Search

To evaluate the estimation and spatiotemporal variation characteristics of nitrous oxide emissions from agricultural sources in Anhui Province, the nitrous oxide emissions generated during crop cultivation and manure management were assessed based on the recommended methods in the “Guidelines for Provincial Greenhouse Gas Inventories” and official statistical data. The results showed that the overall emission of nitrous oxide from agricultural land showed a downward trend, reaching a valley value in 2019 with an emission of 2.83 × 104 tons. The annual average emissions of nitrous oxide from agricultural land and manure management account for 80.98% and 19.02% of the total annual average emissions of nitrous oxide from agricultural activities in Anhui Province, respectively. Both agricultural land emissions and livestock manure management show a trend of nitrous oxide emissions decreasing from the northern region of Anhui > central region of Anhui > southern region of Anhui. In this paper, we explored and discussed the intrinsic driving factors behind the spatiotemporal changes in nitrous oxide emissions, and analyzed the potential for future emission reductions. It is suggested that the emissions of nitrous oxide from agricultural sources can be reduced through measures such as reasonable nitrogen application, adjustment of aquaculture structures, and the improvement of manure treatment methods, providing a theoretical reference for the estimation of greenhouse gas emissions from agricultural sources.

Abstract Background Somatic mutations of cancer driver genes are found to be responsible for vascular malformations with clinical manifestations ranging from cutaneous birthmarks to life-threatening systemic anomalies. Till now, only a limited number of cases and mutations were reported in Chinese population. The purpose of this study was to describe the somatic mutation spectrum of a cohort of Chinese pediatrics with vascular malformations. Methods Pediatrics diagnosed with various vascular malformations were collected between May 2019 and October 2020 from Beijing Children’s Hospital. Genomic DNA of skin lesion of each patient was extracted and sequenced by whole-exome sequencing to identify pathogenic somatic mutations. Mutations with variant allele frequency less than 5% were validated by ultra-deep sequencing. Results A total of 67 pediatrics (33 males, 34 females, age range: 0.1–14.8 years) were analyzed. Exome sequencing identified somatic mutations of corresponding genes in 53 patients, yielding a molecular diagnosis rate of 79.1%. Among 29 PIK3CA mutations, 17 were well-known hotspot p.E542K, p.E545K and p.H1047R/L. Non-hotspot mutations were prevalent in patients with PIK3CA-related overgrowth spectrum, accounting for 50.0% (11/22) of detected mutations. The hotspot GNAQ p.R183Q and TEK p.L914F mutations were responsible for the majority of port-wine stain/Sturge–Weber syndrome and venous malformation, respectively. In addition, we identified a novel AKT1 p.Q79K mutation in Proteus syndrome and MAP3K3 p.E387D mutation in verrucous venous malformation. Conclusions The somatic mutation spectrum of vascular malformations in Chinese population is similar to that reported in other populations, but non-hotspot PIK3CA mutations may also be prevalent. Molecular diagnosis may help the clinical diagnosis, treatment and management of these pediatric patients with vascular malformations.

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.

Background and PurposeThis study was conducted to explore the plasma drug concentration of propranolol in Chinese Han patients with infantile haemangioma (IH) and the influencing factors, as well as the relationship among plasma drug concentrations of propranolol, β1-AR mutation and CYP2D6 188C>T, efficacy, and safety.Experimental ApproachFrom January 2018 to April 2019, 140 patients with IH who were admitted to the hospital for oral propranolol and agreed to have their plasma concentration of propranolol tested, including 112 patients with β1-AR and CYP2D6 gene tested.Key Results and Conclusions and ImplicationsThe mean peak blood levels of propranolol, 4-hydroxypropranolol (4-OH-P), and N-deisopropylpropranolol (NDP) were 60.35 ± 37.90, 1.90 ± 2.37, and 0.24 ± 0.18 ng/ml, respectively. The mean trough blood levels of propranolol, 4-OH-P, and NDP were 24.98 ± 17.68, 0.45 ± 0.52, and 0.05±0.05 ng/ml, respectively. The higher the dose of propranolol, the higher the plasma concentration of propranolol (p = 0.031). The plasma concentration of propranolol was not related to the treatment efficacy.

ObjectiveTo compare the clinical efficacy and safety of two different doses of propranolol in the treatment of cutaneous kaposiform hemangioendothelioma (KHE).MethodsThe cohort of this prospective case–control study comprised 11 children with KHE treated from October 2015 to August 2018 in our institution. All participants were clinically and pathologically diagnosed as having cutaneous KHE. The children were allocated to two groups: six children in Group A (low-dose group) received oral propranolol 1.5 mg/kg/d, whereas five in Group B (high-dose group) received oral propranolol 2 mg/kg/d. The children were checked and photographed before and after treatment. Changes in the tumors were tracked by clinical and ultrasound examination. Follow-up visits to monitor for adverse reactions occurred regularly.ResultsGrade I, Grade II, and Grade IV improvements in tumors were each noted in one child in Group A (three improved in total) and Grade III in two and Grade IV in another two children in Group B (four improved in total). Oral propranolol was effective in 50 and 80% of children in Groups A and B, respectively; this difference is statistically significant (P < 0.01). Minor adverse reactions occurred in eight of the 11 children.ConclusionsPropranolol treatment is effective against cutaneous KHE. There were no serious adverse reactions, and the treatment was safe in the long term. A dose of 2 mg/kg/d was more effective than 1.5 mg/kg/d in the treatment of KHE and did not increase the rate of adverse reactions. Children with KHE should be treated with propranolol 2 mg/kg/d orally.

Organogels are excellent drug carrier for controlled release. Organogels based on amino acid derivatives has been widely used in the area of drug delivery. In this study, a series of the organogel system based on amino acid derivatives gelators was designed and prepared to investigate the structure-property correlation in organogels. To investigate the factors that influence the property of drug release, we varied the formulation in the organogels: gelator structure, gelator concentration, volume of antigelation solvent, and drug loading. Through the Box–Behnken tests, the optimum organogel formulation in vitro was obtained. The self-healing properties of the organogel have been utilised for injection of a model lipophilic risperidone in situ, and sustained release of the drug has been studied over about one week in vivo. In conclusion, the gelation ability of gelators could be adjusted by the gelator structure. Gel property is related with the whole composition of the formulation. As drug carrier, the drug release property of organogels is affected by multiple factors. Our investigation of the gel release property will play a theoretical guiding role in the application in the in situ drug delivery system.

Deep neural networks are difficult to train due to the large number of unknown parameters. To increase the trainable performance, we present a novel network model with moderate depth for three-dimensional reconstruction. The proposed network model, called SFResNet, only has eight layers, and sparse feedbacks were added in the middle and last layers, which is mainly used to add the constraints and improve the stability of the network model. In addition, a joint strategy is proposed to reduce the artificial Mosaic trace at the seam of the patches; hence, SFResNet can also evaluate an input image of any size. Visually pleasing output results can be produced with a reconstructed shape and normal surface. The experimental results show the effectiveness of the proposed method.

A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or noisy images. Experimental results show the effectiveness of our designed model and method.

Oral squamous cell carcinoma (OSCC) is an aggressive, invasive malignancy of epithelial origin. The progression from premalignant lesions—oral leukoplakia (OLK) and oral lichen planus (OLP)—to OSCC involves complex inflammatory processes that have not been elucidated. We investigated the roles of inflammatory mediators and infiltrating immunocytes in the pathogenic progression of OLK and OLP to OSCC. The occurrence of regulatory T-cells (Tregs) and tumor-associated macrophages (TAMs) and the expression of anti-inflammatory cytokines and proinflammatory cytokines were investigated in OLK, OLP, and OSCC tissues. Immunohistochemical staining of CD4, FOXP3, CD68, TGF-β1, IL-10, IL-4, IFN-γ, and MCP-1 showed that the occurrence of Tregs and TAMs increased in parallel with disease progression in OLK and OSCC. IL-10 gradually increased during the early stages of OLK and in OSCC. Infiltrating IL-4+ macrophages were seen with increasing frequency in OLK tissue during the progression of oral dysplasia. Fewer TGF-β1+ macrophages were seen in OSCC than in OLK and OLP. The expression of IFN-γ decreased gradually with the OLK development and had the lowest expression in OSCC. MCP-1 expression did not change significantly during the development of OSCC. The results suggested that the immunosuppression induced by chronic inflammation promotes tumorigenesis in OSCC, rather than initiating it.

Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.