Charging a Stylus using Mobile Device Near Field Communication (NFC) Coil

This disclosure describes techniques to charge a stylus using the existing near field communication (NFC) integrated circuit and coil on the mobile device. The storage slot for a stylus in a case that holds a mobile device is designed such that upon insertion, the stylus NFC coil automatically aligns with the phone NFC coil, thereby enabling charging during storage. Effectively, the storage slot functions as a charging dock for the stylus. Since the storage slot of the phone case is the normal home for the stylus, charging takes place in the background and becomes a seamless experience for the user. A Hall sensor on the main logic board of the mobile device is used to detect the presence of the stylus; no other hardware changes are required. The device NFC mode is automatically configured to allow other NFC functionality while selectively charging the stylus when the battery level of the stylus falls below a threshold

Localized Sparse Incomplete Multi-view Clustering

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC.Comment: Published in IEEE Transactions on Multimedia (TMM). The code is available at Github https://github.com/justsmart/LSIMV

Information Recovery-Driven Deep Incomplete Multiview Clustering Network

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.Comment: Accepted by TNNLS 2023. Please contact me if you have any questions: [email protected]. The code is available at: https://github.com/justsmart/RecForme

Targeting cellular senescence in senile osteoporosis: therapeutic potential of traditional Chinese medicine

Senile osteoporosis (SOP) is a prevalent manifestation of age-related bone disorders, resulting from the dysregulation between osteoblast (OB)-mediated bone formation and osteoclast (OC)-mediated bone resorption, coupled with the escalating burden of cellular senescence. Traditional Chinese medicine (TCM) herbs, renowned for their remarkable attributes encompassing excellent tolerability, low toxicity, heightened efficacy, and minimal adverse reactions, have gained considerable traction in OP treatment. Emerging evidence substantiates the therapeutic benefits of various TCM formulations and their active constituents, including Zuogui wan, Fructus Ligustri Lucidi, and Resveratrol, in targeting cellular senescence to address SOP. However, a comprehensive review focusing on the therapeutic efficacy of TCM against SOP, with a particular emphasis on senescence, is currently lacking. In this review, we illuminate the pivotal involvement of cellular senescence in SOP and present a comprehensive exploration of TCM formulations and their active ingredients derived from TCM, delineating their potential in SOP treatment through their anti-senescence properties. Notably, we highlight their profound effects on distinct aging models that simulate SOP and various senescence characteristics. Finally, we provide a forward-looking discussion on utilizing TCM as a strategy for targeting cellular senescence and advancing SOP treatment. Our objective is to contribute to the unveiling of safer and more efficacious therapeutic agents for managing SOP

Current development and future challenges in microplastic detection techniques: a bibliometrics-based analysis and review.

Microplastics have been considered a new type of pollutant in the marine environment and have attracted widespread attention worldwide in recent years. Plastic particles with particle size less than 5 mm are usually defined as microplastics. Because of their similar size to plankton, marine organisms easily ingest microplastics and can threaten higher organisms and even human health through the food chain. Most of the current studies have focused on the investigation of the abundance of microplastics in the environment. However, due to the limitations of analytical methods and instruments, the number of microplastics in the environment can easily lead to overestimation or underestimation. Microplastics in each environment have different detection techniques. To investigate the current status, hot spots, and research trends of microplastics detection techniques, this review analyzed the papers related to microplastics detection using bibliometric software CiteSpace and COOC. A total of 696 articles were analyzed, spanning 2012 to 2021. The contributions and cooperation of different countries and institutions in this field have been analyzed in detail. This topic has formed two main important networks of cooperation. International cooperation has been a common pattern in this topic. The various analytical methods of this topic were discussed through keyword and clustering analysis. Among them, fluorescent, FTIR and micro-Raman spectroscopy are commonly used optical techniques for the detection of microplastics. The identification of microplastics can also be achieved by the combination of other techniques such as mass spectrometry/thermal cracking gas chromatography. However, these techniques still have limitations and cannot be applied to all environmental samples. We provide a detailed analysis of the detection of microplastics in different environmental samples and list the challenges that need to be addressed in the future

Urine Metabolomics Profiling of Lumbar Disc Herniation and its Traditional Chinese Medicine Subtypes in Patients Through Gas Chromatography Coupled With Mass Spectrometry

Lumbar disc herniation (LDH) possesses complex pathogenesis, which has not been well elucidated yet. To date, specific or early diagnosis of LDH remains unavailable, resulting in missed opportunity for effective treatment. According to Traditional Chinese medicine (TCM) theory, LDH can be divided into two subtypes (reality syndrome and deficiency syndrome). The purpose of this study was to analyze the metabolic disorders of LDH and its TCM subtypes and screen out potential biomarkers for LDH diagnosis. Gas chromatography coupled with mass spectrometry (GC-MS) was applied to test the urine samples from 66 participants (30 healthy volunteers, 18 LDH patients with deficiency syndrome and 18 patients with reality syndrome). PCA analysis showed a distinct separation tendency between the healthy subjects and LDH patients but no obvious separation between the different syndromes (reality syndrome and deficiency syndrome) of LDH patients. As a result, 23 metabolites were identified significantly altered in the LDH patients, as compared with the healthy subjects. The altered metabolites belong to amino acid metabolism, nucleic acid metabolism, carbohydrate metabolism, and vitamin metabolism, which are related to osteoporosis and inflammation. Our results indicate metabolic disorders of LDH and thereby propose a group of metabolic biomarkers for potential application in early diagnosis of LDH in clinic, which provide a reasonable explanation for the pathogenesis of LDH

Mechanisms of action and synergetic formulas of plant-based natural compounds from traditional Chinese medicine for managing osteoporosis: a literature review

Osteoporosis (OP) is a systemic skeletal disease prevalent in older adults, characterized by substantial bone loss and deterioration of microstructure, resulting in heightened bone fragility and risk of fracture. Traditional Chinese Medicine (TCM) herbs have been widely employed in OP treatment owing to their advantages, such as good tolerance, low toxicity, high efficiency, and minimal adverse reactions. Increasing evidence also reveals that many plant-based compounds (or secondary metabolites) from these TCM formulas, such as resveratrol, naringin, and ginsenoside, have demonstrated beneficial effects in reducing the risk of OP. Nonetheless, the comprehensive roles of these natural products in OP have not been thoroughly clarified, impeding the development of synergistic formulas for optimal OP treatment. In this review, we sum up the pathological mechanisms of OP based on evidence from basic and clinical research; emphasis is placed on the in vitro and preclinical in vivo evidence-based anti-OP mechanisms of TCM formulas and their chemically active plant constituents, especially their effects on imbalanced bone homeostasis regulated by osteoblasts (responsible for bone formation), osteoclasts (responsible for bone resorption), bone marrow mesenchymal stem cells as well as bone microstructure, angiogenesis, and immune system. Furthermore, we prospectively discuss the combinatory ingredients from natural products from these TCM formulas. Our goal is to improve comprehension of the pharmacological mechanisms of TCM formulas and their chemically active constituents, which could inform the development of new strategies for managing OP