Explore the vast range of titles available.

It has been suggested that retrieving episodic information can involve adopting a cognitive state or set: retrieval mode. In a series of studies, an event-related potential (ERP) index of retrieval mode has been identified in designs which cue participants on a trial-by-trial basis to switch between preparing for and then completing an episodic or non-episodic retrieval task. However, a confound in these studies is that along with task type the content of what is to be retrieved has varied. Here we examined whether the ERP index of retrieval mode remains when the contents of an episodic and non-episodic task are highly similar – both requiring a location judgement. In the episodic task participants indicated the screen location where words had been shown in a prior study phase (left/right/new); whereas in the perceptual task they indicated the current screen location of the word (top/middle/bottom). Consistent with previous studies the ERPs elicited while participants prepared for episodic retrieval were more positive-going at right-frontal sites than when they prepared for the perceptual task. This index was observed, however, on the first trial after participants had switched tasks, rather than on the second trial, as has been observed previously. Potential reasons for this are discussed, including the critical manipulation of similarity in contents between tasks, as well as the use of a predictable cue sequence. •People were cued to switch between episodic and non-episodic cognitive tasks.•Both tasks required judgements about stimulus location.•ERPs were acquired in response to the cues signalling which task to complete.•Preparatory ERPs for episodic retrieval had different timings than in prior studies.•These outcomes offer new insights into processes that facilitate episodic retrieval.

Successful memory retrieval depends not only on memory fidelity but also on the mental preparedness on the part of the subject. ERP studies of recognition memory have identified two topographically distinct ERP components, the FN400 old/new effect and the late posterior component (LPC) old/new effect, commonly associated with familiarity and recollection, respectively. Here we used a task-switching paradigm to examine the extent to which adoption of a retrieval task-set influences FN400 and LPC old/new effects, in light of the presumption that recollection, as a control process, relies on the adoption of a retrieval task-set, but that familiarity-based retrieval does not. Behavioral accuracy indicated that source memory (experiment 2), but not item recognition (experiment 1), improved with task-set adoption. ERP data demonstrated a larger LPC on stay trials when a task-set had been adopted even with a simple recognition memory judgment. We conclude that adopting a retrieval task-set impacts recollection memory but not familiarity. These data indicate that attentional state immediately prior to retrieval can influence objective measures of recollection memory. ► Using a task-switching paradigm we examined the impact of preparation on retrieval. ► The FN400, typically associated with familiarity was uninfluenced by preparation. ► The LPC, typically associated with recollection increased with preparation. ► Source memory, but not item recognition showed significant switch costs. ► Results suggest familiarity and recollection are differently impacted by preparation.

This chapter contains sections titled: Antigen Retrieval (AR) Development Background—A Continuum of Past, Present, and Future Two Philosophically Different Approaches for Cell/Tissue Sample Preparation References

Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real‐world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT’s capability to identify drug-disease associations with an accuracy of 74.6–83.5% and 96.2–97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.

The scope of this volume will encompass a collection of research papers related to indexing and retrieval of online non-text information. In recent years, the Internet has seen an exponential increase in the number of documents placed online that are not in textual format. These documents appear in a variety of contexts, such as user-generated content sharing websites, social networking websites etc. and formats, including photographs, videos, recorded music, data visualizations etc. The prevalence of these contexts and data formats presents a particularly challenging task to information indexing and retrieval research due to many difficulties, such as assigning suitable semantic metadata, processing and extracting non-textual content automatically, and designing retrieval systems that \"speak in the native language\" of non-text documents.

Recently a novel method based on horizontal sperm migration in injection dishes has been introduced as an additional tool for preparation of semen sample in assisted reproductive technology (ART) procedures. In the present study, we evaluated both timing and reproductive outcomes in a randomized controlled study including 1034 intra-cytoplasmic sperm injection (ICSI) procedures followed by fresh embryo transfer. Couples enrolled were divided into two sub-groups, namely conventional swim-up method (Group A), and horizontal sperm migration in injection dishes (Group B). No significant differences were found between groups with respect to fertilization rate, implantation success, clinical pregnancy outcomes and ongoing pregnancies. On the contrary, both cleavage and blastocyst rates were statistically higher in Group B, suggesting superior efficiency and safety of this innovative technique also including time-saving and cheaper costs as compared to the classical swim-up sperm preparation. Our data support the interpretation of the horizontal sperm migration as a promising procedure for semen preparation in ART cycles.

Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries.

Background As pharmacogenomics data becomes increasingly integral to clinical treatment decisions, appropriate data storage and sharing protocols need to be adopted. One promising option for secure, high-integrity storage and sharing is Ethereum smart contracts. Ethereum is a blockchain platform, and smart contracts are immutable pieces of code running on virtual machines in this platform that can be invoked by a user or another contract (in the blockchain network). The 2019 iDASH (Integrating Data for Analysis, Anonymization, and Sharing) competition for Secure Genome Analysis challenged participants to develop time- and space-efficient Ethereum smart contracts for gene-drug relationship data. Methods Here we design a specific smart contract to store and query gene-drug interactions in Ethereum using an index-based, multi-mapping approach. Our contract stores each pharmacogenomics observation, a gene-variant-drug triplet with outcome, in a mapping searchable by a unique identifier, allowing for time and space efficient storage and query. This solution ranked in the top three at the 2019 IDASH competition. We further improve our ”challenge solution” and develop an alternate ”fastQuery” smart contract, which combines together identical gene-variant-drug combinations into a single storage entry, leading to significantly better scalability and query efficiency. Results On a private, proof-of-authority network, both our challenge and fastQuery solutions exhibit approximately linear memory and time usage for inserting into and querying small databases (<1,000 entries). For larger databases (1000 to 10,000 entries), fastQuery maintains this scaling. Furthermore, both solutions can query by a single field (”0-AND”) or a combination of fields (”1- or 2-AND”). Specifically, the challenge solution can complete a 2-AND query from a small database (100 entries) in 35ms using 0.1 MB of memory. For the same query, fastQuery has a 2-fold improvement in time and a 10-fold improvement in memory. Conclusion We show that pharmacogenomics data can be stored and queried efficiently using Ethereum blockchain. Our solutions could potentially be used to store a range of clinical data and extended to other fields requiring high-integrity data storage and efficient access.

The Institute for Quality and Efficiency in Health Care (IQWiG) assesses the added benefit of new drugs by means of company dossiers. The pharmaceutical company performs the information retrieval, which is then assessed by IQWiG. Our aim was to determine whether PubMed's Related Citations (RelCits) and/or a simple-structured Boolean search (SSBS) are efficient and reliable search techniques to assess the completeness of an evidence base consisting of published randomized controlled trials (RCTs). Retrospective analysis of citations included as relevant in systematic reviews (SRs) of drugs. The proportion of relevant citations identified by the above-mentioned search techniques was determined. Relative sensitivity, precision, and the number needed to read (NNR) were then calculated. A total of 19 SRs included 330 relevant PubMed citations. The single techniques yielded either insufficient completeness, reliability, or efficiency. The first 20 RelCits plus SSBS achieved high completeness and reliability (sensitivity: 98.1%, range: 80–100%) and sufficient efficiency (precision: 5.0%, NNR: 25). The first 50 RelCit plus SSBS achieved slightly better completeness and reliability, but slightly worse efficiency. Combining the first 20 RelCits and an SSBS in PubMed is a suitable method to assess the completeness of an evidence base of published RCTs.

Dynamic predictive modeling using electronic health record data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is, in part, determined by the stages preceding the model development: data extraction from electronic health record systems and data preparation. In this paper, we identified over 40 challenges encountered during these stages and provided actionable recommendations for addressing them. These challenges are organized into 4 categories: cohort definition, outcome definition, feature engineering, and data cleaning. This comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings.