ASG-YOLOv5: Improved YOLOv5 unmanned aerial vehicle remote sensing aerial images scenario for small object detection based on attention and spatial gating.

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git.

Chloride Ions-Responsive Intelligent Coatings for the Active Protection of Degradable Biomedical Mg Alloys.

In this work, the hydroxyapatite (HA) microspheres are utilized as carriers for 8-hydroxyquinoline (8-HQ) inhibitors with a sodium alginate-silver nitrate layer (Ag-SA) added to confer chloride-responsive properties. These 8-HQ@Ag-SA-HA microspheres are subsequently integrated into poly(lactic acid) (PLA) coatings to produce biocompatible coatings. The resulting 8-HQ@Ag-SA-HA microsphere exhibits a spherical structure with a diameter of 3.16 µm. Thermogravimetric analysis indicates that the encapsulated 8-HQ inhibitors are approximately 11.83 wt %. Furthermore, the incorporation of these microspheres fills the micropores within the PLA coating, leading to a denser coating surface, enhanced wettability (contact angle value = 88°), and improved adhesion strength, thereby reinforcing the physical barrier effect. Corrosion tests reveal that the coatings exhibit increased resistance to corrosion in simulated body fluid (SBF) solutions. The released 8-HQ inhibitors in response to chloride ions form a protective layer of Mg(HQ)2, providing the coatings with self-healing properties and ensuring their durability in the SBF environment. Additionally, the cell test demonstrates a significant presence of MG-63 cells, accompanied by a low hemolysis rate of 3.81%, confirming the exceptional biocompatibility of the coatings. These findings offer valuable insights into the development of stimuli-responsive biocompatible coatings for effectively protecting Mg alloys.

AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion.

Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%.

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma.

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning.

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today's digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance-prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method's effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.

A review and analysis of key biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Research on the inhibitory properties and mechanism of carboxymethyl cellulose-modified sulfur quantum dots towards calcium sulfate and calcium carbonate.

An eco-friendly antimicrobial sulfur quantum dot scale inhibitor (CMC-SQDs) synthesized using carboxymethyl cellulose (CMC) showed strong inhibition of calcium sulfate (CaSO4) at a concentration just below 1 mg/L, with an inhibition efficiency exceeding 99 %. However, the precise interaction process between CMC-SQDs and CaSO4 remains unclear. This article investigates the effectiveness of SQDs in inhibiting the formation of CaSO4 and calcium carbonate (CaCO3) scales. Through static scale inhibition tests, molecular dynamics simulations, and quantum chemical calculations, the study aims to elucidate the different impacts of CMC-SQDs on CaSO4 and CaCO3 scale formation. The research focuses on understanding the relationship between the structural activity of CMC-SQDs and their scale-inhibiting performance and delving into the underlying mechanisms of scale inhibition. The findings describe the role of SQDs in a water-based solution, acting as persistent "nanodusts" that interact with calcium (Ca2+) ions and sulfate ions. CMC forms complexes with Ca2+ ions, and the presence of SQDs enhances the van der Waals force, indirectly increasing the resistance of associated ions and the binding energy on the surface of precipitated gypsum. Conversely, SQDs exhibit weak surface stability and have minimal binding energy when interacting with calcite, leading to limited occupation of available adsorption sites.

Cellulose nanocrystal-based polymer hydrogel embedded with iron oxide nanorods for efficient arsenic removal.

A cellulose nanocrystal (CNC) polymer hydrogel containing magnetic iron oxide nanorods (Fe3O4NRs) was prepared for As(III) removal in water. Systematic studies on the performance of these prepared CNC-based composite hydrogels for the removal of As(III) have been undertaken. The maximum adsorption capacity of the CNC-g-PAA/qP4VP (CPqP) hydrogel was 241.3 mg/g. After introduction of Fe3O4NRs in the hydrogel, the maximum adsorption capacity of the resulting Fe3O4NRs@CNC-g-PAA/qP4VP (FN@CPqP) hydrogel was further improved to 263.0 mg/g. The high adsorption performance can be attributed to the facts that the 3D interconnected porous network of the hydrogel allows As species to easily enter into the hydrogel, the quaternized P4VP chains provides more adsorption sites, Fe3O4NRs uniformly distributed in the internal cavity of the hydrogel significantly reduces the nanoparticle aggregation. The adsorption kinetics indicated that the adsorption of arsenic by the hydrogel was mainly chemisorption. The isotherm analysis revealed that the adsorption of arsenic by the hydrogel was principally monolayer adsorption on a homogeneous surface. Moreover, the as-prepared CNC-based polymer hydrogels exhibited good stability and reusability with negligible performance loss after five adsorption-desorption cycles. The novel FN@CPqP hydrogel demonstrates great potential as a cost-effective adsorbent for the removal of arsenic contaminants from wastewater.

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network.

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Self-powered biosensing platform for Highly sensitive detection of soluble CD44 protein.

In this study, a self-powered biosensor based on an enzymatic biofuel cell was proposed for the first time for the ultrasensitive detection of soluble CD44 protein. The as-prepared biosensor was composed of the co-exist aptamer and glucose oxidase bioanode and bilirubin oxidase modified biocathode. Initially, the electron transfer from bioanode to biocathode was hindered due to the presence of the aptamer with high insulation, generating a low open-circuit voltage (EOCV). Once the target CD44 protein was present, it was recognized and captured by the aptamer at the bioanode, thus the interaction between the target CD44 protein and the immobilized aptamer caused the structural change at the surface of the electrode, which facilitated the transfer of electrons. The EOCV showed a good linear relationship with the logarithm of the CD44 protein concentrations in the range of 0.5-1000 ng mL-1 and the detection limit was 0.052 ng mL-1 (S/N = 3). The sensing platform showed excellent anti-interference performance and outstanding stability that maintained over 97% of original EOCV after 15 days. In addition, the relative standard deviation (1.40-1.96%) and recovery (100.23-101.31%) obtained from detecting CD44 protein in real-life blood samples without special pre-treatment indicated that the constructed biosensor had great potential for early cancer diagnosis.

Ferrocenylselenoether and its cuprous cluster modified TiO2 as visible-light photocatalyst for the synergistic transformation of N-cyclic organics and Cr(vi).

In this study, fcSe@TiO2 and [Cu2I2(fcSe)2]n@TiO2 nanosystems based on ferrocenylselenoether and its cuprous cluster were developed and characterized by X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray spectroscopy (EDX), and electron paramagnetic resonance (EPR). Under optimized conditions, 0.2 g L-1 catalyst, 20 mM H2O2, and initial pH 7, good synergistic visible light photocatalytic tetracycline degradation and Cr(vi) reduction were achieved, with 92.1% of tetracycline and 64.5% of Cr(vi) removal efficiency within 30 minutes. Mechanistic studies revealed that the reactive species ˙OH, ˙O2-, and h+ were produced in both systems through the mutual promotion of Fenton reactions and photogenerated charge separation. The [Cu2I2(fcSe)2]n@TiO2 system additionally produced 1O2 from Cu+ and ˙O2-. The advantages of the developed nanosystems include an acidic surface microenvironment provided by Se⋯H+, resourceful product formation, tolerance of complex environments, and excellent adaptability in refractory N-cyclic organics.

Channel semantic mutual learning for visible-thermal person re-identification.

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval issue aiming to match the same pedestrian between visible and infrared cameras. Thus, the modality discrepancy presents a significant challenge for this task. Most methods employ different networks to extract features that are invariant between modalities. While we propose a novel channel semantic mutual learning network (CSMN), which attributes the difference in semantics between modalities to the difference at the channel level, it optimises the semantic consistency between channels from two perspectives: the local inter-channel semantics and the global inter-modal semantics. Meanwhile, we design a channel-level auto-guided double metric loss (CADM) to learn modality-invariant features and the sample distribution in a fine-grained manner. We conducted experiments on RegDB and SYSU-MM01, and the experimental results validate the superiority of CSMN. Especially on RegDB datasets, CSMN improves the current best performance by 3.43% and 0.5% on the Rank-1 score and mINP value, respectively. The code is available at https://github.com/013zyj/CSMN.

Nanogap engineering of 3D nanoraspberries into 2D plasmonic nanoclusters toward improved SERS performance.

3D raspberry-like core/satellite nanostructures were prepared by controlled surface functionalization of silica spheres using crosslinked poly(4-vinylpyridine) (P4VP) chains with known binding affinity for gold nanoparticles (AuNPs). The 3D SiO2-g-P(4VP-co-DVB)/AuNP nanoraspberries can be further transformed into 2D plasmonic nanoclusters by etching the silica core with hydrofluoric acid (HF). After the transformation, the interparticle distance between the AuNPs dramatically reduced from a 10 nm scale to sub 2 nm. Owing to the strong electromagnetic field generated by the plasmonic coupling between AuNPs in very close proximity, the established P(4VP-co-DVB)/AuNP nanoclusters provided strong and undisturbed Raman signals as a SERS substrate. In addition, benefiting from the stabilizing effect of the crosslinked P(4VP-co-DVB) network, the prepared SERS substrate has the advantages of good uniformity, stability and reproducibility, as well as strong SERS enhancement, endowing it with great potential for rapid and efficient SERS detection.

Whole genome sequencing and annotation of Scleroderma yunnanense, the only edible Scleroderma species.

Scleroderma yunnanense, an ectomycorrhizal fungus, is a popular edible mushroom within the Yunnan Province of Southwest China that holds great ecological and economic implications. However, despite its significance, there remains limited information about this species. Therefore, we sequenced S. yunnanense genome to identify the functional genes of S. yunnanense involved in secondary metabolite and carbohydrate production pathways. First, we present the 40.43 Mb high-quality reference genome for S. yunnanense, distributed across 35 contigs; moreover, the N50 contig size was found to reach 3.31 Mb and contained 8877 functional genes. Finally, genome annotation was conducted to compare the functional genes of S. yunnanense with protein sequences from different publicly available databases. Taken together, we identified 12 biosynthetic gene clusters across 10 contigs; among these were 13 key mevalonate (MVA) pathway enzymes, a key tyrosinase enzyme in the 3,4-dihydroxyphenylalanine (DOPA) pathway that is responsible for producing DOPA melanins, and 16 enzymes involved in uridine diphosphate glucose biosynthesis. Overall, this study presents the first genome assembly and annotation of S. yunnanense; ultimately, this information will be important in the elucidation of the biological activities and artificial domestication of this fungus.

AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network.

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.

Graphic Intelligent Diagnosis of Hypoxic-Ischemic Encephalopathy Using MRI-Based Deep Learning Model.

INTRODUCTION: Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HIE identification model (termed as DLCRN, deep learning clinical-radiomics nomogram) based on conventional structural MRI and clinical characteristics. METHODS: In this retrospective case-control study, full-term neonates with HIE and healthy controls were collected in two different medical centers from January 2015 to December 2020. Multivariable logistic regression analysis was implemented to establish the DLCRN model based on conventional MRI sequences and clinical characteristics. Discrimination, calibration, and clinical applicability were used to evaluate the model in the training and validation cohorts. Grad-class activation map algorithm was implemented to visualize the DLCRN. RESULTS: 186 HIE patients and 219 healthy controls were assigned to the training, internal validation, and independent validation cohorts. Birthweight was incorporated with deep radiomics signatures to create the final DLCRN model. The DLCRN model achieved better discriminatory power than simple radiomics models, with an area under the curve (AUC) of 0.868, 0.813, and 0.798 in the training, internal validation, and independent validation cohorts, respectively. The DLCRN model was well calibrated and has clinical potential. Visualization of the DLCRN highlighted the lesion areas that conformed to radiological identification. CONCLUSION: Visualized DLCRN may be a useful tool in the objective and quantitative identification of HIE. Scientific application of the optimized DLCRN model may save time for screening early mild HIE, improve the consistency of HIE diagnosis, and guide timely clinical management.

Insights into identifying resistance genes for cold and disease stresses through chromosome-level reference genome analyses of Poncirus polyandra.

Poncirus polyandra, a plant species with extremely small populations in China, has become extinct in the wild. This study aimed to identify functional genes that improve tolerance to abiotic and biotic stresses. Here, we present a high-quality chromosome-scale reference genome of P. polyandra. The reference genome is 315.78 Mb in size, with an N50 scaffold size of 32.07 Mb, and contains nine chromosomes with 20,815 protein-coding genes, covering 97.82% of the estimated gene space. We identified 17 rapidly evolving nucleotide-binding-site (NBS) genes, three C-repeat-binding factors (CBF) genes, 19 citrus greening disease (Huanglongbing, HLB) tolerance genes, 11 citrus tristeza virus (CTV) genes, and one citrus nematode resistance gene. A divergence time of 1.96 million years ago was estimated between P. polyandra and P. trifoliata. This is the first genome-scale assembly and annotation of P. polyandra, which will be useful for genetic, genomic, and molecular research and provide guidance for the development of conservation strategies.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Isoform-specific effects of neuronal repression of the AMPK catalytic subunit on cognitive function in aged mice.

AMP-activated protein kinase (AMPK) functions as a molecular sensor that plays a critical role in maintaining cellular energy homeostasis. Dysregulation of the AMPK signaling has been linked to synaptic failure and cognitive impairments. Our recent study demonstrates abnormally increased AMPK activity in the hippocampus of aged mice. The kinase catalytic subunit of AMPK exists in two isoforms α1 and α2, and their specific roles in aging-related cognitive deficits are unknown. Taking advantage of the unique transgenic mice (AMPKα1/α2 cKO) recently developed by our group, we investigated how isoform-specific suppression of the neuronal AMPKα may contribute to the regulation of cognitive and synaptic function associated with aging. We found that aging-related impairment of long-term object recognition memory was improved with suppression of AMPKα1 but not AMPKα2 isoform. Moreover, aging-related spatial memory deficits were unaltered with suppression of either AMPKα isoform. Biochemical experiments showed that the phosphorylation levels of the eukaryotic initiation factor 2 α subunit (eIF2α) were specifically decreased in the hippocampus of the AMPKα1 cKO mice. We further performed large-scale unbiased proteomics analysis and revealed identities of proteins whose expression is differentially regulated with AMPKα isoform suppression. These novel findings may provide insights into the roles of AMPK signaling pathway in cognitive aging.