Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text)
(363 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Guo JofPathInformatics2016 7.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out|Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The adoption of digital pathology offers benefits over labor-intensive, time-consuming, and error-prone manual processes. However, because most workflow and [[laboratory]] transactions are centered around the anatomical pathology laboratory information system (APLIS), adoption of digital pathology ideally requires integration with the APLIS. A digital pathology system (DPS) integrated with the APLIS was recently implemented at our institution for diagnostic use. We demonstrate how such integration supports digital workflow to sign-out anatomical pathology cases.
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
 
''Recently featured'':
Workflow begins when pathology cases get accessioned into the APLIS ([[Cerner Corporation|CoPathPlus]]). Glass slides from these cases are then digitized (Omnyx VL120 scanner) and automatically uploaded into the DPS (Omnyx; Integrated Digital Pathology (IDP) software v.1.3). The APLIS transmits case data to the DPS via a publishing web service. The DPS associates scanned images with the correct case using barcode labels on slides and information received from the APLIS. When pathologists remotely open a case in the DPS, additional information (e.g. gross pathology details, prior cases) gets retrieved from the APLIS through a query web service. ('''[[Journal:Digital pathology and anatomic pathology laboratory information system integration to support digital pathology sign-out|Full article...]]''')<br />
{{flowlist |
<br />
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
''Recently featured'':  
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
: ▪ [[Journal:A polyglot approach to bioinformatics data integration: A phylogenetic analysis of HIV-1|A polyglot approach to bioinformatics data integration: A phylogenetic analysis of HIV-1]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
: ▪ [[Journal:The systems biology format converter|The systems biology format converter]]
}}
: ▪ [[Journal:Chemozart: A web-based 3D molecular structure editor and visualizer platform|Chemozart: A web-based 3D molecular structure editor and visualizer platform]]

Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63551"