Communication Signals Research Articles

SnRNA-seq reveals differential functional transcriptional pathway alterations in three mutant types of dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a leading cause of heart failure, characterized by ventricular dilation, thinning of the ventricular walls, and systolic dysfunction in either the left or both ventricles, often accompanied by fibrosis. Human cardiac tissue is composed of various cell types, including cardiomyocytes (CMs), fibroblasts (FBs), endothelial cells (ECs), macrophages, lymphocytes and so on. In DCM patients, these cells frequently undergo functional and phenotypic changes, contributing to contractile dysfunction, inflammation, fibrosis, and cell death, thereby increasing the risk of heart failure. This study focuses on DCM patients with mutations (LMNA, RBM20, and TTN) and analyzes functional changes in subpopulations of four cardiac cell types. The study involves functional annotation of subpopulations within each cell type and explores the association between gene mutations and specific functions and pathways. Additionally, the SCENIC method is employed of a particular cell subpopulation with significant functional importance, aiming to identify key transcriptional regulators in specific cell states. By analyzing the expression levels of ligand-receptor pairs in vCM4, vFB2, EC5.0, T cells, and NK cells across the DCM mutant genotypes, we predicted their signaling pathways and communications. This research provides insights into the molecular mechanisms of DCM and potential therapeutic targets.

Atmosphere turbulence effects on a multiple apertures free space optical communication systems

Abstract Wireless communication signals experienced fluctuations in intensity due to atmospheric turbulence. The presented work uses a multi-input-multi-output (MIMO) system to study the performance free-space optical (FSO) communication over a diverse atmospheric turbulence condition channel. The findings produced demonstrate how various channel models perform in FSO system at varying levels of channel turbulence. From 7.9 for the Weak turbulence to 45.3 for the strong turbulence, the turbulence attenuation rises linearly. The data could be transmitted via a link length of up to 1 km with strong turbulence. Strong turbulence could have higher Q-factor, yet is constrained by short distance (1 km) in comparison with weak turbulence, which has greater distance (3.8 km), therefore communication quality is lower. For moderate and weak turbulence, the maximum link is around 1.5 km and 3.8 km, respectively. This study is useful for designing a stable FSO system for regional environments suffer from frequent fluctuated weather turbulence such as the Middle East and South Asia.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Infants Produce Optimally Informative Points to Satisfy the Epistemic Needs of Their Communicative Partner.

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee's inferred epistemic needs. For instance, when necessary, adults flexibly and appropriately modify their communicative gestures to provide their partner the relevant information she lacks about the situation. To investigate this ability in infants, we designed a cooperative task in which 18-month-olds were asked to point at the target object they wanted to receive. In Experiment 1, we found that when their desired object was placed behind a distractor object, infants appropriately modified their prototypical pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their cooperative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target's location - as opposed to being knowledgeable about it - infants pointed differentially more often at the target and employed modified pointing gestures more frequently as a function of the amount of relevant information that their partner needed to retrieve their desired object from its correct location. These findings demonstrate that when responding to a verbal request in a cooperative task 18-month-old infants can take into account their communicative partner's epistemic states and when necessary provide her with the relevant information she lacks through sufficiently informative deictic gestures. Our results indicate that infants possess an early emerging, species-unique cognitive adaptation specialized for communicative mindreading and pragmatic inferential communication which enable the efficient exchange of relevant information between communicating social partners in cooperative contexts.

Non-cyclic nucleotide EPAC1 activators suppress lipopolysaccharide-regulated gene expression, signalling and intracellular communication in differentiated macrophage-like THP-1 cells

This study explores the anti-inflammatory effects of non-cyclic nucleotide EPAC1 activators, PW0577 and SY007, on lipopolysaccharide (LPS)-induced responses in differentiated THP-1 macrophage-like cells. Both activators were found to selectively activate EPAC1 in THP-1 macrophages, leading to the activation of the key down-stream effector, Rap1. RNA sequencing analysis of LPS-stimulated THP-1 macrophages, revealed that treatment with PW0577 or SY007 significantly modulates gene expression related to fibrosis and inflammation, including the suppression of NLRP3, IL-1β, and caspase 1 protein expression in LPS-stimulated cells. Notably, these effects were independent of p65 NFκB phosphorylation at Serine 536, indicating a distinct mechanism of action. The study further identified a shared influence of both activators on LPS signalling pathways, particularly impacting extracellular matrix (ECM) components and NFκB-regulated genes. Additionally, in a co-culture model involving THP-1 macrophages, vascular smooth muscle cells, and human coronary artery endothelial cells, EPAC1 activators modulated immune-vascular interactions, suggesting a broader role in regulating cellular communication between macrophages and endothelial cells. These findings enhance our understanding of EPAC1's role in inflammation and propose EPAC1 activators as potential therapeutic agents for treating inflammatory and fibrotic conditions through targeted modulation of Rap1 and associated signalling pathways.

Recognition Of Digits From EEG Signals: An Innovative Approach

Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. This study proposes a methodological combination of EEG signal processing techniques and SVM models for the classification of digit. Also, it explores DCT and LDA techniques for feature extraction and selection. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.

EphA-Mediated Regulation of Stomatin Expression in Prostate Cancer Cells.

Tumor growth and progression are affected by interactions between tumor cells and stromal cells within the tumor microenvironment. We previously showed that the expression of an integral membrane protein, called stomatin, was increased in cancer cells following their association with stromal cells. Additionally, stomatin impaired the Akt signaling pathway to suppress tumor growth. However, it remains unclear how stomatin expression is regulated. To explore this, we examined the cell surface molecules that can transduce the intercellular communication signals between cancer cells and stromal cells. Among these molecules, EphA3 and EphA7 receptors and their ligand ephrin-A5 were found to be expressed in prostate cancer cells, but not in prostate stromal cells. Cell-to-cell contact of prostate cancer cells through the EphA-ephrin-A interaction suppressed stomatin expression, while knockdown of EphA3/7 or ephrin-A5 increased stomatin expression. This increase contributed to an inhibition of prostate cancer cell proliferation. Intracellularly, the binding of ephrin-A to EphA attenuated extracellular signaling-regulated kinase (ERK) activation that promoted stomatin expression. Furthermore, ELK1 and ELK4, which are Ets family transcription factors phosphorylated by ERK, were involved in the induction of stomatin expression. We also found that higher Gleason score prostate cancer tissue samples had increased activation of EphA, while the stomatin expression and activated ERK and ELK levels were all low. In the mouse xenograft tumor samples generated by implantation of prostate cancer cells, EphA3 phosphorylation was attenuated and the ERK-ELK signaling and stomatin expression were enhanced in the area where stromal cells infiltrated the tumor. The EphA-mediated signaling suppresses the ERK-ELK pathway, leading to the reduction of stomatin expression that affects prostate cancer malignancy.

Optical dark-field spectroscopy of single plasmonic nanoparticles for molecular biosciences.

An ideal sensor capable of quantifying analytes in minuscule sample volumes represents a significant technological advancement. Plasmonic nanoparticles integrated with optical dark-field spectroscopy have reached this capability, demonstrating versatility and expanding applicability across in vitro and in vivo subjects. This review underscores the applicability of optical dark-field spectroscopy with single plasmonic nanoparticles to elucidate a wide range of biomolecular characteristics, including binding constants, molecular dynamics, distances, and forces, as well as recording cell communication signals. Perspectives highlight the potential for the development of implantable nanosensors for metabolite detection in animal models, illustrating the technique's efficacy without the need for labeling molecules. In summary, this review aims to consolidate knowledge of this adaptable and robust technique for decoding molecular biological phenomena within the nano- and bio-scientific community.

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Enhancing safety in narrow-band transformerless PLC couplers with a passive symmetrical filter

As the demand for efficient data transmission over existing power infrastructure grows, power-line communication (PLC) systems have become increasingly vital. However, integrating communication signals with high-voltage power lines poses significant safety challenges, emphasizing the need for reliable, safe, and high-performance components. To address cost and safety concerns, this article introduces a symmetrical third-order band-pass filter design that employs a transformerless coupler, avoiding the expense and bulk of traditional transformers while enhancing both equipment and human safety in narrow-band PLC systems. The design, functionality, and safety considerations are discussed, with a focus on implementing a passive analog Butterworth filter, chosen for its flat frequency response in the passband. Additionally, we address the safety aspects of transformerless couplers in PLC applications, emphasizing their reliability and efficiency in maintaining signal integrity while minimizing the risks associated with high-voltage environments. Simulations validated the proposed design, confirming its effectiveness in achieving the desired frequency response. The design is cost-effective due to the elimination of the transformer and ensures safety through the strategic rearrangement of the coupler components, which isolates the power line from the communication side.

Single-cell and bulk transcriptional profiling of mouse ovaries reveals novel genes and pathways associated with DNA damage response in oocytes

Immature oocytes enclosed in primordial follicles stored in female ovaries are under constant threat of DNA damage induced by endogenous and exogenous factors. Checkpoint kinase 2 (CHEK2) is a key mediator of the DNA damage response (DDR) in all cells. Genetic studies have shown that CHEK2 and its downstream targets, p53, and TAp63, regulate primordial follicle elimination in response to DNA damage. However, the mechanism leading to their demise is still poorly characterized. Single-cell and bulk RNA sequencing were used to determine the DDR in wild-type and Chek2-deficient ovaries. A low but oocyte-lethal dose of ionizing radiation induces ovarian DDR that is solely dependent on CHEK2. DNA damage activates multiple response pathways related to apoptosis, p53, interferon signaling, inflammation, cell adhesion, and intercellular communication. These pathways are differentially employed by different ovarian cell types, with oocytes disproportionately affected by radiation. Novel genes and pathways are induced by radiation specifically in oocytes, shedding light on their sensitivity to DNA damage, and implicating a coordinated response between oocytes and pregranulosa cells within the follicle. These findings provide a foundation for future studies on the specific mechanisms regulating oocyte survival in the context of aging, therapeutic and environmental genotoxic exposures.

Problem Behaviours and Relinquishment: Challenges Faced by Clinical Animal Behaviourists When Assessing Fear and Frustration.

Fear and frustration are two emotions thought to frequently contribute to problem behaviour, often leading to relinquishment. Inferring these emotions is challenging as they may present with some similar general signs, but they potentially require different treatment approaches to efficiently address the behaviour of concern. Although behavioural assessment frameworks have been proposed, it is largely unknown how clinical animal behaviourists (CABs) assimilate information about the emotional state of an animal to inform their behavioural assessment. In other fields (such as both in human and veterinary medicine), the use of intuition and gut feelings, without the concurrent use of an assessment framework, can lead to higher rates of error and misdiagnosis. Therefore, this study used semi-structured interviews of ten CABs and qualitative methods to explore the ways they conceptualise, recognise and differentiate fear and frustration in dogs. Although interviewees perceived fear and frustration as negative affective states that lead to changes in an animal's behaviour, there was little consensus on the definition or identification or differentiation of these emotions. The use of a scientific approach (i.e., hypothesis-driven and based on falsification of competing hypotheses) for behavioural assessment was highly variable, with individual assessment processes often characterised by tautology, intuition, circular reasoning and confirmation bias. Assessment was typically based on professional judgment, amalgamating information on interpretation of communicative signals, motivation, learning history, breed, genetics and temperament. Given the lack of consensus in the definition of these states, it is clearly important that authors and clinicians define their interpretation of key concepts, such as fear and frustration, when trying to communicate with others.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

Abstract C044: PI5P4Kα regulates cell fitness through iron homeostasis in pancreatic cancer

Abstract PI5P4Ks (Phosphatidylinositol 5-phosphate 4-kinases) are a family of lipid kinases that phosphorylate phosphatidylinositol 5-monophosphate (PI-5-P) at the 4-position to generate distinct pools of phosphatidylinositol 4,5-bisphosphate (PI-4,5-P2) largely on intracellular membranes. These lipid species are involved in cellular and metabolic stress responses, signaling and organelle communication. PI5P4Ks have been implicated in multiple cancers, with compelling evidence showing synthetic lethality when PI5P4Ks are inhibited in TP53-mutant cancer cells. Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive metabolic rewiring but despite the critical roles of PI5P4Ks in cellular metabolism, their role in PDAC remains completely unexplored. We have found that depletion of PI5P4Kα selectively induces apoptotic cell death in PDAC cells. To gain insights into the metabolic mechanisms underlying this apoptotic cell death, we quantified polar metabolites using gas chromatography- mass spectrometry (GC/MS) and found significant reductions in the ratio of monounsaturated to saturated fatty acids, lactate and TCA metabolites. Orthogonal rescue assays demonstrated that exogenous supplementation with fatty acids, pyruvate or lactate fail to rescue the observed apoptosis in PI5P4Kα-knockdown cells; however, TCA cycle metabolites did reverse the extent of apoptosis. Interestingly, we identify that these altered metabolic read-outs and the effects on cell fitness are dependent on iron. Consistent with these findings, intracellular iron levels are reduced upon PI5P4Kα inhibition, and this is directly linked to PI5P4Kα depletion disturbing the intracellular labile iron pools via iron import. Using a heterotopic xenograft mouse model, we demonstrate that PI5P4Kα knockdown leads to the abrogation of PDAC tumor growth due to increased intratumoral cell death. Validating the physiological relevance of PI5P4Kα in human PDAC, we find that PIP4K2A, the gene that encodes PI5P4Kα, is upregulated in human PDAC specimens relative to normal pancreas, and that PIP4K2A gene expression is strongly correlated with iron uptake related gene signatures. This study highlights the critical role of PI5P4Kα in PDAC and suggests that PI5P4Kα inhibition has therapeutic potential in pancreatic cancer. Citation Format: Gurpreet Kaur Arora, Ryan Loughran, Kyanh Ly, Cheska Marie Galapate, Alicia Llorente, Taylor R Anderson, Chantal Pauli, David A Scott, Yoav Altman, Cosimo Commisso, Brooke M Emerling. PI5P4Kα regulates cell fitness through iron homeostasis in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C044.

Passive Sensing Using Multiple Types of Communication Signal Waveforms for Internet of Everything

Passive Sensing Using Multiple Types of Communication Signal Waveforms for Internet of Everything

The gut microbiome and the brain.

The importance of the gut microbiome for human health and well-being is generally accepted, and elucidating the signaling pathways between the gut microbiome and the host offers novel mechanistic insight into the (patho)physiology and multifaceted aspects of healthy aging and human brain functions. The gut microbiome is tightly linked with the nervous system, and gut microbiota are increasingly emerging as important regulators of emotional and cognitive performance. They send and receive signals for the bidirectional communication between gut and brain via immunological, neuroanatomical, and humoral pathways. The composition of the gut microbiota and the spectrum of metabolites and neurotransmitters that they release changes with increasing age, nutrition, hypoxia, and other pathological conditions. Changes in gut microbiota (dysbiosis) are associated with critical illnesses such as cancer, cardiovascular, and chronic kidney disease but also neurological, mental, and pain disorders, as well as chemotherapies and antibiotics affecting brain development and function. Dysbiosis and a concomitant imbalance of mediators are increasingly emerging both as causes and consequences of diseases affecting the brain. Understanding the microbiota's role in the pathogenesis of these disorders will have major clinical implications and offer new opportunities for therapeutic interventions.

Seamlessly merging radar ranging and imaging, wireless communications, and spectrum sensing for 6G empowered by microwave photonics

To facilitate intelligent interconnection among people, machines, and things, the next generation of communication technology must incorporate various sensing functions besides high-speed wireless communications. Integration of radar, wireless communications, and spectrum sensing is being investigated for 6G with increased spectral efficiency, enhanced system integration, and reduced cost. Microwave photonics, a technique that combines microwave engineering and photonic technology is considered an effective solution for implementing the integration and breaking the bottleneck problems of electronic solutions. Here, we show a photonics-assisted joint radar, wireless communications, and spectrum sensing system that enables precise perception of the surrounding physical and electromagnetic environments while maintaining high-speed communication. Communication signals and frequency-sweep signals are merged optically using a shared system architecture and hardware to achieve signal level sharing, ultimately simultaneously achieving high-accuracy radar ranging and imaging with a measurement error within ± 4 cm and an imaging resolution of 25 × 24.7 mm, high-data-rate wireless communications at 2 Gbaud, and wideband spectrum sensing with a frequency measurement error within ±10 MHz in a 6 GHz bandwidth.