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Given the advent of the digital era, digital transformation has become necessary for enterprise development. Political connections are the most important resources for enterprise development in most countries. However, the impact of political connections on corporate digital transformation has yet to be verified. This study uses ERNIE, a large language model, to construct a measurement of corporate digital transformation from the perspective of digital technology application through a textual analysis of the annual reports of A-share privately listed companies from 2008 to 2020 and analyzes the impact of political connections on corporate digital transformation and its mechanism of action. The findings demonstrate that political connections have a significant inhibitory effect on corporate digital transformation. This conclusion still holds after a series of robustness and endogeneity tests. The mechanism analyses demonstrate that political connections primarily affect corporate digital transformation through three mechanisms: weakening risk, inhibiting innovation, and enhancing resource crowding. We theoretically expand the understanding of the economic impact of political connections and provide new ideas for accelerating enterprise digital transformation from the perspective of policy makers.

Despite broad consensus on the importance of enterprise digital transformation, significant discrepancies persist regarding its actual effects.This divergence stems primarily from two key measurement challenges: (1) a lack of clear and consistent definitions of enterprise digital transformation, and (2) a lack of rigorous and accurate measurement methodologies.These shortcomings lead to research findings that are incomparable, difficult to replicate, and often conflicting.To effectively address the aforementioned challenges, this paper employs machine learning and large language models (LLMs) to construct a novel set of indicators for enterprise digital transformation.The work begins by manually annotating sentences from annual reports of listed companies in China from 2006 to 2020.These labeled sentences are then used to train and fine-tune several machine learning models, including LLMs.The ERNIE model, demonstrating the best classification performance among the models tested, is selected as the sentence classifier to predict sentence labels across the full text of the annual reports, ultimately constructing the enterprise digital transformation metrics.Both theoretical analysis and multiple data crossvalidations demonstrate that the metrics developed in this paper are more accurate than existing approaches.Based on these metrics, the paper empirically examines the impact of enterprise digital transformation on financial performance.Our findings reveal three key points: (1) enterprise digital transformation significantly enhances financial performance, with big data, AI, mobile internet, cloud computing, and the Internet of Things (IoT) all playing a significant role; however, blockchain technology does not show a significant effect; (2) the significant positive effect of digital transformation on financial performance is primarily observed in firms with weaker initial financial performance; and (3) enterprise digital transformation improves financial performance mainly through enhancing efficiency and reducing costs.This research has practical implications for promoting enterprise digital transformation and fostering high-quality economic development.

Lipid membranes are found in most intracellular organelles, and their heterogeneities play an essential role in regulating the organelles’ biochemical functionalities. Here we report a Spectrum and Polarization Optical Tomography (SPOT) technique to study the subcellular lipidomics in live cells. Simply using one dye that universally stains the lipid membranes, SPOT can simultaneously resolve the membrane morphology, polarity, and phase from the three optical-dimensions of intensity, spectrum, and polarization, respectively. These high-throughput optical properties reveal lipid heterogeneities of ten subcellular compartments, at different developmental stages, and even within the same organelle. Furthermore, we obtain real-time monitoring of the multi-organelle interactive activities of cell division and successfully reveal their sophisticated lipid dynamics during the plasma membrane separation, tunneling nanotubules formation, and mitochondrial cristae dissociation. This work suggests research frontiers in correlating single-cell super-resolution lipidomics with multiplexed imaging of organelle interactome. Lipid membranes are heterogeneous and dynamically regulated in cells. Here the authors report a Spectrum and Polarisation Optical Tomography (SPOT) method where they use Nile Red dye to resolve membrane morphology, polarity and phase in cells.

Vaccination is the most cost-effective and sustainable way to prevent and eliminate infectious diseases. Compared with traditional vaccines, novel vaccines have better stability, longer duration and require less antigen usage. In addition, novel vaccines have better immune effects and significantly less toxic side effects. However, both novel vaccines and traditional vaccines require carrier molecules or adjuvants to produce an optimal immune response. There is an increasing demand for vaccine adjuvants and delivery systems that can induce stronger immune response whilst reducing production cost and the dose of vaccine. In recent years, nanotechnology has played an important role in the development of novel vaccine adjuvants and nano-delivery systems. Biodegradable materials have also received a lot of attention in medical science because they have excellent biocompatibility, biodegradability and low toxicity, which can protect antigens from degradation, increase antigen stability and provide slow release; resulting in enhanced immunogenicity. Therefore, biodegradable nanoparticles have attracted much attention in the formulation of vaccines. In this review, we outline some key features of biodegradable nanomaterials in the developing safer and more effective vaccines. The properties, structural characteristics, advantages and disadvantage of the biodegradable nanomaterials will be systematically reviewed. Additionally, applications, research progress and future prospects of biodegradable nanomaterials are discussed. This review will be help in future research work directed at developing biodegradable vaccine adjuvants or delivery carriers.

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles and plays a vital role in studying molecular structures and dynamics of bio-complexes. However, current techniques remain difficult to resolve the dipole assemblies on subcellular structures and their dynamics in living cells at super-resolution level. Here we report polarized structured illumination microscopy (pSIM), which achieves super-resolution imaging of dipoles by interpreting the dipoles in spatio-angular hyperspace. We demonstrate the application of pSIM on a series of biological filamentous systems, such as cytoskeleton networks and λ-DNA, and report the dynamics of short actin sliding across a myosin-coated surface. Further, pSIM reveals the side-by-side organization of the actin ring structures in the membrane-associated periodic skeleton of hippocampal neurons and images the dipole dynamics of green fluorescent protein-labeled microtubules in live U2OS cells. pSIM applies directly to a large variety of commercial and home-built SIM systems with various imaging modality. Polarization microscopy has been combined with single-molecule localization, but it’s often limited in either speed or resolution. Here the authors present polarized Structured Illumination Microscopy (pSIM), a method that uses polarized laser excitation to measure dye orientation during fast super-resolution live cell imaging.

Electron correlations play a dominant role in the charge dynamics of the cuprates. We use resonant inelastic X-ray scattering (RIXS) to track the doping dependence of the collective charge excitations in electron doped La2−xCexCuO4 (LCCO). From the resonant energy dependence and the out-of-plane momentum dependence, the charge excitations are identified as three-dimensional (3D) plasmons, which reflect the nature of the electronic structure and Coulomb repulsion on both short and long lengthscales. With increasing electron doping, the plasmon excitations increase monotonically in energy, a consequence of the electron correlation effect on electron structure near the Fermi surface (FS). Importantly, the plasmon excitations evolve from a broad feature into a well-defined peak with much increased life time, revealing the evolution of the electrons from incoherent states to coherent quasi-particles near the FS. Such evolution marks the reduction of the short-range electronic correlation, and thus the softening of the Mottness of the system with increasing electron doping.

The distribution range, time-varying characteristics, and sources of lunar water are still controversial. Here we show the Chang’E-5 in-situ spectral observations of lunar water under Earth’s magnetosphere shielding and relatively high temperatures. Our results show the hydroxyl contents of lunar soils in Chang’E-5 landing site are with a mean value of 28.5 ppm, which is on the weak end of lunar hydration features. This is consistent with the predictions from remote sensing and ground-based telescopic data. Laboratory analysis of the Chang’E-5 returned samples also provide critical clues to the possible sources of these hydroxyl contents. Much less agglutinate glass contents suggest a weak contribution of solar wind implantation. Besides, the apatite present in the samples can provide hydroxyl contents in the range of 0 to 179 ± 13 ppm, which shows compelling evidence that, the hydroxyl-containing apatite may be an important source for the excess hydroxyl observed at this young mare region. Laboratory analysis of returned Chang’E-5 samples from the lunar surface show their hydroxyl contents to be on the weak end of lunar hydration features.

Studying organelles’ interactome at system level requires simultaneous observation of subcellular compartments and tracking their dynamics. Conventional multicolor approaches rely on specific fluorescence labeling, where the number of resolvable colors is far less than the types of organelles. Here, we use a lipid-specific dye to stain all the membrane-associated organelles and spinning-disk microscopes with an extended resolution of ~143 nm for high spatiotemporal acquisition. Due to the chromatic polarity sensitivity, high-resolution ratiometric images well reflect the heterogeneity of organelles. With deep convolutional neuronal networks, we successfully segmented up to 15 subcellular structures using one laser excitation. We further show that transfer learning can predict both 3D and 2D datasets from different microscopes, different cell types, and even complex systems of living tissues. We succeeded in resolving the 3D anatomic structure of live cells at different mitotic phases and tracking the fast dynamic interactions among six intracellular compartments with high robustness. The authors demonstrate an AI-enhanced imaging method combining lipid-specific dyes and super-resolution microscopy enables simultaneous tracking of up to 15 organelles in living cells, revealing dynamic interactions across diverse biological systems.

Low density polyethylene (LDPE) has been widely used commercially for decades; however, as a non-degradable material, its continuous accumulation has contributed to serious environmental issues. A fungal strain, Cladosporium sp. CPEF-6 exhibiting a significant growth advantage on MSM-LDPE (minimal salt medium), was isolated and selected for biodegradation analysis. LDPE biodegradation was analyzed by weight loss percent, change in pH during fungal growth, environmental scanning electron microscopy (ESEM), and Fourier transformed infrared spectroscopy (FTIR). Inoculation with the strain Cladosporium sp. CPEF-6 resulted in a 0.30 ± 0.06% decrease in the weight of untreated LDPE (U-LDPE). After heat treatment (T-LDPE), the weight loss of LDPE increased significantly and reached 0.43 ± 0.01% after 30 days of culture. The pH of the medium was measured during LDPE degradation to assess the environmental changes caused by enzymes and organic acids secreted by the fungus. The fungal degradation of LDPE sheets was characterized by ESEM analysis of topographical alterations, such as cracks, pits, voids, and roughness. FTIR analysis of U-LDPE and T-LDPE revealed the appearance of novel functional groups associated with hydrocarbon biodegradation as well as changes in the polymer carbon chain, confirming the depolymerization of LDPE. This is the first report demonstrating the capacity of Cladosporium sp. to degrade LDPE, with the expectation that this finding can be used to ameliorate the negative impact of plastics on the environment.