a-protocol-for-constructing-a-rat-wound-model-of-type-1-diabetes

A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes

A single high dose of streptozotocin injection followed by full-thickness skin excision on the dorsum of rats is a common method for constructing animal ...

phenol-red-thread-based-sampling-procedure-for-untargeted-tear-fluid

Phenol Red Thread-based Sampling Procedure for Untargeted Tear Fluid Lipidomics in Biomarker Discovery

Tear lipids are crucial for tear film stability and ocular surface health. Changes in tear composition could be associated with ocular and systemic ...

Chengdu University of Traditional Chinese Medicine

The Hong Kong Polytechnic University

The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the whole breast anatomy. To facilitate the reading of AWBUS images and support the breast density estimation, an automatic breast anatomy segmentation method for AWBUS images is proposed in this study. The problem at hand is quite challenging as it needs to address issues of low image quality, ill-defined boundary, large anatomical variation, etc. To address these issues, a new deep learning encoder-decoder segmentation method based on a self-co-attention mechanism is developed. The self-attention mechanism is comprised of spatial and channel attention module (SC) and embedded in the ResNeXt (i.e., Res-SC) block in the encoder path. A non-local context block (NCB) is further incorporated to augment the learning of high-level contextual cues. The decoder path of the proposed method is equipped with the weighted up-sampling block (WUB) to attain class-specific better up-sampling effect. Meanwhile, the co-attention mechanism is also developed to improve the segmentation coherence among two consecutive slices. Extensive experiments are conducted with comparison to several the state-of-the-art deep learning segmentation methods. The experimental results corroborate the effectiveness of the proposed method on the difficult breast anatomy segmentation problem on AWBUS images.

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound.

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction.

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Nanomaterials for Flexible Neuromorphics.

Buckwheat, a nutrient-rich pseudocereal, is known for its various biological properties, but its antinutritional factors, such as phytic acid and tannins, can hinder nutrient absorption. Fermentation improves buckwheat's nutritional profile by enhancing bioactive compounds, increasing digestibility, and reducing antinutritional factors. This review comprehensively examines the effects of fermentation and microbial strains on the nutritional composition and functional properties of buckwheat, highlighting their impact on health benefits and potential applications in diverse food products. Fermentation significantly boosts essential nutrients, including amino acids, vitamins, minerals, and bioactive compounds, while reducing antinutritional factors like phytic acid and protease inhibitors. It also enhances antioxidant, antidiabetic, hypolipidemic, anti-inflammatory, and gut microbiota-regulating properties. However, there are notable gaps in research, including limited understanding of fermentation process control, heavy metal transformation, and pathogenic microorganism effects during fermentation. Addressing these gaps is crucial for optimizing the functional properties and ensuring the safety of fermented buckwheat in the food industry. Overall, fermented buckwheat holds significant potential as a functional ingredient for gluten-free foods, nondairy beverages, and other health-promoting products that cater to specific dietary needs.

ArticleTotal : 2	Year
Publication title Cited by 7	2023
Publication title	2025

Article	Year
Self-co-attention neural network for anatomy segmentation in whole breast ultrasound. Medical image analysis\| PubMed ID: 32574986	2020
Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction. The journal of physical chemistry letters\| PubMed ID: 39167716	2024
Nanomaterials for Flexible Neuromorphics. Chemical reviews\| PubMed ID: 39499851	2024
Revisiting fermented buckwheat: a comprehensive examination of strains, bioactivities, and applications. Critical reviews in food science and nutrition\| PubMed ID: 39989084	2025

Hang Li

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