| 1. |
- Beal, Jacob, et al.
(författare)
-
Robust estimation of bacterial cell count from optical density
- 2020
-
Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
-
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.
|
|
| 2. |
- Dong, Yi-Min, et al.
(författare)
-
Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia
- 2021
-
Ingår i: Clinical Infectious Diseases. - : Oxford University Press. - 1058-4838 .- 1537-6591. ; 72:4, s. 652-660
-
Tidskriftsartikel (refereegranskat)abstract
- Background. The outbreak of coronavirus disease 2019 (COVID-19) has spread worldwide and continues to threaten peoples' health as well as put pressure on the accessibility of medical systems. Early prediction of survival of hospitalized patients will help in the clinical management of COVID-19, but a prediction model that is reliable and valid is still lacking. Methods. We retrospectively enrolled 628 confirmed cases of COVID-19 using positive RT-PCR tests for SARS-CoV-2 in Tongji Hospital, Wuhan, China. These patients were randomly grouped into a training (60%) and a validation (40%) cohort. In the training cohort, LASSO regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of patients with COVID-19. A nomogram based on the 3 variables was built for clinical use. AUCs, concordance indexes (C-index), and calibration curves were used to evaluate the efficiency of the nomogram in both training and validation cohorts. Results. Hypertension, higher neutrophil-to-lymphocyte ratio, and increased NT-proBNP values were found to be significantly associated with poorer prognosis in hospitalized patients with COVID-19. The 3 predictors were further used to build a prediction nomogram. The C-indexes of the nomogram in the training and validation cohorts were 0.901 and 0.892, respectively. The AUC in the training cohort was 0.922 for 14-day and 0.919 for 21-day probability of in-hospital survival, while in the validation cohort this was 0.922 and 0.881, respectively. Moreover, the calibration curve for 14- and 21-day survival also showed high coherence between the predicted and actual probability of survival. Conclusions. We built a predictive model and constructed a nomogram for predicting in-hospital survival of patients with COVID-19. This model has good performance and might be utilized clinically in management of COVID-19.
|
|
| 3. |
- Dong, Yi-Min, et al.
(författare)
-
Reply to Collins et al
- 2021
-
Ingår i: Clinical Infectious Diseases. - : Oxford University Press. - 1058-4838 .- 1537-6591. ; 73:3, s. 558-559
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 4. |
- Yu, Wenjin, et al.
(författare)
-
Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
-
Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
-
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.
|
|
| 5. |
- Kreibich, Heidi, et al.
(författare)
-
Panta Rhei benchmark dataset : Socio-hydrological data of paired events of floods and droughts
- 2023
-
Ingår i: Earth System Science Data. - : Copernicus Publications. - 1866-3508 .- 1866-3516. ; 15:5, s. 2009-2023
-
Tidskriftsartikel (refereegranskat)abstract
- As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, 10.5880/GFZ.4.4.2023.001).
|
|
| 6. |
- Kreibich, Heidi, et al.
(författare)
-
The challenge of unprecedented floods and droughts in risk management
- 2022
-
Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 608:7921, s. 80-86
-
Tidskriftsartikel (refereegranskat)abstract
- Risk management has reduced vulnerability to floods and droughts globally, yet their impacts are still increasing. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change.
|
|
| 7. |
- Liang, Wenbiao, et al.
(författare)
-
Synthesis of single-crystal LiNi0.8Co0.1Mn0.1O2 materials for Li-ion batteries by microfluidic technology
- 2023
-
Ingår i: Chemical Engineering Journal. - : Elsevier. - 1385-8947 .- 1873-3212. ; 464
-
Tidskriftsartikel (refereegranskat)abstract
- Single-crystal LiNixMnyCo1-x-yO2 (SC-NMC) cathode with electro-chemo-mechanically compliant microstructure is regarded as a promising candidate for high-energy-density lithium ion battery. However, the research of Ni-rich SC-NCM still lags behind its corresponding polycrystalline cathode materials, mainly due to the difficulties in synthesis. Herein, the single-crystal LiNi0.8Mn0.1Co0.1O2 cathode (SC-NCM811) was successfully synthesized by microfluidic technology combined with the solid-state lithiation process. The nano-sized Ni0.8C- o0.1Mn0.1(OH)2 precursor prepared via microfluidic technology enhances its accessibility to lithium salts, thus exhibiting high chemical activity for lithiation reaction. As a result, the optimized SC-NCM811 cathode shows relatively small-scale grain size (<3 mu m), low cation mixing and well layered structure, which is beneficial to electrochemical kinetics and redox reversibility. The electrochemical characterization results further reveal that the optimized SC-NCM811 cathode can well balance the cycle performance and rate capability, showing good electrochemical performance. Overall, microfluidic technology is expected to provide a new strategy for pre-paring single-crystal Ni-rich cathode materials, which may extend to the commercial application of other cathode materials.
|
|
| 8. |
- Liu, Wei, et al.
(författare)
-
Coherent dynamics of multi-spin V-B(-) center in hexagonal boron nitride
- 2022
-
Ingår i: Nature Communications. - : Nature Portfolio. - 2041-1723. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Hexagonal boron nitride (hBN) has recently been demonstrated to contain optically polarized and detected electron spins that can be utilized for implementing qubits and quantum sensors in nanolayered-devices. Understanding the coherent dynamics ofmicrowave driven spins in hBN is of crucial importance for advancing these emerging new technologies. Here, we demonstrate and study the Rabi oscillation and related phenomena of a negatively charged boron vacancy (V-B(-)) spin ensemble in hBN. We report on different dynamics of the V-B(-) spins at weak and strong magnetic fields. In the former case the defect behaves like a single electron spin system, while in the latter case it behaves like a multi-spin system exhibiting multiple-frequency dynamical oscillation as beat in the Ramsey fringes. We also carry out theoretical simulations for the spin dynamics of V-B(-) and reveal that the nuclear spins can be driven via the strong electron nuclear coupling existing in V-B(-) center, which can be modulated by the magnetic field and microwave field.
|
|
