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Chiral metal-organic frameworks (chirMOFs) have been widely considered on enantioselective adsorption/separation, asymmetric catalysis, biological and nonlinear optical applications. However, chirMOFs are facing a great challenge in development of chiroptical thin films with circularly polarized luminescence (CPL) property. Here, we first report CPL thin films by encapsulating achiral lanthanide complexes Ln(acac) 3 (Ln = Eu a Tb b Gd c ) into the pores of surface-coordinated chirMOF thin films (SURchirMOF) [Zn 2 (cam) 2 dabco] n with layer by layer (lbl) encapsulation strategy. Due to the unique combination of chiral porous MOF and adjustable luminescent complexes in the host-guest thin films, the obtained Ln(acac) 3 @SURchirMOF possess strong and tunable CPL property with high dissymmetry factors. The compared CPL and fluorescent lifetime results show that the advantage of this preparation strategy can effectively achieve energy transfer from Ln(acac) 3 to SURchirMOF, resulting in an excellent CPL performance. This study not only provides a novel strategy to develop new types of chiral thin films but also offers an efficient approach for tunable chiroptical applications.

is potential probiotic in that its type strain ATCC BAA-835 has beneficial effects upon obesity and diabetes. However, whether can improve inflammatory bowel diseases (IBD), which is a form of chronic intestinal dysbiosis, is unknown. Hence, we used an isolated murine strain (designated 139) and type strain ATCC, to investigate their anti-inflammatory properties in cell models and in Dextran Sulfate Sodium (DSS)-induced chronic colitis of mice. , the two strains exerted similar anti-inflammatory properties as they both reduced IL-8 production by TNF-α-stimulated HT-29 cells. However, neither of the strains showed capacity to increase the differentiation of regulatory T (Treg)-cells from CD4+ T cell populations significantly. , both strains exerted anti-inflammatory effects on chronic colitis as they improved clinical parameters including spleen weight, colon inflammation index, and colon histological score. They also down-regulated the expression of the pro-inflammatory cytokines including TNF-α and IFN-γ in the colon of mice. However, the anti-inflammatory effects of strain ATCC were stronger than strain 139 in that ATCC significantly reduced spleen weight, colon inflammation index, and fecal lipocalin-2 content in mice with chronic colitis, while strain 139 was not. Dysbiosis of the gut microbiota was observed in mice with chronic colitis. Both strains facilitated the normalization of the gut microbiota. The specific capacity of strain ATCC to modulate the differentiation of Tregs as well as increase production of short chain fatty acids, demonstrated strain-specific characteristics for these two strains. This study suggests the potential beneficial effect of on IBD and the importance of the future study of the function of at the strain-level.

Endoperoxide-containing natural products are a group of compounds with structurally unique cyclized peroxide moieties. Although numerous endoperoxide-containing compounds have been isolated, the biosynthesis of the endoperoxides remains unclear. NvfI from Aspergillus novofumigatus IBT 16806 is an endoperoxidase that catalyzes the formation of fumigatonoid A in the biosynthesis of novofumigatonin. Here, we describe our structural and functional analyses of NvfI. The structural elucidation and mutagenesis studies indicate that NvfI does not utilize a tyrosyl radical in the reaction, in contrast to other characterized endoperoxidases. Further, the crystallographic analysis reveals significant conformational changes of two loops upon substrate binding, which suggests a dynamic movement of active site during the catalytic cycle. As a result, NvfI installs three oxygen atoms onto a substrate in a single enzyme turnover. Based on these results, we propose a mechanism for the NvfI-catalyzed, unique endoperoxide formation reaction to produce fumigatonoid A. Many endoperoxide-containing natural products have been isolated, but the biosynthesis of the endoperoxides remains unclear. Here, the authors report the structural and functional analysis of the NvfI endoperoxidase that catalyzes the formation of fumigatonoid A in the biosynthesis of novofumigatonin, and show that it does not employ tyrosyl radical in the reaction.

Lignin is the most abundant aromatic substrate on Earth and its valorization technologies are still under developed. Depolymerization and fragmentation are the predominant preparatory strategies for valorization of lignin to chemicals and fuels. However, due to the structural heterogeneity of lignin, depolymerization and fragmentation typically result in diverse product species, which require extensive separation and purification procedures to obtain target products. For lignin valorization, bacterial-based systems have attracted increasing attention because of their diverse metabolisms, which can be used to funnel multiple lignin-based compounds into specific target products. Here, recent advances in lignin valorization using bacteria are critically reviewed, including lignin-degrading bacteria that are able to degrade lignin and use lignin-associated aromatics, various associated metabolic pathways, and application of bacterial cultures for lignin valorization. This review will provide insight into the recent breakthroughs and future trends of lignin valorization based on bacterial systems.

Rapidly growing interest in the nanoparticle-mediated delivery of DNA and RNA to plants requires a better understanding of how nanoparticles and their cargoes translocate in plant tissues and into plant cells. However, little is known about how the size and shape of nanoparticles influence transport in plants and the delivery efficiency of their cargoes, limiting the development of nanotechnology in plant systems. In this study we employed non-biolistically delivered DNA-modified gold nanoparticles (AuNPs) of various sizes (5–20 nm) and shapes (spheres and rods) to systematically investigate their transport following infiltration into Nicotiana benthamiana leaves. Generally, smaller AuNPs demonstrated more rapid, higher and longer-lasting levels of association with plant cell walls compared with larger AuNPs. We observed internalization of rod-shaped but not spherical AuNPs into plant cells, yet, surprisingly, 10 nm spherical AuNPs functionalized with small-interfering RNA (siRNA) were the most efficient at siRNA delivery and inducing gene silencing in mature plant leaves. These results indicate the importance of nanoparticle size in efficient biomolecule delivery and, counterintuitively, demonstrate that efficient cargo delivery is possible and potentially optimal in the absence of nanoparticle cellular internalization. Overall, our results highlight nanoparticle features of importance for transport within plant tissues, providing a mechanistic overview of how nanoparticles can be designed to achieve efficacious biocargo delivery for future developments in plant nanobiotechnology. A study of gold nanospheres and nanorods shows that, even without internalization, they are very efficient for siRNA delivery and inducing gene silencing in mature plant leaves.

High-precision localization through multi-sensor fusion has become a popular research direction in unmanned driving. However, most previous studies have performed optimally only in open-sky conditions; therefore, high-precision localization in complex urban environments required an urgent solution. The complex urban environments employed in this study include dynamic environments, which result in limited visual localization performance, and highly occluded environments, which yield limited global navigation satellite system (GNSS) performance. In order to provide high-precision localization in these environments, we propose a vision-aided GNSS positioning method using semantic information by integrating stereo cameras and GNSS into a loosely coupled navigation system. To suppress the effect of dynamic objects on visual positioning accuracy, we propose a dynamic-simultaneous localization and mapping (Dynamic-SLAM) algorithm to extract semantic information from images using a deep learning framework. For the GPS-challenged environment, we propose a semantic-based dynamic adaptive Kalman filtering fusion (S-AKF) algorithm to develop vision aided GNSS and achieve stable and high-precision positioning. Experiments were carried out in GNSS-challenged environments using the open-source KITTI dataset to evaluate the performance of the proposed algorithm. The results indicate that the dynamic-SLAM algorithm improved the performance of the visual localization algorithm and effectively suppressed the error spread of the visual localization algorithm. Additionally, after vision was integrated, the loosely-coupled navigation system achieved continuous high-accuracy positioning in GNSS-challenged environments.

Electrochemical modification of animal skin is a new material preparation method and new direction of research exploration. In this study, under the action of the electric field using NaCl as the supporting electrolyte, the effect of electrolysis on Glycyl-glycine(GlyGl), gelatin(Gel) and Three-dimensional rawhide collagen(3DC) were determined. The amino group of GlyGl is quickly eliminated within the anode region by electrolysis isolated by an anion exchange membrane. Using the same method, it was found that the molecular weight of Gel and the isoelectric point of the Gel decreased, and the viscosity and transparency of the Gel solution obviously changed. The electrolytic dissolution and structural changes of 3DC were further investigated. The results of TOC and TN showed that the organic matter in 3DC was dissolved by electrolysis, and the tissue cavitation was obvious. A new approach for the preparation of collagen-based multi-pore biomaterials by electrochemical method was explored.

Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. Current Transformer-based approaches demonstrate superior long-range dependency modeling, but still suffer from the problem of progressive information loss during hierarchical feature encoding. Therefore, this study proposed a new semantic segmentation network named SegTDformer to extract buildings in remote sensing images. We designed a Dynamic Atrous Attention (DAA) fusion module that integrated multi-scale features from Transformer, constructing an information exchange between global information and local representational information. Among them, we introduced the Shift Operation module and the Self-Attention module, which adopt a dual-branch structure to respectively capture local spatial dependencies and global correlations, and perform weight coupling to achieve highly complementary contextual information fusion. Furthermore, we fused triplet attention with depth-wise separable convolutions, reducing computational requirements and mitigating potential overfitting scenarios. We benchmarked the model on three different datasets, including Massachusetts, INRIA, and WHU, and the results show that the model consistently outperforms existing models. Notably, on the Massachusetts dataset, the SegTDformer model achieved benchmark in mIoU, F1-score, and Overall Accuracy of 75.47%, 84.7%, and 94.61%, respectively, superseding other deep learning models. The proposed SegTDformer model exhibits enhanced precision in the extraction of urban structures from intricate environments and manifests a diminished rate of both omission and internal misclassification errors, particularly within the context of diminutive and expansive edifices.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

To evaluate the dosimetric benefits and clinical feasibility of deep inspiratory breath-hold (DIBH) combined with volumetric modulated arc therapy (VMAT) in left-sided postmastectomy radiotherapy (PMRT). Eligible patients with left-sided breast cancer undergoing DIBH-based PMRT were prospectively included. Chest wall, supra/infraclavicular fossa, and/or internal mammary node irradiation (IMNI) were planned with a prescription dose of 43.5 Gy in 15 fractions. VMAT plans were designed on free breathing (FB)—and DIBH-CT to compare dosimetric parameters in heart, left anterior descending artery (LAD) and lung. Cone-beam computed tomography (CBCT) was performed before and after treatment to evaluate inter- and intra-fractional setup errors. Heart position and dose variations during treatment were estimated by fusing CBCT with DIBH-CT scans.Twenty patients were included with 10 receiving IMNI. In total, 193 pre-treatment and 39 pairs pre- and post-treatment CBCT scans were analyzed. The D mean , D max , and V 5−40 of the heart, LAD, and left lung were significantly lower in DIBH than FB ( p  < 0.05 for all), except for V5 of LAD ( p  = 0.167). The cardiopulmonary dosimetric benefits were maintained regardless of IMNI. The inter- and intra-fractional setup errors were < 0.3 cm; and the overall estimated PTV margins were < 1.0 cm. During treatment, the mean dice similarity coefficient of heart position and the mean ratio of heart D mean between CBCT and DIBH-CT plans was 0.95 (0.88–1.00) and 100% (70.6–119.5%), respectively. DIBH-VMAT could effectively reduce the cardiopulmonary doses with acceptable reproducibility and stability in left-sided PMRT regardless of IMNI.