| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)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. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 3. |
- Zhang, Chuang, et al.
(författare)
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Weather Visibility Prediction Based on Multimodal Fusion
- 2019
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 74776-74786
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Tidskriftsartikel (refereegranskat)abstract
- Visibility affects all forms of traffic: roads, sailing, and aviation. Visibility prediction is meaningful in guiding production and life. Different from weather prediction, which relies solely on atmosphere factors, the factors that affect meteorological visibility are more complicated, such as the air pollution caused by factory exhaust emission. However, the current prediction of visibility is mostly based on the numerical prediction method similar to the weather prediction. We proposed a method using multimodal fusion to build a weather visibility prediction system in this paper. An advanced numerical prediction model and a method for emission detection were used to build a multimodal fusion visibility prediction system. We used the most advanced regression algorithm, XGBoost, and LightGBM, to train the fusion model for numerical prediction. Through the estimation of factory emission by the traditional detector in the satellite image, we propose to add the result of estimation based on Landsat-8 satellite images to assist the prediction. By testing our numerical model in atmosphere data of various meteorological observation stations in Beijing-Tianjin-Hebei region from 2002 to 2018, our numerical prediction model turns out to be more accurate than other existing methods, and after fusing with emission detection method, the accuracy of our visibility prediction system has been further improved.
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| 4. |
- Chen, Yue, et al.
(författare)
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A novel nanoparticle system targeting damaged mitochondria for the treatment of Parkinson's disease
- 2022
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Ingår i: Biomaterials Advances. - : Elsevier BV. - 2772-9516 .- 2772-9508. ; 138
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Tidskriftsartikel (refereegranskat)abstract
- Mitochondrial damage is one of the primary causes of neuronal cell death in Parkinson's disease (PD). In PD patients, the mitochondrial damage can be repaired or irreversible. Therefore, mitochondrial damage repair becomes a promising strategy for PD treatment. In this research, hyaluronic acid nanoparticles (HA-NPs) of different molecular weights are used to protect the mitochondria and salvage the mild and limited damage in mitochondria. The HA-NPs with 2190 k Dalton (kDa) HA can improve the mitochondrial function of SH-SY5Y cells and PTEN induced putative kinase 1 (PINK1) knockout mouse embryo fibroblast (MEF) cells. In cases of irreversible damage, NPs with ubiquitin specific peptidase 30 (USP30) siRNA are used to promote mitophagy. Meanwhile, by adding PINK1 antibodies, the NPs can selectively target the irreversibly damaged mitochondria, preventing the excessive clearance of healthy mitochondria.
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| 5. |
- Dong, Beibei, et al.
(författare)
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AI-based Dynamic Modelling for CO2 Capture
- 2023
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Ingår i: Energy Proceedings.
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Konferensbidrag (refereegranskat)abstract
- Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate and energy consumption with the mean absolute percentage error of 6.2% and 2.7% respectively.
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| 6. |
- Li, Peiran, et al.
(författare)
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Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning
- 2021
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Ingår i: Advances in Applied Energy. - : Elsevier BV. - 2666-7924. ; 4, s. 100057-100057
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Tidskriftsartikel (refereegranskat)abstract
- The photovoltaic (PV) industry boom and increased PV applications call for better planning based on accurate and updated data on the installed capacity. Compared with the manual statistical approach, which is often time-consuming and labor-intensive, using satellite/aerial images to estimate the existing PV installed capacity offers a new method with cost-effective and data-consistent features. Previous studies investigated the feasibility of segmenting PV panels from images involving machine learning technologies. However, due to the particular characteristics of PV panel semantic-segmentation, the machine learning tools need to be designed and applied with careful considerations of the issue formulation, data quality, and model explainability. This paper investigated the characteristics of PV panel semantic-segmentation from the perspective of computer vision. The results reveal that the PV panel image data has several specific characteristics: highly class-imbalance and non-concentrated distribution; homogeneous texture and heterogenous color features; and the notable resolution threshold for effective semantic-segmentation. Moreover, this paper provided recommendations for data obtaining and model design, aiming at each observed character from the viewpoints of recent solutions in computer vision, which can be helpful for future improvement of the PV panel semantic-segmentation.
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| 7. |
- Li, Wenjing, et al.
(författare)
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PredLife : Predicting Fine-Grained Future Activity Patterns
- 2023
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Ingår i: IEEE Transactions on Big Data. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2332-7790. ; 9:6, s. 1658-1669
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Tidskriftsartikel (refereegranskat)abstract
- Activity pattern prediction is a critical part of urban computing, urban planning, intelligent transportation, and so on. Based on a dataset with more than 10 million GPS trajectory records collected by mobile sensors, this research proposed a CNN-BiLSTM-VAE-ATT-based encoder-decoder model for fine-grained individual activity sequence prediction. The model combines the long-term and short-term dependencies crosswise and also considers randomness, diversity, and uncertainty of individual activity patterns. The proposed results show higher accuracy compared to the ten baselines. The model can generate high diversity results while approximating the original activity patterns distribution. Moreover, the model also has interpretability in revealing the time dependency importance of the activity pattern prediction.
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| 8. |
- Sheng, Jinyu, et al.
(författare)
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Designing P-type bi-stable overcrowded alkene-based chiroptical photoswitches
- 2023
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Ingår i: Chemical Science. - : Royal Society of Chemistry. - 2041-6520 .- 2041-6539. ; 14:16, s. 4328-4336
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Tidskriftsartikel (refereegranskat)abstract
- Overcrowded alkene based molecular motors and switches constitute a unique class of photo-responsive systems due to their intrinsic chirality near the core C(sic )C bond, making them highly suitable candidates for the construction of light-switchable dynamic systems, i.e., for controlling molecular motion, modulation of material chiroptical properties and supramolecular assembly. However, the lack of general design principles, along with the challenging synthesis of these molecules, precludes full exploitation of their dynamic structures. Therefore, systematic investigations of the key parameters are crucial for the further development of these systems. Here we provide a facile alternative synthetic route, elucidate the influence of substituents on the photochemistry of overcrowded alkene-derived bistable chiroptical photoswitches, and show nearly quantitative bidirectional photoswitching. The established structure-property relationship constitutes a practical guideline for the design of these photochromes tailored to a specific application.
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| 9. |
- Zhang, Haoran, et al.
(författare)
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1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown
- 2021
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Ingår i: Applied Energy. - : ELSEVIER SCI LTD. - 0306-2619 .- 1872-9118. ; 283
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Tidskriftsartikel (refereegranskat)abstract
- Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge. Based on millions of financial transaction records and 44 thousand photovoltaic installation records, we tracked the spatiotemporal sale network of the distributed photovoltaic market and explored the extent of market slowdown. We found that a two-month lockdown duration can be assessed as a high-risk threshold value. When the lockdown duration exceeds the threshold value, the monthly value-added loss reaches 67.7%, and emission reduction capacity is cut by 64.2% over the whole year. We show that risks of a slowdown in PV deployment due to COVID19 lockdowns can be mitigated by comprehensive incentive strategies for the distributed PV market amid market uncertainties.
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| 10. |
- Zhang, Haoran, et al.
(författare)
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Epidemic versus economic performances of the COVID-19 lockdown : A big data driven analysis
- 2022
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Ingår i: Cities. - : Elsevier BV. - 0264-2751 .- 1873-6084. ; 120
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Tidskriftsartikel (refereegranskat)abstract
- Lockdown measures have been a “panacea” for pandemic control but also a violent “poison” for economies.Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governmentsmeet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive andquantified guides. Based on millions of financial transaction records, and billions of mobility data, we trackedspatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sidedframework to assess the epidemiological performance and economic damage of different lockdown policies. Wefound that the pandemic duration under the strictest lockdown is less about two months than that under thelightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economicallyefficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue thatcutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helpedgovernments figure out the Pareto optimal solution set of lockdown strategy.
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