A Degeneration Gradient of Poplar Trees Contributes to the Taxonomic,Functional, and Resistome Diversity of Bacterial Communities in Rhizosphere Soils

Bacterial communities associated with roots influence the health and nutrition of the host plant. However, the microbiome discrepancy are not well understood under different healthy conditions. Here, we tested the hypothesis that rhizosphere soil microbial diversity and function varies along a degeneration gradient of poplar, with a focus on plant growth promoting bacteria (PGPB) and antibiotic resistance genes. Comprehensive metagenomic analysis including taxonomic investigation, functional detection, and ARG (antibiotics resistance genes) annotation revealed that available potassium (AK) was correlated with microbial diversity and function. We proposed several microbes, Bradyrhizobium, Sphingomonas, Mesorhizobium, Nocardioides, Variovorax, Gemmatimonadetes, Rhizobacter, Pedosphaera, Candidatus Solibacter, Acidobacterium, and Phenylobacterium, as candidates to reflect the soil fertility and the plant health. The highest abundance of multidrug resistance genes and the four mainly microbial resistance mechanisms (antibiotic efflux, antibiotic target protection, antibiotic target alteration, and antibiotic target replacement) in healthy poplar rhizosphere, corroborated the relationship between soil fertility and microbial activity. This result suggested that healthy rhizosphere soil harbored microbes with a higher capacity and had more complex microbial interaction network to promote plant growing and reduce intracellular levels of antibiotics. Our findings suggested a correlation between the plant degeneration gradient and bacterial communities, and provided insight into the role of high-turnover microbial communities as well as potential PGPB as real-time indicators of forestry soil quality, and demonstrated the inner interaction contributed by the bacterial communities.     

Real-Time Monitoring the Effect of Cytopathic Hypoxia on Retinal Pigment Epithelial Barrier Functionality Using Electric Cell-Substrate Impedance Sensing (ECIS) Biosensor Technology

Disruption of retinal pigment epithelial (RPE barrier integrity is a hallmark feature of various retinal blinding diseases, including diabetic macular edema and age-related macular degeneration, but the underlying causes and pathophysiology are not completely well-defined. One of the most conserved phenomena in biology is the progressive decline in mitochondrial function with aging leading to cytopathic hypoxia, where cells are unable to use oxygen for energy production. Therefore, this study aimed to thoroughly investigate the role of cytopathic hypoxia in compromising the barrier functionality of RPE cells. We used Electric Cell-Substrate Impedance Sensing (ECIS) system to monitor precisely in real time the barrier integrity of RPE cell line (ARPE-19) after treatment with various concentrations of cytopathic hypoxia-inducing agent, Cobalt(II) chloride (CoCl₂). We further investigated how the resistance across ARPE-19 cells changes across three separate parameters: R_b (the electrical resistance between ARPE-19 cells), α (the resistance between the ARPE-19 and its substrate), and C_m (the capacitance of the ARPE-19 cell membrane). The viability of the ARPE-19 cells and mitochondrial bioenergetics were quantified with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay and seahorse technology, respectively. ECIS measurement showed that CoCl₂ reduced the total impedance of ARPE-19 cells in a dose dependent manner across all tested frequencies. Specifically, the ECIS program’s modelling demonstrated that CoCl₂ affected R_b as it begins to drastically decrease earlier than α or C_m, although ARPE-19 cells’ viability was not compromised. Using seahorse technology, all three concentrations of CoCl₂ significantly impaired basal, maximal, and ATP-linked respirations of ARPE-19 cells but did not affect proton leak and non-mitochondrial bioenergetic. Concordantly, the expression of a major paracellular tight junction protein (ZO-1) was reduced significantly with CoCl_2-treatment in a dose-dependent manner. Our data demonstrate that the ARPE-19 cells have distinct dielectric properties in response to cytopathic hypoxia in which disruption of barrier integrity between ARPE-19 cells precedes any changes in cells’ viability, cell-substrate contacts, and cell membrane permeability. Such differences can be used in screening of selective agents that improve the assembly of RPE tight junction without compromising other RPE barrier parameters.     

强碱树脂在木糖应用中存在的问题及解决方法

木糖是一种在化工、食品等领域有着广泛用途的五碳糖,工业生产是用稀强酸将玉米芯中含有的大量多缩戊糖分解为单个的木糖分子,然后通过离子交换除盐、脱色,精制提取,但在强碱离子交换柱中,木糖分子容易被部分催化异构转化为阿拉伯糖,使得木糖纯度下降,结晶困难。考察了强碱树脂再生浓度、淋洗终点pH值、交换温度以及流速对木糖分子异构的影响,得出最佳的交换工艺条件:再生剂浓度为2%、淋洗终点pH值为7.5、交换温度20℃,流速为1BV/h。     

基于组合方向的对偶单纯形法

线性规划问题已经在各行各业得到了应用。求解线性规划问题也得到了很大发展。该文提出了求解线性规划问题的一种新的改进算法,利用组合方向来改进对偶单纯形方法。通过对对偶问题可行域结构的分析,找到一个组合的可行下降方向,沿此方向迭代,将绕过很多极点,若绕过产生退化的极点,会很大程度上避免退化带来的停顿现象,提高算法的效率。     

功能微生物对炼厂剩余污泥减量化效果的研究

研究了投加酵母菌群、光合细菌群和混合菌群等多功能微生物菌群促进炼厂的剩余污泥的减量化效果。通过多次污泥减量试验,观察到在好氧消化基础上投加功能菌群能显著促进炼厂剩余污泥减量化。结果表明,与仅好氧曝气条件下相比,投加酵母菌群、光合细菌群、混合菌群的MLSS去除率明显提高了13.81%、30.58%、37.94%;投加功能菌群作用后的污泥沉降性能均优于投加前;pH值均逐渐降低。功能微生物降解效应使石油组分发生明显的变化,强化了对低中碳正构烷烃的降解程度,有利于将高碳链烃降解为低碳链烃。     

深度学习在主动脉中膜变性病理图像分类中的应用

胸主动脉瘤和夹层（TAAD）是严重的心血管疾病之一,而中膜变性（MD）的组织学改变对疾病的诊断及早期干预具有重要的临床意义。针对病理图像的高度复杂性使得MD的诊断过程耗时费力且一致性差的问题,提出了一种基于深度学习的病理图像分类方法,并将其应用于四种MD病变类型以进行性能验证。该方法使用了一种改进的基于GoogLeNet的卷积神经网络模型,首先采用迁移学习来将先验知识应用于TAAD病理图像的表达,然后使用Focal loss和L2正则化来解决数据不平衡问题,从而进一步优化模型性能。实验结果表明,所提模型的平均四分类准确率达到98.78%,表现出较好的泛化性能。可见所提方法可以有效地提升病理学家的诊断效率。     

钇基重稀土镁球化剂的应用研究

研究了钇基重稀土镁球化剂的组成，试验了钇基重稀土镁复合球化剂对抗球化衰退、抗石墨球畸变的作用。工厂试验结果表明，钇基重稀土镁球化剂具有脱硫、球化、抗球化衰退、抗石墨畸变能力强，细化基体组织、白口倾向小及提高厚大断面球铁本体力学性能的作用。     

带退化因子的人工代谢算法在多对象物流配送优化中的应用

在多对象优化的物流配送中,需要对配送对象实行均衡调度。本文提出了一种含退化因子的人工代谢算法。每一种配送对象对应了不同的代谢底物,每一种配送对象的优先级对应了相应的催化酶,每一次配送对应一次代谢过程。为了降低代谢算法的复杂度,完成配送任务后,初始化时形成的催化酶需要有规律地退出代谢体系。通过对代谢算子等参数的分析,为人工酶设立了不同等级的退化因子。根据对酶优先级的辨识,按照代谢优化的进程,使已完成寻优搜索的代谢路径有序地退出本次代谢网络,提高整个代谢网络运行效率,为后续的物流配送路径缩小搜索空间,实现多对象配送过程的优化。     

High resolution X-ray phase contrast synchrotron imaging of normal and ligation damaged rat sciatic nerves

This study was performed to apply synchrotron radiation (SR) imaging to a neuropathologic evaluation technique after treatment of peripheral nerve blocks. A phase contrast synchrotron images of normal and ligation damaged rat sciatic nerve were obtained with an 8 KeV monochromatic beam and 20-mum thick CsI(TI) scintillation crystal. The visual image was magnified using a 20x microscope objective and captured using an analog CCD camera. Obtained images were compared with conventional light microscopic findings from the same nerve samples. By using an edge enhancement effect of phase const with SR, we could easily discriminate each nerve fiber and identify the arrangement of nerve fibers within a whole thickness (about 1 mm in diameter) of peripheral nerve without sectioning and fixation. The composite SR image of a ligation damaged rat sciatic nerve sample showed that the response to nerve injury was different on each side of the site of injury. The SR image of damaged distal lesion showed destruction of neural microarchitecture and typical extensive Wallerian degeneration of nerve fibers as clearly as histologic image. We could get very detailed morphologic data for Wallerian degeneration of nerve fibers by using the SR imaging technique. We believe that the phase contrast synchrotron imaging has great potential as an imaging tool in the bioscience and medical science.     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.