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Using wheat B starch as raw material, optimize the one-step enzymatic hydrolysis process for preparing fermentable sugars and subsequent fermentation of alcohol by brewing yeast to improve its utilization value. Initially, the elemental composition of Wheat B starch was quantified. A composite enzymatic catalyst containing α-amylase and saccharase were then employed to generate fermentable sugars from wheat B starch through enzymatic hydrolysis efficiently. Various parameters, including composite enzyme concentration, solid-liquid ratio, enzyme temperature, enzymatic duration, and pH were thoroughly investigated to assess their effects on reducing sugar yield. An orthogonal optimization approach was conducted to determine the optimal conditions for enzymatic hydrolysis. Results showed that wheat B starch contained 85.61% starch, with approximately 2% protein and mineral content, and pentosan composition nearing 5%. Following the orthogonal optimization, the most effective parameters for enzymatic saccharification were identified: A composite enzyme dosage of 4 mL, a solid-liquid ratio of 1:25 g/mL, an enzymatic digestion temperature of 60 ℃, an enzymatic digestion duration of 10 h, and a pH of 5.0. Under these specific conditions, the yield of reducing sugar reached 94.6%. Subsequently, wheat B starch was converted into alcohol through yeast-mediated fermentation. This process led to a final alcohol concentration 12.76 g/L, accompanied by an alcohol yield of 31.90%. In conclusion, wheat B starch holded remarkable promise for large-scale bioethanol production, providing valuable insights for maximizing the utilization of this resource on a broad scale.

Sweet potato residue (SPR) is the by-product of starch extraction from fresh sweet potatoes and is rich in carbohydrates, making it a suitable substrate for bioethanol production. An amylolytic industrial yeast strain with co-expressing α-amylase and glucoamylase genes would combine enzyme production, SPR hydrolysis, and glucose fermentation into a one-step process. This consolidated bioprocessing (CBP) shows great application potential in the economic production of bioethanol. In this study, a convenient heterologous gene integration method was developed. Eight copies of a Talaromyces emersonii α-amylase expression cassette and eight copies of a Saccharomycopsis fibuligera glucoamylase expression cassette were integrated into the genome of industrial diploid Saccharomyces cerevisiae strain 1974. The resulting recombinant strains exhibited clear transparent zones in the iodine starch plates, and SDS-PAGE analysis indicated that α-amylase and glucoamylase were secreted into the culture medium. Enzymatic activity analysis demonstrated that the optimal temperature for α-amylase and glucoamylase was 60–70°C, and the pH optima for α-amylase and glucoamylase was 4.0 and 5.0, respectively. Initially, soluble corn starch with a concentration of 100 g/L was initially used to evaluate the ethanol production capability of recombinant amylolytic S. cerevisiae strains. After 7 days of CBP fermentation, the α-amylase-expressing strain 1974-temA and the glucoamylase-expressing strain 1974-GA produced 33.03 and 28.37 g/L ethanol, respectively. However, the 1974-GA-temA strain, which expressed α-amylase and glucoamylase, produced 42.22 g/L ethanol, corresponding to 70.59% of the theoretical yield. Subsequently, fermentation was conducted using the amylolytic strain 1974-GA-temA without the addition of exogenous α-amylase and glucoamylase, which resulted in the production of 32.15 g/L ethanol with an ethanol yield of 0.30 g/g. The addition of 20% glucoamylase (60 U/g SPR) increased ethanol concentration to 50.55 g/L, corresponding to a theoretical yield of 93.23%, which was comparable to the ethanol production observed with the addition of 100% α-amylase and glucoamylase. The recombinant amylolytic strains constructed in this study will facilitate the advancement of CBP fermentation of SPR for the production of bioethanol.

Abstract Selective hydrogenation of carbon monoxide (CO) to higher alcohols (C2+OH) is a promising non-petroleum route for producing high-value chemicals, in which precise regulations of both C-O cleavage and C-C coupling are highly essential but remain great challenges. Herein, we report that highly selective CO hydrogenation to C2-4OH is achieved over a potassium-modified edge-rich molybdenum disulfide (MoS2) catalyst, which delivers a high CO conversion of 17% with a superior C2-4OH selectivity of 45.2% in hydrogenated products at 240 °C and 50 bar, outperforming previously reported non-noble metal-based catalysts under similar conditions. By regulating the relative abundance of edge to basal plane, C2-4OH to methanol selectivity ratio can be overturned from 0.4 to 2.2. Mechanistic studies reveal that sulfur vacancies at MoS2 edges boost carbon-chain growth by facilitating not only C-O cleavage but also C-C coupling, while potassium promotes the desorption of alcohols via electrostatic interaction with hydroxyls, thereby enabling preferential formation of C2-4OH.

Based on the radar data direct assimilation system established by the Chongqing Meteorological Bureau in cooperation with the Center for Analysis and Prediction of Storms of the University of Oklahoma, a set of radar data assimilation and forecasting experiments for a continuous month in the 2020 are carried out by using ensemble Kalman filter (EnKF) assimilation and hybrid ensemble three-dimensional variational (Hybrid Ensemble Three-dimensional Variational, Hybrid En3DV) assimilation with different hybrid weights (with 0%, 20%, 50%, 80% and 100% of static background error covariance). The performance of different direct assimilation methods of radar data on the short-term precipitation is explored. The results of one month continuous experiment indicate that the precipitation forecasting skills of different assimilation experiments show significant difference in the forecast with short leading-time (within 3 h), and then the difference gradually decreases with the increase of the forecast leading-time. In general, the short-term precipitation forecasting skills of EnKF are much better than the others. In the Hybrid En3DV assimilation experiments, the experiment with only the ensemble-based background error covariance performs the best, suggesting that the static background error covariance has no positive effect on radar data assimilation. The results of case study show that with the increase of ensemble-based background error covariance weights, the spin-up time of model decreases, suggesting that the ensemble-based background error covariance can make the variables in assimilation analysis filed more balanced compared with the static background error covariance.

In order to study the performance differences of different ensemble forecast products for temperature and precipitation forecasts in Chongqing, based on the Chongqing Convective-scale Ensemble Prediction System (CQCEPS) which has been operationally implemented, a comprehensive comparison and analysis are carried out for the differences as well as temporal and spatial distribution characteristics in the forecast performance of ensemble forecast application products of surface elements such as the control forecast of 24 h cumulative precipitation and 2 m temperature, ensemble mean forecast and ensemble quantile forecast for the whole year of 2020, as well as the probability matched mean forecast of 24 h cumulative precipitation. The results show that (1) the precipitation prediction performance of each ensemble forecast product decreases with the increase of predicting lead time. When predicting lead time is consistent, ensemble mean forecast, probability matched mean forecast, and 60% and higher quantile forecasts are better than control forecast. The 90% quantile forecast is the best among quantile forecast products. (2) In summer and autumn, the forecast results of each product are quite different, and the 90% quantile forecast, probability matched mean forecast and ensemble mean forecast are better than others. (3) The SEEPS scores of control forecast, ensemble mean forecast, probability matched mean forecast and the 90% quantile forecast show higher accuracy in eastern Sichuan and poorer accuracy in the southeast and northeast of Chongqing, indicating that the prediction performance may have a certain relationship with the terrain. (4) For temperature forecasts with different lead times, overall, the ensemble mean forecast performs the best among all of the prediction products, and the 70% quantile forecast performs the best among all quantile products.

This work achieves highly active and selective electrocatalytic hydrogenation of acetylene to ethylene (HAE) under mild conditions over Cu catalyst to replace the classic thermocatalytic HAE route requiring harsh conditions and costly H2.

Achieving CO oxidation at room temperature is significant for gas purification but remains challenging to perform. Here, the authors report design of graphene-isolated Pt from cobalt-nickel nanoparticles for efficiently catalytic CO oxidation in an oxygen-rich atmosphere.

The lack of efficient, inexpensive catalysts hinders large-scale application of hydrogen evolution reaction (HER). Here, the authors report a MoS2 nanofoam catalyst with co-confined Se in the surface and Co in the inner layer, exhibiting high large-current-density HER activity and durability.

This paper is concerned with the fixed-time stability of delayed neural networks with impulsive perturbations. By means of inequality analysis technique and Lyapunov function method, some novel fixed-time stability criteria for the addressed neural networks are derived in terms of linear matrix inequalities (LMIs). The settling time can be estimated without depending on any initial conditions but only on the designed controllers. In addition, two different controllers are designed for the impulsive delayed neural networks. Moreover, each controller involves three parts, in which each part has different role in the stabilization of the addressed neural networks. Finally, two numerical examples are provided to illustrate the effectiveness of the theoretical analysis.

Abstract Background A global public health emergency triggered by the Coronavirus Disease 2019 (COVID-19) epidemic may have are markable psychological impact on the population. There is still limited psychological research on police officers, especially prison officers in the process of enforcing the law. The present study aims to identify prevalence and influencing factors on mental health status among frontline prison officers in China during the prevention and control of the COVID-19 epidemic. Methods A cross-sectional survey with a sample of 981 frontline prison officers was conducted using snowball sampling approach. The self-administered questionnaire consisted of 4 parts: (i) informed consent form; (ii) socio-demographic section; (iii) work and life situations during the prevention and control of the COVID-19 epidemic; (iv) the Chinese version of the 12-item General Health Questionnaire (GHQ-12). Univariate analysis and multivariable logistic regression were performed to identify factors influencing mental health status. Results The prevalence of being prone to mental health problems (GHQ-12 score ≥ 4) was 33.43% among frontline prison officers. The results of GHQ-12 factors analysis indicated that the prison officers suffered from psychological issues was related to anxiety and depression, which main symptoms were unhappy and depressed, lost sleep over worry and constantly under strain. Multivariate logistic regression analysis revealed that male (OR = 1.573, 95% CI:1.385–1.853), lockdown shift inside the prison(OR = 2.203, 95% CI:2.139–2.297), more night shifts (OR = 2.163, 95% CI:2.031–2.317; OR = 2.749, 95% CI:2.194–2.901), more smoking (OR = 1.100, 95% CI:1.037–2.168), poor self-reported physical condition (OR = 1.947, 95% CI:1.478–2.250), chronic or serious illness history(OR = 1.870, 95% CI:1.314–2.660; OR = 2.214, 95% CI:1.460–2.812) were risk factors for mental health among frontline prison officers, while regular diet (OR = 0.779, 95% CI:0.539–0.928), more physical exercise (OR = 0.702, 95% CI:0.548–0.899; OR = 0.641, 95% CI:0.316–0.887), more communication with family members (OR = 0.437, 95% CI:0.295–0.616) were protective factors. Conclusion Chinese frontline prison officers experienced different psychological stress coming from the prevention and control of this epidemic. Therefore, continued surveillance of psychological problems and targeted mental health care for frontline prison officers were urgent.

In this paper, we discuss the stability problem of the impulsive differential systems with state-dependent impulses. By using the linear decomposition methods, some sufficient conditions ensuring stability of the impulsive differential systems with state-dependent impulses are obtained and the estimate of the solution of such nonlinear systems is also acquired. Our results improve and generalize some of the known results given in earlier references. An example is given to demonstrate our results. [ABSTRACT FROM AUTHOR]

In the study of polymer flooding, researchers usually ignore the genetic stress properties of viscoelastic fluids. In this paper, we investigate the process of viscoelastic fluid flooding the remaining oil in the dead end. This work uses the fractional-order Maxwell in the traditional momentum equation. Furthermore, a semi-analytic solution of the flow control equation for fractional-order viscoelastic fluids is derived, and the oil-repelling process of viscoelastic fluids is simulated by a secondary development of OpenFOAM. The results show that velocity fractional-order derivative α significantly affects polymer solution characteristics, and increasing the elasticity of the fluid can significantly improve the oil repelling efficiency. Compared to the Newtonian fluid flow model, the fractional order derivative a and relaxation time b in the two-parameter instanton equation can accurately characterize the degree of elasticity of the fluid. The smaller the a, the more elastic the fluid is and the higher the oil-repelling efficiency. The larger the b, the less elastic the fluid is and the lower the cancellation efficiency. Moreover, the disturbance of the polymer solution to the dead end is divided into two elastic perturbation areas. The stronger the elasticity of the polymer solution, the higher the peak value of the area in the dead end and the higher the final oil displacement efficiency.

BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.

Graphene has shown great potential for improving growth of many plants, but its effect on woody plants remains essentially unstudied. In this work, Pinus tabuliformis Carr. bare-rooted seedlings grown outdoors in pots were irrigated with a graphene solution over a concentration range of 0-50 mg/L for six months. Graphene was found to stimulate root growth, with a maximal effect at 25 mg/L. We then investigated root microstructure and carried out transcript profiling of root materials treated with 0 and 25 mg/L graphene. Graphene treatment resulted in plasma-wall separation and destruction of membrane integrity in root cells. More than 50 thousand of differentially expressed genes (DEGs) were obtained by RNA sequencing, among which 6477 could be annotated using other plant databases. The GO enrichment analysis and KEGG pathway analysis of the annotated DEGs indicated that abiotic stress responses, which resemble salt stress, were induced by graphene treatment in roots, while responses to biotic stimuli were inhibited. Numerous metabolic processes and hormone signal transduction pathways were altered by the treatment. The growth promotion effects of graphene may be mediated by encouraging proline synthesis, and suppression of the expression of the auxin response gene SMALL AUXIN UP-REGULATED RNA 41 (SAUR41), PYL genes which encode ABA receptors, and GSK3 homologs.

Deriving aerosol optical depth (AOD) from space-borne observations is still challenging due to uncertainties associated with sensor calibration drift, cloud screening, aerosol type classification, and surface reflectance characterization. As an initial step to understanding the physical processes impacting these uncertainties in satellite AOD retrievals, this study outlines a theoretical approach to estimate biases in the satellite aerosol retrieval algorithm affected by surface albedo and prescribed aerosol optical properties using a simplified radiative transfer model with a traditional error propagation approach. We expand the critical surface reflectance concept to obtain the critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP). The top-of-atmosphere (TOA) reflectance is not sensitive to significant variability in aerosol loading (AOD) at the critical value; thus, the AOD cannot be determined. Results show that 5% bias in surface albedo (A), single scattering albedo (SSA), or asymmetry parameter (g) lead to large retrieved AOD errors, especially high under conditions when A, SSA, or g are close to their critical values. The results can be useful for future research related to improvements of satellite aerosol retrieval algorithms and provide a preliminary framework to analytically quantify AOD uncertainties from satellite retrievals.

In this paper, we constructed a fluorescence resonance energy transfer (FRET) system between amino-functionalized carbon dots (C-dots) and gold nanoparticles (AuNPs). In this system, C-dots were treated as energy donors, while AuNPs were treated as energy acceptors. We optimized some important factors including incubation time, AuNPs concentration and media pH, which would affect the efficiency of the FRET system. Under the optimized experimental conditions, melamine could be detected based on fluorescence intensity of C-dots. We could get a linear relationship between 50 nM and 500 nM and the detection limit was 36 nM. The proposed method was applied to the determination of melamine in milk samples with satisfactory results. Compared with previous reports, the proposed method manifested great advantages including high sensitivity, short analysis time, low cost and ease of operation. [ABSTRACT FROM AUTHOR]