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Cryptochrome is an important class of blue-light receptors involved in various physiological activities such as photomorphogenesis and abiotic stress regulation in plants. In order to investigate the molecular mechanism of blue-light-induced color change in Chimonobambusa sichuanensis, we screened and cloned the gene encoding the blue-light receptor Cryptochrome. In order to investigate the molecular mechanism of blue-light-induced color change in Chimonobambusa sichuanensis, we screened and cloned the gene encoding the blue-light receptor Cryptochrome in Ch.sichuanensis, and analyzed the expression characteristics of the Cryptochrome gene in Ch.sichuanensis under different light intensities, light quality, and temperatures by qRT-PCR. Through homologous cloning, a total of four CsCRY genes were obtained in the Ch.sichuanensis genome, namely, CsCRY1a, CsCRY1b, CsCRY2, and CsCRY3. Structural domain analyses of the encoded proteins of the four genes revealed that all CsCRYs proteins had the typical photoreceptor structural domain, PRK (protein kinase C-related kinase). Phylogenetic tree analyses revealed that the four genes CsCRY1a, CsCRY1b, CsCRY2, and CsCRY3 could be categorized into three subfamilies, with CsCRY1a and CsCRY1b clustered in subfamily I, CsCRY2 classified in subfamily II, and CsCRY3 belonging to subfamily III. All CsCRYs proteins lacked signal peptides and the instability index was higher than 40, among which the isoelectric points of CsCRY1a, CsCRY1b, and CsCRY2 were around five. qRT-PCR analysis revealed that the expression of all four CsCRYs genes was up-regulated at 75 µmol·m−2·s−1 blue-light illumination for 4 h. In addition, under treatments of different light quality, the expression of CsCRY2 genes was significantly higher under blue light than under red light and a mixture of red light and blue light with a light intensity of 1:1; the expression of CsCRY1a and CsCSY1b was significantly higher in the mixed light of red and blue light than in the single light treatment, while under different temperature gradients, CsCRYs genes were highly expressed under low-temperature stress at −5 °C and 0 °C. This study provides a basis for further research on blue-light-induced color change in Ch.sichuanensis and expands the scope of Cryptochrome gene research.

With the rapid development of society and industry, novel technologies and materials related to pavement engineering are constantly emerging. However, with the continuous improvement of people’s demands, pavement engineering also faces more and more enormous challenges that the pavement materials must have excellent engineering properties and environmental benefits. Meanwhile, the intelligence is the mainstream development direction of modern society, and the development trend of future transportation infrastructure. Materials Genome Initiative, a program for the development of new materials that materials design is conducted by up-front simulations and predictions, followed by key validation experiments, the rapid development of science and technology and AI toolset (big data and machine learning) provide new opportunities and strong technical supports for pavement materials development that shorten the development-application cycle of new material, reduce cost and promote the application of new carriers such as intelligent sensing components in transportation engineering, to achieve the intelligence of transportation engineering. However, traditional pavement materials possess several unavoidable shortcomings, indicating that it is exceedingly difficult for them to meet the above requirements for future pavement materials. Therefore, the development of future new pavement materials, which can be designed on-demand as well as possessing enough mechanical properties, high durability, practical functionality, and high environmental protection, is urgent. In recent years, as a “designable” polymer material with various excellent engineering performances, polyurethane (PU) has been widely applied in pavement practices by changing the chemical structures of raw materials and their mix proportions, for instance pavement repairing material, permeable pavement material, tunnel paving material and bridge deck paving materials, etc. Although PU material has been widely applied in practices, a systematically summarization is still quite necessary for further understanding the working mechanism of PU materials and optimization it’s engineering applications. To fill the gap, this article puts forward the special requirements for future transportation infrastructure materials, and introduces the basic properties and working mechanism of PU materials in order to make up for the defects of conventional road materials. Based on this, this article also summarizes the engineering performances and environmental benefits of applying PU as the binder for different road infrastructure materials in recent years. Considering the gene-editable nature of polyurethane, further research of the on-demand design principles of PU pavement materials is recommended. The establishment of raw material gene database, material terminal performance database and their structure-activity relationship are highlighted. The current research is essential to the practice guidance and further optimization of the PU materials for road infrastructures, which in line with the future Carbon neutral policy.

Urban areas exhibit significant gradients in Fine Particulate Matter (PM 2.5 ) concentration variability. Understanding the spatiotemporal distribution and formation mechanisms of PM 2.5 is crucial for public health, environmental justice, and air pollution mitigation strategies. Here, we utilized machine learning and integrated air quality sensor monitoring networks consisting of 200 mobile cruising vehicles and 614 fixed micro–stations to reconstruct PM 2.5 pollution maps for Jinan’s urban area with a high spatiotemporal resolution of 500 m and 1 h. Our study demonstrated that pollution mapping can effectively capture spatiotemporal variations at the urban microscale. By optimizing the spatial design of monitoring networks, we developed a cost-effective air quality monitoring strategy that reduces expenses by nearly 70% while maintaining high precision. The results of multi-model coupling indicated that secondary inorganic aerosols were the primary driving factors for PM2.5 pollution in Jinan. Our work offers a unique perspective on urban air quality monitoring and pollution attribution.

Ticks are hematophagous vectors that transmit a variety of pathogens, posing significant threats to the health of both humans and animals. Tick midgut proteins play essential roles in blood digestion, feeding, toxic waste processing, and pathogen transmission. Dermacentor nuttalli is the primary vector of tick-borne pathogens, including rickettsioses in the Qinghai-Tibet Plateau. However, there is a lack of genomic, transcriptomic, and proteomic information regarding the biology of D. nuttalli . In this study, we assembled and compared the midgut transcriptomes of female D. nuttalli ticks at 0, 24, 48, 72, and 96 h during blood feeding, identifying the genes with differentially regulated expression following feeding. The obtained data were compiled and annotated in multiple databases including Nr, NT, PFAM, KOG, KEGG, and GO. The high-quality clean readings of midgut tissue at the different blood-feeding times were recorded as 22,524,912, 23,752,325, 20,377,718, 21,300,710, and 20,378,658, respectively. The transcripts were classified into eight large categories, including immunogenic proteases (8.37%), protease inhibitors (0.85%), transporters (3.96%), ligand binding proteins (1.98%), ribosomal function proteins (0.94%), heat shock proteins (0.30%), other proteases and miscellaneous proteins (57.61%), and unknown proteins (26.00%). Significant differences were observed in the genes obtained at 0, 24, 48, 72, and 96 h during blood feeding. The differentially expressed genes include catalytic proteins that play an important role in accelerating biochemical reactions, binding activity proteins which are involved in various molecular interactions, and proteins that actively participate in multiple metabolic pathways and cellular processes. Notably, the gene expression in the midgut of D. nuttalli shows dynamic changes every 24 h throughout the blood-feeding process. This change may represent an equivalent strategy of antigenic variation for ticks, designed to protect their essential feeding function against the host’s immune system. The tick antigens identified in this study may serve as promising candidates for the development of effective vaccines or as drug targets for acaricides.

The scarcity and low spatial resolution of hyperspectral images (HSIs) have become a major problem limiting the application of the images. In recent years, spectral reconstruction (SR) has been applied to convert multispectral images (MSIs) with abundant quantities and high spatial resolution into HSIs. With the launch of several new multispectral (MS) satellites with a short repeat period, the simultaneous acquisition of images from multiple MS sensors in the same area is gradually becoming feasible. Unfortunately, existing SR methods only consider the reconstruction of the MSIs of a single sensor without considering using MSIs from different MS sensors to obtain a better construction effect through their complementary bands. However, multi-sensor SR is characterized by two problems: inconsistency in the amplitude information of real multisensor imaging and difficulty in the extraction of the complex correlations of bands from different sensors. To solve these problems, this paper proposes a multi-sensor SR framework based on a two-step approach in which the problems of amplitude inconsistency and band information extraction are solved using an ideal projection network and an ideal multi-sensor SR network, respectively. The effectiveness of the proposed method is verified by experiments on three datasets.

Nitro-aromatic compounds (NACs) are important atmospheric pollutants that impact air quality, atmospheric chemistry, and human health. Understanding the relationship between NAC formation and key environmental driving factors is crucial for mitigating their environmental and health impacts. In this work, we combined an ensemble machine learning (EML) model with the SHapley Additive exPlanation (SHAP) and positive matrix factorization (PMF) model to identify the key driving factors for ambient particulate NACs, covering primary emissions, secondary formation, and meteorological conditions based on field observations at urban, rural, and mountain sites in eastern China. The EML model effectively reproduced ambient NACs and recognized that anthropogenic emissions (i.e., coal combustion, traffic emission, and biomass burning) were the most important driving factors, with a total contribution of 49.3 %, while significant influences from meteorology (27.4 %) and secondary formation (23.3 %) were also confirmed. Seasonal variation analysis showed that direct emissions presented positive responses to NAC concentrations in spring, summer, and autumn, while lower temperatures had the largest positive impact in winter. By evaluating NAC formation and loss under various locations in winter, we found that anthropogenic sources played a dominant role in increasing NAC levels in urban and rural sites, while reduced ambient temperature, along with secondary formation from gas-phase oxidation, was the main reason for relatively high particulate NAC levels at the mountain site. This work provides a reliable modeling method for understanding the dominant sources and influencing factors for atmospheric NACs and highlights the necessity of strengthening emission source controls to mitigate organic aerosol pollution.

The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin state, thus influencing interactions with polysulfides and resulting in diverse Li-S bond breaking and lithium migration barriers. Using a carbon-coated Fe/Co electrocatalyst (synthesized using recycled Li-ion battery electrodes as raw materials) at the positive electrode of a Li | |S pouch cell with high sulfur loading and lean electrolyte conditions, we report an initial specific energy of 436 Wh kg −1 (whole mass of the cell) at 67 mA and 25 °C. The atomic-level interactions among electrocatalytic sites in Li | |S batteries remain unclear. Here, authors propose a multiview machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, thus enhancing the understanding of electrocatalytic sites.

Impaired angiogenesis is one of the predominant reasons for non-healing diabetic wounds. Herein, a nanofiber/hydrogel core–shell scaffold with three-dimensional (3D) multilayer patterned structure (3D-PT-P/GM) was introduced for promoting diabetic wound healing with improved angiogenesis. The results showed that the 3D-PT-P/GM scaffolds possessed multilayered structure with interlayer spacing of about 15–80 μm, and the hexagonal micropatterned structures were uniformly distributed on the surface of each layer. The nanofibers in the scaffold exhibited distinct core–shell structures with Gelatin methacryloyl (GelMA) hydrogel as the shell and Poly ( d , l -lactic acid) (PDLLA) as the core. The results showed that the porosity, water retention time and water vapor permeability of the 3D-PT-P/GM scaffolds increased to 1.6 times, 21 times, and 1.9 times than that of the two-dimensional (2D) PDLLA nanofibrous scaffolds, respectively. The in vitro studies showed that the 3D-PT-P/GM scaffolds could significantly promote cell adhesion, proliferation, infiltration and migration throughout the scaffolds, and the expression of cellular communication protein-related genes, as well as angiogenesis-related genes in the same group, was remarkably upregulated. The in vivo results further demonstrated that the 3D-PT-P/GM scaffolds could not only effectively absorb exudate and provide a moist environment for the wound sites, but also significantly promote the formation of a 3D network of capillaries. As a result, the healing of diabetic wounds was accelerated with enhanced angiogenesis, granulation tissue formation, and collagen deposition. These results indicate that nanofiber/hydrogel core–shell scaffolds with 3D multilayer patterned structures could provide a new strategy for facilitating chronic wound healing. Graphical Abstract

Geotextile bags are widely used in revetment engineering due to their simple fabrication and cost-effectiveness. However, prolonged exposure to natural environments can lead to aging and damage, compromising their performance. To enhance the durability of geotextile bags in practical applications, this study conducted microscopic examinations and strength tests, employing a slurry spraying method to form a protective surface layer. Adhesion tests and orthogonal experiments were performed to evaluate the impact of spraying parameters on performance. The optimal parameter combination was determined through range analysis, variance analysis, and projection pursuit regression (PPR) analysis, with the durability improvement verified by accelerated aging tests. Results demonstrated that sediment significantly reinforced the internal fibers and mechanical properties of the geotextile. Artificial slurry spraying effectively adhered to the geotextile surface, with clay slurry exhibiting the strongest adhesion. By integrating range analysis, variance analysis, and PPR analysis, the key influencing factors were identified as spraying thickness, geotextile thickness, and clay content. The optimal parameter combination was selected for accelerated aging tests and electron microscopy observation, revealing that the spraying treatment significantly improved the geotextile’s strength retention rate, delayed performance degradation under UV and high-temperature conditions, and protected the fiber structure. These findings provide valuable insights in terms of enhancing the durability of geotextile bags.