Bioinspired cellulose-integrated MXene-based hydrogels for multifunctional sensing and electromagnetic interference shielding

Bioinspired hydrogels are complex materials with distinctive properties comparable to biological tissues. Their exceptional sensitivity to various external stimuli leads to substantial application potential in wearable smart devices. However, these multifaceted hydrogels are often challenging to be combined with pattern customization, stimulus responsiveness, self-healing, and biocompatibility. Herein, inspired by mussel secretions, a printable, self-healing, and biocompatible MXene-based composite hydrogel was designed and prepared by incorporating Ti3C2Tx MXene nanosheets into the hydrogel framework through the chelation of calcium ions (Ca2+) with polyacrylic acid and cellulose nanofibers at alkaline conditions. The biocompatible conductive hydrogel exhibited sensitivity (gauge factor of 2.16), self-healing (within 1 s), recognition, and adhesion, distinguishing it as an ideal candidate for wearable multifunctional sensors toward strain sensing, vocal sensing, signature detection, and Morse code transmission. Additionally, the multifunctional hydrogel manifested efficient electromagnetic interference shielding properties (reaching more than 30 dB at a thickness of 2.0 mm), protecting electronics and humans from electromagnetic radiation and pollution. Therefore, the presented work represents a versatile strategy for developing environmentally friendly conductive hydrogels, demonstrating the perspectives of intelligent hydrogels for multifunctional applications

Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

A weighted network model based on the correlation degree between nodes

Many complex networks in practice can be described by weighted network models, and the BBV model is one of the most classical ones. In this paper, by introducing the concept of correlation degree between nodes, a new weighted network model based on the BBV model is proposed. The model takes the both node strength and node correlation into consideration during the network evolution, which better reveals the evolving mechanisms behind various real-world networks. Results from theoretical analysis and numerical simulation have demonstrated the scale-free property and small-world property of the network model, which have been widely observed in many real-world networks. Compared with the BBV model, the added correlation preferential attachment rule in the model leads to a faster network propagation velocity.Location : Shenzhen, ChinaDate : 16-18 December 201

Research progress of Claudin-low breast cancer

Claudin-low breast cancer (CLBC) is a subgroup of breast cancer discovered at the molecular level in 2007. Claudin is one of the primary proteins that make up tight junctions, and it plays crucial roles in anti-inflammatory and antitumor responses as well as the maintenance of water and electrolyte balance. Decreased expression of claudin results in the disruption of tight junction structures and the activation of downstream signaling pathways, which can lead to tumor formation. The origin of Claudin-low breast cancer is still in dispute. Claudin-low breast cancer is characterized by low expression of Claudin3, 4, 7, E-cadherin, and HER2 and high expression of Vimentin, Snai 1/2, Twist 1/2, Zeb 1/2, and ALDH1, as well as stem cell characteristics. The clinical onset of claudin-low breast cancer is at menopause age, and its histological grade is higher. This subtype of breast cancer is more likely to spread to lymph nodes than other subtypes. Claudin-low breast cancer is frequently accompanied by increased invasiveness and a poor prognosis. According to a clinical retrospective analysis, claudin-low breast cancer can achieve low pathological complete remission. At present, although several therapeutic targets of claudin-low breast cancer have been identified, the effective treatment remains in basic research stages, and no animal studies or clinical trials have been designed. The origin, molecular biological characteristics, pathological characteristics, treatment, and prognosis of CLBC are extensively discussed in this article. This will contribute to a comprehensive understanding of CLBC and serve as the foundation for the individualization of breast cancer treatment

Correlation-induced symmetry-broken states in large-angle twisted bilayer graphene on MoS2

Strongly correlated states are commonly emerged in twisted bilayer graphene (TBG) with magic-angle, where the electron-electron (e-e) interaction U becomes prominent relative to the small bandwidth W of the nearly flat band. However, the stringent requirement of this magic angle makes the sample preparation and the further application facing great challenges. Here, using scanning tunneling microscopy (STM) and spectroscopy (STS), we demonstrate that the correlation-induced symmetry-broken states can also be achieved in a 3.45{\deg} TBG, via engineering this non-magic-angle TBG into regimes of U/W > 1. We enhance the e-e interaction through controlling the microscopic dielectric environment by using a MoS2 substrate. Simultaneously, the bandwidth of the low-energy van Hove singularity (VHS) peak is reduced by enhancing the interlayer coupling via STM tip modulation. When partially filled, the VHS peak exhibits a giant splitting into two states flanked the Fermi level and shows a symmetry-broken LDOS distribution with a stripy charge order, which confirms the existence of strong correlation effect in our 3.45{\deg} TBG. Our result paves the way for the study and application of the correlation physics in TBGs with a wider range of twist angle