Influencing Factors of Smart Community Service Quality: Evidence from China

Smart community is an important constituent part of a smart city and an extension and deepening of the concept of the latter. When it comes to smart community, the digitalization upgrading of traditional community service is conducted via information technology, in an effort to improve the service experience of community residents and elevate their happiness index. From social functions, smart community also has the advantages in facilitating the smart transformation of cities, promoting the harmonious society construction, and improving governmental efficiency and image, among others. However, various problems persist in the construction and development process of a smart community, such as mismatching service contents and low service quality. To explore the influencing factors of smart community service quality, a total of 16 influencing factors were extracted from 5 dimensions: service object, service subject, government role, management system, and service content. The relationships among the influencing factors were analyzed via the decision-making trial and evaluation laboratory (DEMATEL)-interpretative structural modeling (ISM) composite model, and a multi-order explanation model was constructed for these influencing factors. Result shows that the legal guarantee is the root cause influencing the smart community service quality. Development standard, basic service, and expected service are deep influencing factors that play mediating roles. Middle-layer factors such as service and operating systems have a direct bearing on quality perception. The surface-layer factors directly decide residential assessment on the smart community service quality. This study has also manifested the feasibility of the integrated DEMATEL-ISM method in analyzing the action mechanism of influencing factors for smart community service quality, providing a new analytical idea and modeling method for the smart community service quality

Understanding the substrate specificity of the heparan sulfate sulfotransferases by an integrated biosynthetic and crystallographic approach

Heparan sulfates (HSs) have potential therapeutic value as anti-inflammatory and antimetastasis drugs, in addition to their current use as anticoagulants. Recent advances in chemoenzymatic synthesis of HS provide a way to conveniently produce homogenous HS with different biological properties. Crystal structures of sulfotransferases involved in this process are providing atomic detail of their substrate binding clefts and interactions with their HS substrates. In theory, the flexibility of this method can be increased by modifying the specificities of the sulfotransferases based on the structures, thereby producing a new array of products

Uncovering Biphasic Catalytic Mode of C 5 -epimerase in Heparan Sulfate Biosynthesis

Heparan sulfate (HS), a highly sulfated polysaccharide, is biosynthesized through a pathway involving several enzymes. C5-epimerase (C5-epi) is a key enzyme in this pathway. C5-epi is known for being a two-way catalytic enzyme, displaying a “reversible” catalytic mode by converting a glucuronic acid to an iduronic acid residue, and vice versa. Here, we discovered that C5-epi can also serve as a one-way catalyst to convert a glucuronic acid to an iduronic acid residue, displaying an “irreversible” catalytic mode. Our data indicated that the reversible or irreversible catalytic mode strictly depends on the saccharide substrate structures. The biphasic mode of C5-epi offers a novel mechanism to regulate the biosynthesis of HS with the desired biological functions

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM