Search

Anxiety disorder is a universal disease related to neuro-inflammation. Solanesol has shown positive effects because of its anti-inflammatory, anti-tumor, and anti-ulcer properties. This study focused on determining whether solanesol could ameliorate anxiety-like behaviors in a mouse model of neuro-inflammation and identify its working targets. Complete Freund's adjuvant (CFA)-induced mice that were intra-peritoneally administered with solanesol (50 mg/kg) for 1 week showed a statistically significant reduction in anxiety-like behaviors, as measured by open field and elevated plus-maze tests. Western blot analysis revealed that CFA-induced upregulation of the levels of pro-inflammatory cytokines interleukin (IL)-1β and tumor necrosis factor α (TNF-α), which played crucial roles in regulating anxiety, returned to normal in the anterior cingulate cortex (ACC) after solanesol treatment. The level of T cell-restricted intracellular antigen-1 (TIA1), a key component of stress granules, also decreased in the ACC. Moreover, immunofluorescence results indicated that solanesol suppressed CFA-induced microglial and astrocytic activation in the ACC. CFA was injected in the hind paws of TIA1Nestin conditional knockout (cKO) mice to confirm whether TIA1 is a potential modulatory molecule that influences pro-inflammatory cytokines and anxiety-like behaviors. Anxiety-like behaviors could not be observed in cKO mice after CFA injection with IL-1β and TNF-α levels not remarkedly increasing. Our findings suggest that solanesol inhibits neuro-inflammation by decreasing the TIA1 level to reduce IL-1β and TNF-α expression, meanwhile inhibiting microglial and astrocytic activation in the ACC and ultimately ameliorating anxiety-like behaviors in mice. [ABSTRACT FROM AUTHOR]

Ceramsites are extensively employed as substrates for adsorbents in studies focused on phosphorus adsorption, leaving ceramsites crafted from diatomite less explored. Diatomite-based ceramsite, with its distinct porous architecture and surface silanol functionalities, is adept at supporting a variety of metal oxides, presenting a distinct advantage over other ceramsite variants. In light of this, the present study embarked on producing diatomite-based ceramsite using diatomite as the foundational material, subsequently enhancing it through the incorporation of Fe, thus yielding an Fe-modified diatomite-based ceramsite. Through adsorption testing, the modified ceramsite demonstrated a significantly improved adsorption capacity of 4.06 mg P/g, marking a substantial enhancement from the initial capacity of 0.9 mg P/g. The process of phosphorus adsorption exhibited a strong alignment with the Langmuir isotherm model and the pseudo-second-order kinetic model. In-depth analyses employing XRD, FTIR, zeta potential, and XPS techniques have revealed that the principal mechanisms driving the adsorption process are centered on interactions involving electrostatic forces, the formation of chemical precipitates, and the exchange of ligands. This investigation not only opens new avenues for the application of diatomite-based ceramsite but also lays down a theoretical foundation for its modification, thereby enriching the spectrum of its utility. [ABSTRACT FROM AUTHOR]

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

Elucidating the response mechanism of vegetation change trends is of great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking the Shendong mining area as an example, based on the Google Earth Engine cloud platform, this study used the kernel Normalized Vegetation Index (kNDVI) to study the spatiotemporal change characteristics of vegetation cover during 1994–2022. Then, it carried out an attribution analysis through the partial derivative analysis method to explore the driving mechanism behind vegetation greening. The results showed that (1) the growth rate of vegetation cover change from 1994 to 2022 was 0.0052/a. The area with an upward trend of kNDVI accounted for 94.11% of the total area of the study area. The greening effect was obvious, and the kNDVI change would continue to rise. (2) Under the scenario of regional climate warming and humidifying, kNDVI responds slightly differently to different climatic factors, and kNDVI is positively correlated with temperature and precipitation in 85.20% of the mining area. The average contribution of precipitation, temperature, and human activities to kNDVI change in the Shendong mining area were 0.00094/a, 0.00066/a, and 0.0036/a, respectively. The relative contribution rates of human activities and climate change were 69.23% and 30.77%, respectively. Thus, human activities are the main driving factor for the changing of vegetation cover in this mining area, and climate change is the secondary driving factor. (3) The dynamic change in land use presents an increase in forest area under the ecological restoration project. The results of this study can provide a scientific basis for the future ecological construction of the Shendong mining area and help in the realization of regional green sustainable development goals. [ABSTRACT FROM AUTHOR]

Underground coal mining in coal-grain overlapped areas leads to land subsidence and deformation above the goaf, damaging cultivated land. Understanding the influencing process of coal mining on cultivated land and crops is important for carrying out timely land reclamation and stabilizing crop yield. Research has been carried out by using crop growth parameters to evaluate the damaging degree of cultivated land when the mining subsidence is stable, but few studies focus on the influence of land damage on crop growth when the subsidence is unstable during coal mining. Therefore, this study tracked the three growth stages of winter wheat by using UAV multispectral imagery to explore the dynamic influence of underground mining on winter wheat aboveground biomass (AGB). Firstly, a winter-wheat-AGB estimation model (R2 = 0.89, RMSE = 2.18 t/ha) was developed by using vegetation indexes (VIs), textures, and terrain data extracted from UAV imagery. Secondly, based on the winter-wheat-AGB estimation model, the winter wheat AGB was successfully estimated and mapped at different growth stages. The AGB of winter wheat in the coal mining-affected area was approximately 5.59 t/ha at the reviving stage, 8.2 t/ha at the jointing stage, and 15.6 t/ha at the flowering stage. Finally, combined with the progress of coal mining, the dynamic changing process of crops during underground mining can be inferred by analyzing the spatiotemporal variation in winter wheat AGB. Results showed that, in the dip direction, winter wheat AGB at the flowering stage was the highest at the compression zone, followed by the inner stretch zone, outer stretch zone, and neutral zone. The distance from the waterlogged area and the existence of cracks were found to be the important moderating variables affecting the crop growth status in the mining subsidence area. In the strike direction, there were significant differences in the wheat AGB-affected area as the mining proceeded. Even areas where AGB had previously significantly increased gradually transitioned to significant decreases with the end of mining. The research explores the dynamic changes in winter wheat AGB and land damage status during coal mining. It provides a rapid and non-destructive land-damage-monitoring method to protect cultivated land in mining areas. [ABSTRACT FROM AUTHOR]

Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergence of deep learning has dramatically helped in plant disease detection. However, the accuracy of deep learning models largely depends on the quantity and quality of training data. To solve the inter-class imbalance problem and improve the generalization ability of the classification model, this paper proposes a cycle-consistent generative-adversarial-network-based Transformer model to generate diseased tomato leaf images for data augmentation. In addition, this paper uses a Transformer model and densely connected CNN architecture to extract multilevel local features. The Transformer module is utilized to capture global dependencies and contextual information accurately to expand the sensory field of the model. Experiments show that the proposed model achieved 99.45% accuracy on the PlantVillage dataset. The 2018 Artificial Intelligence Challenger dataset and the private dataset attained accuracies of 98.30% and 95.4%, and the proposed classification model achieved a higher accuracy and smaller model size compared to previous deep learning models. The classification model is generalizable and robust and can provide a stable theoretical framework for crop disease prevention and control. [ABSTRACT FROM AUTHOR]

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + Hdsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R2 increase. The performance of Hdsm was verified by including it either as one input feature or not. Results showed that Hdsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R2. Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + Hdsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R2 of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring. [ABSTRACT FROM AUTHOR]

Mn-based catalysts have attracted significant attention in the field of catalytic research, particularly in NOx catalytic reductions and CO catalytic oxidation, owing to their good catalytic activity at low temperatures. In this review, we summarize the recent progress of Mn-based catalysts for the removal of NOx and CO. The effects of crystallinity, valence states, morphology, and active component dispersion on the catalytic performance of Mn-based catalysts are thoroughly reviewed. This review delves into the reaction mechanisms of Mn-based catalysts for NOx reduction, CO oxidation, and the simultaneous removal of NOx and CO. Finally, according to the catalytic performance of Mn-based catalysts and the challenges faced, a possible perspective and direction for Mn-based catalysts for abating NOx and CO is proposed. And we expect that this review can serve as a reference for the catalytic treatment of NOx and CO in future studies and applications. [ABSTRACT FROM AUTHOR]

The effects of four distinct zinc species (ZnCl2, Zn(NO3)2, ZnSO4, and ZnCO3) on a Mn-Ce co-doped CuX (MCCX)catalyst were investigated and contrasted in the low-temperature NH3-SCR process. Aqueous solutions of ZnCl2, Zn(NO3)2, ZnSO4, and ZnCO3 were used to poison the catalysts. The catalytic activity of all catalysts was assessed, and their physicochemical properties were studied. There was a notable drop trend in catalytic activity in the low temperature range (200 °C) after zinc species poisoning on MCCX catalyst. Interestingly, ZnSO4 and ZnCO3 on MCCX catalyst had more serious effect on catalytic activity than Zn(NO3)2 and ZnCl2 from 150 °C to 225 °C, in which NO conversion of the MCCX-Zn-S and MCCX-Zn-C catalysts dropped about 20–30% below 200 °C compared with the fresh MCCX catalyst. The zeolite X structure was impacted by Zn species doping on the MCCX catalyst, and the Zn-poisoned catalysts had less acidic and lower redox ability than fresh Mn-Ce/CuX catalysts. Through the results of in situ DRIFTS spectroscopy experiments, all catalysts were governed by both Langmuir–Hinshelwood (L–H) and Eley–Rideal (E–R) mechanisms, and the possible mechanism for poisoning the Mn-Ce/CuX catalyst using various zinc species was revealed. [ABSTRACT FROM AUTHOR]

On a cold single-phase test stand, the effect of central air on the exit flow field of Babcock, Germany, burner was investigated. Industrial measurements were taken for a 700 MW wall-fired pulverized-coal utility boiler with above burners. Gas temperature, gas composition and concentration in the burner area were measured at 444 MW, 522 MW and 645 MW loads, respectively. Only when the central air mass flow was zero did a center reflux zone exist in the burner outlet area. The steady combustion of faulty coal was aided by early mixing of primary and secondary air, which was made possible by the decreased central air mass flow. At all different loads, the pulverized coal in center region took a long distance to ignite. The temperature in center steadily dropped as central air mass flow decreased, while the temperature in secondary air region gradually rose. Within 1.5 m from the primary air duct outlet, the highest CO concentration was 25 ppm and the highest O2 concentration was close to 21% under all loads. The gas concentration near sidewall was more influenced by load. With all valves opening of burner center air at 30%, the boiler was able to operate safely and stably without oil at a load of 262 MW. The furnace chamber temperature in burner area reached 1056.1 °C. [ABSTRACT FROM AUTHOR]

Featured Application: The method proposed can be used for the identification and control of multivariable nonlinear systems. A novel dynamic inverse control method based on a dynamical neural network (DNN) is proposed for the trajectory tracking control of a flexible air-breathing hypersonic vehicle (FAHV). Firstly, considering that the accurate model of FAHV is difficult to obtain, the FAHV is regarded as a completely unknown system, and a DNN is designed to identify its nonlinear model. On the basis of Lyapunov's second law, the weight vectors of the DNN are adaptively updated. Then, a dynamic inverse controller is designed based on the identification model, which avoids the transformation of the nonlinear model of FAHV, thereby simplifying the controller design process. The simulation results verify that the DNN can identify FAHV accurately, and velocity and altitude can track the given reference signal accurately with the proposed dynamic inverse control method. Compared with the back-stepping control method, the proposed method has better tracking accuracy, and the amplitude of the initial control law is smaller. [ABSTRACT FROM AUTHOR]

In this study, a series of zeolite-X-supported different crystal phases of MnO2 (α-MnO2, β-MnO2, γ-MnO2, and σ-MnO2) catalysts were prepared via a solid-state diffusion method and high-heat treatment method to explore their low-temperature NH3-SCR performance. All of the catalysts featured typical octahedral zeolite X structures and manganese dioxides species of various crystal types dispersed across the support surface. Throughout the entire temperature range of the reaction, γ-MnO2/X catalyst had the highest NO conversion. Additionally, β-MnO2/X, γ-MnO2/X, and σ-MnO2/X catalysts had nearly 100% of N2 selectivity, whereas the α-MnO2/X catalyst had the lowest N2 selectivity (about 90%) below 125 °C. Moreover, the γ-MnO2/X catalyst demonstrated superior acidity capacity and reduction ability compared with the other three catalysts. All the catalysts contained the essential intermediates NH2NO and NH4NO3 species, which are essential to the SCR reaction. More acid sites and nitrate species existed on the γ-MnO2/X catalyst than on the other catalysts, thereby boosting the SCR reaction. [ABSTRACT FROM AUTHOR]

Simple Summary: Humanin is a small mitochondrial-derived peptide still under study, with a number of potential therapeutic applications for various age-related diseases. It plays an important role in the body against several pathophysiological conditions; for instance, humanin has been shown to exert neuroprotective effects, which may make it a promising therapeutic candidate for Alzheimer's and Parkinson's disease. Moreover, it can help reduce inflammation and oxidative stress, thus interfering with the development of cardiovascular conditions and autoimmune diseases. Humanin also helps maintain the proper functioning of mitochondria, the dysfunction of which can contribute to a number of health problems, including neurodegenerative diseases and metabolic disorders, such as diabetes and obesity. It has been shown to improve glucose metabolism and insulin sensitivity in animal models. Treatment with humanin on mice is able to increase both their lifespan and health span. The mechanisms behind the protective effects of humanin in different diseases are slowly being unveiled and appear to be connected with autophagy and cytoprotective activity. Background: Senescence is a cellular aging process in all multicellular organisms. It is characterized by a decline in cellular functions and proliferation, resulting in increased cellular damage and death. These conditions play an essential role in aging and significantly contribute to the development of age-related complications. Humanin is a mitochondrial-derived peptide (MDP), encoded by mitochondrial DNA, playing a cytoprotective role to preserve mitochondrial function and cell viability under stressful and senescence conditions. For these reasons, humanin can be exploited in strategies aiming to counteract several processes involved in aging, including cardiovascular disease, neurodegeneration, and cancer. Relevance of these conditions to aging and disease: Senescence appears to be involved in the decay in organ and tissue function, it has also been related to the development of age-related diseases, such as cardiovascular conditions, cancer, and diabetes. In particular, senescent cells produce inflammatory cytokines and other pro-inflammatory molecules that can participate to the development of such diseases. Humanin, on the other hand, seems to contrast the development of such conditions, and it is also known to play a role in these diseases by promoting the death of damaged or malfunctioning cells and contributing to the inflammation often associated with them. Both senescence and humanin-related mechanisms are complex processes that have not been fully clarified yet. Further research is needed to thoroughly understand the role of such processes in aging and disease and identify potential interventions to target them in order to prevent or treat age-related conditions. Objectives: This systematic review aims to assess the potential mechanisms underlying the link connecting senescence, humanin, aging, and disease. [ABSTRACT FROM AUTHOR]

In this experiment, we investigated the effects of different reactions on the growth process and morphology of h-MoO3 and α-MoO3, and their optical properties and photocatalytic activities were also investigated. Orthogonal experiments were designed to investigate the effects of four influencing factors, namely the amount of ammonium molybdate tetrahydrate (AHM), the type of acid, the reaction temperature and the holding time, on the morphology of h-MoO3 by a microwave hydrothermal method. The phase and morphology were analyzed by using advanced physicochemical techniques. The XRD results showed that the samples produced by the microwave hydrothermal method had sharp diffraction peaks, high crystallinity and complete crystalline shape. AHM generates h-MoO3 in both hydrochloric and nitric acid environments. In particular, when the temperature rises to 200 °C, the generated h-MoO3 will be converted to α-MoO3 in a nitric acid environment, which will be generated in a sulfuric acid environment. Therefore, increasing the reaction temperature will result in the conversion of h-MoO3 to α-MoO3 in sulfuric acid solution. SEM results show that the sample prepared from hydrochloric acid solution has a complete hexagonal prism morphology, while the sample prepared from sulfuric acid solution presents a long fibrous morphology, and the sample prepared from nitric acid solution has many defects on the surface of the hexagonal prism morphology. Interestingly, sample A11 prepared in nitric acid solution showed a spherical structure. Since the generated A3, A6 and A9 samples are all stable phase α-MoO3, they have a wider band gap compared with other samples. Their particle size is up to the nanometer scale, so they have strong adsorption properties. The spherical sample A11 has excellent adsorption and photocatalytic activity. [ABSTRACT FROM AUTHOR]

We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks. [ABSTRACT FROM AUTHOR]

Senescence is a cellular aging process in all multicellular organisms. It is characterized by a decay in cellular functions and proliferation, resulting in increased cellular damage and death. This condition plays an essential role in the aging process and significantly contributes to the development of age-related complications. On the other hand, ferroptosis is a systemic cell death pathway characterized by excessive iron accumulation followed by the generation of reactive oxygen species (ROS). Oxidative stress is a common trigger of this condition and may be induced by various factors such as toxins, drugs, and inflammation. Ferroptosis is linked to numerous disorders, including cardiovascular disease, neurodegeneration, and cancer. Senescence is believed to contribute to the decay in tissue and organ functions occurring with aging. It has also been linked to the development of age-related pathologies, such as cardiovascular diseases, diabetes, and cancer. In particular, senescent cells have been shown to produce inflammatory cytokines and other pro-inflammatory molecules that can contribute to these conditions. In turn, ferroptosis has been linked to the development of various health disorders, including neurodegeneration, cardiovascular disease, and cancer. Ferroptosis is known to play a role in the development of these pathologies by promoting the death of damaged or diseased cells and contributing to the inflammation often associated. Both senescence and ferroptosis are complex pathways that are still not fully understood. Further research is needed to thoroughly investigate the role of these processes in aging and disease, and to identify potential interventions to target such processes in order to prevent or treat age-related conditions. This systematic review aims to assess the potential mechanisms underlying the link connecting senescence, ferroptosis, aging, and disease, and whether they can be exploited to block or limit the decay of the physiological functions in elderly people for a healthy longevity. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the psychometric properties of the Brief Interpersonal Competence Questionnaire (ICQ-15) administered to Chinese adolescents. A sample of 1705 adolescents (Mean age = 14.08, SD = 3.22, 46.5% male) completed a questionnaire including the Chinese version of the ICQ-15, as well as measurements of well-being, psychological resilience, and depression. To examine the psychometric properties of the ICQ-15, item analyses (item–total correlation and normality test), confirmatory factor analysis, concurrent validity analyses, multi-group analyses, and internal consistency analyses were performed. The results of the item analyses suggested a good item–total correlation, and the item scores were distributed approximately normally. The confirmatory factor analysis showed that the five-factor model had acceptable fit indices. The concurrent validity analyses indicated that the Chinese version of the ICQ-15 had a satisfactory concurrent validity. The multi-group analyses proved the measurement invariance across females and males, as well as participants in early, middle, and late adolescence. The ICQ-15 demonstrated satisfactory internal consistency reliability among Chinese adolescents. The ICQ-15 presents good psychometric properties and can be used to assess interpersonal competence in Chinese adolescents. [ABSTRACT FROM AUTHOR]

This paper aims to investigate and categorize all inequivalent and irreducible Z + -modules of a commutative unit Z + -ring A , equipped with set {1, x, y, x y } satisfying x 2 = 1 , y 2 = 1 as a Z + -basis by using matrix equations, which was part of a call for a Special Issue about matrix inequalities and equations by Symmetry. If the rank of the Z + -module n ≤ 2 , we prove that there are finitely many inequivalent and irreducible Z + -modules, respectively, one and three. However, if n ≥ 3 , there is no irreducible Z + -module. [ABSTRACT FROM AUTHOR]

Quantum cascade lasers (QCLs) have broken the spectral barriers of semiconductor lasers and enabled a range of applications in the mid-infrared (MIR) and terahertz (THz) regimes. However, until recently, generating ultrashort and intense pulses from QCLs has been difficult. This would be useful to study ultrafast processes in MIR and THz using the targeted wavelength-by-design properties of QCLs. Since the first demonstration in 2009, mode-locking of QCLs has undergone considerable development in the past decade, which includes revealing the underlying mechanism of pulse formation, the development of an ultrafast THz detection technique, and the invention of novel pulse compression technology, etc. Here, we review the history and recent progress of ultrafast pulse generation from QCLs in both the THz and MIR regimes. [ABSTRACT FROM AUTHOR]

This study aims at improving the efficiency of remote sensing scene classification (RSSC) through lightweight neural networks and to provide a possibility for large-scale, intelligent and real-time computation in performing RSSC for common devices. In this study, a lightweight RSSC model is proposed, which is named RSCNet. First, we use the lightweight ShuffleNet v2 network to extract the abstract features from the images, which can guarantee the efficiency of the model. Then, the weights of the backbone are initialized using transfer learning, allowing the model to learn by drawing on the knowledge of ImageNet. Second, to further improve the classification accuracy of the model, we propose to combine ShuffleNet v2 with an efficient channel attention mechanism that allows the features of the input classifier to be weighted. Third, we use a regularization technique during the training process, which utilizes label smoothing regularization to replace the original loss function. The experimental results show that the classification accuracy of RSCNet is 96.75% and 99.05% on the AID and UCMerced_LandUse datasets, respectively. The floating-point operations (FLOPs) of the proposed model are only 153.71 M, and the time spent for a single inference on the CPU is about 2.75 ms. Compared with existing RSSC methods, RSCNet achieves relatively high accuracy at a very small computational cost. [ABSTRACT FROM AUTHOR]

Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%. [ABSTRACT FROM AUTHOR]

Petroleum extraction, transportation, and consumption in the marine environment contribute to a large portion of anthropogenic oil spills into the ocean. While previous research focuses more on large oil spill accidents from oil tankers or offshore oil platforms, there are few systematic records on occasional regional oil spills. In this study, optical imagery from Landsat-8 OLI was used to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). The source of the slicks was identified through datasets from traffic density and platform locations. A total of 632 oil slicks were detected in the NSCS from 2015 to 2019, where 57 were from platforms sources, and 490 were from ships. The average area of the detected slicks was 4.8 km2, and half of the slicks had areas <1.7 km2. Major oil spill hot spots included coastal Guangdong (ship origins), southeast and northeast Dongsha Island (ship origins), middle of south Beibu Gulf (ship and platform origins), and southeast Pearl River Estuary (platform origins). Through this study, we demonstrate the capability of medium-resolution optical imagery in monitoring regional oil spills. Such results and methods may help in near real-time oil spill monitoring and further environmental assessments. [ABSTRACT FROM AUTHOR]

With the rapid development of the Chinese marine economy, the anti-corrosion of ships and marine engineering facilities has become urgent to be solved. In this paper, a stable zinc-hydroxyapatite (ZnO-HAP) nanocomposite anti-corrosive pigment was prepared by using the hydrothermal in situ synthesis technique, which supported nano-ZnO onto the hydroxyapatite (HAP) surface. The phase composition, microstructure, and performance of the ZnO-HAP nanocomposite were investigated by X-ray diffraction (XRD), Fourier transform infrared (FT-IR), electron probe microanalysis (EPMA), and electrochemical impedance spectroscopy (EIS), and the anti-corrosion mechanism of the ZnO-HAP nanocomposite was discussed. The results show that the corrosion resistance of the ZnO-HAP/epoxy is better than that of the pure epoxy resin coating. The optimum condition to fabricate ZnO-HAP/epoxy with suitable corrosion resistance was found to be a ZnO/HAP ratio of 0.65/0.35. The synergistic complementation mechanism of ZnO and HAP enriches the metallic anti-corrosion theory and provides a new idea for the synthesis of novel and promising anti-corrosive pigments. [ABSTRACT FROM AUTHOR]

(1) Background: Although there are extensive data on admission co-variates and outcomes of persons with coronavirus infectious disease-2019 (COVID-19) at diverse geographic sites, there are few, if any, subject-level comparisons between sites in regions and countries. We investigated differences in hospital admission co-variates and outcomes of hospitalized people with COVID-19 between Wuhan City, China and the New York City region, USA. (2) Methods: We retrospectively analyzed clinical data on 1859 hospitalized subjects with COVID-19 in Wuhan City, China, from 20 January to 4 April 2020. Data on 5700 hospitalized subjects with COVID-19 in the New York City region, USA, from 1 March to 4 April 2020 were extracted from an article by Richardson et al. Hospital admission co-variates (epidemiological, demographic, and laboratory co-variates) and outcomes (rate of intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], major organ failure and death, and length of hospital stay) were compared between the cohorts. (3) Results: Wuhan subjects were younger, more likely female, less likely to have co-morbidities and fever, more likely to have a blood lymphocyte concentration > 1 × 109/L, and less likely to have abnormal liver and cardiac function tests compared with New York subjects. There were outcomes data on all Wuhan subjects and 2634 New York subjects. Wuhan subjects had higher blood nadir median lymphocyte concentrations and longer hospitalizations, and were less likely to receive IMV, ICU hospitalization, and interventions for kidney failure. Amongst subjects not receiving IMV, those in Wuhan were less likely to die compared with New York subjects. In contrast, risk of death was similar in subjects receiving IMV at both sites. (4) Conclusions: We found different hospital admission co-variates and outcomes between hospitalized persons with COVID-19 between Wuhan City and the New York region, which should be useful developing a comprehensive global understanding of the SARS-CoV-2 pandemic and COVID-19. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) have been explored as a surface modification strategy to promote the oxygen reduction reaction (ORR) on Pt/C and their chemical structures were identified to have strong influence on the ORR activities. To better understand the roles of anion and cation of ILs on the catalytic reaction, two cations ([MTBD]+ and [bmim]+) were paired with three anions ([TFSI]−, [beti]−, and [C4F9SO3]−) to form various IL structures. By systematically varying the IL combinations and studying their effects on the electrochemical behaviors, such as electrochemical surface area and specific ORR activities, it was found that cation structure had a higher influence than anion, and the impact of the [MTBD]+ series was stronger than the [bmim]+ series. In addition to the investigation in the half-cell, studies were also extended to the membrane electrode assembly (MEA). Considerable performance enhancements were demonstrated in both the kinetic region and high current density region with the aid of IL. This work suggests that IL modification can provide a complementary approach to improve the performance of proton exchange membrane fuel cells. [ABSTRACT FROM AUTHOR]

To improve interfacial adhesion between glass fiber (GF) and poly(ethylene-grafted-styrene)-based cation exchange membranes (CEM), GF was modified by four coupling agents: [3-(Methacryloxy)propyl] trimethoxy silane (3-MPS), 1,6-bis (trimethoxysilyl) hexane (1,6 bis), Poly(propylene-graft-maleic anhydride) (PP-g-MA) and Triethoxyvinylsilane (TES). The results indicated the addition of modified GF increased tensile strength, tensile modulus, storage modulus and interfacial adhesion of GF/CEM composite but degraded the strains. The composite with 3-MPS modified GF obtained superior mechanical properties and interfacial adhesion, whereas the modified effect of TES was inconspicuous. The addition of unmodified GF even had negative effects on GF/CEM mechanical properties. The field emission scanning electron microscopes (FE-SEM) showed that the GF treated by 3-MPS and PP-g-MA have better compatibility with the CEM matrix than 1,6 bis and TES-treated GF. The Fourier-transform infrared spectroscopy (FT-IR) verified that the strengthening effects from modified GF were attributed to the formation of Si-O-Si and Si-O-C bonds. The additions of modified GF in CEM positively influence water uptake ability but negatively influence ion exchange capacity (IEC). This research provided a way of strengthening GF/CEM composite and pointed out which functional groups included in coupling agents could be useful to GF-reinforced composite. [ABSTRACT FROM AUTHOR]

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year's weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions. [ABSTRACT FROM AUTHOR]

In this study, the poisoning effect of CaO on activated carbon (AC)-based Mn-Ce catalysts was discussed. Loading CaO inhibited the catalytic activity of the catalyst and the NO conversion of the catalyst decreased from 69.5% to 38.2% at 75 °C. The amount of MnO2 in AC surface decreased in the process of loading CaO, which was detrimental to the Selective Catalytic Reduction (SCR) performance of the catalyst. The change of manganese oxide form inhibited generation rate for the chemisorption oxygen and NO2, which was the most critical reason for the decrease of catalytic activity. Besides, loaded CaO entered into the pores of the catalyst, which led to the blockage of the pores and further resulted in the decrease of the Brunauer-Emmett-Teller (BET) surface area and total pore volume. It also destroyed the oxygen-containing functional groups and acid site on the surface of AC. All of these caused the deactivation of Mn-Ce/AC catalyst after loading CaO. [ABSTRACT FROM AUTHOR]

RapidSCAN is a portable active canopy sensor with red, red-edge, and near infrared spectral bands. The objective of this study is to develop and evaluate a RapidSCAN sensor-based precision nitrogen (N) management (PNM) strategy for high-yielding rice in Northeast China. Six rice N rate experiments were conducted from 2014 to 2016 at Jiansanjiang Experiment Station of China Agricultural University in Northeast China. The results indicated that the sensor performed well for estimating rice yield potential (YP0) and yield response to additional N application (RIHarvest) at the stem elongation stage using normalized difference vegetation index (NDVI) (R2 = 0.60–0.77 and relative error (REr) = 6.2–8.0%) and at the heading stage using normalized difference red edge (NDRE) (R2 = 0.70–0.82 and REr = 7.3–8.7%). A new RapidSCAN sensor-based PNM strategy was developed that would make N recommendations at both stem elongation and heading growth stages, in contrast to previously developed strategy making N recommendation only at the stem elongation stage. This new PNM strategy could save 24% N fertilizers, and increase N use efficiencies by 29–35% as compared to Farmer N Management, without significantly affecting the rice grain yield and economic returns. Compared with regional optimum N management, the new PNM strategy increased 4% grain yield, 3–10% N use efficiencies and 148 $ ha−1 economic returns across years and varieties. It is concluded that the new RapidSCAN sensor-based PNM strategy with two in-season N recommendations using NDVI and NDRE is suitable for guiding in-season N management in high-yield rice management systems. Future studies are needed to evaluate this RapidSCAN sensor-based PNM strategy under diverse on-farm conditions, as well as to integrate it into high-yield rice management systems for food security and sustainable development. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages. [ABSTRACT FROM AUTHOR]

Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods. [ABSTRACT FROM AUTHOR]

Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer's typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information. [ABSTRACT FROM AUTHOR]

During the casting of high aluminum steel, the dramatic increase in the Al2O3/SiO2 ratio is inevitable, resulting in significant changes of the crystallization behavior, which would result in heat transfer and lubrication problems. Crystallization products and structure characterization of glassy CaO-SiO2-based mold fluxes with different Al2O3/SiO2 ratios were experimentally investigated using a differential scanning calorimetry technique and Raman spectroscopy. With increasing Al2O3/SiO2 ratios, the following results were obtained. The crystallization temperature and the crystallization products are changed. With increasing Al2O3/SiO2 ratios from 0.088 to 0.151, the crystallization temperature first increases greatly from 1152 °C to 1354 °C, and then moderately increases. The crystallization ability of the mold flux is strengthened. The species of the precipitated crystalline phase change from two kinds, i.e., Ca4Si2O7F2 and Ca2SiO4, to four kinds, i.e., Ca4Si2O7F2, Ca2SiO4, 2CaO·Al2O3·SiO2 and Ca12Al14O32F2, the crystallization ability of Ca4Si2O7F2 is gradually attenuated, but other species show the opposite trend. The results of Raman spectroscopy indicate that Al3+ mainly acts as a network former by the information of [AlO4]-tetrahedral structural units, which can connect with [SiO4]-tetrahedral by the formation of new bridge oxygen of Al–O–Si linkage, but there is no formation of Al–O–Al linkage. The linkage of Al–O–Si increases and that of Si–O–Si decreases. The polymerization degree of the network and the average number of bridging oxygens decrease. Further, the relatively strong Si–O–Si linkage gradually decreases and the relatively weak Al–O–Si gradually increases. The change of the crystalline phase was interpreted from the phase diagram and structure. [ABSTRACT FROM AUTHOR]

Renal impairment (RI) is one of the hallmarks of multiple myeloma (MM) and carries a poor prognosis. Microvesicles (MVs) are membrane vesicles and play an important role in disease progression. Here, we investigated the role of MVs derived from MM cells (MM-MVs) in RI of MM. We found that MM-MVs significantly inhibited viability and induced apoptosis, but not epithelial-mesenchymal transition in human kidney-2 (HK-2), a human renal tubular epithelial cell line. The protein levels of cleaved caspase-3, 8, and 9, and E-cadherin, were increased, but vementin levels were decreased in the HK-2 cells treated with MM-MVs. Through a comparative sequencing and analysis of RNA content between the MVs from RPMI8226 MM cells (RPMI8226-MVs) and K562 leukemia cells, RPMI8226-MVs were enriched with more renal-pathogenic miRNAs, in which the selective miRNAs may participate in the up-regulation of the levels of cleaved caspase-3. Furthermore, the levels of CD138+ circulating MVs (cirMVs) in the peripheral blood were positively correlated with the severity of RI in newly-diagnosed MM. Our study supports MM-MVs representing a previously undescribed factor and playing a potential role in the development of RI of MM patients, and sheds light on the potential application of CD138+ cirMV counts in precise diagnosis of RI in MM and exploring MM-MVs as a therapeutic target.