Collision dynamics between soliton molecules and a single soliton: exploding soliton pair and periodic soliton explosions.

Inherent periodic collisions in dual-wavelength mode-locked fiber lasers (MLFLs) stimulate various intra-cavity collision dynamic phenomena. Analogous to the collision of matter particles, collisions between optical soliton molecules (SMs) and single solitons (SSs) have been observed by the real-time spectral measurements. It is demonstrated that the energy accumulation after the collision caused by internal motion within bound pulses leads to soliton pair (SP) explosions, while the periodic soliton explosions with another cavity parameter setting are almost unaffected by the collision. Additionally, the collision between a SP and a SS is reproduced through numerical simulations, and the collision-induced double Hopf-type bifurcation of SP is predicted. These findings provide novel insights, to the best of our knowledge, for further understanding the complex collision dynamics in dual-wavelength MLFLs and will help in the design of high-performance dual-comb sources.

Bistable response and quasi-periodicity excitation of the internal dynamics of soliton molecules.

Soliton molecules, a frequently observed phenomenon in most mode-locked lasers, have intriguing characteristics comparable to their matter molecule counterparts. However, there are rare explorations of the deterministic control of the underlying physics within soliton molecules. Here, we demonstrate the bistable response of intramolecular motion to external stimuli and identify a general approach to excite their quasi-periodic oscillations. By introducing frequency-swept gain modulation, the intrinsic resonance frequency of the soliton molecule is observed in the simulation model. Applying stronger modulation, the soliton molecule exhibits divergent response susceptibility to up- and down-sweeping, accompanied by a jump phenomenon. Quasi-periodic intramolecular oscillations appear at the redshifted resonance frequency. Given the leading role of bistability and quasi-periodic dynamics in nonlinear physics, our research provides insights into the complex nonlinear dynamics within dissipative soliton molecules. It may pave the way to related experimental studies on synchronization and chaos at an ultrafast time scale.

Collision-induced Hopf-type bifurcation reversible transitions in a dual-wavelength femtosecond fiber laser.

Collisions refer to a striking nonlinear interaction process in dissipative systems, revealing the particle-like properties of solitons. In dual-wavelength mode-locked fiber lasers, collisions are inherent and periodic. However, how collisions influence the dynamical transitions in the dual-wavelength mode-locked state has not yet been explored. In our work, dispersion management triggers the complex interactions between solitons in the cavity. We reveal the smooth or Hopf-type bifurcation reversible transitions of dual-color soliton molecules (SMs) during the collision by the real-time spectral measurement technique of time-stretch Fourier transform. The reversible transitions between stationary SMs and vibrating SMs, reveal that the cavity parameters pass through a bifurcation point in the collision process without active external intervention. The numerical results confirm the universality of collision-induced bifurcation behavior. These findings provide new insights into collision dynamics in dual-wavelength ultrafast fiber lasers. Furthermore, the study of inter-molecular collisions is of great significance for other branches of nonlinear science.

Stress granule activation attenuates lipopolysaccharide-induced cardiomyocyte dysfunction.

BACKGROUND: Sepsis is the leading cause of death in intensive care units. Sepsis-induced myocardial dysfunction, one of the most serious complications of sepsis, is associated with higher mortality rates. As the pathogenesis of sepsis-induced cardiomyopathy has not been fully elucidated, there is no specific therapeutic approach. Stress granules (SG) are cytoplasmic membrane-less compartments that form in response to cellular stress and play important roles in various cell signaling pathways. The role of SG in sepsis-induced myocardial dysfunction has not been determined. Therefore, this study aimed to determine the effects of SG activation in septic cardiomyocytes (CMs). METHODS: Neonatal CMs were treated with lipopolysaccharide (LPS). SG activation was visualized by immunofluorescence staining to detect the co-localization of GTPase-activating protein SH3 domain binding protein 1 (G3BP1) and T cell-restricted intracellular antigen 1 (TIA-1). Eukaryotic translation initiation factor alpha (eIF2α) phosphorylation, an indicator of SG formation, was assessed by western blotting. Tumor necrosis factor alpha (TNF-α) production was assessed by PCR and enzyme-linked immunosorbent assays. CMs function was evaluated by intracellular cyclic adenosine monophosphate (cAMP) levels in response to dobutamine. Pharmacological inhibition (ISRIB), a G3BP1 CRISPR activation plasmid, and a G3BP1 KO plasmid were employed to modulate SG activation. The fluorescence intensity of JC-1 was used to evaluate mitochondrial membrane potential. RESULTS: LPS challenge in CMs induced SG activation and resulted in eIF2α phosphorylation, increased TNF-α production, and decreased intracellular cAMP in response to dobutamine. The pharmacological inhibition of SG (ISRIB) increased TNF-α expression and decreased intracellular cAMP levels in CMs treated with LPS. The overexpression of G3BP1 increased SG activation, attenuated the LPS-induced increase in TNF-α expression, and improved CMs contractility (as evidenced by increased intracellular cAMP). Furthermore, SG prevented LPS-induced mitochondrial membrane potential dissipation in CMs. CONCLUSION: SG formation plays a protective role in CMs function in sepsis and is a candidate therapeutic target.

Switchable generation of a sub-200 fs dissipative soliton and a noise-like pulse in a normal-dispersion Tm-doped mode-locked fiber laser.

We report on the switchable generation of a dissipative soliton (DS) pulse and a noise-like pulse (NLP) in an all-fiberized Tm-doped fiber laser in the normal-dispersion region. Mode-locking operation is achieved through a nonlinear polarization rotation component, and the cavity dispersion is compensated using ultra-high numerical aperture (UHNA4) fiber that is easy to integrate and low in cost. At a pump threshold of 510 mW, DS operation can first be achieved without additional filter. The 3 dB spectrum bandwidth of the DS pulse is greater than 50 nm, and the duration of the de-chirped pulse is 193 fs. By increasing the pump power to 880 mW, the mode-locking state can evolve into NLP operation with proper cavity polarization state. The 3 dB spectrum bandwidth and duration of de-chirped coherence spike are 105.6 nm and 121 fs, respectively. Meanwhile, ultra-broadband NLP (over 150 nm considering 3 dB spectrum width) can also be observed with the appropriate cavity parameters. All the proposed pulse patterns present good capacity for achieving narrow pulse width and withstanding high pulse energy.

Self-organized structures of bound states in a 2 µm dispersion-managed mode-locked fiber laser.

We report on the experimental generation of various self-organized structures of bound states in a near zero-dispersion mode-locked fiber laser. When the pump power is fixed at 492 mW, appropriately adjusting polarization controllers, the switching of the cavity feedback results in the evolution from the single pulse to the dispersion-managed soliton (i.e., stretched-pulse) pair. With the increase of pump power, bound states composed of more than two pulses can also be observed. Our results of the self-organized structures might enlarge the data-carrying capacity of current fiber-optical communication systems and benefit the investigation of nonlinear dynamics of bound states in fiber lasers at 2 µm.

Observation of sub-400fs, wavelength-switchable stretched pulse and bound-state pulse in Tm-doped fiber laser.

We report on the experimental observation of wavelength-switchable stretched pulse and bound-state pulse in a dispersion-managed Tm-doped laser. At a pump power of 572 mW, a stretched pulse with a pulse duration of 389 fs can be first obtained at 1961 nm. By increasing the pump power and appropriately adjusting the cavity polarization state, the mode-locking wavelength can be switched from 1961 nm to 1980 nm caused by the birefringence filtering effect based on nonlinear polarization rotation, and the corresponding pulse duration is 371 fs. Meanwhile, loosely bound states of two pulses and three pules at 1980 nm can be observed with appropriate cavity parameters.

Fast and Strong Carbon Nanotube Yarn Artificial Muscles by Electro-osmotic Pump.

Previous electrochemically powered yarn muscles cannot be usefully operated between extreme negative and extreme positive potentials, since generated stresses during anion injection and cation injection partially cancel because they are in the same direction. We here report an ionomer-infiltrated hybrid carbon nanotube (CNT) yarn muscle that shows unipolar stress behavior in the sense that stress generation between extreme potentials is additive, resulting in an enhanced stress generation. Moreover, the stress generated by this muscle unexpectedly increases with the potential scan rate, which contradicts the fact that scan-rate-induced stress decreases for neat CNT muscles. It is revealed by the electro-osmotic pump effect that the effective ion size injected into the muscle increases with an increase in the scan rate. We demonstrate an electrochemically powered gel-elastomer-yarn muscle adhesive that generates and delivers muscle-contraction-mimicking stimulation to a target tissue.

The role of mammalian Sirtuin 6 in cardiovascular diseases and diabetes mellitus.

Cardiovascular diseases are severe diseases posing threat to human health because of their high morbidity and mortality worldwide. The incidence of diabetes mellitus is also increasing rapidly. Various signaling molecules are involved in the pathogenesis of cardiovascular diseases and diabetes. Sirtuin 6 (Sirt6), which is a class III histone deacetylase, has attracted numerous attentions since its discovery. Sirt6 enjoys a unique structure, important biological functions, and is involved in multiple cellular processes such as stress response, mitochondrial biogenesis, transcription, insulin resistance, inflammatory response, chromatin silencing, and apoptosis. Sirt6 also plays significant roles in regulating several cardiovascular diseases including atherosclerosis, coronary heart disease, as well as cardiac remodeling, bringing Sirt6 into the focus of clinical interests. In this review, we examine the recent advances in understanding the mechanistic working through which Sirt6 alters the course of lethal cardiovascular diseases and diabetes mellitus.

Recent Advances in Carbon Nanotube-Based Energy Harvesting Technologies.

There has been enormous interest in technologies that generate electricity from ambient energy such as solar, thermal, and mechanical energy, due to their potential for providing sustainable solutions to the energy crisis. One driving force behind the search for new energy-harvesting technologies is the desire to power sensor networks and portable devices without batteries, such as self-powered wearable electronics, human health monitoring systems, and implantable wireless sensors. Various energy harvesting technologies have been demonstrated in recent years. Among them, electrochemical, hydroelectric, triboelectric, piezoelectric, and thermoelectric nanogenerators have been extensively studied because of their special physical properties, ease of application, and sometimes high obtainable efficiency. Multifunctional carbon nanotubes (CNTs) have attracted much interest in energy harvesting because of their exceptionally high gravimetric power outputs and recently obtained high energy conversion efficiencies. Further development of this field, however, still requires an in-depth understanding of harvesting mechanisms and boosting of the electrical outputs for wider applications. Here, various CNT-based energy harvesting technologies are comprehensively reviewed, focusing on working principles, typical examples, and future improvements. The last section discusses the existing challenges and future directions of CNT-based energy harvesters.

Aircraft Image Recognition Network Based on Hybrid Attention Mechanism.

With the deepening of deep learning research, progress has been made in artificial intelligence. In the process of aircraft classification, the precision rate of aircraft picture recognition based on traditional methods is low due to various types of aircraft, large similarities between different models, and serious texture interference. In this article, the hybrid attention network model (BA-CNN) to implement an aircraft recognition algorithm is proposed to solve the above problems. Using two-channel ResNet-34 as a characteristic extraction function, the depth of network is increased to improve fine-grained characteristic extraction capability without increasing the output characteristic dimension. In the network to introduce a hybrid attention mechanism, respectively, between the residual units of two ResNet-34 channels, channel attention and spatial attention modules are added, more abundant mixed characteristics of attention are obtained, space and characteristics of the local characteristics of the channel response are focused, the characteristics of redundancy are reduced, and the fine-grained characteristics of learning ability are further enhanced. Trained and tested on FGVC-aircraft, a public fine-grained pictures dataset, the recognition precision rate of the BA-CNN networks model reached 89.2%. It can be seen from the experimental results, the recognition precision rate of the original model is improved effectively by using this method, and the recognition precision rate is higher than most of the existing mainstream aircraft recognition ways.

Large Margin and Local Structure Preservation Sparse Representation Classifier for Alzheimer's Magnetic Resonance Imaging Classification.

Alzheimer's disease (AD) is a progressive dementia in which the brain shrinks as the disease progresses. The use of machine learning and brain magnetic resonance imaging (MRI) for the early diagnosis of AD has a high probability of clinical value and social significance. Sparse representation classifier (SRC) is widely used in MRI image classification. However, the traditional SRC only considers the reconstruction error and classification error of the dictionary, and does not consider the global and local structural information between images, which results in unsatisfactory classification performance. Therefore, a large margin and local structure preservation sparse representation classifier (LMLS-SRC) is developed in this manuscript. The LMLS-SRC algorithm uses the classification large margin term based on the representation coefficient, which results in compactness between representation coefficients of the same class and a large margin between representation coefficients of different classes. The LMLS-SRC algorithm uses local structure preservation term to inherit the manifold structure of the original data. In addition, the LMLS-SRC algorithm imposes the ℓ 2,1 -norm on the representation coefficients to enhance the sparsity and robustness of the model. Experiments on the KAGGLE Alzheimer's dataset show that the LMLS-SRC algorithm can effectively diagnose non AD, moderate AD, mild AD, and very mild AD.

MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer's disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50.

miR­141 impairs mitochondrial function in cardiomyocytes subjected to hypoxia/reoxygenation by targeting Sirt1 and MFN2.

Mitochondrial oxidative stress and dysfunction are major pathogenic features of cardiac injury induced by ischemia/reperfusion (I/R). MicroRNA-141 (miR-141) has been implicated in the mitochondrial dysfunction in cell-based models of oxidant stress. Thus, the main aim of the present study was to systematically assess the role of miR-141 in cardiomyocyte injury induced by simulated I/R. The challenge of HL-1 cardiomyocytes with hypoxia/reoxygenation (H/R) decreased cell viability, which was also associated with an increase in miR-141 expression. The H/R-induced cell injury was mitigated by a miR-141 inhibitor and exacerbated by a miR-141 mimic. Furthermore, H/R induced mitochondrial superoxide production, dysfunction (decreased oxygen utilization and membrane depolarization), as well as ultrastructural damage. These mitochondrial effects were mitigated by a miR-141 inhibitor and intensified by a miR-141 mimic. Luciferase reporter assay, reverse transcription-quantitative PCR, and western blot analyses identified sirtuin-1 (Sirt1) and mitofusin-2 (MFN2) as targets of miR-141. The silencing of Sirt1 reduced the MFN2 cardiomyocyte levels and reversed the alleviating effects of miR-141 inhibitor on mitochondrial function during H/R. Collectively, these findings suggest that miR-141 functions as a causative agent in cardiomyocyte injury induced by I/R, primarily by interfering with two mitochondrial regulatory proteins, Sirt1 and MFN2.

TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer.

In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition.

Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract global and local features of facial emotions in order to capture part of the feature relationships between different scales, thereby improving the model's ability to discriminate subtle features of facial emotions. Second, a hierarchical interactive feature fusion module is designed to address the issue of loss of useful feature information caused by layer-by-layer convolution and pooling of convolutional neural networks. In addition, the attention mechanism is also used between convolutional layers at different levels. Improve the neural network's discriminative ability by increasing the saliency of information about different features on the layers and suppressing irrelevant information. Finally, we use the angular distance loss function to improve the proposed model's inter-class feature separation and intra-class feature clustering capabilities, addressing the issues of large intra-class differences and high inter-class similarity in facial emotion recognition. We conducted comparison and ablation experiments on the FER2013 dataset. The results illustrate that the performance of the proposed MIFAD-Net is 1.02-4.53% better than the compared methods, and it has strong competitiveness.

Noise Robustness Low-Rank Learning Algorithm for Electroencephalogram Signal Classification.

Electroencephalogram (EEG) is often used in clinical epilepsy treatment to monitor electrical signal changes in the brain of patients with epilepsy. With the development of signal processing and artificial intelligence technology, artificial intelligence classification method plays an important role in the automatic recognition of epilepsy EEG signals. However, traditional classifiers are easily affected by impurities and noise in epileptic EEG signals. To solve this problem, this paper develops a noise robustness low-rank learning (NRLRL) algorithm for EEG signal classification. NRLRL establishes a low-rank subspace to connect the original data space and label space. Making full use of supervision information, it considers the local information preservation of samples to ensure the low-rank representation of within-class compactness and between-classes dispersion. The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL. The aLS-SVM finds the maximum quantile distance between the two classes of samples based on the pinball loss function, which further improves the noise robustness of the model. Several classification experiments with different noise intensity are designed on the Bonn data set, and the experiment results verify the effectiveness of the NRLRL algorithm.

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification.

As a hot research topic, sports video classification research has a wide range of applications in switched TV, video on demand, smart TV, and other fields and is closely related to people's lives. Under this background, sports video classification research has aroused great interest in people. However, the existing methods usually use manual video classification, which the workers themselves often influence. It is challenging to ensure the accuracy of the results, leading to the wrong classification. Due to these limitations, we introduce neural network technology to the automatic classification of sports. This paper proposed a novel attention-based graph convolution-guided third-order hourglass network (AGTH-Net) classification model. First, we designed a kind of figure convolution model based on the attention mechanism. The model is the key to introduce the attention mechanism for neighborhood node weights' allocation. It reduces the impact of error nodes in the neighborhood while avoiding manual weight assignment. Second, according to the sports complex video image characteristics, we use the third-order hourglass network structure. It is used for the extraction and fusion of multiscale characteristics of sports. In addition, in the hourglass, internal network residual-intensive modules are introduced, realizing characteristics in different levels of network transfer and reuse. It is helpful for maximum details to feature extracting and enhancing the network expression ability. Comparison and ablation experiments are also carried out to prove the effectiveness and superiority of the proposed algorithm.