remote sensing Table .Table 4.Natural Disasters, Safety and SecurityNatural Disasters. Due to changing climatic conditions, natural disasters such as floods, landslides, droughts, earthquakes are nowadays becoming common events. These events create a substantial volume of data that needs to be processed in real time and thus avoid, for instance, suffering and/or death of the people affected. Advancements in the field of IoT, machine learning, big data, remote sensing, mobile applications can improve the effectiveness of disaster management strategies and facilitate implementation of evacuation processes. The requirements faced by ICT developers are similar to those in the other domains already discussedthe need to integrate multimodal data ;the need to syncronize the activities of many stakeholders involved in four aspects of emergency ;the need to install measuring devices for collecting and real-time analysis in order to understand changes ;the need to visualize information;the need to communicate with people (first responders and/or affected people and track their responses and behaviour) or to alert officials to initiate rescue measures.The application of big data analytics techniques is specially important for the Safety and Security industry as it can extract hidden value  from security-related data, derive actionable intelligence, and propose new forms of surveillance and prevention. Additionally, the number of connected devices is expected to rapidly increase in the coming years with the use of AI-defined 5G networks [477]. Intelligent fire risk monitor based on Linked Open Data [442].The global market offers a wide range of emergency solutions (in the form of web and/or mobile solutions) with intuitive mapping, live field monitoring and multimedia data sharing, such as CommandWearSafety and Security of Critical Infrastructures. Big data processing is especially important for protecting critical infrastructures like airports, railway/metro systems, and power grids. Large infrastructures are difficult to monitor due to their complex layout and the variety of entities that they may contain such as rooms and halls of different sizes, restricted areas, shops, etc. In emergency situations, various control and monitoring systems, e.g. fire protection systems, heating, ventilation and air conditioning systems, evacuation and access control systems and flight information display systems among others, can send altogether thousands of events to the control room each second [309]. By streaming these low-level events and combining them in a meaningful way, increased situation awareness can be achieved. Using big data tools, stream processing solutions, complex event processing/event-condition-action (CEP/ECA) paradigm and combining events, state and emergency management procedures, a wide range of emergency scenarios and emergency procedures can be pre-defined. Besides processing the large amount of heterogeneous data extracted from multiple sources while considering the challenges of volume, velocity and variety, what is also challenging today isreal-time visualization and subsequent interaction with computational modules in order to improve understanding and speed-up decision making;development of advanced semantic analytics and Machine Learning techniques for new pattern recognition that will build upon pre-defined emergency scenarios (e.g. based on rules) and generate new early warning procedures or reliable action plans.TelecommunicationsFollowing the already mentioned impact of using smart mobile phones as data sources, the telecommunications industry must also be considered when discussing big data. The 5th generation of cellular network (5G) that is now live in 24 markets  will provide real-time data collection and analysis and open possibilities for business intelligence and artificial intelligence-based systems.Mobile, television and internet service providers have customer retention as their core interest in order to maintain a sustainable business. Therefore, in order to prevent customer churn, behaviour patterns are analysed in order to provide predictions on customers looking to switch their provider and allow the company to act in time and offer various incentives or contract benefits in due course. Also, besides this business aspect, telecommunication companies using big data analytic solutions on data collected from mobile users can use the information generated in this way to assess problems with their network and perform optimizations, thus improving the quality of their service. Since almost all modern mobile phones rely on wireless 4G (and 5G in the years to come) networks to communicate when their users are not at home or work, all communication is passed through the data provider’s services, and in processing this data still lie many useful bits of information as only time will tell what useful applications are yet to be discovered. Papers covering this aspect include Yazti [479] and He [191] outlining mobile big data analytics, while Amin [15] talks about preventing and predicting the mentioned phenomena of customer churn, and Liu [286] talks about collecting data from mobile (phone and wearable) devices.ManufacturingIndustry 4.0 is about automating processes, improving the efficiency of processes, and introducing edge computing in a distributed and intelligent manner. As discussed previously, more complex requirements are imposed in process operations while the process frequently forfeits robustness, complicating process optimization. In the Industry 4.0 era, smart manufacturing services have to operate over multiple data streams, which are usually generated by distributed sensors in almost real-time. Similarly to other industrial sectors, transforming plants into full digital production sites requires an efficient and flexible infrastructure for data integration and management connected to powerful computational systems and cognitive reasoning engines. Edge computing  plays an important role in smart manufacturing. Data has to be transferred, stored, processed and transferred again back  to both users and providers in order to transmit the inferred knowledge from sensor data. In the layered infrastructure see Fig. , cognitiKumar [268] proposes using the MapReduce framework for automatic pattern recognition based on fault diagnosis in cloud-based manufacturing. Fault diagnosis significantly contributes to reduce product testing cost and enhances manufacturing quality;Vater [443] discusses how new technologies, such as IoT, big data, data analytics and cloud computing, are changing production into the next generation of industry.Human-Computer Interaction. In complex situations, operators and machines need to quickly analyze situations, communicate and cooperate with each other, coordinate emergency response efforts, and find reasonable solutions for emerging problems. In such situations, collaborative intelligence services are needed that require fewer human-driven decisions as well as easy-to-use interfaces that accelerate information-seeking and human response. Interpretability and explainability are crucial for achieving fair, accountable and transparent (FAT) machine learning, complying with the needs and standards of the business sector.Dynamic process adaptation. Many industrial processes are hard to adapt to changes . This affects product quality and can cause damage to equipment and production lines. Hence, a semantic framework for storing contextual information and an explainable AI approach can be used for fine-tuning of process parameters to optimize environmental resources, fast reconfiguration of machines to adapt to production change, or advance fault diagnosis and recovery.In the smart manufacturing ecosystem, cognitive applications make use of process data (processed on the edge) and provide high level supervisory control and support the process operators and engineers. Data analytics and AI techniques are combined with digital twins and real-life feedback from the shop floor or production facility to improve the quality of products and processes. Example areas where semantic processing and artificial intelligence can advance this sector areThis chapter presented applications of big data approaches in different sectors. Research into real-time data analytics by addressing the volume and velocity dimension of big data is a significant area in emerging smart grid technology, for instance, where different predictive models and optimization algorithms serve to improve end-to-end performance, end-user energy efficiency and allow increasing amounts of renewable energy sources to be embedded within the distribution networks (e.g. solar photovoltaic (PV), wind power plants). Next, analytics on real-time data streams combined with GIS and weather data improves detection of significant events, enhances situational awareness and helps identify hazardous road conditions (e.g. snow), which may assist drivers and emergency responders in avoiding such conditions and allow for faster emergency vehicle routing and improved response time. Solutions that address the variety dimension, integration of heterogeneous data sources  and advanced machine learning algorithms have found application in customer relation management and fraud detection . For instance, the ability to cross-relate private information on consumer preferences and products with information from Facebook, tweets, blogs, product evaluations, and other sources opens a wide range of possibilities for organisations to understand the needs of their customers, predict their needs and demands, and optimise their use of resources. This chapter also discussed challenges that can be addressed and overcome using the semantic processing approaches and knowledge reasoning approaches discussed in this book.


Dear editor,According to the US Census Bureau, recent immigrants to the United States are more likely to have a college education than earlier immigrants or the native born.To be considered as a candidate for the Alternate Pathway, an applicant must meet several requirements, including completing a Structured Mentorship Program (SMP). The SMP isconducted through a sponsoring department at an institution that has a residency program accredited through the Commission on Accreditation of Medical Physics Education Programs (CAMPEP),under a supervising medical physicist who is a diplomate of the ABR,for a minimum of 3 years in the same institution.The ABR lists six high‐level competencies that must be met along with minimum SMP portfolio activities. These competencies are not as granularly defined as those of CAMPEP’s standards for residency programs. Thus, implementing an SMP allows for flexibility in tailoring the Training Plan to the candidate, which can also be a daunting undertaking.At Duke University, we planned a 3 yr SMP that was approved by the ABR Medical Physics Credentials Evaluation Committee in May 2017. The candidate completed the SMP, on schedule, in 2020. In this letter we would like to share our experience to provide useful information for other institutions and international graduates who want to follow the same pathway.1The objective of the SMP at Duke University Medical Center was to provide a mentorship program for medical physicists trained in foreign countries to meet the requirements of the ABR and become board certified in Diagnostic Physics. Duke conducts an Imaging Physics Residency Program that is accredited through the CAMPEP. The SMP candidate maintained a level of apprenticeship comparable to that of residents in the CAMPEP Residency Program to ensure the SMP candidate meets the minimum level of competence sufficient to perform all aspects of routine diagnostic imaging tasks. Additional flexibility was considered in areas where the candidate had a strong demonstrated proficiency. An overall objective was maintained for the candidate to contribute to the oversight of safe and accurate imaging procedures. In addition, the SMP provided an environment for the candidate to demonstrate competent performance in other aspects of an imaging physicist’s responsibilities such as teaching, research, radiation safety, and administration.http://cipg.duhs.duke.edu), who provide clinical physics support for the imaging operations across the Duke University Health System. The SMP candidate had access to clinical resources and testing equipment through the CIPG. Staff of the Radiation Safety Division in the Duke Occupational & Environmental Safety Office were also involved in the mentorship program to provide rotational mentor support and track the candidates’ radiation safety training and occupational dosimetry badge reports.The SMP was closely associated with both the Imaging Physics Residency Program and the Medical Physics Graduate Program, which together offer an extensive array of basic and advanced medical physics graduate courses and practical training for a candidate. A candidate’s progress was managed by a Facility Supervisor (the Residency Program Director), assisted by the Imaging Physics Residency Program’s Assistant Director, as well as several rotation mentors. All clinical rotation mentors were members of the Duke Clinical Imaging Physics Group (CIPG, 2The duration of the SMP totaled 36 consecutive months. Following the ABR guidelines, the mentorship included oversight in both clinical and technical areas of imaging physics for imaging modalities: radiography, fluoroscopy and interventional radiology, mammography, computed tomography, ultrasound, and magnetic resonance. All modalities included routine competency requirements of physics fundamentals, instrumentation and system operation, regulatory requirements, image quality attributes and parameters, and QC and QA program evaluation. Additionally, each modality had a unique set of competencies, listed below.2.1Radiation safety; Equipment evaluation including collimation accuracy, beam quality assessment, automatic exposure control performance, phantom image quality evaluation, and computed radiography image reader performance; Dosimetry measurements including exposure reproducibility/linearity and patient entrance skin exposure.2.2Radiation safety; Equipment evaluation including collimation, resolution, beam quality assessment, automatic exposure control performance, and phantom image quality evaluation; Dosimetry including measuring exposure rates and evaluating patient entrance skin dose exposure.2.3Performing medical physicist testing following MQSA/ACR requirements including collimation, artifact evaluation, detector uniformity, system resolution, and SNR/CNR measurements; Dosimetry including measuring and evaluating breast entrance dose, average glandular dose, and automatic exposure control performance.2.4Radiation safety; Dosimetry principles, including measuring CT dose index; Equipment evaluation including slice thickness accuracy, CT number accuracy, uniformity, and high/low contrast resolution.2.5Equipment evaluation including resolution, uniformity, depth of penetration, and Doppler/color‐flow evaluation.2.6MRI safety; Equipment evaluation including phase stability, magnetic field homogeneity, radio frequency calibration, SNR, intensity uniformity, MR spectroscopy water and metabolic peak areas, and volume‐of‐interest accuracy.3For each modality rotation, the candidate reported a list of completed competencies for review and evaluation by the Facility Supervisor and rotation mentor. The candidate also maintained rotation portfolios for each module including:List of competencies the candidate had mastered;List of the physics responsibilities carried out during the rotation;List of completed equipment evaluations performed;Summary of additional activities, including operational improvements and clinically oriented projects;Training resources and references used for the module;Evaluations of the candidate by rotation mentor and Facility Supervisor.The candidate completed the six clinical rotations, performed 51 equipment evaluations, and three clinical quality improvement projects, which led to four conference presentations and two peer‐reviewed publications.4The 3‐yr SMP was successfully completed as planned and on‐schedule. The international graduate, who was qualified as a medical physicist in his country of origin and had practiced there, reviewed and demonstrated clinical competency of diagnostic imaging modalities, techniques, and procedures. The ABR approved the proposed SMP and admitted the candidate to Part‐2 exam. The ABR International Medical Graduates Alternate Pathway provided the candidate specific competencies to practice diagnostic imaging physics independently offering alongside opportunities to improve in both clinical and research activities.


In agricultural cropping systems, relatively large amounts of nitrogen (N) are applied for plant growth and development, and to achieve high yields. However, with increasing N application, plant N use efficiency generally decreases, which results in losses of N into the environment and subsequently detrimental consequences for both ecosystems and human health. A strategy for reducing N input and environmental losses while maintaining or increasing plant performance is the development of crops that effectively obtain, distribute, and utilize the available N. Generally, N is acquired from the soil in the inorganic forms of nitrate or ammonium and assimilated in roots or leaves as amino acids. The amino acids may be used within the source organs, but they are also the principal N compounds transported from source to sink in support of metabolism and growth. N uptake, synthesis of amino acids, and their partitioning within sources and toward sinks, as well as N utilization within sinks represent potential bottlenecks in the effective use of N for vegetative and reproductive growth. This review addresses recent discoveries in N metabolism and transport and their relevance for improving N use efficiency under high and low N conditions. Nitrogen (N) is essential for general plant functions as it is a component of amino acids, which are the elemental units of protein and enzymes. Amino acids are also precursors or N donors for many fundamental compounds including nucleic acids, hormones, chlorophyll, ureides, and other metabolites required for primary metabolism and specialized biological functions, respectively , tobacco (Nicotiana tabacum), and rice (Oryza sativa) showed that overexpression of the hyperactive chimeric nitrate transporter AtNC4N in the phloem of old leaves results in improved NUE under high and low N status  as well as aquaporins or cation channels  influences N uptake and NUE, including under varying soil amino acid concentrations.Root importers for amino acids include amino acid permeases AAP1 and AAP5, proline transporter ProT2, and lysine-histidine-type transporters LHT1 and LHT6  as well as re-assimilation of ammonium produced during photorespiration  and glutamate synthase (GOGAT) into glutamine and glutamate, respectively, which serve as N donors for the biosynthesis of other amino acids and amides  mutants, essential roles in plant growth and seed development have been demonstrated for both Fd-GOGAT and NADH-GOGAT  or GOGAT is overexpressed  may be avoided  or NADH-dependent (NADH-GOGAT) GOGAT  and alanine aminotransferase (AlaAT)  seems to only be successful at high N status  and/or low N  caused increased photosynthesis rates, tuber numbers and tuber dry weight, as well as improved NUE at moderate and low N levels  or Eurotium cheralieri (EcGDH) were expressed  in the pea (Pisum sativum) phloem (as well as in the seed cotyledons) resulted in increased amino acid allocation to sinks and co-regulated carbon capture and source-sink allocation. The overall outcome was increased vegetative growth, seed number, and seed protein levels at high N supply , in the phloem  allocated to sinks  in the storage parenchyma cells of the cotyledons  involved in regulating carbon flow toward methionine synthesis  by manipulating a serine acetyltransferase (AtSAT)  in Arabidopsis  have been successfully expressed in seeds of rice  were expressed in rice  achieved higher seed albumin levels under high and low N supply, while seed yield was not changed at high N but decreased at low N fertilization. However, since sulfur was sequestered in the sulfur-rich protein, the negative effects were mainly attributed to sulfur rather than N deficiency  have been successfully increased in rice  N uptake and partitioning within cells and over short and long distances, and (ii) N (re)assimilation, amino acid synthesis and catabolism, and protein synthesis. Since any of these processes could potentially present a bottleneck for the efficient use of N for biomass and seed production, associated key genes have been targets for improving plant performance and NUE, but, as reviewed above, with varying success –3. The iNUE is a complex polygenic trait making its genetic dissection and engineering challenging. In fact, despite the notable progress that has been made in understanding plant NUE, successful agronomic applications of engineered crop plants with altered N transport and metabolism or other physiological functions have not yet been achieved. To better grasp the genetic basis of NUE for genetic manipulation, future strategies might take greater advantage of the available natural variation for NUE between and within crop species (Hawkesford and Griffiths, All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Non-communicable diseases (NCDs) represent a growing health burden in low-income and middle-income countries (LMICs). Operational research (OR) has been used globally to support the design of effective and efficient public policies. Equity is emphasised in the Sustainable Development Goal (SDG) framework introduced in 2015 and can be analysed within OR studies.We systematically searched MEDLINE, Embase, Scopus and Web of Science for studies published between 2015 and 2018 at the intersection of five domains . We categorised the type of policy intervention and described any concern for equity, which we defined as either analysis of differential impact by subgroups or, policy focus on disadvantaged groups or promoting universal health coverage (UHC).A total of 149 papers met the inclusion criteria. The papers covered a number of policy types and a broad range of NCDs, although not in proportion to their relative disease burden. A concern for equity was demonstrated by 88 of the 149 papers (59%), with 8 (5%) demonstrating differential impact, 47 (32%) targeting disadvantaged groups, and 68 (46%) promoting UHC.Overall, OR for NCD health policy in the SDG era is being applied to a diverse set of interventions and conditions across LMICs and researchers appear to be concerned with equity. However, the current focus of published research does not fully reflect population needs and the analysis of differential impact within populations is rare. Operational research (OR) has been widely used to aid decision makers in low-income and middle-income countries (LMICs) to prioritise health interventions.There is a growing and disproportionate burden of non-communicable disease (NCD) on LMICs, and on the most vulnerable populations within these countries.The Sustainable Development Goals (SDGs) emphasised the need for interventions which target NCDs while simultaneously promoting equity.The 149 papers included in this review demonstrated that OR methods are being used to provide valuable evidence across a wide range of NCD disease burdens and policy options in the SDG era.The number of papers were not proportional to the LMIC disease burden attributable to each NCD.OR in the SDG era demonstrates a strong concern for equity, although this was often limited to a focus on disadvantaged groups or universal coverage without robust consideration of differential impact.OR methods continue to provide valuable evidence across a broad range of policy options and NCD disease burdens in LMICs.In future, operational researchers should consider including the differential impact of the policies they are evaluating, thereby ensuring that equity remains a central concern.Non-communicable diseases (NCDs) account for 73% of global deaths and 62% of global disease burden in 2017, increasing from 48% to 62% respectively in 2000.7Despite these growing challenges, there are a wide range of interventions available to policymakers and healthcare practitioners to reduce the burden of NCDs in LMICs. An effective ‘package’ of measures may encompass diverse approaches including: fiscal policies and regulations to reduce the key modifiable risk factors such as tobacco use and unhealthy diets; population-wide screening to achieve early diagnosis and treatment of NCDs and access to effective and affordable individual-level healthcare interventions.14The discipline of operational research (OR) can provide quantitative evidence to guide scale-up of interventions for NCDs19A key characteristic of NCDs is their strong link with health (in)equity in LMIC contexts. NCDs are important contributors to (within-country) health inequity insofar as modifiable risk factors for NCDs are typically concentrated in population subgroups who have low socioeconomic status (SES) and/or live in poor or marginalised communities.et al,healthcare equity, particularly in relation to hard-to-reach or disadvantaged subgroups. For OR to have impact, they recommended researchers embed stakeholder engagement, use contextually appropriate data and vary communication channels for outputs. An earlier, 2006, review by Mulligan et alPrevious reviews include Bradley The 2015 Sustainable Development Goals (SDGs) include good health and well-being (SDG 3) and emphasise equity throughout. Given these goals, and the particular challenges posed by NCDs in LMICs, we investigated the use of OR in informing NCD-related health policy in LMICs, and whether a concern for equity informed the analyses. Here, we present results of a scoping review of studies published in peer-reviewed journals during the SDG era, from January 2015 to May 2018, which apply quantitative OR methods to evaluate policies for NCD prevention and treatment in LMICs. We provide a narrative summary by key themes, including geographical setting, policy/intervention type and disease area, with attention to if/how OR methods have been used to support a concern for equity.This scoping review follows a systematic search strategy and analysis to provide a broad overview of the size and scope of the literature. A scoping review can provide a preliminary assessment of the literature without the requirement of formal quality appraisals required by systematic literature reviews for the synthesis of evidence.et al,Web of Science, Scopus, Embase and MEDLINE were searched to cover both health and social science. Free-text searches were undertaken for Web of Science and Scopus. Free text plus Emtree and MESH subject headings were used for Embase and MEDLINE, respectively. The database free-text search strategy drew on the work of Bradley 10.1136/bmjgh-2019-002259.supp1Supplementary dataet alTwo researchers (NG and JK) independently sifted all articles, first according to title and abstract and second using the full text. The inclusion criteria drew on those used by Bradley The first criteria was for the study to use OR methods, including: optimisation models ; simulation models ; dynamic state transition models  and static cohort-level models .Second, the study must explore a decision-making/policy-making problem compatible with OR. This could be: several competing interventions or policy options are compared to propose the best/optimal strategy (including comparing current status quo vs a new option); outcomes are modelled for competing intervention or policy scenarios/options, including disease burden, budget impact and cost-effectiveness; issues of logistics, supply chain, distribution, scheduling are explored, including studies that highlight operational inefficiencies or poor performance; what if scenarios are tested .Third, the study explores an OR problem with a health or healthcare focus. This can include public or population health  and/or healthcare delivery .Fourth, the study focuses on a low-income or middle-income setting, either specific country or within-country or supranational region.Finally, the study must focus on an NCD. The list of NCDs was taken from the WHO Global Health EstimatesAt the end of each screening stage, the researchers met to compare results and resolve decision discrepancies. Ambiguous cases were arbitrated by the other authors (JB and PD).Standard bibliographic information was extracted alongside data for the following topics: geographic coverage, policy area, disease/risk area and equity consideration.We conducted a narrative synthesis of study characteristics with a special focus on two themes, namely: (i) application area of policy/intervention and (ii) equity consideration adopted by the study (if any). Both of these were further broken down and/or categorised as follows:Given the variety of global health solutions , studies were inductively categorised into the following application areas, which arose through reviewing of the papers:Public health policy: policy working via a non-healthcare mechanism to modify key risk factors for NCDs; examples include taxation of sugar-sweetened beverages and regulations to create smoke-free areas.Population screening: screening of general populations at risk of a specific NCD before provision of relevant healthcare to diagnosed persons; examples include national cervical cancer screening programmes and diabetes screening in primary care.Healthcare provisionAccess: provision of healthcare to defined patient groups who previously had no access to a comparable standard of care; examples include charitable provision of surgery to patients with cancer who otherwise have no access to oncology treatment and provision of dialysis to patients with end-stage renal disease who otherwise would have received basic supportive care only.Comparative: effectiveness and/or cost-effectiveness studies of two or more alternative healthcare interventions of comparable standards for a defined patient group; examples include comparison of two chemotherapy regimens for patients with breast cancer and comparison of two insulin-based treatments for patients with diabetes to achieve superior control of given disease and/or lower intervention cost.Within each application area, the included studies were further grouped by risk area (for studies under category A) or targeted disease area (for studies under categories B and C). Application areas were mutually exclusive.Studies were categorised by whether and how they demonstrated a concern for equity, this categorisation was inductive:Differential subgroup impact: the study evaluates the differential impact of intervention outcomes on population subgroups delineated by social, economic, demographic and geographic or any other predisposing environmental factor beyond individual control. The outcomes may include health ), healthcare access or financial risk protection from NCD-induced healthcare expenditure.Disadvantaged group targeting: the intervention targets healthcare access and/or health improvement in a population group deemed to be socially disadvantaged; examples of such groups include women, infants and children, the elderly and rural populations.Universal coverage of healthcare: the intervention aims to expand access to preventive or treatment services.Preventive services: widening the provision of preventative healthcare. For example, a new national cancer screening programme is compared with no screening or opportunistic screening at healthcare centres, or where coverage levels are varied and evaluated within the study.Treatment services: provision of clinical care to patients who previously had no access. We only include cases representing an improvement in coverage of public sector healthcare, that is, where the cost of relevant healthcare is at least partly borne by the government.Studies under category (I) must explicitly highlight differential impact as an equity concern: simple stratification of results  with no discussion of equity was deemed insufficient for inclusion. These equity categories are not mutually exclusive and a study was able to fall into multiple categories.As this paper is a scoping review comprising an assessment of the academic literature, there was no direct patient and public engagement on the paper.The PRISMA flow diagram illustrates the search process, including article screening and study inclusion . The datThere were 29 (19%) studies evaluating public health policies working via non-healthcare mechanisms to reduce key modifiable risk factors for NCDs and 30 (20%) that evaluated population screening interventions. Of the 90 (60%) studies that evaluated healthcare provision for defined patient groups, 41 were primarily concerned with providing access to new or higher-standard healthcare, while 49 included evaluation of strategies with a comparative standard of care. When classified by application area and income group of the country/countries included in the study, a very high proportion, 76% and 85%, of public health policy and comparative evaluation studies, respectively, incorporated only upper-middle-income countries . At the The 149 included studies covered a broad range of risk factors and disease areas . Neoplas10.1136/bmjgh-2019-002259.supp2Supplementary dataWhen the proportion of NCD burden across LMICs, using Global Burden of Disease data,Of the 149 papers, 88 (59%) fell into at least one of the three equity categories .The eight subgroup impact papers mainly comprised extended cost-effectiveness analysis (ECEA) (n=7). ECEA requires the inclusion of OOP expenditure and at least one measure of financial risk protection, providing an explicit link between health and poverty .A total of 47 papers evaluated policies specifically targeted at socially disadvantaged groups. The most populous group was women (n=28), followed by neonates (n=9), children (n=9), rural dwellers (n=3), older adults (n=2) and economically disadvantaged (n=1). Four of the papers concerned the intersection of two groups, including rural and neonatal (n=2), rural and women (n=1) and women and neonatal (n=1).All 41 of the healthcare provision access papers fell into this category as did an additional 4 comparative papers which examined different funding strategies and 23 of the population screening papers, totalling 68. Screening papers which did not fall into this category were either concerned with the cost-effectiveness of comparable screening strategies (n=4), budget distribution between screening and treatment (n=1) or were paid for exclusively by private insurance (n=1).The intersection between application area and equity approach taken is included in Population screening demonstrated concern for equity in 29 papers (97%) mainly via a focus on disadvantaged groups, of which the majority were women (n=15), and extending preventative healthcare services via universal provision of screening policies. The screening interventions were dominated by cervical cancer (n=8), congenital disease (n=6) and breast cancer (n=4).All 41 healthcare provision access studies by definition fell into the universal health coverage, however, many papers also fell into the other equity categories. Four papers used ECEA to demonstrate the impact of the healthcare treatment by income/wealth quintiles including charitable surgery in Uganda, epilepsy treatment in India, mental health treatment in Ethiopia and a package of diverse healthcare treatments in Ethiopia. There was also range of disadvantaged groups covered across 12 of the papers including children (n=5), women (n=4), the elderly (n=1), neonates (n=1) and lower socioeconomic groups (n=1).The healthcare provision comparative studies had the lowest percentage of papers considering equity with just 11 (23%). Eight covered disadvantaged groups, including women (n=6) and children (n=2). The four papers in this application area which evaluated alternative cancer treatments demonstrated concern for universal coverage by incorporating scenarios involving different levels of public funding for the treatment.The range of studies using quantitative OR methods (broadly understood) demonstrates the breadth of public health and clinical interventions available to policymakers for addressing the burden of NCDs in LMICs. The much greater number of OR studies evaluating NCD interventions included in this review, compared with previous reviews,Equity was considered in 59% of the 149 papers included in the review. This supports the renewed focus on health equity as it relates to differential impact between subpopulations, targeting of disadvantaged groups and extending universal healthcare. In a previous scoping review of OR studies only 4% of studies included an equity consideration and these studies focused primarily on access to healthcare.Despite this strong focus on equity only eight papers explored differential impact, seven of which used extended cost-effectiveness analysis. These papers, across all three application areas, demonstrate the prominence of ECEA as a methodological approach and its flexibility of application to diverse health interventions. However, a number of papers which modelled universal upstream public health interventions did not incorporate a concern for differential impact, including SSB taxes, unhealthy goods taxes and salt reduction policies,The finding that 44% of papers included authors affiliated with governmental departments, quasi-governmental institutions or specific hospitals in the country of interest infers a degree of stakeholder engagement in OR. However, it should be noted that 15% of papers had no author at all from the country of study so there is still significant progress to be made in embedding research in its local context.We argue that OR shows much potential to improve the quality of decision-making and guide improvements in population health and equity. Researchers should attempt to incorporate differential impact within their research wherever possible, although we acknowledge that barriers exist, including data availability, capacity constraints and researcher familiarity with methods. Understanding which groups are of interest, or indeed which intersectionalities , is critical to the pursuit of equity and is highly context-dependent. Closer collaboration between researchers and decision makers and implementers would enable a deeper and more nuanced understanding of the equity impact of the policy and its implementation.Greater stakeholder engagement would also strengthen the development and validation of models, and the translation of findings into policy and practice. Further research into the embeddedness of OR research in local decision-making processes would provide greater insight into the role that local stakeholders play in setting the research agenda as well as in the research process itself and in uptake of key findings.To the best of our knowledge, this scoping review is the most comprehensive one to date covering all approaches to reduce NCD burden and promote health and healthcare equity in the SDG era. However, there are several limitations to this research, which in part reflect the nature of a scoping review. Databases were chosen to cover medicine and social science, but engineering databases may have provided additional papers. The search period was deliberately designed to provide a cross-sectional snapshot of the SDG era (2015–2018), however this prevents the consideration of OR applications before this period and of trends over time. Important studies may have been missed due to date range alone, for example, those evaluating excise tax on SSB in IndiaIn summary, this review found that OR is being used to evaluate a broad range of policies across a range of NCDs in LMICs, although the focus of the papers was disproportionate to the relative disease burden in these countries. A majority of papers incorporated a concern for equity, although this is primarily through targeted policies and expansion of healthcare. The included papers demonstrated that OR methods can be used to illustrate differential impact, highlighting vulnerable groups, across a range of application areas and disease types. This however, appears to currently be an underused approach in relation to OR on NCD policy in LMICs.