Modulation Of Balance Research Articles

Rapid Selenoprotein Activation by Selenium Nanoparticles to Suppresses Osteoclastogenesis and Pathological Bone Loss.

Osteoclast hyperactivation stands as a significant pathological factor contributing to the emergence of bone disorders driven by heightened oxidative stress levels. The modulation of the redox balance to scavenge reactive oxygen species emerges as a viable approach to addressing this concern. Selenoproteins, characterized by selenocysteine (SeCys2) as the active center, are crucial for selenium-based antioxidative stress therapy for inflammatory diseases. This study reveals that surface-active elemental selenium (Se) nanoparticles, particularly lentinan-Se (LNT-Se), exhibit enhanced cellular accumulation and accelerated metabolism to SeCys2, the primary active Se form in biological systems. Consequently, LNT-Se demonstrates significant inhibition of osteoclastogenesis. Furthermore, in vivo studies underscore the superior therapeutic efficacy of LNT-Se over SeCys2, potentially attributable to the enhanced stability and safety profile of LNT-Se. Specifically, LNT-Se effectively modulates the expression of the selenoprotein GPx1, thereby exerting regulatory control over osteoclastogenesis inhibition, and the prevention of osteolysis. In summary, these results suggest that the prompt activation of selenoproteins by Se nanoparticles serves to suppress osteoclastogenesis and pathological bone loss by upregulating GPx1. Moreover, the utilization of bioactive Se species presents a promising avenue for effectively managing bone disorders.

MiRNA-148a-containing GMSC-derived EVs modulate Treg/Th17 balance via IKKB/NF-κB pathway and treat a rheumatoid arthritis model.

Mesenchymal stem cells (MSCs) have demonstrated potent immunomodulatory properties that have shown promise in the treatment of autoimmune diseases, including rheumatoid arthritis (RA). However, the inherent heterogeneity of MSCs triggered conflicting therapeutic outcomes, raising safety concerns and limiting their clinical application. This study aimed to investigate the potential of extracellular vesicles derived from human gingival mesenchymal stem cells (GMSC-EVs) as a therapeutic strategy for RA. Through in vivo experiments using an experimental RA model, our results demonstrate that GMSC-EVs selectively homed to inflamed joints and recovered Treg and Th17 cell balance, resulting in the reduction of arthritis progression. Our investigations also uncovered miR-148a-3p as a critical contributor to the Treg/Th17 balance modulation via IKKB/NF-κB signaling orchestrated by GMSC-EVs, which was subsequently validated in a model of human xenograft versus host disease (xGvHD). Furthermore, we successfully developed a humanized animal model by utilizing synovial fibroblasts obtained from patients with RA (RASFs). We found that GMSC-EVs impeded the invasiveness of RASFs and minimized cartilage destruction, indicating their potential therapeutic efficacy in the context of patients with RA. Overall, the unique characteristics - including reduced immunogenicity, simplified administration, and inherent ability to target inflamed tissues - position GMSC-EVs as a viable alternative for RA and other autoimmune diseases.

Neuronal Mitochondrial Calcium Uniporter (MCU) Deficiency Is Neuroprotective in Hyperexcitability by Modulation of Metabolic Pathways and ROS Balance.

Epilepsy is one of the most common neurological disorders in the world. Common epileptic drugs generally affect ion channels or neurotransmitters and prevent the emergence of seizures. However, up to a third of the patients suffer from drug-resistant epilepsy, and there is an urgent need to develop new therapeutic strategies that go beyond acute antiepileptic (antiseizure) therapies towards therapeutics that also might have effects on chronic epilepsy comorbidities such as cognitive decline and depression. The mitochondrial calcium uniporter (MCU) mediates rapid mitochondrial Ca2+ transport through the inner mitochondrial membrane. Ca2+ influx is essential for mitochondrial functions, but longer elevations of intracellular Ca2+ levels are closely associated with seizure-induced neuronal damage, which are underlying mechanisms of cognitive decline and depression. Using neuronal-specific MCU knockout mice (MCU-/-ΔN), we demonstrate that neuronal MCU deficiency reduced hippocampal excitability in vivo. Furthermore, in vitro analyses of hippocampal glioneuronal cells reveal no change in total Ca2+ levels but differences in intracellular Ca2+ handling. MCU-/-ΔN reduces ROS production, declines metabolic fluxes, and consequently prevents glioneuronal cell death. This effect was also observed under pathological conditions, such as the low magnesium culture model of seizure-like activity or excitotoxic glutamate stimulation, whereby MCU-/-ΔN reduces ROS levels and suppresses Ca2+ overload seen in WT cells. This study highlights the importance of MCU at the interface of Ca2+ handling and metabolism as a mediator of stress-related mitochondrial dysfunction, which indicates the modulation of MCU as a potential target for future antiepileptogenic therapy.

Resistance Training to Muscle Failure with Variable Load Intensities: Implications for Post-Exercise Blood Pressure and Heart Rate Variability in Trained Men.

Background: The modulation of cardiac sympathovagal balance alters following acute resistance training (RT) sessions. Nevertheless, the precise influence of RT at varying load intensities on this physiological response remains to be fully elucidated. Therefore, the aim of this study was to compare the time course of recovery following low- (40%), moderate- (60%), and high- (80%) load-intensity RT protocols performed up to muscle repetition failure in resistance-trained men. Method: Sixteen young, resistance-trained men (mean age: 21.6 ± 2.5 years, mean height: 175.7 ± 8.9 cm, mean weight: 77.1 ± 11.3 kg) participated in a randomized crossover experimental design involving three sessions, each taken to the point of muscle failure. These sessions were characterized by different load intensities: low (40% of 1-repetition maximum, 1RM), moderate (60% of 1RM), and high (80% of 1RM). The exercise regimen comprised four exercises-back squat (BS), bench press (BnP), barbell row (BR), and shoulder press (SP)-with each exercise consisting of three sets. Throughout each session, heart rate variability (HRV) and blood pressure (BP) parameters were assessed both pre-exercise and during a 40 min post-exercise period, segmented into 10 min intervals for stabilization. Statistical analysis involved the use of a repeated measures ANOVA. Results: It was observed that the 40% and 60% RT sessions resulted in a significantly higher root mean square of successive R-R intervals (RMSSD) value compared to the 80% RT session in the post-exercise recovery process in 30 min (respectively, p = 0.025; p = 0.028) and 40 min (respectively, p = 0.031; p = 0.046), while the 40% and 60% RT sessions produced similar responses. The 40% RT session was significantly higher in the high frequency (HF) value post-exercise in 40 min compared to the 80% RT session (p = 0.045). Conclusions: Our findings suggest that engaging in resistance training (RT) sessions to muscle failure at an intensity of 80% induces acute increases in sympathetic activity, potentially leading to elevated cardiovascular stress. For individuals with normal blood pressure, it is advisable to opt for lighter loads and higher repetition volumes when prescribing RT, as heavier-load RT may carry an increased risk of cardiac-related factors.

THE USE OF LINGUISTIC MODALITY AMONG MALAYSIAN LECTURERS IN LANGUAGE EDUCATION CLASSROOM FOR TEACHER CANDIDATES

This study investigates the use of linguistic modality by Malaysian educators within the context of teaching English as a Second Language (TESL) programmes, focusing on its impact on teacher education and the engagement of prospective teachers. Six classroom observations and corresponding audio recordings were analysed using mixed-method content analysis to identify the frequencies and contexts of modal expressions. The findings revealed a predominant use of modalisation with high probability and a balanced modulation of obligation, alongside a notable use of low-inclination modalities. The lecturers used high-probability modal verbs to convey authority and certainty, while strategically using lower-probability modals to enhance engagement and promote critical thinking. Furthermore, the use of Malay modal expressions contributed to inclusivity and linguistic adaptability in the classroom. These results demonstrated the critical role of modality by educators in shaping the pedagogical communication of prospective teachers. The study highlights the essential need for educators to adeptly manage language choices to meet diverse educational needs and cultural contexts, thus improving teaching strategies in multilingual settings.

CMNet: Cascaded context fusion and multi-attention network for multiple lesion segmentation of diabetic retinopathy images

Diabetic retinopathy (DR) is a leading cause of blindness in humans, and regular screening is helpful to early detection and containment of DR. Therefore, automated and accurate lesion segmentation is crucial for DR grading and diagnosis. However, it is full of challenges due to complex structures, inconsistent scales and blurry edges of different kinds of lesions. To address the above issues, this paper proposes a cascaded context fusion and multi-attention network (CMNet) for multiple lesion segmentation of DR images. The CMNet includes triple attention module (TAM), cascaded context fusion module (CFM) and balanced attention module (BAM). Firstly, TAM is proposed to extract channel attention, spatial attention and pixel-point attention features for addition fusion to ensure selectivity and consistency of information representation. Moreover, CFM applies adaptive average pooling and non-local operation to capture local and global contextual features for concatenation fusion to expand the receptive field of lesions. Finally, BAM computes foreground, background and boundary attention maps of lesions, as well as uses Squeeze-and-Excitation block to weigh the feature channels and rebalance attention of three regions to make the network focus on the edge details for fine segmentation. We performed comprehensive comparison experiments and ablation studies on three public datasets of DDR, IDRiD and E-Ophtha, and mAUC of four lesions segmentation reached 0.6765, 0.7466 and 0.6710, respectively. The experimental results show that the proposed model outperforms current state-of-the-arts, which overcomes the adverse interference of background noise, and preserves contour details for realizing precise segmentation of multi-scale lesion.

Research on the design and balancing strategy with multi-section lithium battery protection IC

This study uses 0.5um BCD technology to design a multi section lithium battery protection chip suitable for small mobile electronic devices such as Bluetooth earphones and smart bracelets. By providing a complete system structure diagram, the principles and design methods of several key circuits in the chip were introduced. This chip can effectively monitor the voltage of lithium batteries, take protective measures against abnormal situations such as overcharging, discharging, and overcurrent, and has a balancing module with dynamic power consumption management function. The balancing module adopts a passive balancing architecture, and the balancing algorithm used is an average based voltage detection algorithm. Using this algorithm can make the balancing system structure simple and reduce system power consumption, reduce the differences between batteries within the group, and thus improve the uniformity of the battery pack. The overcharging detection accuracy of the designed chip is ±25mV, the overcharging detection accuracy is 50mV, and the static working current of the chip is 7uA.

Synergistic effects of biochar and potassium co-application on growth, physiological attributes, and antioxidant defense mechanisms of wheat under water deficit conditions

Global wheat production faces a severe threat from drought stress, necessitating innovative strategies for enhanced crop resilience. This study examined the synergistic impact of biochar and potassium co-application on the growth, physiological attributes, and antioxidant defense system of wheat under water deficit conditions at crown root initiation (CRI), anthesis, and grain development stage. Drought-induced reactive oxygen species (ROS) accumulation, particularly pronounced at the CRI stage, adversely affected all growth stages. At CRI, co-application of biochar and foliar potassium delivered significant improvements in growth parameters, including increased plant height (15.4%), spike length (50%), grain yield (43.0%), photosynthetic performance (chlorophyll content 125.8%), and relative water content (11.2%), compared to untreated drought-exposed counterparts. The combined application of biochar and potassium effectively reduced hydrogen peroxide production, electrolyte leakage, proline accumulation, and malondialdehyde generation, while increasing relative water content and glutathione levels under both well-irrigated and drought stress conditions. Furthermore, the combined biochar and potassium treatment was effective in mitigating oxidative stress and enhancing physiological resilience in wheat, particularly during the anthesis stage of drought stress. Specifically, the combined treatment ameliorated the effects of drought by reducing ROS levels through enhanced antioxidant enzyme activities and elevating osmoprotectants levels. The synergistic modulation of tissue osmotic balance and relative water content holds promise for mitigating drought-induced stress, offering an innovative and practical strategy for resilient wheat production in water-limited environments.

Fully integrated and automated centrifugal microfluidic chip for point-of-care multiplexed molecular diagnostics

Public health events caused by pathogens have imposed significant economic and societal burdens. However, conventional methods still face challenges including complex operations, the need for trained operators, and sophisticated instruments. Here, we proposed a fully integrated and automated centrifugal microfluidic chip, also termed IACMC, for point-of-care multiplexed molecular diagnostics by harnessing the advantages of active and passive valves. The IACMC incorporates multiple essential components including a pneumatic balance module for sequential release of multiple reagents, a pneumatic centrifugation-assisted module for on-demand solution release, an on-chip silicon membrane module for nucleic acid extraction, a Coriolis force-mediated fluid switching module, and an amplification module. Numerical simulation and visual validation were employed to iterate and optimize the chip's structure. Upon sample loading, the chip automatically executes the entire process of bacterial sample lysis, nucleic acid capture, elution quantification, and isothermal LAMP amplification. By optimizing crucial parameters including centrifugation speed, direction of rotation, and silicone membrane thickness, the chip achieves exceptional sensitivity (twenty-five Salmonella or forty Escherichia coli) and specificity in detecting Escherichia coli and Salmonella within 40 min. The development of IACMC will drive advancements in centrifugal microfluidics for point-of-care testing and holds potential for broader applications in precision medicine including high-throughput biochemical analysis immune diagnostics, and drug susceptibility testing.

SWAT‐GL: A new glacier routine for the hydrological model SWAT

AbstractThe hydrological model Soil Water Assessment Tool (SWAT) is widely used in water resources management worldwide. It is also used to simulate catchment hydrology in high‐mountainous regions where glaciers play an important role. However, SWAT considers glaciers in a simplistic way. Although some efforts were done to overcome this limitation, there is no official version available that considers glaciers adequately. This strongly impairs its applicability in glacierized catchments. In this technical note, we propose a novel version of the traditional SWAT, called SWAT‐GL, which introduces (1) a mass balance module and (2) a glacier evolution routine to represent dynamic glacier changes. Mass balance calculations are based on a conceptual degree‐day approach, similar to the snow routine implemented in SWAT. Glacier evolution is realized using the delta‐h (∆h) parameterization, which requires a minimum of data and is thus suitable in data‐scarce regions. The approach allows users to simulate spatially distributed glacier changes. Annual mass balance changes are translated to distributed ice thickness changes depending on the glacier elevation. We demonstrate how SWAT‐GL is technically integrated into SWAT and how glaciers are merged with the existing spatial units. Model code and test data are freely accessible to promote further model development efforts and a wide application. Ultimately, SWAT‐GL aims to make SWAT easily applicable in glacierized catchments without the need of additional tools.

Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation

Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT). Despite the good robust performance, the existing ARD methods are still impractical to deploy in natural high-security scenes due to these methods rely entirely on original or publicly available data with a similar distribution. In fact, these data are almost always private, specific, and distinctive for scenes that require high robustness. To tackle these issues, we propose a challenging but significant task called Data-Free Adversarial Robustness Distillation (DFARD), which aims to train small, easily deployable, robust models without relying on data. We demonstrate that the challenge lies in the lower upper bound of knowledge transfer information, making it crucial to mining and transferring knowledge more efficiently. Inspired by human education, we design a plug-and-play Interactive Temperature Adjustment (ITA) strategy to improve the efficiency of knowledge transfer and propose an Adaptive Generator Balance (AGB) module to retain more data information. Our method uses adaptive hyperparameters to avoid a large number of parameter tuning, which significantly outperforms the combination of existing techniques. Meanwhile, our method achieves stable and reliable performance on multiple benchmarks.

FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers

Recent advancements in deep learning have greatly improved the efficiency of auxiliary medical diagnostics. However, concerns over patient privacy and data annotation costs restrict the viability of centralized training models. In response, federated semi-supervised learning has garnered substantial attention from medical institutions. However, it faces challenges arising from knowledge discrepancies among local clients and class imbalance in non-independent and identically distributed data. Existing methods like class balance adaptation for addressing class imbalance often overlook low-confidence yet valuable rare samples in unlabeled data and may compromise client privacy. To address these issues, we propose a novel framework with class awareness balance and dual teacher distillation called FedCD. FedCD introduces a global-local framework to balance and purify global and local knowledge. Additionally, we introduce a novel class awareness balance module to effectively explore potential rare classes and encourage balanced learning in unlabeled clients. Importantly, our approach prioritizes privacy protection by only exchanging network parameters during communication. Experimental results on two medical datasets under various settings demonstrate the effectiveness of FedCD. The code is available at https://github.com/YunzZ-Liu/FedCD.

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance.

A partial energy current‐sharing method for interleaved LLC converter with phase shift current balance module

AbstractA partial energy current‐sharing method for an interleaved LLC resonant converter with a phase shift current balance module (CBM) is presented in this paper. The current sharing is achieved by controlling the phase shift angle of the bridge in the CBM. The proposed method maintains excellent soft‐switching performance, minimizes power losses, and ensures high overall efficiency as only partial energy is processed. This method simplifies current sharing in the resonant converter by using phase shift control, making it easy to implement and extend to multiple phases. A 500‐kHz two‐phase interleaved LLC prototype was built to validate the effectiveness of this method in achieving current balancing.

Balanced transformer: efficient classification of glioblastoma and primary central nervous system lymphoma

Objective. Primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) are malignant primary brain tumors with different biological characteristics. Great differences exist between the treatment strategies of PCNSL and GBM. Thus, accurately distinguishing between PCNSL and GBM before surgery is very important for guiding neurosurgery. At present, the spinal fluid of patients is commonly extracted to find tumor markers for diagnosis. However, this method not only causes secondary injury to patients, but also easily delays treatment. Although diagnosis using radiology images is non-invasive, the morphological features and texture features of the two in magnetic resonance imaging (MRI) are quite similar, making distinction with human eyes and image diagnosis very difficult. In order to solve the problem of insufficient number of samples and sample imbalance, we used data augmentation and balanced sample sampling methods. Conventional Transformer networks use patch segmentation operations to divide images into small patches, but the lack of communication between patches leads to unbalanced data layers. Approach. To address this problem, we propose a balanced patch embedding approach that extracts high-level semantic information by reducing the feature dimensionality and maintaining the geometric variation invariance of the features. This approach balances the interactions between the information and improves the representativeness of the data. To further address the imbalance problem, the balanced patch partition method is proposed to increase the receptive field by sampling the four corners of the sliding window and introducing a linear encoding component without increasing the computational effort, and designed a new balanced loss function. Main results. Benefiting from the overall balance design, we conducted an experiment using Balanced Transformer and obtained an accuracy of 99.89%, sensitivity of 99.74%, specificity of 99.73% and AUC of 99.19%, which is far higher than the previous results (accuracy of 89.6% ∼ 96.8%, sensitivity of 74.3% ∼ 91.3%, specificity of 88.9% ∼ 96.02% and AUC of 87.8% ∼ 94.9%). Significance. This study can accurately distinguish PCNSL and GBM before surgery. Because GBM is a common type of malignant tumor, the 1% improvement in accuracy has saved many patients and reduced treatment times considerably. Thus, it can provide doctors with a good basis for auxiliary diagnosis.

Apoptosis signal-regulating kinase 1 (Ask1) deficiency alleviates MPP+-induced impairment of evoked dopamine release in the mouse hippocampus.

The dopaminergic system is susceptible to dysfunction in numerous neurological diseases, including Parkinson's disease (PD). In addition to motor symptoms, some PD patients may experience non-motor symptoms, including cognitive and memory deficits. A possible explanation for their manifestation is a disturbed pattern of dopamine release in brain regions involved in learning and memory, such as the hippocampus. Therefore, investigating neuropathological alterations in dopamine release prior to neurodegeneration is imperative. This study aimed to characterize evoked hippocampal dopamine release and assess the impact of the neurotoxin MPP+ using a genetically encoded dopamine sensor and gene expression analysis. Additionally, considering the potential neuroprotective attributes demonstrated by apoptosis signal-regulating kinase 1 (Ask1) in various animal-disease-like models, the study also aimed to determine whether Ask1 knockdown restores MPP+-altered dopamine release in acute hippocampal slices. We applied variations of low- and high-frequency stimulation to evoke dopamine release within different hippocampal regions and discovered that acute application of MPP+ reduced the amount of dopamine released and hindered the recovery of dopamine release after repeated stimulation. In addition, we observed that Ask1 deficiency attenuated the detrimental effects of MPP+ on the recovery of dopamine release after repeated stimulation. RNA sequencing analysis indicated that genes associated with the synaptic pathways are involved in response to MPP+ exposure. Notably, Ask1 deficiency was found to downregulate the expression of Slc5a7, a gene encoding a sodium-dependent high-affinity choline transporter that regulates acetylcholine levels. Respective follow-up experiments indicated that Slc5a7 plays a role in Ask1 deficiency-mediated protection against MPP+ neurotoxicity. In addition, increasing acetylcholine levels using an acetylcholinesterase inhibitor could exacerbate the toxicity of MPP+. In conclusion, our data imply that the modulation of the dopamine-acetylcholine balance may be a crucial mechanism of action underlying the neuroprotective effects of Ask1 deficiency in PD.

Research on the expansion and optimization control strategy of cascaded rectifier H-bridge

Extensive research on the multi-bridge extension of cascaded rectifier stages is deemed important to meet the requirements of high-voltage and high-power applications. Two control structures for capacitor voltage balancing in cascaded rectifier stages are analyzed in this paper. On this basis, the balance strategy for the multi-bridge extension of single-phase cascaded rectifiers is explored. The balance method of ultra-fast balance modulation trajectories is introduced into single-phase multi-bridge cascaded rectification, and the optimal control strategy is selected through simulation verification and comparative analysis. During the actual experiments, some disturbance factors were introduced into the system, causing an instantaneous unbalanced state. However, through the control strategy proposed in this paper, the system can quickly regain balance. Through simulation validation and analysis of the experimental results, the reliability and innovation of this control strategy have been demonstrated.

Real-Time Low-Light Imaging in Space Based on the Fusion of Spatial and Frequency Domains

Due to the low photon count in space imaging and the performance bottlenecks of edge computing devices, there is a need for a practical low-light imaging solution that maintains satisfactory recovery while offering lower network latency, reduced memory usage, fewer model parameters, and fewer operation counts. Therefore, we propose a real-time deep learning framework for low-light imaging. Leveraging the parallel processing capabilities of the hardware, we perform the parallel processing of the image data from the original sensor across branches with different dimensionalities. The high-dimensional branch conducts high-dimensional feature learning in the spatial domain, while the mid-dimensional and low-dimensional branches perform pixel-level and global feature learning through the fusion of the spatial and frequency domains. This approach ensures a lightweight network model while significantly improving the quality and speed of image recovery. To adaptively adjust the image based on brightness and avoid the loss of detailed pixel feature information, we introduce an adaptive balancing module, thereby greatly enhancing the effectiveness of the model. Finally, through validation on the SID dataset and our own low-light satellite dataset, we demonstrate that this method can significantly improve image recovery speed while ensuring image recovery quality.

MAPoseNet: Animal pose estimation network via multi-scale convolutional attention

Animal pose estimation serves as an upstream task for recognizing and understanding animal behavior. Over the last year, the accuracy of the deep learning-based method has steadily improved, but at the expense of the model’s inference speed. This paper uses an efficient and powerful model to improve inference speed and accuracy. The classic encoder–decoder architecture is chosen. For estimating animal pose, our model based on a feature pyramid and a multi-scale asymmetric convolution attention mechanism is developed and named MAPoseNet (Animal Pose Estimation Network Via Multi-scale Convolutional Attention). MAPoseNet consists of an encoder and a decoder. Rather than typical self-attention, the encoder’s attention mechanism comprises multi-scale, asymmetric convolutions that are lightweight and instrumental in improving inference speed. A feature pyramid and a feature balance module make up the decoder. The public dataset AP-10K is used to train and test MAPoseNet. A series of experimental results demonstrate that the MAPoseNet model provides cutting-edge performance. MAPoseNet outperforms HRFormer by 1.3 AP and 0.8 AR, with 33.7% fewer FLOPs and 66% faster inference speed. And our model surpasses HRNet and HRFormer on the Animal Pose dataset as well. Our model has achieved a win-win situation regarding inference speed and accuracy.

SMYD3 Modulates AMPK-mTOR Signaling Balance in Cancer Cell Response to DNA Damage.

Cells respond to DNA damage by activating a complex array of signaling networks, which include the AMPK and mTOR pathways. After DNA double-strand breakage, ATM, a core component of the DNA repair system, activates the AMPK-TSC2 pathway, leading to the inhibition of the mTOR cascade. Recently, we showed that both AMPK and mTOR interact with SMYD3, a methyltransferase involved in DNA damage response. In this study, through extensive molecular characterization of gastrointestinal and breast cancer cells, we found that SMYD3 is part of a multiprotein complex that is involved in DNA damage response and also comprises AMPK and mTOR. In particular, upon exposure to the double-strand break-inducing agent neocarzinostatin, SMYD3 pharmacological inhibition suppressed AMPK cascade activation and thereby promoted the mTOR pathway, which reveals the central role played by SMYD3 in the modulation of AMPK-mTOR signaling balance during cancer cell response to DNA double-strand breaks. Moreover, we found that SMYD3 can methylate AMPK at the evolutionarily conserved residues Lys411 and Lys424. Overall, our data revealed that SMYD3 can act as a bridge between the AMPK and mTOR pathways upon neocarzinostatin-induced DNA damage in gastrointestinal and breast cancer cells.