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<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> | <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: June 10–16:</h2> | ||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Schabacker FrontBioengBiotech2019 7.jpg|240px]]</div> | |||
'''"[[Journal:Assessing cyberbiosecurity vulnerabilities and infrastructure resilience|Assessing cyberbiosecurity vulnerabilities and infrastructure resilience]]"''' | |||
The convergence of advances in [[biotechnology]] with [[laboratory automation]], access to data, and computational biology has democratized biotechnology and accelerated the development of new therapeutics. However, increased access to biotechnology in the digital age has also introduced additional security concerns and ultimately spawned the new discipline of cyberbiosecurity, which encompasses [[cybersecurity]], cyber-physical security, and biosecurity considerations. With the emergence of this new discipline comes the need for a logical, repeatable, and shared approach for evaluating facility and system vulnerabilities to cyberbiosecurity threats. In this paper, we outline the foundation of an assessment framework for cyberbiosecurity, accounting for both security and resilience factors in the physical and cyber domains. This is a unique problem set, yet despite the complexity of the cyberbiosecurity field in terms of operations and governance, previous experience developing and implementing physical and cyber assessments applicable to a wide spectrum of critical infrastructure sectors provides a validated point of departure for a cyberbiosecurity assessment framework. ('''[[Journal:Assessing cyberbiosecurity vulnerabilities and infrastructure resilience|Full article...]]''')<br /> | |||
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|<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: June 3–9:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Haugsbakken NordicJOfSciTechStud2018 6-1.png|240px]]</div> | |||
'''"[[Journal:What is the meaning of sharing: Informing, being informed or information overload?|What is the meaning of sharing: Informing, being informed or information overload?]]"''' | |||
In recent years, several Norwegian public organizations have introduced enterprise social media platforms (ESMPs). The rationale for their implementation pertains to a goal of improving internal communications and work processes in organizational life. Such objectives can be attained on the condition that employees adopt the platform and embrace the practice of sharing. Although sharing work on ESMPs can bring benefits, making sense of the practice of sharing constitutes a challenge. In this regard, the paper performs an analysis on a case whereby an ESMP was introduced in a Norwegian public organization. The analytical focus is on the challenges and experiences of making sense of the practice of sharing. The research results show that users faced challenges in making sense of sharing. ('''[[Journal:What is the meaning of sharing: Informing, being informed or information overload?|Full article...]]''')<br /> | |||
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|<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: May 27–June 2:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Murch FrontBioengBiotech2019 6.jpg|240px]]</div> | |||
'''"[[Journal:Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy|Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy]]"''' | |||
Cyberbiosecurity is being proposed as a formal new enterprise which encompasses cybersecurity, cyber-physical security, and biosecurity as applied to biological and biomedical-based systems. In recent years, an array of important meetings and public discussions, commentaries, and publications have occurred that highlight numerous vulnerabilities. While necessary first steps, they do not provide a systematized structure for effectively promoting communication, education and training, elucidation, and prioritization for analysis, research, development, testing and evaluation, and implementation of scientific and technological standards of practice, policy, or regulatory or legal considerations for protecting the bioeconomy. Further, experts in biosecurity and cybersecurity are generally not aware of each other's domains, expertise, perspectives, priorities, or where mutually supported opportunities exist for which positive outcomes could result. ('''[[Journal:Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy|Full article...]]''')<br /> | |||
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|<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: May 20–26:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Duncan FrontBioengBiotech2019 7.jpg|240px]]</div> | |||
'''"[[Journal:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system]]"''' | |||
Our national data and infrastructure security issues affecting the “bioeconomy” are evolving rapidly. Simultaneously, the conversation about cybersecurity of the U.S. [[Agriculture industry|food and agricultural system]] (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse, and they require advanced technologies and efficiencies that rely on computer technologies, big data, [[Cloud computing|cloud-based]] data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bioeconomy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safety management Hazard Analysis Critical Control Point (HACCP) system concept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). ('''[[Journal:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|Full article...]]''')<br /> | |||
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|<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: May 13–19:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Perez-Castillo Sensors2018 18-9.png|240px]]</div> | |||
'''"[[Journal:DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data|DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data]]"''' | |||
The [[internet of things]] (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various smart, connected products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to [[Quality assurance|assure]] that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While data quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers, and vendors should align their data quality management mechanisms and artifacts with well-known best practices and [[Specification (technical standard)|standards]], as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. ('''[[Journal:DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data|Full article...]]''')<br /> | |||
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|<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: May 06–12:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig17 Pinheiro Sensors2018 18-3.png|240px]]</div> | |||
'''"[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services]]"''' | |||
[[Cloud computing]] is considered an interesting paradigm due to its scalability, availability, and virtually unlimited storage capacity. However, it is challenging to organize a cloud storage service (CSS) that is safe from the client point-of-view and to implement this CSS in public clouds since it is not advisable to blindly consider this configuration as fully trustworthy. Ideally, owners of large amounts of data should trust their data to be in the cloud for a long period of time, without the burden of keeping copies of the original data, nor of accessing the whole content for verification regarding data preservation. Due to these requirements, [[Data integrity|integrity]], availability, [[Information privacy|privacy]], and trust are still challenging issues for the adoption of cloud storage services, especially when losing or leaking [[information]] can bring significant damage, be it legal or business-related. With such concerns in mind, this paper proposes an architecture for periodically monitoring both the information stored in the cloud and the service provider behavior. ('''[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|Full article...]]''')<br /> | |||
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|<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 29–May 05:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Al-Jefri FrontInMedicine2018 5.jpg|240px]]</div> | |||
'''"[[Journal:What Is health information quality? Ethical dimension and perception by users|What Is health information quality? Ethical dimension and perception by users]]"''' | |||
The popularity of seeking health [[information]] online makes information quality (IQ) a public health issue. The present study aims at building a theoretical framework of health information quality (HIQ) that can be applied to websites and defines which IQ criteria are important for a website to be trustworthy and meet users' expectations. We have identified a list of HIQ criteria from existing tools and assessment criteria and elaborated them into a questionnaire that was promoted via social media and, mainly, the university. Responses (329) were used to rank the different criteria for their importance in trusting a website and to identify patterns of criteria using hierarchical cluster analysis. HIQ criteria were organized in five dimensions based on previous theoretical frameworks, as well as on how they cluster together in the questionnaire response. We could identify a top-ranking dimension (scientific completeness) that describes what the user is expecting to know from the websites (in particular: description of symptoms, treatments, side effects). ('''[[Journal:What Is health information quality? Ethical dimension and perception by users|Full article...]]''')<br /> | |||
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|<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 22–28:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Teixeira FutureInternet2018 10-8.png|240px]]</div> | |||
'''"[[Journal:SCADA system testbed for cybersecurity research using machine learning approach|SCADA system testbed for cybersecurity research using machine learning approach]]"''' | |||
This paper presents the development of a [[supervisory control and data acquisition]] (SCADA) system testbed used for [[cybersecurity]] research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br /> | |||
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|<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 15–21:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Trellet JOfIntegBioinfo2018 15-2.jpg|240px]]</div> | |||
'''"[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data]]"''' | |||
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and [[Data analysis|analyzing]] such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br /> | |||
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|<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 8–14:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div> | |||
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"''' | |||
Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|Full article...]]''')<br /> | |||
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|<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 1–7:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Swaminathan FrontInGenetics2018 9.jpg|240px]]</div> | |||
'''"[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital]]"''' | |||
The adoption rate of [[Genomics|genome sequencing]] for clinical diagnostics has been steadily increasing, leading to the possibility of improvement in diagnostic yields. Although [[Laboratory|laboratories]] generate a summary clinical report, sharing raw genomic data with healthcare providers is equally important, both for secondary research studies as well as for a deeper analysis of the data itself, as seen by the efforts from organizations such as American College of Medical Genetics and Genomics, as well as Global Alliance for Genomics and Health. Here, we aim to describe the existing protocol of genomic data sharing between a certified [[clinical laboratory]] and a healthcare provider and highlight some of the lessons learned. This study tracked and subsequently evaluated the data transfer workflow for 19 patients, all of whom consented to be part of this research study and visited the genetics clinic at a tertiary pediatric hospital between April 2016 and December 2016. ('''[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Full article...]]''')<br /> | |||
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|<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:Fig4 Yu JOnWireCommNet2019 2019.png|240px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 Yu JOnWireCommNet2019 2019.png|240px]]</div> | ||
'''"[[Journal:Research on information retrieval model based on ontology|Research on information retrieval model based on ontology]]"''' | '''"[[Journal:Research on information retrieval model based on ontology|Research on information retrieval model based on ontology]]"''' |
Revision as of 01:40, 19 June 2019
If you're looking for other "Article of the Week" archives: 2014 - 2015 - 2016 - 2017 - 2018 - 2019 |
Featured article of the week archive - 2019
Welcome to the LIMSwiki 2019 archive for the Featured Article of the Week.
Featured article of the week: June 10–16:"Assessing cyberbiosecurity vulnerabilities and infrastructure resilience" The convergence of advances in biotechnology with laboratory automation, access to data, and computational biology has democratized biotechnology and accelerated the development of new therapeutics. However, increased access to biotechnology in the digital age has also introduced additional security concerns and ultimately spawned the new discipline of cyberbiosecurity, which encompasses cybersecurity, cyber-physical security, and biosecurity considerations. With the emergence of this new discipline comes the need for a logical, repeatable, and shared approach for evaluating facility and system vulnerabilities to cyberbiosecurity threats. In this paper, we outline the foundation of an assessment framework for cyberbiosecurity, accounting for both security and resilience factors in the physical and cyber domains. This is a unique problem set, yet despite the complexity of the cyberbiosecurity field in terms of operations and governance, previous experience developing and implementing physical and cyber assessments applicable to a wide spectrum of critical infrastructure sectors provides a validated point of departure for a cyberbiosecurity assessment framework. (Full article...)
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