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With the wide application of nanomaterials, the concentration of nanomaterials in natural water continues to increase, which poses a severe threat to the water environment. However, the influence of organic matter and nanomaterials rich in natural water on the toxic effect of algae growth is still unclear. In this study, the effects of humic acid (HA) and nano-cerium oxide (nCeO2) on the physiology and transcriptome of Chlorella sp. were analyzed, and the mechanism of the toxic effect of HA on Chlorella sp. under nCeO2 stress was revealed. Under 20–200 mg/L nCeO2 stress, the growth of Chlorella cells was inhibited and the highest inhibition rate reached 52% within 200 mg/L nCeO2. The Fv/Fm and ETRmax values of Chlorella sp. decreased from 0.490 and 24.45 (20 mg/L nCeO2) to 0.488 and 23.4 (100 mg/L nCeO2), respectively. Under the stimulation of nCeO2, the level of reactive oxygen species in algal cells was increased, accompanied by lipid peroxidation and membrane damage. However, the addition of HA at concentrations of 5–10 mg/L effectively alleviated the toxic effect of nCeO2 on Chlorella sp. Transcriptome analysis showed that 10 mg/L HA could alleviate the cellular stress at 100 mg/L nCeO2 on Chlorella sp. by regulating genes related to photosynthesis and metabolism pathways. Moreover, the downregulation of genes (e.g., Lhca1, Lhcb1, AOC3, and AOC2) indicated that HA reduced the level of oxidative stress in Chlorella sp. These findings offer novel insights of evaluating the ecotoxicity nCeO2 and HA in natural water environment and their impact on Chlorella sp. [ABSTRACT FROM AUTHOR]

In this study, to explore the relationship between environmental factors and fungal diversity in the Shenzhen River ecosystem, multiple methods including chemical analysis, culture isolation, qPCR analysis of fungal ITS region and ITS-based Illumina next-generation-sequencing were integrated. A total of 115 isolates were finally isolated and could be classified into 23 genera. Top three abundant genera isolated were Meyerozyma (18 strains), Aspergillus (17 strains) and Penicillium (14 strains). Based on the Illumina sequencing approach, 829 OTUs were affiliated to seven phyla, 17 known classes, and 162 genera, indicating the Shenzhen estuary sediments are rich in fungal diversity. The major fungal genera were Meyerozyma, Trichoderma and Talaromyces. Environmental factors showed a gradient change in Shenzhen estuary, and fungal abundance was only significantly correlated with NH4+. Shannon index was significantly correlated with pH and IC (P < 0.05). Principal coordinate analysis based on OTU level grouped into three clusters among sampling sites along with the IC and pH gradient. Functional guilds analysis suggests most of the fungi in this studying area were almost all saprotrophs, suggesting a large number of saprophytic fungi may play a significant role in the organic matter decomposition and nutrient cycling process. In summary, this study will deepen our understanding of fungi community in Shenzhen River ecosystem and their distribution and potential function shaped by environmental factors. [ABSTRACT FROM AUTHOR]

As the most common cancer disease in the world, breast cancer is the main cause of death of female cancer patients. The number of women in China is far greater than that in other countries. Therefore, the absolute death toll is often high. Even a small increase in incidence rate will lead to a severe increase in deaths. Therefore, accurate preoperative diagnosis of benign and malignant breast cancer and the status prediction of various clinical indicators are urgently needed in clinical practice. Deep learning based on neural network has been widely used in diagnosis. Therefore, this paper attempts to explore the influence of deep learning on the auxiliary diagnosis technology of small tumor in mammography. In this paper, 200 cases of breast disease patients in a hospital of our city were taken as the research object, and the artificial neural network model was established. Through the experimental simulation, the results showed that the diagnostic sensitivity, specificity and overall accuracy of BP neural network for test set samples were 95.3%, 96.7 and 96.2%, respectively. The experimental results confirmed its generalization ability. In this paper, the core idea of multi feature kernel hash, combined with a variety of features and deep kernel hash network framework, constructs a new multi feature based deep learning network, which can effectively express the image features of breast tumor, and complete the task of breast micro tumor detection with good performance. [ABSTRACT FROM AUTHOR]

The poor mechanical properties of self-healing hydrogels limited their applications in the fields of biomedicine and industry. Here, a series of self-healing polymeric ionic liquid (PIL) hydrogels with high mechanical strength and electrical conductivity were prepared through hydrophobic association. Hydrophilic monomer vinyl ionic liquids (VILs) based on choline and amino acids, acrylamide (AAm) and hydrophobic monomers stearyl methacrylate (C18) were copolymerized in a micellar solution of sodium dodecyl sulfate (SDS); meanwhile, bacterial cellulose was introduced to enhance the mechanical strength of hydrogels. The resultant hydrogels exhibited excellent mechanical strength (5.8 MPa), extensive elongation at break (4250%) and outstanding self-healing efficiency (85%) without any external intervention. Even after healing, the tensile strength of most hydrogels could reach 2.5–3.9 MPa. At the same time, the incorporation of ILs endowed hydrogels with good electrical conductivity (a maximum of 1.258 S/m). These excellent properties predicted the potential application of the obtained PIL hydrogels in the fields of biomedicine and industry. [ABSTRACT FROM AUTHOR]