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Given that fingerprint localization methods can be effectively modeled as supervised learning problems, machine learning has been employed for indoor localization tasks based on fingerprint methods. However, it is often challenging for popular machine learning models to effectively capture the unstructured data features inherent in fingerprint data that are generated in diverse propagation environments. In this paper, we propose an indoor localization algorithm based on a high-order graph neural network (HoGNNLoc) to enhance the accuracy of indoor localization and improve localization stability in dynamic environments. The algorithm first designs an adjacency matrix based on the spatial relative locations of access points (APs) to obtain a graph structure; on this basis, a high-order graph neural network is constructed to extract and aggregate the features; finally, the designed fully connected network is used to achieve the regression prediction of the location of the target to be located. The experimental results on our self-built dataset show that the proposed algorithm achieves localization accuracy within 1.29 m at 80% of the cumulative distribution function (CDF) points. The improvements are 59.2%, 51.3%, 36.1%, and 22.7% compared to the K-nearest neighbors (KNN), deep neural network (DNN), simple graph convolutional network (SGC), and graph attention network (GAT). Moreover, even with a 30% reduction in fingerprint data, the proposed algorithm exhibits stable localization performance. On a public dataset, our proposed localization algorithm can also show better performance.

Mutations in the p53 gene are frequently observed in various cancers, prompting the initiation of efforts to restore p53 function as a therapeutic approach several decades ago. Nevertheless, only a limited number of drug development initiatives have progressed to late-stage clinical trials, and to date, no p53-targeted therapies have received approval in the USA or Europe. This situation can be attributed primarily to the characteristics of p53 as a nuclear transcription factor, which lacks the conventional features associated with drug targets and has historically been considered “undruggable”. In recent years, however, several promising strategies have emerged, including the enhanced iterations of previous approaches and novel techniques aimed at targeting proteins that have traditionally been considered undruggable. There is a growing interest in small molecules that can restore the tumor-suppressive functions of mutant p53 proteins, and the development of drugs specifically designed for particular p53 mutation types is currently underway. Other approaches aim to deplete mutant p53 or exploit vulnerabilities associated with its expression. Additionally, genetic therapy strategy and approaches have rekindled interest. Advances in mutant p53 biology, compound mechanisms, treatment modalities, and nanotechnology have opened up new avenues for p53-based therapies. However, significant challenges remain in clinical development. This review reassesses the progress in targeting p53-mutant cancers, discusses the obstacles in translating these approaches into effective therapies, and highlights p53-based therapies via nanotechnology.

Background/Objectives: Eggshell quality is a critical factor influencing consumer preference and the economic benefits of poultry enterprises, and the uterus is the key site for eggshell synthesis. Yaoshan chicken (YS), an indigenous chicken breed in China, is renowned for its flavorful meat and high-quality eggs. However, its egg production is lower compared to specialized strains. Therefore, the GYR crossbreed was developed by three-line hybridization for YS chicken, which can produce green-shelled eggs with better eggshell thickness and strength than YS chicken (p < 0.01). To explore the molecular mechanisms underlying the differences in eggshell quality between GYR and YS chickens, we conducted an integrated transcriptomic and metabolomic analysis. Methods: Twelve uterus samples (six from GYR and six from YS chickens) were collected during the period of eggshell calcification at 260 days of age. RNA sequencing (RNA-seq) and liquid chromatography–mass spectrometry (LC-MS/MS) were performed to identify differentially expressed genes (DEGs) and differential metabolites (DMs), respectively. Results: A total of 877 DEGs were identified in the GYR group, including 196 upregulated and 681 downregulated genes (|log2 (fold change)| > 1, p-value < 0.05). Additionally, 79 DMs were detected, comprising 50 upregulated and 29 downregulated metabolites (|log₂ (fold change)| > 1, VIP > 1). Notably, the key DEGs (SLCO1B3, SLCO1B1, PTGR1, LGR6, MELTF, CRISP2, GVINP1, and OVSTL), important DMs (prostaglandin-related DMs and biliverdin) and signaling pathways (calcium signaling, neuroactive ligand–receptor interaction, arachidonic acid metabolism, bile secretion, and primary bile acid biosynthesis) were major regulators of the eggshell quality. Furthermore, an integrated transcriptomic and metabolomic analysis revealed two significant gene–metabolite pairs associated with eggshell quality: PTGDS–prostaglandin E2 and PTGS1–prostaglandin E2. Conclusions: This study provides a theoretical foundation for the improved eggshell quality of Yaoshan chicken.

To investigate the relationship between the rhizosphere microbial community structure and lotus root texture, the biological properties, and the rhizosphere microbial composition of mealy (ML) and crunchy lotus (CL) varieties were all analyzed using traditional and high-throughput sequencing technologies. The results showed that the ML varieties exhibited significantly lower moisture but higher starch contents than those of CL. Meanwhile, the rhizosphere fungal richness of ML was also significantly higher than that of CL. Moreover, the relative abundances of bacterial phyla and genera, such as Nitrospirota, Bacteroidota, Proteobacteria, and Bacillus, alongside fungal phyla and genera, i.e., Ascomycota and Emericellopsis, were enriched in rhizosphere of ML compared to CL. Functional prediction also revealed that elevated nitrogen cycling, polysaccharide degradation and cellulose breakdown functions could be detected in ML, potentially driving starch accumulation and cell wall modification. These results suggest that rhizosphere microbial composition, particularly nitrogen-cycling bacteria and lignocellulose-degrading fungi, may contribute to texture formation between texture-differentiated lotus root varieties.

This dissertation delves into three projects within the realms of image processing, computer vision, and machine/deep learning. The primary objective of the first project is the detection of heavy metal particle concentrations using microfluidic paper-based devices. The introduction to this project is separated into several sections: firstly, an in-depth discussion regarding the calibration process of the fabrication device (wax printer) is provided. Subsequently, the rationale behind the design of pipelines and algorithms for extracting regions of interest from the paper device, as well as the evaluation metric that pertains to the color-changing properties of the reactions occurring on the paper device, is expounded upon. Additionally, the integration of machine learning techniques for data analysis within the established pipeline is elucidated.The second project revolves around the analysis of crystals within microscopic images. A pipeline is proposed, emphasizing image processing techniques such as adaptive thresholding, morphological operations, and pixel-level manipulations to derive foreground images of crystals. Furthermore, a segmentation algorithm based on crystal structures is introduced to facilitate accurate separation of each crystal. The third project centers around zero-shot action recognition in video sequences, utilizing a multi-modality deep learning framework that is refined through prompt tuning to enhance its performance. The zero-shot capability is facilitated by vision-language pretraining of the underlying framework. To seamlessly bridge the disparity between image pretraining and video-based tasks, the framework undergoes fine-tuning on a large video dataset. Following this, the model is zero-shot evaluated on unseen video samples from different datasets to gauge its effectiveness and generalization ability.While seemingly disparate, these projects share a common thread of deep learning and computer vision, reflective of the evolving trends observed throughout the duration of my Ph.D. studies. The exploration of diverse domains is driven by both personal interest and the dynamic landscape of the computer vision field. Each project holds significance in shaping my Ph.D. journey and contributes to the broader discourse in the field.

Histopathological level imaging in a non-invasive manner is important for clinical diagnosis, which has been a tremendous challenge for current imaging modalities. Recent development of ultra-high-field (UHF) magnetic resonance imaging (MRI) represents a large step toward this goal. Nevertheless, there is a lack of proper contrast agents that can provide superior imaging sensitivity at UHF for disease detection, because conventional contrast agents generally induce T2 decaying effects that are too strong and thus limit the imaging performance. Herein, by rationally engineering the size, spin alignment, and magnetic moment of the nanoparticles, we develop an UHF MRI-tailored ultra-sensitive antiferromagnetic nanoparticle probe (AFNP), which possesses exceptionally small magnetisation to minimize T2 decaying effect. Under the applied magnetic field of 9 T with mice dedicated hardware, the nanoprobe exhibits the ultralow r 2 / r 1 value (~1.93), enabling the sensitive detection of microscopic primary tumours (<0.60 mm) and micrometastases (down to 0.20 mm) in mice. The sensitivity and accuracy of AFNP-enhanced UHF MRI are comparable to those of the histopathological examination, enabling the development of non-invasive visualization of previously undetectable biological entities critical to medical diagnosis and therapy. Ultra-high-field (UHF) magnetic resonance imaging (MRI) has potential for imaging disease including cancer metastasis. Here, the authors develop an ultra-sensitive antiferromagnetic nanoparticle probe with a small magnetisation for use in UHF MRI and demonstrate the ability to detect small primary tumours and micrometastases in mice.

The design of photocatalysts capable of generating localized surface plasmon resonance (LSPR) effects represents a promising strategy for enhancing photocatalytic activity. However, the mechanistic role of plasmonic nanoparticles-induced interfacial electric fields in driving photocatalytic processes remains poorly understood. To produce a Schottky junction, varying amounts of Au nanoparticles widely utilized to broaden the light absorption were loaded onto ultrathin carbon nitride sheets (Au/UCN). The Au/UCN-20 Schottky junction exhibits exceptional photocatalytic activity, achieving a hydrogen evolution rate (14.2 mmol·g−1 over a 4 h period) while maintaining robust stability through five consecutive photocatalytic cycles. The LSPR activity of Au nanoparticles are responsible for the broadened light absorption spectrum of Au/UCN nanocomposites. The interfacial electric field generated at the Au /UCN heterojunction is proposed to enhance charge-transfer efficiency through Schottky barrier penetration of photocarriers, mediated by electric field-driven carrier migration, according to surface potential and finite-difference time-domain (FDTD). These findings uncover a previously obscured photocatalytic mechanism driven by LSPR-induced interfacial electric fields, pioneering a quantum-dot-directed strategy to precisely engineer charge dynamics in advanced photocatalysts via targeted manipulation of nanoscale electric field effects.

Sulfide-based all-solid-state lithium metal batteries (ASSLMBs) have received extensive attention due to their high energy density and high safety, while the poor interface stability between sulfide electrolyte and lithium metal anode limits their development. Hence, a hybrid SEI (LICl/LiF/LiZn) was constructed at the interface between Li 5.5 PS 4.5 Cl 1.5 sulfide electrolyte and lithium metal. The LiCl and LiF interface phases with high interface energy effectively induce the uniform deposition of Li + and reduce the overpotential of Li + deposition, while the LiZn alloy interface phase accelerates the diffusion of lithium ions. The synergistic effect of the above functional interface phases inhibits the growth of lithium dendrites and stabilizes the interface between the sulfide electrolyte and lithium metal. The hybrid SEI strategy exhibits excellent electrochemical performance on symmetric batteries and all-solid-state batteries. The symmetrical cell exhibits stable cycling performance over long duration over 500 h at 1.0 mA cm −2 . Moreover, the LiNbO 3 @NCM712/Li 5.5 PS 4.5 Cl 1.5 /Li-10%ZnF 2 battery exhibits excellent cycle stability at a high rate of 0.5 C, with a capacity retention rate of 76.4% after 350 cycles.

Contrast agents can improve the sensitivity and resolution of magnetic resonance imaging (MRI) by accelerating the relaxation times of surrounding water protons. The MRI performances of contrast agents are closely related to their structural characteristics, including size, shape, surface modification, and so on. Recently, dynamically switchable MRI contrast agents that can undergo structural changes and imaging functional activations upon reaching the disease microenvironment have been developed for high performance MRI. This perspective highlights the ingenious design, controllable structural transformation, and tunable imaging property of dynamic MRI contrast agents. Additionally, the current challenges of the dynamic MRI contrast agents for medical diagnosis are discussed. Furthermore, the future integration of high‐resolution ultra‐high field MRI technology and cutting‐edge dynamic MRI contrast agents for non‐invasive histopathological level accurate detection of microscopic lesions are commented. This perspective highlights the recent development of dynamically switchable MRI contrast agents that can achieve structural transformation and controllable MR signal activation at the disease site in response to various pathological stimuli. Moreover, current challenges and future perspectives of dynamic contrast agents for the accurate detection of microscopic lesions are commented.

Surface segregation--the enrichment of one element at the surface, relative to the bulk--is ubiquitous to multi-component materials. Using the example of a Cu-Au solid solution, we demonstrate that compositional variations induced by surface segregation are accompanied by misfit strain and the formation of dislocations in the subsurface region via a surface diffusion and trapping process. The resulting chemically ordered surface regions acts as an effective barrier that inhibits subsequent dislocation annihilation at free surfaces. Using dynamic, atomic-scale resolution electron microscopy observations and theory modelling, we show that the dislocations are highly active, and we delineate the specific atomic-scale mechanisms associated with their nucleation, glide, climb, and annihilation at elevated temperatures. These observations provide mechanistic detail of how dislocations nucleate and migrate at heterointerfaces in dissimilar-material systems.