Multicopy Expression of the Marine Antimicrobial Peptide Spgillcin 177–189 in Pichia pastoris for High-Yield Production and Potent Activity Against Foodborne Pathogens

Bacterial foodborne contamination poses a dual challenge of chemical preservative risks and antibiotic resistance, drives the need for green production of natural antimicrobial alternatives. The reported cationic antimicrobial peptide (AMP) Spgillcin177−189 derived from the Scylla paramamosain, has strong antimicrobial activity against Staphylococcus aureus and clinical isolation strains. To meet industry demand in future, large-scale production of Spgillcin177− 189 is essential. In the study, Pichia pastoris expression system was established for production of the recombinant Spgillcin177− 189 (rSp­ gillcin177−189). Then, multicopy strategy was selectively designed by employing the Golden Gate assembly technology to efficiently construct multi-copy plasmid s, which significantly enhanced the expression level of Spgillcin177− 189. A yield of 126.1 mg/L was harvested with 2.75-fold higher that of the single-copy strain. In addition, the recombinant Spgill­ cin177 − 189 exhibited potent antibacterial activity against multiple foodborne pathogens within a MIC range of 5.25–84 µg/ mL. It also showed effective bactericidal activity and anti-biofilm activity against Staphylococcus aureus and Vibrio parahaemolyticus. rSpgillcin177 − 189 exhibited good thermostability, with no obvious cytotoxicity and hemolytic activity. rSpgillcin177 − 189 may interact with microbial surface components via hydrogen bonding, which were vital for peptide activity in combating bacteria. The rSpgillcin177 − 189 specifically targeting the cell membrane, disrupted bacterial mem­ brane integrity and leading to cell death. This study provided a very feasible genetic engineering strategy for large-scale production of rSpgillcin177 − 189, which will be applied at a lower cost in agricultural and food industries in future.

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A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms. In the segmentation step, clustering techniques with/without group sparsity approach were tested to extract region of interests from the images. Subsequently, a novel publicly available morphological sperm image data set, whose labels were identified by experts as non-sperm, normal and abnormal sperm, was created as the ground truths of classification step. In the c lassification step, conventional and ensemble machine learning methods were applied to domain-specific features that were extracted by using wavelet transform and descriptors. Additionally, as an alternative to conventional features, three deep neural network architectures, which can extract high-level features from raw images after using statistical learning, were employed to increase the proposed method’s performance. The results show that, for the conventional features, the highest classification accuracies were achieved as 80.5% and 83.8% by using the waveletand descriptor-based features that were fed to the Support Vector Machines respectively. On the other hand, the Mobile-Net, which is a very convenient network for smartphones, achieved 87% accuracy. In the light of obtained results, it is seen that a fully automatic hybrid system, which uses the group sparsity to enhance segmentation performance and the Mobile-Net to obtain high-level robust features, can be an effective m obile solution for the sperm morphology analysis problem.

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When the mindful ones experience flow: a moderated-mediation model of

Purpose – The emerging live streaming technology has provided a novel means for streamers to interact with viewers, allowing for synchronous and vivid demonstrations of products for sale. However, individual streamers as sellers still struggle to improve sales in their live stores. Drawing upon flow theory, our study proposes and tests a moderated-mediation model that explores (1) the indirect influences of telepresence and social presence as two important live streaming affordances on viewers’ purchase intentions through the immersive state of flow and (2) the dynamic contingency embedded in the indirect relationships between presence and purchases through flow as created by mindfulness. Design/methodology/approach – We collected survey data from 251 experienced consumers of a three-year Kuaishou store run by an Inner Mongolian singer in China. We applied a covariance-based structural equation modeling approach to examine the first-stage moderated-mediation model. Findings – Our results show that viewers’ flow state mediated the effects of telepresence and social presence on purchase intentions of both virtual gifts and physical products. Additionally, mindfulness toward live streaming strengthened the mediation effect of flow on the relationship between telepresence and purchase intentions but weakened its mediation effect between social presence and purchase intentions. Originality/value – Our study not only expands the existing knowledge on live commerce but also systematically addresses the theoretical tension between flow and mindfulness as two important user states that coexist in the live commerce context. Our findings also reveal practical implications for streamers, managers and designers of live commerce.

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Risk of Revision and Reoperation After ACL Reconstruction: Comparison of Quadriceps Tendon, Patellar Tendon, and Hamstring Autografts Stratified by Patient Sex and Age

(Abstract not found)

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