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The rapid expansion of aquaculture ponds at the end of the last century raised concerns about their environmental and ecological impacts in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study aims to comprehensively investigate the spatiotemporal evolutionary characteristics and explore the influencing factors of aquaculture development in the GBA. An automated approach was established to extract the aquaculture pond areas on the Google Earth Engine (GEE) platform using Landsat images and showed high accuracy. The aquaculture development in the GBA underwent three phases of rapid expansion (1986–1996), constant transformation (1996–2010), and drastic contraction (2010–2020), and was closely related to the important aquaculture policies in China and Guangdong Province. The local aquaculture ponds showed three evolutionary patterns of continuous shrinkage, earlier shrinkage, and later shrinkage, which were mainly influenced by the economic development level and urbanization in each city. Rapid urbanization created great demands for aquaculture products and intense land use competition, which drove the conversion of aquaculture ponds into impervious land and migration from the central GBA towards the surrounding coast and upstream areas. Government policies promoted the initial rapid increase and regulated the aquaculture ponds to develop towards a more environment-friendly and intensive mode, which led to a steady increase of aquaculture products despite the drastic decrease in aquaculture pond areas in the past decade. These evolutionary features are of significance for policymakers and farmers to maintain the sustainable development of aquaculture in the GBA and Guangdong Province.

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

Both oil spill and microplastics (MPs) are major issues in marine environments. After a spill, the oil film may disperse into the water column as droplets under the function of sea waves. The oil dispersion may be affected due to the attachment of oil to MPs. In this paper, the impacts of salinity and temperature on the dispersion of spilled oil in the presence of MPs were individually studied by batch conical flask oscillation experiments. The results indicated that the rise in salinity or temperature displayed a more significant effect on promoting, rather than inhibiting, oil dispersion with MPs. When the salinity rose from 15% to 35%, the oil dispersion efficiency (ODE) with the 13 μm polyethylene (PE) and polystyrene (PS) MPs was increased by 10.4% and 12.9%, respectively; when the temperature rose from 10 °C to 25 °C, the corresponding ODE was increased by 15.6% and 12.7%, respectively. In addition, the volumetric mean diameter (VMD) of the dispersed oil droplets decreased with an increase in salinity or temperature. Furthermore, the 13 μm MPs showed a higher impact on the oil dispersion than 106 μm of MPs, and the ODE with PE MPs was greater than that with PS MPs. The findings of this study expanded the understanding of the migration of spilled oil in seawater in the presence of MPs and may further improve the capability of predicting the impact of oil spills by marine environment managers.

Synthetic aperture radar (SAR), with all-day and all-weather observation capabilities, can capture the phenology of crops with short growth cycles to improve land cover classification results. The present study carried out a land cover classification using multitemporal RADARSAT-2 fully polarimetric SAR (PolSAR) images that are 24 days apart. The objectives of this study were 1) examining the land cover classification capacity of multitemporal fully PolSAR data processed with multiple polarimetric decomposition methods, 2) investigating the contribution of multi-type polarimetric decomposition methods to multitemporal PolSAR image classification, 3) and determining optimal image acquisition dates and polarimetric parameters for land cover classification. Overall accuracy and kappa coefficient attained using the multitemporal PolSAR data were 96.55% and 0.96, respectively, which were improved by as much as 16.77% and 0.20, respectively, compared with those obtained with a single scene. Compared with the multitemporal PolSAR image classification using coherency matrices alone, the use of polarimetric decomposition methods improved overall accuracy and kappa value by 2.22% and 0.03, respectively. Using decision tree algorithms for feature selection, we found that April 14, May 8, June 1, and June 25 and Pauli, Cloude, Neumann3, An&Yang4, Freeman3, Barnes2, and MCSM5 decomposition methods were optimal for the land cover classification.

Printing and dyeing wastewater is characterized with complex water quality and poor biodegradability. In this study, a pilot-scale anaerobic baffled reactor (ABR) with packing was verified to effectively degrade the complex organic pollutants in the wastewater through the hydrolysis and acidification of anaerobic microorganisms. At a hydraulic retention time (HRT) of 12 h and an organic loading rate (OLR) of 2.0–2.5 kg COD/(m3·d), the ABR stabilized the fluctuation range of pH and achieved an average colority removal rate of 10.5%, which provided favorable conditions for subsequent aerobic treatment. During the early operation period, the reactor increased the alkalinity of the wastewater; after 97 days of operation, the volatile fatty acid (VFA) content in the wastewater decreased. To demonstrate the suitability of the support vector regression (SVR) technology in predicting the performance of the reactor, two SVR algorithms with three kernel functions were employed to relate the chemical oxygen demand (COD) removal rate to its influencing factors, and the predictions of both the training and validation groups agreed with the measurements. The results obtained from this study can contribute to the design and optimal operation of the anaerobic treatment project of the industrial wastewater treatment plant.

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

This research aims to advance our understanding towards forest transition, which is about the shift from net deforestation to net reforestation over a given area during certain period, through a case study of a western city in China from 1990 to 2015. Three main contributions are made to the theory and practice of forest land management. First, this research enriches forest transition theory with a meso-level exploration on forest land change in subtropic Chongqing, which echoes the “U” shape transition rules widely observed in the Euro-American context but was found a time lag of the turning point until 2000s. Second, it intentionally identifies the subtype of forests—the artificial plantation, which is considered influential on performance of forest’s carbon sequestration but not paid sufficient attention to. Third, it adds knowledge on forest transition pathway at an intra-urban scale through the identified significance degree of forest transition dynamics, which implies that economic development matters but is less important than topography in a mountainous city like Chongqing, and different dimensions of economy impact differently on forest transition.

Agricultural land resources have been the central issue for the Chinese government in its attempts to secure food and agricultural sustainability. Yet strict land use control does not protect the agricultural land from erosion by urban expansion. Identifying the specific patterns and mechanisms of the agricultural land conversion, thus, is critical for land management and related decision making. Based on the annual nominal 30 m land use/land cover datasets (called CLUD-A), this study goes below the national/regional level to examine agricultural land conversion in Chongqing from a multiscale perspective. At the metropolis and its subdivision’s scales, the volume of the conversion area has been generally increasing, from 122.40 km2 in 1980–1990, 162.26 km2 in 1990–2000, and 706.14 km2 in 2000–2010, to 684.83 km2 in 2010–2015. Such a conversion in the main city area and its surroundings far outweighed that in the rural outskirts, as 68.9% (1990–2000), 92.2% (2000–2010), and 82.7% (2010–2015) of the conversion happened in the former. Moreover, values of Gini coefficients and coefficient of variation (CV) based on the county/district scale (Gini [0.46, 0.64], CV [0.69, 0.99] throughout the four periods) are much lower than those based on the town/village scale (Gini [0.88, 0.94], CV [3.18, 4.47] throughout the four periods), suggesting the uneven extent of spatial distribution of the agricultural land conversion trickles down along with the downscale of administration: the lower the administrative level, the more severe the unbalance. The policy rationale behind this transition is also discussed. This research argues for tangible approaches to a sustainable rural-urban transformation.

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

The formation of sunken oils is mainly dominated by the interaction between spilled oils and sediments. Due to their patchiness and invisibility, cleaning operations become difficult. As a result, sunken oils may cause long-term and significant damage to marine benthonic organisms. In the present study, a bench experiment was designed and conducted to investigate the quantitative distribution of polycyclic aromatic hydrocarbons (PAHs) in sunken oils in the presence of chemical dispersant and sediment. The oil sinking efficiency (OSE) of 16 priority total PAHs in the sediment phase was analyzed with different dosages of dispersant. The results showed that the synergistic effect of chemical dispersant and sediment promoted the formation of sunken oils, and the content of PAHs partitioned in the sunken oils increased with the increase of dispersant-to-oil ratios (DORs). Furthermore, with the addition of chemical dispersant, due to the solubility and hydrophobicity of individual PAHs, the high molecular weight (HMW) PAHs with 4−6 rings tended to partition to sediment compared with low molecular weight (LMW) PAHs with 2−3 rings. The synergistic effect of chemical dispersant and sediment could enhance the OSE of HMW PAHs in sunken oils, which might subsequently cause certain risks for marine benthonic organisms.

The floating oil can drift to the coastal areas and interact with the shoreline substrates after oil spill accidents. This process is demonstrated to be the cause of the formation of oil-contaminated sediments, which has attracted much attention. However, no systematic study has concerned the desorption process of oil from oil-contaminated sediments when the coastal hydrodynamic conditions change. This work determines the effects of wave conditions and particle size on the release of oil from artificially prepared sediments in a wave tank. Nonlinear fitting results show that the oil release kinetic curves can be correctly estimated with the Lagrangian first-order (LFO) first-order equation. Under different test conditions. The oil concentration in the water increases rapidly within 6 h. However, the oil desorption is inhibited thereafter and the process of sorption occurs dominantly. Under higher wave energy, the process of desorption is significantly enhanced and more large oil droplets release from sediments. Under the same wave condition, small oil droplets firstly release from the sediments. Besides, more oil especially with a larger size can release from larger sediment while oil releases more quickly from smaller sediment.

A pilot-scale six-compartment hybrid anaerobic baffled reactor (HABR) with effective volume of 18 m3 was used to treat dyeing wastewater. The HABR system was able to treat the wastewater efficiently after FeSO4 pretreatment, as indicated by removal efficiencies of 33.7% for chemical oxygen demand (COD), 39.9% for suspended solid (SS), and 22.5% for sulfate (SO42−) during steadily operational period. Gas chromatography–mass spectrometry (GC-MS) showed that the concentrations of alkanes, amides, organic acids, ketones, phenols, and esters were much lower in the effluent than those in the influent; many high-molecular-weight compounds such as cyclanes, quinolines, and phenols were successfully transformed to low-molecular-weight ones. As illustrated from the results of generalized grey relational analysis (GGRA), COD removal efficiency was more closely associated with flow rate, organic loading rate (OLR), water temperature, and influent SS among the whole selected possible factors. Based on the overall treating effectiveness and the GGRA study, the optimized operation strategy of the dyeing wastewater treatment by HABR was obtained as the hydraulic retention time (HRT) of 12 h for steady-state operation with an up-flow velocity of 1.7 m/h as well as OLR of 1.5–2.0 kg COD/(m3·d).

Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

A coal mine dual prevention construction evaluation model based on association rule mining and combined weighting cloud model is proposed. Firstly, based on association rule mining, the evaluation indicators for the effectiveness evaluation system of the dual prevention mechanism construction in coal mines are determined. Secondly, the analytic hierarchy process and anti entropy weight method are used to obtain the subjective and objective weights of the indicators. Then, the composite normalization method is used to obtain the combined weights of the indicators, and a cloud theory model is introduced to construct an evaluation model for coal mine dual prevention construction based on the cloud model. Finally, by analyzing a coal mine as a sample, the evaluation results of the model are compared with those of the analytic hierarchy process cloud model and the anti entropy weight method cloud model. The results indicate that the evaluation model is more scientific and accurate in the evaluation of dual prevention construction in coal mines, which helps to assist coal mines in establishing a scientific and reasonable “dual prevention” mechanism.

Abstract A ground-breaking roadmap of III-nitride solid-state deep-ultraviolet light emitters is demonstrated to realize the wafer-scale fabrication of devices in vertical injection configuration, from 2 to 4 inches. The epitaxial device structure is stacked on a GaN template instead of conventionally adopted AlN, where the primary concern of the tensile strain for Al-rich AlGaN on GaN is addressed via an innovative decoupling strategy, making the device structure decoupled from the underlying GaN template. Moreover, the strategy provides a protection cushion against the stress mutation during the removal of substrates. As such, large-sized wafers can be obtained without surface cracks, even after the removal of the sapphire substrates by laser lift-off. Wafer-scale fabrication of 280 nm vertical injection deep-ultraviolet light-emitting diodes is eventually demonstrated, where a light output power of 65.2 mW is achieved at a current of 200 mA, largely thanks to the significant improvement of light extraction. This work will definitely speed up the application of III-nitride solid-state deep-ultraviolet light emitters featuring high performance and scalability.

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical.

The Japanese giant salamander, Andrias japonicus, is an iconic but declining species of aquatic amphibian. A population within the Nawa River Basin of Tottori Prefecture persists but faces two significant threats. The first major threat is waterway obstructions, in the form of dams, weirs, and concrete embankments, which severely fragment and degrade the habitat. The second is water pollution, primarily caused by runoff from animal farms. Both threats to A. japonicus can be mitigated with proper actions. We provide science-based recommendations, advocating for the modification of dams and weirs by implementing ladderways to restore habitat connectivity and the provisioning of adequate breeding habitat in the form of artificial nest boxes. We also recommend the implementation of regular water quality monitoring and the enforcement of regulations regarding allowed runoff from animal farms. The Nawa River Basin could also be designated as an official sanctuary area for the Japanese Giant Salamander, lending additional formal protection and providing ecotourism opportunities for economically declining rural areas. Though the threats to A. japonicus are serious, the implementation of these mitigation measures and continual monitoring can allow this remarkable species to thrive.

ABSTRACTThe present study focused on the examination of the seed and pollen morphology of nine native Chinese species of the genus Sorbus L. using Light Microscope (LM) and Scanning Electron Microscope (SEM). The crucial features investigated for seeds include color, shape, epidermal cell size, and seeds coat ornamentation. On the other hand, the pollen grains are analyzed based on quantitative traits as well as the following quantitative traits: polar and equatorial view, shape, apertures and exine ornamentation. Pollens of the studied taxa were monad and trizonocolporate. The most important pollen features include exine ornamentation based on traits of striae and grooves (length and width) and perforations (number and diameter). The variability of the observed morphological features for both seeds and pollen provide important characteristics to differentiate the studied Sorbus species.

Abstract Background Early stage lung adenocarcinomas manifested as ground-glass nodules (GGNs) are increasingly being detected, but screening and diagnosis for GGN-featured lung adenocarcinomas in different risk populations reach no agreement. Objectives To analyze the clinical, pathological, imaging and genetic features of GGN-featured lung adenocarcinomas on high-resolution computed tomography (HRCT) in different risk groups. Methods Include patients with GGNs on HRCT surgically diagnosed as lung adenocarcinoma in the West China Hospital, Sichuan University from 2009 to 2021, and their clinical, pathological, imaging and gene sequencing data. Results According to Chinese Expert Consensus on Screening and Management of Lung Cancer, 1,800 patients with GGN-featured lung adenocarcinoma, 545 males (incl. 269 smokers) and 1,255 females (incl. 16 smokers), were divided into high-risk (509) and non-high-risk (1,291) groups. Among them, 1,095 were detected via physical examination. The mean age at diagnosis was 54.78 (23–84) and the mean time from detection to diagnosis was 9.59 months. There were more males than females in the high-risk group [288 (56.58%) vs 221 (43.42%)], just the opposite in the non-high-risk group [1,034 (80.09%) vs 257 (19.91%)] (both P 0.05). The frequency of invasive adenocarcinoma was higher in the high-risk group, while those of precursor lesions and minimally invasive adenocarcinoma were higher in the non-high-risk group (all P

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

Improving light extraction efficiency (LEE) of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs) has been attempted by thinning the p-AlGaN/p-GaN layer and adopting Ni/Au/Al composite electrodes. It is found that the thin p-AlGaN/p-GaN layer can reduce the light absorption and the Ni/Au/Al electrodes achieve high reflectivity and Ohmic contact to ensure the enhancement of the light extraction and maintain fine electrical properties. By this approach, the maximum external quantum efficiency of the DUV-LEDs with optimized Ni/Au/Al reflective electrodes is increased by 40%, compared to that with conventional Ni/Au electrodes over the whole current range.

Abstract To solve the problems of voltage crossing, offset and fluctuation caused by the high proportion of distributed photovoltaic (PV) access to the distribution network and reduce the risk of grid operation, proposing a Mamdani fuzzy logic based multi‐mode voltage control strategy for high ratio PV distribution network. Based on the voltage scale value of the grid connection, the control strategy combines multi‐mode operation with fuzzy logic and divides the PV inverter operation into three modes: Overvoltage suppression, undervoltage suppression, and network loss/power factor optimization. The control strategy is designed with different fuzzy controllers for each mode of operation to suit different operating conditions. The fuzzy rules are formulated based on the logical relationship between the inputs and the reactive power output of the inverter, and the reactive power output standardized value of the PV inverter is obtained by the defuzzification process. The simulation results show that the proposed method can alleviate the voltage crossing, voltage drift, and voltage fluctuation problems. Moreover, the voltage crossing and voltage drift are effectively suppressed, and the power factor is improved compared with the reactive power sag control, multi‐mode control, and traditional fuzzy control.

Abstract The immunity of patients who recover from coronavirus disease 2019 (COVID-19) could be long lasting but persist at a lower level. Thus, recovered patients still need to be vaccinated to prevent reinfection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or its mutated variants. Here, we report that the inactivated COVID-19 vaccine can stimulate immunity in recovered patients to maintain high levels of anti-receptor-binding domain (RBD) and anti-nucleocapsid protein (NP) antibody titers within 9 months, and high neutralizing activity against the prototype, Delta, and Omicron strains was observed. Nevertheless, the antibody response decreased over time, and the Omicron variant exhibited more pronounced resistance to neutralization than the prototype and Delta strains. Moreover, the intensity of the SARS-CoV-2-specific CD4+ T cell response was also increased in recovered patients who received COVID-19 vaccines. Overall, the repeated antigen exposure provided by inactivated COVID-19 vaccination greatly boosted both the potency and breadth of the humoral and cellular immune responses against SARS-CoV-2, effectively protecting recovered individuals from reinfection by circulating SARS-CoV-2 and its variants.

Background Pregnancy complicated with tuberculosis is increasingly common. The clinical characteristics of pregnancy complicated with miliary tuberculosis are summarized in this study. Methods A retrospective analysis of pregnant patients with miliary tuberculosis was performed in terms of epidemiology, demography, clinical characteristics, laboratory tests, treatment, and prognosis. Results Of the 23 patients that were included, 12 became pregnant after in vitro fertilization combined with embryo transfer (IVF-ET). The average gestational age at symptom onset was 13.96 weeks, and the average time from symptom onset to diagnosis was 33 days. Clinical symptoms included fever, dyspnoea, cough, headache, abdominal pain, and chest pain. Extrapulmonary tuberculosis occurred in 10 patients, respiratory failure in 11 patients, and ARDS in 9 patients. Chest HRCT showed diffusely distributed miliary nodules in all patients. Six patients were on mechanical ventilation, two underwent ECMO, and one died. Symptoms appeared in the first trimester of nine pregnancies after IVF-ET and in the second trimester of seven natural pregnancies. Conclusions Miliary tuberculosis can occur in pregnant patients, especially in patients after IVF-ET. Symptoms often appear in the first trimester of pregnancy after IVF-ET and in the second trimester of natural pregnancy. Lacking specificity, the common clinical characteristics include elevated inflammation markers, anaemia, low lymphocyte count, and multiple miliary nodules shown on a chest HRCT scan. Half of patients with miliary tuberculosis may develop respiratory failure, and some may progress to ARDS. Therefore, infertile patients should be required to undergo TB screening before undergoing IVF-ET, and preventive anti-TB treatment should be given to patients with latent TB infections or untreated TB disease.Key Message Miliary tuberculosis can occur in pregnant patients, especially in pregnant patients after IVF-ET. Symptoms often appear in the first trimester of pregnancy after IVF-ET and in the second trimester of natural pregnancy. Many patients develop respiratory failure or ARDS.

BackgroundInteraction between retinal vascular endothelial cells and neurons plays a critical role in the pathogenesis of diabetic retinopathy (DR). This study aims to compare an in vitro model over a monoculture model to simulate the neurovascular coupling under the hyperglycemic microenvironment of diabetes.MethodsRat retinal vascular endothelial cells (RRMECs) and ganglion cells (RGCs) were seeded mono- or co-cultured in a normal (NG, 5.5 mM) and high (HG, 75 mM) glucose concentrations culture medium. Cell viability was detected by the cell counting kit-8 (CCK-8) assay. The ability of migration and lumen formation of RRMECs were determined by scratch wound, transwell migration, and lumen formation assays. The apoptosis index of cells was calculated and detected by propidium iodide (PI)/Hoechst staining. Quantitative and morphological analysis of RGCs was performed through the labeling of RGCs by brain-specific homeobox/POU domain protein 3A (BRN3A) and anti-beta-III tubulin (TUJ1). The gene and protein expression levels of occludin (OCLN) and zonula occludens-1 (ZO-1) were evaluated by quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay.ResultsThe viability, migration, and lumen formation abilities of RRMECs in the HG group significantly increased (P

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

Aiming at steering the selection of optimal combinations of polarimetric SAR (PolSAR) frequency bands for different land cover classification schemes, this study investigates the land cover classification capabilities of all the possible combinations of L-band ALOS PALSAR fully PolSAR data, C-band RADARSAT-2 fully PolSAR data, and X-band TerraSAR-X HH SAR data. A method that integrates polarimetric decomposition, object-based image analysis, decision tree algorithms, and support vector machines is used for the classification. Polarimetric decomposition theorems are used to interpret the scattering mechanisms at the different frequency bands to reveal the effect mechanisms of PolSAR frequency variation on the classification capability. This study finds that (1) X-band HH SAR is not necessary for classifying the land cover types involved in this study when C- or L-band fully PolSAR are used; (2) C-band fully PolSAR alone is adequate for classifying primitive land cover types, namely, water, bare land, vegetation, and built-up areas; and (3) L-band fully PolSAR alone is adequate for distinguishing between various vegetation types, such as crops, banana trees, and forests. [ABSTRACT FROM AUTHOR]

A desorption-tailoring strategy is demonstrated to steadily prepare self-assembled p-AlGaN superlattices with sub-nanometer ultrathin barriers by MOCVD, which juggle the hole concentration and transport.

Abstract Background Early identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. Methods This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. Results The deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. Conclusions Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.

Glioblastoma is the most malignant primary brain tumor, the prognosis of which remains dismal even with aggressive surgical, medical, and radiation therapies. Glioblastoma stem cells (GSCs) promote therapeutic resistance and cellular heterogeneity due to their self-renewal properties and capacity for plasticity. To understand the molecular processes essential for maintaining GSCs, we performed an integrative analysis comparing active enhancer landscapes, transcriptional profiles, and functional genomics profiles of GSCs and non-neoplastic neural stem cells (NSCs). We identified sorting nexin 10 (SNX10), an endosomal protein sorting factor, as selectively expressed in GSCs compared with NSCs and essential for GSC survival. Targeting SNX10 impaired GSC viability and proliferation, induced apoptosis, and reduced self-renewal capacity. Mechanistically, GSCs utilized endosomal protein sorting to promote platelet-derived growth factor receptor β (PDGFRβ) proliferative and stem cell signaling pathways through posttranscriptional regulation of the PDGFR tyrosine kinase. Targeting SNX10 expression extended survival of orthotopic xenograft–bearing mice, and high SNX10 expression correlated with poor glioblastoma patient prognosis, suggesting its potential clinical importance. Thus, our study reveals an essential connection between endosomal protein sorting and oncogenic receptor tyrosine kinase signaling and suggests that targeting endosomal sorting may represent a promising therapeutic approach for glioblastoma treatment.

ObjectiveComplete cervical spinal cord injury (CSCI) is a devastating injury that usually requires surgical treatment. Tracheostomy is an important supportive therapy for these patients. To evaluate the effectiveness of early one-stage tracheostomy during surgery compared with necessary tracheostomy after surgery, and to identify clinical factors for one-stage tracheostomy during surgery in complete cervical spinal cord injury.DesignData from 41 patients with complete CSCI treated with surgery were retrospectively analyzed.Participants and interventionsTen patients (24.4%) underwent one-stage tracheostomy during surgery, thirteen (31.7%) underwent tracheostomy when necessary after surgery, and eighteen (43.9%) did not have a tracheostomy.Main resultsOne-stage tracheostomy during surgery significantly reduced the development of pneumonia at 7 days after tracheostomy (p = 0.025), increased the PaO2 (p

Glioblastoma ranks among the most aggressive and lethal of all human cancers. Self-renewing, highly tumorigenic glioblastoma stem cells (GSCs) contribute to therapeutic resistance and maintain cellular heterogeneity. Here, we interrogated superenhancer landscapes of primary glioblastoma specimens and patient-derived GSCs, revealing a kelch domain–containing gene, specifically Kelch domain containing 8A (KLHDC8A) with a previously unknown function as an epigenetically driven oncogene. Targeting KLHDC8A decreased GSC proliferation and self-renewal, induced apoptosis, and impaired in vivo tumor growth. Transcription factor control circuitry analyses revealed that the master transcriptional regulator SOX2 stimulated KLHDC8A expression. Mechanistically, KLHDC8A bound chaperonin-containing TCP1 (CCT) to promote the assembly of primary cilia to activate hedgehog signaling. KLHDC8A expression correlated with Aurora B/C Kinase inhibitor activity, which induced primary cilia and hedgehog signaling. Combinatorial targeting of Aurora B/C kinase and hedgehog displayed augmented benefit against GSC proliferation. Collectively, superenhancer-based discovery revealed KLHDC8A as what we believe to be a novel molecular target of cancer stem cells that promotes ciliogenesis to activate the hedgehog pathway, offering insights into therapeutic vulnerabilities for glioblastoma treatment.

This study investigated the effects of ultrasonic, freeze–thaw, and combined pretreatments on corn starch oil absorption. Low-field nuclear magnetic resonance (LF NMR) was used to study the oil absorption changes after frying of corn starch (CS) subjected to different treatments. The structural characteristics of samples were evaluated using various techniques. Scanning electron microscopy, contact angle, and particle size analysis showed that corn starch subjected to combined ultrasonic and freeze–thaw treatment generated larger, coarser particles with a denser structure. Furthermore, X-ray diffraction, Fourier transform infrared spectroscopy, and differential scanning calorimetry showed that combined treatment improved the order and thermal stability of CS molecules, thereby inhibiting oil absorption during frying. The results showed that combined ultrasonic and freeze–thaw pretreatment significantly reduced the oil absorption of corn starch before and after frying.

It is important to describe lineages before they go extinct, as we can only protect what we know. This is especially important in the case of microendemic species likely to be relict populations, such as Hynobius salamanders in southern China. Here, we unexpectedly sampled Hynobius individuals in Fujian province, China, and then worked on determining their taxonomic status. We describe Hynobius bambusicolus sp. nov. based on molecular and morphological data. The lineage is deeply divergent and clusters with the other southern Chinese Hynobius species based on the concatenated mtDNA gene fragments (>1500 bp), being the sister group to H. amjiensis based on the COI gene fragment, despite their geographic distance. In terms of morphology, the species can be identified through discrete characters enabling identification in the field by eye, an unusual convenience in Hynobius species. In addition, we noted some interesting life history traits in the species, such as vocalization and cannibalism. The species is likely to be incredibly rare, over a massively restricted distribution, fitting the definition of Critically Endangered following several lines of criteria and categories of the IUCN Red List of Threatened Species.

Chromosome numbers of nine taxa in Sorbus subgenera Aria and Micromeles were determined. All are diploid with 2n = 2x = 34. Eight out of them: S. alnifolia var. angulata, S. alnifolia var. lobulata, S. dunnii, S. folgneri, S. hemsleyi, S. lushanensis, S. ochracea and S. yuana, which are endemic to China, were reported for the first time. One remaining Asian native S. alnifolia var. alnifolia, 2n = = 34 was confirmed here. The chromosomes size and the total haploid chromosome length varied from 0.91 µm to 3.51 µm and 23.43 μm to 50.04 μm respectively. A predominance of metacentric chromosomes were perceived. Satellites were observed only in taxa of subgenera Micromeles. Nine taxa were classified as 1A, 2A and 2B according to the Stebbins classification. S. yuana in subgenus Aria presents the most symmetrical karyotype according to all the 11 quantitative indices analyzed, while S. dunnii and S. ochracea in Micromeles present the most asymmetrical karyotypes according to different indices.

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor, containing GBM stem cells (GSCs) that contribute to therapeutic resistance and relapse. Exposing potential GSC vulnerabilities may provide therapeutic strategies against GBM. Here, we interrogated the role of adenosine-to-inosine (A-to-I) RNA editing mediated by adenosine deaminase acting on RNA 1 (ADAR1) in GSCs and found that both ADAR1 and global RNA editomes were elevated in GSCs compared with normal neural stem cells. ADAR1 inactivation or blocking of the upstream JAK/STAT pathway through TYK2 inhibition impaired GSC self-renewal and stemness. Downstream of ADAR1, RNA editing of the 3′-UTR of GM2A, a key ganglioside catabolism activator, proved to be critical, as interference with ganglioside catabolism and disruption of ADAR1 showed a similar functional impact on GSCs. These findings reveal that RNA editing links ganglioside catabolism to GSC self-renewal and stemness, exposing a potential vulnerability of GBM for therapeutic intervention.

The quality and yield of cashmere closely affect the economic benefits of cashmere goat farming. Studies have shown that controlling light can have an important impact on cashmere but can also affect the concentration of harmful gases. In order to explore the impact of a short photoperiod on the growth of cashmere and harmful gases in goat houses, 130 female (non-pregnant) Shanbei white cashmere goats, aged 4–5 years with similar body weights, were randomly divided into a control group and a treatment group, with 65 goats in each group. The dietary nutrition levels of the experimental goats were the same, and completely natural light was used in the control group; the light control group received light for 7 h every day (9:30–16:30), and the rest of the time (16:30–9:30 the next day) they did not receive light. The light control treatment was carried out in a control house, and the gas content was analyzed. It was found that a shortened period of light exposure could increase the annual average cashmere production by 34.5%. The content of each gas has a certain functional relationship with the measurement time period, but at the same time, we found that the content of NH3 also changes seasonally. In summary, the use of shortened light periods when raising cashmere goats can significantly increase cashmere production and quality, but at the same time, it will increase the concentration of harmful gases in the goat barn, and ventilation should be increased to ensure the health of the goats and the air quality in the barn.

Objective: This paper reports the clinical manifestation and auxiliary examination features of 15 Chinese patients with glial fibrillary acidic protein (GFAP) autoimmunity. Methods: From June 2016 to December 2019, patients suspected to have neurological autoimmune disease after having their serum and cerebrospinal fluid (CSF) tested for conventional neural antibodies were scanned for additional autoantibodies by immunohistochemistry. Samples that showed a characteristic immunoreactive pattern reminiscent of the GFAP of astrocytes were selected and confirmed by cell-based assay using cells-expressing human GFAPα. Results: A total of 15 patients (eight male and seven female) with a median age at onset of 53 years (range 28–72) were identified as GFAP-IgG-positive. Fourteen cases had GFAP-IgG detected in the CSF, while serum GFAP-IgG was detected in 11 cases. Eleven of the fifteen patients (73.3%) presented with an acute monophasic course, of which 10 (90.9%) had antecedent flu-like symptoms. The predominant phenotype was meningoencephalitis (46.7%), followed by meningoencephalomyelitis in 40% of the cases. The most common clinical features included long tract signs, brainstem symptoms, tremors, headaches, and psychiatric symptoms. Magnetic resonance imaging (MRI) revealed the enhancement of the meninges, the surface of the brainstem, the cerebellum, and the spinal cord as predominant. Inflammatory CSF showed mild lymphocyte-predominant pleocytosis with a median of 51/μL and elevated protein with a median of 87.5 mg/dL. Five patients had coexisting antibodies, including NMDAR-IgG in three patients and Yo and MOG-IgG in one patient each. One patient underwent a stereotactic brain biopsy, and the neuropathology diagnosis was diffuse large B-cell lymphoma. One patient had ovarian teratoma. Eleven of the fifteen (73.3%) patients received both intravenous immunoglobulin and steroids. Among them, three patients also received immunosuppressive agents later. During a two-year follow-up, 9 of the 15 (60%) patients achieved complete clinical remission. Conclusions: The clinical presentation of GFAP astrocytopathy is heterogeneous. It can be characterized by an acute monophasic course and a chronic relapsing course. Tremors are a prominent clinical manifestation in patients with an acute monophasic course with GFAP-IgG antibodies only. Most patients responded well to immunotherapy. In patients with GFAP autoimmunity, presenting with a chronic relapsing course, one should actively search for immunogenic factors and the culprit antibodies. In the case of primary central nervous system lymphoma, GFAP autoimmunity does not always equate to autoimmune GFAP astrocytopathy.

Abstract Epitaxial growth of III‐nitrides on 2D materials enables the realization of flexible optoelectronic devices for next‐generation wearable applications. Unfortunately, it is difficult to obtain high‐quality III‐nitride epilayers on 2D materials such as hexagonal BN (h‐BN) due to different atom hybridizations. Here, the epitaxy of single‐crystalline GaN films on the chemically activated h‐BN/Al2O3 substrates is reported, paying attention to interface atomic configuration. It is found that chemical‐activated h‐BN provides BON and NO bonds, where the latter ones act as effective artificial dangling bonds for following GaN nucleation, leading to Ga‐polar GaN films with a flat surface. The h‐BN is also found to be effective in modifying the compressive strain in GaN film and thus improves indium incorporation during the growth of InGaN quantum wells, resulting in the achievement of pure green light‐emitting diodes. This work provides an effective way for III‐nitrides epitaxy on h‐BN and a possible route to overcome the epitaxial bottleneck of high indium content III‐nitride light‐emitting devices.

A novel method which integrates polarimetric decomposition, object-oriented image analysis, and decision tree algorithms is presented for land-use and land-cover (LULC) classification using RADARSAT-2 polarimetric SAR (POLSAR) data. Polarimetric decomposition which is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects can be used to support the classification of POLSAR data. The main purposes of object-oriented image analysis are delineating image objects as well as extracting various textural and spatial features from image objects to improve classification accuracy. A decision tree algorithm provides on efficient way to select features and implement classification. Compared with the Wishart supervised classification which is based on the coherency matrix, the proposed method can significantly improve the overall accuracy and kappa value of LULC classification by 17.45 percent and 0.24, respectively. Further investigation was carried out on the contribution of polarimetric decomposition, object-oriented image analysis, and decision tree algorithms to the improvement achieved by the proposed method. The investigation shows that all these three methods contribute to the improvement achieved by the proposed method. [ABSTRACT FROM AUTHOR]

The paper deals with change detection using time series SAR images. SAR provides a unique opportunity for detecting land-use changes within short intervals (e.g., monthlyl in tropical and sub-tropical regions with cloud cover. Traditional change detection methods mainly rely on per-pixel spectral information but ignore per-object structural inform ation. In this study, a new method is presented that integrates object-oriented analysis with case-based reasoning (cBR) for change detection. Object-oriented analysis is carried out to retrieve a variety of features, such as tone, shape, texture, area, and context. An incremental segmentation technique is proposed for deriving change objects from multi-temporal Radarsat images. Feature selection based on genetic algorithms is carried out to determine the optimal set of features for change detection. A CBR matching algorithm is developed to identify the temporal positions and the kind of changes. It is based on the weighted k-Nearest Neighbor classification using an accumulative similarity measure. The comparison of the four combinations of change detection methods, object-based or pixel-based plus case-based or rule-based, is carried out to validate the performance of this proposed method. The analysis shows that this integrated approach has provided an efficient way of detecting land-use changes at monthly intervals by using multi-temporal SAR images. [ABSTRACT FROM AUTHOR]

Background and objective Lung cancer is the most common malignancy and is the leading cause of cancer-related death worldwide. Thus, this disease severely threatens human health. This study aims to identify the clinical epidemiology and histological characteristics of lung cancer patients in Sichuan areas. Methods We enrolled 3,761 lung-cancer patients who were identified as residents of Sichuan province and treated in West China Hospital from 2008 to 2013. Results A total of 3,663 patients from Central, Southern, North, and Western areas in Sichuan province, respectively, were enrolled. The average age of patients was 59.6 years, and patients were predominantly male (68.4%). Significant statistical differences were observed among the average age of patients, male, and pathological types in different regions (all P 60 yr) patients, and with adenocarcinoma of the lung. Patients of 2013 had lower rates of adenocarcinoma and squamous-cell carcinoma but higher rates of early-stage lung cancer and lymph-node metastasis. Furthermore, the 3-year OS was not the same in different regions.

Pancreatic cancer is a fatal malignancy worldwide and urgently requires valid therapies. Previous research showed that the HDAC inhibitor chidamide is a promising anti-cancer agent in pancreatic cancer cell lines. In this study, we elucidate a probable underlying anti-cancer mechanism of chidamide involving the degradation of Mcl-1. Mcl-1 is frequently upregulated in human cancers, which has been demonstrated to participate in oxidative phosphorylation, in addition to its anti-apoptotic actions as a Bcl-2 family member. The pancreatic cancer cell lines BxPC-3 and PANC-1 were treated with chidamide, resulting in Mcl-1 degradation accompanied by induction of Mcl-1 ubiquitination. Treatment with MG132, a proteasome inhibitor reduced Mcl-1 degradation stimulated by chidamide. Chidamide decreased O2 consumption and ATP production to inhibit aerobic metabolism in both pancreatic cancer cell lines and primary cells, similar to knockdown of Mcl-1, while overexpression of Mcl-1 in pancreatic cancer cells could restore the aerobic metabolism inhibited by chidamide. Furthermore, chidamide treatment or Mcl-1 knockdown significantly induced cell growth arrest in pancreatic cancer cell lines and primary cells, and Mcl-1 overexpression could reduce this cell growth inhibition. In conclusion, our results suggest that chidamide promotes Mcl-1 degradation through the ubiquitin-proteasome pathway, suppressing the maintenance of mitochondrial aerobic respiration by Mcl-1, and resulting in inhibition of pancreatic cancer cell proliferation. Our work supports the claim that chidamide has therapeutic potential for pancreatic cancer treatment.

Progestagen-associated endometrial protein (PAEP) is a glycoprotein of the lipocalin family that acts as a negative regulator of T cell receptor-mediated activation. However, the function of tumor-derived PAEP on the human immune system in the tumor microenvironment is unknown. PAEP is highly expressed in intermediate and thick primary melanomas (Breslow's 2.5mm or greater) and metastatic melanomas, correlating with its expression in daughter cell lines established in vitro. The current study investigates the role of melanoma cell-secreted PAEP protein in regulating T cell function. Upon the enrichment of CD3+, CD4+ and CD8+ T cells from human peripheral blood mononuclear cells, each subset was then mixed with either melanoma-derived PAEP protein or PAEP-poor supernatant of gene-silenced tumor cells. IL-2 and IFN-γ secretion of CD4+ T cells significantly decreased with the addition of PAEP-rich supernatant. And the addition of PAEP-positive cell supernatant to activated lymphocytes significantly inhibited lymphocyte proliferation and cytotoxic T cell activity, while increasing lymphocyte apoptosis. Our result suggests that melanoma cell-secreted PAEP protein immunosuppresses the activation, proliferation and cytotoxicity of T lymphocytes, which might partially explain the mechanism of immune tolerance induced by melanoma cells within the tumor microenvironment.

Glycogen synthase kinase 3 beta (GSK3β) is centrally involved in diverse cellular processes, including proliferation and apoptosis. This study aimed to investigate the influence of GSK3β expression on the prognosis of human non-small cell lung cancer (NSCLC) and the effects of GSK3β inhibition in NSCLC cell lines.Immunohistochemical and western blot assays were used to evaluate the GSK3β expression level in human NSCLC tissues. Lentiviral RNA interference was performed to inhibit the expression of GSK3β in the A549, H292, H1299 and SK-MES-1 cell lines. Cell survival, apoptosis and motility were evaluated in vivo and in vitro.The levels of GSK3β were greater in NSCLC tissues (n = 211) than in control tissues (n = 194) (P