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Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own. [ABSTRACT FROM AUTHOR]

Background The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments. Methods The design and implementation of PHIRI have been developed upon: (i) the data visiting principle—data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a 'master–worker' federated computational architecture that supported the development of four uses cases. Results Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles. Conclusion PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes. [ABSTRACT FROM AUTHOR]

UV light is a potent mutagen that induces bulky DNA damage in the form of cyclobutane pyrimidine dimers (CPDs). Photodamage and other bulky lesions occurring in nuclear genomes can be repaired through nucleotide excision repair (NER), where incisions on both sides of a damaged site precede the removal of a single-stranded oligonucleotide containing the damage. Mitochondrial genomes (mtDNAs) are also susceptible to damage from UV light, but current evidence suggests that the only way to eliminate bulky mtDNA damage is through mtDNA degradation. Damage-containing oligonucleotides excised during NER can be captured with antidamage antibodies and sequenced (XR-seq) to produce high-resolution maps of active repair locations following UV exposure. We analyzed previously published datasets from Arabidopsis thaliana, Saccharomyces cerevisiae , and Drosophila melanogaster to identify reads originating from the mtDNA (and plastid genome in A. thaliana). In A. thaliana and S. cerevisiae , the mtDNA-mapping reads have unique length distributions compared to the nuclear-mapping reads. The dominant fragment size was 26 nt in S. cerevisiae and 28 nt in A. thaliana with distinct secondary peaks occurring in regular intervals. These reads also show a nonrandom distribution of di-pyrimidines (the substrate for CPD formation) with TT enrichment at positions 7–8 of the reads. Therefore, UV damage to mtDNA appears to result in production of DNA fragments of characteristic lengths and positions relative to the damaged location. The mechanisms producing these fragments are unclear, but we hypothesize that they result from a previously uncharacterized DNA degradation pathway or repair mechanism in mitochondria. [ABSTRACT FROM AUTHOR]

In epidemiology and the social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the " within " approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the " across " approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers. [ABSTRACT FROM AUTHOR]

Clinical quality registries can be a transformational tool to improve healthcare delivery. Clinical registries with an incorporated quality emphasis identify evidence-practice gaps, inform quality improvement, and provide foundational research data to examine and improve health-related outcomes. For registries to create an impact it is essential that clinicians and researchers understand historical context, importance, advantages, and key criticisms. This methodological paper highlights the skills and capabilities required to build and maintain a robust clinical quality registry. This includes key measures to ensure data security, quality control, ongoing operational components, and benchmarking of care outcomes. Graphical Abstract [ABSTRACT FROM AUTHOR]

The lack of sex-specific variables, such as reproductive system history (RSH), in cardiovascular research studies is a missed opportunity to address the cardiovascular disease (CVD) burden, especially among women who face sex-specific risks of developing CVD. Collecting RSH data from women enrolled in research studies is an important step towards improving women's cardiovascular health. In this paper, we describe two approaches to collecting RSH in CVD research: extracting RSH from the medical record and participant self-report of RSH. We provide specific examples from our own research and address common data management and statistical analysis problems when dealing with RSH data in research. [ABSTRACT FROM AUTHOR]

Background Blockchain technology has made great strides in many industries but has yet to impact the world of public health. Population health issues such as outbreak surveillance and controlled substance tracking during emergencies all require a secure, easily accessible database. While the healthcare industry is typically slow to adapt to change, blockchain technology lends itself well to many healthcare issues. Methods We utilized a 3D framework using difficulty, novelty and necessity to examine the adoption of blockchain technology in population health, based on the 2D framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by Iansiti and Lakhani in The Harvard Business Review. Results We find that by implementing the third dimension of necessity into an evaluation framework, we can better predict the adoption of technology. We found how different areas of population health fit into the evaluation framework and how necessity can eliminate barriers from implementing novel technologies. Conclusion The byproduct of this paper will be the extension of the Iansiti and Lakhani framework. We will show that blockchain, in all of these domains, shows promise to improve population health as we move past COVID-19 and into the future of healthcare. [ABSTRACT FROM AUTHOR]

Purpose Structural and financial changes in US healthcare are the driving forces reshaping care delivery and the technologies that clinicians use in their practice. Structural-level changes in healthcare organizations bringing facilities varying in size, systems, and complexity under the new integrated care delivery networks create unprecedented challenges. This paper develops guiding principles for formulary management in automated systems that facilitate medication management and alignment of medication information technology (IT) solutions in healthcare organizations ranging in size from a single facility to very large enterprise/multifacility networks. Summary This paper describes formulary management best practices for healthcare organizations as they follow crucial medication management processes: formulary item builds, urgent and nonformulary medication requests, and formulary item build testing and maintenance. As medication management practices are embedded in automated technologies that are similar yet nuanced, we pay special attention to practices and change control processes that maximize coordination among systems while preserving medication data integrity and reducing medication errors. Conclusion For increasingly complex healthcare systems with multiple medication management system demands, formulary drug database builds and maintenance are challenging and time-consuming. Formulary item build, testing, and maintenance processes involve multifunctional teams collaborating to ensure the integrity of data, prevent unexpected system incompatibilities, and improve patient safety by reducing medication errors. Ideally, formulary item build, testing, and maintenance processes produce consistent final drug records in IT systems meeting user needs. [ABSTRACT FROM AUTHOR]

The Preconception Period Analysis of Risks and Exposures Influencing Health and Development (PrePARED) Consortium creates a novel resource for addressing preconception health by merging data from numerous cohort studies. In this paper, we describe our data harmonization methods and results. Individual-level data from 12 prospective studies were pooled. The crosswalk-cataloging-harmonization procedure was used. The index pregnancy was defined as the first postbaseline pregnancy lasting more than 20 weeks. We assessed heterogeneity across studies by comparing preconception characteristics in different types of studies. The pooled data set included 114,762 women, and 25,531 (22%) reported at least 1 pregnancy of more than 20 weeks' gestation during the study period. Babies from the index pregnancies were delivered between 1976 and 2021 (median, 2008), at a mean maternal age of 29.7 (standard deviation, 4.6) years. Before the index pregnancy, 60% of women were nulligravid, 58% had a college degree or more, and 37% were overweight or obese. Other harmonized variables included race/ethnicity, household income, substance use, chronic conditions, and perinatal outcomes. Participants from pregnancy-planning studies had more education and were healthier. The prevalence of preexisting medical conditions did not vary substantially based on whether studies relied on self-reported data. Use of harmonized data presents opportunities to study uncommon preconception risk factors and pregnancy-related events. This harmonization effort laid the groundwork for future analyses and additional data harmonization. [ABSTRACT FROM AUTHOR]

Researchers often face the problem of how to address missing data. Multiple imputation is a popular approach, with multiple imputation by chained equations (MICE) being among the most common and flexible methods for execution. MICE iteratively fits a predictive model for each variable with missing values, conditional on other variables in the data. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrectly specified, they may produce biased estimates of the imputed data, yielding inconsistent parameter estimates and invalid inference. Given the set of modeling choices that must be made in conducting multiple imputation, in this paper we propose a data-adaptive approach to model selection. Specifically, we adapt MICE to incorporate an ensemble algorithm, Super Learner, to predict the conditional mean for each missing value, and we also incorporate a local kernel-based estimate of variance. We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. These results suggest that using a flexible machine learning imputation approach can be useful in settings where data are missing at random, especially when the relationships among the variables are complex. [ABSTRACT FROM AUTHOR]

The article discusses various papers published in the Practice of Epidemiology section of this journal, including one by Brookhart et al. on the use of propensity scores in simulation studies, one by Benowitz et al. on the National Health and Nutrition Examination Survey and one by Stuart et al. on the application of multiple imputation to account for missing data.

Historians need to recognize the profound changes in the records currently being created by businesses, government and private individuals, and the implications this will have on the way they undertake research with these records in the future. An electronic revolution has already occurred over the last decade in access to records, but the records accessed either remained non-electronic in themselves, or were surrogates of non-electronic originals. The new challenge relates to the ‘born-digital’ emails, word processed documents, spreadsheets and databases that will only ever exist electronically. This will affect the application of legislation, the selection and destruction of records, their preservation, their access and even the nature and meaning of the veracity of the record. Historians need not only to recognize how this affects them, but also to become involved in these developments. [ABSTRACT FROM PUBLISHER]

Sequences of treatments that adapt to a patient's changing condition over time are often needed for the management of chronic diseases. An adaptive treatment strategy (ATS) consists of personalized treatment rules to be applied through the course of a disease that input the patient's characteristics at the time of decision-making and output a recommended treatment. An optimal ATS is the sequence of tailored treatments that yields the best clinical outcome for patients sharing similar characteristics. Methods for estimating optimal adaptive treatment strategies, which must disentangle short- and long-term treatment effects, can be theoretically involved and hard to explain to clinicians, especially when the outcome to be optimized is a survival time subject to right-censoring. In this paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with survival outcomes. Using data from the Clinical Practice Research Datalink, a large primary-care database, we illustrate how it can answer an important clinical question about the treatment of type 2 diabetes. We identify an ATS pertaining to which drug add-ons to recommend when metformin in monotherapy does not achieve the therapeutic goals. [ABSTRACT FROM AUTHOR]

Background: Study-based registers facilitate systematic reviews through shortening the process for review team and reducing considerable waste during the review process. Such a register also provides new insights about trends of trials in a sub-specialty. This paper reports development and content analysis of Cochrane Schizophrenia Group's Study-Based Register. Methods: The randomized controlled trials were collected through systematic searches of major information sources. Data points were extracted, curated and classified in the register. We report trends using regression analyses in Microsoft Excel and we used GIS mapping (GunnMap 2) to visualize the geographical distribution of the origin of schizophrenia trials. Results: Although only 17% of trials were registered, the number of reports form registered trials is steadily increasing and registered trials produce more reports. Clinical trial registers are main source of trial reports followed by sub-specialty journals. Schizophrenia trials have been published in 23 languages from 90 countries while 105 nations do not have any reported schizophrenia trials. Only 9.7% of trials were included in at least one Cochrane review. Pharmacotherapy is the main target of trials while trials targeting psychotherapy are increasing in a continuous rate. The number of people randomized in trials is on average 114 with 60 being the most frequent sample size. Conclusions: Curated datasets within the register uncover new patterns in data that have implications for research, policy, and practice for testing new interventions in trials or systematic reviews. [ABSTRACT FROM AUTHOR]

Information systems ontology is intended to facilitate interoperability among the many applications which are now becoming available on the Internet. In particular, it is intended to facilitate the development of intelligent agents which can automate a large part of the task of a user achieving some end employing multiple autonomous applications. A large number of ontologies exist supporting specific kinds of interoperation among selected, generally mutually aware, applications. The intent of the upper ontology movement is to develop an abstract description of what there is in the world, in an application-independent form, which can be used both to help build specific ontologies and to help in finding common ground among them. This paper argues that, for the purposes of information systems interoperation and the semantic web, application-independent upper ontologies are unlikely to be successful because of semantic heterogeneity. However, the paper argues for a distinction in upper ontologies between formal and material ontologies, based on analogies with concepts in Kant's synthetic a priori, and that formal ontologies whose focus is on how we see the world are more likely to be successfully developed in the absence of applications than are material ontologies, which attempt to catalog the world a priori. Categories and Descriptors: C.2.4 [Distributed Systems] Distributed Applications, Distributed Databases D.2.12 [Software Engineering] InteroperabilityData Mapping; H.3.5 [Information Storage and Retrieval] Online Information ServicesData Sharing and Web-based Services; H.2.1 [Database Management] Systems. [ABSTRACT FROM AUTHOR]

Enabled by the continued advances in storage technologies, the amounts of on-line data grow at a rapidly increasing pace. This development is witnessed in many data warehouse-type applications, including so-called data webhouses that accumulate click streams from portals. The presence of very large and continuously growing amounts of data introduces new challenges, one of them being the need for effective management of aged data. In very large and growing databases, some data eventually becomes inaccurate or outdated and may be of reduced interest to the database applications. This paper offers a mechanism, termed persistent views, that aids in flexibly reducing the volume of data, for example, by enabling the replacement of such ‘low-interest’, detailed data with aggregated data. The paper motivates persistent views and precisely defines and contrasts these with the related mechanisms of views, snapshots and physical deletion. The paper also offers a provably correct foundation for implementing persistent views. [ABSTRACT FROM PUBLISHER]

In recent years, much new data on intestinal alkaline phosphatase (IAP) have been published, and major breakthroughs have been disclosed. The aim of the present review is to critically analyze the publications released over the last 5 years. These breakthroughs include, for example, the direct implication of IAP in intestinal tight junction integrity and barrier function maintenance; chronic intestinal challenge with low concentrations of Salmonella generating long-lasting depletion of IAP and increased susceptibility to inflammation; the suggestion that genetic mutations in the IAP gene in humans contribute to some forms of chronic inflammatory diseases and loss of functional IAP along the gut and in stools; stool IAP as an early biomarker of incipient diabetes in humans; and omega-3 fatty acids as direct inducers of IAP in intestinal tissue. Many recent papers have also explored the prophylactic and therapeutic potential of IAP and other alkaline phosphatase (AP) isoforms in various experimental settings and diseases. Remarkably, nearly all data confirm the potent anti-inflammatory properties of (I)AP and the negative consequences of its inhibition on health. A simplified model of the body AP system integrating the IAP compartment is provided. Finally, the list of nutrients and food components stimulating IAP has continued to grow, thus emphasizing nutrition as a potent lever for limiting inflammation. [ABSTRACT FROM AUTHOR]

For cloud data storage, data privacy and security are two key concerns. Although sensitive data can be encrypted before they are stored in the cloud, the encrypted data can hardly be processed efficiently. Hence, a lightweight solution is required to satisfy both high security and high efficiency requirements. In this paper, we study the problem of range query over encrypted data. The main idea is to transform the range comparison to a privacy-preserving set intersection operation. To protect record privacy, our scheme builds searchable encrypted indexes for records that are secure against inference attack. To ensure the privacy of range queries, non-deterministic encryption, which has not been achieved in range query before, is proposed to hide the search pattern of queries. During range comparison, our scheme neither leaks the order relationship between the upper/lower bound of a range query and the encrypted index, nor produces false positives in the query results. We have implemented our scheme and evaluated its performance in comparison with other schemes. The comparison results indicate that our scheme has a shorter index size and search time than the order-revealing encryption (ORE) scheme when the processing unit is large. Meanwhile, our scheme only leaks the access pattern, and is proved to be more secure than existing schemes. [ABSTRACT FROM AUTHOR]

Background Health systems in the European Union (EU) are being questioned over their effectiveness and sustainability. In pursuing both goals, they have to conciliate coexisting, not always aligned, realities. Methods This paper originated from a workshop entitled 'Health systems for the future' held at the European Parliament. Experts and decision makers were asked to discuss measures that may increase the effectiveness and sustainability of health systems, namely: (i) increasing citizens' participation; (ii) the importance of primary care in providing integrated services; (iii) improving the governance and (iv) fostering better data collection and information channels to support the decision making process. Results In the parliamentary debate, was discussed the concept that, in the near future, health systems' effectiveness and sustainability will very much depend on effective access to integrated services where primary care is pivotal, a clearer shift from care-oriented systems to health promotion and prevention, a profound commitment to good governance, particularly to stakeholders participation, and a systematic reuse of data meant to build health data-driven learning systems. Conclusions Many health issues, such as future health systems in the EU, are potentially transformative and hence an intense political issue. It is policy-making leadership that will mostly determine how well EU health systems are prepared to face future challenges. [ABSTRACT FROM AUTHOR]

There is an increasing demand to efficiently process emerging types of queries, such as progressive queries (PQs), from contemporary database applications including telematics, e-commerce and social media. Unlike conventional queries, a PQ consists of a set of step-queries (SQ). A user formulates a new SQ on the fly based on the result(s) from the previous SQ(s). Existing database management systems were not designed to efficiently process such queries. In this paper, we present a novel technique to efficiently process a special type of PQ, called monotonic linear PQs, based on dynamically materialized views. The key idea is to create a superior relationship graph for SQs from historical PQs that can be used to estimate the benefit of keeping the current SQ result as a materialized view. The materialized views are used to improve the performance of future SQs. A new storage structure for the materialized views set is designed to facilitate efficient search for a usable view to answer a given SQ. Algorithms/strategies to efficiently construct a superior relationship graph, dynamically select materialized views, effectively manage the materialized views set and efficiently search for usable views are discussed. Experiment results demonstrate that our proposed technique is quite promising. [ABSTRACT FROM AUTHOR]

The author comments on a study on ways to reduce unwanted variation in the delivery of high-quality hip fracture services in England and Wales, which references a research paper by R. Patel et al., published within the issue. It discusses the use of big data in understanding how health care systems and processes impact outcomes, the influence of big data on thinking and actions, and a limitation of the study.

The Database-As-a-Service (DaaS) model provides seamless mechanisms for database management and service. As a countermeasure to security treats, encryption technologies have been widely adopted in database systems. Accordingly, privacy-preserving queries on encrypted databases has become an important research topic. In this paper, we present the first solution of authorized equi-join for multiple data contributors in the PKC-based setting. With the improved ciphertext security under checkability, our underlying cryptographic construction could be used independently as a standard procedure for many other relational operations, for instance, intersection, difference, and set union. As a feature, our scheme allows flexible authorization policies for the join operation by the controlled trapdoor transitivity, which has not yet been studied thoroughly in the literature. Our proposed scheme is efficient in encryption and execution and has a reasonable ciphertext size. It can be applied as a building block for data query in the DaaS model. [ABSTRACT FROM AUTHOR]

Forensic Sci Res. 2022; 7. 2 Miamoto P, Uehara C. Personal identification and missing persons initiatives in Santa Catarina state, Brazil: forensic perspectives from 2019 to 2021. Invited authors were forensic anthropology practitioners in Brazil representing the main cities in which practice is being pursued. What is the current state of Forensic Anthropology in Brazil and where do we stand when identification is concerned?. [Extracted from the article]

Owing to the great advances in cloud computing and Internet technologies, data owners (DOs) have been motivated to outsource the storage of their data to remote cloud servers (CSs) in order to enjoy great data management service with an efficient cost. For security purposes, DOs usually have to encrypt their data prior to outsourcing it to the untrusted CSs. But encryption makes searching the encrypted data a challenging task. Recently, several approaches have been provided to enable searching over encrypted data. However, the majority of these systems are limited to handling an exact search, not a similarity search; but the latter is an important need for real-world applications. In this paper, we propose an efficient yet secure scheme to search encrypted cloud data, while recovering the misspellings and typographical errors that exist frequently both in the search request and in the source data. To do so, we use a metric space to construct a tree-based index, which allows retrieving only the relevant entries with a minimum number of distance evaluations. String embedding techniques are used to refine the relevant entries efficiently and securely. Our index construction maintains the privacy of the keyword trapdoors as well as the stored data. Comparing our scheme with other similarity searchable encryption systems via experiments shows that our scheme is efficient in terms of search time and storage overhead. [ABSTRACT FROM AUTHOR]

Background: the worldwide population is ageing. One expected consequence of this is an increase in morbidity and an associated increased demand for long-term care. Physical rehabilitation is beneficial in older people, but relatively little is known about effects in residents of long-term care facilities.Objective: to examine the effects of physical rehabilitation on activities of daily living (ADL) in elderly residents of long-term care facilities.Methods: systematic review with meta-analysis of randomised controlled trials. We included studies that compared the effect of a physical rehabilitation intervention on independence in ADL with either no intervention or an alternative intervention in older people (over 60 years) living in long-term care facilities. We searched 19 databases including the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, AMED, Web of Knowledge and Google Scholar. Two researchers independently screened papers and extracted data. Outcomes of included studies were combined in a standardised mean difference random-effects meta-analysis.Results: thirteen of 14 studies identified were included in the meta-analysis. Independence in ADL was improved by 0.24 standard units (95% CI: 0.11–0.38; P = 0.0005). This is equivalent to 1.3 points on the Barthel Index (0–20 scale). No significant differences in effect were found based on participant or intervention characteristics. Larger sample size and low attrition were associated with smaller estimates of effect. All studies were assessed to be at risk of bias.Conclusions: physical rehabilitation may improve independence for elderly long-term care facility residents, but mean effects are small. It is unclear which interventions are most appropriate. [ABSTRACT FROM AUTHOR]

The selectivity estimation (estimating the number of records that satisfy query conditions) is an important task for query optimization in modern database management systems (DBMS). For this reason, many DBMS maintain histograms to approximate the frequency distribution of values in the attributes of relations. In this paper, we present wavelet packets as an alternative method to the standard wavelet decomposition for building histograms of the distribution of data stored in relations to the database. This method is tested by approximating the cumulative data distribution of one attribute and the joint distributions of two attributes in a relation using various error measures. Experiments performed on synthetically generated data distributions show that our histograms offer improvements in accuracy over standard wavelet decomposition methods. [ABSTRACT FROM AUTHOR]

There are more than 1.2 billion biological specimens in the world's museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences. Nevertheless, from their conception they are almost entirely documented on paper, either as labels attached to the specimens or in catalogues linked with catalogue numbers. In order to make the best use of these data and to improve the findability of these specimens, these data must be transcribed digitally and made to conform to standards, so that these data are also interoperable and reusable. Through various digitization projects, the authors have experimented with transcription by volunteers, expert technicians, scientists, commercial transcription services and automated systems. We have also been consumers of specimen data for taxonomical, biogeographical and ecological research. In this paper, we draw from our experiences to make specific recommendations to improve transcription data. The paper is split into two sections. We first address issues related to database implementation with relevance to data transcription, namely versioning, annotation, unknown and incomplete data and issues related to language. We then focus on particular data types that are relevant to biological collection specimens, namely nomenclature, dates, geography, collector numbers and uniquely identifying people. We make recommendations to standards organizations, software developers, data scientists and transcribers to improve these data with the specific aim of improving interoperability between collection datasets. [ABSTRACT FROM AUTHOR]

The financial support available to students on social work qualifying programmes appears to be a neglected topic in published social work research. This article draws on a literature review and secondary data analysis of an existing dataset to discuss what is known about this topic and specifically considers the impact of a financial incentive to undertake social work qualifying education in England, the social work bursary. In the context of major changes to the funding of higher education in England, it suggests that the introduction of the social work bursary has helped increase the number of students enrolling on social work qualifying programmes in England and supported some students whose personal and financial circumstances might have prevented them from undertaking social work qualifying education. While students report their appreciation of financial assistance in the form of the bursary, many have additional needs in the form of support for children and other dependants, and for help in defraying the costs of travel while undertaking practice placements. The paper concludes that attention needs to be paid to the specific needs of social work students when considering the impact of changes to funding higher education. [ABSTRACT FROM AUTHOR]

Background. To date, there has been no attempt to describe or compare physical therapy as practiced globally, nor any evidence that an international data set exists to support this effort. It is known that research evidence can be used in strategic and tactical ways, especially within the highly politicized context of the policy arena. The International Private Practitioners Association recognized the potential value a global evidence base could have in influencing policy and supporting professional development in a number of countries, yet it lacked a mechanism to achieve these aims. Objectives. The purposes of this study were: (1) to identify and test an international data set, definitions, and means of data collection and (2) to establish views in relation to the value of international collaborations. Design. A mixed, prospective design was used in the study. Method. Phase 1 (2006-2007) involved the development of a data set, definitions, and Web-based and paper-based data collection options involving 98 physical therapists from 68 physical therapy practices in 7 countries. Phase 2 (2008-2009) involved testing of the data set in 34 practices involving 3,195 patient episodes and included physical therapist feedback of experience, local relevance of the data set, and value of international collaborations. Results. Testing confirmed the relevance and reliability of the data set and definitions and a preference for Web-based data collection (74.0%). Physical therapist feedback supported these findings. Most respondents (60.0%-100.0%) reported the value of further international collaborations for their profession nationally or internationally. Limitations. Although a true international collaboration, the limited sample size should be recognized. Conclusions. It is possible to develop an agreed-upon international data set and means of data collection. Testing appears to support its acceptability and relevance for use in practice. Participants highly valued the opportunity to undertake international collaborations that may benefit their profession nationally and internationally. Further testing and use of the data set are advocated before final validation is sought. [ABSTRACT FROM AUTHOR]

When a compiler encounters a fixed integer divisor, which is not a power of 2, it can calculate its inverse to be multiplied by the run-time integer dividends to obtain the quotients, using our very efficient, recently published [Cavagnino, D. and Werbrouck, A.E. (2008) Efficient algorithms for integer division by constants using multiplication. Comp. J., 51, 470–480] division by multiplication algorithms. Essentially our algorithms permit a complete partition of a defined number space into non-adverse and adverse divisors on the basis of whether a dividend associated with each divisor is, or is not, greater than the maximum dividend size. In this paper, we demonstrate useful relations between the dividends associated with all divisors and with their multiples by positive powers of 2 leading to rapid iterative algorithms for calculating full sets of associated dividends. [ABSTRACT FROM AUTHOR]

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This paper argues that, currently, significant change is taking place in economics because (1) technological changes in analytic and computing methods are opening up new avenues of study, and (2) the 'low hanging fruit' from previous approaches and methods have already been picked. It offers a vision of the future of economics that sees economists focusing less on the study of infinitely bright agents operating in information rich environments and more on the study of reasonably bright individuals operating in information-poor environments. Agent-based models and computer analysis of data will increase in importance, and deductive analytics will decrease in importance. [ABSTRACT FROM AUTHOR]

When variable length codes are used in the presence of error, slippage can occur. The number of symbols decoded before synchronization is regained can differ from the number of encoded symbols, causing misinterpretation of data. In previous work certain synchronization schemes have been shown to be satisfactory in preventing permanent loss of synchronization and slippage in the presence of random error. In this paper we demonstrate that these schemes can also be satisfactory in the presence of burst errors, and that their relative merits remain the same. [ABSTRACT FROM PUBLISHER]

Many data compression techniques are essentially variable length codes. Such codes suffer from a vulnerability to error propagation. This occurs when an error causes the correspondence between the data stream and the stream of encoded symbols to be lost and continues until synchronization between the two is re-established. This paper compares a number of strategies for synchronization in which redundant synchronizing words are used with a mechanism for labelling or counting them. In each case the strategy is investigated for the prevention of error propagation, ensuring the re-establishment of the correspondence with the correct point in the data stream. Three classes of potential synchronization error are explored: the false recognition of spurious words, the loss of synchronizing words and synchronization misinterpretation, when one synchronizing word is confused with another. The probabilities of synchronization error are analysed. It is shown that the strategies are successful against random errors when they incorporate some mechanism for consistency checking. The relative merits of different schemes are demonstrated. The schemes are also compared with the technique known as bitstuffing. This is considered on its own and in combination with the other schemes. [ABSTRACT FROM AUTHOR]

A database management system (DBMS) performs query optimization based on statistical information about data in the underlying database. Out-of-date statistics may lead to inefficient query processing in the system. The existing utility method, which collects statistics in batch mode, suffers from drawbacks such as heavy administrative burden, high system load and tardy updates. In this paper, we study approaches to performing statistical analysis on the fly during query execution, taking advantage of data already resident in main memory. We propose a framework for on-the-fly statistics collection, which we term piggybacking, and analyze the tradeoffs of piggybacking various statistics collection techniques on top of query execution plans. We present a multiple-granularity interleaving algorithm to integrate a set of piggyback operations with an execution plan, and show how the algorithm can be incorporated into an existing query optimizer. Our experiments demonstrate that useful statistics can be obtained via the piggyback method with a small overhead. [ABSTRACT FROM AUTHOR]

There are several areas in applied logic where deletion from databases is involved in one way or another: Belief contraction (as formulated in the AGM theory of revision and contraction) Triggers of the form ‘If condition then remove A’, which are extensively used in database management systems Resource considerations as in relevance and linear logics, where addition or removal of resource can affect provability Free logic and the like, where existence and non-existence of individuals affects quantification. All of these areas have certain logical difficulties relating to the removal of elements. These difficulties are normally overcome by working on the meta-level. This paper offers a comprehensive model in which the various forms of removal are treated on the object level, i.e., as connectives of the object language. [ABSTRACT FROM PUBLISHER]

B-trees with lazy parent split (lps) are B-tree variants, according to which parent splits are postponed until a future access of the latter node. This way, the number of splits during an insert is decreased and the number of locks is also decreased. Consequently, better concurrency is achieved. In this paper 2–3 trees with lps are studied. Fringe analysis is used to obtain bounds on some performance metrics of 2–3 trees with lps. The performance metrics of 2–3 tree with lps are compared with those of the classical 2–3 trees. The conclusions are that 2–3 trees with lps have slightly better performance, more keys in the fringe, larger storage utilization and a slightly shorter path length from the root to the leaves. [ABSTRACT FROM PUBLISHER]

In interleaved memories, interference between concurrently active vector streams results in memory bank conflicts and reduced bandwidth. In this paper, we present two schemes for reducing inter-vector interference. First, we propose a memory module partitioning technique in which disjoint access sets are created for each of the concurrent vectors. Various properties of the involved address mapping are presented. Then we present an interlaced data placement scheme, where the simultaneously accessed vectors are interlaced and stored in the memory. The performance of the two schemes is evaluated by simulation. It is observed that the schemes have significant merit in reducing the interference in interleaved memories and increasing the effective memory bandwidth. The schemes are applicable to memory systems for superscalar microprocessors, vector supercomputers and parallel processors. [ABSTRACT FROM PUBLISHER]

Developing and implementing the scientific agenda of the Antibacterial Resistance Leadership Group (ARLG) by soliciting input and proposals, transforming concepts into clinical trials, conducting those trials, and translating trial data analyses into actionable information for infectious disease clinical practice is the collective role of the Scientific Leadership Center, Clinical Operations Center, Statistical and Data Management Center, and Laboratory Center of the ARLG. These activities include shepherding concept proposal applications through peer review; identifying, qualifying, training, and overseeing clinical trials sites; recommending, developing, performing, and evaluating laboratory assays in support of clinical trials; and designing and performing data collection and statistical analyses. This article describes key components involved in realizing the ARLG scientific agenda through the activities of the ARLG centers. [ABSTRACT FROM AUTHOR]

There are national and global moves to improve effective digital data design and application in healthcare. This New Horizons commentary describes the role of digital data in healthcare of the ageing population. We outline how health and social care professionals can engage in the proactive design of digital systems that appropriately serve people as they age, carers and the workforce that supports them. Key Points Healthcare improvements have resulted in increased population longevity and hence multimorbidity. Shared care records to improve communication and information continuity across care settings hold potential for older people. Data structure and coding are key considerations. A workforce with expertise in caring for older people with relevant knowledge and skills in digital healthcare is important. [ABSTRACT FROM AUTHOR]

Multi-site research studies redefine cohort studies by simultaneously providing a cross-sectional snapshot of patients and monitoring them over time, to evaluate outcomes. However, careful design is crucial to minimize potential biases, such as seasonal variations, that may arise during the study period. Addressing snapshot study challenges requires strategic solutions: implementing multi-stage sampling for representativeness, providing rigorous data collection training, using translation techniques and content validation for cultural and linguistic appropriateness, streamlining ethical approval processes, and applying comprehensive data management for follow-up and missing data. These strategies can optimize the efficacy and ethicality of snapshot studies. [ABSTRACT FROM AUTHOR]

SUMMARY This paper considers some of the problems of managing data on a computer, concentrating mainly on the storage and accessing requirements of basic data structures such as the data matrix. The similarity between scans of these structures for different purposes will be noted and common operations identified. The distinction between the detailed structure of the data and the structure as assumed by the scanning program will be clarified and examples of structure scans for different purposes will be described. Related problems of the maintenance of directories keeping track of structures, and the differences between the use of magnetic tape and disc will be included. The main purpose of the paper is to stress the advantages of establishing the requirements of access to the data, identifying the characteristics of a scan of a data structure, and identifying the basic operations that make up such scans. The advantages of a systematic approach to the problems of data management include (1) a clearer program organization, (2) a clearer division of responsibilities for programming and debugging, (3) changing of programs to meet new requirements or the writing of new parts is much easier, (4) duplication of programming effort both within one program and between programs is avoided, (5) time-consuming operations are better identified and defined and the chances of reducing the time they take are increased. [ABSTRACT FROM AUTHOR]

Currently, there are no generally applicable legal regulations in the field of digital succession in the majority of legal orders. However, such legal provisions are needed because digital assets are getting more and more significant in the contemporary world. Entry into force of legal regulations in this area should be preceded by creation of legal theory of digital succession. Therefore, it is necessary to consider fundamental principles of digital asset inheritance system. The author of this work reviews current theoretical and practical approaches to the matters of digital succession. Considerations included in this work lead to answer to the question how to create fundamental principles of digital succession system. Furthermore, there are separate recommendations for legislators, end-users, digital service providers and judiciary in this work. Hence, this article seems not only theoretical but also practical. [ABSTRACT FROM AUTHOR]

Background Systematic continuous thinking about the future helps organizations, professions and communities to both prepare for, and shape, the future. This becomes ever more critical given the accelerating rate at which new data emerge, and in some cases uncertainties around their reliability and interpretation. Businesses with the capability to filter and analyse vast volumes of data to create knowledge and insights requiring action have a competitive advantage. Similarly Government and the public sector, including public health can be more effective and efficient through the early identification of emerging issues (both threats and opportunities). Methods Horizon scanning approaches, and the use of resulting intelligence related to health protection and improvement were reviewed. Results Public health horizon scanning systems have to date focussed on health technologies and infectious diseases. While these have been successful there is a major gap in terms of non-infectious hazards and health improvement. Conclusion Any system to meet this need must recognize the changed environment for delivering front line public health services and the critical role of local authorities and the local democratic process. This presents opportunities and challenges and this paper explores those dynamics describing an existing environment and health horizon scanning system which could readily and rapidly be re-engineered to provide a national service. [ABSTRACT FROM AUTHOR]

Complete-case analyses can be biased if missing data are not missing completely at random. We propose simple sensitivity analyses that apply to complete-case estimates of treatment effects; these analyses use only simple summary data and obviate specifying the precise mechanism of missingness and making distributional assumptions. Bias arises when treatment effects differ between retained and nonretained participants or, among retained participants, the estimate is biased because conditioning on retention has induced a noncausal path between the treatment and outcome. We thus bound the overall treatment effect on the difference scale by specifying: 1) the unobserved treatment effect among nonretained participants; and 2) the strengths of association that unobserved variables have with the exposure and with the outcome among retained participants ("induced confounding associations"). Working with the former sensitivity parameter subsumes certain existing methods of worst-case imputation while also accommodating less-conservative assumptions (e.g. that the treatment is not detrimental on average even among nonretained participants). As an analog to the E-value for confounding, we propose the M-value, which represents, for a specified treatment effect among nonretained participants, the strength of induced confounding associations required to reduce the treatment effect to the null or to any other value. These methods could help characterize the robustness of complete-case analyses to potential bias due to missing data. [ABSTRACT FROM AUTHOR]

Community schools respond to a variety of risk factors that impede student learning and development. School and community leaders in Canyons School District (CSD) have created community schools across one highly impacted feeder pattern to strengthen academic learning efforts, school climate and youth development opportunities, parent and family engagement efforts, health and social services, and community partnerships. Over the past decade, school improvement processes and practices have been supported through key partnerships with colleges and universities. This article describes how universities and colleges assisted five community schools in CSD and illustrates outcomes associated with ongoing evaluation and continuous improvement efforts. The myriad of ways higher education institutions can assist community schools in highly impacted school communities are described, highlighting the various ways schools can leverage university expertise, time, personnel, and capital to help implement expanded models of schooling. [ABSTRACT FROM AUTHOR]

Urban policymakers, city officials, and community residents utilize neighborhood revitalization initiatives to establish safe and empowered neighborhoods. In 2016, leaders in Columbus, Ohio, launched a neighborhood revitalization effort designed to improve safety, access to opportunities, and economic development in the historically underserved Linden neighborhood. A priority focus involved strengthening Linden schools through the development of two university-assisted community schools (UACS). Using the community collaboration model as a guide, leaders from the schools, university, nonprofit, and local government sectors partnered to support school improvement processes in two Linden K–6 elementary schools. Annual stakeholder surveys have demonstrated marked improvements in perceptions of neighborhood safety, school climate, and the overall learning support system. The prevalence of behavioral incidences among students has decreased. Further, during the COVID-19 pandemic, the UACS model helped sustain student engagement and virtual learning and keep families connected to the schools. This article describes implementation outputs and evaluation outcomes associated with adopting the UACS model in these two Linden elementary schools. Findings contribute to a greater understanding of how UACS can serve as partners in neighborhood revitalization efforts. [ABSTRACT FROM AUTHOR]

Scientists from nursing and allied health disciplines frequently examine data with complex distributions. Key examples include data on cost that typically are skewed, and count data like the number of hospitalizations that regularly have greater variation than expected and a majority of observations at zero. Common approaches to handling complex data involve transformations that can interfere with interpretation, or force-fitting of data into linear or logistic regression. In this article, worked examples of generalized linear models, which allow for flexibility in the distribution of data, involving cost and count outcomes, are presented to help expose researchers to their nuances. [ABSTRACT FROM AUTHOR]

Objectives A vast data mining project called 'TRACking and moniToring Occupational Risks in agriculture' (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance. Methods Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases. Results After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period. Conclusions The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers. [ABSTRACT FROM AUTHOR]

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and Brain Pharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources. [ABSTRACT FROM AUTHOR]