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Liver diseases pose a significant threat to human health. Lactate, a byproduct of glycolysis, serves various biological functions, including acting as an energy source, a signaling molecule, and a substrate for lactylation. Lactylation is a novel lactate-dependent post-translational modification that plays a role in tumor proliferation, the regulation of immune cell function, and the modulation of gene expression. In this paper, we summarize the roles of lactate and lactylation in energy metabolism, inflammatory immunity, and the tumor microenvironment, while also elucidating recent research advancements regarding lactate and lactylation in the context of hepatic fibrosis, non-alcoholic fatty liver disease, and hepatocellular carcinoma. Furthermore, lactate and lactylation are proposed as promising new targets for the treatment of liver diseases.

Nitrous oxide (N 2 O) emissions from constructed wetlands (CWs) are accompanying problems and have attracted much attention in recent years. CWs filled with different substrates (gravel, biochar, zeolite, and pyrite) were constructed to investigate the nitrogen removal performance and N 2 O emissions, which named C-CWs, B-CWs, Z-CWs, and P-CWs, respectively. C-CWs showed the poorest nitrogen removal performance in all CWs. Although B-CWs exhibited the highest fluxes of N 2 O emissions, the percentage of N 2 O emissions in nitrogen removal (0.15%) was smaller than that of C-CWs (0.18%). In addition, microbiological analysis showed that compared with C-CWs, CWs filled with biochar, zeolite, and pyrite had higher abundance of nitrifying and denitrifying microorganisms and lower abundance of N 2 O producing bacteria. In conclusion, biochar, zeolite, and pyrite were more favorable kinds of substrate than the conventional substrates of gravel for the nitrogen removal and reduction of N 2 O emissions from CWs.

Rural livability is the essence of people’s well-being and rural revitalization, in which social networks in daily life play important roles. However, the role of social interactions has long been ignored. This study aims to develop an index system for rural livability evaluation considering social interactions. We chose Ezhou city as an example, and two potential social networks were constructed, and the multi-scale characteristics of social networks at the village level and patch level were used as the proxy indicator of social convenience, then together with ecological livability and economic vitality to comprehensively evaluate rural livability. The empirical results showed a heterogeneous spatial distribution in two social networks. It also presented a tendency of “poor social convenience, general economic vitality and benign ecological livability” of rural areas in each dimension, and a complex pattern of stripped and concentric distribution in the spatial distribution of the total rural livability. It further verified that the social interactions had a direct impact on rural livability by a comparison of two evaluation results. This study advances our understanding of the role of social interactions in rural livability evaluation and provide reasonable suggestions for policymakers in future construction of livable countryside.

A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features. Here, we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging, low sample variability and high interpretability, making it suitable for 3D histology studies. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples, achieving consistent staining quality and enhancing contrast across cortical layer boundaries. Additionally, we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks, allowing for visualization of meso-scale vessel networks in the white matter. We believe that our technique has the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures. Enhanced 3D brain imaging modality by integrating serial-sectioning OCT with semi-supervised digital staining.

Halide perovskite materials have garnered significant research attention due to their remarkable performance in both photoharvesting photovoltaics and photoemission applications. Recently, self-assembled CsPbBr3 superstructures (SSs) have been demonstrated to be promising lasing materials. In this study, we report the ultrastable two-photon-pumped amplified stimulated emission from a CsPbBr3 SS/Ag hybrid microcavity with a low threshold of 0.8 mJ/cm2 at room temperature. The experimental results combined with numerical simulations show that the CsPbBr3 SS exhibits a significant enhancement in the electromagnetic properties in the hybrid microcavity on Ag film, leading to the uniform spatial temperature distribution under the irradiation of a pulsed laser, which is conducive to facilitate the recrystallization process of the QDs and improve their structural integrity and optical properties. This study provides a new idea for the application of CsPbBr3/Ag hybrid microcavity in photonic devices, demonstrating its potential in efficient optical amplification and upconversion lasers.

Measurement of blood oxygen saturation (sO2) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO2-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO2 prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO2 shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.

In order to tackle increasingly serious environmental problems, China has been promoting the development of a green economy and guiding the green transformation of various regions and industries through environmental regulation in recent years. By participating in international trade, Hebei Province has been embedded in the global value chain. However, Hebei’s involvement in the high-energy-consuming and polluting manufacturing sector and its lower position in the global value chain have led to serious environmental issues. In practice, the government has promulgated environmental regulations to restrict economic activities of enterprises. What role does environmental regulation play in Hebei’s manufacturing industry’s participation in the global value chain? In order to explore the impact of environmental regulation on Hebei’s manufacturing industry in the global value chain, this paper constructs a fixed-effect econometric model based on the panel data of the embedding level of the value chain of 12 manufacturing sectors in Hebei Province. The research results show that: first, the R & D capacity of the manufacturing industry in Hebei Province still needs to be improved. Second, environmental regulation has promoted the global value chain position of Hebei’s 12 manufacturing sectors. Third, environmental regulation will show obvious heterogeneity to manufacturing industries with different capital intensities and different pollution levels. The impact of environmental regulation on the manufacturing industry with different intensities is different. Therefore, the government should formulate targeted environmental regulation to promote the position of Hebei’s manufacturing industry in the global value chain, such as further improving environmental regulation and increasing the intensity of environmental regulation and increasing the investment of human capital, and cultivating innovative talents.

Label-free imaging modalities offer numerous advantages, such as the ability to avoid the time-consuming and potentially disruptive process of physical staining. However, one challenge that arises in label-free imaging is the limited ability to extract specific structural or molecular information from the acquired images. To overcome this limitation, a novel approach known as digital staining or digital labeling has emerged. Digital staining leverages the power of deep learning algorithms to virtually introduce labels or stains into label-free images, thereby enabling the extraction of detailed information that would typically require physical staining. The integration of digital staining with label-free imaging holds great promise in expanding the capabilities of imaging techniques, facilitating improved analysis, and advancing our understanding of biological systems at both the cellular and tissue level. In this thesis, I explore supervised and semi-supervised methodologies of digital staining and the applications in augmenting label-free imaging modalities, particularly in the context of cell imaging and brain imaging.In the first part of the thesis, I demonstrate the novel integration of multi-contrast dark-field reflectance microscopy and supervised deep learning to enable subcellular immunofluorescence labeling and cell cytometry from label-free imaging. By leveraging the rich structural information and sensitivity of reflectance microscopy, this method accurately predicts subcellular features without the need for physical staining. As a result of the use of a novel multi-contrast modality, the digital labeling approach demonstrates significant improvements over the state-of-the-art techniques, achieving up to 3× prediction accuracy. In addition to fluorescence prediction, the method successfully reproduces single-cell level structural phenotypes related to cell cycles. The multiplexed readouts obtained through digital labeling enable accurate multi-parametric single-cell profiling across a large cell population.In the second part, I investigated a novel digital staining optical coherence tomography (DS-OCT) modality combining advantages of serial sectioning OCT and semi-supervised deep learning and demonstrated several advantages for the application of 3D histological brain imaging. The DS model is trained using a semi-supervised learning framework that incorporates unpaired translation, a biophysical model, and cross-modality image registration, which manifests broad applicability to other weakly-paired bioimaging modalities. The DS model enables the translation of S-OCT images to Gallyas silver staining, providing consistent staining quality across different samples. I further show that DS enhances contrast across cortical layer boundaries and enables reliable cortical layer differentiation. Additionally, DS-OCT preserves 3D-geometry on centimeter-scale brain tissue blocks. My pilot study demonstrates promising results on other anatomical regions acquired from different S-OCT systems, highlighting its potential for generalization in various imaging contexts.Overall, I investigate the problems of augmenting label-free imaging modalities with deep learning generated digital stains. I explored both supervised and semi-supervised methods for building novel DS frameworks. My work showcased two important applications in the field of immunofluorescence cell imaging and 3D histological brain imaging. On the one hand, the integration of DS techniques with multi-contrast microscopy has the potential to enhance the throughput of single-cell imaging cytometry, and phenotyping. On the other hand, integrating DS techniques with S-OCT holds great potential for high-throughput human brain imaging, enabling comprehensive studies on the structure and function of the brain. Through the exploration, I aim to shed light on the impact of digital staining in the field of computational imaging and its implications for various scientific disciplines.

Halide perovskite materials have garnered significant research attention due to their remarkable performance in both photoharvesting photovoltaics and photoemission applications. Recently, self-assembled CsPbBr superstructures (SSs) have been demonstrated to be promising lasing materials. In this study, we report the ultrastable two-photon-pumped amplified stimulated emission from a CsPbBr SS/Ag hybrid microcavity with a low threshold of 0.8 mJ/cm at room temperature. The experimental results combined with numerical simulations show that the CsPbBr SS exhibits a significant enhancement in the electromagnetic properties in the hybrid microcavity on Ag film, leading to the uniform spatial temperature distribution under the irradiation of a pulsed laser, which is conducive to facilitate the recrystallization process of the QDs and improve their structural integrity and optical properties. This study provides a new idea for the application of CsPbBr /Ag hybrid microcavity in photonic devices, demonstrating its potential in efficient optical amplification and upconversion lasers.

Halide perovskite materials have garnered significant research attention due to their remarkable performance in both photoharvesting photovoltaics and photoemission applications. Recently, self-assembled CsPbBr[sub.3] superstructures (SSs) have been demonstrated to be promising lasing materials. In this study, we report the ultrastable two-photon-pumped amplified stimulated emission from a CsPbBr[sub.3] SS/Ag hybrid microcavity with a low threshold of 0.8 mJ/cm[sup.2] at room temperature. The experimental results combined with numerical simulations show that the CsPbBr[sub.3] SS exhibits a significant enhancement in the electromagnetic properties in the hybrid microcavity on Ag film, leading to the uniform spatial temperature distribution under the irradiation of a pulsed laser, which is conducive to facilitate the recrystallization process of the QDs and improve their structural integrity and optical properties. This study provides a new idea for the application of CsPbBr[sub.3] /Ag hybrid microcavity in photonic devices, demonstrating its potential in efficient optical amplification and upconversion lasers.