| 1. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Andersson, Dag, et al.
(author)
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Smart access to small lot manufacturing for systems integration
- 2018
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In: 2018 Pan Pacific Microelectronics Symposium, Pan Pacific 2018. - 9781944543044 ; , s. 1-9
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Conference paper (peer-reviewed)abstract
- The three year EU project SMARTER-SI that ends in January 2018 has tested a new concept for small lot manufacturing for SMEs which we call the Cooperative Foundry Model (CFM). During previous research, all RTOs have completed building blocks, i.e. components or parts of systems which are readily available and characterized by their high Technology Readiness Level (TRL). These building blocks are combined and integrated in so-called Application Experiments (AEs), thereby creating innovative Smart Systems that serve the SMEs' needs. Four pre defined AEs have been presented before [1] and in this paper, six additional AEs will be presented: i) a smart sensor for pneumatic combined clutch and brakes, ii) smart well plates for tissue engineering integrating continuous, non-invasive TEER iii) microclimate sensor for moisture applications, iv) LTCC-Si-Pressure Sensor, v) miniaturized capillary electrophoresis system for bio analysis, and vi) a MEMS sensor module for respiratory applications. Finally, a brief description of ongoing standardization efforts is presented.
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| 3. |
- Dai, Wenbin, et al.
(author)
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Modelling Industrial Cyber-Physical Systems using IEC 61499 and OPC UA
- 2018
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Conference paper (peer-reviewed)abstract
- Industrial Cyber-Physical Systems (iCPS) are considered as the enabling technology for achieve Industry 4.0. One main characteristic of the iCPS is the information transparency to allow interoperability among various devices and systems. The OPC UA provides a common information model for connecting Industry 4.0 components. On the other hand, the IEC 61499 is commonly used as an executable modeling language for iCPS. The IEC 61499 function block network provides an abstract view of the system configuration. By combining IEC 61499 and OPC UA, a visual executable model for iCPS is completed. In this paper, the mapping between two standards are provided and a case study of the proposed mapping is given.
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| 4. |
- Mosley, Jonathan D., et al.
(author)
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Probing the Virtual Proteome to Identify Novel Disease Biomarkers
- 2018
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In: Circulation. - 1524-4539. ; 138:22, s. 2469-2481
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Journal article (peer-reviewed)abstract
- BACKGROUND: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. METHODS: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). RESULTS: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β. CONCLUSIONS: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
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| 5. |
- Wang, Wenhuan, et al.
(author)
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Digital economy sectors are key CO2 transmission centers in the economic system
- 2023
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In: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 407
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Journal article (peer-reviewed)abstract
- The rapid growth of the digital economy has driven economic development, but the massive demand for elec-tricity from digital reforms, coupled with China's coal-based power generation, has created a significant CO2 emission problem. Previous studies have assessed digital economy sectors with an incomplete scope and a lack of carbon emissions assessment at the intermediary-side. To fill these gaps, this study assessed CO2 emissions using input-output modeling of the core industry sector of the digital economy and the industrial digitalization sector at the production, intermediary, and demand sides, and identified key CO2 transmission centers. The results show the following: (1) Digital economy sectors had a high betweenness and were important CO2 transmission centers in the economic system, transmitting more than 4.08 billion tonnes of betweenness-based CO2 emissions; (2) specifically, the industrial digitalization sector transmitted the most CO2 in the economic system, and the digital product manufacturing sector was the core industry sector with the highest betweenness and a strong trans-mission effect on the CO2 emissions in the supply chain; (3) digital economy sectors had large CO2 emissions on the production, intermediary, and demand sides, and transmitted CO2 more through the demand-side and key transmission centers. These results suggest that digital economy sectors can decarbonize and reduce CO2 emissions by (1) improving material use efficiency in the digital product manufacturing sector, (2) reducing the use of carbon-intensive energy and materials in the digital economy sectors, and (3) establishing CO2 emission disclosure rules, incentives, and penalties.
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