To cater for the diverse experiment requirements at the High Energy Photon Source (HEPS) with often limited human resources, Bluesky was chosen as the basis for our software framework, Mamba. In our attempt to address Bluesky's lack of integrated graphical user interfaces (GUIs), command injection with feedback was chosen as the main way for the GUIs to cooperate with the command line interface; a remote-procedure-call service is also provided, which covers functionalities unsuitable for command injection, as well as pushing of status updates. In order to fully support high-frequency applications like fly scans, Bluesky's support for asynchronous control is being improved; furthermore, to support high-throughput experiments, Mamba Data Worker is being developed to cover the complexity in asynchronous online data processing for these experiments. To systematically simplify the specification of metadata, scan parameters and data-processing graphs for each type of experiment, an experiment parameter generator will be developed; experiment-specific modules to automate preparation steps will also be made. The integration of off-the-shelf code in Mamba for domain-specific needs is under investigation, and Mamba GUI Studio is being developed to simplify the implementation and integration of GUIs.

With the advancements in instrumentations of next-generation synchrotron light sources, methodologies for small-angle X-ray scattering (SAXS)/wide-angle X-ray diffraction (WAXD) experiments have dramatically evolved. Such experiments have developed into dynamic and multiscale in situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time or dose may result in noisier images with a lower signal-to-noise ratio, requiring powerful denoising mechanisms for physical information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small yet effective machine-learning model for experimental SAXS/WAXD image denoising, allowing more redundancy for exposure time or dose reduction. Compared with classic models developed for natural image scenarios, our model provides a bespoke denoising solution, demonstrating superior performance on highly textured SAXS/WAXD images. The model is versatile and can be applied to denoising in other synchrotron imaging experiments when data volume and image complexity is concerned.

High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data acquisition, processing, visualization and storage ends are becoming increasingly complex and essential for enhancing beamline performance. Mamba Data Worker (MDW) has been developed to address the data challenges for the forthcoming High Energy Photon Source (HEPS). It is an important component of the Mamba experimental control and data acquisition software ecosystem, which enables fast data acquisition and transmission, dynamic configuration of data processing pipelines, data multiplex in streaming, and customized data and metadata assembly. This paper presents the architecture and development plan of MDW, outlines the essential technologies involved, and illustrates its current application at the Beijing Synchrotron Radiation Facility (BSRF).

Introduction: Osteoporotic-related fractures remains a significant public health concern, thus imposing substantial burdens on our society. Excessive activation of osteoclastic activity is one of the main contributing factors for osteoporosis-related fractures. While polylactic acid (PLA) is frequently employed as a biodegradable scaffold in tissue engineering, it lacks sufficient biological activity. Microdroplets (MDs) have been explored as an ultrasound-responsive drug delivery method, and mesenchymal stem cell (MSC)-derived exosomes have shown therapeutic effects in diverse preclinical investigations. Thus, this study aimed to develop a novel bioactive hybrid PLA scaffold by integrating MDs-NFATc1-silencing siRNA to target osteoclast formation and MSCs-exosomes (MSC-Exo) to influence osteogenic differentiation (MDs-NFATc1/PLA-Exo). Methods: Human bone marrow-derived mesenchymal stromal cells (hBMSCs) were used for exosome isolation. Transmission electron microscopy (TEM) and confocal laser scanning microscopy were used for exosome and MDs morphological characterization, respectively. The MDs-NFATc1/PLA-Exo scaffold was fabricated through poly(dopamine) and fibrin gel coating. Biocompatibility was assessed using RAW 264.7 macrophages and hBMSCs. Osteoclast formations were examined via TRAP staining. Osteogenic differentiation of hBMSCs and cytokine expression modulation were also investigated. Results: MSC-Exo exhibited a cup-shaped structure and effective internalization into cells, while MDs displayed a spherical morphology with a well-defined core-shell structure. Following ultrasound stimulation, the internalization study demonstrated efficient delivery of bioactive MDs into recipient cells. Biocompatibility studies indicated no cytotoxicity of MDs-NFATc1/PLA-Exo scaffolds in RAW 264.7 macrophages and hBMSCs. Both MDs-NFATc1/PLA and MDs-NFATc1/PLA-Exo treatments significantly reduced osteoclast differentiation and formation. In addition, our results further indicated MDs-NFATc1/PLA-Exo scaffold significantly enhanced osteogenic differentiation of hBMSCs and modulated cytokine expression. Discussion: These findings suggest that the bioactive MDs-NFATc1/PLA-Exo scaffold holds promise as an innovative structure for bone tissue regeneration. By specifically targeting osteoclast formation and promoting osteogenic differentiation, this hybrid scaffold may address key challenges in osteoporosis-related fractures.

With pre-trained large models and their associated fine-tuning paradigms being constantly applied in deep learning, the performance of large models achieves a dramatic boost, mostly owing to the improvements on both data quantity and quality. Next-generation synchrotron light sources offer ultra-bright and highly coherent X-rays, which are becoming one of the largest data sources for scientific experiments. As one of the most data-intensive scanning-based imaging methodologies, ptychography produces an immense amount of data, making the adoption of large deep learning models possible. Here, we introduce and refine the architecture of a neural network model to improve the reconstruction performance, through fine-tuning large pre-trained model using a variety of datasets. The pre-trained model exhibits remarkable generalization capability, while the fine-tuning strategy enhances the reconstruction quality. We anticipate this work will contribute to the advancement of deep learning methods in ptychography, as well as in broader coherent diffraction imaging methodologies in future.

Synchrotron tomography experiments are transitioning into multifunctional, cross-scale, and dynamic characterizations, enabled by new-generation synchrotron light sources and fast developments in beamline instrumentation. However, with the spatial and temporal resolving power entering a new era, this transition generates vast amounts of data, which imposes a significant burden on the data processing end. Today, as a highly accurate and efficient data processing method, deep learning shows great potential to address the big data challenge being encountered at future synchrotron beamlines. In this review, we discuss recent advances employing deep learning at different stages of the synchrotron tomography data processing pipeline. We also highlight how applications in other data-intensive fields, such as medical imaging and electron tomography, can be migrated to synchrotron tomography. Finally, we provide our thoughts on possible challenges and opportunities as well as the outlook, envisioning selected deep learning methods, curated big models, and customized learning strategies, all through an intelligent scheduling solution.