Quality Characteristics and Fungal Diversity during Natural and Inoculated Fermentation of “Hutai 8” Wine

In order to increase the added value of table grape products and consequently improve the income of fruit farmers, this experiment systematically studied the relationship between fungal diversity and “Hutai 8 ” wine quality during the winemaking process. Fungal diversity changes during natural and inoculated wine fermentation were studied by high-throughput sequencing, and changes in flavor characteristics were evaluated as well. The results showed that compared with inoculated fermentation, naturally fermented wine had lower alcohol content and higher acetic acid content. Moreover, the contents of volatile alcohols and esters were higher than those of the wine produced by inoculated fermentation, and the content of fatty acids was lower. At the early stage of natural fermentation, Hanseniaspora was dominant in the microbial community of naturally fermented wine, a large number of Saccharomyces appeared at the middle stage, and there were still a large number of Hanseniaspora at the late stage. S. cerevisiae remained dominant during inoculated fermentation. Ethyl acetate and some alcohols, such as benzyl alcohol, benzyl alcohol, hexanol, cis-3-nonene-1-ol, were correlated with the genera Alternaria, Acremonium and Cladosporium. In addition, there could be a risk of naturally fermented “Hutai 8” wine deteriorating and producing a large amount of ethyl acetate, which can adversely affect the aroma. Therefore, inoculated fermentation is recommended to produce “Hutai 8” wine

Tightly-bound and room-temperature-stable excitons in van der Waals degenerate-semiconductor Bi4O4SeCl2 with high charge-carrier density

Excitons, which represent a type of quasi-particles consisting of electron-hole pairs bound by the mutual Coulomb interaction, were often observed in lowly-doped semiconductors or insulators. However, realizing excitons in the semiconductors or insulators with high charge carrier densities is a challenging task. Here, we perform infrared spectroscopy, electrical transport, ab initio calculation, and angle-resolved-photoemission spectroscopy studies of a van der Waals degenerate-semiconductor Bi4O4SeCl2. A peak-like feature (i.e., alpha peak) is present around ~ 125 meV in the optical conductivity spectra at low temperature T = 8 K and room temperature. After being excluded from the optical excitations of free carriers, interband transitions, localized states and polarons, the alpha peak is assigned as the exciton absorption. Moreover, assuming the existence of weakly-bound excitons--Wannier-type excitons in this material violates the Lyddane-Sachs-Teller relation. Besides, the exciton binding energy of ~ 375 meV, which is about an order of magnitude larger than those of conventional semiconductors, and the charge-carrier concentration of ~ 1.25 * 10^19 cm^-3, which is higher than the Mott density, further indicate that the excitons in this highly-doped system should be tightly bound. Our results pave the way for developing the optoelectronic devices based on the tightly-bound and room-temperature-stable excitons in highly-doped van der Waals degenerate semiconductors.Comment: Accepted by Communications Material

Crop pest image classification based on improved densely connected convolutional network

IntroductionCrop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.MethodsTo address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.ResultsExperimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality

Translating Human Cancer Sequences Into Personalized Porcine Cancer Models

The global incidence of cancer is rapidly rising, and despite an improved understanding of cancer molecular biology, immune landscapes, and advancements in cytotoxic, biologic, and immunologic anti-cancer therapeutics, cancer remains a leading cause of death worldwide. Cancer is caused by the accumulation of a series of gene mutations called driver mutations that confer selective growth advantages to tumor cells. As cancer therapies move toward personalized medicine, predictive modeling of the role driver mutations play in tumorigenesis and therapeutic susceptibility will become essential. The development of next-generation sequencing technology has made the evaluation of mutated genes possible in clinical practice, allowing for identification of driver mutations underlying cancer development in individual patients. This, combined with recent advances in gene editing technologies such as CRISPR-Cas9 enables development of personalized tumor models for prediction of treatment responses for mutational profiles observed clinically. Pigs represent an ideal animal model for development of personalized tumor models due to their similar size, anatomy, physiology, metabolism, immunity, and genetics compared to humans. Such models would support new initiatives in precision medicine, provide approaches to create disease site tumor models with designated spatial and temporal clinical outcomes, and create standardized tumor models analogous to human tumors to enable therapeutic studies. In this review, we discuss the process of utilizing genomic sequencing approaches, gene editing technologies, and transgenic porcine cancer models to develop clinically relevant, personalized large animal cancer models for use in co-clinical trials, ultimately improving treatment stratification and translation of novel therapeutic approaches to clinical practice

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

BackgroundThe novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.MethodsWe retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.ResultsThe AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).ConclusionThe nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively

Biochim. Biophys. Acta-Biomembr.

Stabilization of cells in a desiccated state can significantly simplify the storage and transportation and save expenses for clinical applications. Introduction of the impermeable disaccharide, trehalose, into cells is an important step to improve the desiccation tolerance of cells. In this study, a novel cell penetrating peptide, KRKRWHW, was developed based on molecular simulations. The peptide exhibited little cytotoxicity and high penetrating efficiency into mammalian cells. The cell viability of mouse embryonic fibroblasts (MEFs) after the incubation with various concentrations of KRKRWHW from 0.01 mM to 5 mM at 37 degrees C for 4 h was maintained at around 100%. The peptide was able to penetrate into MEFs within 1 h at 37 degrees C with an efficiency of around 90% at 0.1 mM. Trehalose, as a cargo coupled with the peptide of KRKRWHW through hydrogen bond and pi-pi bond, was successfully loaded into the MEFs. This novel peptide provides a novel approach for the delivery of trehalose into mammalian cells. (C) 2014 Elsevier B.V. All rights reserved.Stabilization of cells in a desiccated state can significantly simplify the storage and transportation and save expenses for clinical applications. Introduction of the impermeable disaccharide, trehalose, into cells is an important step to improve the desiccation tolerance of cells. In this study, a novel cell penetrating peptide, KRKRWHW, was developed based on molecular simulations. The peptide exhibited little cytotoxicity and high penetrating efficiency into mammalian cells. The cell viability of mouse embryonic fibroblasts (MEFs) after the incubation with various concentrations of KRKRWHW from 0.01 mM to 5 mM at 37 degrees C for 4 h was maintained at around 100%. The peptide was able to penetrate into MEFs within 1 h at 37 degrees C with an efficiency of around 90% at 0.1 mM. Trehalose, as a cargo coupled with the peptide of KRKRWHW through hydrogen bond and pi-pi bond, was successfully loaded into the MEFs. This novel peptide provides a novel approach for the delivery of trehalose into mammalian cells. (C) 2014 Elsevier B.V. All rights reserved

Stochastic Material Point Method for Analysis in Non-Linear Dynamics of Metals

A stochastic material point method is proposed for stochastic analysis in non-linear dynamics of metals with varying random material properties. The basic random variables are parameters of equation of state and those of constitutive equation. In conjunction with the material point method, the Taylor series expansion is employed to predict first- and second-moment characteristics of structural response. Unlike the traditional grid methods, the stochastic material point method does not require structured mesh; instead, only a scattered cluster of nodes is required in the computational domain. In addition, there is no need for fixed connectivity between nodes. Hence, the stochastic material point method is more suitable than the stochastic method based on grids, when solving dynamics problems of metals involving large deformations and strong nonlinearity. Numerical examples show good agreement between the results of the stochastic material point method and Monte Carlo simulation. This study examines the accuracy and convergence of the stochastic material point method. The stochastic material point method offers a new option when solving stochastic dynamics problems of metals involving large deformation and strong nonlinearity, since the method is convenient and efficient