Selective Channels Research Articles

Ligand-Targeted Ag9 Cluster Self-Cascade Antioxidants for Enhanced Reactive Oxygen Species Clearance in the Treatment of Inflammatory Bowel Disease.

Atomically precise metal nanoclusters (MNCs) with flexible peripheral ligands provide selective channels and unique electronic structures to the rigid metal core, showing great application potential in many fields such as biomimetic nanozymes. In this study, we developed cost-effective, low-toxicity, and water-soluble MNC based self-cascade antioxidants for enhanced reactive oxygen species clearance. Compared to Ag nanoparticles, the Ag9 NCs with a monovalent Ag core coordinated by nonmetallic S and O show superior superoxide dismutase-like (inhibition 86.7%) and catalase-like (Km = 20.37 mM, Vmax = 3.13 × 10-3 mM s-1) activities. Interestingly, the -COOH groups on the ligands endow Ag9 NCs with a -42.5 mV negative surface charge, enhancing the targeting to inflammatory cells. Additionally, Ag9 NCs' remarkable therapeutic effects have been confirmed in cell cultures and an inflammatory bowel disease (IBD) model. Thus, this study proposes a novel cluster-based nanozyme that utilizes ligands for targeting and monovalent metal cores for catalysis, demonstrating significant therapeutic potential for IBD treatment.

BSPKTM-SIFE-WST: bispectrum based channel selection using set-based-integer-coded fuzzy granular evolutionary algorithm and wavelet scattering transform for motor imagery EEG classification

BSPKTM-SIFE-WST: bispectrum based channel selection using set-based-integer-coded fuzzy granular evolutionary algorithm and wavelet scattering transform for motor imagery EEG classification

Structural insights into light-gating of potassium-selective channelrhodopsin

Structural information on channelrhodopsins’ mechanism of light-gated ion conductance is scarce, limiting its engineering as optogenetic tools. Here, we use single-particle cryo-electron microscopy of peptidisc-incorporated protein samples to determine the structures of the slow-cycling mutant C110A of kalium channelrhodopsin 1 from Hyphochytrium catenoides (HcKCR1) in the dark and upon laser flash excitation. Upon photoisomerization of the retinal chromophore, the retinylidene Schiff base NH-bond reorients from the extracellular to the cytoplasmic side. This switch triggers a series of side chain reorientations and merges intramolecular cavities into a transmembrane K+ conduction pathway. Molecular dynamics simulations confirm K+ flux through the illuminated state but not through the resting state. The overall displacement between the closed and the open structure is small, involving mainly side chain rearrangements. Asp105 and Asp116 play a key role in K+ conductance. Structure-guided mutagenesis and patch-clamp analysis reveal the roles of the pathway-forming residues in channel gating and selectivity.

Cognitive Fish Swarm Optimization for Multi-Objective Routing in IoT-based Wireless Sensor Networks utilized in Greenhouse Agriculture

This research presents the working mechanism of Cognitive Fish Swarm Optimization (CFSO) for multi-objective routing and channel selection in Internet of Things (IoT)-based Wireless Sensor Networks (IWSNs). CFSO is inspired by the collective intelligence and cooperation observed in fish swarms. The model involves three main components: perception, cognition, and behavior. Each fish in the swarm perceives the network conditions by gathering information from its surrounding environment, including signal strength, channel availability, and network congestion. The fish then utilizes its cognitive abilities to evaluate different routing paths and channel options based on specific objectives, namely energy efficiency, packet delivery ratio, and delay. This evaluation process involves analyzing historical information and utilizing heuristics to create notified results. Each fish adapts its behavior by adjusting its movement pattern and selecting optimal routing paths and channels. This adaptive behavior is critical for achieving reliable and efficient data transmission in IWSNs. The fish swarm balances exploration and exploitation strategies to search for optimal solutions comprehensively. Exploration allows for discovering new paths and channels, while exploitation focuses on refining the best-known solutions. The efficiency of the CFSO method in enhancing data transmission efficiency in greenhouse agriculture applications was validated through extensive simulations in the NS-3 network simulation framework. The findings suggest that the CFSO method is a promising technique for addressing routing and channel selection challenges in IWSN by leveraging the collective intelligence of fish swarms. The CFSO model portrayed a superior throughput and Network Lifetime (NLT) values of 71.34% and 77.20%, respectively, significantly outpacing SSEER and CRP across overall node counts.

An enhanced deep learning model based on smoothed pseudo Wigner-Ville distribution technique for emotion recognition with channel selection

An enhanced deep learning model based on smoothed pseudo Wigner-Ville distribution technique for emotion recognition with channel selection

Research on failure diagnosis analysis of plunger gas lift system using convolutional neural network with multi-scale channel attention mechanism based on wavelet transform

Plunger gas lift technology has been extensively utilized in unconventional gas fields, owing to its distinct engineering benefits. However, as development challenges intensify, fault diagnosis has grown more intricate, and conventional diagnostic techniques exhibit delays and inaccuracies. With advancements in computer science, machine learning methods have demonstrated their prowess in establishing robust correlations between data features and prediction outcomes. Consequently, this paper introduces a convolutional neural network fault diagnosis model that incorporates a multi-scale channel attention mechanism based on wavelet transform. This model dissects features across various scales using wavelet transform and leverages channel attention to adaptively select channels encompassing fault features, thereby enhancing diagnostic and recognition accuracy. Furthermore, the model integrates a hyperparameter search optimization algorithm to refine the model’s architecture and comprehensively bolster its generalization capability. A comparison with actual field data reveals that the fault diagnosis accuracy of the WT-MACNN model stands at 83.33%. Digital ablation experiments underscore the limited accuracy of the basic CNN model in fault diagnosis, but the sequential introduction of the channel attention mechanism and wavelet transform layers significantly elevates model performance. When juxtaposed with SVM, KNN, and decision tree models, the WT-MACNN model exhibits a diagnostic accuracy improvement of 73.81%, 57.13%, and 54.76%, respectively. Additionally, to assess the model’s adaptability under diverse well conditions, this study reacquired 128 sets of field data. The verification results indicate that the model’s prediction accuracy across different well conditions is 83.59%. Despite occasional misjudgments among certain operating conditions, the overall performance remains outstanding, offering dependable support for diagnosing real-world scenarios. The outcomes of numerical experiments highlight the profound advantages of the proposed deep learning model in terms of generalization ability and diagnostic accuracy, validating its superiority in plunger gas lift fault identification and carrying substantial guidance for the application of this technology.

A Practical and Highly Efficient Procedure for the Synthesis of the Trifluoromethylpyrazol-Derived Canonical Transient Receptor ­Potential Channel (TRPC3) Inhibitor Pyr3 and Its Derivatives

AbstractCanonical transient receptor potential (TRPC) channels play a variety of diverse biological functions. Among the subgroups of the TRPC family (TRPC3, TRPC6, and TRPC7), TRPC3 has been implicated in several diseases, including neurological and cardiovascular diseases, cancer, and neurodegeneration. A pyrazole-based compound Pyr3 is a selective TRPC3 channel inhibitor which is now a standard tool compound to study conditions associated with TRPC3 dysregulation. However, the reported literature syntheses of Pyr3 either provided low yield or involved microwave reactions and harsh reaction conditions. We have developed a practical and highly efficient procedure for the synthesis of Pyr3 and its derivatives in high yields which is suitable for scale-up synthesis.

Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG

IntroductionApproximately 15 million premature infants are born each year, many of whom face risks of neurological impairments. Accurate assessment of brain maturity is crucial for timely intervention and treatment planning. Electroencephalography (EEG) is a noninvasive method commonly used for this purpose. However, using all channels and features for brain maturity assessment can lead to high computational burden and overfitting, which can decrease the performance of the prediction system.MethodsIn this study, we propose an automatic prediction framework based on EEG to predict functional brain age (FBA) for assessing brain maturity in preterm infants. To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. For feature selection, we combine the Pearson Correlation Coefficient (PCC), Recursive Feature Elimination (RFE), and Support Vector Regression (SVR) model.ResultsThe proposed framework achieved a prediction accuracy of 76.71% within ±1 week and 94.52% within ±2 weeks. Effective channel and feature selection significantly improved model performance while reducing computational costs.DiscussionThese results demonstrate that optimizing channel and feature selection can enhance the performance of FBA prediction in preterm infants, offering a more efficient and accurate tool for brain maturity assessment.

A Reinforcement Learning Inspired Approach for Efficient Cognitive Radio Network Routing

Introduction: One fundamental characteristic of Cognitive Radio Networks (CRNs) is their dynamic operating environment, where network conditions, such as the activities of Primary Users (PUs), undergo continuous changes over time. While Secondary Users (SUs) are engaged in communication, if a PU reappears on an SU's channel, the SU is required to vacate the channel and switch to another available channel. Thus, finding a stable route that minimizes frequent channel switches is a challenging task in CRNs. Method: Existing solutions to reduce PU interference often overlook the energy consumption of nodes when forming clusters, focusing solely on the minimum number of common channels in a cluster. Consequently, these schemes suffer from frequent channel switches due to PU appearances. The proposed Cognitive Radio Network Routing (CRNR) approach aims to minimize frequent channel switches by employing a Reinforcement Learning (RL) technique called Q-Learning to select stable routes with channels exhibiting higher OFF-state probabilities. Result: This strategy ensures that selected routes avoid rerouting by prioritizing channels with higher off-state probabilities. Experimental studies demonstrate that the CRNR approach enhances network throughput and reduces interference when compared with existing techniques. CRNR introduces a novel application of AI, use of Q-Learning, a reinforcement learning technique in wireless networks. Conclusion: This bridges the gap between machine learning and network design, showcasing how intelligent algorithms can optimize communication decisions in real-time, which could inspire further exploration of AI-driven techniques in network management and beyond. conclusion: The CRNR approach, through the use of Q-Learning and channel selection based on OFF-state probabilities, provides a more stable routing solution in CRNs. It enhances network performance by reducing rerouting and interference while addressing energy consumption concerns during cluster formation.

Advances in the development of TRPM2 channel inhibitors.

The studies on specific transient receptor potential melastatin 2(TRPM2) channel inhibitors can deepen our understanding of the pathological mechanism of related diseases, and discover novel, effective targets for therapeutic drugs. The development of TRPM2 channel inhibitors can be broadly classified into four categories with distinct characteristics: reutilization and structural modification of homologous ion channel modulators to produce a diverse array of TRPM2 channel inhibitors with strong inhibitory effects; TRPM2 channel inhibitors developed on channel gating mechanism with high specificity; inhibitors developed through high-throughput screening with novel chemical structures; inhibitors developed from natural antioxidants with higher safety. In recent years, the application of computer-aided drug design has significantly accelerated the development of TRPM2 channel inhibitors. Several promising compounds such as ZA18, A1 and D9 have been discovered, and it is expected that more potent and selective TRPM2 channel inhibitor scaffolds will be discovered in the future. This article reviews the advances on the studies of TRPM2 channel inhibitors, aiming to provide insights for the further research and clinical application of TRPM2 channel inhibitors.

A cross-domain-based channel selection method for motor imagery.

Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental resultsvalidated that CDCS is effective in selecting important channels.

Automatic channel selection using multi-objective prioritized jellyfish search (MPJS) algorithm for motor imagery classification using modified DB-EEGNET

Automatic channel selection using multi-objective prioritized jellyfish search (MPJS) algorithm for motor imagery classification using modified DB-EEGNET

Supplier Encroachment Channel Selection on an Online Retail Platform

Supplier Encroachment Channel Selection on an Online Retail Platform

Targeting TRPC channels for control of arthritis-induced bone erosion.

Arthritis leads to bone erosion due to an imbalance between osteoclast and osteoblast function. Our prior investigations revealed that the Ca2+-selective ion channel, Orai1, is critical for osteoclast maturation. Here, we show that the small-molecule ELP-004 preferentially inhibits transient receptor potential canonical (TRPC) channels. While ELP-004 minimally affected physiological RANKL-induced osteoclast maturation in murine bone marrow- and spleen-derived myeloid cells (BMSMCs) and human PBMC-derived cells, it potently interfered with osteoclast maturation driven by TNFα or LTB4. The contribution of TRPC channels to osteoclastogenesis was examined using BMSMCs derived from TRPC4-/- or TRPC(1-7)-/- mice, again revealing preferential interference with osteoclastogenesis driven by proinflammatory cytokines. ELP-004 also reduced bone erosion in a mouse model of rheumatoid arthritis. These investigations reveal TRPC channels as critical mediators of inflammatory bone erosion and provide insight into the major target of ELP-004, a drug currently in preclinical testing as a therapeutic for inflammatory arthritis.

Simultaneous Determination of Three Active Forms of Vitamin B12 In Situ Produced During Fermentation by LC-MS/MS.

The in situ fortification of vitamin B12 (VB12) in foods through fermentation is an effective strategy to address the deficiency of this micronutrient, and precise monitoring of VB12 production is crucial for developing VB12-fortified functional foods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is advantageous for analyzing trace substances in food due to its high sensitivity. In the present study, an LC-MS/MS method capable of rapidly and accurately quantifying three active forms of VB12, namely adenosylcobalamin (AdoCbl), methylcobalamin (MeCbl), hydroxocobalamin (OHCbl), in 8 min were developed. Meanwhile, the quantitative result of this method is not affected by pseudo-VB12 because the selected ion channels include fragments of active VB12. Maintaining light-shielding during extraction and purification is essential, as light exposure during the process can decrease the content of detected VB12 by about 30%. At last, the developed method was applied for the determination of VB12 in fermented rice bran and the cell mass of Propionibacterium freudenreichii. The results showed that AdoCbl was the predominant form of VB12 during fermentation, and the addition of cobalt did not influence the proportions of the three VB12 types. The present study reported a rapid and accurate method for the simultaneous determination of three active forms of VB12, which can effectively support the development of foods with VB12 fortification.

Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field.

Coherent Optics for Passive Optical Networks: Flexible Access, Rapid Burst Detection, and Simplified Structure

With the development of the Internet of Things, cloud networking, and 4K/8K high-definition video, global internet traffic has seen a dramatic increase. This surge in traffic has placed higher demands on the performance of optical networks, featuring higher data rates, lower latency, and lower cost. The passive optical network (PON) is a representative scenario of optical access networks. Issues such as burst-mode detection in upstream PON scenarios, flexible rate allocation in downstream scenarios, and the simplification of hardware complexity at the optical network unit (ONU) side have attracted considerable attention. Compared to intensity modulation/direct detection (IM/DD), a recently proposed coherent PON incorporates a local oscillator laser at the receiver, enabling superior receiver sensitivity, spectrally efficient modulation, linear optical field recovery, and flexible channel selection. These features significantly enhance the flexibility and data rates of PON systems. This paper provides a comprehensive review of the development of coherent PONs, particularly in aspects of preamble design for burst-mode detection in upstream scenarios, the design of flexible rate PONs in downstream scenarios, and solutions for reducing hardware complexity at the ONU side.

Application of Evolutionary Game to Analyze Dual-Channel Decisions: Taking Consumer Loss Aversion into Consideration

Manufacturers and consumers are boundedly rational and ultimately seek evolutionarily stable strategies through trial and error, imitation, and learning. It is important to study the pricing strategies of manufacturers and the purchasing channel decisions of consumers in the context of increasingly fierce competition in online channels, in addition to consumers’ loss aversion due to increasingly confusing promotional strategies; accordingly, in this paper, an evolutionary game including both parties is constructed, and the loss aversion factor from prospect theory is introduced. Based on data from Chinese media reports on the cosmetics industry, simulation and sensitivity analyses were conducted using Matlab R2024a. The results indicate that—in addition to channel services affecting the evolutionarily stable strategy for purchasing channel selection—a decrease in consumer loss aversion will help consumers reach the evolutionarily stable strategy faster. For manufacturers, channel services do not affect their evolution to a unified pricing strategy; however, when consumer loss aversion increases, manufacturers’ evolutionarily stable strategy will shift from a unified pricing strategy to a differentiated pricing strategy.

PH-induced conformational changes in the selectivity filter of a potassium channel lead to alterations in its selectivity and permeation properties.

The Selectivity Filter (SF) in tetrameric K+ channels, has a highly conserved sequence, TVGYG, at the extracellular entry to the channel pore region. There, the backbone carbonyl oxygens from the SF residues, create a stack of K+ binding sites where dehydrated K+ binds to induce a conductive conformation of the SF. This increases intersubunit interactions and confers a higher stability to the channel against thermal denaturation. Indeed, the fit of dehydrated K+ to its binding sites is fundamental to define K+ selectivity, an important feature of these channels. Nonetheless, the SF conformation can be modified by different effector molecules. Such conformational plasticity opposes selectivity, as the SF departs from the "induced-fit" conformation required for K+ recognition. Here we studied the KirBac1.1 channel, a prokaryotic analog of inwardly rectifying K+ channels, confronted to permeant (K+) and non-permeant (Na+) cations. This channel is pH-dependent and transits from the open state at neutral pH to the closed state at acidic pH. KirBac1.1 has the orthodox TVGYG sequence at the SF and thus, its behavior should resemble that of K+-selective channels. However, we found that when at neutral pH, KirBac1.1 is only partly K+ selective and permeates this ion causing the characteristic "induced-fit" phenomenon in the SF conformation. However, it also conducts Na+ with a mechanism of ion passage reminiscent of Na+ channels, i.e., through a wide-open pore, without increasing intersubunit interactions within the tetrameric channel. Conversely, when at acidic pH, the channel completely loses selectivity and conducts both K+ and Na+ similarly, increasing intersubunit interactions through an apparent "induced-fit"-like mechanism for the two ions. These observations underline that KirBac1.1 SF is able to adopt different conformations leading to changes in selectivity and in the mechanism of ion passage.

Deep learning-enabled filter-free fluorescence microscope.

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering. This approach allows for automatic fluorescence channel selection after image acquisition and accurate prediction of fluorescence by computing color changes due to spectral shifts with the presence of excitation scattering. Fluorescence prediction for cells and tissues labeled with various fluorophores was demonstrated under different magnification powers. The technique offers accurate identification of labeling with robust sensitivity and specificity, achieving consistent results with the reference standard. Beyond fluorescence microscopy, the deep learning-enabled spectral filtering strategy has the potential to drive the development of other biomedical applications, including cytometry and endoscopy.