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Matysiak BM, Thomas D, Cronin L. Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis. Angew Chem Int Ed Engl 2024; 63:e202315207. [PMID: 38155102 DOI: 10.1002/anie.202315207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/12/2023] [Accepted: 12/27/2023] [Indexed: 12/30/2023]
Abstract
Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time-consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on-line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron-demand Diels-Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer-readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning.
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Affiliation(s)
| | - Dean Thomas
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Leroy Cronin
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
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Rajaseger G, Chan KL, Yee Tan K, Ramasamy S, Khin MC, Amaladoss A, Kadamb Haribhai P. Hydroponics: current trends in sustainable crop production. Bioinformation 2023; 19:925-938. [PMID: 37928497 PMCID: PMC10625363 DOI: 10.6026/97320630019925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/30/2023] [Accepted: 09/30/2023] [Indexed: 11/07/2023] Open
Abstract
The combination of Hydroponics with smart technology in farming is novel and has promise as a method for effective and environmentally friendly crop production. This technology eliminates the need for soil and reduces water usage by providing nutrients straight to the plant's roots. The Internet of Things (IoT), sensors, and automation are all used in "smart farming," which allows for constant monitoring of soil conditions, nutrient levels, and plant vitality to facilitate fine-grained management and optimization. The technology-driven strategy improves crop output, quickens growth rates, and keeps conditions ideal all year round regardless of weather or other environmental circumstances. In addition, smart farming lessens the need for organic chemical inputs, promotes environmentally safe methods of pest management, and minimizes the amount of waste produced. This ground-breaking strategy may significantly alter the agricultural sector by encouraging regionalized food production, enhancing food security, and adding to more resilient farming practices. This comprehensive review delves into current trends in Hydroponics, highlighting recent advancements in smart farming systems, such as Domotics, Data Acquisition, Remote Cultivation, and automated AI systems. The review also underscores the various types and advantages of smart farming hydroponic technology, emphasizing the requirements for achieving efficiency in this innovative domain. Additionally, it explores future goals and potential developments, paving the way for further advancements in hydroponic smart farming.
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Affiliation(s)
- Ganapathy Rajaseger
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Kit Lun Chan
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Kay Yee Tan
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Shan Ramasamy
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Mar Cho Khin
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Anburaj Amaladoss
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
| | - Patel Kadamb Haribhai
- Centre for Research & Opportunities in Plant Science (CROPS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
- Centre Centre for Aquaculture and Veterinary Science (CAVS), School of Applied Science, Temasek Polytechnic, 21 Tampines Ave 1, Singapore 529757
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Oberloier S, Whisman NG, Hafting F, Pearce JM. Open source framework for a Broadly Expandable and Reconfigurable data acquisition and automation device (BREAD). HardwareX 2023; 15:e00467. [PMID: 37711733 PMCID: PMC10498007 DOI: 10.1016/j.ohx.2023.e00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 07/25/2023] [Accepted: 08/24/2023] [Indexed: 09/16/2023]
Abstract
Though open source data acquisition (DAQ) systems have been published, closed source proprietary systems are the standard despite often being prohibitively expensive. High costs, however, limit access to high-quality DAQ in low-resource settings. In many cases the functions executed by the closed source and proprietary DAQ cards could be carried out by an open source alternative; however, as desired function count increases, the simplicity of integrating the designs decreases substantially. Although the global library of open source electronic designs is expanding rapidly, and there is clear evidence they can reduce costs for scientists one device at a time, they are generally made to carry a function well, but are often not capable of scaling up or easily being integrated with other designs. Just as other open source projects have found success by having modular frameworks and clearly documented specifications, a framework to unify and enable interoperation of these open source electronics systems would be greatly beneficial to the scientific community. To meet these needs and ensure greater accessibility to high-quality electronics sensing and DAQ systems, this article shares and tests a news framework where new open source electronics can be developed and have plug-and-play functionality. The Broadly Reconfigurable and Expandable Automation Device (BREAD), consists of a basic set of guidelines and requirements to which others can contribute. Here 7 slices (boards) are provided, demonstrated, and validated: 1) Amplified Analog Input, 2) Audio Analysis / Fourier Transform, 3) +/- 10A Current Sensor, 4) 4-Channel Relay Controller 5) 4 Channel Stepper Motor Controller, 6) 4 Channel Type-K Thermocouple Reader and 7) 2 Channel USB Port. Implementing systems using BREAD rather than closed source and proprietary alternatives can result in cost savings of up to 93%.
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Affiliation(s)
- Shane Oberloier
- Department of Electrical & Computer Engineering, Michigan Technological University, Houghton MI 49931 USA
| | - Nicholas G. Whisman
- Department of Electrical & Computer Engineering, Michigan Technological University, Houghton MI 49931 USA
| | - Finn Hafting
- Department of Electrical & Computer Engineering, Western University, London, ON, Canada
| | - Joshua M. Pearce
- Department of Electrical & Computer Engineering, Western University, London, ON, Canada
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson S, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. ArXiv 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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Ridgeway WK, Millar DP, Williamson JR. Vectorized data acquisition and fast triple-correlation integrals for Fluorescence Triple Correlation Spectroscopy. Comput Phys Commun 2013; 184:1322-1332. [PMID: 23525193 PMCID: PMC3601675 DOI: 10.1016/j.cpc.2012.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Fluorescence Correlation Spectroscopy (FCS) is widely used to quantitate reaction rates and concentrations of molecules in vitro and in vivo. We recently reported Fluorescence Triple Correlation Spectroscopy (F3CS), which correlates three signals together instead of two. F3CS can analyze the stoichiometries of complex mixtures and detect irreversible processes by identifying time-reversal asymmetries. Here we report the computational developments that were required for the realization of F3CS and present the results as the Triple Correlation Toolbox suite of programs. Triple Correlation Toolbox is a complete data analysis pipeline capable of acquiring, correlating and fitting large data sets. Each segment of the pipeline handles error estimates for accurate error-weighted global fitting. Data acquisition was accelerated with a combination of off-the-shelf counter-timer chips and vectorized operations on 128-bit registers. This allows desktop computers with inexpensive data acquisition cards to acquire hours of multiple-channel data with sub-microsecond time resolution. Off-line correlation integrals were implemented as a two delay time multiple-tau scheme that scales efficiently with multiple processors and provides an unprecedented view of linked dynamics. Global fitting routines are provided to fit FCS and F3CS data to models containing up to ten species. Triple Correlation Toolbox is a complete package that enables F3CS to be performed on existing microscopes.
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Affiliation(s)
- William K Ridgeway
- Dept. of Molecular Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
- Dept. of Chemistry, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
| | - David P Millar
- Dept. of Molecular Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
| | - James R Williamson
- Dept. of Molecular Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
- Dept. of Chemistry, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla CA 92037, USA
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