Development and Characterization of Two Types of Surface Displayed Levansucrases for Levan Biosynthesis

Levan has wide applications in chemical, cosmetic, pharmaceutical and food industries. The free levansucrase is usually used in the biosynthesis of levan, but the poor reusability and low stability of free levansucrase have limited its large-scale use. To address this problem, the surface-displayed levansucrase in Saccharomyces cerevisiae were generated and evaluated in this study. The levansucrase from Zymomonas mobilis was displayed on the cell surface of Saccharomyces cerevisiae EBY100 using a various yeast surface display platform. The N-terminal fusion partner is based on a-agglutinin, and the C-terminal one is Flo1p. The yield of levan produced by these two whole-cell biocatalysts reaches 26 g/L and 34 g/L in 24 h, respectively. Meanwhile, the stability of the surface-displayed levansucrases is significantly enhanced. After six reuses, these two biocatalysts retained over 50% and 60% of their initial activities, respectively. Furthermore, the molecular weight and polydispersity test of the products suggested that the whole-cell biocatalyst of levansucrase displayed by Flo1p has more potentials in the production of levan with low molecular weight which is critical in certain applications. In conclusion, our method not only enable the possibility to reuse the enzyme, but also improves the stability of the enzyme

Outlier Detection for Spotting Micro-expressions

Facial expression, as a basic communication method, is an important way of emotion expression and cognition. Facial emotional expression impairment seriously affects interpersonal communication and social life. Micro-expressions (MEs) are involuntary and instant facial dynamics that occurs when the subject failed to suppress their genuine emotions, especially in high-stake situations. Psychological research has shown that MEs can reflect people&rsquo;s true emotions, which is of great help to the treatment of Facial emotional expression impairment. ME spotting aims to locate the apex frame positions of MEs from long videos, which is the first step in ME analysis. Unlike previous researches that used binary classification or maximum feature difference for analysis, in this paper, we apply the idea of outlier detection to spot ME for the first time. MEs are unusual facial dynamics whose movement patterns diverge from others, so they can be regarded as outliers in the feature space of long videos. Our proposed method uses Gaussian model to estimate the probability density function and locates outliers by analyzing the statistical features of long videos to achieve ME spotting. This method was evaluated on CASME I, CASME II and SAMM datasets that only include spontaneous MEs in long videos. The results show that this method can efficiently locate apex frames of ME efficiently in long videos and also provide a new perspective for ME spotting.</p

Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma

Abstract Background The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images. Methods This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram. Results Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619–0.843), 0.810(95%CI, 0.696–0.907), 0.841(95%CI, 0.743–0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130. Conclusion The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application

Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma

Abstract Introduction The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features. Methods We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signal intensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability. Results The mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS. The SI of tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior dimension, transverse dimension, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. The AUC was 0.997 of the combined of ADCmax, REAmax and REVmax. Our study showed that ADCmax threshold value of 789.05 (10–3mm2/s) can differentiate UCS from EAC with 100% sensitivity, 76.5% specificity, and 0.76 AUC, REAmax threshold value of 0.45 can differentiate UCS from EAC with 88.2% sensitivity, 100% specificity, and 0.88 AUC. Conclusion Multiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC

Phylogenetic, Structural and Functional Evolution of the LHC Gene Family in Plant Species

Light-harvesting chlorophyll a/b-binding (LHC) superfamily proteins play a vital role in photosynthesis. Although the physiological and biochemical functions of LHC genes have been well-characterized, the structural evolution and functional differentiation of the products need to be further studied. In this paper, we report the genome-wide identification and phylogenetic analysis of LHC genes in photosynthetic organisms. A total of 1222 non-redundant members of the LHC family were identified from 42 species. According to the phylogenetic clustering of their homologues with Arabidopsis thaliana, they can be divided into four subfamilies. In the subsequent evolution of land plants, a whole-genome replication (WGD) event was the driving force for the evolution and expansion of the LHC superfamily, with its copy numbers rapidly increasing in angiosperms. The selection pressure of photosystem II sub-unit S (PsbS) and ferrochelatase (FCII) families were higher than other subfamilies. In addition, the transcriptional expression profiles of LHC gene family members in different tissues and their expression patterns under exogenous abiotic stress conditions significantly differed, and the LHC genes are highly expressed in mature leaves, which is consistent with the conclusion that LHC is mainly involved in the capture and transmission of light energy in photosynthesis. According to the expression pattern and copy number of LHC genes in land plants, we propose different evolutionary trajectories in this gene family. This study provides a basis for understanding the molecular evolutionary characteristics and evolution patterns of plant LHCs

Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies

Abstract Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes

Production of Microbial Lipids by <i>Saitozyma podzolica</i> Zwy2-3 Using Corn Straw Hydrolysate, the Analysis of Lipid Composition, and the Prediction of Biodiesel Properties

Although Saitozyma podzolica Zwy2-3 can use the enzymatic hydrolysate of corn stalks treated with an ammonium carbonate-steam explosion (EHCS-ACSE) as a substrate for lipid accumulation, the inefficient conversion of sugars from EHCS-ACSE into lipids necessitates the further optimization of fermentation parameters. Response surface design was used to optimize the primary fermentation parameters. Under the optimized conditions of the reducing sugar concentration of 89.44 g/L, yeast extract concentration of 3.88 g/L, rotational speed of 219 rpm, and incubation time of 122 h, the maximum lipid production achieved 11.45 g/L, which was 2.28 times higher than the results of the previous study. In addition, lipid profiling showed the presence of four fatty acid methyl esters, with the highest percentage being 61.84% oleic acid, followed by 21.53% palmitic acid, 13.05% stearic acid, and 3.58% linoleic acid. It is noteworthy that the composition and relative abundance of microbial lipids remained constant under different culture conditions. The characteristics of Zwy2-3 biodiesel, such as the iodine value (62.09), cetane number (59.29), density (0.87 g/cm3), and oxidation stability (35.53), meet the international standards (ASTM D6751-02 and EN 14214) for biodiesel. The present study further demonstrated that S. podzolica Zwy2-3 can efficiently utilize EHCS-ACSE for microbial lipid accumulation, and its lipids have favorable qualities that make them suitable for biodiesel production

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids’ contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet

Oxygen-producing and pH-responsive targeted DNA nanoflowers for enhanced chemo-sonodynamic therapy of lung cancer

Lung cancer is the deadliest kind of cancer in the world, and the hypoxic tumor microenvironment can significantly lower the sensitivity of chemotherapeutic drugs and limit the efficacy of different therapeutic approaches. In order to overcome these problems, we have designed a drug-loaded targeted DNA nanoflowers encoding AS1411 aptamer and encapsulating chemotherapeutic drug doxorubicin and oxygen-producing drug horseradish peroxidase (DOX/HRP-DFs). These nanoflowers can release drugs in response to acidic tumor microenvironment and alleviate tumor tissue hypoxia, enhancing the therapeutic effects of chemotherapy synergistic with sonodynamic therapy. Owing to the encoded drug-loading sequence, the doxorubicin loading rate of DNA nanoflowers reached 73.24 ± 3.45%, and the drug could be released quickly by disintegrating in an acidic environment. Furthermore, the AS1411 aptamer endowed DNA nanoflowers with exceptional tumor targeting properties, which increased the concentration of chemotherapeutic drug doxorubicin in tumor cells. It is noteworthy that both in vitro and in vivo experiments demonstrated DNA nanoflowers could considerably improve the hypoxia of tumor cells, which enabled the generation of sufficient reactive oxygen species in combination with ultrasound, significantly enhancing the therapeutic effect of sonodynamic therapy and evidently inhibiting tumor growth and metastasis. Overall, this DNA nanoflowers delivery system offers a promising approach for treating lung cancer