| 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. |
- Luo, Yifei, et al.
(författare)
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Technology Roadmap for Flexible Sensors
- 2023
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Ingår i: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
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Forskningsöversikt (refereegranskat)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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| 3. |
- Dai, Ting, et al.
(författare)
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Assessment of Fast Fluid Dynamics with Different Turbulence Models for Simulating Airflow and Pollutant Dispersion Around Buildings
- 2023
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Ingår i: Proceedings of the 5th International Conference on Building Energy and Environment. - : Springer Nature. ; , s. 51-59
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Konferensbidrag (refereegranskat)abstract
- Fast fluid dynamics (FFD) could provide efficient airflow and concentration simulation. The commonly used turbulence model in FFD was RNG k- ε turbulence model which solved two transport equations to obtain eddy viscosity. To improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating single-building case and comparing with experiment and CFD, this study assessed accuracy and efficiency of FFD with those turbulence models. Compared with CFD, FFD improved computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased computing speed while ensuring the same accuracy as RNG k- ε turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. This investigation suggested FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.
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| 4. |
- Dai, Ting, et al.
(författare)
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Evaluation of fast fluid dynamics with different turbulence models for predicting outdoor airflow and pollutant dispersion
- 2022
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Ingår i: Sustainable cities and society. - : Elsevier BV. - 2210-6707. ; 77, s. 103583-
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Tidskriftsartikel (refereegranskat)abstract
- Fast fluid dynamics (FFD) could provide informative and efficient airflow and concentration simulation. The commonly used turbulence model in FFD was Re-Normalization Group (RNG) k-epsilon turbulence model which solved two transport equations to obtain eddy viscosity. To reduce this part of time and further improve computing speed, this investigation implemented no turbulence model, Smagorinsky model and dynamic Smagorinsky model which calculated eddy viscosity without solving equation in FFD in an open-source program, OpenFOAM. By simulating several outdoor cases of varying complexity and comparing with experiment and CFD, this study assessed the accuracy and computing efficiency of FFD with four turbulence models. Compared with CFD, FFD greatly improved the computing speed without reducing accuracy. The simulation of FFD without turbulence model was fast but inaccurate. FFD with Smagorinsky model increased the computing speed while ensuring the same accuracy as RNG k-epsilon turbulence model. FFD with dynamic Smagorinsky model provided accurate results with high efficiency. Computation errors arose mainly from inaccurate prediction of turbulence dispersion. The computing cost was associated with the number of transport equations and calculation method of model coefficient. This investigation recommended the use of FFD with dynamic Smagorinsky model for outdoor airflow and pollutant dispersion studies.
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| 5. |
- Li, Wu Lue, et al.
(författare)
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Multiple Origins and Genomic Basis of Complex Traits in Sighthounds
- 2023
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Ingår i: Molecular biology and evolution. - : Oxford University Press (OUP). - 0737-4038 .- 1537-1719. ; 40:8
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Tidskriftsartikel (refereegranskat)abstract
- Sighthounds, a distinctive group of hounds comprising numerous breeds, have their origins rooted in ancient artificial selection of dogs. In this study, we performed genome sequencing for 123 sighthounds, including one breed from Africa, six breeds from Europe, two breeds from Russia, and four breeds and 12 village dogs from the Middle East. We gathered public genome data of five sighthounds and 98 other dogs as well as 31 gray wolves to pinpoint the origin and genes influencing the morphology of the sighthound genome. Population genomic analysis suggested that sighthounds originated from native dogs independently and were comprehensively admixed among breeds, supporting the multiple origins hypothesis of sighthounds. An additional 67 published ancient wolf genomes were added for gene flow detection. Results showed dramatic admixture of ancient wolves in African sighthounds, even more than with modern wolves. Whole-genome scan analysis identified 17 positively selected genes (PSGs) in the African population, 27 PSGs in the European population, and 54 PSGs in the Middle Eastern population. None of the PSGs overlapped in the three populations. Pooled PSGs of the three populations were significantly enriched in "regulation of release of sequestered calcium ion into cytosol"(gene ontology: 0051279), which is related to blood circulation and heart contraction. In addition, ESR1, JAK2, ADRB1, PRKCE, and CAMK2D were under positive selection in all three selected groups. This suggests that different PSGs in the same pathway contributed to the similar phenotype of sighthounds. We identified an ESR1 mutation (chr1: g.42,177,149-T > C) in the transcription factor (TF) binding site of Stat5a and a JAK2 mutation (chr1: g.93,277,007-T > A) in the TF binding site of Sox5. Functional experiments confirmed that the ESR1 and JAK2 mutation reduced their expression. Our results provide new insights into the domestication history and genomic basis of sighthounds.
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| 6. |
- Lin, Yi-Ting, et al.
(författare)
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Differences in the Microbial Composition of Hemodialysis Patients Treated with and without β-Blockers
- 2021
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Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 11:3
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Tidskriftsartikel (refereegranskat)abstract
- β-blockers are commonly prescribed to treat cardiovascular disease in hemodialysis patients. Beyond the pharmacological effects, β-blockers have potential impacts on gut microbiota, but no study has investigated the effect in hemodialysis patients. Hence, we aim to investigate the gut microbiota composition difference between β-blocker users and nonusers in hemodialysis patients. Fecal samples collected from hemodialysis patients (83 β-blocker users and 110 nonusers) were determined by 16S ribosomal RNA amplification sequencing. Propensity score (PS) matching was performed to control confounders. The microbial composition differences were analyzed by the linear discriminant analysis effect size, random forest, and zero-inflated Gaussian fit model. The α-diversity (Simpson index) was greater in β-blocker users with a distinct β-diversity (Bray–Curtis Index) compared to nonusers in both full and PS-matched cohorts. There was a significant enrichment in the genus Flavonifractor in β-blocker users compared to nonusers in full and PS-matched cohorts. A similar finding was demonstrated in random forest analysis. In conclusion, hemodialysis patients using β-blockers had a different gut microbiota composition compared to nonusers. In particular, the Flavonifractor genus was increased with β-blocker treatment. Our findings highlight the impact of β-blockers on the gut microbiota in hemodialysis patients.
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| 7. |
- Xiao, Tianxiao, et al.
(författare)
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Autonomous self-healing hybrid energy harvester based on the combination of triboelectric nanogenerator and quantum dot solar cell
- 2024
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Ingår i: Nano Energy. - : Elsevier BV. - 2211-2855 .- 2211-3282. ; 125
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Tidskriftsartikel (refereegranskat)abstract
- Realization of multi-source energy harvesting with one single device would maximize power output. Thus, it is emerging as a promising strategy towards renewable energy generation and has attracted worldwide attention in the past decades. Capable of capturing mechanical energy that is ubiquitous in the ambient environment, triboelectric nanogenerator (TENG) has been considered a novel yet effective source towards next-generation energy harvesting. In this work, a flexible hybrid energy harvester (HEH) is developed via the rational integration of autonomous self-healing TENG and high bending-stable lead sulfide quantum dot (PbS QD) solar cell, enabling independent electricity generation by two different mechanisms. The single-electrode mode TENG component with self-healing is realized by a polydimethylsiloxane/Triton X-100 (PDMS/TX100) mixture as the dielectric layer and the shared gold (Au) electrode, which generates 0.39 µA of output current (Iout), 24.6 V of output voltages (Vout), 15.4 nC of transfer charges (Qsc), and 7.80 mW m−2 of output power peak density. The thin-film solar cell component is based on a PbS QD layer as the light absorber with a planar structure fabricated under low-cost and compatible conditions, achieving 22.8 mA cm−2 of short-circuit current density (Jsc) and 4.92% of power conversion efficiency (PCE). As a proof of concept, an electronic watch is successfully powered by harnessing ambient mechanical and solar energy with a hybridized energy cell. This approach will offer more opportunities to construct a versatile platform towards remote monitoring and smart home systems.
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| 8. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 9. |
- Lind, Lars, et al.
(författare)
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Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)
- 2021
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Ingår i: eLife. - : eLife Sciences Publications Ltd. - 2050-084X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions.
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| 10. |
- Liu, Wei, et al.
(författare)
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Microencapsulated phase change material through cellulose nanofibrils stabilized Pickering emulsion templating
- 2023
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Ingår i: advanced composites and hybrid materials. - 2522-0128. ; 6:4
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Tidskriftsartikel (refereegranskat)abstract
- Phase change materials (PCMs) possess remarkable capability to store and release substantial amounts of energy during the processes of melting and crystallization across a wide temperature range, thus holding great promise in applications related to temperature regulation and thermal energy storage. Herein, to effectively address PCM leakage and enhance thermal conduction, PCM microcapsules with melamine-formaldehyde resin (MF) shell were prepared using in situ polymerization of Pickering emulsions stabilized by cellulose nanofibrils (CNFs). CNFs were selected as the stabilizers for the Pickering emulsions and as reinforcing nanofillers for the MF shell, owing to their excellent emulsifying capability, high mechanical strength, and sustainable nature. Paraffin wax (PW) was utilized as the PCM material. The resulting PCM microcapsules with MF resin shells and PW core had a diameter ranging from 2 to 4 & mu;m. Results showed that microcapsule with the core-shell ratio of 2 (Micro-2.0) exhibited the highest latent heat of crystallization and latent heat of fusion, measuring approximately 128.40 J/g and 120.23 J/g, respectively. The encapsulation efficiency of Micro-2.0 was determined to be approximately 79.84%.
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