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Ovarian ageing is a key factor in the decline of female fertility. It is primarily characterised by diminished oocyte quality, follicular depletion, and dysregulated hormone levels. In recent years, mitochondria-driven inflammation has emerged as a potential mechanism in ovarian ageing. Mitochondrial dysfunction results in the accumulation of reactive oxygen species (ROS) and the release of mitochondrial DNA (mtDNA), as well as the leakage of mitochondrial components and metabolites into the cytosol or extracellular space. These elements act as damage-associated molecular patterns (DAMPs), activating inflammasomes like NLRP3, thereby initiating and amplifying innate immune responses and contributing to sustained inflammation. Furthermore, an imbalance in mitochondrial quality control mechanisms can worsen the spread and persistence of inflammatory responses. In this study, we present a comprehensive overview of the signalling origins, molecular mechanisms of amplification, and key regulatory nodes involved in mitochondria-driven inflammation during ovarian ageing. Finally, we summarise potential therapeutic strategies targeting mitochondria-driven inflammation, offering novel perspectives and targets for delaying ovarian ageing and enhancing female reproductive health.

Wheat ( Triticum aestivum L.) is a major staple food for more than one-third of the world’s population. Tiller number is an important agronomic trait in wheat, but only few related genes have been cloned. Here, we isolate a wheat mutant, tiller number1 ( tn1 ), with much fewer tillers. We clone the TN1 gene via map-based cloning: TN1 encodes an ankyrin repeat protein with a transmembrane domain (ANK-TM). We show that a single amino acid substitution in the third conserved ankyrin repeat domain causes the decreased tiller number of tn1 mutant plants. Resequencing and haplotype analysis indicate that TN1 is conserved in wheat landraces and modern cultivars. Further, we reveal that the expression level of the abscisic acid (ABA) biosynthetic gene TaNCED3 and ABA content are significantly increased in the shoot base and tiller bud of the tn1 mutants; TN1 but not tn1 could inhibit the binding of TaPYL to TaPP2C via direct interaction with TaPYL. Taken together, we clone a key wheat tiller number regulatory gene TN1 , which promotes tiller bud outgrowth probably through inhibiting ABA biosynthesis and signaling. Tiller number is an important agronomic trait of wheat. Here, the authors clone a positive regulator of wheat tiller number and show that the encoded ankyrin repeat protein can promote tiller bud outgrowth through inhibiting ABA biosynthesis and signaling.

Excitatory projection neurons and inhibitory interneurons primarily accomplish the neural activity of the cerebral cortex, and an imbalance of excitatory-inhibitory neural networks may lead to neuropsychiatric diseases. Gamma-aminobutyric acid (GABA)ergic interneurons mediate inhibition, and the embryonic medial ganglionic eminence (MGE) is a source of GABAergic interneurons. After transplantation, MGE cells migrate to different brain regions, differentiate into multiple subtypes of GABAergic interneurons, integrate into host neural circuits, enhance synaptic inhibition, and have tremendous application value in diseases associated with interneuron disorders. In the current review, we describe the fate of MGE cells derived into specific interneurons and the related diseases caused by interneuron loss or dysfunction and explore the potential of MGE cell transplantation as a cell-based therapy for a variety of interneuron disorder-related diseases, such as epilepsy, schizophrenia, autism spectrum disorder, and Alzheimer's disease.

Different athletes have different requirements and coping methods for concentration. Research based on visual attention models can help identify individual differences and propose targeted improvement strategies to improve the concentration level of each athlete, thereby enhancing their performance in competition. Currently, visual attention models are difficult to help athletes extract important information. This article conducted a questionnaire survey on the concentration level of athletes and studied a visual attention model. Taking fencing as an example, this article studied the commonly used evaluation indicators for athletes’ concentration level. In addition to objective measurement indicators, it also conducted an attention questionnaire survey in a sports college to collect relevant data on fencers. It applied a visual attention model for data analysis to explore the characteristics and changes in the concentration level of fencers. This article took the average number of matches played by athletes within one year as the direction. It is found that the winning rate of male athletes in the control group was 79.6%, and that of female athletes was 89.1%. The winning rate of male athletes in the intervention group was 90.1%, and that of female athletes was 97.7%. After the effectiveness of this model was confirmed, the attention level of athletes was actually improved. The athlete attention level analysis method based on visual attention model, due to its high accuracy, can help people find different degrees of differences between people, thus providing a basis for formulating targeted training plans.

Few-shot object detection (FSOD) in remote sensing images (RSIs) faces challenges such as data scarcity, difficulty in detecting small objects, and underutilization of frequency-domain information. Existing methods often rely on spatial-domain features, neglecting the complementary insights from low- and high-frequency characteristics. Additionally, their performance in detecting small objects is hindered by inadequate feature extraction in cluttered backgrounds. To tackle these problems, we propose a novel detection framework of Spatial-Frequency Interaction and Distribution Matching (SFIDM), which significantly enhances FSOD performance in RSIs. SFIDM focuses on rapid adaptation to target datasets and efficient fine-tuning with limited data. First, to improve feature representation, we introduce the Spatial-Frequency Interaction (SFI) module, which leverages the complementarity between low-frequency and high-frequency information. By decomposing input images into their frequency components, the SFI module extracts features critical for classification and precise localization, enabling the framework to capture fine details essential for detecting small objects. Secondly, to resolve the limitations of traditional label assignment strategies when dealing with small bounding boxes, we construct the Distribution Matching (DM) module, which models bounding boxes as 2D Gaussian distributions. This allows for the accurate detection of subtle offsets and overlapping or non-overlapping small objects. Moreover, to leverage the learned base-class information for improved performance on novel class detection, we employ a feature reweighting module, which adaptively fuses features extracted from the backbone network to generate representations better suited for downstream detection tasks. We conducted extensive experiments on two benchmark FSOD datasets to demonstrate the effectiveness and performance improvements achieved by the proposed SFIDM framework.

Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with little to no labeled data from the target domain. However, the distribution shifts among multiple source domains make it more challenging to align the distributions between the target domain and all source domains concurrently. Moreover, relying solely on global alignment risks losing fine-grained information for each class, especially in the task of RS scene classification. To alleviate these issues, we present a Multi-Source Subdomain Distribution Alignment Network (MSSDANet), which introduces novel network structures and loss functions for subdomain-oriented MSDA. By adopting a two-level feature extraction strategy, this model attains better global alignment between the target domain and multiple source domains, as well as alignment at the subdomain level. First, it includes a pre-trained convolutional neural network (CNN) as a common feature extractor to fully exploit the shared invariant features across one target and multiple source domains. Secondly, a dual-domain feature extractor is used after the common feature extractor, which maps the data from each pair of target and source domains to a specific dual-domain feature space and performs subdomain alignment. Finally, a dual-domain feature classifier is employed to make predictions by averaging the outputs from multiple classifiers. Accompanied by the above network, two novel loss functions are proposed to boost the classification performance. Discriminant Semantic Transfer (DST) loss is exploited to force the model to effectively extract semantic information among target and source domain samples, while Class Correlation (CC) loss is introduced to reduce the feature confusion from different classes within the target domain. It is noteworthy that our MSSDANet is developed in an unsupervised manner for domain adaptation, indicating that no label information from the target domain is required during training. Extensive experiments on four common RS image datasets demonstrate that the proposed method achieves state-of-the-art performance for cross-domain RS scene classification. Specifically, in the dual-source and three-source settings, MSSDANet outperforms the second-best algorithm in terms of overall accuracy (OA) by 2.2% and 1.6%, respectively.

The depletion of ovarian reserve is a major factor contributing to the decline in female fertility. It is characterized by a simultaneous reduction in the quantity and quality of oocytes and the follicular pools. The cyclic recruitment of primordial follicles and the preservation of oocyte quality involve complex and tightly regulated biological processes. Granulosa cells, which surround the oocytes, play a pivotal role in follicular development and the determination of follicular fate. Programmed cell death (PCD), a genetically regulated process of cell elimination, is a key factor in the regulation of ovarian reserve dynamics. Emerging evidence suggests that natural products derived from medicinal plants, dietary components, animals, and microorganisms may modulate PCD in granulosa cells through various molecular mechanisms and signaling pathways. These natural products have demonstrated preliminary effects in delaying ovarian aging and preserving ovarian reserve in preclinical models. This review discusses the roles and underlying mechanisms of various forms of PCD in diminished ovarian reserve, while summarizing the current findings on natural products that influence granulosa cells PCD to protect ovarian function. These insights may contribute to the future development of novel, targeted strategies aimed at preserving female reproductive potential.

Background There was inconsistency in optimal endometrial preparation protocol for frozen-thawed embryo transfer (FET) in patients with endometriosis. We conducted this study to investigate the effect of different endometrial preparation protocols on the pregnancy outcomes in patients with endometriosis undergoing FET cycles, and determine the optimal number of GnRHa injections in GnRHa-HRT protocols. Method(s) This was a retrospective cohort analysis of women with endometriosis who underwent FET cycles at a single university-based center. This study retrospectively analyzed 2048 FET cycles in our center from 2011 to 2020. According to the endometrial preparation protocols, patients were divided into 4 groups: gonadotropin releasing hormone agonist-hormone replacement therapy(GnRHa-HRT), hormone replacement therapy(HRT), ovulation induction(OI), and natural cycle(NC). In the GnRHa-HRT group, patients were further divided into 3 groups: one injection of GnRHa, two injections of GnRHa, and three or more injections of GnRHa. The primary outcome was the clinical pregnancy rate. Propensity score matching was used to adjust for potential non-similarities among the groups. Multivariate logistic regression analysis was performed to figure out the risk factors for pregnancy outcomes. Result(s) There were no statistical differences in pregnancy outcomes among the four endometrial preparation protocols in FET cycles with endometriosis patients, the results retained after propensity score matching(PSM). And in endometriosis patients complicated with adenomyosis, the results remained similar. In patients with GnRHa-HRT protocol, there were no differences in clinical pregnancy rate and live birth rate with different numbers of GnRHa injections, the early miscarriage rate were 18% in the two injections of GnRHa group and 6.5% in the one injection of GnRHa group(P = 0.017). Multifactorial logistic regression analysis showed that two injections of GnRHa before FET was associated with increased early miscarriage rate compared with one injection of GnRHa[adjusted OR (95% CI): 3.116(1.079–8.998),p = 0.036]. Conclusion(s) The four kinds of endometrial preparation protocols for FET, GnRHa-HRT, HRT, OI and NC had similar pregnancy outcomes in patients with endometriosis. In endometriosis patients complicated with adenomyosis, the results remained similar. In patients with endometriosis undergoing GnRHa-HRT protocol for FET, more injections of GnRHa had no more advantages in pregnancy outcomes, on the contrary, it might increase the early miscarriage rate.

Tiller angle is a critical determinant of wheat plant architecture, which profoundly impacts yield potential. However, the regulatory mechanisms of tiller angle in wheat remain largely unexplored. To explore the underlying mechanisms, we identify two EMS-mutagenized wheat mutants tiller angle 1 ( ta1 ) and ta2 with enlarged tiller angles. Molecular characterization reveals that TA1 and TA2 encode the DELLA protein Rht-A1 and TaLA1-D, respectively. Biochemical analyses demonstrate that the Rht-A1 ta1 variant acquires enhanced protein stability, whereas TaLA1-D ta2 exhibits destabilization. We establish a mechanistic framework that Rht-A1 physically associates with TaPROG1 to synergistically repress TaLA1-D transcription. Moreover, we show that TaGSK3 directly interacts with and phosphorylates TaLA1-D to enhance its stability in reducing wheat tiller angles. Population genomic analyses uncover a significant selection for the elite TaLA1-D Hap1 allele during modern wheat breeding, correlating with compact plant architecture and elevated thousand-grain weight. This study provides new insights into plant architecture regulation and target genes for improving yield potential in wheat. This study identifies a Rht-A1 – TaLA1-D module controlling wheat tiller angle, elucidates its mechanism, and traces the selection trajectory of TaLA1-D during wheat breeding, providing new insights for developing ideotype wheat varieties.

Background: Vascular cognitive impairment (VCI) is the second most common type of cognitive impairment in the world after Alzheimer’s disease (AD). At present, there is no specific drug for VCI. This study aims to confirm the role of electroacupuncture (EA) preconditioning in improving the long‐term potentiation (LTP) of chronic cerebral hypoperfusion (CCH) rats with human embryonic stem cell (hESC)‐derived medial ganglionic eminence (MGE) neural progenitor transplantation and to investigate its possible mechanism. Methods: Rats with two‐vessel occlusion (2VO) were selected as models for the study of VCI. The rats in the 2VO + cell + EA group were given EA for 7 days after modeling. On the 7 th day, MGE neural progenitors were transplanted into the hippocampus of CCH rats. 2 weeks after transplantation, we detected the expressions of Iba1, CX3CL1/CX3CR1, Bcl2/Bax, brain‐derived neurotrophic factor (BDNF), and tyrosine receptor kinase B (TrkB) in the hippocampus of rats by western blot. Immunofluorescence staining was used to observe the morphologies of microglia and the survival and differentiation of transplanted cells. Microglial morphologies were quantitatively analyzed using the AnalyzeSkeleton. 8 weeks after transplantation, the LTP in the hippocampus of brain slices was detected to evaluate the learning and memory function of the rats with CCH. Results: 2 weeks after transplantation, we observed that MGE neural progenitors survived and differentiated into neurons in the hippocampus of CCH rats. Inflammation and apoptosis appeared in the hippocampus of rats after the interruption of cerebral blood flow. EA preconditioning notably alleviated the inflammatory response and inhibited cell apoptosis in the hippocampus. Moreover, we detected that the expressions of BDNF and TrkB were increased in the hippocampus of rats in the 2VO + cell group and 2VO + cell + EA groups, especially in the 2VO + cell + EA groups. 8 weeks after transplantation, the electrophysiological experiment results showed that the LTP value in the 2VO group was 103.1% ± 2.316%. Compared with the 2VO group, LTP value increased in the 2VO + cell group and 2VO + cell + EA group, which were 136.2% ± 1.603% and 170.8% ± 15.82%, respectively. The increase of LTP value in the 2VO + cell + EA group was more obvious. Conclusion: MGE neural progenitor transplantation improves the LTP of CCH rats, and EA preconditioning can enhance the efficacy of cell transplantation. This enhancement mechanism may be attributed to the effect of EA preconditioning on ameliorating the ischemic microenvironment.