Spatially resolved transcriptomics (SRT) is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition. To fully understand the organizational complexity and tumor immune escape mechanism, we propose stMGATF, a multiview graph attention fusion model that integrates gene expression, histological images, spatial location, and gene association. To better extract information, stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view. A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation. Applied to diverse SRT datasets, stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms. In particular, stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains. Importantly, considering the associations between genes in tumors, stMGATF can identify the spatial dark genes ignored by traditional methods, which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms, providing theoretical evidence for the development of new immunotherapeutic strategies.

Epilepsy severely impairs the cognitive behavior of patients. It remains unclear whether epilepsy-induced cognitive impairment is associated with neuronal activities in the medial entorhinal cortex (MEC), a region known for its involvement in spatial cognition. To explore this neural mechanism, we recorded the spikes and local field potentials from MEC neurons in lithium–pilocarpine-induced epileptic rats using self-designed microelectrode arrays. Through the open field test, we identified spatial cells exhibiting spatially selective firing properties and assessed their spatial representations in relation to the progression of epilepsy. Meanwhile, we analyzed theta oscillations and theta modulation in both excitatory and inhibitory neurons. Furthermore, we used a novel object recognition test to evaluate changes in spatial cognitive ability of epileptic rats. After the epilepsy modeling, the spatial tuning of various types of spatial cells had suffered a rapid and pronounced damage during the latent period (1 to 5 d). Subsequently, the firing characteristics and theta oscillations were impaired. In the chronic period (>10 d), the performance in the novel object experiment deteriorated. In conclusion, our study demonstrates the detrimental effect on spatial representations and electrophysiological properties of MEC neurons in the epileptic latency, suggesting the potential use of these changes as a “functional biomarker” for predicting cognitive impairment caused by epilepsy.

Topological data analysis can extract effective information from higher-dimensional data. Its mathematical basis is persistent homology. The persistent homology can calculate topological features at different spatiotemporal scales of the dataset, that is, establishing the integrated taxonomic relation among points, lines, and simplices. Here, the simplicial network composed of all-order simplices in a simplicial complex is essential. Because the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values, a method based on the concept of critical simplices can be developed by searching all-order spanning trees. Employing this new method, not only the Morse function values with the theoretical minimum number of critical simplices can be obtained, but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated quickly. Finally, this method is used to analyze some examples and compared with other methods, showing its effectiveness and feasibility.

As extensively distributed tissues throughout the human body, glands play a critical role in various physiological processes. Therefore, the construction of biomimetic gland models in vitro has aroused great interest in multiple disciplines. In the biological field, the researchers focus on optimizing the cell sources and culture techniques to reconstruct the specific structures and functions of glands, such as the emergence of organoid technology. From the perspective of biomedical engineering, the generation of biomimetic gland models depends on the combination of engineered scaffolds and microfluidics, to mimic the in vivo environment of glandular tissues. These engineered stratagems endowed gland models with more biomimetic features, as well as a wide range of application prospects. In this review, we first describe the biomimetic strategies for constructing different in vitro gland models, focusing on the role of microfluidics in promoting the structure and function development of biomimetic glands. After summarizing several common in vitro models of endocrine and exocrine glands, the applications of gland models in disease modelling, drug screening, regenerative medicine, and personalized medicine are enumerated. Finally, we conclude the current challenges and our perspective of these biomimetic gland models.

Introducing and stabilizing oxygen vacancies in oxide catalysts is considered to be a promising strategy for improving catalytic activity and durability. Herein, we quantitatively create oxygen vacancies in the lattice of porous single-crystalline β-Ga₂O₃ monoliths by reduction treatments and stabilize them through the long-range ordering of crystal lattice to enhance catalytic activity and durability. The combination analysis of time-of-flight neutron powder diffraction and extended x-ray absorption fine structure discloses that the preferential generation of oxygen vacancy tends to occur at the site of tetrahedral coordination oxygen ions (O_III sites), which contributes to the formation of unsaturated Ga–O coordination in the monoclinic phase. The oxygen vacancies are randomly distributed in lattice even though some of them are present in the form of domain defect in the PSC Ga₂O₃ monoliths after the reduction treatment. The number of oxygen vacancies in the reduced monoliths gives 2.32 × 10¹³, 2.87 × 10¹³, and 3.45 × 10¹³ mg⁻¹ for the Ga₂O_2.952, Ga₂O_2.895, and Ga₂O_2.880, respectively. We therefore demonstrate the exceptionally high C₂H₄ selectivity of ~100% at the C₂H₆ conversion of ~37% for nonoxidative dehydrogenation of C₂H₆ to C₂H₄. We further demonstrate the excellent durability even at 620 °C for 240 h of continuous operation.

Classical-wave topological materials lacking intrinsic half-integer spins are less robust while more tunable. Here, we explore a single 3-dimensional phononic topological crystalline insulator that simultaneously exhibits a whole family of first-order quadratic surface, second-order hinge, and third-order corner states within the same bandgap. Such a topological crystalline insulator hosting all-order phases originates from the different topological nature when hierarchically projected onto different facets and lower dimensions, thus free from trivial cladding crystals. Our work offers an ideal platform for either robust wave propagation or localization in on-demand dimensions and may facilitate dimension division multiplexing technology.

The influence of a mechanical structure’s volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m³. This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs.

The identification of protein–protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

Achieving color-tunable emission in single-component organic emitters with multistage stimuli-responsiveness is of vital significance for intelligent optoelectronic applications, but remains enormously challenging. Herein, we present an unprecedented example of a color-tunable single-component smart organic emitter (DDOP) that simultaneously exhibits multistage stimuli-responsiveness and multimode emissions. DDOP based on a highly twisted amide-bridged donor–acceptor–donor structure has been found to facilitate intersystem crossing, form multimode emissions, and generate multiple emissive species with multistage stimuli-responsiveness. DDOP pristine crystalline powders exhibit abnormal excitation-dependent emissions from a monomer-dominated blue emission centered at 470 nm to a dimer-dominated yellow emission centered at 550 nm through decreasing the ultraviolet (UV) excitation wavelengths, whereas DDOP single crystals show a wide emission band with a main emission peak at 585 nm when excited at different wavelengths. The emission behaviors of pristine crystalline powders and single crystals are different, demonstrating emission features that are closely related to the aggregation states. The work has developed color-tunable single-component organic emitters with simultaneous multistage stimuli-responsiveness and multimode emissions, which is vital for expanding intelligent optoelectronic applications, including multilevel information encryption, multicolor emissive patterns, and visual monitoring of UV wavelengths.

Photodeformable polymer materials have a far influence in the fields of flexibility and intelligence. The stimulation energy is converted into mechanical energy through molecular synergy. Among kinds of photodeformable polymer materials, liquid crystalline polymer (LCP) photodeformable materials have been a hot topic in recent years. Chromophores such as azobenzene, α-cyanostilbene, and 9,10-dithiopheneanthracene have been widely used in LCP, which are helpful for designing functional molecules to increase the penetration depth of light to change physical properties. Due to the various applications of photodeformable polymer materials, there are many excellent reports in intelligent field. In this review, we have systematized LCP containing azobenzene into 3 categories depending on the degree of crosslinking liquid crystalline elastomers, liquid crystalline networks, and linear LCPs. Other structural, typical polymer materials and their applications are discussed. Current issues faced and future directions to be developed for photodeformable polymer materials are also summarized.