Topological Inverse Band Theory in Waveguide Quantum Electrodynamics.

Topological phases play a crucial role in the fundamental physics of light-matter interaction and emerging applications of quantum technologies. However, the topological band theory of waveguide QED systems is known to break down, because the energy bands become disconnected. Here, we introduce a concept of the inverse energy band and explore analytically topological scattering in a waveguide with an array of quantum emitters. We uncover a rich structure of topological phase transitions, symmetric scale-free localization, completely flat bands, and the corresponding dark Wannier states. Although bulk-edge correspondence is partially broken because of radiative decay, we prove analytically that the scale-free localized states are distributed in a single inverse energy band in the topological phase and in two inverse bands in the trivial phase. Surprisingly, the winding number of the scattering textures depends on both the topological phase of inverse subradiant band and the odevity of the cell number. Our Letter uncovers the field of the topological inverse bands, and it brings a novel vision to topological phases in light-matter interactions.

Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation.

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks. First, the artifact recognition network AR-Classifier first differentiates common artifacts from normal tissues, e.g., tissue folds, marking dye, tattoo pigment, spot, and out-of-focus, and also catalogs artifact patches by their restorability. Then, the succeeding artifact restoration network AR-CycleGAN performs de-artifact processing where stain styles and tissue structures can be maximally retained. We construct a benchmark for performance evaluation, curated from both clinically collected WSIs and public datasets of colorectal and breast cancer. The functional structures are compared with state-of-the-art methods, and also comprehensively evaluated by multiple metrics across multiple tasks, including artifact classification, artifact restoration, downstream diagnostic tasks of tumor classification and nuclei segmentation. The proposed system allows full automation of deep learning based histology image analysis without human intervention. Moreover, the structure-independent characteristic enables its processing with various artifact subtypes. The source code and data in this research are available at https://github.com/yunboer/AR-classifier-and-AR-CycleGAN.

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks.

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution's difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

Enantiomeric profiling of a chiral benzothiazole necroptosis inhibitor.

Necroptosis is a form of programmed cell death that contributes to the pathophysiology of multiple diseases. Development of small-molecule anti-necroptosis agents has great promising clinical therapeutic relevance. The benzothiazole compounds were discovered by our group from an in-house fluorine-containing compound library as potent necroptosis inhibitors. Herein, a chiral dimethylcyclopropyl benzothiazole necroptosis inhibitor was developed and the enantiomeric profiling resulted that the (S) form was generally more potent than the (R) counterpart in 2 ~ 4-fold toward cell necroptosis, receptor-interacting protein (RIP) kinases 1 and 3. The chiral compounds could significantly inhibit the expression of the phosphorylation of RIPK1, RIPK3 and MLKL in necroptotic cells. The molecular modelling studies predicted the binding modes of the enantiomers with RIP and explained their activity differences, guiding further rational design of the chiral necroptosis inhibitors.

Discovery of bardoxolone derivatives as novel orally active necroptosis inhibitors.

Necroptosis is a form of programmed cell death that contributes to the pathophysiology of cerebral ischemia/reperfusion (I/R) injury. In this study, bardoxolone (CDDO, 7) was an inhibitor of necroptosis identified from an in-house natural product library. Further optimization led to identify a more potent analogue 20. Compound 20 could effectively protect against necroptosis in human and mouse cells. The antinecroptotic effect could also be synergized with other necroptosis inhibitors. It blocked necrosome formation by targeting Hsp90 to inhibit the phosphorylation of RIPK1 and RIPK3 in necroptotic cells. In vivo, this compound was orally active to alleviate TNF-induced systemic inflammatory response syndrome (SIRS) and cerebral I/R injury. Our results suggested that 20 could be a lead compound for discovering necroptosis inhibitors in I/R treatment.

Direct inhibition of Keap1-Nrf2 Protein-Protein interaction as a potential therapeutic strategy for Alzheimer's disease.

The Kelch-like ECH-associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor 2 (Nrf2) pathway works as the key regulator against oxidative stress damage in many cells and organs. It has been a widely proposed therapeutic target for neurodegenerative diseases, including Alzheimer's disease (AD). This study aimed at determining the neuroprotective activity of 9 (NXPZ-2), a small-molecule compound that directly inhibits the Keap1-Nrf2 protein-protein interaction, in an amyloid beta 1-42 (Aß1-42) oligomer intracerebroventricularly (i.c.v.) injected mouse model. Behavioral tests showed that NXPZ-2 treatment dose-relatedly ameliorated learning and memory dysfunction in Aß1-42-treated mice. HE and Nissl staining showed that NXPZ-2 improved brain tissue pathological changes in AD mice by increasing neuron quantity and function. Western blot analysis of the hippocampus and cortex showed up-regulated Nrf2 in whole cell lysate, with increased nuclear translocation to increase Nrf2-targeted antioxidant enzymes (HO-1, NQO-1) and decreased p-Tau in NXPZ-2-treated mice. ELISA results showed that NXPZ-2 treatment increased serum Nrf2 and significantly decreased serum Aß1-42 levels in AD mice. Furthermore, hippocampal and cortical superoxide dismutase (SOD) and glutathione (GSH) levels increased, while malondialdehyde (MDA) levels decreased. No obvious toxicity was observed in primary cultured mouse cortical neurons and organs with NXPZ-2 treatment. No ameliorative effect was observed of NXPZ-2 in Nrf2 knockout AD mice. Collectively, our findings demonstrated that NXPZ-2 could be a promising therapeutic agent against AD, and provided the first set of experimental evidence, in a mouse model, to support Keap1-Nrf2 interaction as a validated target for the Nrf2 reactivation in AD.

Intracellular MicroRNA Imaging with MoS2-Supported Nonenzymatic Catassembly of DNA Hairpins.

Amplification strategies for low-level microRNA detection in living cells are pivotal for gene diagnosis and many cellular bioprocesses. In this work, we develop an amplification strategy for microRNA-21 (miRNA-21) imaging in living cells with MoS2-supported catassembly of DNA hairpins. The MoS2 nanosheet with low cytotoxicity serves as the nanocarrier and excellent fluorescence quencher, which can transfer fluorescent metastable hairpin DNA into the cells easily in a nondestructive manner and significantly reduce background signals. The three-branched catalyzed hairpin assembly (TB-CHA) probes contain three types of designed DNA molecular beacons with the modification of Cy3 in the terminal. In the presence of miRNA-21, the catalyzed hairpin assembly (CHA) reaction would be triggered and a "Y"-shaped three-branched duplex nanostructure would be formed, which would release from the surface of the MoS2 nanosheet due to the reduced affinity between the DNA duplex and MoS2 nanosheet. The multisite fluorescence modification and the circular reaction of TB-CHA probes allowed a significant fluorescence recovery in a live-cell microenvironment. The ultrasensitive detection of miRNA-21 is achieved with a detection limit of 75.6 aM, which is ∼5 orders of magnitude lower than that of a simple strand displacement-based strategy (detection limit: 8.5 pM). This method offers great opportunities for the ultrasensitive live-cell detection of miRNAs and helps in gaining a deeper understanding of the physiological functions of miRNAs in cancer research and life processes.

Group-based rewiring rules of binary opinion competition dynamics.

The dynamics of competing opinions on networks has attracted multi-disciplinary research. Most modelling approaches assume uniform or heterogeneous behaviour among all individuals, while the role of distinctive group behaviour is rarely addressed. Here, we consider competition occurring between two opinion groups with bound rewiring rules, i.e., opinion-preferred rewiring, degree-preferred rewiring and random rewiring. When two opinions share a balanced initial proportion, opinion-preferred rewiring is superior to the other rules under low rewiring rates, and coexistence occurs under high rewiring rates. For unbalanced proportions, the best response rule for the minority/majority is unfixed, and this depends on the initial proportion and rewiring frequency. Furthermore, we find evolution processes for all competing cases belong to two categories. Evolution Category I shows an obvious correlation between opinion proportions and the density of discordant edges (connecting nodes with different opinions), and these trends can be effectively described by numerical approximations. However, for Evolution Category II, no such correlation exists for individuals or linking pairs, and an analysis of local structures reveals the emergence of large numbers of open triads with the same opinions, denoting group prevalence. This work broadens the understanding of opinion competition and inspires exploring group strategies employed in social dynamic systems.

Poly-adenine-mediated fluorescent spherical nucleic acid probes for live-cell imaging of endogenous tumor-related mRNA.

Identification of tumor-related mRNA in living cells hold great promise for early cancer diagnosis and pathological research. Herein, we present poly-adenine (polyA)-mediated fluorescent spherical nucleic acid (FSNA) probes for intracellular mRNA detection with regulable sensitivities by programmably adjusting the loading density of DNA on gold nano-interface. Gold nanoparticles (AuNPs) functionalized with polyA-tailed recognition sequences were hybridized to fluorescent "reporter" strands to fabricate fluorescence-quenched FSNA probes. While exposed to target gene, the "reporter" strands were released from FSNA through strand displacement and fluorescence was recovered. With polyA20 tail as the attaching block, the detection limit of FSNA probes was calculated to be 0.31 nM, which is ~55 fold lower than that of thiolated probes without surface density regulation. Quantitative intracellular mRNA detection and imaging could be achieved with polyA-mediated FSNA probes within 2 hours, indicating their application potential in rapid and sensitive intracellular target imaging.

Label-Free Electrochemical Sensing Platform for MicroRNA-21 Detection Using Thionine and Gold Nanoparticles Co-Functionalized MoS2 Nanosheet.

Herein, we demonstrated a label-free and simple electrochemical sensing platform to detect microRNA-21 (miR-21) with high sensitivity by using MoS2 nanosheet functionalized with thionine and gold nanoparticles (MoS2-Thi-AuNPs). Interestingly, thionine (Thi) was used as a reducing agent to successfully synthesize MoS2-Thi-AuNPs nanohybrid and as a signaling molecule to monitor DNA-RNA hybridization, which provided an ideal platform for label-free miR-21 detection. Upon hybridization with miR-21, the formation of the DNA-RNA duplex on the electrode would greatly hinder the electron transfer, which caused the electrochemical signal decrease of Thi. After optimization of experimental conditions, the signal change of peak currents of Thi has a linear relationship with the logarithm of miR-21 concentration ranging from 1.0 pM to 10.0 nM and the limit of detection (LOD) was 0.26 pM. Moreover, this biosensor could detect miR-21 in biological samples like human serum with satisfactory results.

Coupling effect of nodes popularity and similarity on social network persistence.

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes' popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.