Lingling Chen

Hyperspectral imaging (HSI) captures full spectra at each pixel, enabling applications in environmental monitoring, industrial inspection, and biomedical research. Its effectiveness, however, is often limited by complex noise, and existing deep learning denoising methods require large-scale paired datasets, which are scarce due to HSI’s high dimensionality and susceptibility to corruption. Here, we present FT-DSES, a few-shot domain-adaptive hyperspectral denoising framework designed to address data scarcity and cross-system variability common in optical imaging. The method integrates a specifically designed spectral-enhanced spatial attention module with an adaptive dynamic quantile pooling mechanism, enabling noise-aware spatial encoding and precise preservation of spectral signatures under varying illumination and noise statistics. A two-stage adaptation scheme selectively recalibrates quantile parameters, allowing rapid transfer to new instruments and modalities using only 5-8 paired samples. Trained on synthetic complex photon-noise models and minimally fine-tuned in the target domain, FT-DSES achieves high-fidelity reconstruction (up to ∼36.47 dB in PSRN and ∼0.15 in SAM) with reduced computational burden (∼61.42% parameter and ∼57.86% time). Experiments across real-world remote sensing, reflectance imaging, and fluorescence microscopy demonstrate significant gains in PSNR and SAM, demonstrating robust cross-source, cross-domain, and cross-modal generalization in various photon-limited and data-limited regimes.

Membrane‐based nanoconfinement catalytic oxidation (MNCO) offers promise for degrading persistent organic micropollutants (OMPs) in aqueous environments. Nevertheless, the intrinsic efficiency‐flux‐stability trade‐off impedes its further large‐scale engineering implementation. This study addresses this fundamental challenge through a first‐of‐its‐kind tripartite strategy encompassing atomic‐scale material design, mechanistic innovation, and structural engineering. A novel Co‐N‐Ti _3−x C ₂ T _y membrane is developed through vacancy‐mediated atomic trapping, constructing asymmetric Co‐N ₁ C ₂ sites on Ti‐defective MXene, synergizing hierarchical mass transfer optimization (Knudsen‐like flow, expanded external nanopores, minimized transport distance) with the nanoconfinement effect. This achieves an unprecedented high water flux of 2157 LMH, surpassing conventional membranes by 2−3 orders of magnitude, while maintaining 100% removal efficiency for various OMPs within 13.2 ms of retention time. Crucially, nanoconfinement and electronic delocalization at Co‐N ₁ C ₂ sites synergistically activate peroxymonosulfate to generate reactive oxygen species and accelerate catalyst‐mediated electron transfer, reducing formation energy barriers and diffusion distances, collectively culminating in 10 ⁵ −10 ⁷ ‐fold reaction kinetic enhancement relative to non‐confined analogues. Co single‐atom incorporation via Ti vacancies enhances structural integrity, ensuring stability in real water matrices and robustness for >130 h with <3% Co loss. This integrated design overcomes the catalytic membrane trilemma, enabling sustainable ultrafast decontamination and advancing MNCO for high‐performance, eco‐friendly purification systems toward global water security.

Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems, yet it remains a major challenge due to the trade-off among imaging speed, light exposure, illumination power, and image quality. Here, we present a deep-learning enhanced light sheet fluorescence microscopy (LSFM) approach that addresses the restoration of rapid volumetric time-lapse imaging with less than 0.03% light exposure and 3.3% acquisition time compared to a typical standard acquisition. We demonstrate that the convolutional neural network (CNN)-transformer network developed here, namely U-net integrated transformer (UI-Trans), successfully achieves the mitigation of complex noise-scattering-coupled degradation and outperforms state-of-the-art deep learning networks, due to its capability of faithfully learning fine details while comprehending complex global features. With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM (LS-LSFM) and conventional LSFM, this method achieves a three- to five-fold signal-to-noise ratio (SNR) improvement and ~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D (3D morphology + time) imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.

Hyperspectral imaging provides the full spectrum at each point of the whole field-of-view, and thus is being extensively employed in remote sensing, surveillance, medical diagnostics and biological research. However, the intrinsically limited photons for each spectral band and the inevitable noise during acquisition result in complex degradation of hyperspectral images (HSIs) that adversely impacts the subsequent data analysis. Yet, it remains challenging for current HSI denoising methods to effectively address HSI datasets that are significantly contaminated by complex noise, especially in terms of spectral recovery. In this paper, we propose a spatial-spectral collaborative denoising network (SSCDN) that makes full use of spatial-spectral correlation information for HSI denoising. Through the combination of attention mechanism and specifically designed spatial-spectral collaborative attention module along with a multi-loss joint optimization strategy, the proposed model achieves superior denoising performance while well-preserving spectral and spatial features for complex degradation. Extensive experimental results on simulated and real data for remote sensing and biomedical applications demonstrate that the proposed SSCDN outperforms other state-of-the-art competitive HSI denoising methods under various noise settings, especially in terms of structural-spectral fidelity and the model robustness against noise.

This paper explores the use of the Jupyter platform in teaching C++ programming at the higher education level. It includes a comparative analysis between traditional integrated development environments and online judge systems. The study discusses a teaching approach that leverages JupyterHub and Xeus-Cling to create an interactive learning setting. This approach was pilot-tested during a C++ lab session at Shenzhen Technology University, and student feedback was subsequently gathered and analyzed. A substantial number of students found value in Jupyter's interactive capabilities and the seamless integration of code execution and report writing. However, others experienced difficulties with the platform due to adaptation hurdles, code compatibility issues, and the lack of functionality compared to traditional integrated development environments. The paper further explores the potential of integrating Jupyter with the Visual Studio Code environment to mitigate these limitations and amplify its benefits. The paper concludes by recommending ongoing research and evaluation to refine and adapt this innovative teaching strategy.

Profound understanding of fouling behaviors and underlying mechanisms is fundamentally important for fouling control in membrane-based environmental applications. Therefore, it entails novel noninvasive analytical approaches for in situ characterizing the formation and development of membrane fouling processes. This work presents a characterization approach based on hyperspectral light sheet fluorescence microscopy (HSPEC-LSFM), which is capable of discriminating various foulants and providing their 2-dimensional/3-dimensional spatial distributions on/in membranes in a label-free manner. A fast, highly sensitive and noninvasive imaging platform was established by developing a HSPEC-LSFM system and further extending it to incorporate a laboratory-scale pressure-driven membrane filtration system. Hyperspectral data sets with a spectral resolution of ∼1.1 nm and spatial resolution of ∼3 μm as well as the temporal resolution of ∼8 s/plane were obtained, and the fouling formation and development process of foulants onto membrane surfaces, within the pores and on the pore walls were clearly observed during the ultrafiltration of protein and humic substances solutions. Pore blocking/constriction at short times while cake growth/concentration polarization at longer times was found to have coupled effects for the flux decline in these filtration tests, and yet the contribution of each effect as well as the transition of the governing mechanisms was found distinct. These results demonstrate in situ label-free characterization of membrane fouling evolution with the recognition of foulant species during filtration and provide new insights into membrane fouling. This work offers a powerful tool to investigate dynamic processes for a wide range of membrane-based explorations.

Two-dimensional black phosphorus (BP) has layer-dependent band gap, high specific surface area, moderate carrier mobility, excellent photothermal effect, inherent photoacoustic properties, excellent biodegradability and biocompatibility, making it an ideal option for applications in biomedicine such as photothermal therapy, disease diagnosis, biological imaging and so on. Despite of these advantages, the two-dimensional BP has its own obstacle limiting its practical application, such as its instability, and there is no practical method for synthetization of large size BP flakes for industrial application. This review firstly introduces interesting properties of BP such as its biocompatibility, biodegradability, anisotropy, moderate carrier mobility and so on. Then summarizes the preparation methods of two-dimensional BP: non-liquid phase methods (e.g. mechanical peeling and chemical vapor deposition) and liquid phase methods (e.g. liquid-phase exfoliation and wet chemistry methods). The application of BP in the biomedical field such as photoacoustic imaging, fluorescence imaging, photothermal imaging, circulating tumor DNA detection, cancer treatment and treatment of neurodegenerative diseases are introduced later. Then we discussed the current problems of BP and methods to enhance the stability of BP including encapsulation, functionalization, liquid phase environmental protection and doping. Finally, some future trends in the research of two-dimensional BP are envisaged. It is believed that two-dimensional BP will have a broad application prospect in various fields especially in biomedicine due to its biocompatibility and biodegradability.

We develop a multidimensional fluorescence imaging technique by implementing a wide-field time-gated fluorescence lifetime imaging into digital scanned laser light-sheet microscopy (FLIM-DSLM) to measure 3D fluorescence lifetime distribution in mesoscopic specimens with high resolution. This is achieved by acquiring a series of time-gated images at different relative time delays with respect of excitation pulses at different depths. The lifetime is determined for each voxel by iteratively fitting to single exponential decay. The performance of the developed system is evaluated with the measurements of a lifetime reference Rhodamine 6G solution and a subresolution fluorescent bead phantom. We also demonstrate the application performances of this system to ex vivo and in vivo imaging of Tg(kdrl:EGFP) transgenic zebrafish embryos, illustrating the lifetime differences between the GFP signal and the autofluorescence signal. The results show that FLIM-DSLM can be used for sample size up to a few millimetres and can be utilised as a powerful and robust method for biomedical research, for example as a readout of protein-protein interactions via Forster resonance energy transfer.