Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by approximating the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.

UGT1A7 altered HER2-positive breast cancer response to trastuzumab by affecting epithelial-to-mesenchymal transition: A potential biomarker to identify patients resistant to trastuzumab treatment.

HER2-positive (HER2+) breast cancer accounts for 20-30% of all breast cancers. Although trastuzumab has significantly improved the survival of patients with HER2+ breast cancer, more than 70% of patients develop drug resistance within one year of treatment. Differential-gene-expression analysis of trastuzumab-sensitive and resistant HER2+ breast cancer cell lines from GSE15043 was performed to identify the biomarkers associated with trastuzumab resistance. Differential biomarker expression was confirmed in FFPE tissues collected from clinical HER2+ breast cancer tumor samples that were sensitive or resistant to trastuzumab treatment. UGT1A7, a member of the uronic acid transferase family, was associated with trastuzumab resistance. UGT1A7 expression was downregulated in trastuzumab-resistant tumor tissues and in a cell line that developed trastuzumab resistance (BT474TR). Overexpressing UGT1A7 in BT474TR restored their sensitivity to trastuzumab treatment, whereas downregulating UGT1A7 expression in parental cells led to trastuzumab resistance. Importantly, UGT1A7 localized to the endoplasmic reticulum and altered stress responses. Furthermore, downregulating UGT1A7 expression promoted epithelial-to-mesenchymal transition (EMT) by affecting TWIST, SNAIL, and GRP78 expression and the AMP-activated protein kinase signaling pathway, thus contributing to trastuzumab resistance. This study demonstrated the important role and novel mechanisms of UGT1A7 in tumor responses to trastuzumab. Low UGT1A7 expression plays an important role in EMT and contributes to trastuzumab resistance. UGT1A7 has the potential to be developed as a biomarker for identifying patients who are resistant to trastuzumab treatment.

Differentiated Anchor Quantity Assisted Incomplete Multiview Clustering Without Number-Tuning.

Incomplete multiview clustering (IMVC) generally requires the number of anchors to be the same in all views. Also, this number needs to be tuned with extra manual efforts. This not only degenerates the diversity of multiview data but also limits the model's scalability. For generating differentiated numbers of anchors without tuning, in this article we devise a novel framework named DAQINT. To be specific, the most perfect solution is to jointly find the optimal number of anchors that belongs to respective view. Regretfully, it is extremely time consuming. In view of this, we choose to first offer a set of anchor numbers for each view, and then integrate their contributions by adaptive weighting to approximate the optimal number. In particular, these offered numbers are all predefined and do not require any tuning. Through adaptively weighting them, we hold that this equivalently makes each view enjoy a different number of anchors. Accordingly, the bipartite graphs generated on all views are with diverse scales. Besides exploring multiview features more deeply, they also balance the importance between views. Then, to fuse these multiscale bipartite graphs, we design a combination strategy that owns linear computation and storage overheads. Afterward, to solve the resulting optimization problem, we also carefully develop a three-step iterative algorithm with linear complexities and demonstrated convergence. Experiments on the multiple public datasets validate the superiority of DAQINT against several advanced IMVC methods, such as on Mfeat, DAQINT surpasses the competitors like MKC, EEIMVC, FLSD, DSIMVC, IMVC-CBG, and DCP by 36.65%, 6.33%, 48.53%, 22.46%, 15.06%, and 32.04%, respectively, in ACC.

Systematic review and meta-analysis of current evidence in uterine artery embolization vs myomectomy for symptomatic uterine fibroids.

This review compares the efficacy of Uterine Artery Embolization (UAE) and Myomectomy (MYO) in managing symptomatic Uterine Fibroids (UFs) in women who do not want hysterectomy. A meta-analysis was performed on all available studies that evaluated the relative benefits and harms of MYO and UEA for the management of patients suffering from UFs. Outcomes evaluated reintervention, UFs scores for quality of life (QOL) and symptom severity, and other complications. To determine mean differences (MDs) or odds ratios (ORs) with 95% confidence intervals (CIs), a random or fixed-effects model was utilized. A meta-analysis of 13 studies (9 observational and 4 randomized controlled trials) was conducted. The results indicated that UAE had a higher reintervention rate (OR 1.84; 95% CI 1.62-2.10; P < 0.01; I2 = 39%), hysterectomy rate (OR 4.04; 95% CI 3.45-4.72; P < 0.01; I2 = 59%), and symptom-severity score (OR - 4.02; 95% CI 0.82, 7.22; P = 0.01; I2 = 0%) compared to MYO at a four-year follow-up. However, UAE was associated with a lower rate of early complications (OR 0.44; 95% CI 0.20-0.95; P = 0.04; I2 = 25%), and readmission rate (OR 1.16; 95% CI 1.01-1.33; P = 0.04; I2 = 0%) compared to MYO. Furthermore, both procedures had comparable improvement in pregnancy rates and abnormal uterine bleeding. In conclusion, UAE and MYO are effective in treating symptomatic UFs but they have different outcomes. The decision on which procedure to choose should be made based on individual preferences and the physician's expertise.

TFMKC: Tuning-Free Multiple Kernel Clustering Coupled With Diverse Partition Fusion.

Clustering is a popular research pipeline in unsupervised learning to find potential groupings. As a representative paradigm in multiple kernel clustering (MKC), late fusion-based models learn a consistent partition across multiple base kernels. Despite their promising performance, a common concern is the limited representation capacity caused by the inflexible fusion mechanism. Concretely, the representations are constrained by truncated- k Eigen-decomposition (EVD) without fully exploiting potential information. An intuitive idea to alleviate this concern is to generate a set of augmented partitions and then select the optimal partition by fine-tuning. However, this is overlimited by: 1) introducing undesired hyperparameters and dataset-related consequences; 2) neglecting rich information across diverse partitions; and 3) expensive parameter-tuning costs. To address these problems, we propose transforming the challenging problem of directly determining the optimal partition (optimal parameter) into a diverse partition fusion (parameter ensemble) problem. We design a novel flexible fusion mechanism called tuning-free multiple kernel clustering coupled with diverse partition fusion (TFMKC) by reweighting diverse partitions through optimization, achieving an optimal consensus partition by integrating diverse and complementary information rather than traditional fine-tuning, and distinguishing our work from existing methods. Extensive experiments verify that TFMKC achieves competitive effectiveness and efficiency over comparison baselines. The code can be accessed at https://github.com/ZJP/TFMKC.

Scalable and Structural Multi-view Graph Clustering with Adaptive Anchor Fusion.

Anchor graph has been recently proposed to accelerate multi-view graph clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs and combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a separable two-step procedure-anchor graph construction and individual graph fusion, which may degrade the clustering performance. (ii)These methods determine the number of selected anchors to be equal among all the views, which may destruct the data distribution diversity. A more flexible multi-view anchor graph fusion framework with diverse magnitudes is desired to enhance the representation ability. (iii)During the latter fusion process, current anchor graph fusion framework follows simple linearly-combined style while the intrinsic clustering structures are ignored. To address these issues, we propose a novel scalable and flexible anchor graph fusion framework for multi-view graph clustering method in this paper. Specially, the anchor graph construction and graph alignment are jointly optimized in our unified framework to boost clustering quality. Moreover, we present a novel structural alignment regularization to adaptively fuse multiple anchor graphs with different magnitudes. In addition, our proposed method inherits the linear complexity of existing anchor strategies respecting to the sample number, which is time-economical for large-scale data. Experiments conducted on various benchmark datasets demonstrate the superiority and effectiveness of the newly proposed anchor graph fusion framework against the existing state-of-the-arts over the clustering performance promotion and time expenditure. Our code is publicly available at https://github.com/wangsiwei2010/SMVAGC-SF.

Whole genome sequencing and genomic characteristics analysis of carbapenem-resistant Acinetobacter baumannii clinical isolates in two hospitals in China.

Nosocomial outbreaks caused by carbapenem-resistant Acinetobacter baumannii (CRAB) strains are rapidly emerging worldwide and are cause for concern. Herein, we aimed to describe the genomic characteristics of CRAB strains isolated from two hospitals in China in 2023. The A. baumannii isolates were mainly collected from the ICU and isolated from the sputum (71.43%, 15/21), followed by urine (14.29%, 3/21). Twenty-one A. baumannii strains possessed a multidrug-resistant (MDR) profile, and whole-genome sequencing showed that they all carried blaOXA-23. Based on the Pasteur multilocus sequence typing (MLST) scheme, all strains were typed into a sequence type 2 (ST2). Based on the Oxford MLST scheme, six strains belonged to ST540, three of which were ST208, and four strains were assigned to ST784. Kaptive showed most of the strains (38.10%, 8/21) contained KL93. As for the lipoolygosaccharide (OC locus) type, OCL1c and OCL1d were identified, accounting for 33.33% (7/21) and 66.67% (14/21), respectively. Based on the BacWGSTdb server, we found that the strains belonging to ST540 and ST784 were all collected from China. However, the ST938 strains were isolated from Malaysia and Thailand. Comparative genomics analysis showed that the AB10 strain had a closed relationship with SXAB10-SXAB13 strains, suggesting the transmission happened in these two hospitals and other hospital in China. In addition, the 4300STDY7045869 strain, which was collected from Thailand, possessed near genetic relationship with our isolates in this study, suggesting the possible spread among various countries. Additionally, 3-237 single nucleotide polymorphisms were observed among these strains. In conclusion, this study conducted a genome-based study for A. baumannii strains collected from two hospitals in China and revealed their epidemiological and molecular features. Clone spreading occurred in these two hospitals. Hence, there is an urgent need for increased surveillance in hospitals and other clinical settings to prevent and control CRAB spreading.

Mechanism of immune activation mediated by genomic instability and its implication in radiotherapy combined with immune checkpoint inhibitors.

Various genetic and epigenetic changes associated with genomic instability (GI), including DNA damage repair defects, chromosomal instability, and mitochondrial GI, contribute to development and progression of cancer. These alterations not only result in DNA leakage into the cytoplasm, either directly or through micronuclei, but also trigger downstream inflammatory signals, such as the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway. Apart from directly inducing DNA damage to eliminate cancer cells, radiotherapy (RT) exerts its antitumor effects through intracellular DNA damage sensing mechanisms, leading to the activation of downstream inflammatory signaling pathways. This not only enables local tumor control but also reshapes the immune microenvironment, triggering systemic immune responses. The combination of RT and immunotherapy has emerged as a promising approach to increase the probability of abscopal effects, where distant tumors respond to treatment due to the systemic immunomodulatory effects. This review emphasizes the importance of GI in cancer biology and elucidates the mechanisms by which RT induces GI remodeling of the immune microenvironment. By elucidating the mechanisms of GI and RT-induced immune responses, we aim to emphasize the crucial importance of this approach in modern oncology. Understanding the impact of GI on tumor biological behavior and therapeutic response, as well as the possibility of activating systemic anti-tumor immunity through RT, will pave the way for the development of new treatment strategies and improve prognosis for patients.

Treatment of infections caused by carbapenem-resistant Acinetobacter baumannii.

Patients with severe carbapenem-resistant Acinetobacter baumannii (CRAB) infections currently face significant treatment challenges. When patients display signs of infection and the clinical suspicion of CRAB infections is high, appropriate treatment should be immediately provided. However, current treatment plans and clinical data for CRAB are limited. Inherent and acquired resistance mechanisms, as well as host factors, significantly restrict options for empirical medication. Moreover, inappropriate drug coverage can have detrimental effects on patients. Most existing studies have limitations, such as a restricted sample size, and are predominantly observational or non-randomized, which report significant variability in patient infection severity and comorbidities. Therefore, a gold-standard therapy remains lacking. Current and future treatment options of infections due to CRAB were described in this review. The dose and considerable side effects restrict treatment options for polymyxins, and high doses of ampicillin-sulbactam or tigecycline appear to be the best option at the time of initial treatment. Moreover, new drugs such as durlobactam and cefiderocol have substantial therapeutic capabilities and may be effective salvage treatments. Bacteriophages and antimicrobial peptides may serve as alternative treatment options in the near future. The advantages of a combination antimicrobial regimen appear to predominate those of a single regimen. Despite its significant nephrotoxicity, colistin is considered a primary treatment and is often used in combination with antimicrobials, such as tigecycline, ampicillin-sulbactam, meropenem, or fosfomycin. The Infectious Diseases Society of America (IDSA) has deemed high-dose ampicillin-sulbactam, which is typically combined with high-dose tigecycline, polymyxin, and other antibacterial agents, the best option for treating serious CRAB infections. A rational combination of drug use and the exploration of new therapeutic drugs can alleviate or prevent the effects of CRAB infections, shorten hospital stays, and reduce patient mortality.

The Conservation Implications of the Gut Microbiome for Protecting the Critically Endangered Gray Snub-Nosed Monkey (Rhinopithecus brelichi).

The gut microbiota plays a crucial role in regulating energy metabolism, facilitating nutrient absorption, and supporting immune function, thereby assisting the host in adapting to seasonal dietary changes. Here, we compare the gut microbiome composition of wild gray snub-nosed monkeys during winter (from October to December) and spring (from January to March) to understand differences in seasonal nutrient intake patterns. Snub-nosed monkeys are foregut fermenters and consume difficult-to-digest carbohydrates and lichen. To examine the digestive adaptations of gray snub-nosed monkeys, we collected 14 fresh fecal samples for DNA analysis during the winter and spring. Based on 16S rRNA sequencing, metagenomic sequencing, and functional metagenomic analyses, we identified that Firmicutes, Actinobacteria, Verrucomicrobia, and Bacteroidetes constitute a keystone bacterial group in the gut microbiota during winter and spring and are responsible for degrading cellulose. Moreover, the transition in dietary composition from winter to spring was accompanied by changes in gut microbiota composition, demonstrating adaptive responses to varying food sources and availability. In winter, the bacterial species of the genera Streptococcus were found in higher abundance. At the functional level, these bacteria are involved in fructose and mannose metabolism and galactose metabolism c-related pathways, which facilitate the breakdown of glycogen, starch, and fiber found in fruits, seeds, and mature leaves. During spring, there was an increased abundance of bacteria species from the Prevotella and Lactobacillus genera, which aid the digestion of protein-rich buds. Combined, these findings reveal how the gut microbiota adjusts to fluctuations in energy balance and nutrient intake across different seasons in this critically endangered species. Moreover, we also identified Pseudomonas in two samples; the presence of potential pathogens within the gut could pose a risk to other troop members. Our findings highlight the necessity of a conservation plan that focuses on protecting vegetation and implementing measures to prevent disease transmission for this critically endangered species.

Synthesis of Fully Substituted Difluoromethylpyrazoles by Cyclization of Difluoroacetohydrazonoyl Bromides with 2-Acylacetonitriles or Malononitrile.

A concise and efficient method for the construction of fully substituted difluoromethylpyrazoles is achieved by a cyclization reaction between difluoroacetohydrazonoyl bromides and 2-acylacetonitrile or malononitrile. The method features advantages such as mild reaction conditions, broad substrate scope, good product yields, and high regioselectivity.

Accuracy of immediate anterior implantation using static and robotic computer-assisted implant surgery: A retrospective study.

OBJECTIVES: To investigate the accuracy of immediate anterior implantation using static computer-assisted implant surgery (s-CAIS) and robotic computer-assisted implant surgery (r-CAIS). MATERIALS AND METHODS: One hundred and six implants were immediately inserted in the anterior zone of 69 patients using a freehand technique, s-CAIS or r-CAIS. Postoperative cone-beam computed tomography scans were matched with preoperative plans to evaluate the deviations between the planned and placed implant positions. RESULTS: The global coronal deviations in the freehand, s-CAIS, and r-CAIS groups were 1.29 ± 0.52 mm, 1.01 ± 0.41 mm, and 0.62 ± 0.28 mm, respectively. Significant differences were observed in the r-CAIS group compared to both the s-CAIS group and the freehand group (p < 0.05). However, no significant differences were found between the s-CAIS group and the freehand group (p > 0.05). The global apical deviations in the freehand, s-CAIS and r-CAIS groups were 1.78 ± 0.59 mm, 1.24 ± 0.52 mm and 0.65 ± 0.27 mm, respectively, while the angular deviations in the freehand, s-CAIS and r-CAIS groups were 6.46 ± 2.21°, 2.94 ± 1.71° and 1.46 ± 0.57°, respectively. Significant differences were observed in both the global apical deviations and angular deviations among the three groups (p < 0.05). CONCLUSIONS: The accuracy of immediate anterior implantation with r-CAIS was better than that with s-CAIS. This difference is attributed to better control of the coronal, vertical and axial errors during r-CAIS. CLINICAL SIGNIFICANCE: This study provides significant evidence to support the use of r-CAIS as a potential alternative in immediate anterior implantation.

Insecticidal potential and risk assessment of diamide pesticides against Spodoptera frugiperda in maize crops.

The effectiveness, tolerance, and safety of pesticides must be established before their scientific or rational. This study evaluates the field control efficacy of broflanilide, tetraniliprole, and chlorantraniliprole in combating Spodoptera frugiperda in maize crops, as well as the resistance of S. frugiperda to these three diamide pesticides after exposure. By assessing field control efficiency, toxicity, effects on development and reproduction, and detoxification enzyme activity of these diamide pesticides on S. frugiperda, highlights broflanilide's significant insecticidal potential. A highly sensitive and efficient method using QuEChERS/HPLCMS/MS was developed to simultaneously detect residues of these three pesticides on maize. Initial concentrations of broflanilide, tetraniliprole, and chlorantraniliprole ranged from 2.13 to 4.02 mg/kg, with their respective half-lives varying between 1.23 and 1.51 days. Following foliar application, by the time of harvest, the terminal residue concentrations of these pesticides were all under 0.01 mg/kg. Chronic dietary intake risk assessments and cumulative chronic dietary exposure for three pesticides indicated that the general population's terminal residue concentration was within acceptable limits. Not only does this research provide valuable insights into field control efficiency, insecticidal effects, resistance, residues, and risk assessment results of broflanilide, tetraniliprole, and chlorantraniliprole on maize, but additionally, it also paves the way for setting suitable Maximum Residue Limits (MRLs) values based on pre-harvest interval values, rational dosage, and application frequency.

Comparative evaluating laser ionization and iKnife coupled with rapid evaporative ionization mass spectrometry and machine learning for geographical authentication of Larimichthys crocea.

Larimichthys crocea (LYC) holds significant economic value as a marine fish species. However, inaccuracies in labeling its origin can adversely affect consumer interests. Herein, a laser assisted rapid evaporative ionization mass spectrometry (LA-REIMS) and machine learning (ML) was developed for geographical authentication. When compared to iKnife, the LA demonstrated to be superior owing to reduced thermal damage to sample tissue, enhanced automation, and ease of use. Analysis of LYC from six distinct geographical origins across China revealed a total of 798 ions, which were then subjected to six classifiers to establish ML models. Following hyperparameter optimization and feature engineering, the Chi2(15%)-KNN model exhibited the highest training and testing accuracy, achieving 98.4 ± 0.9% and 98.5 ± 1.4%, respectively. This LA-REIMS/ML methodology offers a rapid, accurate, and intelligent solution for tracing the origin of LYC, thereby providing valuable technical support for the establishment of traceability systems in the aquatic product industry.

Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.

BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends. RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

Structural basis for recruitment of peptidoglycan endopeptidase MepS by lipoprotein NlpI.

Peptidoglycan (PG) sacculi surround the cytoplasmic membrane, maintaining cell integrity by withstanding internal turgor pressure. During cell growth, PG endopeptidases cleave the crosslinks of the fully closed sacculi, allowing for the incorporation of new glycan strands and expansion of the peptidoglycan mesh. Outer-membrane-anchored NlpI associates with hydrolases and synthases near PG synthesis complexes, facilitating spatially close PG hydrolysis. Here, we present the structure of adaptor NlpI in complex with the endopeptidase MepS, revealing atomic details of how NlpI recruits multiple MepS molecules and subsequently influences PG expansion. NlpI binding elicits a disorder-to-order transition in the intrinsically disordered N-terminal of MepS, concomitantly promoting the dimerization of monomeric MepS. This results in the alignment of two asymmetric MepS dimers respectively located on the two opposite sides of the dimerization interface of NlpI, thus enhancing MepS activity in PG hydrolysis. Notably, the protein level of MepS is primarily modulated by the tail-specific protease Prc, which is known to interact with NlpI. The structure of the Prc-NlpI-MepS complex demonstrates that NlpI brings together MepS and Prc, leading to the efficient MepS degradation by Prc. Collectively, our results provide structural insights into the NlpI-enabled avidity effect of cellular endopeptidases and NlpI-directed MepS degradation by Prc.

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal.

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According to the graph types, existing KGR models can be roughly divided into three categories, i.e., static models, temporal models, and multi-modal models. Early works in this domain mainly focus on static KGR, and recent works try to leverage the temporal and multi-modal information, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the models are reviewed based on bi-level taxonomy, i.e., top-level (graph types) and base-level (techniques and scenarios). Besides, the performances, as well as datasets, are summarized and presented. Moreover, we point out the challenges and potential opportunities to enlighten the readers. The corresponding open-source repository is shared on GitHub https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.

Adaptive Zone Learning for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) stands as a pivotal endeavor within the realm of computer vision, entailing the location of objects utilizing merely image-level labels. Contemporary approaches in WSOL have leveraged FPMs, yielding commendable outcomes. However, these existing FPM-based techniques are predominantly confined to rudimentary strategies of either augmenting the foreground or diminishing the background presence. We argue for the exploration and exploitation of the intricate interplay between the object's foreground and its background to achieve efficient object localization. In this manuscript, we introduce an innovative framework, termed adaptive zone learning (AZL), which operates on a coarse-to-fine basis to refine FPMs through a triad of adaptive zone mechanisms. First, an adversarial learning mechanism (ALM) is employed, orchestrating an interplay between the foreground and background regions. This mechanism accentuates coarse-grained object regions in a mutually adversarial manner. Subsequently, an oriented learning mechanism (OLM) is unveiled, which harnesses local insights from both foreground and background in a fine-grained manner. This mechanism is instrumental in delineating object regions with greater granularity, thereby generating better FPMs. Furthermore, we propose a reinforced learning mechanism (RLM) as the compensatory mechanism for adversarial design, by which the undesirable foreground maps are refined again. Extensive experiments on CUB-200-2011 and ILSVRC datasets demonstrate that AZL achieves significant and consistent performance improvements over other state-of-the-art WSOL methods.

Apigenin suppresses epithelial-mesenchymal transition in high glucose-induced retinal pigment epithelial cell by inhibiting CBP/p300-mediated histone acetylation.

Epithelial mesenchymal transition (EMT) is a critical process implicated in the pathogenesis of retinal fibrosis and the exacerbation of diabetic retinopathy (DR) within retinal pigment epithelium (RPE) cells. Apigenin (AP), a potential dietary supplement for managing diabetes and its associated complications, has demonstrated inhibitory effects on EMT in various diseases. However, the specific impact and underlying mechanisms of AP on EMT in RPE cells remain poorly understood. In this study, we have successfully validated the inhibitory effects of AP on high glucose-induced EMT in ARPE-19 cells and diabetic db/db mice. Notably, our findings have identified CBP/p300 as a potential therapeutic target for EMT in RPE cells and have further substantiated that AP effectively downregulates the expression of EMT-related genes by attenuating the activity of CBP/p300, consequently reducing histone acetylation alterations within the promoter region of these genes. Taken together, our results provide novel evidence supporting the inhibitory effect of AP on EMT in RPE cells, and highlight the potential of specifically targeting CBP/p300 as a strategy for inhibiting retinal fibrosis in the context of DR.

Laponite-activated AIE supramolecular assembly with modulating multicolor luminescence for logic digital encryption and perfluorinated pollutant detection.

Recently, the non-covalently activated supramolecular scaffold method has become a prominent research area in the field of intelligent materials. Here, the inorganic clay (LP) promoted the AIE properties of 4,4',4″,4‴-(ethene-1,1,2,2-tetrayltetrakis(benzene-4,1-diyl))tetrakis(1-ethylpyridin-1-ium) (P-TPE), showing an astonishing 42-fold enhancement of the emission intensity of the yellow-green luminescence and a 34-fold increase of the quantum yield via organic-inorganic supramolecular strategy as well as the efficient light-harvesting properties (energy transfer efficiency up to 33 %) after doping with the dye receptor Rhodamine B. Furthermore, the full-color spectral regulation, including white light, was achieved by adjusting the ratio of the donor to the acceptor component and co-assembling with the carbon dots (CD). Interestingly, this TPE-based non-covalently activated full-color supramolecular light-harvesting system (LHS) could be achieved not only in aqueous media but also in the hydrogel and the solid state. More importantly, this panchromatic tunable supramolecular LHS exhibited the multi-mode and quadruple digital logic encryption property as well as the specific detection ability towards the perfluorobutyric acid and the perfluorobutanesulfonic acid, which are harmful to human health in drinking water. This result develops a simple, convenient and effective approach for the intelligent anti-counterfeiting and the pollutant sensing.