Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.

MOTIVATION: Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS: In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY: The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

MOTIVATION: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION: https://github.com/XuLew/MIDTI.

Increased gene dosage of RFWD2 causes autistic-like behaviors and aberrant synaptic formation and function in mice.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interactions, communication deficits and repetitive behaviors. A study of autistic human subjects has identified RFWD2 as a susceptibility gene for autism, and autistic patients have 3 copies of the RFWD2 gene. The role of RFWD2 as an E3 ligase in neuronal functions, and its contribution to the pathophysiology of ASD, remain unknown. We generated RFWD2 knockin mice to model the human autistic condition of high gene dosage of RFWD2. We found that heterozygous knockin (Rfwd2+/-) male mice exhibited the core symptoms of autism. Rfwd2+/- male mice showed deficits in social interaction and communication, increased repetitive and anxiety-like behavior, and spatial memory deficits, whereas Rfwd2+/- female mice showed subtle deficits in social communication and spatial memory but were normal in anxiety-like, repetitive, and social behaviors. These autistic-like behaviors in males were accompanied by a reduction in dendritic spine density and abnormal synaptic function on layer II/III pyramidal neurons in the prelimbic area of the medial prefrontal cortex (mPFC), as well as decreased expression of synaptic proteins. Impaired social behaviors in Rfwd2+/- male mice were rescued by the expression of ETV5, one of the major substrates of RFWD2, in the mPFC. These findings indicate an important role of RFWD2 in the pathogenesis of autism.

Endothelial Autophagy Promotes Atheroprotective Communication Between Endothelial and Smooth Muscle Cells via Exosome-Mediated Delivery of miR-204-5p.

BACKGROUND: Cellular communication among different types of vascular cells is indispensable for maintaining vascular homeostasis and preventing atherosclerosis. However, the biological mechanism involved in cellular communication among these cells and whether this biological mechanism can be used to treat atherosclerosis remain unknown. We hypothesized that endothelial autophagy mediates the cellular communication in vascular tissue through exosome-mediated delivery of atherosclerosis-related genes. METHODS: Rapamycin and adeno-associated virus carrying Atg7 short hairpin RNA under the Tie (TEK receptor tyrosine kinase) promoter were used to activate and inhibit vascular endothelial autophagy in high-fat diet-fed ApoE-/- mice, respectively. miRNA microarray, in vivo and in vitro experiments, and human vascular tissue were used to explore the effects of endothelial autophagy on endothelial function and atherosclerosis and its molecular mechanisms. Quantitative polymerase chain reaction and miRNA sequencing were performed to determine changes in miRNA expression in exosomes. Immunofluorescence and exosome coculture experiments were conducted to examine the role of endothelial autophagy in regulating the communication between endothelial cells and smooth muscle cells (SMCs) via exosomal miRNA. RESULTS: Endothelial autophagy was inhibited in thoracic aortas of high-fat diet-fed ApoE-/- mice. Furthermore, rapamycin alleviated high-fat diet-induced atherosclerotic burden and endothelial dysfunction, while endothelial-specific Atg7 depletion aggravated the atherosclerotic burden. miRNA microarray, in vivo and in vitro experiments, and human vascular tissue analysis revealed that miR-204-5p was significantly increased in endothelial cells after high-fat diet exposure, which directly targeted Bcl2 to regulate endothelial cell apoptosis. Importantly, endothelial autophagy activation decreased excess miR-204-5p by loading miR-204-5p into multivesicular bodies and secreting it through exosomes. Moreover, exosomal miR-204-5p can effectively transport to SMCs, alleviating SMC calcification by regulating target proteins such as RUNX2 (runt-related transcription factor 2). CONCLUSIONS: Our study revealed the exosomal pathway by which endothelial autophagy protects atherosclerosis: endothelial autophagy activation transfers miR-204-5p from endothelial cells to SMCs via exosomes, both preventing endothelial apoptosis and alleviating SMC calcification. REGISTRATION: URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR2200064155.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. RESULTS: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. CONCLUSIONS: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.

Klebsiella pneumoniae carbapenemase variants: the new threat to global public health.

Klebsiella pneumoniae carbapenemase (KPC) variants, which refer to the substitution, insertion, or deletion of amino acid sequence compared to wild blaKPC type, have reduced utility of ceftazidime-avibactam (CZA), a pioneer antimicrobial agent in treating carbapenem-resistant Enterobacterales infections. So far, more than 150 blaKPC variants have been reported worldwide, and most of the new variants were discovered in the past 3 years, which calls for public alarm. The KPC variant protein enhances the affinity to ceftazidime and weakens the affinity to avibactam by changing the KPC structure, thereby mediating bacterial resistance to CZA. At present, there are still no guidelines or expert consensus to make recommendations for the diagnosis and treatment of infections caused by KPC variants. In addition, meropenem-vaborbactam, imipenem-relebactam, and other new ß-lactam-ß-lactamase inhibitor combinations have little discussion on KPC variants. This review aims to discuss the clinical characteristics, risk factors, epidemiological characteristics, antimicrobial susceptibility profiles, methods for detecting blaKPC variants, treatment options, and future perspectives of blaKPC variants worldwide to alert this new great public health threat.

A De Novo Strategy To Improve Pharmacokinetics of Proteins from mRNA Therapeutics via Zwitterionic Polypeptide Fusion.

Achieving therapeutic efficacy in protein replacement therapies requires sustaining pharmacokinetic (PK) profiles, while maintaining the bioactivity of circulating proteins. This is often achieved via PEGylation in protein-based therapies, but it remains challenging for proteins produced in vivo in mRNA-based therapies due to the lack of a suitable post-translational modification method. To address this issue, we integrated a genetically encoded zwitterionic polypeptide, EKP, into mRNA constructs to enhance the PK properties of product proteins. Composed of alternating glutamic acid (E), lysine (K), and proline (P), EKP exhibits unique superhydrophilic properties and low immunogenicity. Our results demonstrate that EKP fusion significantly extends the circulation half-life of proteins expressed from mRNA while preserving their bioactivity using human interferon alpha and Neoleukin-2/15 as examples. This EKP fusion technology offers a new approach to overcoming the current limitations in mRNA therapeutics and has the potential to significantly advance the development of mRNA-based protein replacement therapy.

Learning from Classic: DNA-Based Conditional Equilibrium Constant To Regulate Affinity "On-the-Fly" for Bioassays.

Artificial programming of affinity is beneficial to optimize responsiveness in biomolecules for various applications. In one classical theory, one comprehensive parameter, conditional equilibrium constant (K'EDTA), can accurately and quantitatively define the affinity of ethylene diamine tetraacetic acid (EDTA) for metal ions. Learning from the classic, we have proposed a novel DNA-based conditional equilibrium constant (K'DNA) to regulate DNA probes' affinity and response "on-the-fly", long after the probe design and synthesis. Artificial regulation of affinity over several magnitudes has been simply realized via short oligonucleotides with different lengths, concentrations, and combinations. The thermodynamic response can be quantitatively simulated by one DNA-based conditional equilibrium constant (K'DNA), acting as an analogue to the classical EDTA system. The proof of concept of affinity programming also allows improved discrimination of single-nucleotide variants as well as assaying ribonuclease and doxycycline in a homogeneous solution. Therefore, the theory of DNA-based conditional equilibrium constant (K'DNA) will enable to engineer versatile DNA switches with programmable affinity in assays and bionanotechnology.

Photocatalytic Aptamer Chemiluminescent System for a Homogeneous, Reliable, Label-Free, Generic Assay of Small Molecules.

Chemiluminescence is a powerful analytical technique with many advantages, while aptamers are well-known as good molecular recognition units. However, many aptamer-based chemiluminescence assays employed interface sensing, which often needed several immobilization, separation, and washing steps. To minimize the risks of contamination and false-positive, we for the first time proposed a photocatalytic aptamer chemiluminescent system for a homogeneous, label-free, generic assay of small molecules. After binding to a DNA aptamer, thioflavin T has a unique photocatalytic oxidase activity to activate the system's luminol chemiluminescence. Then, the specific binding between the aptamer and target molecules will compete with the above process. Therefore, we can realize the efficient assay of different analytes including estradiol and adenosine. Such a homogeneous chemiluminescent system allowed a direct assay of small molecules with limits of detection in a nM level. Several control tests were carried out to avoid possible false-positive results, which were originated from the interactions between analytes and sensing interfaces previously. This homogeneous chemiluminescent system provides a useful strategy to reliably assay various analytes in the pharmacy or biology field.

Postoperative radiotherapy with docetaxel versus cisplatin for high-risk oral squamous cell carcinoma: a randomized phase II trial with exploratory analysis of ITGB1 as a potential predictive biomarker.

BACKGROUND: Oral squamous cell carcinoma (OSCC) causes significant mortality and morbidity worldwide. Surgical resection with adjuvant radiotherapy remains the standard treatment for locally advanced resectable OSCC. Results from landmark trials have established postoperative concurrent cisplatin-radiotherapy (Cis-RT) as the standard treatment for OSCC patients with high-risk pathologic features. However, cisplatin-related toxicity limits usage in clinical practice. Given the need for effective but less toxic alternatives, we previously conducted a single-arm trial showing favorable safety profiles and promising efficacy of concurrent docetaxel-radiotherapy (Doc-RT). METHODS: In this randomized phase 2 trial, we aimed to compare Doc-RT with the standard Cis-RT in postoperative OSCC patients. Eligible patients had AJCC stage III-IV resectable OSCC with high-risk pathologic features. Two hundred twenty-four patients were enrolled and randomly assigned to receive concurrent Doc-RT or Cis-RT. The primary endpoint was 2-year disease-free survival (DFS). Secondary endpoints included overall survival (OS), locoregional-free survival (LRFS), distant metastasis-free survival (DMFS), and adverse events (AEs). Integrin ß1 (ITGB1) expression was analyzed as a biomarker for efficacy. RESULTS: After a median 28.8-month follow-up, 2-year DFS rates were 63.7% for Doc-RT arm and 56.1% for Cis-RT arm (p = 0.55). Meanwhile, Doc-RT demonstrated comparable efficacy to Cis-RT in OS, LRFS, and DMFS. Doc-RT resulted in fewer grade 3 or 4 hematological AEs. Low ITGB1 was associated with improved Doc-RT efficacy versus Cis-RT. CONCLUSIONS: This randomized trial directly compared Doc-RT with Cis-RT for high-risk postoperative OSCC patients, with comparable efficacy and less toxicity. ITGB1 merits further validation as a predictive biomarker to identify OSCC patients most likely to benefit from Doc-RT. Findings indicate docetaxel may be considered as a concurrent chemoradiation option in this setting. TRIAL REGISTRATION: www. CLINICALTRIALS: gov . NCT02923258 (date of registration: October 4, 2016).

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.

MOTIVATION: Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. RESULTS: In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. AVAILABILITY AND IMPLEMENTATION: https://github.com/pxystudy/MHADTI.

Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information.

MOTIVATION: In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging. RESULTS: To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA. AVAILABILITY AND IMPLEMENTATION: https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.

A novel three-plasmid packaging system for chimeric SFV/SIN VRPs derived from Semliki Forest virus and Sindbis virus as a candidate gene delivery vector.

Semliki Forest virus (SFV) viral replicon particles (VRPs) have been frequently used in various animal models and clinical trials. Chimeric replicon particles offer different advantages because of their unique biological properties. We here constructed a novel three-plasmid packaging system for chimeric SFV/SIN VRPs. The capsid and envelope of SIN structural proteins were generated using two-helper plasmids separately, and the SFV replicon contained the SFV replicase gene, packaging signal of SIN, subgenomic promoter followed by the exogenous gene, and 3' UTR of SIN. The chimeric VRPs carried luciferase or eGFP as reporter genes. The fluorescence and electron microscopy results revealed that chimeric VRPs were successfully packaged. The yield of the purified chimeric VRPs was approximately 2.5 times that of the SFV VRPs (1.38 × 107 TU/ml vs. 5.41 × 106 TU/ml) (p < 0.01). Furthermore, chimeric VRPs could be stored stably at 4°C for at least 60 days. Animal experiments revealed that mice immunized with chimeric VRPs (luciferase) had stronger luciferase expression than those immunized with equivalent amount of SFV VRPs (luciferase) (p < 0.01), and successfully expressed luciferase for approximately 12 days. Additionally, the chimeric VRPs expressed the RBD of SARS-CoV-2 efficiently and induced robust RBD-specific antibody responses in mice. In conclusion, the chimeric VRPs constructed here met the requirements of a gene delivery tool for vaccine development and cancer therapy.

Three-dimensional laser micromachining system with integrated sub-100†nm resolution in-situ measurement.

In this study, a three-dimensional (3D) laser micromachining system with an integrated sub-100 nm resolution in-situ measurement system was proposed. The system used the same femtosecond laser source for in-situ measurement and machining, avoiding errors between the measurement and the machining positions. It could measure the profile of surfaces with an inclination angle of less than 10°, and the measurement resolution was greater than 100 nm. Consequently, the precise and stable movement of the laser focus could be controlled, enabling highly stable 3D micromachining. The results showed that needed patterns could be machined on continuous surfaces using the proposed system. The proposed machining system is of great significance for broadening the application scenarios of laser machining.

Coherent polarization control of terahertz emission from graphene under excitation of two-color co-circularly polarized laser fields.

Coherent polarization control of terahertz (THz) emission is crucial for applications in the THz field. Here, we demonstrate that the polarization of THz waves emitted from graphene through quantum interference can be coherently controlled by varying the relative phase between the co-circularly polarized laser fields. The polarization state of the THz wave emitted from graphene remains linearly polarized, while its direction can be arbitrarily changed by varying the relative phase. This work not only achieves the coherent polarization control of the THz waves emitted from graphene but also promotes the fundamental research of THz photonics in graphene.

Frequency-domain terahertz optoacoustics for non-contact quantitative detection of gas, liquid, and solid samples.

Terahertz optoacoustics (THz-OA) combines the advantages of abundant molecular characteristic absorptions in a terahertz band and the low attenuation through ultrasonic detection. Frequency-domain THz-OA, benefiting from the compact and the low cost of a continuous-wave THz source, has been used in gas detection and sensing. However, liquid and solid detections are hard to achieve due to the sensitivity limitation of existing technologies. Here we present a high-sensitivity frequency-domain THz-OA system with customized optoacoustic cells to accomplish non-contact quantitative detection of gas, liquid, and solid samples. The relationships between signal amplitudes and sample concentration, volume and temperature are discussed separately, revealing a potential application of this technology.

Non-Abelian Holonomy in Degenerate Non-Hermitian Systems.

Non-Abelian holonomy, a noncommutative process that measures the parallel transport of non-Abelian gauge fields, has so far been realized in degenerate Hermitian systems with degenerate eigenstates or nondegenerate non-Hermitian systems with exceptional points. Here, we introduce non-Abelian holonomy into degenerate non-Hermitian systems possessing degenerate exceptional points and degenerate energy topologies. The interplay between energy degeneracy and energy topology around exceptional points leads to a non-Abelian holonomy with multiple energy levels and multiple degenerate levels simultaneously, going beyond that in degenerate Hermitian systems with a single energy level, or in nondegenerate non-Hermitian systems with a single degenerate level. We exploit an on-chip photonic platform to experimentally demonstrate the holonomy induced non-Abelian phenomenon, including the switching of eigenstates associated with different degenerate exceptional points and sequence-dependent holonomic outcomes. Our work shifts the paradigm of non-Abelian holonomy and adds new degrees of freedom for non-Abelian applications.

Persistent increase of accumbens cocaine ensemble excitability induced by IRK downregulation after withdrawal mediates the incubation of cocaine craving.

The incubation phenomenon, cue-induced drug craving progressively increasing over prolonged withdrawal, accounts for persistent relapse, leading to a dilemma in the treatment of cocaine addiction. The role of neuronal ensembles activated by initial cocaine experience in the incubation phenomenon was unclear. In this study, with cocaine self-administration (SA) models, we found that neuronal ensembles in the nucleus accumbens shell (NAcSh) showed increasing activation induced by cue-induced drug-seeking after 30-day withdrawal. Inhibition or activation of NAcSh cocaine-ensembles suppressed or promoted craving for cocaine, demonstrating a critical role of NAcSh cocaine-ensembles in incubation for cocaine craving. NAcSh cocaine-ensembles showed a specific increase of membrane excitability and a decrease of inward rectifying channels Kir2.1 currents after 30-day withdrawal. Overexpression of Kir2.1 in NAcSh cocaine-ensembles restored neuronal membrane excitability and suppressed cue-induced drug-seeking after 30-day withdrawal. Expression of dominant-negative Kir2.1 in NAcSh cocaine-ensembles enhanced neuronal membrane excitability and accelerated incubation of cocaine craving. Our results provide a cellular mechanism that the downregulation of Kir2.1 functions in NAcSh cocaine-ensembles induced by prolonged withdrawal mediates the enhancement of ensemble membrane excitability, leading to incubation of cocaine craving.