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Lignocellulosic biomass is an abundant and renewable bioresource for the production of biofuels and biochemical products. The classical biorefinery process for lignocellulosic degradation and conversion comprises three stages, i.e., pretreatment, enzymatic saccharification, and fermentation. However, the complicated pretreatment process, high cost of cellulase production, and insufficient production performance of fermentation strains have restricted the industrialization of biorefinery. Consolidated bioprocessing (CBP) technology combines the process of enzyme production, enzymatic saccharification, and fermentation in a single bioreactor using a specific microorganism or a consortium of microbes and represents another approach worth exploring for the production of chemicals from lignocellulosic biomass. The present review summarizes the progress made in research of CBP technology for lignocellulosic biomass conversion. In this review, different CBP strategies in lignocellulose biorefinery are reviewed, including CBP with natural lignocellulose-degrading microorganisms as the chassis, CBP with biosynthetic microorganisms as the chassis, and CBP with microbial co-culturing systems. This review provides new perspectives and insights on the utilization of low-cost feedstock lignocellulosic biomass for production of biochemicals.

During disease progression to AIDS, HIV-1 infected individuals become increasingly immunosuppressed and susceptible to opportunistic infections. It has also been demonstrated that multiple subsets of dendritic cells (DC), including DC-SIGN⁺ cells, become significantly depleted in the blood and lymphoid tissues of AIDS patients, which may contribute to the failure in initiating effective host immune responses. The mechanism for DC depletion, however, is unclear. It is also known that vast quantities of viral envelope protein gp120 are shed from maturing HIV-1 virions and form circulating immune complexes in the serum of HIV-1-infected individuals, but the pathological role of gp120 in HIV-1 pathogenesis remains elusive. Here we describe a previously unrecognized mechanism of DC death in chronic HIV-1 infection, in which ligation of DC-SIGN by gp120 sensitizes DC to undergo accelerated apoptosis in response to a variety of activation stimuli. The cultured monocyte-derived DC and also freshly-isolated DC-SIGN⁺ blood DC that were exposed to either cross-linked recombinant gp120 or immune-complex gp120 in HIV⁺ serum underwent considerable apoptosis after CD40 ligation or exposure to bacterial lipopolysaccharide (LPS) or pro-inflammatory cytokines such as TNFα and IL-1β. Furthermore, circulating DC-SIGN⁺ DC that were isolated directly from HIV-1⁺ individuals had actually been pre-sensitized by serum gp120 for activation-induced exorbitant apoptosis. In all cases the DC apoptosis was substantially inhibited by DC-SIGN blockade. Finally, we showed that accelerated DC apoptosis was a direct consequence of excessive activation of the pro-apoptotic molecule ASK-1 and transfection of siRNA against ASK-1 significantly prevented the activation-induced excessive DC death. Our study discloses a previously unknown mechanism of immune modulation by envelope protein gp120, provides new insights into HIV immunopathogenesis, and suggests potential therapeutic approaches to prevent DC depletion in chronic HIV infection.

Abstract Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical camouflage evaluation methods, this paper proposes a grayscale clustering camouflage effect evaluation method based on multifeature descriptions of hyperspectral images using similarity indicators that reflect different spectral characteristics of the target and background. From the perspective of spectrum and human visual contrast, a comprehensive evaluation index system including spectral distance feature, spectral derivative feature, curve shape feature and spatial texture feature is constructed by combining spatial–spectral multi-feature constraints. At the same time, an improved Delphi method is proposed to simulate the expert decision-making process, and better evaluation weights are obtained by comparison and screening. The comprehensive evaluation of camouflage effect based on whitening function gray clustering is realized. The proposed method can not only give the “excellent” and “bad” of camouflage effect qualitatively, but also calculate the comprehensive score of camouflage effect by model.

Most of the existing camouflage effect evaluation methods are for static images, and the evaluation methods have problems of singularity and subjectivity. Therefore, this paper takes the camouflage of moving objects in video as the research object and proposes a comprehensive camouflage effect evaluation method based on multifeature constraints. This method has two parts: the Homo-F (homography transformation and optical flow) target detection module and the camouflage effect evaluation module. The former uses the optical flow method to correct the target detection results obtained by the homography transformation. The latter performs statistical analysis on the target detection results and the feature information of the neighborhood background and describes the effect of camouflage from multiple angles such as the degree of target fusion, the repetition rate and the target detection stability in the video sequence. The experimental results of the comprehensive camouflage evaluation of moving targets show that the proposed method can objectively and accurately evaluate moving targets with different levels of camouflage, which verifies the reliability and effectiveness of the method.

Digital camouflage is a common countermeasure against military reconnaissance. In the face of high-tech imaging reconnaissance, battlefield detection means tend to be automated and refined. In order to adapt to the concealment requirements under various environmental backgrounds, combined with the camouflage performance of digital camouflage and its feedback mechanism in camouflage pattern design, this paper proposed a digital camouflage pattern design method based on biased random walk. Firstly, the original background is preprocessed, and the background texture’s direction, corner, step length, and pixel intensity difference are statistically analyzed, and the boundary probability between pixel nodes is estimated. Then, a biased random walk is used to outline the camouflage patches. The edge scatter is enriched according to the density of the patches, and the camouflage patches are filled according to the proportion of the main color of the background. Finally, a digital camouflage pattern is obtained. The quantitative analysis results show that the mean heart rate of the digital camouflage pattern based on multiscene design is at least 31.0% higher than that of the original background segmentation texture, and the standard deviation index of equivalent diameter is increased by 14.9% on average. In addition, the results of simulation camouflage image detection in multiple scenes show that the proposed method can effectively deal with camouflage target detection on the basis of fully retaining the original background texture information and has strong camouflage concealment effect in the scene.