Efficient and low-complexity variable-to-variable length coding for DNA storage.

BACKGROUND: Efficient DNA-based storage systems offer substantial capacity and longevity at reduced costs, addressing anticipated data growth. However, encoding data into DNA sequences is limited by two key constraints: 1) a maximum of h consecutive identical bases (homopolymer constraint h), and 2) a GC ratio between [ 0.5 - c GC , 0.5 + c GC ] (GC content constraint c GC ). Sequencing or synthesis errors tend to increase when these constraints are violated. RESULTS: In this research, we address a pure source coding problem in the context of DNA storage, considering both homopolymer and GC content constraints. We introduce a novel coding technique that adheres to these constraints while maintaining linear complexity for increased block lengths and achieving near-optimal rates. We demonstrate the effectiveness of the proposed method through experiments on both randomly generated data and existing files. For example, when h = 4 and c GC = 0.05 , the rate reached 1.988, close to the theoretical limit of 1.990. The associated code can be accessed at GitHub. CONCLUSION: We propose a variable-to-variable-length encoding method that does not rely on concatenating short predefined sequences, which achieves near-optimal rates.

Flow Cytometry Sorting for Random Access in DNA Data Storage: Encapsulation for Enhanced Stability and Sequence Integrity of DNA.

As digital data undergo explosive growth, deoxyribonucleic acid (DNA) has emerged as a promising storage medium due to its high density, longevity, and ease of replication, offering vast potential in data storage solutions. This study focuses on the protection and retrieval of data during the DNA storage process, developing a technique that employs flow cytometry sorting (FCS) to segregate multicolored fluorescent DNA microparticles encoded with data and facilitating efficient random access. Moreover, the encapsulated fluorescent DNA microparticles, formed through layer-by-layer self-assembly, preserve structural and sequence integrity even under harsh conditions while also supporting a high-density DNA payload. Experimental results have shown that the encoded data can still be successfully recovered from encapsulated DNA microparticles following de-encapsulation. We also successfully demonstrated the automated encapsulation process of fluorescent DNA microparticles using a microfluidic chip. This research provides an innovative approach to the long-term stability and random readability of DNA data storage.

A location-based service scheme with attribute information privacy.

In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved.

Unlocking Real-World Health Data from Non-University Locations Through Effective Provision of Tools and Knowledge.

INTRODUCTION: The Medical Informatics Initiative (MII) in Germany has pioneered platforms such as the National Portal for Medical Research Data (FDPG) to enhance the accessibility of data from clinical routine care for research across both university and non-university healthcare settings. This study explores the efficacy of the Medical Informatics Hub in Saxony (MiHUBx) services by integrating Klinikum Chemnitz gGmbH (KC) with the FDPG, leveraging the Fast Healthcare Interoperability Resources Core Data Set of the MII to standardize and harmonize data from disparate source systems. METHODS: The employed procedures include deploying installation packages to convert data into FHIR format and utilizing the Research Data Repository for structured data storage and exchange within the clinical infrastructure of KC. RESULT: Our results demonstrate successful integration, the development of a comprehensive deployment diagram, additionally, it was demonstrated that the non-university site can report clinical data to the FDPG. DISCUSSION: The discussion reflects on the practical application of this integration, highlighting its potential scalability to even smaller healthcare facilities and to pave the way to access to more medical data for research. This exemplary demonstration of the interplay of different tools provides valuable insights into technical and operational challenges, setting a precedent for future expansions and contributing to the democratization of medical data access.

Semi-Automatic Export of Electrophysiological Metadata to NFDI4Health Local Data Hubs: Use Case of Microneurography odML-Tables - A Technical Case Report.

INTRODUCTION: The Local Data Hub (LDH) is a platform for FAIR sharing of medical research (meta-)data. In order to promote the usage of LDH in different research communities, it is important to understand the domain-specific needs, solutions currently used for data organization and provide support for seamless uploads to a LDH. In this work, we analyze the use case of microneurography, which is an electrophysiological technique for analyzing neural activity. METHODS: After performing a requirements analysis in dialogue with microneurography researchers, we propose a concept-mapping and a workflow, for the researchers to transform and upload their metadata. Further, we implemented a semi-automatic upload extension to odMLtables, a template-based tool for handling metadata in the electrophysiological community. RESULTS: The open-source implementation enables the odML-to-LDH concept mapping, allows data anonymization from within the tool and the creation of custom-made summaries on the underlying data sets. DISCUSSION: This concludes a first step towards integrating improved FAIR processes into the research laboratory's daily workflow. In future work, we will extend this approach to other use cases to disseminate the usage of LDHs in a larger research community.

Extending the TOP Framework with an Ontology-Based Text Search Component.

INTRODUCTION: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other. METHOD: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing. RESULTS: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents. CONCLUSION: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.

Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems - A Technical Case Report.

INTRODUCTION: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis. BACKGROUND: The EAV model is widely used in clinical information systems due to its adaptability, but it often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. OBJECTIVE: The objective of this study is to develop a methodological approach to address the handling of these generic data structures, Methods: We introduce a five-step methodological approach: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. RESULTS: Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion reduced data sparsity and improved the manageability of schema changes without affecting other classes of data. CONCLUSION: The developed approach provides a systematic method for handling complex data relationships and maintaining data integrity in clinical systems using EAV models. This approach facilitates the secondary use of clinical data, enhancing its utility for medical research and practice.

Classification of Veterinary Subjects in Medical Literature and Clinical Summaries.

INTRODUCTION: Human and veterinary medicine are practiced separately, but literature databases such as Pubmed include articles from both fields. This impedes supporting clinical decisions with automated information retrieval, because treatment considerations would not ignore the discipline of mixed sources. Here we investigate data-driven methods from computational linguistics for automatically distinguishing between human and veterinary medical texts. METHODS: For our experiments, we selected language models after a literature review of benchmark datasets and reported performances. We generated a dataset of around 48,000 samples for binary text classification, specifically designed to differentiate between human medical and veterinary subjects. Using this dataset, we trained and fine-tuned classifiers based on selected transformer-based models as well as support vector machines (SVM). RESULTS: All trained classifiers achieved more than 99% accuracy, even though the transformer-based classifiers moderately outperformed the SVM-based one. DISCUSSION: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval.

Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries.

BACKGROUND: The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability. METHODS: The automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers. RESULTS: Deployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner. CONCLUSIONS: The research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.

PELMI: Realize robust DNA image storage under general errors via parity encoding and local mean iteration.

DNA molecules as storage media are characterized by high encoding density and low energy consumption, making DNA storage a highly promising storage method. However, DNA storage has shortcomings, especially when storing multimedia data, wherein image reconstruction fails when address errors occur, resulting in complete data loss. Therefore, we propose a parity encoding and local mean iteration (PELMI) scheme to achieve robust DNA storage of images. The proposed parity encoding scheme satisfies the common biochemical constraints of DNA sequences and the undesired motif content. It addresses varying pixel weights at different positions for binary data, thus optimizing the utilization of Reed-Solomon error correction. Then, through lost and erroneous sequences, data supplementation and local mean iteration are employed to enhance the robustness. The encoding results show that the undesired motif content is reduced by 23%-50% compared with the representative schemes, which improves the sequence stability. PELMI achieves image reconstruction under general errors (insertion, deletion, substitution) and enhances the DNA sequences quality. Especially under 1% error, compared with other advanced encoding schemes, the peak signal-to-noise ratio and the multiscale structure similarity address metric were increased by 10%-13% and 46.8%-122%, respectively, and the mean squared error decreased by 113%-127%. This demonstrates that the reconstructed images had better clarity, fidelity, and similarity in structure, texture, and detail. In summary, PELMI ensures robustness and stability of image storage in DNA and achieves relatively high-quality image reconstruction under general errors.

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system.

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Backward induction-based deep image search.

Conditional image retrieval (CIR), which involves retrieving images by a query image along with user-specified conditions, is essential in computer vision research for efficient image search and automated image analysis. The existing approaches, such as composed image retrieval (CoIR) methods, have been actively studied. However, these methods face challenges as they require either a triplet dataset or richly annotated image-text pairs, which are expensive to obtain. In this work, we demonstrate that CIR at the image-level concept can be achieved using an inverse mapping approach that explores the model's inductive knowledge. Our proposed CIR method, called Backward Search, updates the query embedding to conform to the condition. Specifically, the embedding of the query image is updated by predicting the probability of the label and minimizing the difference from the condition label. This enables CIR with image-level concepts while preserving the context of the query. In this paper, we introduce the Backward Search method that enables single and multi-conditional image retrieval. Moreover, we efficiently reduce the computation time by distilling the knowledge. We conduct experiments using the WikiArt, aPY, and CUB benchmark datasets. The proposed method achieves an average mAP@10 of 0.541 on the datasets, demonstrating a marked improvement compared to the CoIR methods in our comparative experiments. Furthermore, by employing knowledge distillation with the Backward Search model as the teacher, the student model achieves a significant reduction in computation time, up to 160 times faster with only a slight decrease in performance. The implementation of our method is available at the following URL: https://github.com/dhlee-work/BackwardSearch.

Measuring and understanding information storage and transfer in a simulated human gut microbiome.

Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI). In a first step, we derive information theoretic measures from the simulated abundance data, and, in a second step, relate them to the metabolic processes underlying the abundance data. Our study provides further evidence on the role of active information storage as an indicator of unexpected structural change in the observed system. Besides, we show that information transfer reflects coherent behavior in the microbial community, both as a reaction to environmental changes and as a result of direct effective interaction. In this sense, purely abundance-based information theoretic measures can provide meaningful insight on metabolic interactions within bacterial communities. Furthermore, we shed light on the important however little noticed technical aspect of distinguishing immediate and delayed effects in the interpretation of local information theoretical measures.

Factors impacting hospitalisation and related health service costs in cancer survivors in Australia: Results from a population data linkage study in Queensland (COS-Q).

BACKGROUND: The global economic cost of cancer and the costs of ongoing care for survivors are increasing. Little is known about factors affecting hospitalisations and related costs for the growing number of cancer survivors. Our aim was to identify associated factors of cancer survivors admitted to hospital in the public system and their costs from a health services perspective. METHODS: A population-based, retrospective, data linkage study was conducted in Queensland (COS-Q), Australia, including individuals diagnosed with a first primary cancer who incurred healthcare costs between 2013 and 2016. Generalised linear models were fitted to explore associations between socio-demographic (age, sex, country of birth, marital status, occupation, geographic remoteness category and socio-economic index) and clinical (cancer type, year of/time since diagnosis, vital status and care type) factors with mean annual hospital costs and mean episode costs. RESULTS: Of the cohort (N = 230,380) 48.5% (n = 111,820) incurred hospitalisations in the public system (n = 682,483 admissions). Hospital costs were highest for individuals who died during the costing period (cost ratio 'CR': 1.79, p < 0.001) or living in very remote or remote location (CR: 1.71 and CR: 1.36, p < 0.001) or aged 0-24 years (CR: 1.63, p < 0.001). Episode costs were highest for individuals in rehabilitation or palliative care (CR: 2.94 and CR: 2.34, p < 0.001), or very remote location (CR: 2.10, p < 0.001). Higher contributors to overall hospital costs were 'diseases and disorders of the digestive system' (AU$661 m, 21% of admissions) and 'neoplastic disorders' (AU$554 m, 20% of admissions). CONCLUSIONS: We identified a range of factors associated with hospitalisation and higher hospital costs for cancer survivors, and our results clearly demonstrate very high public health costs of hospitalisation. There is a lack of obvious means to reduce these costs in the short or medium term which emphasises an increasing economic imperative to improving cancer prevention and investments in home- or community-based patient support services.

Advanced DNA Amplification for Efficient Data Storage.

DNA amplification technologies have significantly advanced biotechnology, particularly in DNA storage. However, adaptation of these technologies to DNA storage poses substantial challenges. Key bottlenecks include achieving high throughput to manage large data sets, ensuring rapid and efficient DNA amplification, and minimizing bias to maintain data fidelity. This perspective begins with an overview of natural and artificial amplification strategies, such as polymerase chain reaction and isothermal amplification, highlighting their respective advantages and limitations. It then explores the prospective applications of these techniques in DNA storage, emphasizing the need to optimize protocols for scalability and robustness in handling diverse digital data. Concurrently, we identify promising avenues, including advancements in enzymatic processes and novel amplification methodologies, poised to mitigate existing constraints and propel the field forward. Ultimately, we provide insights into how to utilize advanced DNA amplification strategies poised to revolutionize the efficiency and feasibility of data storage, ushering in enhanced approaches to data retrieval in the digital age.

Exploring librarians' practices when teaching advanced searching for knowledge synthesis: results from an online survey.

Objective: There is little research available regarding the instructional practices of librarians who support students completing knowledge synthesis projects. This study addresses this research gap by identifying the topics taught, approaches, and resources that academic health sciences librarians employ when teaching students how to conduct comprehensive searches for knowledge synthesis projects in group settings. Methods: This study applies an exploratory-descriptive design using online survey data collection. The final survey instrument included 31 open, closed, and frequency-style questions. Results: The survey received responses from 114 participants, 74 of whom met the target population. Some key results include shared motivations to teach in groups, including student learning and curriculum requirements, as well as popular types of instruction such as single session seminars, and teaching techniques, such as lectures and live demos. Conclusion: This research demonstrates the scope and coverage of librarian-led training in the knowledge synthesis research landscape. Although searching related topics such as Boolean logic were the most frequent, librarians report teaching throughout the review process like methods and reporting. Live demos and lectures were the most reported approaches to teaching, whereas gamification or student-driven learning were used rarely. Our results suggest that librarian's application of formal pedagogical approaches while teaching knowledge synthesis may be under-utilized, as most respondents did not report using any formal instructional framework.

Finding full texts in bulk: a comparison of EndNote 20 versus Zotero 6 using the University of York's subscriptions.

Objective: To understand the performance of EndNote 20 and Zotero 6's full text retrieval features. Methods: Using the University of York's subscriptions, we tested and compared EndNote and Zotero's full text retrieval. 1,000 records from four evidence synthesis projects were tested for the number of: full texts retrieved; available full texts retrieved; unique full texts (found by one program only); and differences in versions of full texts for the same record. We also tested the time taken and accuracy of retrieved full texts. One dataset was tested multiple times to confirm if the number of full texts retrieved was consistent. We also investigated the available full texts missed by EndNote or Zotero by: reference type; whether full texts were available open access or via subscription; and the content provider. Results: EndNote retrieved 47% of available full texts versus 52% by Zotero. Zotero was faster by 2 minutes 15 seconds. Each program found unique full texts. There were differences in full text versions retrieved between programs. For both programs, 99% of the retrieved full texts were accurate. Zotero was less consistent in the number of full texts it retrieved. Conclusion: EndNote and Zotero do not find all available full texts. Users should not assume full texts are correct; are the version of record; or that records without full texts cannot be retrieved manually. Repeating the full text retrieval process multiple times could yield additional full texts. Users with access to EndNote and Zotero could use both for full text retrieval.

The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid).

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature.

Effect of librarian collaboration on otolaryngology systematic review and meta-analysis quality.

Objective: To determine if librarian collaboration was associated with improved database search quality, search reproducibility, and systematic review reporting in otolaryngology systematic reviews and meta-analyses. Methods: In this retrospective cross-sectional study, PubMed was queried for systematic reviews and meta-analyses published in otolaryngology journals in 2010, 2015, and 2021. Two researchers independently extracted data. Two librarians independently rated search strategy reproducibility and quality for each article. The main outcomes include association of librarian involvement with study reporting quality, search quality, and publication metrics in otolaryngology systematic reviews and meta-analyses. Categorical data were compared with Chi-Squared tests or Fisher's Exact tests. Continuous variables were compared via Mann Whitney U Tests for two groups, and Kruskal-Wallis Tests for three or more groups. Results: Of 559 articles retrieved, 505 were analyzed. More studies indicated librarian involvement in 2021 (n=72, 20.7%) compared to 2015 (n=14, 10.4%) and 2010 (n=2, 9.0%) (p=0.04). 2021 studies showed improvements in properly using a reporting tool (p<0.001), number of databases queried (p<0.001), describing date of database searches (p<0.001), and including a flow diagram (p<0.001). Librarian involvement was associated with using reporting tools (p<0.001), increased number of databases queried (p<0.001), describing date of database search (p=0.002), mentioning search peer reviewer (p=0.02), and reproducibility of search strategies (p<0.001). For search strategy quality, librarian involvement was associated with greater use of "Boolean & proximity operators" (p=0.004), "subject headings" (p<0.001), "text word searching" (p<0.001), and "spelling/syntax/line numbers" (p<0.001). Studies with librarian involvement were associated with publication in journals with higher impact factors for 2015 (p=0.003) and 2021 (p<0.001). Conclusion: Librarian involvement was associated with improved reporting quality and search strategy quality. Our study supports the inclusion of librarians in review teams, and journal editing and peer reviewing teams.

Building a Natural Language Interface for FHIR Clinical Terminology Server.

While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or deviations in word sequence might lead to incorrect search outcomes. For retrieval, queries to the server must strictly follow the FHIR application programming interface format, which requires users to know the syntax and remember the attribute codes they wish to retrieve. To improve its functionalities, a natural language interface was built, that harnesses the capabilities of two preeminent large language models, along with other cutting-edge technologies such as speech-to-text conversion, vector semantic searching, and conversational artificial intelligence. Preliminary evaluation shows promising results in building a natural language interface for the FHIR clinical terminology system.