The Implementation of Federated Digital Identifiers in Health Care: Rapid Review.

BACKGROUND: Federated digital identifiers (FDIs) have been cited to improve the interoperability of data and information management while enhancing the privacy of individuals verifying their identity on the web. Many countries around the world have implemented FDIs in various sectors, such as banking and government. Similarly, FDIs could improve the experience for those wanting to access their health care information; however, they have only been introduced in a few jurisdictions around the world, and their impact remains unclear. OBJECTIVE: The main objective of this environmental scan was to describe how FDIs have been established and implemented to enable patients' access to health care. METHODS: We conducted this study in 2 stages, with the primary stage being a rapid review, which was supplemented by a targeted gray literature search. Specifically, the rapid review was conducted through a database search of MEDLINE and Embase, which generated a list of countries and their services that use FDIs in health care. This list was then used to conduct a targeted gray literature search using the Google search engine. RESULTS: A total of 93 references from the database and targeted Google searches were included in this rapid review. FDIs were implemented in health care in 11 countries (Australia, Belgium, Canada, Denmark, Estonia, Finland, Iceland, Norway, Singapore, Sweden, and Taiwan) and exclusively used with a patient-accessible electronic health record system through a single sign-on interface. The most common FDIs were implemented nationally or provincially, and establishing them usually required individuals to visit a bank or government office in person. In contrast, some countries, such as Australia, allow individuals to verify their identities entirely on the web. We found that despite the potential of FDIs for use in health care to facilitate the amalgamation of health information from different data sources into one platform, the adoption of most health care services that use FDIs remained below 30%. The exception to this was Australia, which had an adoption rate of 90%, which could be correlated with the fact that it leveraged an opt-out consent model. CONCLUSIONS: This rapid review highlights key features of FDIs across regions and elements associated with higher adoption of the patient-accessible electronic health record systems that use them, like opt-out registration. Although FDIs have been reported to facilitate the collation of data from multiple sources through a single sign-on interface, there is little information on their impact on care or patient experience. If FDIs are used to their fullest potential and implemented across sectors, adoption rates within health care may also improve.

Formación de competencias informacionales en Bioinformática desde los estudios de pregrado en la Universidad de las Ciencias Informáticas / Development of information competencies in bioinformatics since undergraduate training at the Information Sciences University

El objetivo de este trabajo fue implementar un modelo didáctico para formar competencias informacionales en estudiantes de la Ingeniería Bioinformática pertenecientes a la Universidad de las Ciencias Informáticas. Se realizó un estudio de caso en el que se aplicó la prueba no paramétrica Dócima de Wilcoxon, así como métodos, técnicas y procedimientos investigativos. Se obtuvo como resultado un modelo didáctico en la asignatura Ingeniería de Software, centrado en los proyectos de investigación que realizan los estudiantes en el Centro de Ingeniería Genética y Biotecnología. Se concluye que el modelo aplicado es adecuado al lograrse niveles de formación de competencias informacionales en estudiantes de Ingeniería en Bioinformática(AU)

The purpose of the study was to implement a didactic model for the development of information competencies in bioinformatics engineering students from the Information Sciences University. A case study was performed based on the Wilcoxon non-parametric test as well as other research methods, techniques and procedures. The result obtained was a didactic model for the Software Engineering course, centered on the research projects conducted by students at the Center for Genetic Engineering and Biotechnology, in Cuba. It is concluded that the model applied is appropriate, since Bioinformatics Engineering students achieve satisfactory information competence levels(AU)

Communication in Theory and Research on Transactive Memory Systems: A Literature Review.

Transactive memory systems (TMS) theory has attracted considerable attention in the scholarly fields of cognitive, organizational, and social psychology; communication; information science; and management. A central theme underlying and connecting these scholarly fields has been the role of interpersonal communication in explaining how members of dyads, groups, and teams learn "who knows what," specialize in different information domains, and retrieve information from domain experts. However, because theoretical and empirical evidence is scattered across related, yet distinct scholarly fields, it is difficult to determine how and why communication influences TMS and related outcomes. Thus, this paper reviews literature on the relationships between communication, TMS, and outcomes in dyads, groups, and teams, and proposes avenues for future research.

[Medicine 4.0, the importance of electronics, information technology and microsystems in modern medicine - the case of customized chemotherapy]. / « Médecine 4.0 ¼ ou de l'importance des nouvelles technologies dans la médecine moderne - Le cas de la chimiothérapie personnalisée.

A paradigm shift seems to emerge, not only in industrial engineering ("Industry 4.0") but also in medicine: we are on the threshold to "Medicine 4.0". For many years, molecular biology had a leading position in life sciences, but today scientists start realizing that microelectronic systems, due to an increasing miniaturization, are reaching the scale of human cells and consequently can be used for therapeutic approaches. This article shows how microelectronics can play a major role in modern medicine, through the example of customized chemotherapy. This consists in determining, before the beginning of the treatment, what kind of chemotherapy or drug combination will be most effective for a given patient, and at which dose. This of course allows the lessening of a patient burden during treatment, but also to be more efficient and, in the long run, to save money. In order to do this, we have developed the Intelligent Microplate Reader (IMR), which allows us to accurately test different drugs on living cells by mimicking part of their usual environment.

Materials Informatics: Statistical Modeling in Material Science.

Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties. Here we provide a comparison between medicinal chemistry/drug design and materials-related QSAR modeling and highlight the importance of developing new, materials-specific descriptors. We survey some of the most recent QSAR models developed in materials science with focus on energetic materials and on solar cells. Finally we present new examples of material-informatic analyses of solar cells libraries produced from metal oxides using combinatorial material synthesis. Different analyses lead to interesting physical insights as well as to the design of new cells with potentially improved photovoltaic parameters.

The information science of microbial ecology.

A revolution is unfolding in microbial ecology where petabytes of 'multi-omics' data are produced using next generation sequencing and mass spectrometry platforms. This cornucopia of biological information has enormous potential to reveal the hidden metabolic powers of microbial communities in natural and engineered ecosystems. However, to realize this potential, the development of new technologies and interpretative frameworks grounded in ecological design principles are needed to overcome computational and analytical bottlenecks. Here we explore the relationship between microbial ecology and information science in the era of cloud-based computation. We consider microorganisms as individual information processing units implementing a distributed metabolic algorithm and describe developments in ecoinformatics and ubiquitous computing with the potential to eliminate bottlenecks and empower knowledge creation and translation.

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Title and Abstract Screening and Evaluation in Systematic Reviews (TASER): a pilot randomised controlled trial of title and abstract screening by medical students.

BACKGROUND: The production of high quality systematic reviews requires rigorous methods that are time-consuming and resource intensive. Citation screening is a key step in the systematic review process. An opportunity to improve the efficiency of systematic review production involves the use of non-expert groups and new technologies for citation screening. We performed a pilot study of citation screening by medical students using four screening methods and compared students' performance to experienced review authors. METHODS: The aims of this pilot randomised controlled trial were to provide preliminary data on the accuracy of title and abstract screening by medical students, and on the effect of screening modality on screening accuracy and efficiency. Medical students were randomly allocated to title and abstract screening using one of the four modalities and required to screen 650 citations from a single systematic review update. The four screening modalities were a reference management software program (EndNote), Paper, a web-based systematic review workflow platform (ReGroup) and a mobile screening application (Screen2Go). Screening sensitivity and specificity were analysed in a complete case analysis using a chi-squared test and Kruskal-Wallis rank sum test according to screening modality and compared to a final set of included citations selected by expert review authors. RESULTS: Sensitivity of medical students' screening decisions ranged from 46.7% to 66.7%, with students using the web-based platform performing significantly better than the paper-based group. Specificity ranged from 93.2% to 97.4% with the lowest specificity seen with the web-based platform. There was no significant difference in performance between the other three modalities. CONCLUSIONS: Medical students are a feasible population to engage in citation screening. Future studies should investigate the effect of incentive systems, training and support and analytical methods on screening performance. SYSTEMATIC REVIEW REGISTRATION: Cochrane Database CD001048.

Blended particle filters for large-dimensional chaotic dynamical systems.

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below.

Some solved and unsolved problems of chemoinformatics.

The field of chemoinformatics has developed from different roots, starting in the 1960s. These branches have now merged into a scientific discipline of its own, exchanging ideas and methods across different areas of chemistry. In the last 40 years chemoinformatics has achieved a lot. Without access to the databases in chemistry developed with chemoinformatics methods, modern chemical research would not be able to work at its present high level of competence. However, there are quite a few challenges, such as drug design and understanding the effect of chemicals on human health and on the environment, as well as furthering our knowledge of chemistry and of biological systems, that can benefit from a more intensive use of chemoinformatics methods. Approaches to meet these challenges will be briefly outlined. All this emphasizes that chemoinformatics has matured into a scientific discipline of its own that reaches out to many other chemical fields and will increase in attractiveness to students and researchers.

Verification and optimal control of context-sensitive probabilistic Boolean networks using model checking and polynomial optimization.

One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs.

Evolutionary algorithms with segment-based search for multiobjective optimization problems.

This paper proposes a variation operator, called segment-based search (SBS), to improve the performance of evolutionary algorithms on continuous multiobjective optimization problems. SBS divides the search space into many small segments according to the evolutionary information feedback from the set of current optimal solutions. Two operations, micro-jumping and macro-jumping, are implemented upon these segments in order to guide an efficient information exchange among "good" individuals. Moreover, the running of SBS is adaptive according to the current evolutionary status. SBS is activated only when the population evolves slowly, depending on general genetic operators (e.g., mutation and crossover). A comprehensive set of 36 test problems is employed for experimental verification. The influence of two algorithm settings (i.e., the dimensionality and boundary relaxation strategy) and two probability parameters in SBS (i.e., the SBS rate and micro-jumping proportion) are investigated in detail. Moreover, an empirical comparative study with three representative variation operators is carried out. Experimental results show that the incorporation of SBS into the optimization process can improve the performance of evolutionary algorithms for multiobjective optimization problems.

TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

Due to the vagueness of real-world environments and the subjective nature of human judgments, it is natural for experts to estimate their judgements by using incomplete interval fuzzy preference relations. In this paper, based on the technique for order preference by similarity to ideal solution method, we present a consensus model for group decision-making (GDM) with incomplete interval fuzzy preference relations. To do this, we first define a new consistency measure for incomplete interval fuzzy preference relations. Second, a goal programming model is proposed to estimate the missing interval preference values and it is guided by the consistency property. Third, an ideal interval fuzzy preference relation is constructed by using the induced ordered weighted averaging operator, where the associated weights of characterizing the operator are based on the defined consistency measure. Fourth, a similarity degree between complete interval fuzzy preference relations and the ideal one is defined. The similarity degree is related to the associated weights, and used to aggregate the experts' preference relations in such a way that more importance is given to ones with the higher similarity degree. Finally, a new algorithm is given to solve the GDM problem with incomplete interval fuzzy preference relations, which is further applied to partnership selection in formation of virtual enterprises.

Features of effective medical knowledge resources to support point of care learning: a focus group study.

OBJECTIVE: Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. METHODS: We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. RESULTS: We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. CONCLUSION: The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field.

Governance strategies for living technologies: bridging the gap between stimulating and regulating technoscience.

The life sciences present a politically and ethically sensitive area of technology development. NBIC convergence-the convergence of nanotechnology, biotechnology, and information and cognitive technology-presents an increased interaction between the biological and physical sciences. As a result the bio-debate is no longer dominated by biotechnology, but driven by NBIC convergence. NBIC convergence enables two bioengineering megatrends: "biology becoming technology" and "technology becoming biology." The notion of living technologies captures the latter megatrend. Accordingly, living technology presents a politically and ethically sensitive area. This implies that governments sooner or later are faced with the challenge of both promoting and regulating the development of living technology. This article describes four current political models to deal with innovation promotion and risk regulation. Based on two specific developments in the field of living technologies-(psycho)physiological computing and synthetic biology-we reflect on appropriate governance strategies for living technologies. We conclude that recent pleas for anticipatory and deliberative governance tend to neglect the need for anticipatory regulation as a key factor in guiding the development of the life sciences from a societal perspective. In particular, when it is expected that a certain living technology will radically challenge current regulatory systems, one should opt for just such a more active biopolitical approach.

An efficient two-tier causal protocol for mobile distributed systems.

Causal ordering is a useful tool for mobile distributed systems (MDS) to reduce the non-determinism induced by three main aspects: host mobility, asynchronous execution, and unpredictable communication delays. Several causal protocols for MDS exist. Most of them, in order to reduce the overhead and the computational cost over wireless channels and mobile hosts (MH), ensure causal ordering at and according to the causal view of the Base Stations. Nevertheless, these protocols introduce certain disadvantage, such as unnecessary inhibition at the delivery of messages. In this paper, we present an efficient causal protocol for groupware that satisfies the MDS's constraints, avoiding unnecessary inhibitions and ensuring the causal delivery based on the view of the MHs. One interesting aspect of our protocol is that it dynamically adapts the causal information attached to each message based on the number of messages with immediate dependency relation, and this is not directly proportional to the number of MHs.

Criticality in conserved dynamical systems: experimental observation vs. exact properties.

Conserved dynamical systems are generally considered to be critical. We study a class of critical routing models, equivalent to random maps, which can be solved rigorously in the thermodynamic limit. The information flow is conserved for these routing models and governed by cyclic attractors. We consider two classes of information flow, Markovian routing without memory and vertex routing involving a one-step routing memory. Investigating the respective cycle length distributions for complete graphs, we find log corrections to power-law scaling for the mean cycle length, as a function of the number of vertices, and a sub-polynomial growth for the overall number of cycles. When observing experimentally a real-world dynamical system one normally samples stochastically its phase space. The number and the length of the attractors are then weighted by the size of their respective basins of attraction. This situation is equivalent, for theory studies, to "on the fly" generation of the dynamical transition probabilities. For the case of vertex routing models, we find in this case power law scaling for the weighted average length of attractors, for both conserved routing models. These results show that the critical dynamical systems are generically not scale-invariant but may show power-law scaling when sampled stochastically. It is hence important to distinguish between intrinsic properties of a critical dynamical system and its behavior that one would observe when randomly probing its phase space.

Hierarchical information clustering by means of topologically embedded graphs.

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.

CentiLib: comprehensive analysis and exploration of network centralities.

UNLABELLED: CentiLib is a library and plug-in for the comprehensive analysis and exploration of network centralities. It provides 17 different node centrality and four graph centrality measures in a user-friendly interface and supports the exploration of analysis results within the networks. Its architecture allows for easy adaption to Java-based network analysis, simulation and visualization tools, which is demonstrated by providing the plug-in for two popular network analysis tools-Cytoscape and Vanted. With the ability to quantitatively analyze biological networks in an interactive and visual manner, CentiLib supports a better understanding of complex biological networks and processes. AVAILABILITY AND IMPLEMENTATION: Software with manual and tutorials is freely available at http://centilib.ipk-gatersleben.de/.

Nanoscale communication with molecular arrays in nanonetworks.

Molecular communication is a promising nanoscale communication paradigm that enables nanomachines to exchange information by using molecules as communication carrier. Up to now, the molecular communication channel between a transmitter nanomachine (TN) and a receiver nanomachine (RN) has been modeled as either concentration channel or timing channel. However, these channel models necessitate exact time synchronization of the nanomachines and provide a relatively low communication bandwidth. In this paper, the Molecular ARray-based COmmunication (MARCO) scheme is proposed, in which the transmission order of different molecules is used to convey molecular information without any need for time synchronization. The MARCO channel model is first theoretically derived, and the intersymbol interference and error probabilities are obtained. Based on the error probability, achievable communication rates are analytically obtained. Numerical results and performance comparisons reveal that MARCO provides significantly higher communication rate, i.e., on the scale of 100 Kbps, than the previously proposed molecular communication models without any need for synchronization. More specifically, MARCO can provide more than 250 Kbps of molecular communication rate if intersymbol time and internode distance are set to 2 µs and 2 nm, respectively.