DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web ...server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent.
DeepKa is a deep-learning-based protein p
predictor proposed in our previous work. In this study, a web server was developed that enables online protein p
prediction driven by DeepKa. The web server ...provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how p
's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.
DeepKa is a deep-learning-based protein pK a predictor proposed in our previous work. In this study, a web server was developed that enables online protein pK a prediction driven by DeepKa. The web ...server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pK a’s calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.
•The nanoflower-shaped Bi2S3 had a high photoelectric activity and large specific surface area.•Toehold-mediated strand displacement reaction without enzyme was used to recycle target for signal ...amplification.•TSDR strategy improved the sensitivity and reaction efficiency of the biosensor.
Herein, a photoelectrochemical (PEC) biosensor we proposed was used to detect let-7a via studying the photocurrent response change resulted from the change of the toehold-mediated strand displacement reaction (TSDR) between the AuNPs-H2 (a conjugate of gold nanoparticles and DNA strand H2) and let-7a. Without the participation of let-7a, after the alkaline phosphatase (ALP) were immobilized at Bi2S3 surface through modified on the AuNPs-H2, ALP converted the ascorbic acid 2-phosphate (AAP) to produce ascorbic acid (AA), AA could be catalytically oxidized to provide electrons, resulting in the enhancement of photocurrent signal on the Bi2S3. In contrast, the conjugate of ALP@AuNPs-H2 (a conjugate of ALP and AuNPs-H2) were compelled away from Bi2S3 surface when the let-7a participated in the process and hybridized with H1. The fuel DNA assisted the recycling of the let-7a via another TSDR, the recovered let-7a could participate in the next enzyme-free cycle. The PEC biosensor not only exploited TSDR signal amplification strategy, but also achieved good linearity in the range of 0.01 nM-1000 nM with a low detection limit of 6.7 pM. Due to the excellent performance, we believe it will offer more opportunities for bioanalysis and clinical biomedicine.
In order to reduce the influence of differences in human characteristics on the blood pressure prediction model and further improve the accuracy of blood pressure prediction, this paper establishes ...support vector machine regression model and random forest regression model for accurate blood pressure measurement. First, the photoelectric method is used to obtain the photoelectric plethysmography signal (PPG) and ECG signals from people of different ages, and the blood pressure value is roughly estimated based on the high-quality physiological signals and the vascular elastic cavity model; then the human body characteristics are used as the input parameters of the blood pressure prediction model, and the model parameters are used to find the best parameter combination to improve the prediction performance of the model; finally, through a lot of training and learning, the best blood pressure prediction model is selected to achieve accurate measurement of blood pressure values. It has been verified by experiments that the average absolute error of diastolic and systolic blood pressure based on the random forest optimization model meets the standard of less than 5mmHg formulated by AAMI (American Medical Instrument Promotion Association), which is better consistent with the method of mercury sphygmomanometer, and has more excellent performance than support vector machine regression model under the same conditions.
With the continuous depletion of global rhenium resources, the separation and purification of rhenium (Re) from secondary resources became very important. Herein, an imprinted composite membrane with ...“flexible chain” thermo‐responsive imprinted separation layer structure (Re‐TIICM) was prepared to achieve selective separation and recovery of Re from secondary resources with complex composition. Re‐TIICM's thermo‐responsive imprinted separation layer occurred sol/gel phase transition by adjusting the temperature, which effectively mitigated the trade‐off between selectivity and desorption. The structure and performance of Re‐TIICM under optimum process conditions were tested and evaluated. The results showed that Re‐TIICM exhibited a maximum adsorption capacity of 0.1036 mmol/g at 35°C, a separation degree of 4.37 (MnO4− as disturbing ions), and remained good adsorption capacity and selectivity after 9 adsorption/desorption cycles. When Re‐TIICM was applied in secondary resources, the purity of Re increased from 35.412% to 70.208% after one adsorption/desorption cycle, showing a great potential for industrial applications.
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•Fe3O4@OA@Poloxamer NPs exhibit an excellent role in eliminating excess TG.•Fe3O4@OA@Poloxamer NPs exhibit a liposuction effect to absorb excess TG.•Fe3O4@OA@Poloxamer NPs activate ...the lipid-regulated proteins to degrade excess TG.•Fe3O4 NPs eliminate excess TG in vivo by a liposuction effect and a nano-enzyme effect.
Lipid deposition induced various diseases including nonalcoholic fatty liver and hyperlipemia. The excessive accumulation of triglyceride (TG) and the deposition of fat were the two most critical causes. Here, we developed Fe3O4@OA@Poloxamer nanoparticles (NPs) with amphiphilic structures, which exhibited an excellent role in eliminating excess TG. Hydrophobic TG was adsorbed efficiently by Fe3O4@OA@Poloxamer NPs through the “liposuction effect” and the formation of NPs@TG complex was then conducted. The NPs@TG complex was further enclosed by the endosome based on the endocytosis and subsequently was taken into the lysosome, degrading with the help of lipases. Meanwhile, the “nano-enzyme effect” of Fe3O4 NPs recovered the lipid-regulated proteins including PPARα, further triggering biodegradation pathways of TG, although the lipid-regulated proteins were obviously inhibited in the high-fat hepatocytes models. These two mechanisms of Fe3O4@OA@Poloxamer NPs together achieved the down-regulation of TG in vivo and in vitro. Therefore, our findings provided a novel thought in treating these diseases associated with lipid deposition, that is, nanoparticles modified by specific structure exhibit a superior TG removal.
Methane (CH4), the second most significant greenhouse gas after carbon dioxide, contributes significantly to global warming. Owing to its wide monitoring range and long observation time, satellite ...remote sensing has emerged as a popular method for monitoring CH4. Although the existing algorithm for satellite retrievals of CH4 column amount is mature, it has some drawbacks: the retrieval calculation process is complicated, and there is still a discrepancy between the satellite-retried column amount of CH4 and surface CH4 concentrations. To obtain more accurate near-surface CH4 concentrations from satellite observations, this paper proposed a conversion method based on the Extreme Gradient Boosting (XGBoost) algorithm, taking column amount retrieved by the Greenhouse Gas Observation SATellite (GOSAT) satellite, meteorological factors and near-surface methane concentrations as predictor variables. Using this method, we analyzed the importance of the characteristic factors and predicted methane concentrations at four ground monitoring sites in World Center for Greenhouse Gases (WDCGG), and reached the following conclusions: (1) The model performed well on the test set, with a sample-based cross-validation coefficient of determination (R2) of 0.79, a root mean square error (RMSE) of 0.0251 ppm and a mean absolute percentage error (MAPE) of 0.88%. (2) Of the five meteorological input features, surface net solar radiation had a greater impact on the construction of the model than the other four. (3) The CH4 monthly average concentrations predicted by this model are generally consistent with the trend of the CH4 concentrations of ground monitoring stations.
•A machine learning method is used to obtain surface CH4 concentrations reliably.•The R2 between the model estimated value and surface CH4 concentrations is 0.79.•The importance of features in the model is analyzed.
The accurate estimation of a regional ecosystem’s carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function ...enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 109 t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase.
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