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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.

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.