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Intelligent control systems can effectively assist in the construction and management of laboratory animal facilities, improving operational efficiency, ensuring the reliability of animal experimental results, and significantly saving human resources. The intelligent control system for laboratory animal facilities at Shenzhen Institute for Drug Control was completed in April 2021. It includes an intelligent management platform and an information management system for animal laboratories. The intelligent management platform regulates room environment parameters such as temperature, humidity, and pressure through building equipment management system, controlling devices such as the Venturi valve, electric air valve, electric water valve, and steam humidification valve. At the same time, various environmental parameters are monitored online through the environmental monitoring system. The laboratory’s intelligence is further enhanced by systems such as automatic lighting control, full HD video monitoring, automatic access control and door system, independent ventilation and feeding, automatic cleaning, automatic exhaust gas treatment, centralized gas supply, and real-time instrument parameter monitoring. The information management system for animal laboratories integrates inspection, instrument and equipment, personnel, documents, standard substances, reagents, inspection standards, books, records, scientific research management, relevant applications, quality management, and query statistics. For animal experimentation, a management module has been developed to achieve a comprehensive digitization of animal management. Furthermore, real-time collection and recording of data such as balance calibration, sample quality, and animal weight are facilitated through electronic experimental recording. In summary, the Animal Laboratory of Shenzhen Institute for Drug Control has extensively utilized intelligent systems to achieve real-time online control and monitoring, improve efficiency, ensure high-quality facility operation, and meet standard requirements. Smooth execution of all inspection and research activities has been achieved over the past three years. This paper provides insights into the construction, management, and operation of laboratory animal facilities at Shenzhen Institute for Drug Control, offering guidance for the implementation of intelligent control in similar facilities across China.

Abstract Background Cardiovascular disease (CVD) is one among the major causes of mortality all round the globe. Several anti-platelet regimens have been proposed following percutaneous coronary intervention (PCI). In this analysis, we aimed to show the adverse clinical outcomes associated with ticagrelor monotherapy after a short course of dual antiplatelet therapy (DAPT) with ticagrelor and aspirin following PCI in patients with versus without diabetes mellitus (DM). Methods Electronic databases were searched by four authors from September to November 2023. Cardiovascular outcomes and bleeding events were the endpoints of this analysis. Revman 5.4 software was used to conduct this meta-analysis. Risk ratio (RR) and 95% confidence intervals (CI) were used to represent the results which were generated. Results Three studies with a total number of 22,574 participants enrolled from years 2013 to 2019 were included in this analysis. Results of this analysis showed that DM was associated with significantly higher risks of major adverse cardiovascular events (RR: 1.73, 95% CI: 1.49 – 2.00; P = 0.00001), all-cause mortality (RR: 2.15, 95% CI: 1.73 – 2.66; P = 0.00001), cardiac death (RR: 2.82, 95% CI: 1.42 – 5.60; P = 0.003), stroke (RR: 1.78, 95% CI: 1.16 – 2.74; P = 0.009), myocardial infarction (RR: 1.63, 95% CI: 1.17 – 2.26; P = 0.004) and stent thrombosis (RR: 1.74, 95% CI: 1.03 – 2.94; P = 0.04) when compared to patients without DM. However, thrombolysis in myocardial infarction (TIMI) defined minor and major bleedings, bleeding defined according to the academic research consortium (BARC) type 3c (RR: 1.31, 95% CI: 0.14 – 11.90; P = 0.81) and BARC type 2, 3 or 5 (RR: 1.17, 95% CI: 0.85 – 1.62; P = 0.34) were not significantly different. Conclusion In patients who were treated with ticagrelor monotherapy after a short course of DAPT with ticagrelor and aspirin, DM was an independent risk factor for the significantly increased adverse cardiovascular outcomes. However, TIMI and BARC defined bleeding events were not significantly different in patients with versus without DM.

Objective To study the effect of triptolide(TPL)combined with chemotherapy drug doxorubicin(DOX)on apoptosis of human breast cancer cell line MCF-7 and its molecular mechanism. Methods MCF-7 cells were treated with triptolide and different concentrations of doxorubicin alone or in combination. Cell viability was analyzed by CCK-8 method with the combination index calculated. Cell proliferation was detected by plate cloning formation assay. Cell apoptosis and cell cycle were detected by flow cytometry. The expressions of apoptosis-related proteins Bcl-2, Bax, cleaved caspase-3 and proteins in PI3K/AKT signaling pathway were analyzed by Western blot. Results Triptolide combined with different concentrations of doxorubicin significantly inhibited cell proliferation as compared with the doxorubicin group alone after incubation for 24, 48 and 72 hours(P＜0.05). The clone formation was restrained and the cell apoptosis rate was significantly higher(P＜0.01). The expressions of Bax and cleaved caspase-3 was up-regulated, and the expressions of Bcl-2, p-PI3K, p-AKT was down-regulated(P＜0.05). The cells were blocked in S phase after combination treatment of the two drugs(P＜0.05). Conclusions The combination of triptolide and doxorubicin significantly increases the sensitivity of MCF-7 cells to doxorubicin and promotes the apoptosis of cells, which might be related to the inhibition of PI3K/AKT signaling pathway.

To achieve carbon peaking and carbon neutrality, China has deepened its energy revolution with the largest renewable energy power generation capacity in the world.In face of the unstable power supply of large-scale renewable energy, a new power system has been proposed and constantly upgraded, which promoted the construction and development of pumped storage power station.Under terrain constraints and the ecological red line, the site selection of traditional pumped storage power station is a hard nut to crack.At the same time, a large number of closed/abandoned mines and resource exhausted cities emerged in the process of coal capacity reduction.This study gave a detailed overview of the status quo of pumped storage power station in China, and analyzed the environmental benefits of the combination of closed/abandoned mines and pumped storage, such as water resource pressure relief and greenhouse gas emission reduction.Based on the different modes of closed/abandoned mine pumped storage power stations, this study analyzed the application potential of closed/abandoned mine pumped storage.Finally, this paper proposed the development strategy of China's abandoned mine pumped storage, and the prospects of closed/abandoned mine+energy storage in multiple industries.

Background Allergic rhinitis (AR) is a highly prevalent chronic non-communicable disease. The research on the understanding and treatment of AR in China is mainly in otorhinolaryngology specialists, but rarely in general practitioners (GPs) in primary care. And recommendations on the diagnosis and treatment of AR in primary care are also insufficient. Objective To explore the understanding of AR and diagnosis and treatment capacities related to AR in GPs in primary care. Methods By use of simple random sampling, 432 GPs from 21 community health service organizations in Beijing's Chaoyang District were chosen between August and September 2020. The questionnaire was combined with the Guidelines for the Diagnosis and Treatment of Allergic Rhinitis (Tianjin, 2015) (China 2015 AR Guidelines) and Allergic Rhinitis and Its Impact on Asthma (ARIA) Guidelines: 2010 Revision, and its 2016 annual revision, and refinement of the questionnaire after consultation with experts and pre-survey. The information obtained from the questionnaire included basic information about the subjects, understanding level, diagnostic and therapeutic behaviors, training, and support needs regarding AR, et al. Results The 383 (88.7%) of the 432 questionnaires were finally returned. Only 0.8% (3/383) of the GPs correctly responded to all of the questions on typical AR symptoms, diagnosis, treatment philosophies, first-line drug classes, and regimens suggested by guidelines. Guidelines for AR, including China 2015 AR Guidelines and ARIA Guidelines, were known to 32.4% (124/383) of the GPs, Web-based continuing education such as www.haoyisheng.com, www.dxy.cn and WeChat and related training. When treating each patient with a respiratory illness, the prevalence of GPs differentiating AR from others, providing recommendations on environmental control, suggesting nasal rinsing, recommending other treatments instead of immunotherapy, and referring the patient to the specialty department without any treatment, was 59.8% (229/383) , 37.1% (142/383) , 17.8% (68/383) , 49.4% (189/383) , and 13.1% (50/383) , respectively. In terms of pharmacological treatment, 17.5% (67/383) of GPs said their hospital offered all four classes of first-line AR medications. As for AR-related training, 75.7% (366/383) of the GPs reported not having taken any AR-related training in 2019; and 91.7% (266/290) needed the training. And 95.6% (290/383) of the GPs said AR should be handled standardizedly in the community. A multivariate stepwise regression Logistic analysis revealed that when encountering patients with respiratory symptoms, GPs with a master's degree or higher〔OR (95%CI) =2.790 (1.057, 7.366) 〕and a good grasp of AR-related health knowledge〔OR (95%CI) =3.537 (2.015, 6.209) 〕were more likely to make a differential diagnosis of AR from other illnesses, GPs with a good grasp of AR-related health knowledge〔OR (95%CI) =4.397 (0.534, 1.576) 〕were more likely to offer patients guidance on environmental control behaviors, GPs who were familiar with nasal irrigation procedures〔OR (95%CI) =6.592 (3.038, 14.306) 〕were more likely to recommend nasal irrigation, and GPs knowing about immunotherapy〔OR (95%CI) =1.881 (1.087, 3.254) 〕, accurately answering questions on the principles of treatment〔OR (95%CI) =128.330 (16.628, 990.402) 〕or their institution providing some/all laboratory testing services〔OR (95%CI) =2.210 (1.299, 3.760) 〕were prone to recommend immunotherapy. Conclusion Despite their low awareness levels of AR expertise and guidelines, and unsatisfied practice standardization, GPs in primary care in Chaoyang District demonstrated proactive attitude towards continuing education and carrying out standardized AR treatment in primary care. As high awareness levels of AR-related knowledge and guidelines promote the practice standardization, relevant trainings for GPs in primary care should be strengthened, and AR-related guidelines applicable to primary care should be developed, which can provide support for the standardization of AR treatment at the primary care level.

Camellia oleifera, a key economic forestry species in southern China, struggles with low productivity due to suboptimal planting management. Recently, transforming old or unadopted varieties of C. oleifera plantations has been recognized as a means to enhance economic benefits and production. However, the impact of these transformations on soil properties and fungal communities has received little attention. In this study, we targeted pre-renewal old C. oleifera and post-renewal young C. oleifera, Pinus massoniana, and Cunninghamia lanceolata. Through field sampling and soil physicochemical property analysis, we developed a soil quality evaluation system that effectively analyzes fungal community structures and identifies key arbuscular mycorrhizal fungi (AMF) species for soil health. We found that the soil quality evaluation system for this region comprises pH, TK, AK, NO3−, PO4− BG, ACP, F.simpson, AMF.shannon, and AMF.ace, which collectively indicated significant improvements in soil quality following transformation. Notably, the nutritional characteristics of the dominant fungal communities underwent marked changes, with an increase in pathogenic fungi in young C. oleifera and an expansion of ectomycorrhizal fungi in P. massoniana forests. The AMF communities in all four types of forest exhibited aggregation, and Scutellospora and Diversispora emerged as key species in the AMF community of C. oleifera. Additionally, Mortierella and Trichoderma were found to enhance plant resistance to pathogenic fungi. This study demonstrates that forestland transformation positively impacts soil quality and fungal community structure in C. oleifera, which provides valuable insights for future soil management in the region, both in terms of soil quality evaluation and fungal conservation.

Objective To explore the mechanism of phosphorylation of caveolin1 (Cav1) in neurological injury after cerebral ischemia-reperfusion in rats. Methods The rats were randomly divided into sham group, model group and inhibitor group(PP2). Neurological function was evaluated by modified neurological deficit score (mNSS) neurobehavioral score, cerebral infarction volume was evaluated by triphenyltetrazolium chloride (TTC) staining, histomorphology after cerebral ischemia-reperfusion was evaluated by Nissl staining, and apoptosis of peri-infarcted tissue was detected by Tunel staining. Western blot and immunofluorescence were used to detect the protein expression of p-Cav1, glial fibrillary acidic protein (GFAP) and ion calcium binding junction molecule 1 (Iba1) in the tissue around cerebral infarction. Results Compared with the sham operation group, there were significantly increased mNSS score, volume of cerebral infarction, number of apoptotic cells and expression of p-Cav1, GFAP and Iba1 in the model group,while the number of Nissl corpuscles decreased. The inhibitor PP2 reduced the changes of the above-mentioned indexes and protect the neurological function of rats with cerebral ischemia-reperfusion injury. Conclusions Inhibition of p-Cav1 may improve the neurological function after cerebral ischemia-reperfusion in rats with potential mechanism of inhibiting glial cell activation and interaction.

When large-scale photovoltaic is connected to the interconnected power grid, the problem of voltage and frequency stability is prominent due to the shortage of photovoltaic active power output during low voltage fault. Firstly, this paper established the frequency response model of the combination of hydropower and photovoltaic, and theoretically analyzed the correlation between the system frequency and the key parameters of photovoltaic active power of LVRT. Then, through the normalized sensitivity index of photovoltaic parameters, it quantitatively analyzed the impact of key parameters of photovoltaic LVRT active power on transient frequency. Thirdly, based on the above normalized sensitivity, the optimization model of photovoltaic active power parameters meeting multi-scenario transient stability constraints was established with the objective of minimizing frequency deviation. Finally, the adaptability of the optimized parameters was verified through an example. With the AC line N-2 fault as an example, the minimum transient frequency is 49.5 Hz in forward mode, which increases by 1.5 Hz. Meanwhile, the maximum transient frequency is 50.4 Hz in outgoing mode. Both scenarios meet the requirements of frequency stability, which verifies that the proposed method can improve the stability of system frequency.

Remote sensing (RS) scene classification plays an important role in a wide range of RS applications. Recently, convolutional neural networks (CNNs) have been applied to the field of scene classification in RS images and achieved impressive performance. However, to classify RS scenes, most of the existing CNN methods either utilize the high-level features from the last convolutional layer of CNNs, missing much important information existing at the other levels, or directly fuse the features at different levels, bringing redundant and/or mutually exclusive information. Inspired by the attention mechanism of the human visual system, in this article, we explore a novel relation-attention model and design an end-to-end relation-attention network (RANet) to learn powerful feature representations of multiple levels to further improve the classification performance. First, we propose to extract convolutional features at different levels by pretrained CNNs. Second, a multiscale feature computation module is constructed to connect features at different levels and generate multiscale semantic features. Third, a novel relation-attention model is designed to focus on the critical features whilst avoiding the use of redundant and even distractive ones by exploiting the scale contextual information. Finally, the resulting relation-attention features are concatenated and fed into a softmax layer for the final classification. Experiments on four well-known RS scene classification datasets (UC-Merced, WHU-RS19, AID, and OPTIMAL-31) show that our method outperforms some state-of-the-art algorithms.

Prevention and control of waste plastics is an important component of the national strategy in China. In this study, the sources as well as the current situation and measures for recycling of waste plastics in China were comprehensively analyzed. We proposed an all-rounded tactic for the prevention and control of plastic pollution in China based on the life-cycle assessment of plastics. Technically, waste plastic pollution should be prevented and controlled throughout the entire process covering plastic synthesis, processing, utilization, and recycling; specifically, it includes reduction of waste plastics from the origin by developing new degradable raw materials and their environmentally friendly alternatives, design and processing of high-performance plastic products with extended service life, development of new technologies offering efficient and large-scale capacity for recycling waste plastics, as well as safe disposal of the ultimate plastic wastes. At the government level, policy guidance must be strengthened, and administrative supervision be implemented. At the enterprise level, an effective recycling system should be instituted to clarify recycling responsibilities of producers, sellers, and consumers, providing policy and technical suggestions for the prevention and control of waste plastics. At the public level, citizens’ environmental awareness must be aroused to promote a nationwide participation in waste management. These suggestions are favorable to a more green and sustainable development of China’s plastic industry and even national economy.

To explore the capacity value of a concentrating solar power (CSP) station,a method to optimize the thermal collection area and thermal storage capacity of a CSP station based on the credible capacity and levelized cost of energy (LCOE) is proposed in this paper. Firstly,generating efficiency model and economical model of CSP station are established in this paper. Secondly,the generation reliability of power generation system is calculated based on sequential Monte-Carlo method,and the particle swarm optimization (PSO) is utilized to search the credible capacity of the CSP station. Tthen the influences of solar multiple and thermal storage time on capacity credibility and LCOE of CSP station are studied. With credible capacity and levelized cost of energy as the optimization goal,weighted ideal point solution and entropy weight method are utilized to create a single objective optimization function and to determine the index weight respectively. Taking a CSP planning in the northwest as an example,using the region's real solar irradiation data,a model is established. The results show that the capacity credibility of CSP station increases monotonically with the increase of solar multiple and thermal storage time. At the same time,the levelized cost of energy decreases at the first stage and then increases at the second stage. The optimal solar multiple and thermal storage time is obtained under the constraints.

With the vigorous development of Internet technology and the popularization of smart devices, while the amount of multimedia data exploding, their forms become increasingly diverse. People's demand for information is no longer satisfied with single-modal data retrieval. Realizing cross-modal retrieval through knowledge collaboration of different modalities has become a research hotspot in recent years. On the basis of in-depth understanding and analysis of the research background and progress of cross-modal retrieval, with the key technology of cross-modal retrieval, public subspace modeling as the main line, this paper analyzes three types of methods of cross-modal retrieval technology: traditional statistical analysis, deep learning, and Hash learning. This paper conducts a comprehensive and multi-angle comparative analysis on the research content, key technology, limitations, applicability and characteristics from different angles, and experiments are done for more in-depth comparisons. Finally, the difficulties to be solved in cross-modal retrieval, future exploration directions, mainstream design ideas and development trends in recent years are fully prospected to provide a theoretical basis for further research.

Objective To explore the pathophysiologic mechanism of SNCA gene in the early⁃stage of Parkinson's disease (PD). Methods Wild type (WT group, n=10) and SNCA Ala53Thr base substitution mice (TG group, n=10) were selected for further experiments. Endogenous metabolites in olfactory bulb (OB) of transgenic and wild type mice were detected and analyzed by high performance liquid chromatography⁃tandem mass spectrometry (HPLC⁃MS/MS). Then the endogenous metabolites were identified through mzCloud and determined by molecular formula and molecular weight. The differential endogenous metabolites were obtained by principal component analysis (PCA), partial least squares discrimination analysis (PLS⁃DA), orthogonal partial least squares discriminant analysis (OPLS⁃DA) and cluster analysis. Finally, the pathway and interaction network between the differential endogenous metabolites and corresponding pathways were constructed. Results Finally 29 variations were identified as differential metabolites. Among them, the relative expressions of phosphatidylcholine (PC), phosphatidylethanolamine (PE), vitamin C, sphingomyelin (SM), phosphatidylglycerol (PG) and glutamic acid were elevated in TG group (P＜0.05, for all) and the negative relationship was shown between elevated metabolite and decreased metabolite in relative expression, while the relative expressions of phosphalipids, taurine, ceramide valine and γ⁃aminobutyric acid (GABA) were decreased in TG group (P＜0.05, for all) and the relationship of relative expression among them was positive. The related metabolic pathways were mainly associated with the taurine and hypotaurine metabolism, glutamine and glutamate metabolism, ascorbate and aldarate metabolism, glycerophospholipid metabolism and alanine, aspartate and glutamate metabolism, included taurine, D⁃glutamate, vitamine C, phospholipid, LysoPC [18: 2 (9Z, 12Z)] and GABA all 6 significant metabolites. Conclusions The experiment verified pathological changes of OB in the early stage of PD. Meanwhile the differences in phosphatides could be a direct result of SNCA Ala53Thr mutation. Furthermore, malfunction of neurons in OB is also observed and may be contributed to the abnormal phosphatides' metabolism.

In recent years, cardiovascular and cerebrovascular diseases have been the focus of several studies. In this study, oyster protein hydrolysate was produced via enzyme hydrolysis and used as a fermentation substrate to ferment recombinant strain PSP2 to produce nattokinase. Using the synergism strategy, fermentation products with fibrinolytic and angiotensin I-converting enzyme (ACE) inhibitory activities were obtained and evaluated. The fermentation medium contained 1.0% trypsin, 1.0% oyster protein hydrolysate, 2.0% maltose, and 0.5% sodium chloride, with an initial pH of 7.0. The maximum nattokinase activity was 390.23 ± 10.24 FU/mL after 72 h of fermentation. The flavor of the product was improved, and heavy metals and volatile salt nitrogen were partially removed via fermentation. The ACE inhibitory activity (IC50) of the fermentation products was 1.433 mg/mL. This study provides a novel approach for the development of marine functional foods with hypotensive and antithrombotic properties.

Different from the traditional remote sensing (RS) scene classification which uses a single scene label to holistically annotate an image, multilabel RS image classification uses a series of object labels to interpret a scene more deeply. For multilabel RS scene classification, there exist two vital problems. First, the objects with different semantic labels have smaller sizes and more scattered arrangements compared to backgrounds, making meaningful semantic feature extraction and representation severely hard. Second, an RS scene usually contains various kinds of objects, leading to exponential magnification of output label space size with the increase of the number of object categories. To simultaneously solve the challenges in features as well as label space and produce significant performance improvements, this article proposes a novel end-to-end deep learning architecture, which we term the global context-based multilevel feature fusion network. We verify the whole framework by conducting a great number of experiments on two publicly available multilabel datasets, and we also provide an ablation study exploring different modules inclusion in the framework. Experimental results demonstrate that the proposed method is superior to some popular networks for multilabel RS image scene classification.

Understanding the scenes provided by remote sensing (RS) images has drawn increasing attention in the last decade. It is to automatically classify a RS scene image by feature extraction and label assignment. Although much effort has been dedicated to developing discriminative feature extraction as well as automatic classification techniques, it is still very challenging owing to the complex distributions of the ground objects in high spatial resolution scenes. To enhance the ability to represent the RS scenes, an integration of multiple types of features for remote sensing scenes is considered as an effective way. Nevertheless, different kinds of features possess different characteristics, and how to fuse them together is a critical problem. In this paper, to fuse three different but complementary types of features that are extracted to characterize global attributes of scenes together, a discriminant correlation analysis based feature-level fusion approach is proposed, which maximizes the correlation of corresponding features across the two feature sets and in addition de-correlates features that belong to different classes within each feature set. In addition, to further improve the scene classification performance, another kind of information fusion form, called decision-level fusion is adopted in the classification stage. In our proposed decision-level fusion technique, the final results of multiple classifiers are combined via majority voting. Extensive experiments results conducted on the well-known SIRI-WHU dataset, the WHU-RS dataset and the UC Merced Land Use dataset have demonstrated that the proposed remote sensing scene classification method based on heterogeneous feature extraction and multi-level fusion is superior to many state-of-the-art scene classification algorithms.

Based on the energy consumption and economic data of a heavy chemical industrial park in northwest China from 2013 to 2020, the carbon emissions of the park were calculated. The generalized Divisia index method (GDIM) was employed to analyze the effects of eight absolute and ten relative indicators of carbon emission changes in the heavy chemical industrial park. The results show that optimizing the electricity structure, reducing the energy output and carbon output intensities, and improving investment efficiency have a significant effect on inhibiting carbon emission. The inhibitory effect from reducing investment carbon intensity, fossil energy, and the total energy consumption scale was clearly evident. The deterioration of fossil fuel energy structure, and the increase in investment, output, and total electricity consumption scales are the main factors affecting carbon emission growth. Carbon emissions from industrial processes have a limited impact on carbon emissions. Finally, pertinent suggestions are provided.

As important decomposers and plant symbionts, soil fungal communities play a major role in remediating heavy-metal-polluted soils. However, the diversity and structures of fungal communities generally remain unclear in mining areas. This study aimed to assess the rhizospheric fungal-community composition of Masson pine (Pinus massoniana) in the lead-zinc mining area of Suxian District, Hunan Province, China. This experiment undertook the following three treatments: Masson pine inoculated with or not inoculated with Suillus luteus, and bulk soil without plants as a control. The results thereof showed that inoculation of ectomycorrhizal fungi could enlarge plants’ capability to absorb heavy metals and secrete soil enzymes. The richness and diversity of fungi in the rhizospheric soil were significantly higher than of those in the bulk soil (p < 0.05), but no significant difference was noted between the rhizospheric soils inoculated with and not inoculated with ectomycorrhizal (ECM) fungi as the community structure changed. The rhizospheric fungi belonged to 6 phyla, 25 classes, 65 orders, 115 families, and 150 genera, and the dominant phyla were Chytridiomycota (50.49%), Ascomycota (38.54%), and Basidiomycota (9.02%). Through use of LEfSe and heatmapping, the relative abundances of Suillus, Paraglomus, Agaricus, and Tulasnella were found to be the highest in the soil with ECM fungus inoculation. RDA showed that the community structure nearly changed with ECM-fungus inoculation; this was significantly related to soil water content, the carbon–nitrogen ratio, bulk density, available potassium, and soil enzymes. Altogether, inoculation with ECM fungi may change the habitation environments of microorganisms and dominant fungi in soil, providing keystone screenings in heavy-metal-contaminated mining areas.

Urbanization alters land uses and creates heterogeneous environmental conditions in cities and their surroundings, which may directly or indirectly impact soil microorganisms. However, how urbanization affects soil bacterial diversity and community composition, particularly in different land use types, remains largely unknown. In this study, we collected 36 soil samples (18 forest and 18 grass soils) along a rural-suburban-urban gradient in Chang-Zhu-Tan agglomeration. The bacterial diversity and community composition were investigated using 16S rRNA gene sequencing that targeted the V3-V4 region. Our results showed that urbanization induced shifts in bacterial diversity and community composition in both forestlands and grasslands. Specifically, soil bacterial diversity was higher in urban areas than in their suburban and rural counterparts in forests and grasslands, particularly in forests, where significant increases were detected. Urbanization changed the most dominated soil bacterial community from Acidobacteria to Proteobacteria in forestland. Significant decrease and increase were observed in the relative abundance of Acidobacteria (e.g., Acidobacteriales, Acidobacteriia_Subgroup2 and Solibacterales) and Proteobacteria (e.g., Betaproteobacteriales, Myxococcales and Sphingomonadales), respectively, in the forests with increasing urbanization intensity. In contrast, Proteobacteria always dominated the soil bacterial community along the rural-suburban-urban gradient in grassland, and significant decrease and increase in Nitrospirae and Latescibacteria were induced by urbanization, respectively. In addition to urbanization and total nitrogen, total organic carbon and ratio of carbon and nitrogen were the main factors that related with the bacterial community in forest soils, whereas soil water content was the main factor related with soil bacterial community in the grasslands. Together, our results indicate that the urbanization results in shifts in bacterial community composition and diversity, but the extent varied between forest and grassland, which may due to different human management intensity.

Purpose: The combination therapy of rosuvastatin (RSV) and the platelet inhibitor clopidogrel (CP) is widely accepted in the management of cardiovascular diseases. The objective of the present study was to identify the mechanism of RSV–CP DDI and evaluate the risk of hepatotoxicity associated with the concomitant use of CP.Methods: We first studied the effect of CP and its major circulating metabolite, carboxylic acid metabolite (CPC), on RSV transport by overexpressing cells and membrane vesicles. Second, we investigated whether a rat model could replicate this DDI and then be used to conduct mechanistic studies and assess the risk of hepatotoxicity. Then, cytotoxicity assay in hepatocytes, biochemical examination, and histopathology were performed to measure the magnitude of liver injury in the presence and absence of DDI.Results: CP inhibited OATP1B1-mediated transport of RSV with an IC50 value of 27.39 μM. CP and CPC inhibited BCRP-mediated RSV transport with IC50 values of

Abstract Background Chronic kidney disease (CKD) and end stage renal disease (ESRD) are not well characterized in prostate cancer patients. This study aimed to examine the clinical characteristics and postsurgical outcomes of patients with or without CKD and ESRD undergoing radical prostatectomy for prostate cancer. Methods This population-based, retrospective study used patient data from the Nationwide Inpatient Sample, the largest all-payer US inpatient care database. From 2005 to 2014, 136,790 male patients aged > 20 years diagnosed with prostate cancer and who received radical prostatectomy were included. Postoperative complications, postoperative acute kidney injury (AKI) and urinary complications, and length of hospital stay were compared between patients with or without underlying CKD and ESRD. Results After adjusting for relevant factors, the CKD group had a significantly higher risk of postoperative complications than the non-CKD group. In addition, the CKD group had a 5-times greater risk of postoperative AKI and urinary complications than the non-CKD group. Both CKD and ESRD groups had significantly longer hospital stays than the non-CKD group. Patients receiving RARP had a lower risk of postoperative complications than those who received open radical prostatectomy, regardless of having CKD or not. Both non-CKD and CKD patients receiving RARP had shorter hospital stays than those who received open surgery. Conclusions Prostate cancer patients with underlying CKD had significantly greater risk of postoperative complications, postoperative AKI and urinary complications, and longer hospital stays than those without CKD. The use of RARP significantly shortened hospital stays and reduced complications for these patients.

As an extension of traditional sparse representation (SR), kernel SR has received great interest recently in the areas of computer vision and pattern recognition. It shows a considerable capacity to map linearly inseparable data into high-dimensional feature space via nonlinear mapping technique, and has been widely used in target recognition problems. In this paper, we propose a new weighted multi-task kernel sparse representation method to solve the synthetic aperture radar (SAR) target recognition problem. To capture the spatial and spectral information of a SAR target simultaneously, the proposed method explores the monogenic signal transformation to generate multi-scale monogenic features at first. Then, the proposed method provides a unified framework, named multi-task kernel sparse representation, for SAR target classification. The framework implicitly maps monogenic features into a high-dimensional kernel feature space by using the nonlinear mapping associated with a kernel function. In the kernelized subspace, SAR target recognition is formulated as a joint covariate selection problem across a group of related tasks. Furthermore, a multi-task weight optimization scheme is developed to compensate for the heterogeneity of the multi-scale features and enhance the recognition performance. Extensive experimental results tested on the public moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that our proposed method achieves better recognition performance than other existing competitive algorithms.

For remote sensing (RS) scene classification, most of the existing techniques annotate a scene image with merely a single semantic label. However, with the recent advance of remote sensing technology, more abundant information is contained in high-resolution scenes, making a scene image having multiple semantic meanings (i.e., multilabels). Since multi-label RS scene image annotation is a domain full of challenges due to the ambiguities between complicated scene contents and labels, it motivates us to present a novel algorithm which is based on multi-bag integration. First, to describe the semantic content of RS scene image, we propose to partition a scene image into image patches, defined by a regular grid, and extract the heterogeneous features within each. Second, two kinds of image instance bag, namely segmented instance bag (SIB) and layered instance bag (LIB), are designed to represent the scene image. Third, a Mahalanobis distance-based K-Medoids approach is applied to cluster SIB and LIB, respectively, to convert the multi-instance into single-instance, and then the obtained two single-instances are concatenated to generate more powerful scene-aware representation. At last, a multi-class classification technique is used to make predictions on the class labels. Experiments are performed on real remote sensing images and the results show that the proposed method is valid and can achieve superior performance to a number of state-of-the-art approaches.

BackgroundThe main ore-bearing rock of the Husab Uranium Mine in Namibia, southern Africa, is white granite, and the radioactive element thorium content in the ore is high, which has a greater impact on the γ ray logging results.PurposeThis study aims to solve the problem of thorium's interference on the γ ray logging results by developing a γ ray spectrum logging instrument.MethodsA high-resolution LaBr3(Ce) scintillation detector was employed to build a new type of γ ray spectrum logging tool. Combined with cross-segment method of γ ray spectrum and techniques for separating the γ ray spectrum, the counting rates of thorium, uranium and potassium nuclides were greatly improved, so did the measuring range of the tool and logging speed. Experiment and calibration tests of this logging instrument were conducted on control holes, blast holes, national standard uranium calibration pit, etc., to verify its range, speed applicability, stability, repeatability, etc.ResultsTests results show that the γ ray spectrum logging achieves upper limit of uranium content measurement of 5.0% eU, and the logging speed of 0.067 m·s-1, satisfying the rapid quantitative measurement of uranium grades in the blast holes of the Husab Mine and accurately estimated the amount of uranium with superior measuring accuracy to the γ ray logging method.ConclusionsThe γ ray logging tool of this study reaches specification requirements of γ ray logging. It can effectively distinguish thorium, uranium and potassium nuclides, realizing the quantitative measurement of uranium content.

Glucose oxidase (GOx) with high enzyme activity at low temperature (4°C) is potentially useful for food preservation, especially for aquatic products preservation. A cold-active GOx with approximately 83% similarity to known protein sequences, was isolated from Penicillium sp. MX3343 and expressed in Pichia pastoris X33. Through high cell density fermentation, the yield of recombinant enzyme (named GOxP5) reached 458.6 U/mL. GOxP5 showed optimal activity at 30°C and pH 5.5, and was stable at a broad pH range from pH 2–6. Moreover, GOxP5 could maintain 72% maximum activity at 4°C, suggesting its application for the preservation of aquatic products at low-temperatures. Importantly, GOxP5 showed a good antimicrobial effect against common fish pathogenic bacteria (Listeria monocytogenes and Vibrio parahaemolyticus). Moreover, sensory, microbiological (total bacterial count), and physicochemical (total volatile basic nitrogen and pH) systematic analyses proved GOxP5 to be an excellent freshness preserving agent in the context of the grass carp. These favorable enzymatic properties of GOxP5 make it potentially useful in food biopreservation, and the effect was better compared to the commonly used chemical preservatives.

Soil microbial communities potentially serve as indicators for their responses to changes in various ecosystems at scales from a region to the globe. However, changes in wetland soil bacterial communities and how they are related to urbanization intensities remains poorly understood. Here, we collected 60 soil samples along urbanization intensity gradients from 20 wetlands. We measured a range of environmental factors and characterized bacterial communities structure using 16S ribosomal RNA (16S rRNA) gene amplicon sequencing that targeted the V4-V5 region. Our results revealed the dominant soil bacterial phyla included Proteobacteria (39.3%), Acidobacteria (21.4%) and Chloroflexi (12.3%) in the wetlands, and showed a significant divergence of composition in intensive urbanization area (UI_4) than other places. A critical ‘threshold’ exists in the soil bacterial diversity, demonstrating different patterns: a gradual increase in the areas of low-to-intermediate disturbances but a significant decrease in highly urbanized areas where metabolic functions were significantly strong. Additionally, soil pH, total phosphorus (TP), available phosphorus (AP) and ammonia nitrogen (NH _4 ^+ -N) made a significant contribution to variations in bacterial communities, explaining 49.6%, 35.1%, 26.2% and 30.7% of the total variance, respectively. pH and NH _4 ^+ -N were identified as the main environmental drivers to determine bacterial community structure and diversity in the urban wetlands. Our results highlight collective changes in multiple environmental variables induced by urbanization rather than by the proportion of impervious surface area (ISA), which were potentially attributed to the spatial heterogeneity along different urbanization gradients.

The impact of rapid urbanization on food security of China has received considerable attention. It is not clear whether China can strike a balance between urbanization and food security, especially grain security. There have been numerous studies examining the effects of urbanization on grain production or consumption, but few studies have yet analyzed grain balances. Based on the Chinese World Agricultural Regional Market Equilibrium Model (CWARMEM), this paper explores the impacts of urbanization on national and regional grain balances through different scenarios. The CWARMEM is a global partial equilibrium multimarket model which captures the differences between urban and rural residents as well as the effects of globalization. The results show that urbanization has a small negative effect on maintaining grain self-sufficiency. Despite of that, China is able to achieve the objective of grain security set by its policymakers. Moreover, urbanization changes regional grain balances across China: The position of Northeast China and North China, as two dominant grain suppliers of China, will be weaken; Central China and East China will increase dependence on other grain suppliers; the grain surplus of Northwest China will increase slightly. Besides, in terms of grain category, urbanization helps China achieve self-sufficiency in food grain (rice and wheat), while expands deficit of feed grain (maize).

In order to achieve weak target detection, the range migration and Doppler frequency spread induced by the target motion within the coherent processing interval should be compensated to focus the target energy in a certain range-Doppler cell. This article proposes a new integration method based on non-uniform Keystone transform and non-uniform fast Fourier transform (NUFFT) for random pulse repetition interval (PRI) radar. In this method, non-uniform Keystone transform is applied to correct the linear range migration. After that, the acceleration is estimated by dechirp process and the Doppler frequency spread is compensated. Finally, the target energy is accumulated coherently via NUFFT. Compared with the random PRI generalised Radon-Fourier transform (RPRI-GRFT) and the moving target detection method for random PRI radar, the proposed algorithm achieves a good balance between the detection performance and the computational cost. Simulation results are provided to demonstrate the effectiveness of the proposed method.