Nanjing Normal University
Recent publications
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.
Good's buffers have been widely applied in cell/organ culture over the past half a century as biocompatible pH stabilizers. However, the emergence of severe adverse effects, such as cellular uptake, lysosomal autophagic activation, and visible light-induced cytotoxicity, raises serious questions over its biocompatibility while underlying mechanism was unclear. Here we report that riboflavin (RF, component of cell culture medium) generates ¹O2, ·OH, and O2•- under visible light exposure during regular cell manipulation. These short half-life reactive oxygen species (ROS) react with tertiary amine groups of HEPES, producing 106.6 μM of H2O2. Orders of magnitude elevated half-life of ROS in the medium caused severe cytotoxicity and systematic disorder of normal cell functions. We have further designed and validated zwitterionic betaines as the new generation biocompatible organic pH buffers, which is able to completely avoid the adverse effects that found on HEPES and derivate Good's buffers. These findings may also open a new avenue for zwitterionic betaine based materials for biomedical applications.
Development of multiple detection methods to monitor non-steroidal anti-inflammatory drugs (NSAIDs) in food is an effective way to protect human health. Here, we aimed to synthesize fluorescent artificial receptors by molecular imprinting technique to construct a simultaneous detection system targeting NSAIDs. Rhodamine B and fluorescein-functionalized silanes were employed as the fluorescence signal reporters for naproxen and ketoprofen, respectively. Two fluorescent molecularly imprinted polymers (FMIPs) were obtained with high specificity, giving cross-reactivity factors of 6.4-15.8 (naproxen) and 2.6-25.6 (ketoprofen). Both FMIPs also displayed rapid response time (5 min) and high sensitivity (detection limit at ∼ nM level). A simultaneous detection system was constructed based on the FMIPs and applied for sensing the spiked NSAIDs in real samples, showing recoveries of 71-119 %, comparable with the HPLC methods (70-113 %). In summary, use of different FMIPs to construct simultaneous detection systems is practicable, and provides a flexible way for sensing multiple hazards in food samples.
In-depth studies of the extraction mechanism using deep eutectic solvents (DES), especially extraction through the formation of a deep eutectic system (DESys), revealed commonalities between the DES- and ionic liquids (IL)-based extraction systems. New applications of ILs and DES for extraction of nutritional natural products were presented. In this study, the extraction behavior of choline chloride (ChCl) and 1-(2-hydroxyethyl)-3-methylimidazolium chloride ([HMIm][Cl]) in DES and IL, respectively, in mechanochemical extraction of target compounds from Moringa oleifera leaves was systematically studied. The results suggested that both extraction methods were based on the formation of a DESys, either a normal DESys or an IL DESys. Considering the DESys-based one-step extraction improves the extraction efficiency and reduces the preparation time, the same idea can be used in IL for performance improvement. By formation of a new IL deep eutectic system based on hydrogen bond interaction in extraction, similar improvement was obtained.
Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban–rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.
Dissolved organic matter (DOM) is an essential component of environmental systems. It usually originates from two end-members, including allochthonous and autochthonous sources. Previously, links have been established between DOM origins/sources and its biogeochemical reactivities. However, the influence of changes in DOM characteristics driven by end-member mixing on DOM biogeochemical reactivities has not been clarified. In this study, we investigated variations of DOM reactivities responding to the dynamics of DOM characteristics induced by different mixing ratios of two DOM end-members derived from humic acid (HA) and algae, respectively. Four biogeochemical reactivities of DOM were evaluated, including biodegradation, photodegradation, ·OH production, and redox capacity. Results showed that the variations of DOM characteristics due to the two end-members mixing significantly impact its biogeochemical reactivities. However, not all spectral parameters and reactivities followed the conservative mixing behavior. In contrast to reactivities of ·OH production and redox capacity, mixed samples showed apparent deviations from conservative linear relationships in biodegradation and photodegradation due to the interaction between the two end-members. Regarding the role of DOM properties influencing reactivity changes, peak A and M was recognized as the most stable parameters. However, peak C and SUVA254 were identified as the most vital contributors for explaining DOM reactivity variations. These findings suggest that a general model for describing the dynamic relationship between DOM source and reactivity cannot be proposed. Thus, the dynamics of DOM reactivity in diverse ecosystems cannot be estimated simply by the “plus or minus” of the reactivity from individual end-member. The effect of end-member mixing should be evaluated in a given reactivity instead of generalization. This study provides important insights for further understanding the dynamics of DOM’s environmental role in different ecosystems influenced by variations of source inputs. In future, more field investigations are needed to further verify our findings in this study, especially in the scenario of end-member mixing.
Wetlands are considered the hotspots for mercury (Hg) biogeochemistry, garnering global attention. Therefore, it is important to review the research progress in this field and predict future frontiers. To achieve that, we conducted a literature analysis by collecting 15,813 publications about Hg in wetlands from the Web of Science Core Collection. The focus of wetland Hg research has changed dramatically over time: 1) In the initial stage (i.e., 1959–1990), research mainly focused on investigating the sources and contents of Hg in wetland environments and fish. 2) For the next 20 years (i.e., 1991–2010), Hg transformation (e.g., Hg reduction and methylation) and environmental factors that affect Hg bioaccumulation have attracted extensive attention. 3) In the recent years of 2011–2022, hot topics in Hg study include microbial Hg methylators, Hg bioavailability, methylmercury (MeHg) demethylation, Hg stable isotope, and Hg cycling in paddy fields. Finally, we put forward future research priorities, i.e., 1) clarifying the primary factors controlling MeHg production, 2) uncovering the MeHg demethylation process, 3) elucidating MeHg bioaccumulation process to better predict its risk, and 4) recognizing the role of wetlands in Hg circulation. This research shows a comprehensive knowledge map for wetland Hg research and suggests avenues for future studies.
Normalized weight vector determination under bi-polar preferences is important in multi-criteria decision making and its related evaluation problems. In order to determine weights for the elements in partially ordered set which can embody bi-polar preferences, some new methods such as the ordered weighted averaging (OWA) aggregation on lattice using three-set formulation have been proposed. However, when there are no posets and orders but fuzzy relations available, some new effective generalized methods should be proposed. This work differentiates two types of special fuzzy relations, called incomplete fuzzy relation and contra-dictive fuzzy relation. Two objective methods to derive incomplete fuzzy relation from a set of vectors and basic uncertain information (BUI) granules are introduced. Two scaling methods to transform contradictive fuzzy relation into incomplete fuzzy relation are suggested. Based on those derived fuzzy relations and given convex/concave basic unit monotonic (BUM) functions, some weights allocation methods are proposed which can well embody the bi-polar preferences of decision makers. The method further generalizes the OWA aggregation on lattice. Some mathematical properties, four different instances and some numerical examples with application backgrounds or potentials are also provided.
Exhaled breath (EB) may contain metabolites that are closely related to human health conditions. Real time analysis of EB is important to study its true composition, however, it has been difficult. A robust ambient ionization mass spectrometry method using a modified direct analysis in real time (DART) ion source was developed for the online analysis of breath volatiles. The modified DART ion source can provide a confined region for direct sampling, rapid transmission and efficient ionization of exhaled breath. With different sampling methods, offline analysis and near real-time evaluation of exhaled breath were also achieved, and their unique molecular features were characterized. High resolution MS data aided the putative metabolite identification in breath samples, resulting in hundreds of volatile organic compounds being identified in the exhalome. The method was sensitive enough to be used for monitoring the breath feature changes after taking different food and over-the-counter medicine. Quantification was performed for pyridine and valeric acid with fasting and after ingesting different food. The developed method is fast, simple, versatile, and potentially useful for evaluating the true state of human exhaled breath.
In this study, a laboratory sediment resuspension simulation system (RSS) was used to investigate the effect of wind-induced (5.3 and 8.7 m/s) repeated sediment resuspension on internal phosphorus (P) in sediment treated by dredging and La-modified clay (LMC) based inactivation in a shallow lake. The results indicated that the dredged sediment had a better capability to resist repeated wind disturbance than the LMC-inactivated sediment. The concentration of suspended solids (SS) in the inactivated treatment (70.7 mg/L) was 1.7 times that in the dredged treatment (41.7 mg/L) under moderate wind disturbance, and was similar for the two treatments under strong wind disturbance. Nevertheless, dredging performed better than inactivation in reducing 44 % total phosphorus (TP) in overlying water (43 % reduction by inactivation) and 31 % mobile P in sediment (27 % reduction by inactivation) under moderate wind disturbance (p < 0.01) compared with control treatment. Inactivation performed better in reducing 57 % P in porewater (52 % reduction by dredging) and 81 % P flux (13 % reduction by dredging) (p < 0.01) compared with control treatment. Surprisingly, under strong wind disturbance, LMC inactivation could still reduce 18 % P in porewater and 75 % P flux (p < 0.01), whereas dredging increased 25 % P in porewater and 13 % P flux compared with control treatment (p < 0.01). LMC inactivation can increase the sediment P adsorption capacity and decrease the equilibrium P concentration (EPC0) when compared with control treatment. The contrasting control effects of the two methods were probably due to the different P buffer mechanisms for the two treated sediment. The wind disturbance-induced sediment P release was controlled by the inactivation of Fe and co-inactivation of Fe and La at the surface of dredged and LMC-inactivated sediments, respectively. The results of this study indicated inactivation can be a better method to control sediment internal P loading with repeated strong wind disturbances in eutrophic lakes.
Developers need to reuse Web services and create mashups suitable for various scenarios. Currently, it relies on the developer's adequate domain knowledge to be able to find services and verify their compatibility. Although service recommendation systems already exist to assist them, inexperienced developers may not be able to adequately express their requirements, resulting in inappropriate and incompatible recommendations. To tackle this problem, we define a service-keyword correlation graph (SKCG) to capture the relationship between services and keywords , and the compatibility among services. Then, we propose keyword-based Deep Reinforced Steiner Tree Search (K-DRSTS) to recommend services for mashup creation. K-DRSTS models the task of service discovery as a Steiner tree search problem against SKCG. Leveraging deep reinforcement learning, K-DRSTS provides an efficient solution for solving the NP-hard search problem of the Steiner tree. Extensive experiments on real-world data sets have shown the effectiveness of K-DRSTS.
We propose a label enhancement model to solve the multi-label learning (MLL) problem by using the incremental subspace learning to enrich the label space and to improve the ability of label recognition. In particular, we use the incremental estimation of the feature function representing the manifold structure to guide the construction of the label space and to transform the local topology from the feature space to the label space. First, we build a recursive form for incremental estimation of the feature function representing the feature space information. Second, the label propagation is used to obtain the hidden supervisory information of labels in the data. Finally, an enhanced maximum entropy model based on conditional random field is established as the objective, to obtain the predicted label distribution. The enriched label information in the manifold space obtained in first step and the estimated label distributions provided in second step are employed to train this enhanced maximum entropy model by a gradient-descent iterative optimization to obtain the label distribution predictor’s parameters with enhanced accuracy. We evaluate our method on 24 real-world datasets. Experimental results demonstrate that our label enhancement manifold learning model has advantages in predictive performance over the latest MLL methods.
Mitochondria have been shown to play a variety of roles in tumorigenesis and progression, and thus present an attractive therapeutic target for cancer treatment. Herein, we developed multifunctional celastrol (cela) nanoparticles with mitochondrial alkaline drug release and feature a positive core and a negative outer layer. First, the mitochondria-targeting material, triphenyl phosphonium-tocopherol polyethylene glycol succinate (TPP-TPGS, TT), was synthesised, and TT/[email protected] nanoparticles (NPs) were prepared. Then, the positive charge on the surface was neutralised using tumor-targeting, pH-sensitive chondroitin sulfate-folic acid (CS-FA) to generate CS-FA/TT/[email protected] NPs. Characterisation revealed these NPs to be globular particles with smooth surfaces and an average diameter of 100 nm - characteristics that could enhance their uptake by 4T1 murine breast cancer cells. After CS-FA/TT/[email protected] NPs entered 4T1 cells, CS-FA was degraded and the positively charged TT/[email protected] NPs were exposed and able to target the mitochondria after performing lysosomal escape. Celastrol is released upon exposure of TT/[email protected] NPs to the alkaline mitochondrial environment, and assessment of mitochondrial respiration and membrane potential revealed celastrol to induce mitochondrial injury and damage. Exposure of 4T1 cells to these nanoparticles significantly upregulated proapoptotic protein expression in vitro, and generated robust anticancer effects in vivo. Together, these results indicate that CS-FA/TT/[email protected] NPs with mitochondrial alkaline drug release represent a promising advancement in breast cancer treatment.
Entrepreneurship education and pedagogic reforms advocating the increased use of progressive educational methods have been promoted by the Chinese government. In practice, this has led to a fusion of the more traditional teaching approach and more progressive approaches. This has led to calls for entrepreneurship education to be contextualized within the Chinese context, against the backdrop of the progressive pedagogic reforms. This paper addresses this by exploring how Chinese educators are responding to directives encouraging progressive pedagogic entrepreneurship education, by applying the lens of Sfard's knowledge acquisition and participation-orientation learning metaphors. Interviews were conducted with fifteen educators and analysis of their narratives of practice was undertaken to identify knowledge acquisition and participation-orientation metaphors to elicit the approaches adopted in the classroom. The results indicate that both acquisition and participation approaches are adopted by educators, but in a way that reflects the traditional and cultural heritage that values knowledge. Educators still relied heavily on the transmission-acquisition metaphor, however the encouragement to introduce more progressive practices could be observed in two ways, the constructivist acquisition metaphor, and the participation metaphor. The former appeared more developed and the latter less so, although both are desirable in the light of the education reforms.
Large amounts of microplastics (MPs) accumulate in the sludge anaerobic digestion system after being treated by the wastewater treatment plants, inevitably leading to aging and chemicals leaching. However, no information is available about the effects of aged MPs and leachates on the anaerobic digestion of sludge. In this study, the effects of different aged MPs ((polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polylactic acid (PLA)) and leachates on anaerobic methanogenesis of sludge were investigated. PLA-related treatments caused no adverse effects on anaerobic digestion. While PE-, PET-, and PVC-related treatments significantly inhibited methane production with an order of leachates (26.4-42.4 %) > MPs (16.1-22.9 %) > aged MPs (2.4-11.8 %). For different leachates, PET leachate caused the strongest inhibitory effects. The same order was found for the methane potential and hydrolysis coefficient. These results suggest that the inhibition of MPs on methanogenesis is mainly caused by the leachates. Based on biochemical and microbial community analysis, the primary mechanism is that the leachates induce oxidative stress, damaging microbial cells and reducing microbial activity, consequently inhibiting methanogenesis. Furthermore, via effect-directed analysis, methyl benzoate (MB), dimethyl phthalate (DMP), and 2,4-Di-tert-butylphenol (DTBP) were identified as key components in the PET-leachate inhibiting anaerobic methanogenesis.
Due to the physiological connection with photosynthesis, sun-induced chlorophyll fluorescence (SIF) provides a promising indicator of vegetation physiological changes caused by environmental stress (e.g. water deficiency). SIF response to crop physiological alterations under water stress is complicated by concurrent non-physiological changes. The non-physiological variation stems from crop structure, leaf optical traits (i.e. pigments, leaf water content, and dry matter), and sun-target geometry. This study aims to disentangle the physiological effect from the non-physiological effect on SIF variations caused by water stress, providing more direct insights into the mechanism of SIF response to stress. We parameterized the radiative transfer model (RTM) SCOPE with top-of-canopy (TOC) reflectance and SIF measurements to decouple the joint effects on TOC SIF in sugar beet. SIF and reflectance data were acquired over irrigated and water-stressed plots using an Unmanned Aerial Vehicle (UAV) on two consecutive days. The non-physiological response was quantified with SCOPE by fitting the model parameters to the TOC reflectance measurements. Subsequently, fluorescence emission yield (ΦF) was estimated using SIF measurements to represent the actual physiological status. The results demonstrate that SIF variation caused by water stress both at 687 nm and 760 nm was affected by both the physiological alterations in ΦF and the non-physiological changes. At both 687 nm and 760 nm, the non-physiological contribution to SIF variations was lower than the contribution of ΦF variation induced by water stress. The lower non-physiological contribution was mainly due to the weak combined effect of the fraction of photosynthetically active radiation absorbed by leaf chlorophyll (fAPARchl) and the fluorescence escape fraction (fesc) on SIF responses. This study provided direct insights into the plant physiological status under water stress and further indicated the ability of the approach of combining RTMs, canopy reflectance, and SIF measurements to support the scalable quantitative use of SIF from the leaf to the ecosystem level.
Background: Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. Methods: We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. Results: We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. Conclusion: This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.
Regulating the magnetic properties through structural transformation and multifunctional magnetic bistable materials attracts increasing interest in the research area of molecular magnets. Herein, we have obtained a two-dimensional Dy3+ metal–organic framework {[Dy2(H2L)2(H2O)(CH3OH)](ClO4)2·H2O}n (1) and three dinuclear Dy2 complexes 2–4 based on the new diacylhydrazone ligand H4L. The structural analyses show that the Dy3+ ions have a coordination geometry of Hula-hoop (C2v) and are coordinated by one or two trans phenoxy groups of the ligand H3L− or H2L2− with Dy–Ophenoxy bond distances in the range of 2.175–2.252 Å. The 2D complex (1) exhibits excellent thermal stability, a broad ligand-based fluorescence emission band and selective adsorption capacity for rhodamine B. The dinuclear Dy2 complexes (2–4) undergo consecutive single-crystal to single-crystal transformation in the mother liquor. Alternating-current (ac) susceptibility measurements reveal that 1–3 display typical single-molecule magnetic properties with effective energy barriers of 91.6 K, 106.3 K and 91.9 K, respectively. Ab initio calculations evidence good magnetic anisotropy with the calculated energy of the first excited state as high as 392.5 K (272.8 cm−1) for 1, 257.5 K (179.0 cm−1) for 2 and 250.2 K (173.9 cm−1) for 3, respectively.
Due to the rising demand for green energy, bioethanol has attracted increasing attention from academia and industry. Limited by the bottleneck of bioethanol yield in traditional corn starch dry milling processes, an increasing number of studies focus on fully utilizing all corn ingredients, especially kernel fiber, to further improve the bioethanol yield. This mini-review addresses the technological challenges and opportunities on the way to achieving the efficient conversion of corn fiber. Significant advances during the review period include the detailed characterization of different forms of corn kernel fiber and the development of off-line and in-situ conversion strategies. Lessons from cellulosic ethanol technologies offer new ways to utilize corn fiber in traditional processes. However, the commercialization of corn kernel fiber conversion may be hampered by enzyme cost, conversion efficiency, and overall process economics. Thus, future studies should address these technical limitations.
Recently, researchers have focused on various factors influencing work engagement, particularly in the EFL context. In this vein, this study was carried out to investigate the relationship among proactive personality, flow, and work engagement in China. In so doing, three instruments including Proactive Personality Scale, Work-Related Flow Inventory (WOLF), and Work and Well-Being Survey (UWES) were used. Employing a convenience sampling method, 350 English teachers were selected from 20 provinces in China. Structural equation modeling (SEM) was utilized to analyze the obtained data. The findings demonstrated a positive correlation among EFL teachers’ flow, proactive personality, and work engagement. The results of SEM also indicated that work engagement could be predicted by both flow and proactive personality; however, flow had more predictive power. The findings from this study have both theoretical and practical implications for second/foreign language researchers and different stakeholders.
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1,784 members
Bojian Zhong
  • College of Life Sciences
Hu Chuan-Peng
  • School of Psychology
Nan Shen
  • School of Environment
Fusheng Ma
  • Department of Physics
Zhi-Yuan Gu
  • School of Chemistry and Materials Science
1 Wenyuan, 210023, Jiangsu, China