Difference between revisions of "Main Page/Featured article of the week/2024"
Shawndouglas (talk | contribs) (Added last week's article of the week) |
Shawndouglas (talk | contribs) (Added last week's article of the week) |
||
(9 intermediate revisions by the same user not shown) | |||
Line 17: | Line 17: | ||
<!-- Below this line begin pasting previous news --> | <!-- Below this line begin pasting previous news --> | ||
<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 12–18:</h2> | <h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 15–21:</h2> | ||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div> | |||
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"''' | |||
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 08–14:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Manisha HighConComp2023 3-3.jpg|240px]]</div> | |||
'''"[[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]"''' | |||
Food [[traceability]] is a critical factor that can ensure food safety while enhancing the credibility of the manufactured product, thus achieving heightened user satisfaction and loyalty. The perishable food supply chain (PFSC) requires paramount care for ensuring [[Quality (business)|quality]] owing to the limited product life. The PFSC comprises of multiple organizations with varied interests and is more likely to be hesitant in sharing the traceability details among one another owing to a lack of trust, which can be overcome by using blockchain. In this research, an efficient scheme using a blockchain-enabled deep [[wikipedia:Residual neural network|residual network]] (BC-DRN) is developed to provide food traceability for dairy products. Here, food traceability is determined by using various modular tools ... ('''[[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 01–07:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Patel JofClinDiagRes2023 17-9.jpg|140px]]</div> | |||
'''"[[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]"''' | |||
Good clinical laboratory practices (GCLP) play a vital role in early and accurate diagnosis, providing high-quality data and timely [[Sample (material)|sample]] processing. Adhering to a robust [[quality management system]] (QMS) that complies with GCLP standards is crucial for [[laboratory]] personnel in a [[clinical laboratory]] to deliver outstanding healthcare services and reliable, reproducible reports. [The aim of this study is to] assess the knowledge, attitude, and practice (KAP) of laboratory professionals towards [[Quality (business)|quality]] in the laboratory through GCLP training... ('''[[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 25–31:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Scroggie DigDisc2023 2.gif|240px]]</div> | |||
'''"[[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]"''' | |||
[[Electronic laboratory notebook]]s (ELNs) have expanded the utility of the paper [[laboratory notebook]] beyond that of a simple record keeping tool. Open ELNs offer additional benefits to the scientific community, including increased transparency, reproducibility, and [[Data integrity|integrity]]. A key element underpinning these benefits is facile and expedient knowledge sharing which aids communication and collaboration. In previous projects, we have used [[LabTrove]] and [[Vendor:LabArchives, LLC|LabArchives]] as open ELNs, in partnership with GitHub (an open-source web-based platform originally developed for collaborative coding) for communication and discussion. Here we present our personal experiences using GitHub as the central platform for many aspects of the scientific process ... ('''[[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 18–24:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Nieminen GigaScience2023 12.jpeg|240px]]</div> | |||
'''"[[Journal:SODAR: Managing multiomics study data and metadata|SODAR: Managing multiomics study data and metadata]]"''' | |||
Scientists employing omics in [[Life sciences industry|life science]] studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many [[Sample (material)|samples]] with their [[metadata]]. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for [[omics]] data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface (GUI) for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format ... ('''[[Journal:SODAR: Managing multiomics study data and metadata|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 11–17:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Chang HealthInfoRes2023 29-4.png|240px]]</div> | |||
'''"[[Journal:Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records|Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records]]"''' | |||
Systematic evaluations of the benefits of [[health information technology]] (HIT) play an essential role in enhancing healthcare [[Quality (business)|quality]] by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of [[artificial intelligence]] (AI), the [[internet of things]] (IoT), and [[personal health record]]s (PHR), based on scientific evidence. The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major [[health informatics]] journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains ... ('''[[Journal:Benefits of information technology in healthcare: Artificial intelligence, internet of things, and personal health records|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 04–10:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Villegas-Pérez Foods23 12-22.png|240px]]</div> | |||
'''"[[Journal:A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for ''Trichinella spp.'']]"''' | |||
[[Trichinosis|Trichinellosis]] is a parasitic foodborne zoonotic disease transmitted by ingestion of raw or undercooked meat containing the first larval stage (L1) of the nematode. To ensure the [[Quality (business)|quality]] and safety of food intended for human consumption, meat inspection for detection of ''Trichinella'' spp. larvae is a mandatory procedure per European Union (E.U.) regulations. The implementation of [[quality assurance]] (QA) practices in [[Laboratory|laboratories]] that are responsible for ''Trichinella'' spp. detection is essential given that the detection of this parasite is still a pivotal threat to public health, and it is included in List A of Annex I, Directive 2003/99/EC, which determines the agents to be monitored on a mandatory basis ... ('''[[Journal:A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 26–March 03:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Pineda-Pampliega EFSAJournal2023 20-S2.png|240px]]</div> | |||
'''"[[Journal:Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood|Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood]]"''' | |||
[[Risk assessment|Risk and risk–benefit assessments]] of food are complex exercises, in which access to and use of several disconnected individual stand-alone [[database]]s is required to obtain hazard and exposure information. Data obtained from such databases ideally should be in line with the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR principles]], i.e. the data must be findable, accessible, interoperable, and reusable. However, often cases are encountered when one or more of these principles are not followed. In this project, we set out to assess if existing commonly used databases in risk assessment are in line with the FAIR principles ... ('''[[Journal:Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 19–25:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Henke JMIRMedInfo2023 11.png|240px]]</div> | |||
'''"[[Journal:An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation|An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation]]"''' | |||
In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) data using an extract-transform-load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment ... ('''[[Journal:An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 12–18:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Johnson JofCannRes23 5.png|240px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Johnson JofCannRes23 5.png|240px]]</div> | ||
'''"[[Journal:Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem|Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem]]"''' | '''"[[Journal:Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem|Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem]]"''' |
Revision as of 17:05, 22 April 2024
If you're looking for other "Article of the Week" archives: 2014 - 2015 - 2016 - 2017 - 2018 - 2019 - 2020 - 2021 - 2022 - 2023 - 2024 |
Featured article of the week archive - 2024
Welcome to the LIMSwiki 2024 archive for the Featured Article of the Week.
Featured article of the week: April 15–21:"Data management challenges for artificial intelligence in plant and agricultural research" Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
|