Showing papers by "Jie Zhang published in 2022"

The Evolution of Quantum Key Distribution Networks: On the Road to the Qinternet

Abstract: Quantum key distribution (QKD) constitutes a symmetric secret key negotiation protocol capable of maintaining information-theoretic security. Given the recent advances in QKD networks, they have evolved from academic research to some preliminary applications. A QKD network consists of two or more QKD nodes interconnected by optical fiber or free space links. The secret keys are negotiated between any pair of QKD nodes, and then they can be delivered to multiple users in various areas for ensuring long-term protection and forward secrecy. We commence by introducing the QKD basics, followed by reviewing the development of QKD networks and their implementation in practice. Subsequently, we describe the general QKD network architecture, its elements, as well as its interfaces and protocols. Next, we provide an in-depth overview of the associated physical layer and network layer solutions, followed by the standardization efforts as well as the application scenarios associated with QKD networks. Finally, we discuss the potential future research directions and provide design guidelines for QKD networks.

Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring

Abstract: Primary lung cancer is one of the most common malignant tumors in China. Approximately 60% of lung cancer patients have distant metastasis at the initial diagnosis, so it is necessary to find new tumor markers for early diagnosis and individualized treatment. Tumor markers contribute to the early diagnosis of lung cancer and play important roles in early detection and treatment, as well as in precision medicine, efficacy monitoring, and prognosis prediction. The pathological diagnosis of lung cancer in small biopsy specimens determines whether there are tumor cells in the biopsy and tumor type. Because biopsy is traumatic and the compliance of patients with multiple biopsies is poor, liquid biopsy has become a hot research direction. Liquid biopsies are advantageous because they are nontraumatic, easy to obtain, reflect the overall state of the tumor, and allow for real-time monitoring. At present, liquid biopsies mainly include circulating tumor cells, circulating tumor DNA, exosomes, microRNA, circulating RNA, tumor platelets, and tumor endothelial cells. This review introduces the research progress and clinical application prospect of liquid biopsy technology for lung cancer.

Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring

Abstract: Primary lung cancer is one of the most common malignant tumors in China. Approximately 60% of lung cancer patients have distant metastasis at the initial diagnosis, so it is necessary to find new tumor markers for early diagnosis and individualized treatment. Tumor markers contribute to the early diagnosis of lung cancer and play important roles in early detection and treatment, as well as in precision medicine, efficacy monitoring, and prognosis prediction. The pathological diagnosis of lung cancer in small biopsy specimens determines whether there are tumor cells in the biopsy and tumor type. Because biopsy is traumatic and the compliance of patients with multiple biopsies is poor, liquid biopsy has become a hot research direction. Liquid biopsies are advantageous because they are nontraumatic, easy to obtain, reflect the overall state of the tumor, and allow for real-time monitoring. At present, liquid biopsies mainly include circulating tumor cells, circulating tumor DNA, exosomes, microRNA, circulating RNA, tumor platelets, and tumor endothelial cells. This review introduces the research progress and clinical application prospect of liquid biopsy technology for lung cancer.

Enhanced Electrocatalytic CO2 Reduction to C2+ Products by Adjusting the Local Reaction Environment with Polymer Binders

Abstract: The activity and selectivity of the electrochemical CO2 reduction reaction (CO2RR) are often hindered by the limited access of CO2 to the catalyst surface and overtaken by the competing hydrogen evolution reaction. Herein, it is revealed that polymers used as catalyst binders can effectively modulate the accessibility of CO2 relative to H2O at the vicinity of the catalyst and thus the performance of CO2RR. Three polymers with different hydrophilicities (i.e., polyacrylic acid (PAA), Nafion, and fluorinated ethylene propylene (FEP)) are selected as binders for Cu catalysts. At a thickness of only ≈1.2 nm, these binders strongly affect the activity and selectivity toward multi‐carbon (C2+) products. The FEP coated catalyst exhibits a C2+ partial current density of over 600 mA cm−2 with ≈77% faradaic efficiency at −0.76 V versus RHE. This high performance is attributed to the hydrophobic (aerophilic) properties of FEP, which reduces the local concentration of H2O and enhances that of the reactant (i.e., CO2) and the reaction intermediates (i.e., CO). These findings suggest that tuning the hydrophobicity of electrocatalysts with polymer binders can be a promising way to regulate the performance of electrochemical reactions involving gas–solid–liquid interfaces.

YTHDF3 facilitates triple-negative breast cancer progression and metastasis by stabilizing ZEB1 mRNA in an m6A-dependent manner

Abstract: Background The YTH domain family protein 3 (YTHDF3) is an important N6-methyladenosine (m6A) reader which is involved in multiple cancers. However, the biological role and mechanisms of action for YTHDF3 in triple-negative breast cancer (TNBC) remains to be elucidated. Methods The expression of YTHDF3 in TNBC tissues was evaluated using The Cancer Genome Atlas (TCGA) database, BC-GenExMiner, and immunohistochemistry (IHC) staining. Cell migration, invasion, and epithelial-mesenchymal transition (EMT) were validated by wound healing assays, transwell assays, and Western blot (WB) analyses. The association between YTHDF3 and zinc finger E-box-binding homeobox 1 (ZEB1) was confirmed by Pearson correlation analysis. RNA-binding protein immunoprecipitation (RIP) assays and mRNA actinomycin stability analyses were applied to confirm whether YTHDF3 could interact with ZEB1in an m6A-dependent manner. Results The expression of YTHDF3 was correlated with poorer disease-free survival (DFS) and overall survival (OS) in TNBC patients. Functional experiments indicated that YTHDF3 positively regulated cell migration, invasion, and EMT in TNBC cells. Moreover, ZEB1 was identified as a key downstream target for YTHDF3 and YTHDF3 could enhance ZEB1 mRNA stability in an m6A-dependent manner. Inhibition of YTHDF3 reduced migration, invasion, and EMT, all of which were reversed by rescue experiments overexpressing ZEB1. Conclusions The findings herein confirmed that the YTHDF3/ZEB1 axis plays an important role in the progression and metastasis of TNBC. YTHDF3 is a promising prognosis biomarker and potential therapeutic target for patients with TNBC.

A n-butanol gas sensor with enhanced gas sensing performance based on Co‐doped BiVO4 polyhedrons

Abstract: N-butanol, a poisonous and flammable liquid, has great threat to human life and property security, which is of importance to monitor n-butanol in the environment. In this paper, the pure and Co-doped BiVO4 polyhedrons are successfully synthesized by hydrothermal method. A series of characterizations, such as XRD, XPS, SEM, UV, and EIS, etc., are used to analyze the crystal structure, morphology, element compositions, optical and electrochemical properties of the as-prepared samples. The optimal sample (3Co-BiVO4) exhibits high gas response (51.65) toward 100 ppm n-butanol, which is around four times higher than that of pure BiVO4 at 300 °C. In addition, the 3Co-BiVO4 also has fast response-recovery time, good humidity resistance, favorable selectivity to n-butanol, etc. Based on the above characterizations and gas sensing test results, the enhanced gas sensing mechanism for 3Co-BiVO4 is proposed. This work provides a feasible strategy to fabricate BiVO4 polyhedron by doping Co to greatly enhance the gas sensing properties for n-butanol.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Abstract: Simple Summary The histopathological image is widely considered as the gold standard for the diagnosis and prognosis of human cancers. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology analysis. The aim of our paper is to provide a comprehensive and up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis, including color normalization, nuclei/tissue segmentation, and cancer diagnosis and prognosis. The experimental results of the existing studies demonstrated that deep learning is a promising tool to assist clinicians in the clinical management of human cancers. Abstract With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

Layer-wised Model Aggregation for Personalized Federated Learning

Abstract: Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Researches over the past few years have applied the weighted aggregation manner to produce personalized models, where the weights are determined by calibrating the distance of the entire model parameters or loss values, and have yet to consider the layer-level impacts to the aggregation process, leading to lagged model convergence and inadequate personalization over non-IID datasets. In this paper, we propose a novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. Specifically, we employ a dedicated hyper-network per client on the server side, which is trained to identify the mutual contribution factors at layer granularity. Meanwhile, a parameterized mechanism is introduced to update the layer-wised aggregation weights to progressively exploit the inter-user similarity and realize accurate model personalization. Extensive experiments are conducted over different models and learning tasks, and we show that the proposed methods achieve significantly higher performance than state-of-the-art pFL methods.

Knowledge Mapping of Necroptosis From 2012 to 2021: A Bibliometric Analysis

Abstract: Background Necroptosis, a recently discovered programmed cell death, has been pathologically linked to various diseases and is thus a promising target for treating diseases. However, a comprehensive and objective report on the current status of entire necroptosis research is lacking. Therefore, this study aims to conduct a bibliometric analysis to quantify and identify the status quo and trending issues of necroptosis research in the last decade. Methods Articles were acquired from the Web of Science Core Collection database. We used two bibliometric tools (CiteSpace and VOSviewer) to quantify and identify the individual impact and cooperation information by analyzing annual publications, journals, co-cited journals, countries/regions, institutions, authors, and co-cited authors. Afterwards, we identified the trending research areas of necroptosis by analyzing the co-occurrence and burst of keywords and co-cited references. Results From 2012 to 2021, a total of 3,111 research articles on necroptosis were published in 786 academic journals by 19,687 authors in 885 institutions from 82 countries/regions. The majority of publications were from China and the United States, of which the United States maintained the dominant position in necroptosis research; meanwhile, the Chinese Academy of Sciences and Ghent University were the most active institutions. Peter Vandenabeele published the most papers, while Alexei Degterev had the most co-citations. Cell Death & Disease published the most papers on necroptosis, while Cell was the top 1 co-cited journal, and the major area of these publications was molecular, biology, and immunology. High-frequency keywords mainly included those that are molecularly related (MLKL, TNF-alpha, NF-κB, RIPK3, RIPK1), pathological process related (cell-death, apoptosis, necroptosis, necrosis, inflammation), and disease related (cancer, ischemia/reperfusion injury, infection, carcinoma, Alzheimer’s disease). Conclusion Necroptosis research had a stable stepwise growth in the past decade. Current necroptosis studies focused on its cross-talk with other types of cell death, potential applications in disease treatment, and further mechanisms. Among them, the synergy with ferroptosis, further RIPK1/RIPK3/MLKL studies, its association with inflammation and oxidative stress and translational applications, and the therapeutic potential to treat cancer and neurodegenerative diseases are the trending research area. These might provide ideas for further research in the necroptosis field.

Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

Abstract: This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technology they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the gathered insights (e.g., what is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?). We hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.

Towards Efficient Data Free Blackbox Adversarial Attack

Abstract: Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model. However, these substitute models need to be trained by target models' training data, which is hard to acquire due to privacy or transmission reasons. Recognizing the limited availability of real data for adversarial queries, recent works proposed to train substitute models in a data-free black-box scenario. However, their generative adversarial networks (GANs) based framework suffers from the convergence failure and the model collapse, resulting in low efficiency. In this paper, by rethinking the collaborative relationship between the generator and the substitute model, we design a novel black-box attack framework. The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate. The comprehensive experiments over six datasets demonstrate the effectiveness of our method against the state-of-the-art attacks. Especially, we conduct both label-only and probability-only attacks on the Microsoft Azure online model, and achieve a 100% attack success rate with only 0.46% query budget of the SOTA method [49].

Federated Learning with Label Distribution Skew via Logits Calibration

Abstract: Traditional federated optimization methods perform poorly with heterogeneous data (i.e. , accuracy reduction), especially for highly skewed data. In this paper, we investigate the label distribution skew in FL, where the distribution of labels varies across clients. First, we investigate the label distribution skew from a statistical view. We demonstrate both theoretically and empirically that previous methods based on softmax cross-entropy are not suitable, which can result in local models heavily overfitting to minority classes and missing classes. Additionally, we theoretically introduce a deviation bound to measure the deviation of the gradient after local update. At last, we propose FedLC ( Fed erated learning via L ogits C alibration), which calibrates the logits before softmax cross-entropy according to the probability of occurrence of each class. FedLC applies a fine-grained calibrated cross-entropy loss into local update by adding a pairwise label margin. Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance. Furthermore, integrating other FL methods into our approach can further enhance the performance of the global model.

Understanding the role of metal and N species in M@NC catalysts for electrochemical CO2 reduction reaction

Abstract: The electrochemical CO 2 reduction reaction (CO 2 RR) that transforms CO 2 to CO has attracted great interest. Transition metal nanoparticles encapsulated in nitrogen-doped carbon (M@NC) catalysts exhibit outstanding catalytic performance. However, the role of metal and N species in M@NC catalysts remains unclear. In this work, Co@C, Co@NC, Ni@C, and Ni@NC catalysts were achieved and employed in CO 2 RR. The Ni@NC catalyst exhibits an industry level current density of 220 mA cm −2 and a high Faradaic efficiency of 98% for CO production at − 0.87 V vs. RHE for 100 h. In addition, the N species, especially the pyrrolic-N in the shell of Ni@NC material provide active sites for adsorbing and activating CO 2 molecules, and metal nanoparticles improve the electronic structure of N species, thereby decreasing their ability for radical attack (*COOH, *CO, and *H). Consequently, this work can guide the design of M@NC catalyst for CO 2 RR to CO. • Different kinds of metal nanoparticles encapsulated within carbon shells are achieved. • The Ni@NC catalyst exhibits a large current density of 220 mA cm –2 and a high FE CO % of 98%. • The Ni@NC catalyst has excellent stability in CO 2 RR. • The pyrrolic-N in the shell of Ni@NC material provides active sites for CO 2 molecules. • The decrease of adsorption for radical attack (*COOH, *CO, and *H) improves the catalytic performance of CO 2 RR.

Imipridones affect tumor bioenergetics and promote cell lineage differentiation in diffuse midline gliomas.

Abstract: BACKGROUND Pediatric diffuse midline gliomas (DMGs) are incurable childhood cancers. The imipridone ONC201 has shown early clinical efficacy in a subset of DMGs. However, the anticancer mechanisms of ONC201 and its derivative ONC206 have not been fully described in DMGs. METHODS DMG models including primary human in vitro (n=18), and in vivo (murine and zebrafish) models, and patient (n=20) frozen and FFPE specimens were used. Drug-target engagement was evaluated using in silico ChemPLP and in vitro thermal shift assay. Drug toxicity and neurotoxicity were assessed in zebrafish models. Seahorse XF Cell Mito Stress Test, MitoSOX and TMRM assays, and electron microscopy imaging were used to assess metabolic signatures. Cell lineage differentiation and drug-altered pathways were defined using bulk and single cell RNA-seq. RESULTS ONC201 and ONC206 reduce viability of DMG cells in nM concentrations and extend survival of DMG PDX models (ONC201: 117 days, p=0.01; ONC206: 113 days, p=001). ONC206 is 10X more potent than ONC201 in vitro and combination treatment was the most efficacious at prolonging survival in vivo (125 days, p=0.02). Thermal shift assay confirmed that both drugs bind to ClpP, with ONC206 exhibiting a higher binding affinity as assessed by in silico ChemPLP. ClpP activation by both drugs results in impaired tumor cell metabolism, mitochondrial damage, ROS production, activation of integrative stress response and apoptosis in vitro and in vivo. Strikingly, imipridone treatment triggered a lineage shift from a proliferative, oligodendrocyte precursor-like state to a mature, astrocyte-like state. CONCLUSION Targeting mitochondrial metabolism and ISR activation effectively impairs DMG tumorigenicity. These results supported initiation of a phase 1 pediatric clinical trial (PNOC023, NCT04732065).

A Noise-Robust Self-Supervised Pre-Training Model Based Speech Representation Learning for Automatic Speech Recognition

Abstract: Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.0 has a good robustness against the domain shift, while the noise robustness is still unclear. In this work, we therefore first analyze the noise robustness of wav2vec2.0 via experiments. We observe that wav2vec2.0 pre-trained on noisy data can obtain good representations and thus improve the ASR performance on the noisy test set, which however brings a performance degradation on the clean test set. To avoid this issue, in this work we propose an enhanced wav2vec2.0 model. Specifically, the noisy speech and the corresponding clean version are fed into the same feature encoder, where the clean speech provides training targets for the model. Experimental results reveal that the proposed method can not only improve the ASR performance on the noisy test set which surpasses the original wav2vec2.0, but also ensure a tiny performance decrease on the clean test set. In addition, the effectiveness of the proposed method is demonstrated under different types of noise conditions.

Fairness Testing: A Comprehensive Survey and Analysis of Trends

Abstract: Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing.

Advanced Spatiotemporal Voltammetric Techniques for Kinetic Analysis and Active Site Determination in the Electrochemical Reduction of CO2.

Abstract: ConspectusElectrochemical reduction of the greenhouse gas CO2 offers prospects for the sustainable generation of fuels and industrially useful chemicals when powered by renewable electricity. However, this electrochemical process requires the use of highly stable, selective, and active catalysts. The development of such catalysts should be based on a detailed kinetic and mechanistic understanding of the electrochemical CO2 reduction reaction (eCO2RR), ideally through the resolution of active catalytic sites in both time (i.e., temporally) and space (i.e., spatially). In this Account, we highlight two advanced spatiotemporal voltammetric techniques for electrocatalytic studies and describe the considerable insights they provide on the eCO2RR. First, Fourier transformed large-amplitude alternating current voltammetry (FT ac voltammetry), as applied by the Monash Electrochemistry Group, enables the resolution of rapid underlying electron-transfer processes in complex reactions, free from competing processes, such as the background double-layer charging current, slow catalytic reactions, and solvent/electrolyte electrolysis, which often mask conventional voltammetric measurements of the eCO2RR. Crucially, FT ac voltammetry allows details of the catalytically active sites or the rate-determining step to be revealed under catalytic turnover conditions. This is well illustrated in investigations of the eCO2RR catalyzed by Bi where formate is the main product. Second, developments in scanning electrochemical cell microscopy (SECCM) by the Warwick Electrochemistry and Interfaces Group provide powerful methods for obtaining high-resolution activity maps and potentiodynamic movies of the heterogeneous surface of a catalyst. For example, by coupling SECCM data with colocated microscopy from electron backscatter diffraction (EBSD) or atomic force microscopy, it is possible to develop compelling correlations of (precatalyst) structure-activity at the nanoscale level. This correlative electrochemical multimicroscopy strategy allows the catalytically more active region of a catalyst, such as the edge plane of two-dimensional materials and the grain boundaries between facets in a polycrystalline metal, to be highlighted. The attributes of SECCM-EBSD are well-illustrated by detailed studies of the eCO2RR on polycrystalline gold, where carbon monoxide is the main product. Comparing SECCM maps and movies with EBSD images of the same region reveals unambiguously that the eCO2RR is enhanced at surface-terminating dislocations, which accumulate at grain boundaries and slip bands. Both FT ac voltammetry and SECCM techniques greatly enhance our understanding of the eCO2RR, significantly boosting the electrochemical toolbox and the information available for the development and testing of theoretical models and rational catalyst design. In the future, it may be possible to further enhance insights provided by both techniques through their integration with in situ and in operando spectroscopy and microscopy methods.

Improving Machine Translation Systems via Isotopic Replacement

Abstract: Machine translation plays an essential role in people's daily international communication. However, machine translation systems are far from perfect. To tackle this problem, researchers have proposed several approaches to testing machine translation. A promising trend among these approaches is to use word replacement, where only one word in the original sentence is replaced with another word to form a sentence pair. However, precise control of the impact of word replacement remains an outstanding issue in these approaches. To address this issue, we propose CAT, a novel word-replacement-based approach, whose basic idea is to identify word replacement with controlled impact (referred to as isotopic replacement). To achieve this purpose, we use a neural-based language model to encode the sentence context, and design a neural-network-based algorithm to evaluate context-aware semantic similarity between two words. Furthermore, similar to TransRepair, a state-of-the-art word-replacement-based approach, CAT also provides automatic fixing of revealed bugs without model retraining. Our evaluation on Google Translate and Transformer indicates that CAT achieves significant improvements over TransRepair. In particular, 1) CAT detects seven more types of bugs than TransRe-pair; 2) CAT detects 129% more translation bugs than TransRepair; 3) CAT repairs twice more bugs than TransRepair, many of which may bring serious consequences if left unfixed; and 4) CAT has better efficiency than TransRepair in input generation (0.01s v.s. 0.41s) and comparable efficiency with TransRepair in bug repair (1.92s v.s. 1.34s).

MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software

Abstract: Machine Learning (ML) software can lead to unfair and unethical decisions, making software fairness bugs an increasingly significant concern for software engineers. However, addressing fairness bugs often comes at the cost of introducing more ML performance (e.g., accuracy) bugs. In this paper, we propose MAAT, a novel ensemble approach to improving fairness-performance trade-off for ML software. Conventional ensemble methods combine different models with identical learning objectives. MAAT, instead, combines models optimized for different objectives: fairness and ML performance. We conduct an extensive evaluation of MAAT with 5 state-of-the-art methods, 9 software decision tasks, and 15 fairness-performance measurements. The results show that MAAT significantly outperforms the state-of-the-art. In particular, MAAT beats the trade-off baseline constructed by a recent benchmarking tool in 92.2% of the overall cases evaluated, 12.2 percentage points more than the best technique currently available. Moreover, the superiority of MAAT over the state-of-the-art holds on all the tasks and measurements that we study. We have made publicly available the code and data of this work to allow for future replication and extension.

Smart Polycationic Hydrogel Dressing for Dynamic Wound Healing.

Abstract: It is challenging for traditional wound dressings to adapt to the complex and changeable environment, due to the lack of stable, efficient, and continuous bactericidal activity. They also cannot be satisfied in a multifunctional sensing platform to reconstruct skin sensory functions for human health monitoring. A multifunctional hydrogel dressing is developed here for the treatment of infected wounds and human health monitoring, which is based on alginate and polycation. The in situ polymerization and solvent displacement method are used to functionalize the hydrogel for the improvement of antifreezing, water retention, and environmental adaptability, as well as the adhesion and photothermal property. As a wound dressing, the as-prepared hydrogel exhibits an excellent antibacterial property against both Escherichia coli and Staphylococcus aureus. In a rat model of full-thickness wound infection, it significantly accelerates the healing of infected wounds with a high healing rate of 96.49%. In the further multifunctional sensory tests, the hydrogel shows multiple response modes of strain, pressure and temperature, and sensing stability. An idea is provided here to develop a smart hydrogel dressing that can accelerate wound healing and achieve human health monitoring.

Light-induced dimension crossover dictated by excitonic correlations

Abstract: In low-dimensional systems with strong electronic correlations, the application of an ultrashort laser pulse often yields novel phases that are otherwise inaccessible. The central challenge in understanding such phenomena is to determine how dimensionality and many-body correlations together govern the pathway of a non-adiabatic transition. To this end, we examine a layered compound, 1T-TiSe2, whose three-dimensional charge-density-wave (3D CDW) state also features exciton condensation due to strong electron-hole interactions. We find that photoexcitation suppresses the equilibrium 3D CDW while creating a nonequilibrium 2D CDW. Remarkably, the dimension reduction does not occur unless bound electron-hole pairs are broken. This relation suggests that excitonic correlations maintain the out-of-plane CDW coherence, settling a long-standing debate over their role in the CDW transition. Our findings demonstrate how optical manipulation of electronic interaction enables one to control the dimensionality of a broken-symmetry order, paving the way for realizing other emergent states in strongly correlated systems.