| 1. |
- 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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| 2. |
- Liu, Yawen, et al.
(författare)
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Flexible Lead Bromide Perovskite Solar Cells
- 2020
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Ingår i: ACS Applied Energy Materials. - : AMER CHEMICAL SOC. - 2574-0962. ; 3:10, s. 9817-9823
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Tidskriftsartikel (refereegranskat)abstract
- Lead bromide perovskite solar cells (PSCs) have attracted increasing interest partly because of the high open-circuit voltage that has been obtained. Here, we present a simple way to prepare PSCs based on formamidinium lead tribromide, FAPbBr(3), by adding methylammonium chloride and methylammonium bromide into the precursor solution. With this method, high-quality and pin-hole free perovskite films with large crystal sizes were prepared. These additives result in a power conversion efficiency (PCE) of 7.9%, almost free of hysteresis, for a device on a rigid glass substrate. The first flexible lead bromide PSC is also prepared in this work and the flexible PSC exhibited a high PCE of 5.0%.
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| 3. |
- Ye, Lei, et al.
(författare)
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Toward environmentally friendly direct reduced iron production : A novel route of comprehensive utilization of blast furnace dust and electric arc furnace dust
- 2021
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Ingår i: Waste Management. - : Elsevier. - 0956-053X .- 1879-2456. ; 135, s. 389-396
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a novel method for producing direct reduced iron (DRI) powders based on microwave-assisted self reduction of core-shell composite pellets composed of blast furnace (BF) dust and hazardous electric arc furnace (EAF) dust followed by magnetic separation was reported. The proper core-shell structure of the composite pellets was designed according to the rule of impedance matching and properties of BF dust and EAF dust by adjusting the thickness of shell (i.e., thickness of impedance matching layer) via controlling the C/O molar ratio of the raw materials from 0.55 to 0.70. The results showed that the EAF dust with high content of CaO was beneficial to the mechanical strength of green, dried, and metallized pellets (collected after reduction), while the BF dust with high content of carbon enabled sufficient microwave-assisted reduction of the pellets, facilitating subsequent magnetic separation and also the removal of zinc from EAF dust. By reduction of the core-shell BF dust-EAF dust composite pellets with the C/O molar ratio of 0.65 at 1050 degrees C for 15 min, the resulting metallized pellets showed superior reduction and magnetic separation indexes with higher removal percentages of zinc and lead, in comparison with conventional metallized pellets. The DRI powders obtained after magnetic separation had total iron content of 91.2 wt%, iron metallization degree of 95.8%, yield of 68.1%, and iron recovery of 88.0%. This study provided a good example for efficient and environmentally friendly comprehensive utilization of typical and hazardous wastes in the iron and steel industry.
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| 4. |
- Zhou, Huimin, et al.
(författare)
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Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands
- 2021
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 307
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Tidskriftsartikel (refereegranskat)abstract
- Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.
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| 5. |
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| 6. |
- Qin, Shiyi, et al.
(författare)
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Resource recovery and biorefinery potential of apple orchard waste in the circular bioeconomy
- 2021
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Ingår i: Bioresource Technology. - : Elsevier BV. - 0960-8524 .- 1873-2976.
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Tidskriftsartikel (refereegranskat)abstract
- In this review investigate the apple orchard waste (AOW) is potential organic resources to produce multi-product and there sustainable interventions with biorefineries approaches to assesses the apple farm industrial bioeconomy. The thermochemical and biological processes like anaerobic digestion, composting and , etc., that generate distinctive products like bio-chemicals, biofuels, biofertilizers, animal feed and biomaterial, etc can be employed for AOW valorization. Integrating these processes can enhanced the yield and resource recovery sustainably. Thus, employing biorefinery approaches with allied different methods can link to the progression of circular bioeconomy. This review article mainly focused on the different biological processes and thermochemical that can be occupied for the production of waste to-energy and multi-bio-product in a series of reaction based on sustainability. Therefore, the biorefinery for AOW move towards identification of the serious of the reaction with each individual thermochemical and biological processes for the conversion of one-dimensional providences to circular bioeconomy.
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| 7. |
- Yuan, Feifei, et al.
(författare)
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Changes in precipitation extremes over the source region of the Yellow River and its relationship with teleconnection patterns
- 2020
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Ingår i: Water. - 2073-4441. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- Precipitation extremes and their underlying causes are important processes to understand to plan appropriate adaptation measures. This paper presents an analysis of the spatiotemporal variability and trend of precipitation extremes in the important source region of the Yellow River and explores the connection to global teleconnection patterns and the 850-mb vector wind. Six indices for precipitation extremes were computed and analyzed for assessment of a changing regional climate. Results showed that these indices have a strong gradient from the northwest to the southeast part for the period 1961-2015, due to the great influence from the south-easterly summer monsoon flow. However, no statistically significant trends were found for the defined indices at the majority of stations, and their spatial distribution are noticed by irregularly mixed positive and negative changes except for the maximum number of consecutive wet days (CWD). Singular value decomposition analysis revealed that the precipitation extreme indices-including annual total precipitation when daily precipitation >95th percentile (R95p), annual count of days with daily precipitation ≥10 mm (R10mm), annual maximum consecutive 5-day precipitation (R5d), total precipitation divided by the number of wet days (SDII), and CWD-are negatively related to the El Nino-Southern Oscillation (NINO 3.4) in the first mode, and the maximum number of consecutive dry days (CDD) is positively related to the Scandinavian pattern in the second mode at 0.05 significance level. The 850-mb vector wind analysis showed that the southwestern monsoon originating from the Indian Ocean brings sufficient moisture to this region. Furthermore, the anti-cyclone in the western part of the North Pacific plays a significant role in the transport of moisture to the source region of the Yellow River. The links between precipitation extremes and teleconnection patterns explored in this study are important for better prediction and preparedness of climatic extremes.
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| 8. |
- Zhao, Yadong, 1985-, et al.
(författare)
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Cellulose nanofibrils-stabilized food-grade Pickering emulsions : Clarifying surface charge's contribution and advancing stabilization mechanism understanding
- 2024
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Ingår i: Food Hydrocolloids. - : Elsevier BV. - 0268-005X .- 1873-7137. ; 152
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Tidskriftsartikel (refereegranskat)abstract
- Pickering emulsions stabilized by cellulose nanofibrils (CN) have sparked significant attention, however the fundamental mechanisms underpinning the stabilization process remain insufficiently elucidated. Focusing on an academic debate of surface charge's contribution to stabilization, this study first explored how the varying carboxyl group contents of TEMPO-oxidized CN (TCNs) impacted Pickering emulsions' formation and stability. TCNs with 662 μmol/g carboxyl groups exhibited distinctive attributes, including larger particle sizes (322 nm in length), improved thermal stability (maximum decomposition temperature of 317 °C), and increased viscosity (1.57 Paִִ⋅s) compared to their counterparts with 963–1011 μmol/g charge density. Notably, the former one, with a larger three-phase contact angle (51.5°), higher interfacial tension, and greater detachment energy (21.69 × 10−18 J), resulted in a homogeneous dispersion of spherical oil droplets and super-stable Pickering emulsions with a consistent emulsifying index of 100% over 30 days. These findings clearly clarified that TCNs with a lower charge density exhibit superior emulsifying properties. In addition, for the first time, a distinct oil droplet-decorated fibrillar structure was observed, probably suggesting that TCNs might be able to serve as anchoring matrixes to guide the distribution of oil droplets. These structures seemed to impeded the migration and accumulation of the oil droplets, consequently enhancing the stability of the resulting Pickering emulsions. To sum, this study clearly elucidated the role of surface charge in stabilizing cellulose-based Pickering emulsions and proposed a new model to expound the cellulose-oil interaction mechanisms, thus providing new theoretical and practical insights on utilization of CN as highly effective emulsifier for super-stable food-grade Pickering emulsions.
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