Mechanism Of Modulation Research Articles

AMFANet: Advanced Deep Learning Techniques for High-Quality Image Style Transfer

This paper presents AMFANet, an advanced deep learning model engineered for high-quality image style transfer. AMFANet integrates cutting-edge techniques such as the Adaptive Multi-Scale Feature Fusion (AMSF) module and Hybrid Attention Mechanism (HAM) to significantly improve style consistency, content fidelity, and texture preservation. The model also utilizes Segmented Atrous Spatial Pyramid Pooling (SASPP) for effective multi-scale feature extraction. Comprehensive experimental evaluations demonstrate that AMFANet surpasses current state-of-the-art models like StyleGAN3, ChipGAN, ACL-GAN, and CycleGAN in generating high-fidelity stylized images while preserving intricate details and artistic essence. Future research will focus on optimizing computational efficiency, enabling multi-style transfer, enhancing user interaction, and exploring cross-domain applications. These findings highlight AMFANet’s potential as a robust solution for advanced image style transfer in both artistic and practical domains.

Development of a dual-DOF vibration energy harvester using a foldable spring pendulum mechanism

Abstract The novelty of this design is that it changes the traditional idea of an unchangeable pendulum length to achieve folding and stretching through the shear fork structure. The concept of spring as the modulation mechanism of the vibration energy harvester is introduced to complete the dynamic change of the pendulum length and realize the dual degrees of freedom output of the roll and heave motions. The spring mechanism integrated into the design, including tension and torsion springs, not only realizes the flexible expansion of the structure but also significantly enhances the ability of the vibration energy harvester to capture external vibration energy through the unique energy storage and release mechanism. The vibration energy harvester has been designed, mathematically simulated, constructed, and experimentally tested with good results, characterized by dual mode state, low-frequency characteristics, and high-power output. Experimental results show that the prototype can achieve an average power output of 5.47 W at an excitation frequency of 0.8 Hz, corresponding to a normalized power output of 377.42 W g−2 and a normalized power density of 26.25 W g−2 kg−1.

Copper Increases the Cooperative Gating of Rat P2X2a Receptor Channels.

Background/Objectives: P2X receptor channels are widely expressed in the CNS, where they have multiple functions in health and disease. The rat P2X2a (rP2X2a) receptor channel is modulated by copper, an essential trace element that plays important roles in synaptic modulation and neurodegenerative disorders. Although essential extracellular amino acids that coordinate copper have been identified, the exact mechanism of copper-induced modulation has not been yet elucidated. Methods: We used HEK293T cells expressing rP2X2a channel(s) and performed outside-out single-channel and whole-cell recordings to explore copper's effects on rP2X2 currents and determine whether this metal can increase the cooperative gating of rP2X2a channel. Results: In whole-cell recordings and in patches containing 2 or 3 rP2X2a channels, copper enhanced the ATP-induced currents, significantly reducing the ATP EC50 and increasing the Hill coefficient. Moreover, copper increased the apparent Po in patches containing two or three channels. By contrast, in patches containing only one rP2X2a channel, we did not observe any significant changes in ATP EC50, the Hill coefficient, or Po. Conclusions: Copper modulates the gating of rP2X2a channels, enhancing interchannel cooperativity without altering single-channel conductance or Po. This novel regulatory mechanism could be relevant for understanding the role of P2X2 channels in physiological and pathological processes.

Entropy-engineered perovskite cathodes: A novel approach for efficient and durable CO2 electrolysis

The application of solid oxide electrolysis cells (SOECs) for high-temperature CO2 reduction reaction (CO2RR) is constrained by the electrochemical activity and stability of the cathode materials. In this study, a series of iron-based perovskite oxides, designed by systematically varying A-site configurational entropy, are investigated as cathode materials for the CO2RR. Experimental results reveal that these high-entropy materials, derived from La1/2Sr1/2FeO3-δ (LSF), exhibit high electrocatalytic activity and durability. Notably, the SOEC with La1/5Sr1/5Pr1/5Ba1/5Ca1/5FeO3-δ (LSPBCF) cathode achieves a remarkable current density of 2.14 A cm-2 at 800 °C and 1.5V, maintaining excellent stability over 120h of operation with negligible fluctuations. Density functional theory (DFT) calculations further unveil the electronic structure modulation mechanism of the high-entropy material, revealing that A-site entropy engineering could enhance CO2 adsorption and activation by reducing the oxygen vacancy formation energy. This study underscores the potential of entropy engineering to improve the electrocatalytic performance and stability of other energy conversion systems.

Insight into heterovalent metal modification for lanthanum carbonate to enhance phosphate removal at trace levels

Insight into heterovalent metal modification for lanthanum carbonate to enhance phosphate removal at trace levels

Stochastic Epigenetic Modification and Evolution of Sex Determination in Vertebrates

In this report, we propose a novel mathematical model of the origin and evolution of sex determination in vertebrates that is based on the stochastic epigenetic modification (SEM) mechanism. We have previously shown that SEM, with rates consistent with experimental observation, can both increase the rate of gene fixation and decrease pseudogenization, thus dramatically improving the efficacy of evolution. Here, we present a conjectural model of the origin and evolution of sex determination wherein the SEM mechanism alone is sufficient to parsimoniously trigger and guide the evolution of heteromorphic sex chromosomes from the initial homomorphic chromosome configuration, without presupposing any allele frequency differences. Under this theoretical model, the SEM mechanism (i) predated vertebrate sex determination origins and evolution, (ii) has been conveniently and parsimoniously co-opted by the vertebrate sex determination systems during the evolutionary transitioning to the extant vertebrate sex determination, likely acting "on top" of these systems, and (iii) continues existing, alongside all known vertebrate sex determination systems, as a universal pan-vertebrate sex determination modulation mechanism.

A multi-scale temporal convolutional capsule network with parameter-free attention module-dynamic routing for intelligent diagnosis of rolling bearing

Abstract We proposed a multi-scale temporal convolutional capsule network model coupled with a parameter-free attention module and dynamic routing mechanism to analyze complex vibration signals for diagnosing the health of bearings. The proposed method utilizes a capsule network as the fundamental architecture. Instead of a convolutional neural network, a temporal convolutional network is employed. Additionally, a multi-scale feature fusion module is integrated into the capsule network structure to dynamically extract various layers of features from fault samples, enhancing the discriminatory capability of abnormal data. Subsequently, the parameter-free attention module and dynamic routing mechanism are employed to construct digital capsules. This allows the smallest unit capsule in a single layer to carry more information, enhance the similarity between the instance primary capsule and the fault capsule, reduce the interference of irrelevant features to the model, and improve the accuracy of fault type recognition. Finally, a multi-scale temporal convolutional capsule network model that integrates feature extraction and pattern recognition is established to perform end-to-end diagnosis of the bearing. Experimental findings suggest that the proposed method outperforms other deep learning methods in terms of accuracy and robustness. It can provide a theoretical basis and implementation path for the detection and diagnosis of train wheelset bearing time series abnormal data.

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models

Few-Shot Anomaly Detection (FSAD) in industrial images aims to identify abnormalities using only a few normal images, which is crucial for industrial scenarios where sample training is limited. The recent advances in large-scale pre-trained visual-language models have brought significant improvements to the FSAD, which typically requires hundreds of text prompts to be manually crafted through prompt engineering. However, manually designed text prompts cannot accurately match the informative features of different categories across diverse images, and the domain gap between train and test datasets can severely impact the generalization capability of text prompts. To address these issues, we propose a visual-language model based on fine-grained learnable text prompts as a unified general framework for FSAD in industry. Firstly, we design a Fine-grained Text Prompts Adapter (FTPA) and an associated registration loss to enhance the efficiency of text prompts. The manually designed text prompts are improved and optimized by capturing normal and abnormal semantic information in the image, so that the text prompts can describe the image semantic information at a finer granularity. In addition, we introduce a Dynamic Modulation Mechanism (DMM) to avoid potential errors in text prompts post-training due to the agnostic during cross-dataset detection. This is achieved by explicitly modulating the branch guided by few-shot images and the branch guided by fine-grained text prompts. Extensive experiments demonstrate that our proposed method achieves state-of-the-art few-shot industrial anomaly detection and segmentation performance. In the 4-shot, the AUROC of the anomaly classification and anomaly segmentation achieves 98.3%, 96.3%, and 93.8%, 97.9% on the MVTec-AD and VisA datasets, respectively.

Time-dependent phenotypical changes of microglia drive alterations in hippocampal synaptic transmission in acute slices.

It is widely acknowledged that microglia actively regulate synaptic function in the brain. Remarkably, much of our understanding regarding the role of microglia in synaptic regulation is derived from studies in acute brain slices. However, it is still uncertain to what extent the preparation and maintenance of acute slices can influence microglial function and whether microglial changes may affect synaptic transmission. In this study, we examined the impact of acute slice resting time on hippocampal CA1 microglia, by assessing morphological and functional parameters at two distinct time intervals. We report that after 4 h from slicing microglia undergo morphological, functional, and transcriptional changes, including a decrease in the number of branches and in their movement speed. Furthermore, microglia acquire a reactive phenotype, characterized by increased amplitude of outward rectifying K+ currents, increased expression of the pro-inflammatory cytokine Tnfα and altered expression of the microglial receptors Cx3cr1 and P2y12r. We also examined time-dependent changes of excitatory synaptic transmission in CA1 pyramidal neurons from acute hippocampal slices, reporting time-dependent decrease in both amplitude and frequency of postsynaptic currents (sEPSCs), along with a decrease in spine density. Noticeably, sEPSCs amplitude decrease was absent in slices prepared from PLX5622 microglia-depleted mice, suggesting that this time-dependent effect on synaptic transmission is microglia-dependent. Our findings highlight possible causal relation between microglia phenotypic changes in the hours following slice preparation and concomitant synaptic changes, pointing to the mechanisms of acute synaptic modulation, whose understanding is crucial for unraveling microglia-neurons interplay in nature. Furthermore, they emphasize the potential issues associated with experimental time windows in ex vivo samples.

Wavelength-tunable high-repetition-rate pulse generation in a SOA-based fiber laser with gain instability

Abstract Introducing negative optical feedback into a semiconductor optical amplifier-based laser presents a straightforward and effective approach for generating high-repetition-rate pulses through the mechanism of self-sustaining cross-gain modulation. In this work, we demonstrate, for the first time, the tunability of high-frequency pulse generation over a wide wavelength range by adjusting the central transmission wavelength of a tunable filter. Our laser system achieved a full tuning range from 1480 nm to 1556 nm (76 nm) while maintaining pulsed lasing. Remarkably, the laser exhibited a maximum pulse contrast exceeding 85% over a tuning range of 43 nm, from 1505.7 nm to 1548.7 nm. At any wavelength within this range, stable, self-starting pulse generation was consistently achieved, with a pulse repetition rate of 1.3 GHz.

Mechanism and Application of Optical Modulation of Terahertz Waves in a Perovskite Heterojunction

Mechanism and Application of Optical Modulation of Terahertz Waves in a Perovskite Heterojunction

No-Reference Quality Assessment Based on Dual-Channel Convolutional Neural Network for Underwater Image Enhancement

Underwater images are important for underwater vision tasks, yet their quality often degrades during imaging, promoting the generation of Underwater Image Enhancement (UIE) algorithms. This paper proposes a Dual-Channel Convolutional Neural Network (DC-CNN)-based quality assessment method to evaluate the performance of different UIE algorithms. Specifically, inspired by the intrinsic image decomposition, the enhanced underwater image is decomposed into reflectance with color information and illumination with texture information based on the Retinex theory. Afterward, we design a DC-CNN with two branches to learn color and texture features from reflectance and illumination, respectively, reflecting the distortion characteristics of enhanced underwater images. To integrate the learned features, a feature fusion module and attention mechanism are conducted to align efficiently and reasonably with human visual perception characteristics. Finally, a quality regression module is used to establish the mapping relationship between the extracted features and quality scores. Experimental results on two public enhanced underwater image datasets (i.e., UIQE and SAUD) show that the proposed DC-CNN method outperforms a variety of the existing quality assessment methods.

Innovative active terahertz modulator based on dynamic graphene-silicon arrays

This paper presents an innovative active terahertz (THz) modulator that utilizes dynamic graphene-silicon arrays (DGSAs) to overcome the challenges of dynamically manipulating THz waves. The DGSA combines the exceptional electrical properties of graphene with the enhanced optical absorption capabilities of silicon arrays, enabling dual-mode active control through optical pumping and electrical biasing. An 808 nm laser is used to photoexcite carriers in the silicon arrays for optical pumping while a bias voltage modulates the Fermi level of graphene, subsequently varying Si conductivity and influencing the transmission properties of THz waves. Simulations have demonstrated modulation depths of up to 94% at 2.5 THz in the transmission mode and 89% at 1 THz in the reflection mode. Furthermore, this study delves into a theoretical analysis and numerical simulations, exploring the modulation mechanisms, including the role of graphene’s Drude model and PN junction effects. These insights provide a robust theoretical framework for understanding the DGSA’s operation and lay a solid foundation for the design and optimization of high-performance THz modulation devices.

N-terminal cleavage of cyclophilin D boosts its ability to bind F-ATP synthase

Cyclophilin (CyP) D is a regulator of the mitochondrial F-ATP synthase. Here we report the discovery of a form of CyPD lacking the first 10 (mouse) or 13 (human) N-terminal residues (ΔN-CyPD), a protein region with species-specific features. NMR studies on recombinant human full-length CyPD (FL-CyPD) and ΔN-CyPD form revealed that the N-terminus is highly flexible, in contrast with the rigid globular part. We have studied the interactions of FL and ΔN-CyPD with F-ATP synthase at the OSCP subunit, a site where CyPD binding inhibits catalysis and favors the transition of the enzyme complex to the permeability transition pore. At variance from FL-CyPD, ΔN-CyPD binds OSCP in saline media, indicating that the N-terminus substantially decreases the binding affinity for OSCP. We also provide evidence that calpain 1 is responsible for generation of ΔN-CyPD in cells. Altogether, our work suggests the existence of a novel mechanism of modulation of CyPD through cleavage of its N-terminus that may have significant pathophysiological implications.

Apolipoprotein A1-encoding recombinant adenovirus remodels cholesterol metabolism in tumors and the tumor microenvironment to inhibit hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor requiring effective treatments. Oncolytic viruses induce anti-tumor responses but have limited efficacy. Apolipoprotein A1 (ApoA1) inhibits inflammation, modulates immunity, and promotes anti-oxidation. This study aims to construct an oncolytic adenovirus (Ad5)-ApoA1 for superior anti-tumor effects. We analyzed ApoA1 expression in tumors and its prognostic significance using public databases. Subsequently, we engineered a recombinant oncolytic adenovirus Ad5-ApoA1 and assessed its replication and oncolytic efficacy in vitro and in nude mice. The impact of Ad5-ApoA1 on the tumor microenvironment of HCC was evaluated through flow cytometry, transcriptome sequencing, single-cell sequencing, and other methodologies. Additionally, mechanisms of immune microenvironment modulation by Ad5-ApoA1 were explored. ApoA1 expression was down-regulated with HCC progression and significantly positively correlated with the prognosis of HCC patients. Ad5-ApoA1 exhibited robust oncolytic activity but showed no therapeutic effect on nude mice. However, it significantly inhibited HCC growth and prolonged the survival period of both healthy-immune and humanized immune-reconstituted NCG mice. Furthermore, Ad5-ApoA1 significantly promoted the expression of IFN-γ and GzmB in CD8+ T cells while inhibiting the expression of PD-1 and LAG-3. Notably, the cholesterol content in the CD8+ T cells studied was significantly correlated with the expression of PD-1 and LAG-3, with ApoA1 promoting cholesterol efflux and reducing cholesterol levels. Ad5-ApoA1 activates CD8+ T cells by promoting large-scale viral replication. High levels of ApoA1 protein expression promote cholesterol efflux, inhibit CD8+ T cell depletion, and reduce inflammatory factors, ultimately leading to superior therapeutic effects on hepatocellular carcinoma.

Giant tunnel electroresistance through a Van der Waals junction by external ferroelectric polarization

The burgeoning interest in two-dimensional semiconductors stems from their potential as ultrathin platforms for next-generation transistors. Nonetheless, there persist formidable challenges in fully obtaining high-performance complementary logic components and the underlying mechanisms for the polarity modulation of transistors are not yet fully understood. Here, we exploit both ferroelectric domain-based nonvolatile modulation of Fermi level in transitional metal dichalcogenides (MoS2) and quantum tunneling through nanoscale hexagonal boron nitride (h-BN). Our prototype devices, termed as vertical tunneling ferroelectric field-effect transistor, utilizes a Van der Waals MoS2/h-BN/metal tunnel junction as the channel. The Fermi level of MoS2 is bipolarly tuned by ferroelectric domains and sensitively detected by the direct quantum tunneling strength across the junction, demonstrating an impressive electroresistance ratio of up to 109 in the vertical tunneling ferroelectric field-effect transistor. It consumes only 0.16 fJ of energy to open a ratio window exceeding 104. This work not only validates the effectiveness of tailored tunnel barriers in manipulating electronic flow but also highlights a new avenue for the design flexibility and functional versatility of advanced ferroelectric memory technology.

Polysaccharides of natural products alleviate antibiotic-associated diarrhea by regulating gut microbiota: a review.

Antibiotic-associated diarrhea (AAD) is diarrhea caused by disturbances in intestinal microbiota and metabolism following inappropriate use of antibiotics. With the over-reliance on antibiotics, the incidence of AAD is increasing worldwide. Recently, the role of probiotics and prebiotic preparations in the prevention and treatment of AAD has received increasing attention. Various prebiotics can not only reduce the incidence of AAD, but also effectively shorten the course of the disease and alleviate the symptoms. Notably, many polysaccharides derived from plants and fungi are a class of biologically active and rich prebiotics with great potential to alleviate AAD. Therefore, this review aims to summarize the latest research on natural product polysaccharides to alleviate antibiotic-associated diarrhea by modulating the gut microbiota. It provides a theoretical basis for exploring the mechanism of natural product modulation of gut microbiota to alleviate AAD, and provides a reference for further development of active prebiotics.

A Strong Noise Reduction Network for Seismic Records

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications.

Mechanistic insight into accelerating of light olefin products by surface OH* group on the Fe5C2 catalyst

Mechanistic insight into accelerating of light olefin products by surface OH* group on the Fe5C2 catalyst

Preparation and characterization of Tobacco polysaccharides and its modulation on hyperlipidemia in high-fat-diet-induced mice.

This study aimed to investigate the structural properties of tobacco polysaccharide (TP) and its mechanism of modulating hyperlipidemia in high-fat diet-induced mice. The structural properties of TP were characterized by FT-IR, 1HNMR, SEM, AFM and thermogravimetric analysis. And the regulatory mechanism of TP on lipid metabolism was investigated in hyperlipidemia mice. These results showed that TP had a high composition of reducing monosaccharide and the glycosidic bond type was α-glycosidic bond. The intervention by TP resulted in a significant reduction of body weight and improvement in lipid accumulation. And the modulation mechanism by which TP ameliorated the abnormalities of lipid metabolism was associated with the expression levels of lipid metabolism-related genes and serum exosomes miRNA-128-3p, as well as the modulation of structure and abundance of the gut microbiota in mice. In addition, TP treatment significantly increased the content of short-chain fatty acids (SCFAs) in mice feces. The results of molecular docking and dual-luciferase assay exhibited a good interaction between propionic acid and PPAR-α, and it was hypothesized that the interaction might further ameliorate the hyperlipidemia. Therefore, TP can regulate the expression levels of lipid metabolism-related genes through miRNAs from serum exosomes and SCFAs from gut microbiota.