Selective Channels Research Articles

Remaining Useful Life Prediction of Aero-engine via Temporal Convolutional Network with Gated Convolution and Channel Selection Unit

As a hot spot in the prognostics and health management (PHM), predicting the remaining useful life (RUL) is significant to ensure the availability and reliability of industrial systems. Data-driven methods, such as the traditional temporal convolutional networks (TCNs), have been widely used for RUL prediction. However, traditional TCNs lack effective means to sufficiently extract both the spatial and temporal features of multi-sensory data. Furthermore, the prediction ability of traditional TCNs is constrained by their inflexible network structure. Here, an integrated model called the improved temporal convolutional network with gated convolution and channel selection unit (GCSU-ITCN) is proposed as a solution to the above problems. First, “the loss boundary to mapping ability” (LM) method is introduced to select sensors with superior mapping ability to the RUL. It contributes to the extraction of features from multi-sensory data. Then, the gated convolution and channel selection unit (GCSU) is designed to extract both local and global features of multi-sensory data, encompassing the spatial and temporal dimensions. Last, the improved temporal convolutional network (ITCN) is developed to capture long temporal correlations within the extracted features. The ITCN has a flexible structure that enables it to learn deep temporal features more effectively than traditional TCNs. A series of comparative experiments is conducted on the C-MAPSS dataset to evaluate the prediction capability of the GCSU-ITCN. Furthermore, ablation experiments are also conducted to assess the contribution of each component within the model.

EXPRESS: Beyond the Pair: Media Archetypes and Complex Channel Synergies in Advertising

Prior research on advertising media mixes has mostly focused on single channels (e.g., television), pairwise cross-elasticities, or budget optimization within single campaigns. This is starkly detached from advertising practice where (i) there is an increasingly large number of media channels available to marketers, (ii) media plans employ complex combinations of channels, and (iii) marketers manage complementarities among many (i.e., more than pairs) channels. This research empirically learns complex channel complementaries using Latent Class analysis. Latent classes have three useful properties: (i) they account for non-random selection of channels into campaigns, (ii) they capture pairwise and higher-order interactions between channels, and (iii) they allow for meaningful interpretation. We empirically describe the most common media channel archetypes and estimate their relationship to the effectiveness of a set advertising campaigns on a set of common brand-related performance metrics. We use a dataset of 1,083 advertising campaigns from around the world run between 2008 and 2019. We find that there is not a systematically “best” media mix that correlates to dominant performance across all metrics, but clear patterns emerge given specific metrics. We find that traditional channels (TV, outdoor) are commonly paired with digital channels (Facebook, YouTube) in high-performing campaigns. We also find that current marketing practice appears far from optimal, and simple strategies have the potential to increase brand mindset metric lifts by 50% or more.

Abstract 4137646: Transcriptomic Profiling of Human Myocardium at Sudden Death to Define Vulnerable Substrate for Lethal Arrhythmias

Background: While some chronic pathological substrates for sudden cardiac death (SCD) are well-known (e.g., coronary disease and left ventricular [LV] dysfunction), the vulnerable myocardial state predisposing to fatal arrhythmia remains a critical barrier to near-term SCD prevention. Hypothesis: Myocardium of autopsy-defined SCDs exhibit distinct expression profiles vs. non-cardiac sudden deaths and trauma deaths that reflect its acute vulnerable state in the hours to days before SCD, as expression arrests upon death. Goals/Aims: Define the vulnerable myocardial substrate for lethal arrhythmia by targeted RNA profiling. Methods: We used autopsy to adjudicate arrhythmic from non-arrhythmic causes in 1,265 sudden deaths in San Francisco from 2011-2018. We performed a transcriptomic evaluation of LV sampled at the time of SCD from 245 consented cases using a curated panel of 448 genes with known or hypothesized association with SCD. Results: Comparison between Arrhythmic (n=129) and Non-Arrhythmic (n=116) cohorts revealed 31 differentially upregulated and 36 downregulated genes (p-adj<0.05), related to collagen-containing extracellular matrix (upregulation of FAP , FMOD , LTBP2 ), ion transport (upregulation of KCNA5 and KCNN3 , and downregulation of KCNJ8, KCNK1, KCNJ5 ), and contraction (downregulation of MYH6 ). Fibrosis-related genes showed the highest magnitude increased expression in Arrhythmic vs. Non-arrhythmic deaths and vs. published transcriptomes from end-stage heart failure. After molecular stratification by known markers for mature ( COL1A1, COL1A2, COL3A1) and active ( POSTN , MEOX1 ) fibrosis, cases with highest expression of both had the highest proportion of arrhythmic cause of death (27/36 [75%]) vs. cases with low expression of both (87/181 [38%], p=0.006) or vs. mature only (10/14 [71%]) or active only (5/14 [36%]). Activated fibroblast gene expression was enriched in Arrhythmic female vs. Arrhythmic male cases, among other sex-specific differences in ion channel and myosin (upregulation of SCN4B , SCN8A , KCNAB1 in females, KCNJ4 and MYH7B in males) expression. Conclusions: RNA profiling of myocardium at SCD identifies active fibrosis, undetectable by conventional clinical methods, in the presence of fixed scar and selected ion channel dysregulation (more pronounced among female cases) as an acute vulnerable substrate for fatal arrhythmias, and may identify patients at elevated near-term risk for SCD and novel pathways for intervention.

GO-TO-MARKET STRATEGIES FOR CONSUMER TECHNOLOGICAL PRODUCTS WITH SUBSCRIPTION MONETIZATION MODEL

This study examines go-to-market (GTM) strategies for consumer technological products utilizing subscription monetization models. As the digital landscape evolves, businesses are increasingly adopting subscription-based approaches to generate recurring revenue and establish long-term customer relationships. Our research investigates the key components of successful GTM strategies in this context, focusing on market segmentation, value proposition development, pricing strategies, distribution channel selection, and customer acquisition and retention tactics. Through a comprehensive analysis of scientific papers, industry reports, and case studies, we identify best practices and trends in subscription-based GTM strategies for consumer tech products. Our findings highlight the importance of market segmentation, value proposition development, pricing strategies, marketing and communication, customer acquisition and retention practices in optimizing customer lifetime value. This study contributes to the growing body of literature on subscription business models and provides practical insights for managers and entrepreneurs in the consumer tech industry.

Twinned Metal-Organic Framework Nanoplates for Hydrocarbon Separation Membranes.

Filler morphology control is critical for enhancing the gas separation performance of mixed matrix membranes (MMMs). A vertical transport channel using the crystal twinning phenomenon is designed on the zeolitic imidazolate framework (ZIF) nanoplate. Twinned ZIF-8 (TZIF-8) nanoplate is prepared by controlling the shape of ZIF-L from nanosheet to twinned flake, followed by conversion into the ZIF-8 phase. With the addition of TZIF-8 in 6FDA-DAM polymer, propylene/propane selectivity is dramatically enhanced, showing propylene permeability of 40 Barrer and propylene/propane selectivity of 82. The separation performance surpasses the performance of MMMs reported so far, and the selectivity is comparable to that of polycrystalline ZIF-8 membranes. A transport mechanism study using mathematical models implies that the percolated twin fillers create rapid and selective gas channels for desired molecules and substantial tortuous pathways for undesired molecules.

Cyclodextrin Metal-Organic Framework Functionalized Carbon Materials with Optimized Interface Electronics and Selective Supramolecular Channels for High-Performance Lithium-Sulfur Batteries.

During the reaction process in lithium-sulfur batteries, Lewis acidic lithium polysulfides (LiPSs) affect ion distribution and overall electrolyte stability, degrading battery performance and product distribution (e.g., Li2S). Here, a microenvironment regulation strategy with optimized interface electronics and selective supramolecular channels, is proposed to enhance LiPS reaction kinetics through Lewis basic γ-cyclodextrin metal-organic framework (γ-CDMOF). To validate this concept, γ-CDMOF is rapidly synthesized on 3D graphene foam (GF) via a microwave-assisted method, resulting in a γ-CDMOF/GF cathode for high-performance Li-S batteries. A range of analytical techniques combined with density functional theory (DFT) calculations confirm that introducing a Lewis basic supramolecular microenvironment mitigates the LiPSs shuttle effect, enhances polysulfide capture, and improves sulfur redox conversion. Additionally, COMSOL simulations reveal that the γ-CDMOF framework and oxygen sites significantly reduce volumetric expansion stress during the LiPS solid-liquid phase transition. Impressively, the γ-CDMOF/GF cathode exhibits exceptional performance, including a high specific capacity (1253.01 mAh g⁻¹ at 0.1C), excellent rate performance (589.68 mAh g⁻¹ at 5C), and long cycle life (over 1200 cycles). This study introduces a new concept of supramolecular microenvironment regulation and interfacial interaction strategy, offering a unique approach for the development of multifunctional electrode materials.

Radar-based contactless heart beat detection with a modified Pan–Tompkins algorithm

Background.Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge.Materials and methods.The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins.Results.The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute.Conclusion.The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare.

Nanoarchitecture via Synchronic Stacking of Metallic and Nonmetallic Two-Dimensional Nanosheets for Optimal Light-Driven Ion Transport.

The exceptional selectivity and responsive ion transport in biological channels inspire technology breakthrough in energy, environmental, and resource sectors. However, existing nanofluidic systems with a high photothermal conversion efficiency often exhibit excessive thermal conductivity, which impedes the creation of effective temperature gradients and results in a low ion transport efficiency. In this study, a strategy based on the synchronic stacking of metallic and nonmetallic two-dimensional (2D) nanosheets was presented to construct heterogeneous nanofluidic channels. This specific nanoconfined architecture sustained high temperatures in the illuminated area while maintaining low temperatures in the nonilluminated area, thus obtaining a robust driving force from sunlight for directional ion transport. As a result, our light-responsive ion transport system demonstrated significant potential in solar energy conversion and osmotic energy harvesting, surpassing those of all previously reported nanofluidic systems. Additionally, although it is still at the proof-of-concept stage, it shows great promise in light signal monitoring. This work provides an effective strategy for developing advanced light-responsive ion transport systems and their important applications.

Guanidinium‐Based Covalent Organic Framework Membranes for Superior Mono‐ and Divalent Cations Separation

Biological membranes exhibit extraordinary efficiency in processing ion permeability and selectivity. However, creating artificial membranes for an ideal ion separation is still challenging due to the subtle distinctions in valence and size among different ions. In the realm of biological recognition, the ultimate selectivity of ion channels is considered to stem from the specific binding interactions and appropriate charge density. Designing artificial membranes to achieve similar performance not only helps the understanding of complex ion transport in bioprocesses but also facilitates critical industrial separations. Inspiring by the remarkable performance in biological systems, a guanidinium‐based covalent organic framework membrane is designed, which exhibits an excellent capability to recognize mono‐/divalent cations, achieving K+/Mg2+ selectivity up to 202 in a mixed salt solution. Furthermore, the membrane displays rapid ion transport owing to the uniform sub‐2 nm channels. The experimental results and molecular dynamics simulations illustrate that the charge‐assisted hydrogen bonding sites and the Cl− counter ions within 1D channels play critical roles in cations sieving. Specifically, divalent ions passing through the positively charged channels need to overcome higher energy barriers than monovalent ions. These findings offer promising avenues for the development of advanced multifunctional membranes for efficient ion separation and sustainable water‐related separations.

Cost Efficiency and Profitability of Papaya Marketing Channels in North-eastern Karnataka, India

The study on the economic analysis of papaya marketing in North-Eastern Karnataka, focuses on identifying the existing marketing system for papaya and analyzes the marketing cost, margin, price spread and marketing efficiency of different marketing channels. The study was conducted in four districts of North-Eastern Karnataka region and employed multistage random sampling technique to gather data from 80 papaya cultivators and 20 market intermediaries. Three major marketing channels were identified, each showing distinct cost structures and preferences among farmers. The findings highlighted that channel I was the most preferred among farmers, but it also exhibited the highest price spread compared to other channels. Channel III recorded the highest producer's share in the consumer's rupee (60.60%) and was identified as the most efficient channel based on both the Shepherd's approach (3.84) and the Acharya and Agarwal approach (1.54). The selection of a marketing channel significantly affects profitability and the distribution of risk among stakeholders. It is suggested that, due to the higher marketing costs incurred by farmers, they often opt to sell through pre-harvest contractors (PHCs). Therefore, it is recommended that these intermediaries be regulated through the Agricultural Produce Market Committee (APMC) to streamline the supply chain and improve market access for papaya growers.

Combination of Channel Reordering Strategy and Dual CNN-LSTM for Epileptic Seizure Prediction Using Three iEEG Datasets.

Intracranial electroencephalogram (iEEG) signals are generally recorded using multiple channels, and channel selection is therefore a significant means in studying iEEG-based seizure prediction. For n channels, [Formula: see text] channel cases can be generated for selection. However, by this means, an increase in n can cause an exponential increase in computational consumption, which may result in a failure of channel selection when n is too large. Hence, it is necessary to explore reasonable channel selection strategies under the premise of controlling computational consumption and ensuring high classification accuracy. Given this, we propose a novel method of channel reordering strategy combined with dual CNN-LSTM for effectively predicting seizures. First, for each patient with n channels, interictal and preictal iEEG samples from each single channel are input into the CNN-LSTM model for classification. Then, the F1-score of each single channel is calculated, and the channels are reordered in descending order according to the size of F1-scores (channel reordering strategy). Next, iEEG signals with an increasing number of channels are successively fed into the CNN-LSTM model for classification again. Finally, according to the classification results from n channel cases, the channel case with the highest classification rate is selected. Our method is evaluated on the three iEEG datasets: the Freiburg, the SWEC-ETHZ and the American Epilepsy Society Seizure Prediction Challenge (AES-SPC). At the event-based level, the sensitivities of 100%, 100% and 90.5%, and the false prediction rates (FPRs) of 0.10/h, 0/h and 0.47/h, are achieved for the three datasets, respectively. Moreover, compared to an unspecific random predictor, our method also shows a better performance for all patients and dogs from the three datasets. At the segment-based level, the sensitivities-specificities-accuracies-AUCs of 88.1%-94.0%-93.5%-0.9101, 99.1%-99.7%-99.6%-0.9935, and 69.2%-79.9%-78.2%-0.7373, are attained for the three datasets, respectively. Our method can effectively predict seizures and address the challenge of an excessive number of channels during channel selection.

Reduced-dimension STAP method for conformal array based on sequential convex programming

Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance.

A Statistical Method for EEG Channel Selection

A new statistical analysis of medical signals is proposed based on information extracted for diagnosis. Several specialized tests can be run on electroencephalography (EEG) signal due to its nonlinear behavior. Epileptic EEG signals are the main topic of this research. A statistics-based approach is proposed for the automatic selection of the relevant channels that will not only provide more accurate characterization but also require fewer computing resources. Moreover, the outcomes given by specific electrode pairs might indicate epileptic focus. By selecting the most efficient electrode placement, alarms for detecting inappropriate medical behavior could then be triggered.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).

Neurological disorders affecting speech production adversely impact quality of
life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking
devices and P300 spellers are slow and unnatural for these patients. An
alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech
characteristics, offering a more natural communication mechanism. This research
explores the feasibility of decoding speech features using non-invasive EEG. Nine
neurologically intact participants were equipped with a 63-channel EEG system
with additional sensors to eliminate eye artifacts. Participants read aloud sentences
displayed on a screen selected for phonetic similarity to the English language. Deep
learning models, including Convolutional Neural Networks and Recurrent Neural
Networks with and without attention modules, were optimized with a focus on
minimizing trainable parameters and utilizing small input window sizes for real-time
application. These models were employed for discrete and continuous speech decoding
tasks, achieving statistically significant participant-independent decoding performance
for discrete classes and continuous characteristics of the produced audio signal. A
frequency sub-band analysis highlighted the significance of certain frequency bands
(delta, theta, and gamma) for decoding performance, and a perturbation analysis
was used to identify crucial channels. Assessed channel selection methods did not
significantly improve performance, suggesting a distributed representation of speech
information encoded in the EEG signals. Leave-One-Out training demonstrated
the feasibility of utilizing common speech neural correlates, reducing data collection
requirements from individual participants.

Double Deep Q- energy aware Service allocation based on Dynamic fractional frequency reusable technique for lifetime maximization in HetNet-LTE network

The development of mobile communication in heterogeneous networks is incredible in providing various services through wireless cellular communication through advanced long-term evaluation networks. Increasing multi-concern services and frequencies in spectrum channels are highly layered to select the bandwidth to provide the fastest network without interference. Selecting the channel through macro cell selection is essential to improve network communication and provide the quickest service. Most frequency reuse techniques use service optimality and route selection-based protocols to enrich the packet flow. Still, the improper spectrum delights create more delay tolerance due to short-range service optimality due to energy loss by selecting the short spectrum signal to reuse, which doesn't support the lifetime improvement of the LTE network. To resolve these problems, we propose a Double Deep Q- energy-aware Service allocation based on a Dynamic fractional frequency reusable technique for lifetime maximization in the HetNet-LTE network. Initially, the heterogenous communication environment and node deplanement were carried out to construct the LTE network under the WCC. The communication logs are Route Table (RT), and its services are taken by all node LTE Communication Impact Rate (LTE-CIR). Then, the Backhaul Traffic Algorithm (BTA) is applied to predict the interference on traffic rate from the channel frequency margin. Select the balanced node using the Channel Interference Macro Cell Selection (CIMCS) technique. Considering frequency limits with the Double Deep Q- Network (DDQN) approach, energy-aware selects the optimal route to reuse the frequency level using Frequency Domain Packet Scheduling (FDPS) to improve communication. The proposed system improves the overall throughput by up to 97.8 % with adopted channel selection from the macro unit to improve the latency performance. Also, the interference frequency limits are dynamically reused at an energy optimal level with low-level delay tolerance to improve the link stability by up to 98.4 % with higher lifetime maximation in the LTE network.

Implementation of preamble based GFDM prototype for robust 5G systems

AbstractGeneralized frequency division multiplexing (GFDM) is a flexible block‐structured multi‐carrier scheme recently proposed for next‐generation wireless communication systems. There are various approaches suggested for its analysis and implementation via simulations but testing in real‐time environments is not heavily investigated. This paper carries out the real‐time implementation of the GFDM system utilizing software‐defined radio (SDR) by emphasizing mainly channel estimation and synchronization. Symbol timing, frequency offset, and channel estimate algorithms are applied using a windowed preamble with two identical halves to satisfy low egress noise requirements. Time and frequency estimation is evaluated in terms of residual offsets along with symbol error rate over frequency selective channels. This algorithm is extended to a preamble composed of multiple identical parts. This facilitates a large frequency estimation range at the cost of complexity. For practical validation of the above concepts, the National Instruments (NI) universal software radio peripheral (USRP) 2953R is employed as hardware and it is interfaced with LabVIEW.

Selective ion channel adsorbents facilitate efficient and low environmental impact extraction of liquid lithium resources

As lithium is the cornerstone of green energy development, it is crucial to realize a low environmental impact and efficient lithium extraction process. Ion-sieve adsorption is the most widely used method to extract liquid lithium resources, but this method is only efficient under alkaline conditions for H+ and Mg2+ competing adsorption. Conventional methods are often accompanied by the consumption of quantities of alkali, the generation of solid waste, and the acidification of liquid lithium resources. To address these issues, a selective ion-channel adsorbent was constructed. The composition comprises an ion sieve adsorbent and an organic carrier with a zwitterionic quaternary ammonium base group. This group storages OH- in situ, hinders H+ diffusion, slows down Mg2+ diffusion, and accelerates Li+ diffusion by relying on the difference in binding energies, which reduces the competing adsorption and avoids acidification and solid waste generation. The saturated adsorption capacity (21.38 mg/g) and selectivity of the adsorbent are 4.7 and 24 times higher than that of conventional ion-sieve adsorbent under neutral conditions respectively. The dosage of alkali is 1/256 of the traditional method, the effluent remains neutral and no solid waste is generated. This study presents an environmental and effective adsorbent for lithium extraction.

An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks

Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Scorpion Neurotoxin BeM9 Derivative Uncovers Unique Interaction Mode with Nav1.5 Sodium Channel Isoform

Scorpion α-neurotoxins are classical ligands of voltage-gated sodium channels that inhibit their inactivation. The strength of this effect depends on the organism and channel isoform, and the precise mechanisms explaining the differences in activity are still unknown. Previously, we have shown that scorpion α-toxins are characterized by a modular structure. They consist of a conserved and structurally stable core module and a variable and mobile specificity module, which determines the selectivity for different channels. We noted a higher mobility of the specificity module in toxins active against mammals compared to insect-active toxins. We then hypothesized that the enhanced mobility in mammal toxins was provided by two conserved glycine residues that enclose the N-terminal loop of the specificity module. To test this assumption, we obtained a derivative of the neurotoxin BeM9 from the venom of the scorpion Mesobuthus eupeus with two replacements of amino acid residues in the corresponding positions with glycine (A4G and Y17G). Unexpectedly, it turned out that BeM9GG lost its activity against Nav1.5 channel isoform, characteristic of mammalian cardiac muscle. A comparison of two known structures of voltage-gated sodium channel complexes with scorpion toxins made it possible to explain the observed effect. We hypothesize an essential role of the membrane in the interaction of toxins with the Nav1.5 isoform.