Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus causing acute respiratory tract infection in humans. The virus has the characteristics of rapid transmission, long incubation period and strong pathogenicity, and has spread all over the world. Therefore, it is of great significance to select appropriate animal models for antiviral drug development and therapeutic effect evaluation. Here, we review and compare the current animal models of SARS-CoV-2.

a b s t r a c tThe significance of arbuscular mycorrhizal fungi (AMF) in soil remediation has been widely recognized because of their ability to promote plant growth and increase phytoremediation efficiency in heavy metal (HM) polluted soils by improving plant nutrient absorption and by influencing the fate of the metals in the plant and soil. However, the symbiotic functions of AMF in remediation of polluted soils depend on plantefungusesoil combinations and are greatly influenced by environmental conditions. To better understand the adaptation of plants and the related mycorrhizae to extreme environmental conditions, AMF colonization, spore density and community structure were analyzed in roots or rhizosphere soils of Robinia pseudoacacia. Mycorrhization was compared between uncontaminated soil and heavy metal contaminated soil from a leadezinc mining region of northwest China. Samples were analyzed by restriction fragment length polymorphism (RFLP) screening with AMF-specific primers (NS31 and AM1), and sequencing of rRNA small subunit (SSU). The phylogenetic analysis revealed 28 AMF group types, including six AMF families: Glomeraceae, Claroideoglomeraceae, Diversisporaceae, Acaulosporaceae, Pacisporaceae, and Gigasporaceae. Of all AMF group types, six (21%) were detected based on spore samples alone, four (14%) based on root samples alone, and five (18%) based on samples from root, soil and spore. Glo9 (Rhizophagus intraradices), Glo17 (Funneliformis mosseae) and Acau3 (Acaulospora sp.) were the three most abundant AMF group types in the current study. Soil Pb and Zn concentrations, pH, organic matter content, and phosphorus levels all showed significant correlations with the AMF species compositions in root and soil samples. Overall, the uncontaminated sites had higher species diversity than sites with heavy metal contamination. The study highlights the effects of different soil chemical parameters on AMF colonization, spore density and community structure in contaminated and uncontaminated sites. The tolerant AMF species isolated and identified from this study have potential for application in phytoremediation of heavy metal contaminated areas.

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To select suitable tree species associated with arbuscular mycorrhizal fungi (AMF) for phytoremediation of heavy metal (HM) contaminated area, we measured the AMF status and heavy metal accumulation in plant tissues in a lead-zinc mine area, Northwest China. All 15 tree species were colonized by AM fungi in our investigation. The mycorrhizal frequency (F%), mycorrhizal colonization intensity (M%) and spore density (SP) reduced concomitantly with increasing Pb and Zn levels; however, positive correlations were found between arbuscule density (A%) and soil total/DTPA-extractable Pb concentrations. The average concentrations of Pb, Zn, Cu and Cd in plant samples were 168.21, 96.61, 41.06, and 0.79 mg/kg, respectively. Populus purdomii Rehd. accumulated the highest concentrations of Zn (432.08 mg/kg) and Cu (140.85 mg/kg) in its leaves. Considerable amount of Pb (712.37 mg/kg) and Cd (3.86 mg/kg) were concentrated in the roots of Robinia pseudoacacia Linn. and Populus simonii Carr., respectively. Plants developed different strategies to survive in HM stress environment: translocating more essential metals (Zn and Cu) into the aerial parts, while retaining more toxic heavy metals (Pb and Cd) in the roots to protect the above-ground parts from damage. According to the translocation factor (TF), bioconcentration factor (BCF), growth rate and biomass production, five tree species (Ailanthus altissima (Mill.) Swingle, Cotinus coggygria Scop., P. simonii, P. purdomii, and R. pseudoacacia) were considered to be the most suitable candidates for phytoextraction and/or phytostabilization purposes. Redundancy analysis (RDA) showed that the efficiency of phytoremediation was enhanced by AM symbioses, and soil pH, Pb, Zn, and Cd levels were the main factors influencing the HM accumulation characteristics of plants.

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This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) and the maximum curvature criterion (MCC) are proposed to estimate regularization parameters especially for the non-negativity constrained deconvolution problem. MDC has good theoretical properties (convexity and uniqueness) but requires to choose a reference point. On the contrary, MCC does not need to choose any reference point but does not have interesting theoretical properties. A grid-search-based approach to minimize the computational cost of MDC and MCC is proposed. It results in fast approaches to estimate the regularization parameters. Based on simulated 2D images, the proposed approaches are compared with the state-of-the-art methods, confirming the effectiveness of the MDC and MCC for the non-negativity constrained image deconvolution problem. In the case of non-negative hyperpsectral image deconvolution, the fast MDC yields better performances than the fast MCC. An application to real-world hyperspectral fluorescence microscopy images is also provided; it confirms the superiority of MDC.

Efficient therapeuic proteins’ delivery into mammalian cells and subcellular transport (e.g., fast escape from endolysosomes into cytoplasm) are two key biological barriers that need to be overcome for antigen-based immunotherapy and related biomedical applications. For those purposes, we designed a novel kind of photoresponsive polypeptide-glycosylated poly­(amidoamine) (PAMAM) dendron amphiphiles (PGDAs), and their synthesis, UV-responsive self-assembly, and triggered ovalbumin (OVA) release have been fully investigated. The highly anisotropic PGDA4 with a glycosylated second-generation PAMAM dendron self-assembled into stable polypeptide vesicles (polymersomes) within 20–50 wt % water, which exhibited UV-responsive reassembly, dynamic binding with a lectin of concanavalin A, and an accelerated OVA release in vitro. Moreover, upon 365 nm UV irradiation, the self-assembled polymersomes of those glycopolypeptides were transformed into micellar aggregates in aqueous solution at pH 7.4 but disassembled completely at pH 5. The OVA-loaded polymersomes could efficiently deliver OVA into RAW264.7 cells and achieve enhanced endolysosomes escape upon UV irradiation, as revealed by flow cytometry and confocal laser scanning microscopy (CLSM). Furthermore, the enzyme-linked immunosorbent assay (ELISA) showed that the blank sugar-coated polypeptidosomes activated a high level of tumor necrosis factor α (TNF-α) of 468 pg/mL, playing a better role of immune adjuvant for activating the macrophages. Upon the UV irradiation with a dose of 3 J/cm2, the OVA-loaded polymersomes could further stimulate RAW264.7 and enhance the TNF-α level by about 45%. Consequently, this work provides a versatile platform to construct photosensitive and sugar-coated polymersomes of glycopolypeptides that have potential applications for protein delivery, immune adjuvant, and antigen-based immunotherapy.

Abstract-Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and 1-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.