Pub Date : 2026-01-12DOI: 10.1109/TMBMC.2026.3652361
Mustafa Ozan Duman;Ibrahim Isik;Esme Isik
Bacteria-based nanonetworks (BNs) represent a promising strategy for nanoscale information transfer, utilizing bacterial motility and chemotaxis for targeted message delivery. This study analyzes BN performance through both experimental validation and a custom-developed three-dimensional (3D) simulation program built in MATLAB, focusing on receiver (RX) placement, chemoattractant release rate ($Q$ ), and bacterial lifespan. The simulation employs experimentally validated parameters and models bacterial behavior under various spatial configurations. Results demonstrate that RX positioning significantly affects communication efficiency, with asymmetric placement causing uneven chemoattractant gradients and reduced success rates. While higher $Q$ values improve reach time and delivery success, bacterial lifespan becomes a limiting factor at extended distances. Experimental findings using agar-based assays confirm a threshold distance beyond which bacterial motility becomes ineffective. These insights provide practical guidance for optimizing BN systems by balancing signal strength with biological constraints. Future work should explore adaptive bacterial strategies and dynamic environmental conditions to further enhance BN reliability and applicability in areas such as targeted drug delivery and biosensing.
{"title":"Bacterial Chemotaxis in Molecular Communication: Experimental and Simulation Analysis of Receiver Placement and Gradient Dynamics","authors":"Mustafa Ozan Duman;Ibrahim Isik;Esme Isik","doi":"10.1109/TMBMC.2026.3652361","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3652361","url":null,"abstract":"Bacteria-based nanonetworks (BNs) represent a promising strategy for nanoscale information transfer, utilizing bacterial motility and chemotaxis for targeted message delivery. This study analyzes BN performance through both experimental validation and a custom-developed three-dimensional (3D) simulation program built in MATLAB, focusing on receiver (RX) placement, chemoattractant release rate (<inline-formula> <tex-math>$Q$ </tex-math></inline-formula>), and bacterial lifespan. The simulation employs experimentally validated parameters and models bacterial behavior under various spatial configurations. Results demonstrate that RX positioning significantly affects communication efficiency, with asymmetric placement causing uneven chemoattractant gradients and reduced success rates. While higher <inline-formula> <tex-math>$Q$ </tex-math></inline-formula> values improve reach time and delivery success, bacterial lifespan becomes a limiting factor at extended distances. Experimental findings using agar-based assays confirm a threshold distance beyond which bacterial motility becomes ineffective. These insights provide practical guidance for optimizing BN systems by balancing signal strength with biological constraints. Future work should explore adaptive bacterial strategies and dynamic environmental conditions to further enhance BN reliability and applicability in areas such as targeted drug delivery and biosensing.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"298-308"},"PeriodicalIF":2.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1109/TMBMC.2026.3651917
{"title":"2025 Index IEEE Transactions on Molecular, Biological and Multi-Scale Communications","authors":"","doi":"10.1109/TMBMC.2026.3651917","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3651917","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"1-16"},"PeriodicalIF":2.3,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing reliance on global air and sea transport, the need for effective underwater search mechanisms is becoming more pressing. However, complex marine environments presents substantial challenges for the recovery of crashed aircraft or sunken vessels. Existing underwater search methods rely heavily on acoustic communication, which is constrained in both range and energy. Although molecular communication offers considerable promise, there is a lack of search algorithms applicable to underwater scenarios. Therefore, we propose the hexagonal inverse gradient search (HIGS) algorithm for the search mission of lost objects underwater. HIGS employs the autonomous underwater vehicle (AUV) that navigates using molecular concentration gradients. To overcome the challenge of nearly zero-gradient regions, we develop a set of three-dimensional motion rules based on real-time chemical sensing, allowing AUVs to adjust their trajectories adaptively. Additionally, a zero-gradient escape strategy is incorporated to prevent the AUV from becoming trapped in local optima within complex underwater environments, thereby ensuring persistent and effective target search. Simulation results confirm the effectiveness of the proposed algorithm in underwater search missions.
{"title":"Long-Distance Underwater Target Search and Localization Using an AUV With Chemical Sensing","authors":"Mingyue Cheng;Yingying Zhong;Runhua Chen;Zhangrui Ren;Menghan Zhao;Qiong Huang","doi":"10.1109/TMBMC.2025.3650122","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3650122","url":null,"abstract":"With the increasing reliance on global air and sea transport, the need for effective underwater search mechanisms is becoming more pressing. However, complex marine environments presents substantial challenges for the recovery of crashed aircraft or sunken vessels. Existing underwater search methods rely heavily on acoustic communication, which is constrained in both range and energy. Although molecular communication offers considerable promise, there is a lack of search algorithms applicable to underwater scenarios. Therefore, we propose the hexagonal inverse gradient search (HIGS) algorithm for the search mission of lost objects underwater. HIGS employs the autonomous underwater vehicle (AUV) that navigates using molecular concentration gradients. To overcome the challenge of nearly zero-gradient regions, we develop a set of three-dimensional motion rules based on real-time chemical sensing, allowing AUVs to adjust their trajectories adaptively. Additionally, a zero-gradient escape strategy is incorporated to prevent the AUV from becoming trapped in local optima within complex underwater environments, thereby ensuring persistent and effective target search. Simulation results confirm the effectiveness of the proposed algorithm in underwater search missions.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"218-225"},"PeriodicalIF":2.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1109/TMBMC.2025.3650156
Sahil Imtiyaz;Serafim Rodrigues
We propose a unified mathematical meta-framework for long-distance navigation in birds, based on a bundle-theoretic representation of multisensory integration within evolving combinatorial structures. Various information streams—such as magnetic, celestial, olfactory, and landmark cues—are modeled as typed fibers over a time-varying simplicial base, which is reconstructed from behavioral trajectories and neural co-activity. In this framework, integration is framed as a global consistency problem: coherent system-level representations occur when there are globally compatible assignments across overlapping local contexts. This is equivalent to finding global sections of an evolving bundle. The tension between local and global perspectives is formalized in quantum theory through the concept of contextuality, which expresses the impossibility of a single, non-contextual global assignment that is consistent with all local marginals. In Bell-type scenarios, this aligns with operational non-locality and device-independent signatures of entanglement. We demonstrate that the same constraint semantics provide a precise mathematical connection between canonical contextuality models and biological cue integration, treating contextuality as a calculus for diagnosing and localizing incompatibilities in distributed representations. Our theory introduces two computable topological observables: critical simplices and interface loops. Critical simplices identify discrete remapping pivots where the structural scaffold must be reconfigured to restore consistency, while interface loops detect transient conflict cycles at the boundaries between different information streams. Together, these observables form a diagnostic “compass” that integrates cues onto a common scaffold, localizes incompatible overlaps, and predicts when spatial representations need to be remapped. We validate our framework using standard quantum contextuality scenarios, including Bell’s theorem and Klyachko–Can–Binicioǧlu–Shumovsky scenario (KCBS), successfully recovering established contextuality classifications through bundle obstructions and loop signatures. We then apply this framework to an anatomy-aware model of avian navigation, in which entanglement-capable cryptochrome/radical-pair dynamics serve as a microscopic source of non-classical correlations, without assuming that macroscopic entanglement occurs across neural circuits. In this model, microscopic non-classicality influences adaptive functions by leaving persistent, computable contextual footprints within the evolving biological scaffold, providing testable signatures at the level of remapping events and context-dependent cue integration.
{"title":"Understanding Entanglement Through the Lens of Quantifiable Algebraic Structures: Application to Bird Navigation","authors":"Sahil Imtiyaz;Serafim Rodrigues","doi":"10.1109/TMBMC.2025.3650156","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3650156","url":null,"abstract":"We propose a unified mathematical meta-framework for long-distance navigation in birds, based on a bundle-theoretic representation of multisensory integration within evolving combinatorial structures. Various information streams—such as magnetic, celestial, olfactory, and landmark cues—are modeled as typed fibers over a time-varying simplicial base, which is reconstructed from behavioral trajectories and neural co-activity. In this framework, integration is framed as a global consistency problem: coherent system-level representations occur when there are globally compatible assignments across overlapping local contexts. This is equivalent to finding global sections of an evolving bundle. The tension between local and global perspectives is formalized in quantum theory through the concept of contextuality, which expresses the impossibility of a single, non-contextual global assignment that is consistent with all local marginals. In Bell-type scenarios, this aligns with operational non-locality and device-independent signatures of entanglement. We demonstrate that the same constraint semantics provide a precise mathematical connection between canonical contextuality models and biological cue integration, treating contextuality as a calculus for diagnosing and localizing incompatibilities in distributed representations. Our theory introduces two computable topological observables: critical simplices and interface loops. Critical simplices identify discrete remapping pivots where the structural scaffold must be reconfigured to restore consistency, while interface loops detect transient conflict cycles at the boundaries between different information streams. Together, these observables form a diagnostic “compass” that integrates cues onto a common scaffold, localizes incompatible overlaps, and predicts when spatial representations need to be remapped. We validate our framework using standard quantum contextuality scenarios, including Bell’s theorem and Klyachko–Can–Binicioǧlu–Shumovsky scenario (KCBS), successfully recovering established contextuality classifications through bundle obstructions and loop signatures. We then apply this framework to an anatomy-aware model of avian navigation, in which entanglement-capable cryptochrome/radical-pair dynamics serve as a microscopic source of non-classical correlations, without assuming that macroscopic entanglement occurs across neural circuits. In this model, microscopic non-classicality influences adaptive functions by leaving persistent, computable contextual footprints within the evolving biological scaffold, providing testable signatures at the level of remapping events and context-dependent cue integration.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"279-297"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1109/TMBMC.2025.3646292
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Publication Information","authors":"","doi":"10.1109/TMBMC.2025.3646292","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3646292","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"i-i"},"PeriodicalIF":2.3,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11320849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1109/TMBMC.2025.3648253
Nguyen-Phuc-Xuan Quynh;Hoai-Nhan Tran;Cheng Yan;Jianxin Wang
MicroRNAs are key biomarkers and therapeutic targets due to their role in regulating biological processes and disease progression. Traditional identification of miRNA-disease associations (MDAs) is costly and slow, motivating computational approaches. In this study, we propose HGNNMDA, a hybrid prediction model applying graph neural networks (GNNs) for predicting MDAs. The key novelty of HGNNMDA lies in its interactive dual-branch architecture, where each branch integrates both graph convolutional networks (GCNs) and graph attention networks (GATs) to extract robust features from diverse biological data sources. This design generates complementary feature representations by leveraging GCNs to capture structural associations through neighborhood aggregation, while employing GATs to prioritize the most relevant interactions via attention mechanisms, effectively overcoming limitations of single-architecture GNNs. The obtained feature vectors are then processed by XGBoosts to generate predictive scores, which are subsequently combined for final predictions. HGNNMDA outperforms seven recent methods in experimental validation on three datasets (HMDD v2.0, HMDD v3.2, and an independent dataset) and shows strong results in de novo validation and case studies. The source code in this work is available at https://github.com/npxquynhdhsp/HGNNMDA/.
{"title":"HGNNMDA: Hybrid Graph Neural Networks for MiRNA-Disease Association Prediction","authors":"Nguyen-Phuc-Xuan Quynh;Hoai-Nhan Tran;Cheng Yan;Jianxin Wang","doi":"10.1109/TMBMC.2025.3648253","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3648253","url":null,"abstract":"MicroRNAs are key biomarkers and therapeutic targets due to their role in regulating biological processes and disease progression. Traditional identification of miRNA-disease associations (MDAs) is costly and slow, motivating computational approaches. In this study, we propose HGNNMDA, a hybrid prediction model applying graph neural networks (GNNs) for predicting MDAs. The key novelty of HGNNMDA lies in its interactive dual-branch architecture, where each branch integrates both graph convolutional networks (GCNs) and graph attention networks (GATs) to extract robust features from diverse biological data sources. This design generates complementary feature representations by leveraging GCNs to capture structural associations through neighborhood aggregation, while employing GATs to prioritize the most relevant interactions via attention mechanisms, effectively overcoming limitations of single-architecture GNNs. The obtained feature vectors are then processed by XGBoosts to generate predictive scores, which are subsequently combined for final predictions. HGNNMDA outperforms seven recent methods in experimental validation on three datasets (HMDD v2.0, HMDD v3.2, and an independent dataset) and shows strong results in de novo validation and case studies. The source code in this work is available at <uri>https://github.com/npxquynhdhsp/HGNNMDA/</uri>.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"136-145"},"PeriodicalIF":2.3,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Molecular Communication (MC) is an emerging field of research focused on understanding how cells in the human body communicate and exploring potential medical applications. In theoretical analysis, the goal is to investigate cellular communication mechanisms and develop nanomachine-assisted therapies to combat diseases. Since cells transmit information by releasing molecules at varying intensities, this process is commonly modeled using Poisson channels. In our study, we consider a discrete-time Poisson channel (DTPC). MC is often event-driven, making traditional Shannon communication an unsuitable performance metric. Instead, we adopt the identification framework introduced by Ahlswede and Dueck. In this approach, the receiver is only concerned with detecting whether a specific message of interest has been transmitted. Unlike Shannon transmission codes, the size of identification (ID) codes for a discrete memoryless channel (DMC) increases doubly exponentially with blocklength when using randomized encoding. This remarkable property makes the ID paradigm significantly more efficient than classical Shannon transmission in terms of energy consumption and hardware requirements. Another critical aspect of MC, influenced by the concept of the Internet of Bio-NanoThings, is security. In-body communication must be protected against potential eavesdroppers. To address this, we first analyze the DTPC for randomized identification (RI) and then extend our study to secure randomized identification (SRI). We derive capacity formulas for both RI and SRI, providing a comprehensive understanding of their performance and security implications.
{"title":"Secure Event-Triggered Molecular Communication—Information Theoretic Perspective and Optimal Performance","authors":"Wafa Labidi;Vida Gholamian;Yaning Zhao;Christian Deppe;Holger Boche","doi":"10.1109/TMBMC.2025.3647208","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3647208","url":null,"abstract":"Molecular Communication (MC) is an emerging field of research focused on understanding how cells in the human body communicate and exploring potential medical applications. In theoretical analysis, the goal is to investigate cellular communication mechanisms and develop nanomachine-assisted therapies to combat diseases. Since cells transmit information by releasing molecules at varying intensities, this process is commonly modeled using Poisson channels. In our study, we consider a discrete-time Poisson channel (DTPC). MC is often event-driven, making traditional Shannon communication an unsuitable performance metric. Instead, we adopt the identification framework introduced by Ahlswede and Dueck. In this approach, the receiver is only concerned with detecting whether a specific message of interest has been transmitted. Unlike Shannon transmission codes, the size of identification (ID) codes for a discrete memoryless channel (DMC) increases doubly exponentially with blocklength when using randomized encoding. This remarkable property makes the ID paradigm significantly more efficient than classical Shannon transmission in terms of energy consumption and hardware requirements. Another critical aspect of MC, influenced by the concept of the Internet of Bio-NanoThings, is security. In-body communication must be protected against potential eavesdroppers. To address this, we first analyze the DTPC for randomized identification (RI) and then extend our study to secure randomized identification (SRI). We derive capacity formulas for both RI and SRI, providing a comprehensive understanding of their performance and security implications.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"265-278"},"PeriodicalIF":2.3,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/TMBMC.2025.3645247
T. Sai Krishna Charitha;Lokendra Chouhan;Abhishek K. Gupta;Rik Dey;Eswar Kadali;Sairam Mente;Rituraj;Prabhat K. Sharma
In this work, we develop the channel model of a molecular communication (MC) system with molecules propagating via anomalous diffusion in a confined environment, in particular, inside a spherical region. The MC system consists of an absorbing receiver located at the center of the region and a point transmitter, whereas the outer boundary is fully reflecting. We first obtain the concentration profile of molecules inside the region at a given time. Further, we derive the hitting rate and hitting probability to characterize the channel.
{"title":"Molecular Communication in Bounded Spherical Region With Anomalous Diffusion Phenomenon","authors":"T. Sai Krishna Charitha;Lokendra Chouhan;Abhishek K. Gupta;Rik Dey;Eswar Kadali;Sairam Mente;Rituraj;Prabhat K. Sharma","doi":"10.1109/TMBMC.2025.3645247","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3645247","url":null,"abstract":"In this work, we develop the channel model of a molecular communication (MC) system with molecules propagating via anomalous diffusion in a confined environment, in particular, inside a spherical region. The MC system consists of an absorbing receiver located at the center of the region and a point transmitter, whereas the outer boundary is fully reflecting. We first obtain the concentration profile of molecules inside the region at a given time. Further, we derive the hitting rate and hitting probability to characterize the channel.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"105-110"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Guest Editorial Introduction to the Special Feature on the 9th Workshop on Molecular Communications","authors":"Jens Kirchner;Bhuvana Krishnaswamy;Lin Lin;Laura Galluccio;Nunzio Tuccitto","doi":"10.1109/TMBMC.2025.3637924","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3637924","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"482-485"},"PeriodicalIF":2.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1109/TMBMC.2025.3637917
{"title":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Publication Information","authors":"","doi":"10.1109/TMBMC.2025.3637917","DOIUrl":"https://doi.org/10.1109/TMBMC.2025.3637917","url":null,"abstract":"","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"11 4","pages":"C2-C2"},"PeriodicalIF":2.3,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11302010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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