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The study was conducted to enhance the food security guarantee capability of the main sales area in the event of major public safety emergencies, clarify the provincial grain production capacity and spatial changes in the production and sales areas, and further improve the national food security governance capacity and long-term mechanism under the background of the unified national market in the new era. Using ArcGIS technology combined with statistical analysis methods, the study analyzes the spatial evolution of grain production and area since the founding of the People's Republic of China, by taking the main grain production areas, main sales areas, as well as production and marketing balance areas in China as the research objects. The grain export rate of the main grain production areas from 2011 to 2023 is also calculated. Research has found that the net export of grain in China is more concentrated in four provinces including Heilongjiang, Henan, Shandong, and Anhui, while the grain export rates of provinces such as Sichuan, Jiangsu, Hubei, and Hunan continue to decline, and even show a tendency towards "regional sales". There are structural problems such as regional imbalance strengthening, production and sales mismatch, and grain and feed mismatch in China's food security. The tr ansportation distance between production and sales across provinces is too far and the normalized cost is high. The logistics channels are basically independent of each other, with poor interoperability and connectivity. It is proposed to further strengthen the requirement of ensuring a certain self-sufficiency rate in the main sales areas by adopting an all-encompassing approach to food, focus on improving the grain production and emergency reserve capacity of the main sales areas, establish a long-term mechanism for cross regional cooperation in grain "production and sales"; and enhance the endogenous enthusiasm of grain producers in the main sales areas and avoid "sports style" production methods.

The success of messenger RNA (mRNA) vaccines in controlling COVID-19 has warranted further developments in new technology. Currently, their quality control process largely relies on low-resolution electrophoresis for detecting chain breaks. Here, we present an approach using multi-primer reverse transcription sequencing (MPRT-seq) to identify degradation fragments in mRNA products. Using this in-house-made mRNA containing two antigens and untranslated regions (UTRs), we analyzed the mRNA completeness and degradation pattern at a nucleotide resolution. We then analyzed the sensitive base sequence and its correlation with the secondary structure. Our MPRT-seq mapping shows that certain sequences on the 5′ of bulge–stem–loop structures can result in preferential chain breaks. Our results agree with commonly used capillary electrophoresis (CE) integrity analysis but at a much higher resolution, and can improve mRNA stability by providing information to remove sensitive structures or sequences in the mRNA sequence design.

6:2 fluorotelomer carboxylic acid (6:2 FTCA) is a perfluorooctanoic acid (PFOA) substitute, which is supposedly less accumulative and toxic than PFOA. However, 6:2 FTCA is structurally similar to PFOA, and there had already been reports about its toxicities comparable to PFOA. The aim of the current study is to assess potential effects of developmental exposure to 6:2 FTCA on the development of kidney in chicken embryo and to investigate underlying mechanism. Fertile chicken eggs were exposed to 1.25 mg/kg, 2.5 mg/kg or 5 mg/kg doses of 6:2 FTCA, or 2 mg/kg PFOA, then incubated to hatch. Serum and kidney of hatchling chickens were collected. Blood urea nitrogen (BUN) and creatinine (Cre) levels were measured with commercially available kits. Morphology of kidney was assessed with histopathology. To further reveal molecular mechanism of observed endpoints, IGF signaling molecules were assessed in the kidney samples with qRT-PCR, results indicated that IGFBP3 is a potentially crucial molecule. Lentiviruses overexpressing or silencing IGFBP3 were designed and applied to enhance/suppress the expression of IGFBP3 in developing chicken embryo for further verification of its role in the observed effects. Disrupted nephron formation, in the manifestation of decreased glomeruli number/area and increased serum BUN/Cre levels, was observed in the animals developmentally exposed to 6:2 FTCA. Correspondingly, IGF signaling molecules (IGF1, IGF1R and IGFBP3) were affected by 6:2 FTCA exposure. Meanwhile, overexpression of IGFBP3 effectively alleviated such changes, while silencing of IGFBP3 mimicked observed effects. In conclusion, developmental exposure to 6:2 FTCA is associated with disrupted chicken embryo renal development, in which IGFBP3 seems to be a remarkable contributor, suggesting potential health risks for human and other species. Further risk assessments and mechanistic works are necessary.

Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio680 (PSSR680), and Carotenoid Reflectance Index700 (CPI700). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model’s feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation.

Semiconductor near-infrared lasers have been widely used in lidar systems. However, various source types have different shapes and divergence angles, causing more difficulties for long-distance detection. In this paper, an optical collimation system is designed for a long-range lidar system with a shaped laser source (the wavelength is 905 nm, the emitted spot size is 50 µm long by 10 µm wide, and the divergence angles are 33°and 15°, respectively, which are unconventional). On the basis of the traditional method of aspheric lens setting, a pair of asymmetric aspherical lenses were designed using an extended polynomial. The simulation results show that the spot shapes are all close to circular from 100 mm to 30 m and the spot size always remains the same value. The corrected optical system is put into the designed lidar system for verification. It results show that the average divergence angles in the long and short axis directions are 0.06°and 0.07°, which satisfy the project requirements. This optical system designed provides a collimation scheme and expands the application of vehicle-mounted lidar in the field of long-range detection.

Obtaining high-quality adult human primary cardiomyocytes (hPCM) have been technically challenging due to isolation-induced biochemical and mechanical stress. Building upon a previous tissue slicing-assisted digestion method, we introduced polymers into the digestion solution to reduce mechanical damage to cells. We found that low-viscosity methylcellulose (MC) significantly improved hPCM viability and yield. Mechanistically, it protected cells from membrane damage, which led to decreased apoptosis and mitochondrial reactive oxygen species production. MC also improved the electrophysiological properties of hPCMs by maintaining the density of sodium channels. The effects on cell viability and cell yield effects were not recapitulated by MC of larger viscosities, other cellulose derivatives, nor shear protectants polyethylene glycol and polyvinyl alcohol. Finally, MC also enhanced the isolation efficiency and the culture quality of hPCMs from diseased ventricular myocardium, expanding its potential applications. Our findings showed that the isolation quality of hPCMs can be further improved through the addition of a polymer, rendering hPCMs a more reliable cellular model for cardiac research.

Abstract Background The axonemal microtubules of primary cilium undergo a conserved protein posttranslational modification (PTM) — polyglutamylation. This reversible procedure is processed by tubulin tyrosine ligase-like polyglutamylases to form secondary polyglutamate side chains, which are metabolized by the 6-member cytosolic carboxypeptidase (CCP) family. Although polyglutamylation modifying enzymes have been linked to ciliary architecture and motility, it was unknown whether they also play a role in ciliogenesis. Results In this study, we found that CCP5 expression is transiently downregulated upon the initiation of ciliogenesis, but recovered after cilia are formed. Overexpression of CCP5 inhibited ciliogenesis, suggesting that a transient downregulation of CCP5 expression is required for ciliation initiation. Interestingly, the inhibitory effect of CCP5 on ciliogenesis does not rely on its enzyme activity. Among other 3 CCP members tested, only CCP6 can similarly suppress ciliogenesis. Using CoIP-MS analysis, we identified a protein that potentially interacts with CCP — CP110, a known negative regulator of ciliogenesis, whose degradation at the distal end of mother centriole permits cilia assembly. We found that both CCP5 and CCP6 can modulate CP110 level. Particularly, CCP5 interacts with CP110 through its N-terminus. Loss of CCP5 or CCP6 led to the disappearance of CP110 at the mother centriole and abnormally increased ciliation in cycling RPE-1 cells. Co-depletion of CCP5 and CCP6 synergized this abnormal ciliation, suggesting their partially overlapped function in suppressing cilia formation in cycling cells. In contrast, co-depletion of the two enzymes did not further increase the length of cilia, although CCP5 and CCP6 differentially regulate polyglutamate side-chain length of ciliary axoneme and both contribute to limiting cilia length, suggesting that they may share a common pathway in cilia length control. Through inducing the overexpression of CCP5 or CCP6 at different stages of ciliogenesis, we further demonstrated that CCP5 or CCP6 inhibited cilia formation before ciliogenesis, while shortened the length of cilia after cilia formation. Conclusion These findings reveal the dual role of CCP5 and CCP6. In addition to regulating cilia length, they also retain CP110 level to suppress cilia formation in cycling cells, pointing to a novel regulatory mechanism for ciliogenesis mediated by demodifying enzymes of a conserved ciliary PTM, polyglutamylation.

Abstract Background Recently, it was reported that the adult X. tropicalis heart can regenerate in a nearly scar-free manner after injury via apical resection. Thus, a cardiac regeneration model in adult X. tropicalis provides a powerful tool for recapitulating a perfect regeneration phenomenon, elucidating the underlying molecular mechanisms of cardiac regeneration in an adult heart, and developing an interventional strategy for the improvement in the regeneration of an adult heart, which may be more applicable in mammals than in species with a lower degree of evolution. However, a noninvasive and rapid real-time method that can observe and measure the long-term dynamic change in the regenerated heart in living organisms to monitor and assess the regeneration and repair status in this model has not yet been established. Results In the present study, the methodology of echocardiographic assessment to characterize the morphology, anatomic structure and cardiac function of injured X. tropicalis hearts established by apex resection was established. The findings of this study demonstrated for the first time that small animal echocardiographic analysis can be used to assess the regeneration of X. tropicalis damaged heart in a scar-free perfect regeneration or nonperfect regeneration with adhesion manner via recovery of morphology and cardiac function. Conclusions Small animal echocardiography is a reliable, noninvasive and rapid real-time method for observing and assessing the long-term dynamic changes in the regeneration of injured X. tropicalis hearts.

【】The temporal and spatial change of crop planting structure reflects the change of regional agricultural supply and demand patterns, and is an important basis for the government to formulate regional agricultural development strategy in the future.【Objective】The study aims to explore the temporal and spatial changes of crop planting structures in Guangdong-Hong Kong-Macao Greater Bay Area(GBA) in the past 30 years, and provide a basis for the reasonable adjustment of crop planting structure and agricultural layout next step.【Method】With the regional and county-level crop planting data, the temporal and spatial characteristics of the planting structures in the GBA from 1990 to 2020 were analyzed from the types of planting structure and the change trend of planting proportion by using the methods of time series change trend and spatial agglomeration analysis.【Result】From 1990 to 2020, 82.35% of counties and cities in the GBA adjusted the planting structures. The proportions of grain, oil plants, sugarcane and other important agricultural products decreased gradually, while those of the high-income crops such as vegetables and fruits increased significantly, and the richness of the main planting types of crops increased. In terms of planting structure type, with 2010 as the boundary, the GBA has changed from a rice-dominated type to a vegetable-dominated type accounting for half, supplemented by a rice-vegetable-fruit combination type, showing a balanced layout of rice-dominated type, fruit-dominated type and vegetable-fruit combination type. In terms of spatial pattern, different planting structure types have different spatial aggregation patterns, among which vegetable and rice planting show spatial aggregation characteristics in Guangzhou and Foshan areas with relatively higher urbanization development level.【Conclusion】In the past 30 years, the planting structure of the GBA has changed from food production to diversified planting, and further concentrated on cash crops such as vegetables and fruits. Under the background of increasing resource and environmental constraints, keeping the red line of cultivated land improving comprehensive agricultural service level, and further meetin the demand of urban residents for high-quality agricultural products are the key points to the adjustment of planting structures in the GBA next step.

【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area(hereinafter called "the Greater Bay Area"). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry.

【Objective】The study aims to reveal the spatial and temporal changes of vegetable production in Guangdong Province from 1992 to 2020, with a view to providing a decision basis for the spatial distribution optimization and scientific development strategy of vegetable production in Guangdong Province.【Method】Based on the data of vegetable production in Guangdong Province from 1992 to 2020, the methods of time-series statistics, production scale index, production concentration index, and change monitoring were used to analyze the time-series changes, spatial differences, spatial and temporal distribution and change patterns of vegetable production in Guangdong Province from provincial, municipal and county scales, respectively.【Result】From the provincial scale, from 1992 to 2020, the vegetable sowing area, yield and unit yield in Guangdong Province had been in a fluctuating ascending trend, with the vegetable sowing area increased by 73.23×104 hm2 and vegetable yield increased by 2503.31×104 t. The vegetable unit yield in Guangdong Province increased from 19.07 t/hm2 in 1992 to 27.19 t/hm2 in 2020. From the municipal scale, the regions with larger vegetable sowing area and higher vegetable yield were mainly distributed in Guangzhou, Zhanjiang and Maoming; meanwhile, the regions with higher vegetable unit yield were mainly distributed in Shantou, Jieyang and Chaozhou. From the county scale, counties with vegetable sowing area of more than 2×104 hm2 or yield of more than 50×104 t showed as a significant upward trend, and the distribution pattern of these counties developed from scattered to aggregated contiguous. Further more, counties with vegetable unit yield of more than 30 t/hm2 were dominated by an upward trend, and the distribution pattern of these counties developed from a sporadic distribution in the eastern Guangdong area to a cluster of contiguous development in many regions of the whole province.【Conclusion】The results show that the vegetable production in Guangdong Province showed significant spatial and temporal divergence from 1992 to 2020. Therefore, vegetable production in Guangdong Province should be given full attention to regional advantages, and promote the efficiency, high-quality and specialization of vegetable production in various cities and counties on the basis of ensuring the stable production and supply of vegetables.

【Objective】Quickly gathering information of accurate cropland distribution and spatial and temporal change is an important technical means for scientific protection and effective management of cropland resources, which is an important guiding significance for ensuring regional food security and formulating cropland protection policies. The study of spatial and temporal change of cropland in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA) can provide data support for local cropland conservation and food security assurance.【Method】Based on the multi-temporal Landsat remote sensing image, the support vector machine method was used to obtain the land cover types and cropland distribution of the GBA from 2010 to 2020. Then, the characteristics of spatial and temporal changes of cropland in the GBA were analyzed from three aspects: quantitative changes, spatial transfer and landscape patterns.【Result】In terms of quantitative changes, from 2010to 2020, the area of cropland in the GBA showed a trend of decreasing first and then increasing, from 16 155.56 km2 in 2010 to 15 740.54 km2 in 2015, and then increasing to 16 473.93 km2 in 2020. At the municipal scale, the net increase of cropland in Jiangmen is the largest, with the net increase area of 176.99 km2, and the net decrease of cropland in Dongguan is the largest, with the net decrease area of 74.68 km2. In terms of spatial transfer, the area of 60.25% of the increased cropland were coming from forest, and the area of 35.85% of the increased cropland were coming from water body. Meanwhile, the area of 49.53%of the decrease cropland were converted to forest, and the area of the 43.51% of the decrease cropland were converted to impervious surface. In terms of landscape patterns, the patch density aggregation index of cropland in the GBA showed a trend of increasing and then decreasing.【Conclusion】In total, the change of cropland resources in the GBA maintained a dynamic balance from 2010 to 2020. The increased areas of cropland were mainly coming from forest and water body, and the decrease areas of cropland were converted to forest and impervious surface. Besides, landscape pattern of cropland generally tended to be less fragmented and more aggregated.

Abstract Resource constraints, e.g., limited product inventory or financial strength, may affect consumers’ choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to mine the cue of user preferences or intention in the case of resource-constraint recommendation tasks. For this purpose, we specifically build a largely used car transaction dataset possessing resource-constraint characteristics. Accordingly, we propose a resource-constraint-aware network to predict the user’s future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually recursive recurrent neural networks (MRRNNs) are introduced to capture long-term interactive dependencies, and effective representations of users and items are obtained. To further consider the resource constraint, a resource-constraint branch is built to explore resource variation’s influence on user preferences. Finally, mutual information is introduced to measure the similarity between the future user action and fused historical behavior features to predict future interaction. The fused features come from both MRRNNs and resource-constraint branches. We test the performance on the built used car transaction dataset and the Tmall dataset, and the experimental results verify the effectiveness of our framework.

SAR three-Dimensional (3D) imaging of complex structural facilities is an important and challenging issue in SAR imaging. The existing 3D SAR imaging relies on multiple channels or flights in the elevation direction, exerting high demands on the radar system or data acquisition. This paper proposes a 3D imaging method for complex structural facilities without a prior model, and the full-aspect 3D image of the entire scene in the region with unknown prior information can be obtained solely using a single flight. This method completely utilizes the full-aspect observation as well as the layover and elevation ambiguity resolving capabilities of circular SAR without requiring target pre-modeling and 3D imaging grid construction. Moreover, the method is suitable for fine 3D imaging complex structural facilities in large areas. Significant progress has been made in the practical technology of radar 3D imaging. The full-aspect 3D radar images of the FAST radio telescope are obtained for the first time with the proposed method, thus verifying the correctness and effectiveness of our theory and method.

Abstract The production of tetraethyl orthosilicate (TEOS) from biomass provides a new way for TEOS production and biomass valorization. In this study, rice straw was treated using different fractionation methods, and the content, state, and reactivity of Si in the treated samples were investigated. It was found that acid treatment and ethanol extraction kept most Si in the biomass, while alkali treatment caused significant Si loss. Si was mainly present in the SiOx, Si−O−C, and Si−O−Si states in the surface of raw rice straw, cellulose and Klason lignin. The results showed that the Si−O−Si state in rice straw was beneficial for the formation of TEOS. The removal of lipids from rice straw facilitated the production of TEOS, giving the highest TEOS yield of 76.2 %. In contrast, the production of TEOS from other samples became difficult; the simultaneous conversion of the three organic components of rice straw also facilitated the production of TEOS.

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.

Abstract Objective Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. Methods The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. Results Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. Conclusions In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to construct span semantic vectors, utilizes LSTM and multi-head self-attention mechanism for span feature extraction, multi-head selection mechanism for span-level relation decoding, and introduces span classification task for multi-task learning to decode out the relation triad in a single-stage. Experiments on the classic English dataset NYT and the publicly available Chinese relationship extraction dataset DuIE 2.0 show that this method achieves better results than the baseline method, which verifies the effectiveness of this method. Source code and data are published here(https://github.com/Beno-waxgourd/NLP.git).

Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models’ best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly.

The ocean is one of the most extensive ecosystems on Earth and can absorb large amounts of carbon dioxide. Changes in seawater carbon dioxide concentrations are one of the most important factors affecting marine ecosystems. Excess carbon dioxide can lead to ocean acidification, threatening the stability of marine ecosystems and species diversity. Dissolved carbon dioxide detection in seawater has great scientific significance. Conducting online monitoring of seawater carbon dioxide can help to understand the health status of marine ecosystems and to protect marine ecosystems. Current seawater detection equipment is large and costly. This study designed a low-cost infrared carbon dioxide detection system based on molecular theory. Using the HITRAN database, the absorption spectra and coefficients of carbon dioxide molecules under different conditions were calculated and derived, and a wavelength of 2361 cm−1 was selected as the measurement channel for carbon dioxide. In addition, considering the interference effect of direct light, an infrared post-splitting method was proposed to eliminate the interference of light and improve the detection accuracy of the system. The system was designed for the online monitoring of carbon dioxide in seawater, including a peristaltic pump to accelerate gas–liquid separation, an optical path structure, and carbon dioxide concentration inversion. The experimental results showed that the standard deviation of the gas test is 3.05, the standard deviation of the seawater test is 6.04, and the error range is within 20 ppm. The system can be flexibly deployed and has good stability and portability, which can meet the needs of the online monitoring of seawater carbon dioxide concentration.

Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images. Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy. However, which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood. This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images (i.e. Landsat 8 [30 m], Sentinel-2A [20 m], Sentinel-2A [10 m], and Gaofen-2 [4 m]) in three testing areas. The results indicated that for the medium-spatial resolutions of 30 and 20 m, the support vector machine (SVM) method was considered as the optimal classification method with the highest accuracy of impervious surface extraction. For the high-spatial resolutions of 10 and 4 m, the object based image analysis (OBIA) method obtained the highest accuracy of the impervious surface distribution. Furthermore, the perpendicular impervious surface index (PISI) outperformed the other indices in obtaining the impervious surface distribution, with the highest accuracy for four spatial resolution images. These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.

Abstract Background This study aimed to establish and validate an easy-to-operate novel scoring system based on simple and readily available clinical indices for predicting the progression of chronic kidney disease (CKD). Methods We retrospectively evaluated 1045 eligible CKD patients from a publicly available database. Factors included in the model were determined by univariate and multiple Cox proportional hazard analyses based on the training set. Results Independent prognostic factors including etiology, hemoglobin level, creatinine level, proteinuria, and urinary protein/creatinine ratio were determined and contained in the model. The model showed good calibration and discrimination. The area under the curve (AUC) values generated to predict 1-, 2-, and 3-year progression-free survival in the training set were 0.947, 0.931, and 0.939, respectively. In the validation set, the model still revealed excellent calibration and discrimination, and the AUC values generated to predict 1-, 2-, and 3-year progression-free survival were 0.948, 0.933, and 0.915, respectively. In addition, decision curve analysis demonstrated that the model was clinically beneficial. Moreover, to visualize the prediction results, we established a web-based calculator ( https://ncutool.shinyapps.io/CKDprogression/ ). Conclusion An easy-to-operate model based on five relevant factors was developed and validated as a conventional tool to assist doctors with clinical decision-making and personalized treatment.

Nitropolycyclic aromatic hydrocarbons (NPAHs) present one of the most important airborne pollutants. Recent studies have shown that one of the most abundant NPAHs, 2-Nitrofluorene (NF), was supposed to be converted to endocrine-disrupting metabolites by cytochrome P450 1A1 (CYP1A1) in human cells. However, the mechanism is still largely unexplored. Here the metabolic activation and transformation mechanism of NF catalyzed by CYP1A1 were systematically studied with the aid of Molecular Dynamics, Density Functional Theory and Quantum Mechanics/Molecular Mechanics techniques. We evidence that CYP1A1 can activate NF through two elementary processes: (i) electrophilic addition (12.4 kcal·mol−1) or hydrogen abstraction (38.2 kcal·mol−1) and (ii) epoxidation (5.9 and 8.7 kcal·mol−1) or NIH shift (12.5 and 14.9 kcal·mol−1) or proton shuttle (12.1 kcal·mol−1). Electrophilic addition was found to be the rate-determining step while epoxidation rather than NIH shift or proton shuttle is the more feasible pathway after electrophilic addition. Metabolites 6,7-epoxide-2-nitrofluorene and 7,8-epoxide-2-nitrofluorene were identified as the major epoxidation products. Epoxides are unstable and easy to react with hydrated hydrogen ions and hydroxyls to produce endocrine disrupter 7-hydroxy-2-nitrofluorene. Toxic analysis shows that some of the metabolites are more toxic to model aquatic organisms (e.g. Green algea) than NF. Binding affinity analysis to human sex hormone binding globulin reveals that NF metabolites all have endocrine-disrupting potential. This study provides a comprehensive understanding on the biotransformation process of NF and may aid future studies on various NPAHs activation catalyzed by human P450 enzyme.

Circular synthetic aperture radar (CSAR) has the potential to form 3D images with single-pass single-channel radar data, which is very time-efficient. This article proposes a volumetric neural renderer that utilizes CSAR 2D amplitude images to reconstruct the 3D power distribution of the imaged scene. The innovations are two-fold: Firstly, we propose a new SAR amplitude image formation model that establishes a linear mapping relationship between multi-look amplitude-squared SAR images and a real-valued 4D (spatial location (x, y, z) and azimuth angle θ) radar scattered field. Secondly, incorporating the proposed image formation model and SAR imaging geometry, we extend the neural radiance field (NeRF) methods to reconstruct the 4D radar scattered field using a set of 2D multi-aspect SAR images. Using real-world drone SAR data, we demonstrate our method for (1) creating realistic SAR imagery from arbitrary new viewpoints and (2) reconstructing high-precision 3D structures of the imaged scene.

With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body’s structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body’s motion posture, which has great application prospects in the field of gait analysis.

Global urbanization and post-industrialization have resulted in the emergence of a large number of brownfields. The integration of brownfields into green infrastructure (GI) has been widely recognized as a sustainable development strategy in metropolitan areas. It is important to spatially prioritize brownfields catering for GI, which can enable the greatest enhancement of urban functions. Various studies have assessed brownfield site attributes or urban demands to define the priority of brownfields transformed into GI, but it is key to consider the coupling coordination between urban demands and site attributes in order to achieve more accurate matches. In this paper, an approach is proposed for assessing the priority of brownfields catering for GI in Xuzhou, China; this involved calculating the coupling coordination degree between site attributes and urban function demands, including heat island mediation, stormwater regulation, disaster prevention, landscape aesthetics improvement, and leisure and recreation increments. The results showed that 42.52% of the brownfields have a high degree of coupling coordination (“good coordination” and “primary coordination”) between site attributes and urban demands. Furthermore, 40.82% of the brownfields (120 plots) were selected to be integrated into urban GI; these are not only located in high urban functional demand areas, but also have a high coupling coordination degree. These brownfields were divided into three priority levels, and 4.42% and 17.69% of the total brownfields are of very high and high priority. Our proposed approach offers an accurate decision-making tool for urban GI optimization in high-density built-up metropolitan areas, and offers guidance for brownfield redevelopment.

The widespread presence of mixed pixels in remotely sensed images is a pressing challenge for accurate target detection and classification. Linear spectral mixture analysis (LSMA) is commonly used to address this problem by deriving remotely sensed information at the subpixel level. In the implementation of LSMA, the effects of mixed-pixel spectral interference need to be taken into account; mixed spectra would exhibit as a pure spectral characteristic when the abundance of one endmember in a mixed pixel exceeds a specific threshold. However, the thresholds of endmember abundance resulting in mixed-pixel spectral interference remain unclear. Thus, this study designed an experiment to analyze the effect of the spectral interference of mixed pixels and to identify the thresholds causing such interference by spectral similarity measures (spectral angle and spectral distance). Four types of pure endmember spectra (vegetation, high-albedo impervious surface (HIS), low-albedo impervious surface (LIS), soil) and corresponding representative mixed spectra with endmember abundances of 95%-5% at intervals of 5% were collected from Earth Observing-1 Hyperion imagery. Spectral similarity measures among the pure endmember spectra and representative mixed spectra were used to determine the thresholds of endmember abundance that cause spectral interference. The results verified the effect of the spectral interference of mixed pixels. The thresholds of abundance causing mixed-pixel spectral interference in vegetation, HIS, LIS, and soil endmembers were 70%, 75%, 80%, and 70%, respectively. Therefore, when the endmember abundance within mixed pixels exceeds the abovementioned thresholds, these mixed spectra are interfered and would exhibit as a pure spectral characteristic. Accordingly, interfered mixed pixels have to be removed before applying LSMA or other unmixing methods to avoid the effect of spectral interference.

Nonpharmaceutical interventions (NPIs), particularly contact tracing isolation and household quarantine, play a vital role in effectively bringing the Coronavirus Disease 2019 (COVID-19) under control in China. The pairwise model, has an inherent advantage in characterizing those two NPIs than the classical well-mixed models. Therefore, in this paper, we devised a pairwise epidemic model with NPIs to analyze COVID-19 outbreak in China by using confirmed cases during February 3rd–22nd, 2020. By explicitly incorporating contact tracing isolation and family clusters caused by household quarantine, our model provided a good fit to the trajectory of COVID-19 infections. We calculated the reproduction number R = 1.345 (95% CI: 1.230 − 1.460) for Hubei province and R = 1.217 (95% CI: 1.207 − 1.227) for China (except Hubei). We also estimated the peak time of infections, the epidemic duration and the final size, which are basically consistent with real observation. We indicated by simulation that the traced high-risk contacts from incubated to susceptible decrease under NPIs, regardless of infected cases. The sensitivity analysis showed that reducing the exposure of the susceptible and increasing the clustering coefficient bolster COVID-19 control. With the enforcement of household quarantine, the reproduction number R and the epidemic prevalence declined effectively. Furthermore, we obtained the resumption time of work and production in China (except Hubei) on 10th March and in Hubei at the end of April 2020, respectively, which is broadly in line with the actual time. Our results may provide some potential lessons from China on the control of COVID-19 for other parts of the world.

The increasing and widespread application of antibacterial drugs makes antibiotic resistance a prominent and growing concern in clinical practice. The emergence of multidrug-resistant bacteria presents a global threat. However, the development and use of novel antibacterial agents involves time-consuming and costly challenges that may lead to yet further drug resistance. More recently, researchers have turned to traditional Chinese medicine to stem the rise of antibiotic resistance in pathogens. Many studies have shown traditional Chinese medicines to have significant bacteriostatic and bactericidal effects, with the advantage of low drug resistance. Some of which when combined with antibiotics, have also demonstrated antibacterial activity by synergistic effect. Traditional Chinese medicine has a variety of active components, including flavonoids, alkaloids, phenols, and quinones, which can inhibit the growth of drug-resistant bacteria and be used in combination with a variety of antibiotics to treat various drug-resistant bacterial infections. We reviewed the interaction between the active ingredients of traditional Chinese medicines and antibiotic-resistant bacteria. At present, flavonoids and alkaloids are the active ingredients that have been most widely studied, with significant synergistic activity demonstrated when used in combination with antibiotics against drug-resistant bacteria. The reviewed studies show that traditional Chinese medicine and its active ingredients have antimicrobial activity on antibiotic-resistant bacteria, which may enhance the susceptibility of antibiotic-resistant bacteria, potentially reduce the required dosage of antibacterial agents and the rate of drug resistance. Our results provide direction for finding and developing alternative methods to counteract drug-resistant bacteria, offering a new therapeutic strategy for tackling antibiotic resistance.

Abstract Background Family doctor contract service is an important service item in China’s primary care reform. This research was designed to evaluate the impact of the provision of family doctor contract services on the patient-perceived quality of primary care, and therefore give evidence-based policy suggestions. Methods This cross-sectional study of family doctor contract service policy was conducted in three pilot cities in the Pearl River Delta, South China, using a multistage stratified sampling method. The validated Primary Care Assessment Tool-Adult Edition (PCAT-AS) was used to measure the quality of primary care services. PCAT-AS assesses each of the unique characteristics of primary care including first contact, continuity, comprehensiveness, coordination, family-centeredness, community orientation, culture orientation. Data was collected through face-to-face interviews held from July to November, 2015. Covariate analysis and multivariate Linear Regression were adopted to explore the effect of contract on the quality of primary care by controlling for the socio-demographic status and health care service utilization factors. Results A total of 828 valid questionnaires were collected. Among the interviewees, 453 patients signed the contract (54.7%) and 375 did not (45.3%). Multivariate linear regression showed that contracted patients reported higher scores in dimensions of PCAT total score (β = − 8.98, P

When an emerging acute infectious disease occurs, travel restrictions, one-way or two-way, are often taken to prevent its global spread. In order to investigate the impact of two-way travel restrictions in the global spread of infectious diseases, this paper defines a risk indicator according to the relative infection density. Based on this risk indicator and an intervention time on two-way travel restrictions, we define an adaptive metapopulation network. Then a susceptible-infectious-removed (SIR) metapopulation model on this network is proposed. The mathematical analysis shows that the basic reproduction number is independent of human mobility. Furthermore, this essay compares the effects of one-way travel restrictions and two-way travel restrictions on the global spread of infectious diseases. It is shown that the adaptive metapopulation network under two-way travel restrictions can effectively suppress the global spread of infectious diseases. We also obtain a threshold of risk indicator to prevent the global spread of infectious diseases by simulations. The earlier the intervention time on two-way travel restriction is, the better to curb the global spread of the disease. Even if two-way travel restrictions are not implemented, controlling the mobility of infectious persons would help prevent the global spread of the disease. This work will throw lights on the prevention and control of the globally spreading of an emerging infectious disease.

PurposeCushing’s disease (CD) is a rare disease that contributes to 70–80% hypercortisolemia, which presents similarities and differences between pediatric and adult patients, and even between male and female patients. However, the comparative study of CD between different age groups and different genders is still insufficient. The aim of the study is to make a systematic comparison to reveal the gender differences in children and adult patients of CD, helping clinicians to provide optimal treatment for different groups of patients.MethodsWe conducted a retrospective research consisting of 30 pediatric and 392 adult CD patients in a single center in Peking Union Medical College Hospital. All 422 patients showed symptoms related to hypercortisolism and received adenoma excision surgery in the department of neurosurgery between 2014 and 2020.ResultsFor the accuracy of diagnosis, the sensitivity of BIPSS at baseline in pediatric patients was lower than in adults (75 vs. 91%, P = 0.054) but increased greatly after desmopressin stimulation (94 vs. 95%). However, the accuracy of lateralization for BIPSS was not preferred for prediction. As for clinical manifestations, growth retardation, weight gain, hirsutism, and acne were more prevalent for children, while for adults, hypertension, osteopenia, glucometabolic disorder, easy bruising, hair loss, and weight loss were more frequently seen. As previously reported, we observed a significant difference between the male prevalence of pediatric and adult patients (50 vs. 17%, P < 0.001), which was possibly caused by the more severe and earlier onset of a series of symptoms. Gender-related comparison showed greater morbidity of nephrolithiasis, hypokalemia, hypertension, easy bruising, osteopenia, and striae for male patients, while irregular menses, hirsutism, and hair loss were more common for female patients. Further analysis showed that the secretory activity of the PA axis was higher for males, presenting as the more remarkable alteration of laboratory parameters and contributing to the more severe clinical manifestations. For patients treated with transsphenoidal pituitary surgery (TSS), the immediate prognosis could be predicted by operation history, invasiveness, Ki-67, and information provided by MRI, including tumor size and Knosp grading. However, we still lack methods to predict long-term prognosis.ConclusionsOur study is the first detailed and systematic comparison between pediatric and adult CD patients. Further exploration of the impact of CD on different genders reveals a more severe and probably an earlier-onset pattern of CD for male patients.

A multifunctional Ru/SBA‐15 catalyst is prepared via impregnation and used in the depolymerization of crude waste corn stover enzymatic lignin (CSEL) to produce monophenols and, unexpectedly, the industrially important organic siloxanes. In the depolymerization of CSEL over Ru/SBA‐15, a high phenolic monomer yield of 13.91 wt% with a high selectivity to ethylphenols (≈61%) can be achieved. Unexpectedly, when the reaction is carried out in ethanol, the predictable phenolic products are accompanied by both diethyl dimethyl orthosilicate and tetraethyl orthosilicate with the methyl group likely to come from lignin methoxy groups. Changing to a different alcohol solvent changes the siloxane products accordingly. The characterization of catalysts indicates that Ru0 is the active species in Ru/SBA‐15 catalysts and is well dispersed with small particle size (6.2 nm). This catalyst significantly contributes to both the production of phenols and siloxanes and contributes to the hydrogenolysis/hydrogenation of lignin.

Abstract Background General practitioner (GP) system is proved to be effective in over 50 countries worldwide. Guangdong province, as a reform pilot in China, initiated its patient-GP contract service reform in 2014. This study is designed to assess the patients’ acceptance of General Practitioners Contract (GPC) reform and explore its influencing factors. Methods This survey interviewed 1010 participants from 16 primary health centers (PHCs) chosen from 4 pilot cities in Guangdong during July and December in 2015. Data were collected through face-to-face interviews. The questionnaire was developed to discover the acceptance of GPC and covered three parts: respondents’ socio-demographic characteristics, health service utilization, and the patients’ assessment of primary health care centers. A binary logistic regression model was used to measure the influencing factors of respondents’ acceptance of GPC policy. Results A total of 611(60.5%) participants accepted GPC policy. Compared to patients visited PHCs over 7 times in the previous year, those visited PHCs fewer times reported lower acceptance of GPC policy (OR:0.68, 95% CI:0.49–0.96 for visits ≤3 times and OR:0.57, 95% CI:0.38–0.84 for visits = 4–6 times). Patients’ satisfaction with medical service coverage was positively associated with patients’ acceptance of GPC (OR: 1.72, 95% CI:1.01–3.98 for the satisfied versus the dissatisfied; OR: 1.38, 95% CI:0.92–3.30 for neutral versus the dissatisfied), and the satisfaction with drug list also positively influenced patients’ acceptance of GPC policy (OR: 1.44, 95% CI:1.26–2.73 for the satisfied versus the dissatisfied; OR:1.61, 95% CI:1.36–2.99 for neutral versus the dissatisfied). Meanwhile, age and education had positive impacts on the acceptance of the GPC policy. Conclusion This study finds out that patients’ satisfaction with medical service coverage and drug list are the influencing factors for the acceptance of GPC policy. Therefore, improvement of medical service accessibility such as better follow-up of patients with chronic diseases and enhanced referral service, as well as the expansion of drug list, will improve patients’ acceptance of GPC policy. It also finds that patients using more primary health service are inclined to accept GPC policy, so more attractive and high-quality service should be provided in PHCs.

Bimetallic components welded by Ta and stainless steel have great promise for engineering applications, but there is relatively little relevant research. In this study, 0.6 mm-thick Ta and 304L stainless steel plates were laser welded, and the forming characteristics, microstructure, and mechanical properties of the welded joints were analyzed. First-principles calculations were also performed to explore the structural stabilization mechanisms of the two intermetallic compounds, TaFe2 and TaCr2, in terms of mechanical properties as well as electronic structure. The results showed that the weld surface was smooth and free from any defects. Fe-based solid solutions formed grains, while TaFe2, TaCr2, and some Fe-based solid solutions formed intergranular and eutectic structures. In addition, due to the presence of the brittle phases of TaFe2 and TaFe, the microhardness of the weld area can reach 650HV, with an average hardness of 530HV, which is much higher than that of the base material. The tensile shear of the joint at room temperature was 154.77 N/mm, and the fracture occurred in the weld zone on the steel side, showing brittle fracture. TaFe2 is a brittle intermetallic compound, while TaCr2 is ductile. Both TaFe2 and TaCr2 systems have dual properties of metallic and covalent bonding within them, and metallic bonding dominates.

Ecological changes affected by increasing human activities have highlighted the importance of ecological quality assessments. An appropriate and efficient selection of ecological parameters is fundamental for ecological quality assessments. On the basis of remote sensing data and methods, this study developed an enhanced ecological evaluation index (EEEI) with five integrated ecological parameters by containing pixel and sub-pixel information: normalized difference vegetation index, impervious surface coverage, soil coverage, land surface temperature, and wetness component of tasseled cap transformation. Significantly, the EEEI simultaneously considered the five aspects of land surface ecological conditions (i.e., greenness, human activities, dryness, heat, and moisture), which provided an effective guide for the systematic selection of ecological parameters. The EEEI has a clear theoretical framework, and all the parameters can be obtained quickly on the basis of the remote sensing datasets and methods, which is suitable for the promotion and application of ecological quality assessments to various areas and scales. Furthermore, the EEEI was applied to assess and detect the ecological quality of the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China. Assessment results indicated that the ecological quality of the GBA is currently facing great challenges with a degradation trend from 2000 to 2020, which emphasizes the significance and urgency for eco-environmental protection of the GBA. This provided evidence that the EEEI can be used as an effective index for scientific, objective, quantitative, and comprehensive ecological quality assessment, which can also aid regional environmental management and ecological protection.

Heavy metals are non-biodegradable and carcinogenic pollutants with great bio-accumulation potential. Their ubiquitous occurrence in water and soils has caused serious environmental concerns. Effective strategies that can eliminate the heavy metal pollution are urgently needed. Here the adsorption potential of seven heavy metal cations (Cd2+, Cu2+, Fe3+, Hg2+, Mn2+, Ni2+ and Zn2+) with 20 amino acids was systematically investigated with Density Functional Theory method. The binding energies calculated at B3LYP-D3/def2TZVP level showed that the contribution order of amino acid side chains to the binding affinity was carboxyl > benzene ring > hydroxyl > sulfhydryl > amino group. The affinity order was inversely proportional to the radius and charge transfer of heavy metal cations, approximately following the order of: Ni2+ > Fe3+ > Cu2+ > Hg2+ > Zn2+ > Cd2+ > Mn2+. Compared to the gas-phase in other researches, the water environment has a significant influence on structures and binding energies of the heavy metal and amino acid binary complexes. Collectively, the present results will provide a basis for the design of a chelating agent (e.g., adding carboxyl or a benzene ring) to effectively remove heavy metals from the environment.

Abstract The global transmission of infectious diseases poses huge threats to human. Traditional heterogeneous mean-field models on metapopulation networks ignore the heterogeneity of individuals who are in different disease states in subpopulations with the same degree, resulting in inaccuracy in predicting the spread of disease. In this paper, we take heterogeneity of susceptible and infectious individuals in subpopulations with the same degree into account, and propose a deterministic unclosed general model according to Markov process on metapopulation networks to curve the global transmission of diseases precisely. Then we make the general model closed by putting forward two common assumptions: a two-dimensional constant distribution and a two-dimensional log-normal distribution, where the former is equivalent to the heterogeneous mean-field model, and the latter is a system of weighted ordinary differential equations. Further we make a stability analysis for two closed models and illustrate the results by numerical simulations. Next, we conduct a series of numerical simulations and stochastic simulations. Results indicate that our general model extends and optimizes the mean-field model. Finally, we investigate the impacts of total mobility rate on disease transmission and find that timely and comprehensive travel restriction in the early stage is an effective prevention and control of infectious diseases.

Rhizoma Coptidis (RC) is a widely used traditional Chinese medicine. Although modern research has found that some alkaloids from RC are the pharmacologically active constituents, the differences in their biological effects are not completely clear. This study analyzed the differences in the typical alkaloids in RC at a systematic level and provided comprehensive information on the pharmaceutical mechanisms of the different alkaloids. The ethanol RC extract (RCE) was characterized using HPLC assay. HepG2, 3T3-L1, and RAW264.7 cells were used to detect the cytotoxicity of alkaloids. Transcriptome analyses were performed to elucidate the cellular pathways affected by RCE and alkaloids. HPLC analysis revealed that the typical alkaloids of RCE were berberine, coptisine, and palmatine. Coptisine and berberine displayed a stronger inhibitory effect on cell proliferation than palmatine. The overlapping ratios of differentially expressed genes between RCE and berberine, coptisine, and palmatine were 70.8%, 52.6%, and 42.1%, respectively. Pathway clustering analysis indicated that berberine and coptisine possessed a certain similarity to RCE, and both compounds affected the cell cycle pathway; moreover, some pathways were uniquely enriched by berberine or coptisine. Berberine and coptisine had different regulatory effects on genes involved in lipid metabolism. These results provide comprehensive information on the pharmaceutical mechanisms of the different RC alkaloids and insights into their better combinatory use for the treatment of diseases.

3D reconstruction has raised much interest in the field of CSAR. However, three dimensional imaging results with single pass CSAR data reveals that the 3D resolution of the system is poor for anisotropic scatterers. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell while for the same target point, imaging point will fall into the different positions at different aspect angles. In this paper, we proposed a method for 3D point cloud reconstruction using projections on 2D sub-aperture images. The target and background in the sub-aperture images are separated and binarized. For a projection point of target, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidate points of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set.

Abstract Background Myocardium regeneration in adult mammals is very limited, but has enormous therapeutic potentials. However, we are far from complete understanding the cellular and molecular mechanisms by which heart tissue can regenerate. The full functional ability of amphibians to regenerate makes them powerful animal models for elucidating how damaged mature organs are naturally reconstituted in an adult organism. Like other amphibians, such as newts and axolotls, adult Xenopus displays high regenerative capacity such as retina. So far, whether the adult frog heart processes regenerative capacity after injury has not been well delineated. Results We examined the regeneration of adult cardiac tissues of Xenopus tropicalis after resection of heart apex. We showed, for the first time, that the adult X. tropicalis heart can regenerate perfectly in a nearly scar-free manner approximately 30 days after injury via apical resection. We observed that the injured heart was sealed through coagulation immediately after resection, which was followed by transient fibrous tissue production. Finally, the amputated area was regenerated by cardiomyocytes. During the regeneration process, the cardiomyocytes in the border area of the myocardium adjacent to the wound exhibited high proliferation after injury, thus contribute the newly formed heart tissue. Conclusions Establishing a cardiac regeneration model in adult X. tropicalis provides a powerful tool for recapitulating a perfect regeneration phenomenon and elucidating the underlying molecular mechanisms of cardiac regeneration in an adult heart, and findings from this model may be applicable in mammals.

Background: Prediction of radiotherapeutic response before radiotherapy could help determine individual treatment strategies for patients with acromegaly.Objective: To develop and validate a machine-learning-based multiparametric MRI radiomics model to non-invasively predict radiotherapeutic response in patients with acromegaly.Methods: This retrospective study included 57 acromegaly patients who underwent postoperative radiotherapy between January 2008 and January 2016. Manual lesion segmentation and radiomics analysis were performed on each pituitary adenoma, and 1561 radiomics features were extracted from each sequence. A radiomics signature was built with a support vector machine using leave-one-out cross-validation for feature selection. Multivariable logistic regression analysis was used to select appropriate clinicopathological features to construct a clinical model, which was then combined with the radiomics signature to construct a radiomics model. The performance of this radiomic model was assessed using receiver operating characteristics (ROC) analysis and its calibration, discriminating ability, clinical usefulness.Results: At 3-years after radiotherapy, 25 patients had achieved remission and 32 patients had not. The clinical model incorporating seven clinical features had an area under the ROC (AUC) of 0.86 for predicting radiotherapeutic response, and performed better than any single clinical feature. The radiomics signature constructed with six radiomics features had a significantly higher AUC of 0.92. The radiomics model showed good discrimination abilities and calibration, with an AUC of 0.96. Decision curve analysis confirmed the clinical utility of the radiomics model.Conclusion: Using pre-radiotherapy clinical and MRI data, we developed a radiomics model with favorable performance for individualized non-invasive prediction of radiotherapeutic response, which may help in identifying acromegaly patients who are likely to benefit from radiotherapy.

Dynamic ventilation control based on the concept of thermal comfort is proposed and applied to single head roadway in high-altitude gold mine in Qinghai. Hourly thermal comfort values are determined for typical working conditions, and a thermal comfort curve is generated. The values are adjusted based on the external environment and the changes in the miners’ thermal comfort. The Fluent software is used to simulate the temperature field and wind speed field of the tunnel roadway at three heights, and the results are used to fit the thermal comfort curve in the roadway. The results show that the thermal comfort is lowest at a roadway height of 1 m and a longitudinal length of 16 m because the wind speed is the highest and the temperature is lowest at these locations. When the dynamic ventilation control is used, the optimal thermal comfort levels can be determined for the locations in the single head roadway.

The urban heat island effect caused by the rapid increase in urban anthropogenic heat has gradually become an important factor affecting the living environment of urban residents. Studying the temporal and spatial variation characteristics of urban anthropogenic heat is of great significance for urban planning and urban ecological service systems. In this study, the urban anthropogenic heat flux (AHF) in 2004, 2009, 2014, and 2020 in the central urban area of Guangzhou was retrieved based on Landsat data and the surface energy balance equation, and the temporal and spatial characteristics of different types of anthropogenic heat were explored by combining the transfer matrix and the migration of the gravity center. The results showed that: (1) The overall change trend of anthropogenic heat in the central urban area of Guangzhou was enhanced, and the degree of enhancement was related to the type of urban functional land. (2) Different types of anthropogenic heat had different characteristics in terms of area expansion and spatial changes. Low-value anthropogenic heat (zero-AHF zone, low-AHF zone, medium-AHF zone) changed drastically in terms of area expansion. High-value anthropogenic heat (medium-AHF zone, high-AHF zone) changed more drastically in space. The increase in urban population, rapid economic development, and increased industrial production activities have stimulated the emission of anthropogenic heat, which has a positive impact on the intensity of anthropogenic heat.

Abstract Background In China, most of migrant workers work in the small and medium-sized enterprises (SMEs) and are a vulnerable group for occupational health. Migrant workers are at increased risk of occupational health risks due to poor occupational health behaviours such as the low use of personal protective equipment (PPE). However, there is a lack of solid evidence regarding how to improve the use of PPE among migrant workers in SMEs. The current study will assess the effectiveness of a multi-pronged behavioural intervention designed to promote PPE utilization among migrant workers exposed to organic solvents in SMEs. Methods/Design This is a single blind, three-arm cluster randomized trial with 60 SMEs equally randomized to receive a top-down intervention (i.e. general health education and mHealth intervention provided by researchers) or a comprehensive intervention (which includes both top-down intervention and peer education) or a control condition (participants will not receive the intervention, but study measures will be obtained). Interventions will be conducted at the SMEs level for 6 months and all eligible migrant workers in these SMEs will be enrolled into the trial. The primary outcome is effective use of PPE during the last week. The secondary outcomes are occupational health knowledge and attitude and participation in occupational health check-up. Data will be collected and assessed at baseline; 3 months post baseline and the end of the intervention. Discussion This theory- and evidence based intervention will contribute to the limited evidence of behaviour change intervention in improving PPE utilization of migrant workers in SMEs, and provide timely evidence for the development of basic occupational health services in China and elsewhere with similar industrialization contexts. Trial registration ChiCTR-IOR-15006929 . Registered on 16 August 2015.

The scatterings of many targets are aspect dependent, which is called anisotropy. Multi-angular synthetic aperture radar (SAR) is a suitable means of detecting this kind of anisotropic scattering behavior by viewing targets from different aspect angles. First, the statistical properties of anisotropic and isotropic scatterings are studied in this paper. X-band chamber circular SAR data are used. The result shows that isotropic scatterings have stable distributions in different aspect viewing angles while the distributions of anisotropic scatterings are various. Then the statistical properties of single polarization high-resolution multi-angular SAR images are modeled by different distributions. G 0 distribution performs best in all types of areas. An anisotropic scattering analysis method based on the multi-angular statistical properties is proposed. A likelihood ratio test based on G 0 distribution is used to measure the anisotropy. Anisotropic scatterings can be discriminated from isotropic scatterings by thresholding. Besides, the scattering direction can also be estimated by our method. AHH polarization C-band circular SAR data are used to validate our method. The result of using G 0 distribution is compared with the result of using Rayleigh distribution. The result of using G 0 distribution is the better one.

Digital elevation model (DEM) generation using multi-aspect synthetic aperture radar (SAR) imagery applying radargrammetry has become a hotspot. The traditional radargrammetric method is to solve the rigorous radar projection equations to obtain the three dimensional coordinates of targets. In this paper, we propose a new DEM generation method based on the offset between multi-aspect images formed on ground plane. The ground object will be projected to different positions from different viewing aspect angles if the height of object is not equal to the height of imaging plane. The linear relationship between the offset of imaging positions and height of the object is derived and scale factor is obtained finally. Height information can be retrieved from offset of imaging positions directly through the DEM extraction model presented in this paper. Thus the solution to nonlinear equations point by point can be avoided. Real C band airborne circular SAR images is used to verify the proposed approach. When extracted DEM applied in multi-aspect imaging process, superimposition of multi-aspect images will no longer be defocusing and can achieve finer observation of the scanned scene.

Calcyclin Binding Protein/(Siah-1 interacting protein) (CacyBP/SIP) acts as an oncogene in colorectal cancer. The nuclear accumulation of CacyBP/SIP has been linked to the proliferation of cancer cells. It has been reported that intracellular Ca2+ induces the nuclear translocation of CacyBP/SIP. However, the molecular mechanism of CacyBP/SIP nuclear translocation has yet to be elucidated. The purpose of this study was to test whether the Ca2+-dependent binding partner S100 protein is involved in CacyBP/SIP nuclear translocation in colon cancer SW480 cells.The subcellular localization of endogenous CacyBP/SIP was observed following the stimulation of ionomycin or BAPTA/AM by immunofluorescence staining in SW480 cells. S100A6 small interfering RNAs (siRNA) were transfected into SW480 cells. Immunoprecipitation assays detected whether S100 protein is relevant to the nuclear translocation of CacyBP/SIP in response to changes in [Ca2+]i.We observed that endogenous CacyBP/SIP is translocated from the cytosol to the nucleus following the elevation of [Ca2+]i by ionomycin in SW480 cells. Co-immunoprecipitation experiments showed that the interaction between S100A6 and CacyBP/SIP was increased simultaneously with elevated Ca2+. Knockdown of S100A6 abolished the Ca2+ effect on the subcellular translocation of CacyBP/SIP.Thus, we demonstrated that S100A6 is required for the Ca2+-dependent nuclear translocation of CacyBP/SIP in colon cancer SW480 cells.