A fast and genotype-independent in planta Agrobacterium-mediated transformation method for soybean.

Efficient genotype-independent transformation and genome editing is highly desirable for plant biotechnology research and product development efforts. We have developed a novel approach to enable fast, high-throughput and genotype-flexible Agrobacterium-mediated transformation using the important soybean crop as a test system. This new method is called GiFT (Genotype-independent Fast Transformation) and involves only a few simple steps. The method uses germinated seeds as explants and DNA delivery is achieved through Agrobacterium infection of wounded explants as in conventional in vitro-based method. Following infection, the wounded explants are incubated in liquid medium with sublethal level of selection and then directly transplanted to soil. The transplanted seedlings are then selected with herbicide spray for three weeks. The time required from initiation to fully established healthy T0 transgenic events is about 35 days. The GiFT method requires minimal in vitro manipulation or use of tissue culture media. Since the regeneration is in planta, the GiFT method is thus highly genotype flexible, which we have demonstrated via successful transformation of elite germplasms from diverse genetic backgrounds. We also show that the soybean GiFT method can be applied to both conventional binary vectors and CRISPR-Cas12a vectors for genome editing applications. T1 progeny analyses demonstrated that the events had a high inheritance rate and could be used for genome engineering applications. By minimizing the need for tissue culture, the described novel approach significantly improves operational efficiency while greatly reducing personnel and supply cost. It is the first industry-scale transformation method utilizing in planta selection in a major field crop.

Collagen-based hydrogels induce hyaline cartilage regeneration by immunomodulation and homeostasis maintenance.

Type I collagen (Col I) and hyaluronic acid (HA), derived from the extracellular matrix (ECM), have found widespread application in cartilage tissue engineering. Nevertheless, the potential of cell-free collagen-based scaffolds to induce in situ hyaline cartilage regeneration and the related mechanisms remain undisclosed. Here, we chose Col I and HA to construct Col I hydrogel and Col I-HA composite hydrogel with similar mechanical properties, denoted as Col and ColHA, respectively. Their potential to induce cartilage regeneration was investigated. The results revealed that collagen-based hydrogels could regenerate hyaline cartilage without any additional cells or growth factors. Notably, ColHA hydrogel stood out in this regard. It elicited a moderate activation, recruitment, and reprogramming of macrophages, thus efficiently mitigating local inflammation. Additionally, ColHA hydrogel enhanced stem cell recruitment, facilitated their chondrogenic differentiation, and inhibited chondrocyte fibrosis, hypertrophy, and catabolism, thereby preserving cartilage homeostasis. This study augments our comprehension of cartilage tissue induction theory by enriching immune-related mechanisms, offering innovative prospects for the design of cartilage defect repair scaffolds. STATEMENT OF SIGNIFICANCE: The limited self-regeneration ability and post-injury inflammation pose significant challenges to articular cartilage repair. Type I collagen (Col I) and hyaluronic acid (HA) are extensively used in cartilage tissue engineering. However, their specific roles in cartilage regeneration remain poorly understood. This study aimed to elucidate the functions of Col I and Col I-HA composite hydrogels (ColHA) in orchestrating inflammatory responses and promoting cartilage regeneration. ColHA effectively activated and recruited macrophages, reprogramming them from an M1 to an M2 phenotype, thus alleviating local inflammation. Additionally, ColHA facilitated stem cell homing, induced chondrogenesis, and concurrently inhibited fibrosis, hypertrophy, and catabolism, collectively contributing to the maintenance of cartilage homeostasis. These findings underscore the clinical potential of ColHA for repairing cartilage defects.

Rational design of a setaria-like NiTe2/MoS2 semi-coherent heterogeneous interface for enhancing diffusion kinetics in potassium-ion batteries.

Constructing unique heterostructures is a highly effective approach for enhancing the K+ storage capability of transition metal selenides. Such structures generate internal electric fields that significantly reduce the charge transfer activation energy. However, achieving a flawless interfacial region that maintains the optimal energy level gradient and degree of lattice matching remains a considerable challenge. In this study, we synthesised Setaria-like NiTe2/MoS2@C heterogeneous interfaces at which three-dimensional MoS2 nanosheets are evenly embedded in NiTe2 nanorods to form stabilised heterojunctions. The NiTe2/MoS2 heterojunctions display distinctive electronic configurations and several active sites owing to their low lattice misfits (δ = 13 %), strong electric fields, and uniform carbon shells. A NiTe2/MoS2@C anode in a potassium-ion battery (KIB) exhibited an impressive reversible capacity of 125.8 mAh/g after 1000 cycles at a rate of 500 mA g-1 and a stable reversible capacity of 111.7 mAh/g even after 3000 cycles at 1000 mA g-1. Even the NiTe2/MoS2@C//perylene tetracarboxylic dianhydride full battery configuration maintained a significant reversible capacity of 92.4 mAh/g after 100 cycles at 200 mA g-1, highlighting its considerable potential for application in KIBs. Calculations further revealed that the well-designed NiTe2/MoS2 heterojunction significantly promotes K+ ion diffusion.

Exploring the effects of the dietary fiber compound mediated by a longevity dietary pattern on antioxidation, characteristic bacterial genera, and metabolites based on fecal metabolomics.

BACKGROUND: Age-related dysbiosis of the microbiota has been linked to various negative health outcomes. This study aims to investigate the effects of a newly discovered dietary fiber compound (DFC) on aging, intestinal microbiota, and related metabolic processes. The DFC was identified through in vitro fermentation screening experiments, and its dosage and composition were determined based on a longevity dietary pattern. METHODS: Aged SPF C57BL/6 J mice (65 weeks old) and young mice (8 weeks old) were divided into three groups: a subgroup without dietary fiber (NDF), a low DFC dose subgroup (LDF, 10% DFC), and a high DFC dose subgroup (HDF, 20% DFC). The total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD) activity, malondialdehyde (MDA) content, and glutathione peroxidase (GSH-Px) activity in liver and serum samples of the mice were measured according to the manufacturer's protocol. The expression levels of characteristic bacterial genera and fecal metabolite concentrations in mice were determined using quantitative real-time PCR (qPCR) and nuclear magnetic resonance hydrogen spectroscopy (1H NMR). Metabolomics analysis was further conducted to identify biological functions and potential pathways related to aging. RESULTS: After an 8-weeks dietary intervention, DFC supplementation significantly attenuated age-related weight loss, organ degeneration, and oxidative stress. And promoted the growth of Lactobacillus and Bifidobacterium and inhibited the growth of Escherichia coli (E. coli) and Bacteroides (p < 0.05) in the intestinal tracts of aged mice. Metabolomic analysis identified glycolipid and amino acid metabolic pathway biomarkers associated with aging that were differentially regulated by DFC consumption. Correlation analysis between the identified microbial flora and the biomarkers revealed potential mechanistic links between altered microbial composition and metabolic activity with aging markers. CONCLUSIONS: In conclusion, this study revealed an important mechanism by which DFC consumption impacts healthspan and longevity, shedding light on optimizing dietary fiber or developing fiber-based interventions to improve human health.

Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.

EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals' compressed sensing.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

An pair of an atypical NLR encoding genes confer Asian soybean rust resistance in soybean.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating disease that is present in all major soybean-producing regions. The limited availability of resistant germplasm has resulted in a scarcity of commercial soybean cultivars that are resistant to the disease. To date, only the Chinese soybean landrace SX6907 has demonstrated an immune response to ASR. In this study, we present the isolation and characterization of Rpp6907-7 and Rpp6907-4, a gene pair that confer broad-spectrum resistance to ASR. Rpp6907-7 and Rpp6907-4 encode atypic nucleotide-binding leucine-rich repeat (NLR) proteins that are found to be required for NLR-mediated immunity. Genetic analysis shows that only Rpp6907-7 confers resistance, while Rpp6907-4 regulates Rpp6907-7 signaling activity by acting as a repressor in the absence of recognized effectors. Our work highlights the potential value of using Rpp6907 in developing resistant soybean cultivars.

Difunctional Ag nanoparticles with high lithiophilic and conductive decorate on core-shell SiO2 nanospheres for dendrite-free lithium metal anodes.

Lithium metal is an attractive and promising anode material due to its high energy density and low working potential. However, the uncontrolled growth of lithium dendrites during repeated plating and stripping processes hinders the practical application of lithium metal batteries, leading to low Coulombic efficiency, poor lifespan, and safety concerns. In this study, we synthesized highly lithiophilic and conductive Ag nanoparticles decorated on SiO2 nanospheres to construct an optimized lithium host for promoting uniform Li deposition. The Ag nanoparticles not only act as lithiophilic sites but also provide high electrical conductivity to the Ag@SiO2@Ag anode. Additionally, the SiO2 layer serves as a lithiophilic nucleation agent, ensuring homogeneous lithium deposition and suppressing the growth of lithium dendrites. Theoretical calculations further confirm that the combination of Ag nanoparticles and SiO2 effectively enhances the adsorption ability of Ag@SiO2@Ag with Li+ ions compared to pure Ag and SiO2 materials. As a result, the Ag@SiO2@Ag coating, with its balanced lithiophilicity and conductivity, demonstrates excellent electrochemical performance, including high Coulombic efficiency, low polarization voltage, and long cycle life. In a full lithium metal cell with LiFePO4 cathode, the Ag@SiO2@Ag anode exhibits a high capacity of 133.1 and 121.4 mAh/g after 200 cycles at rates of 0.5 and 1C, respectively. These results highlight the synergistic coupling of lithiophilicity and conductivity in the Ag@SiO2@Ag coating, providing valuable insights into the field of lithiophilic chemistry and its potential for achieving high-performance batteries in the next generation.

EEG Emotion Recognition by Fusion of Multi-Scale Features.

Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping process from low-dimensional feature matrices to high-dimensional ones in emotion recognition algorithms. In this paper, we propose a Multi-scale Deformable Convolutional Interacting Attention Network based on Residual Network (MDCNAResnet) for EEG-based emotion recognition. Firstly, we extract differential entropy features from different channels of EEG signals and construct a three-dimensional feature matrix based on the relative positions of electrode channels. Secondly, we utilize deformable convolution (DCN) to extract high-level abstract features by replacing standard convolution with deformable convolution, enhancing the modeling capability of the convolutional neural network for irregular targets. Then, we develop the Bottom-Up Feature Pyramid Network (BU-FPN) to extract multi-scale data features, enabling complementary information from different levels in the neural network, while optimizing the feature extraction process using Efficient Channel Attention (ECANet). Finally, we combine the MDCNAResnet with a Bidirectional Gated Recurrent Unit (BiGRU) to further capture the contextual semantic information of EEG signals. Experimental results on the DEAP dataset demonstrate the effectiveness of our approach, achieving accuracies of 98.63% and 98.89% for Valence and Arousal dimensions, respectively.

Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

Metal-ion transporter SLC39A8 is required for brain manganese uptake and accumulation.

Manganese (Mn) is an essential nutrient, but is toxic in excess. Whole-body Mn levels are regulated in part by the metal-ion influx transporter SLC39A8, which plays an essential role in the liver by reclaiming Mn from bile. Physiological roles of SLC39A8 in Mn homeostasis in other tissues, however, remain largely unknown. To screen for extrahepatic requirements for SLC39A8 in tissue Mn homeostasis, we crossed Slc39a8-inducible global-KO (Slc39a8 iKO) mice with Slc39a14 KO mice, which display markedly elevated blood and tissue Mn levels. Tissues were then analyzed by inductively coupled plasma-mass spectrometry to determine levels of Mn. Although Slc39a14 KO; Slc39a8 iKO mice exhibited systemic hypermanganesemia and increased Mn loading in the bone and kidney due to Slc39a14 deficiency, we show Mn loading was markedly decreased in the brains of these animals, suggesting a role for SLC39A8 in brain Mn accumulation. Levels of other divalent metals in the brain were unaffected, indicating a specific effect of SLC39A8 on Mn. In vivo radiotracer studies using 54Mn in Slc39a8 iKO mice revealed that SLC39A8 is required for Mn uptake by the brain, but not most other tissues. Furthermore, decreased 54Mn uptake in the brains of Slc39a8 iKO mice was associated with efficient inactivation of Slc39a8 in isolated brain microvessels but not in isolated choroid plexus, suggesting SLC39A8 mediates brain Mn uptake via the blood-brain barrier. These findings establish SLC39A8 as a candidate therapeutic target for mitigating Mn uptake and accumulation in the brain, the primary organ of Mn toxicity.

Major proliferation of transposable elements shaped the genome of the soybean rust pathogen Phakopsora pachyrhizi.

With >7000 species the order of rust fungi has a disproportionately large impact on agriculture, horticulture, forestry and foreign ecosystems. The infectious spores are typically dikaryotic, a feature unique to fungi in which two haploid nuclei reside in the same cell. A key example is Phakopsora pachyrhizi, the causal agent of Asian soybean rust disease, one of the world's most economically damaging agricultural diseases. Despite P. pachyrhizi's impact, the exceptional size and complexity of its genome prevented generation of an accurate genome assembly. Here, we sequence three independent P. pachyrhizi genomes and uncover a genome up to 1.25 Gb comprising two haplotypes with a transposable element (TE) content of ~93%. We study the incursion and dominant impact of these TEs on the genome and show how they have a key impact on various processes such as host range adaptation, stress responses and genetic plasticity.

Functional injectable hydrogel with spatiotemporal sequential release for recruitment of endogenous stem cells and in situ cartilage regeneration.

Articular cartilage is refractory to self-healing due to the absence of vascular, nervous, and lymphatic systems, and its repair remains a clinical challenge. Tissue regeneration through in situ recruitment of stem cells via cell-free scaffolds is a promising alternative strategy. Herein, a kind of functional injectable hydrogel system (Col-Apt@KGN MPs), which is a collagen-based and microsphere-embedded cell-free scaffold, was designed to achieve spatiotemporal regulation of endogenous mesenchymal stem cells (MSCs) recruitment and their chondrogenic differentiation by respective release of aptamer 19S (Apt19S) and kartogenin (KGN). In vitro results confirmed that the Col-Apt@KGN MPs hydrogel had sequential release characteristics. Apt19S was rapidly released from the hydrogel within 6 days, while KGN was slowly released for 33 days via the degradation of poly(lactic-co-glycolic acid) (PLGA) microspheres. When cultured with MSCs, the Col-Apt@KGN MPs hydrogel supported the adhesion, proliferation, and chondrogenic differentiation of MSCs. In vivo results indicated that the Col-Apt@KGN MPs hydrogel effectively promoted the recruitment of endogenous MSCs in a rabbit full-thickness cartilage defect model; furthermore, the Col-Apt@KGN MPs hydrogel enhanced the secretion of cartilage specific extracellular matrix and achieved the reconstruction of subchondral bone. This study demonstrates that the Col-Apt@KGN MPs hydrogel possesses great potential in recruitment of endogenous stem cells and cartilage tissue regeneration.

A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems.

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the "beam squint" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Hydrogen-rich water treatment increased several phytohormones and prolonged the shelf life in postharvest okras.

Hydrogen-rich water (HRW) treatment has been reported to delay the softening and senescence of postharvest okras, but its regulatory mechanism remains unclear. In this paper, we investigated the effects of HRW treatment on the metabolism of several phytohormones in postharvest okras, which act as regulatory molecules in fruit ripening and senescence processes. The results showed that HRW treatment delayed okra senescence and maintained fruit quality during storage. The treatment upregulated all of the melatonin biosynthetic genes such as AeTDC, AeSNAT, AeCOMT and AeT5H, contributing to the higher melatonin content in the treated okras. Meanwhile, increased transcripts of anabolic genes but lower expression of catabolic genes involved in indoleacetic acid (IAA) and gibberellin (GA) metabolism were observed in okras when treated with HRW, which was related to the enhanced levels of IAA and GA. However, the treated okras experienced lower abscisic acid (ABA) content as compared to the non-treated fruit due to the down-regulation of its biosynthetic genes and up-regulation of the degradative gene AeCYP707A. Additionally, there was no difference in γ-aminobutyric acid between the non-treated and HRW-treated okras. Collectively, our results indicated that HRW treatment increased levels of melatonin, GA and IAA, but decreased ABA content, which ultimately delayed fruit senescence and prolonged shelf life in postharvest okras.

A mouse model characterizes the roles of ZIP8 in systemic iron recycling and lung inflammation and infection.

ZIP8 (SLC39A8) is a transmembrane divalent metal ion importer that is most highly expressed in the lung and is inducible by inflammatory stimuli. In addition to zinc and manganese, ZIP8 can transport iron, but its specific roles in iron regulation during homeostatic and pathologic processes remain poorly understood. Using a novel global inducible ZIP8 knockout (KO) mouse, we analyzed the role of ZIP8 in steady-state iron homeostasis and during inflammation and infection. We observed an unexpected phenotype of elevated spleen iron levels and decreased serum iron in ZIP8 KO mice, suggesting that ZIP8 plays a role in iron recycling. We also showed that ZIP8 is expressed on lung distal airspace epithelial cells and transports iron from the airway into lung tissue. LPS-induced inflammation induced ZIP8 expression in the lung, but ZIP8 deletion had no detrimental effect on the severity of LPS-induced acute lung injury or on the outcomes of Klebsiella pneumoniae lung infection. Thus, ZIP8 plays a role in systemic iron homeostasis but does not modulate the severity of inflammatory lung injury or the host defense against a common bacterial cause of pneumonia.

γ-Aminobutyric acid treatment induced chilling tolerance in postharvest kiwifruit (Actinidia chinensis cv. Hongyang) via regulating ascorbic acid metabolism.

The effect of γ-Aminobutyric acid (GABA) treatment on ascorbic acid (AsA) metabolism and chilling injury in postharvest kiwifruit was studied. The results revealed that kiwifruit treated with GABA displayed higher chilling tolerance and better quality maintenance as compared to the controls. Higher AsA was observed in GABA-treated fruit which was beneficial to cell membrane protection and damage alleviation against chilling mediated oxidative stress. Gene expression analysis found the increased expression of AsA anabolic and regenerative genes and down-regulation of its catabolic genes together could contribute to the elevation of AsA levels in kiwifruit after GABA treatment. In addition, the transcripts of several candidate transcription factors such as bHLHs and HZ1 involved in AsA biosynthesis were also enhanced by GABA treatment. Collectively, our results indicated that GABA induced chilling tolerance in postharvest kiwifruit due to the higher AsA content by positively regulating ascorbate metabolic genes and candidate transcription factors.

Apelin-13 Improves Cognitive Impairment and Repairs Hippocampal Neuronal Damage by Activating PGC-1α/PPARγ Signaling.

Alzheimer's disease (AD) is a complex neurodegenerative disease that is prevalent around the world. Both Apelin-13 and proliferator-activated receptor-γ (PPARγ)/PPARγ co-activator 1α (PGC-1α) are regarded as candidate targets for treating AD. The investigation examined whether Apelin-13 exerts neuroprotective effects via PGC-1α/PPARγ signaling. In this study, Apelin-13 improved cognitive deficits in AD mice, while SR-18,292 (a PGC-1α inhibitor) interfered with the therapeutic effects of Apelin-13. Mechanistically, Apelin-13, PGC-1α and PPARγ were decreased in AD mice and oxygen-glucose deprivation (OGD)-induced neuronal cells. Apelin-13 bound to PGC-1α and negatively regulated the expression of PGC-1α and PPARγ. In turn, PGC-1α accelerated the accumulation of Apelin-13 and PPARγ. Additionally, neuronal apoptosis was inhibited, and the abundance of apoptosis-related proteins (Bax, Bcl-2, and cleaved caspase 3) was induced. The content of reactive oxygen species (ROS), malondialdehyde (MDA) and superoxide dismutase (SOD) fluctuated. The level of inflammatory factors (interleukin-6, IL-6, IL-10, tumor necrosis factor-α, TNF-α) was regulated. In short, Apelin-13 exerted anti-apoptosis, anti-oxidant stress and anti-inflammatory effects. Interestingly, PGC-1α silencing promoted neuronal apoptosis, oxidant stress and inflammation, and overexpression of PGC-1α exhibited the opposite. More importantly, inhibition of PGC-1α attenuated Apelin-13-enhanced cognitive impairment and neuronal damage. Therefore, our findings suggested that Apelin-13 exerted neuroprotective effects in part via the PGC-1α/PPARγ pathway.