AgTC and AgETL: open-source tools to enhance data collection and management for plant science research

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.

Aggregated data from the same laboratories participating in two glucose external quality assessment schemes show that commutability and transfers of values to control materials are decisive for the biases found

OBJECTIVES

We report the results of glucose measurements performed during one year by the same measurement procedures (MPs) in 58 Norwegian hospital laboratories using control materials provided by external quality assessment (EQA) schemes from two different providers. The providers used materials with presumed vs. verified commutability and transfers of values using reference material vs. using a highest-order reference MP.

METHODS

Data from six Labquality and three Noklus glucose EQA surveys were aggregated for each MP (Abbott Alinity, Abbott Architect, Roche Cobas, and Siemens Advia) in each scheme. For each EQA result, percent difference from target value (% bias) was calculated. Median percent bias for each MP per scheme was then calculated.

RESULTS

The median % biases observed for each MP in the Labquality scheme were significantly larger than those in the Noklus scheme, which uses verified commutable control materials and highest-order reference MP target values. The difference ranged from 1.2 (Roche Cobas, 2.9 vs. 1.7 %) to 4.4 percentage points (Siemens Advia, 3.2 % vs. -1.2 %). The order of bias size for the various MPs was different in the two schemes. In contrast to the Labquality scheme, the median % biases observed in the Noklus scheme for Abbott Alinity (-0.1 %), Abbott Architect (-0.5 %), and Siemens Advia (-1.2 %) were not significantly different from target value (p>0.756).

CONCLUSIONS

This study underlines the importance of using verified commutable EQA materials and target values traceable to reference MPs in EQA schemes designed for assessment of metrological traceability of laboratory results.

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Addressing controversies in the xylem embolism resistance-vessel diameter relationship

Although xylem embolism is a key process during drought-induced tree mortality, its relationship to wood anatomy remains debated. While the functional link between bordered pits and embolism resistance is known, there is no direct, mechanistic explanation for the traditional assumption that wider vessels are more vulnerable than narrow ones. We used data from 20 temperate broad-leaved tree species to study the inter- and intraspecific relationship of water potential at 50% loss of conductivity (P₅₀ ) with hydraulically weighted vessel diameter (D_h ) and tested its link to pit membrane thickness (T_PM ) and specific conductivity (K_s ) on species level. Embolism-resistant species had thick pit membranes and narrow vessels. While D_h was weakly associated with T_PM , the P₅₀ -D_h relationship remained highly significant after accounting for T_PM . The interspecific pattern between P₅₀ and D_h was mirrored by a link between P₅₀ and K_s , but there was no evidence for an intraspecific relationship. Our results provide robust evidence for an interspecific P₅₀ -D_h relationship across our species. As a potential cause for the inconsistencies in published P₅₀ -D_h relationships, our analysis suggests differences in the range of trait values covered, and the level of data aggregation (species, tree or sample level) studied.

An Energy Efficient Load Balancing Tree-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks

A wireless sensor network (WSN) consists of a very large number of sensors which are deployed in the specific area of interest. A sensor is an electronic device equipped with a small processor and has a small-capacity memory. The WSN has the functions of low cost, easy deployment, and random reconfiguration. In this paper, an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs is proposed. In this scheme, the sensing area is partitioned into many cells of a grid and then the sensor node with the maximum residual energy is elected to be the cell head in each cell. Then, the tree-like path is established by using the minimum spanning tree algorithm. In the tree construction, it must meet the three constraints, which are the minimum energy consumption spanning tree, the network depth, and the maximum number of child nodes. In the data transmission process, the cell head is responsible for collecting the sensing data in each cell, and the collected data are transmitted along the tree-like path to the base station (BS). Simulation results show that the total energy consumption of LB-TBDAS is significantly less than that of GB-PEDAP and PEDAP. Compared to GB-PEDAP and PEDAP, the proposed LB-TBDAS extends the network lifetime by more than 100%. The proposed LB-TBDAS can avoid excessive energy consumption of sensor nodes during multi-hop data transmission and can also avoid the hotspot problem of WSNs.

Creating a data commons: The INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT)

In this article, we will discuss the genesis, evolution, and progress of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT), which aims to foster international research and collaboration focused on pediatric soft tissue sarcoma. We will begin by highlighting the current state of clinical research for pediatric soft tissue sarcomas, including rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcoma. We will then explore challenges and research priorities, describe the development of INSTRuCT, and discuss how the consortium aims to address key research priorities.

HYPO-CHEAT's aggregated weekly visualisations of risk reduce real world hypoglycaemia

Background

Children with congenital hyperinsulinism (CHI) are at constant risk of hypoglycaemia with the attendant risk of brain injury. Current hypoglycaemia prevention methods centre on the prediction of a continuous glucose variable using machine learning (ML) processing of continuous glucose monitoring (CGM). This approach ignores repetitive and predictable behavioural factors and is dependent upon ongoing CGM. Thus, there has been very limited success in reducing real-world hypoglycaemia with a ML approach in any condition.

Objectives

We describe the development of HYPO-CHEAT (HYpoglycaemia-Prevention-thrOugh-CGM-HEatmap-Technology), which is designed to overcome these limitations by describing weekly hypoglycaemia risk. We tested HYPO-CHEAT in a real-world setting to evaluate change in hypoglycaemia.

Methods

HYPO-CHEAT aggregates individual CGM data to identify weekly hypoglycaemia patterns. These are visualised via a hypoglycaemia heatmap along with actionable interpretations and targets. The algorithm is iterative and reacts to anticipated changing patterns of hypoglycaemia. HYPO-CHEAT was compared with Dexcom Clarity's pattern identification and Facebook Prophet's forecasting algorithm using data from 10 children with CHI using CGM for 12 weeks. HYPO-CHEAT's efficacy was assessed via change in time below range (TBR).

Results

HYPO-CHEAT identified hypoglycaemia patterns in all patients. Dexcom Clarity identified no patterns. Predictions from Facebook Prophet were inconsistent and difficult to interpret. Importantly, the patterns identified by HYPO-CHEAT matched the lived experience of all patients, generating new and actionable understanding of the cause of hypos. This facilitated patients to significantly reduce their time in hypoglycaemia from 7.1% to 5.4% even when real-time CGM data was removed.

Conclusions

HYPO-CHEAT's personalised hypoglycaemia heatmaps reduced total and targeted TBR even when CGM was reblinded. HYPO-CHEAT offers a highly effective and immediately available personalised approach to prevent hypoglycaemia and empower patients to self-care.

Goodness of fit tests for random multigraph models

Goodness of fit tests for two probabilistic multigraph models are presented. The first model is random stub matching given fixed degrees (RSM) so that edge assignments to vertex pair sites are dependent, and the second is independent edge assignments (IEA) according to a common probability distribution. Tests are performed using goodness of fit measures between the edge multiplicity sequence of an observed multigraph, and the expected one according to a simple or composite hypothesis. Test statistics of Pearson type and of likelihood ratio type are used, and the expected values of the Pearson statistic under the different models are derived. Test performances based on simulations indicate that even for small number of edges, the null distributions of both statistics are well approximated by their asymptotic χ 2 -distribution. The non-null distributions of the test statistics can be well approximated by proposed adjusted χ 2 -distributions used for power approximations. The influence of RSM on both test statistics is substantial for small number of edges and implies a shift of their distributions towards smaller values compared to what holds true for the null distributions under IEA. Two applications on social networks are included to illustrate how the tests can guide in the analysis of social structure.

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

Development of an Interactive Dashboard for OSSE Rare Disease Registries

BACKGROUND

The Open Source Registry System for Rare Diseases (OSSE), a web-based tool to create rare disease patient registries, currently offers no possibility to view aggregated registry data within the system. Here, we present the development and implementation of a dashboard for the registry of the German NEOCYST (Network for early onset cystic kidney diseases) consortium.

METHODS

Based on user requirements from NEOCYST, we developed a general dashboard for all OSSE registries, which was extended with NEOCYST-specific statistics.

RESULTS

The dashboard now allows users to gain a quick overview of key data, such as patient counts or the availability of biospecimens.

CONCLUSION

This work represents a first prototypical approach for an OSSE dashboard, demonstrated in an existing rare disease registry, to be further evaluated and enhanced in the future.

A group-sequential randomized trial design utilizing supplemental trial data

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

A Lightweight and Privacy-Friendly Data Aggregation Scheme against Abnormal Data

Abnormal electricity data, caused by electricity theft or meter failure, leads to the inaccuracy of aggregation results. These inaccurate results not only harm the interests of users but also affect the decision-making of the power system. However, the existing data aggregation schemes do not consider the impact of abnormal data. How to filter out abnormal data is a challenge. To solve this problem, in this study, we propose a lightweight and privacy-friendly data aggregation scheme against abnormal data, in which the valid data can correctly be aggregated but abnormal data will be filtered out during the aggregation process. This is more suitable for resource-limited smart meters, due to the adoption of lightweight matrix encryption. The automatic filtering of abnormal data without additional processes and the detection of abnormal data sources are where our protocol outperforms other schemes. Finally, a detailed security analysis shows that the proposed scheme can protect the privacy of users’ data. In addition, the results of extensive simulations demonstrate that the additional computation cost to filter the abnormal data is within the acceptable range, which shows that our proposed scheme is still very effective.

The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform

Objective

Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool. This work aims to describe the initiative and illustrate its potential utility through several use cases.

Methods

Cosmos is designed to scale rapidly by leveraging preexisting agreements, clinical health information exchange networks, and data standards. Data are stored centrally as a limited dataset, but the customer facing query tool limits results to prevent patient reidentification.

Results

In 2 years, Cosmos grew to contain EHR data of more than 60 million patients. We present practical examples illustrating how Cosmos could further efforts in chronic disease surveillance (asthma and obesity), syndromic surveillance (seasonal influenza and the 2019 novel coronavirus), immunization adherence and adverse event reporting (human papilloma virus and measles, mumps, rubella, and varicella vaccination), and health services research (antibiotic usage for upper respiratory infection).

Discussion

A low barrier of entry for Cosmos allows for the rapid accumulation of multi-institutional and mostly de-duplicated EHR data to power research and quality improvement queries characteristic of learning healthcare systems. Limitations are being vendor-specific, an “all or none” contribution model, and the lack of control over queries run on an institution’s healthcare data.

Conclusion

Cosmos provides a model for within-vendor data standardization and aggregation and a steppingstone for broader intervendor interoperability.

Quantitating and assessing interoperability between electronic health records

OBJECTIVES

Electronic health records (EHRs) contain a large quantity of machine-readable data. However, institutions choose different EHR vendors, and the same product may be implemented differently at different sites. Our goal was to quantify the interoperability of real-world EHR implementations with respect to clinically relevant structured data.

MATERIALS AND METHODS

We analyzed de-identified and aggregated data from 68 oncology sites that implemented 1 of 5 EHR vendor products. Using 6 medications and 6 laboratory tests for which well-accepted standards exist, we calculated inter- and intra-EHR vendor interoperability scores.

RESULTS

The mean intra-EHR vendor interoperability score was 0.68 as compared to a mean of 0.22 for inter-system interoperability, when weighted by number of systems of each type, and 0.57 and 0.20 when not weighting by number of systems of each type.

DISCUSSION

In contrast to data elements required for successful billing, clinically relevant data elements are rarely standardized, even though applicable standards exist. We chose a representative sample of laboratory tests and medications for oncology practices, but our set of data elements should be seen as an example, rather than a definitive list.

CONCLUSIONS

We defined and demonstrated a quantitative measure of interoperability between site EHR systems and within/between implemented vendor systems. Two sites that share the same vendor are, on average, more interoperable. However, even for implementation of the same EHR product, interoperability is not guaranteed. Our results can inform institutional EHR selection, analysis, and optimization for interoperability.

The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.

Opportunities for the use of health information technology to support research

In the last decade, expanding use of health information technology (IT) across the United States has created opportunities for use of electronic health data for health services and biomedical research, but efforts may be hampered by limited data access, data quality, and system functionality. We identify five opportunities to advance the use of health IT for health services and biomedical research, which informed a federal government-led, collaborative effort to develop a relevant policy and development agenda. In particular, the health IT infrastructure should more effectively support the use of electronic health data for research; provide adaptable technologies; incorporate relevant research-related functionality; support patient and caregiver engagement in research; and support effective integration of knowledge into practice. While not exhaustive, these represent important opportunities that the biomedical and health informatics communities can pursue to better leverage health IT and electronic health data for research.

Electronic health records: a critical appraisal of strengths and limitations

Electronic health record (EHR) was hailed as a major step towards making healthcare more transparent and accountable. All the developed nations digitised their health records which were meant to be safe, secure and could be accessed on demand. This was intended to benefit all stakeholders. However, the jury is still out if the EHR has been worth it. There have been incidences of data breaches despite cybersecurity checks and of manipulation compromising clinicians' integrity and patients' safety. EHRs have also been blamed for doctor burnout in overloading them with a largely avoidable administrative burden. The lack of interoperability amongst various EHR software systems is creating obstacles in seamless workflow. Artificial intelligence is now being used to overcome deficiencies of the EHR. Emerging data from real-world usage of EHR is providing useful inputs which would be helpful in making it a better system. This review critically appraises the current status and issues with the EHR and provides an overview of the key innovations which are being implemented to make the system more efficient for health care providers leading to a reduction in their administrative burden.

P2T2: Protein Panoramic annoTation Tool for the interpretation of protein coding genetic variants

MOTIVATION

Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms.

MATERIALS AND METHODS

P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome.

RESULTS

Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies.

CONCLUSION

The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.

Data Integration to Improve Real-world Health Outcomes Research for Non-Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration

Background

The integration of data from disparate sources could help alleviate data insufficiency in real-world studies and compensate for the inadequacies of single data sources and short-duration, small sample size studies while improving the utility of data for research.

Objective

This study aims to describe and evaluate a process of integrating data from several complementary sources to conduct health outcomes research in patients with non–small cell lung cancer (NSCLC). The integrated data set is also used to describe patient demographics, clinical characteristics, treatment patterns, and mortality rates.

Methods

This retrospective cohort study integrated data from 4 sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Patients with lung cancer who initiated second-line (2L) therapy between November 01, 2015, and April 13, 2018, were identified in the claims and CCQP data. Eligible patients were 18 years or older and received atezolizumab, docetaxel, erlotinib, nivolumab, pembrolizumab, pemetrexed, or ramucirumab in the 2L setting. The main analysis cohort included patients with claims data and data from at least one additional data source (CCQP or MR). Patients without integrated data (claims only) were reported separately. Descriptive and univariate statistics were reported.

Results

Data integration resulted in a main analysis cohort of 2195 patients with NSCLC; 2106 patients had CCQP and 407 patients had MR data. The claims-only cohort included 931 eligible patients. For the main analysis cohort, the mean age was 62.1 (SD 9.27) years, 48.56% (1066/2195) were female, the median length of follow-up was 6.8 months, and for 37.77% (829/2195), death was observed. For the claims-only cohort, the mean age was 66.6 (SD 12.69) years, 52.1% (485/931) were female, the median length of follow-up was 8.6 months, and for 29.3% (273/931), death was observed. The most frequent 2L treatment was immunotherapy (1094/2195, 49.84%), followed by platinum-based regimens (472/2195, 21.50%) and single-agent chemotherapy (441/2195, 20.09%); mean duration of 2L therapy was 5.6 (SD 4.9, median 4) months. We describe challenges and learnings from the data integration process, and the benefits of the integrated data set, which includes a richer set of clinical and outcome data to supplement the utilization metrics available in administrative claims.

Conclusions

The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments.

Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

Decentralized but Globally Coordinated Biodiversity Data

Centralized biodiversity data aggregation is too often failing societal needs due to pervasive and systemic data quality deficiencies. We argue for a novel approach that embodies the spirit of the Web (“small pieces loosely joined”) through the decentralized coordination of data across scientific languages and communities. The upfront cost of decentralization can be offset by the long-term benefit of achieving sustained expert engagement, higher-quality data products, and ultimately more societal impact for biodiversity data. Our decentralized approach encourages the emergence and evolution of multiple self-identifying communities of practice that are regionally, taxonomically, or institutionally localized. Each community is empowered to control the social and informational design and versioning of their local data infrastructures and signals. With no single aggregator to exert centralized control over biodiversity data, decentralization generates loosely connected networks of mid-level aggregators. Global coordination is nevertheless feasible through automatable data sharing agreements that enable efficient propagation and translation of biodiversity data across communities. The decentralized model also poses novel integration challenges, among which the explicit and continuous articulation of conflicting systematic classifications and phylogenies remain the most challenging. We discuss the development of available solutions, challenges, and outline next steps: the global effort of coordination should focus on developing shared languages for data signal translation, as opposed to homogenizing the data signal itself.

Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems

Wearable body area network is a key component of the modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for a secure collection of medical data, there is also a need to process data in real-time. In this article, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications. Specifically, medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example, allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e., chosen-plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.

A Policy and Practice Review of Consumer Protections and Their Application to Hospital-Sourced Data Aggregation and Analytics by Third-Party Companies

The Office of the National Coordinator for Health Information Technology estimates that 96% of all U.S. hospitals use a basic electronic health record, but only 62% are able to exchange health information with outside providers. Barriers to information exchange across EHR systems challenge data aggregation and analysis that hospitals need to evaluate healthcare quality and safety. A growing number of hospital systems are partnering with third-party companies to provide these services. In exchange, companies reserve the rights to sell the aggregated data and analyses produced therefrom, often without the knowledge of patients from whom the data were sourced. Such partnerships fall in a regulatory grey area and raise new ethical questions about whether health, consumer, or health and consumer privacy protections apply. The current opinion probes this question in the context of consumer privacy reform in California. It analyzes protections for health information recently expanded under the California Consumer Privacy Act ("CA Privacy Act") in 2020 and compares them to protections outlined in the Health Information Portability and Accountability Act ("Federal Privacy Rule"). Four perspectives are considered in this ethical analysis: 1) standards of data deidentification; 2) rights of patients and consumers in relation to their health information; 3) entities covered by the CA Privacy Act; 4) scope and complementarity of federal and state regulations. The opinion concludes that the CCPA is limited in its application when health information is processed by a third-party data aggregation company that is contractually designated as a business associate; when health information is deidentified; and when hospital data are sourced from publicly owned and operated hospitals. Lastly, the opinion offers practical recommendations for facilitating parity between state and federal health data privacy laws and for how a more equitable distribution of informational risks and benefits from the sale of aggregated hospital data could be fostered and presents ways both for-profit and nonprofit hospitals can sustain patient trust when negotiating partnerships with third-party data aggregation companies.

Decentralized Privacy-Preserving Data Aggregation Scheme for Smart Grid Based on Blockchain

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Background

Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade.

Objective

The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation.

Methods

We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices.

Results

Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making).

Conclusions

IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

Average Consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights

Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.

Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness

Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.

Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAir™ sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (R n ) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (R a ), which is a function of the friction velocity F*) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

Verifying Data Integration Configurations for Semantical Correctness and Completeness

Data integration is the problem of combining data residing at different sources and providing the user with a unified view of these data. In medical informatics, such a unified view enables retrospective analyses based on more facts and prospective recruitment of more patients than any single data collection by itself. The technical part of data integration is based on rules interpreted by software. These rules define how to perform the translation of source database schemata into the target database schema. Translation rules are formulated by data managers who usually do not have the knowledge about meaning and acquisition methods of the data they handle. The professionals (data providers) collecting the source data who have the respective knowledge again usually have no sufficient technical background. Since data providers are neither able to formulate the transformation rules themselves nor able to validate them, the whole process is fault-prone. Additionally, in continuous development and maintenance of (meta-) data repositories, data structures underlie changes, which may lead to outdated transformation rules. We did not find any technical solution, which enables data providers to formulate transformation rules themselves or which provides an understandable reflection of given rules. Our approach is to enable data providers understand the rules regarding their own data by presenting rules and available context visually. Context information is fetched from a metadata repository. In this paper, we propose a software tool that builds on existing data integration infrastructures. The tool provides a visually supported validation routine for data integration rules. In a first step towards its evaluation, we implement the tool into the DZL data integration process and verify the correct presentation of transformation rules.

PartCrafter: find, generate and analyze BioParts

The field of Synthetic Biology is both practically and philosophically reliant on the idea of BioParts-concrete DNA sequences meant to represent discrete functionalities. While there are a number of software tools which allow users to design complex DNA sequences by stitching together BioParts or genetic features into genetic devices, there is a lack of tools assisting Synthetic Biologists in finding BioParts and in generating new ones. In practice, researchers often find BioParts in an ad hoc way. We present PartCrafter, a tool which extracts and aggregates genomic feature data in order to facilitate the search for new BioParts with specific functionalities. PartCrafter can also turn a genomic feature into a BioPart by packaging it according to any manufacturing standard, codon optimizing it for a new host, and removing forbidden sites. PartCrafter is available at partcrafter.com.

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory

Background

Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties.

Objective

This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing.

Methods

We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties’ sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application.

Results

Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records).

Conclusions

We used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.

A Secure Mutual Batch Authentication Scheme for Patient Data Privacy Preserving in WBAN

The current advances in cloud-based services have significantly enhanced individual satisfaction in numerous modern life areas. Particularly, the recent spectacular innovations in the wireless body area networks (WBAN) domain have made e-Care services rise as a promising application field, which definitely improves the quality of the medical system. However, the forwarded data from the limited connectivity range of WBAN via a smart device (e.g., smartphone) to the application provider (AP) should be secured from an unapproved access and alteration (attacker) that could prompt catastrophic consequences. Therefore, several schemes have been proposed to guarantee data integrity and privacy during their transmission between the client/controller (C) and the AP. Thereby, numerous effective cryptosystem solutions based on a bilinear pairing approach are available in the literature to address the mentioned security issues. Unfortunately, the related solution presents security shortcomings, where AP can with ease impersonate a given C. Hence, this existing scheme cannot fully guarantee C's data privacy and integrity. Therefore, we propose our contribution to address this data security issue (impersonation) through a secured and efficient remote batch authentication scheme that genuinely ascertains the identity of C and AP. Practically, the proposed cryptosystem is based on an efficient combination of elliptical curve cryptography (ECC) and bilinear pairing schemes. Furthermore, our proposed solution reduces the communication and computational costs by providing an efficient data aggregation and batch authentication for limited device's resources in WBAN. These additional features (data aggregation and batch authentication) are the core improvements of our scheme that have great merit for limited energy environments like WBAN.

Health Care Atlases: Informing the General Public About the Situation of the Austrian Health Care System

There is a great number of complex data concerning the Austrian Health Care System. The goal was to process this data and present it to the general public on an easily accessible information platform. The platform focuses on data about the burden of disease of the Austrian Population, the available medical care and the services provided by the physicians. Due to the vast differences in the underlying source data, the methods used for the data acquisition range from statistical linkage over web scraping to aggregating data on the reimbursed services. The results are published on a website and are mainly displayed with interactive graphics. Overall, these dynamic and interactive websites provide a good overview of the situation of the Austrian Health Care System and presents the information in an intuitive and comprehensible manner. Furthermore, the information given in the atlases can contribute to the health care planning in order to identify distinctive service provision in Austria.

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction.

Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection

Intelligent Transport Systems (ITS) are an essential part of the global world. They play a substantial role for facing many issues such as traffic jams, high accident rates, unhealthy lifestyles, air pollution, etc. Public bike sharing system is one part of ITS and can be used to collect data from mobiles devices. In this paper, we propose an efficient, “Internet of Bikes”, IoB-DTN routing protocol based on data aggregation which applies the Delay Tolerant Network (DTN) paradigm to Internet of Things (IoT) applications running data collection on urban bike sharing system based sensor network. We propose and evaluate three variants of IoB-DTN: IoB based on spatial aggregation (IoB-SA), IoB based on temporal aggregation (IoB-TA) and IoB based on spatiotemporal aggregation (IoB-STA). The simulation results show that the three variants offer the best performances regarding several metrics, comparing to IoB-DTN without aggregation and the low-power long-range technology, LoRa type. In an urban application, the choice of the type of which variant of IoB should be used depends on the sensed values.

Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices' privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold (Nt) or the waiting time is equal to the aggregation timer (Tt), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline (Tt) and the aggregation threshold (Nt) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of Tt and Nt to reduce delay, and the nodes near the sink are set to a large value of Tt and Nt to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%.

Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements

Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.

Toward an Information Infrastructure for Global Health Improvement

Profound global challenges to individual and population health, alongside the opportunities to benefit from digital technology, have spawned the concept of the Learning Health System. Learning Health Systems (LHSs)--which can function at organizational, network, regional, and national levels of scale--have the capability of continuous data-driven self-study that promotes change and improvement. The LHS concept, which originated in the U.S. in 2007, is rapidly gaining attention around the world. LHSs require, but also transcend, the secondary use of health data. This paper describes the key features of LHSs, argues that effective and sustainable LHSs must be supported by infrastructures that allow them to function with economies of scale and scope, and describes the services that such infrastructures must provide. While it is relatively straightforward to describe LHSs, achieving them at the high level of capability necessary to promote significant health benefits will require advancements in science and engineering, engaging the field of informatics among a wider range of disciplines. It also follows from this vision that LHSs cannot be built from an imposed blueprint; LHSs will more likely evolve from efforts at smaller scales that compose into larger systems.

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I–IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

Embedding Nursing Interventions into the World Health Organization's International Classification of Health Interventions (ICHI)

OBJECTIVE

The International Classification of Health Interventions, currently being developed, seeks to span all sectors of the health system. Our objective was to test the draft classification's coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions.

MATERIALS AND METHODS

A 2-phase content mapping method was used: (1) three coders independently applied the classification to a dataset comprising 100 high-frequency nursing interventions; (2) the coders reached consensus for each intervention and identified reasons for initial discrepancies.

RESULTS

A consensus code was found for 80 of the 100 source terms; for 34% of these, the code was semantically equivalent to the source term, and for 64% it was broader. Issues that contributed to discrepancies in Phase 1 coding results included concepts in source terms not captured by the classification, ambiguities in source terms, and uncertainty of semantic matching between "action" concepts in source terms and classification codes.

DISCUSSION

While the classification generally provides good coverage of nursing interventions, there remain a number of content gaps and granularity issues. Further development of definitions and coding guidance is needed to ensure consistency of application.

CONCLUSION

This study has produced a set of proposals concerning changes needed to improve the classification. The novel method described here will inform future health terminology and classification content coverage studies.

Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks

Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime.

An Overview of Interrater Agreement on Likert Scales for Researchers and Practitioners

Applications of interrater agreement (IRA) statistics for Likert scales are plentiful in research and practice. IRA may be implicated in job analysis, performance appraisal, panel interviews, and any other approach to gathering systematic observations. Any rating system involving subject-matter experts can also benefit from IRA as a measure of consensus. Further, IRA is fundamental to aggregation in multilevel research, which is becoming increasingly common in order to address nesting. Although, several technical descriptions of a few specific IRA statistics exist, this paper aims to provide a tractable orientation to common IRA indices to support application. The introductory overview is written with the intent of facilitating contrasts among IRA statistics by critically reviewing equations, interpretations, strengths, and weaknesses. Statistics considered include r_wg, rwg*, r′_wg, r_wg(p), average deviation (AD), a_wg, standard deviation (S_wg), and the coefficient of variation (CV_wg). Equations support quick calculation and contrasting of different agreement indices. The article also includes a “quick reference” table and three figures in order to help readers identify how IRA statistics differ and how interpretations of IRA will depend strongly on the statistic employed. A brief consideration of recommended practices involving statistical and practical cutoff standards is presented, and conclusions are offered in light of the current literature.

Channel and Timeslot Co-Scheduling with Minimal Channel Switching for Data Aggregation in MWSNs

Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes.