Keying Modulation Research Articles

Screening of key genes related to M6A methylation in patients with heart failure

ObjectiveThis study aims to identify m6A methylation-related and immune cell-related key genes with diagnostic potential for heart failure (HF) by leveraging various bioinformatics techniques.MethodsThe GSE116250 and GSE141910 datasets were sourced from the Gene Expression Omnibus (GEO) database. Correlation analysis was conducted between differentially expressed genes (DEGs) in HF and control groups, alongside differential m6A regulatory factors, to identify m6A-related DEGs (m6A-DEGs). Subsequently, candidate genes were narrowed down by intersecting key module genes derived from weighted gene co-expression network analysis (WGCNA) with m6A-DEGs. Key genes were then identified through the Least Absolute Shrinkage and Selection Operator (LASSO) analysis. Correlation analyses between key genes and differentially expressed immune cells were performed, followed by the validation of key gene expression levels in public datasets. To ensure clinical applicability, five pairs of blood samples were collected for quantitative real-time fluorescence PCR (qRT-PCR) validation.ResultsA total of 93 m6A-DEGs were identified (|COR| > 0.6, P < 0.05), and five key genes (LACTB2, NAMPT, SCAMP5, HBA1, and PRKAR2A) were selected for further analysis. Correlation analysis revealed that differential immune cells were negatively associated with the expression of LACTB2, NAMPT, and PRKAR2A (P < 0.05), while positively correlated with SCAMP5 and HBA1 (P < 0.05). Subsequent expression validation confirmed significant differences in key gene expression between the HF and control groups, with consistent expression trends observed across both training and validation sets. The expression trends of LACTB2, PRKAR2A, and HBA1 in blood samples from the qRT-PCR assay aligned with the results derived from public databases.ConclusionThis study successfully identified five m6A methylation-related key genes with diagnostic significance, providing a theoretical foundation for further exploration of m6A methylation’s molecular mechanisms in HF.

Development of a Cloud Computing-Based Collaboration Information System

The rapid advancement of information and communication technology has created a demand for efficient and effective information systems to facilitate collaboration among institutions. This study develops a cloud computing-based collaboration information system designed to enhance real-time communication, data management, and collaboration among users. The system comprises key modules, including collaboration management, document management, and integrated collaboration features. Testing involved various scenarios with 10 to 100 active users simultaneously, revealing that the system maintained stable performance with response times under 3 seconds for each user request. Furthermore, security assessments utilizing encryption protocols and two-factor authentication confirmed that the managed data is protected from external threats. The findings underscore the system's potential to improve collaborative efficiency while ensuring data security, making it a valuable solution for future collaborative initiatives.

A Case Study on the Integration of Powerline Communications and Visible Light Communications from a Power Electronics Perspective.

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure.

Towards an End-to-End Personal Fine-Tuning Framework for AI Value Alignment

This study introduces a novel architecture for value, preference, and boundary alignment in large language models (LLMs) and generative AI systems, accompanied by an experimental implementation. It addresses the limitations in AI model trustworthiness stemming from insufficient comprehension of personal context, preferences, and cultural diversity, which can lead to biases and safety risks. Using an inductive, qualitative research approach, we propose a framework for personalizing AI models to improve model alignment through additional context and boundaries set by users. Our framework incorporates user-friendly tools for identification, annotation, and simulation across diverse contexts, utilizing prompt-driven semantic segmentation and automatic labeling. It aims to streamline scenario generation and personalization processes while providing accessible annotation tools. The study examines various components of this framework, including user interfaces, underlying tools, and system mechanics. We present a pilot study that demonstrates the framework’s ability to reduce the complexity of value elicitation and personalization in LLMs. Our experimental setup involves a prototype implementation of key framework modules, including a value elicitation interface and a fine-tuning mechanism for language models. The primary goal is to create a token-based system that allows users to easily impart their values and preferences to AI systems, enhancing model personalization and alignment. This research contributes to the democratization of AI model fine-tuning and dataset generation, advancing efforts in AI value alignment. By focusing on practical implementation and user interaction, our study bridges the gap between theoretical alignment approaches and real-world applications in AI systems.

High-Data-Rate Modulators Based on Graphene Transistors: Device Circuit Co-Design Proposals

The multifunctionality feature of graphene field-effect transistors (GFETs) is exploited here to design circuit building blocks of high-data-rate modulators by using a physics-based compact model. Educated device performance projections are obtained with the experimentally calibrated model and used to choose an appropriate improved feasible GFET for these applications. Phase-shift and frequency-shift keying (PSK and FSK) modulation schemes are obtained with 0.6 GHz GFET-based multifunctional circuits used alternatively in different operation modes: inverting and in-phase amplification and frequency multiplication. An adequate baseband signal applied to the transistors’ input also serves to enhance the device and circuit performance reproducibility since the impact of traps is diminished. Quadrature PSK is also achieved by combining two GFET-based multifunctional circuits. This device circuit co-design proposal intends to boost the heterogeneous implementation of graphene devices with incumbent technologies into a single chip: the baseband pulses can be generated with CMOS technology as a front end of line and the multifunctional GFET-based circuits as a back end of line.

Vispro improves imaging analysis for Visium spatial transcriptomics.

Spatial transcriptomics (ST) is a cutting-edge technology that enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. Within this framework, histological images serve a crucial role, providing spatially cohesive information that is often challenging to capture through gene expression alone. However, the imaging process in ST data presents inherent challenges, such as variability in image quality, artifacts from fiducial markers, and difficulty in distinguishing tissue regions. These challenges can significantly impair the accuracy and effectiveness of downstream analyses. To address these limitations, we developed Vispro, an end-to-end, fully automated image processing tool tailored for ST data. Vispro integrates four key modules: fiducial marker detection, marker removal and image restoration, tissue region detection, and segmentation of disconnected tissue areas. These modules systematically enhance the quality of ST images, reducing artifacts and improving tissue segmentation accuracy. Furthermore, by improving the integrity of image data, we demonstrate that Vispro facilitates downstream analyses such as cell segmentation and image registration, empowering researchers to extract more detailed and accurate biological insights from complex tissue architectures.

Research on vehicle trajectory prediction methods in dense and heterogeneous urban traffic

ABSTRACT In autonomous driving, accurately predicting the trajectories of surrounding vehicles is essential, particularly in dense and heterogeneous urban traffic. We propose a graph-structured model with a category layer to efficiently forecast the target vehicle’s trajectory. The model enables flexible selection of interacting objects based on environmental interactions and extracts spatial-temporal features using a graph convolutional network. A categorical layer is introduced to account for the different influences of dynamic agents, while vehicle dynamics constraints ensure the feasibility of predicted trajectories. We developed a new heterogeneous and dense urban unsignalized intersection dataset (HID), capturing complex urban interactions, and conducted extensive experiments on HID, ApolloScape, and TRAF datasets. Results demonstrate that our model outperforms benchmark methods across diverse urban scenarios, and the integration of key modules significantly enhances prediction accuracy and performance.

PRN: progressive reasoning network and its image completion applications

Ancient murals embody profound historical, cultural, scientific, and artistic values, yet many are afflicted with challenges such as pigment shedding or missing parts. While deep learning-based completion techniques have yielded remarkable results in restoring natural images, their application to damaged murals has been unsatisfactory due to data shifts and limited modeling efficacy. This paper proposes a novel progressive reasoning network designed specifically for mural image completion, inspired by the mural painting process. The proposed network comprises three key modules: a luminance reasoning module, a sketch reasoning module, and a color fusion module. The first two modules are based on the double-codec framework, designed to infer missing areas’ luminance and sketch information. The final module then utilizes a paired-associate learning approach to reconstruct the color image. This network utilizes two parallel, complementary pathways to estimate the luminance and sketch maps of a damaged mural. Subsequently, these two maps are combined to synthesize a complete color image. Experimental results indicate that the proposed network excels in restoring clearer structures and more vivid colors, surpassing current state-of-the-art methods in both quantitative and qualitative assessments for repairing damaged images. Our code and results will be publicly accessible at https://github.com/albestobe/PRN.

Amlexanox reduces new-onset atrial fibrillation risk in sepsis by downregulating S100A12: a Mendelian randomization study.

Sepsis is characterized by high morbidity and mortality rates, alongside limited therapeutic efficacy. Atrial fibrillation (AF), the most common arrhythmia, has been closely linked to sepsis in prior research. However, the specific mechanisms through which sepsis leads to new-onset AF remain poorly understood. This study focuses on identifying critical genes that are dysregulated in the development of new-onset AF within the context of sepsis, with the goal of uncovering new potential targets for its diagnosis and prevention. Our study began by applying Mendelian Randomization (MR) to assess the causal link between sepsis and AF. We then sourced sepsis and AF datasets from the Gene expression Omnibus (GEO) database. Using Weighted Gene Co-expression Network Analysis (WGCNA), we pinpointed key modules and genes associated with both sepsis and AF conditions. Protein-protein interaction (PPI) network was constructed. The Transcriptional Regulatory Relationships Unravelled by Sentence-based Text-mining (TRRUST) database helped build the transcription factor (TF) interaction network. Key genes were scrutinized through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to delve into their roles in new-onset AF's pathophysiology during sepsis. We employed the CIBERSORT algorithm to evaluate immune infiltration and the association between key genes and immune cells. The Connectivity Map (CMap) database facilitated the prediction of potential small molecule compounds targeting key genes. To culminate, an acute sepsis mouse model was developed to validate the implicated mechanisms of key genes involved in new-onset AF during sepsis, and to assess the prophylactic effectiveness of identified drug candidates. MR revealed potential independent risk factors for new-onset AF in sepsis. S100A12 was identified as a core interaction gene with elevated levels in sepsis and AF, underscoring its diagnostic and predictive significance. S100A12, along with associated genes, was mainly linked to immune and inflammatory response signaling pathways, correlating with immune cell levels. Targeting S100A12 identifies five potential small molecule therapeutics: amlexanox, balsalazide, methandriol, olopatadine, and tiboloe. In animal studies, acute sepsis increased S100A12 expression in serum and atrial tissues, correlating positively with inflammatory markers (IL-1β, IL-6, TNF-α) and negatively with heart rate, indicating a predisposition to AF. Early amlexanox administration can reduced S100A12 expression, dampened inflammation, and lessened new-onset AF risk in sepsis. This study demonstrates that sepsis may independently increase the risk of new-onset AF. We identified S100A12 as a key gene influencing the new-onset AF in sepsis through immune regulation, presenting considerable diagnostic and predictive value. Notably, amlexanox, by targeting S100A12 emerges as the most clinical relevant intervention for managing new-onset AF in sepsis patients.

Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors

ABSTRACTAgricultural Internet of Things has become one of the most important data sources of agricultural big data. However, the wireless sensors equipped with agricultural Internet of Things is affected by many factors, and anomalous data inevitably exists during the data collection process. The existence of anomalous data leads to the deterioration of the agricultural data quality, which hinders the efficient development of agricultural data analysis. In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. The model first adds Gaussian noise to the input sequence and converts it into standard time series and image data. Subsequently, it is processed by different modal encoders and fuses the image and sequence modal data using Temporal Cross‐modal Attention module to enhance the perception of anomalous modal information. Finally, the fused data is reconstructed, and anomalous data are identified by the image and sequence decoders. Comparison experiments with several baselines prove the validity of the model proposed in this article, ablation experiments prove the necessity of the key modules in the model, and multiple sets of experiments are also designed to discuss the effects of hyperparameters.

In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, accounting for over 90% of pancreatic cancers, and is characterized by limited treatment options and poor survival rates. Systems biology provides in-depth insights into the molecular mechanisms of PDAC. In this context, novel algorithms and comprehensive strategies are essential for advancing the identification of critical network nodes and therapeutic targets within disease-related protein-protein interaction networks. This study employed a comprehensive computational strategy using the metaheuristic algorithm Trader to enhance the identification of potential therapeutic targets. Analysis of the expression data from the PDAC dataset (GSE132956) involved co-expression analysis and clustering of differentially expressed genes to identify key disease-associated modules. The STRING database was used to construct a network of differentially expressed genes, and the Trader algorithm pinpointed the top 30 DEGs whose removal caused the most significant network disconnections. Enriched gene ontology terms included “Signaling by Rho GTPases,” “Signaling by receptor tyrosine kinases,” and “immune system.” Additionally, nine hub genes—FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1—were identified as central to PDAC pathogenesis. This integrated approach, combining co-expression analysis with protein-protein interaction network analysis using a metaheuristic algorithm, provides valuable insights into PDAC mechanisms and highlights several hub genes as potential therapeutic targets.

Exploring the hub Genes and Potential Mechanisms of Complement system-related Genes in Parkinson Disease: Based on Transcriptome Sequencing and Mendelian Randomization.

An accurate diagnosis of Parkinson's disease (PD) remains challenging and the exact cause of the disease is unclean. The aims are to identify hub genes associated with the complement system in PD and to explore their underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) and key module genes related to PD were mined through differential expression analysis and WGCNA. Then, differentially expressed CSRGs (DE-CSRGs) were obtained by intersecting the DEGs, key module genes and CSRGs. Subsequently, MR analysis was executed to identify genes causally associated with PD. Based on genes with significant MR results, the expression level and diagnostic performance verification were achieved to yield hub genes. Functional enrichment and immune infiltration analyses were accomplished to insight into the pathogenesis of PD. qRT-PCR was employed to evaluate the expression levels of hub genes. After MR analysis and related verification, CD93, CTSS, PRKCD and TLR2 were finally identified as hub genes. Enrichment analysis indicated that the main enriched pathways for hub genes. Immune infiltration analysis found that the hub genes showed significant correlation with a variety of immune cells (such as myeloid-derived suppressor cell and macrophage). In the qRT-PCR results, the expression levels of CTSS, PRKCD and TLR2 were consistent with those we obtained from public databases. Hence, we mined four hub genes associated with complement system in PD which provided novel perspectives for the diagnosis and treatment of PD.

Identification of immune-related hub genes in spinal cord injury

ObjectivesImmune regulation is a pivotal factor in the pathogenesis and repair of spinal cord injury (SCI). This study aims to explore potential immune center genes associated with spinal cord injury.MethodsThe public data set GSE151371 was obtained from the GEO database. The R software package "limma" was used to identify differentially expressed genes (DEGs) in SCI. GO, KEGG and GSEA pathway analyses were performed using the DEGs. The key module genes related to spinal cord injury were selected through WGCNA analysis. Overlapping genes were extracted from WGCNA, DEGs, and immune-related genes. LASSO analysis was employed to identify central genes associated with SCI immunity. Pearson correlation analysis assessed the correlation between hub genes and immune cells in SCI. In addition, we further investigated the hub genes' expression, diagnostic potential, function, and targeted drugs.ResultsWe have identified three immunity-related hub genes (ABHD5, EDNRB, EDN3). Immune infiltration analysis showed that the hub gene was significantly associated with resting NK cells, M2 macrophages, and monocytes in the immune microenvironment of SCI. ROC analysis demonstrated that these hub genes have favorable diagnostic performance for SCI. Functional analysis revealed that ABHD5 is primarily associated with lipid metabolism pathways, while EDN3 and EDNRB are mainly involved in endothelin, downstream GPCR signaling, and ERK signaling transduction. In addition, we identified six potential targeted drugs based on our findings.ConclusionsABHD5, EDNRB, and EDN3 are involved in processes such as SCI progression or repair through immunomodulation and deserve further study.

A 1000FPS@360,000pixels mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for objective detection tasks

Ubiquitous vision sensors continuously collect data from the environment, imposing a significant processing burden. Especially, high-frame-rate convolution processing for large-scale pixel arrays presents substantial challenges. This paper proposes a mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for high-frame-rate object detection tasks. A customized neural network (NN) architecture is designed specifically for object detection. Key circuitry modules, including signal readout, dark current offset, normalization, first-layer convolution, and pooling, are implemented in the analog domain, leading to high-quality information extraction and substantial data compression. Maximum parallelism in the first-layer convolution is realized in our proposed processing flow, ensuring high-frame-rate convolution for large-scale pixel arrays. Additionally, the subsequent NN layers are efficiently deployed on SRAM-based in-memory digital computing. The macro was designed using a 55-nm foundry process. Results show that our macro can achieve 1000 fps@360,000 pixels with a 63.8 TOPS throughput under 4/8-bit hybrid precision, outperforming the state-of-the-art in frame rate by 2–10 times.

Causal-relationship representation enhanced joint extraction model for elements and relationships

The joint extraction of elements such as entities, relations, events, and their arguments, along with their specific interrelationships, is a critical task in natural language processing. Existing research often employs implicit handling of interactions between tasks through techniques like shared encodings or parameter sharing, lacking explicit modeling of the specific relationships between tasks. This limitation hinders the full utilization of inter-task correlation information and impacts effective collaboration between tasks. To address this, we propose a model for joint extraction of elements and relations based on causal relationship representation enhancement (CRE). This model aims to capture specific relationships between tasks in multiple stages, facilitating finer adjustments and optimization of subtasks and thereby enhancing the overall model performance. Specifically, CRE comprises three key modules: feature adaptation, feature interaction, and feature fusion. The feature adaptation module selects and adjusts features from shared encodings based on the requirements of specific tasks to better adapt to semantic differences between different tasks. The feature interaction module employs causal reasoning to comprehensively capture the causal relationships between tasks while mitigating negative transfer brought about by interference from semantic information. The feature fusion module further integrates features to obtain optimized task-specific representations. Ultimately, CRE exhibits a significant improvement in average performance across multiple information extraction tasks.

Identification and RT-qPCR Validation of Biomarkers Based on Butyrate Metabolism-Related Genes to Predict Recurrent Miscarriage.

To date, the cause of recurrent miscarriage (RM) in at least 50% of patients remains unknown. However, no study has explored the correlation between butyrate metabolism-related genes (BMRGs) and RM. RM-related datasets (GSE165004, GSE111974, GSE73025, and GSE179996) were obtained from the Gene Expression Omnibus (GEO) database. First, 595 differentially expressed genes (DEGs) were identified between the RM and control samples in GSE165004. Subsequently, 213 differentially expressed BMRGs (DE-BMRGs) were identified by considering the intersection of DEGs with BMRGs. The protein-protein interaction (PPI)network of DE-BMRGs contained 156 nodes and 250 edges, and a key module was obtained. In total, four biomarkers (ACTR2, ANXA2, PFN1, and OAS1) were acquired through least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF). Immune analysis revealed two immune cells and three immune-related gene sets that were significantly different between the RM and control groups, namely, T helper cells, regulatory T cells (Treg), MHC class I, parainflammation, and type I IFN response. In addition, a TF-mRNA network based on the top 100 nodes ranked in the order of connectivity was created, including 100 nodes and 253 edges, such as MTERF2-ACTR2, NKX23-PFN1, STAT1-OAS1, and SP100-ANXA2. Finally, 3 drugs (withaferin A, N-ethylmaleimide, and etoposide) were predicted to interact with 2 biomarkers (ANXA2 and ACTR2). Eventually, ANXA2 and OAS1 were significantly downregulated, and PFN1 was markedly overexpressed in the RM group, as determined by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Our findings authenticated four butyrate metabolism-related biomarkers for the diagnosis of RM, providing a scientific reference for further studies on RM treatment.

Deciphering the molecular mechanisms of heat stress tolerance in goats: Insights from transcriptome and Gene Co-expression analysis.

Climate change poses a significant threat to the sustainability of livestock production systems in developing countries, particularly impacting small ruminants like goats, which are highly susceptible to heat stress. This stressor not only reduces productivity but also undermines economic viability. This study aimed to delve into the molecular mechanisms underlying heat stress tolerance in goats by conducting a comprehensive transcriptome analysis of heat-tolerant (HT, n=4) and heat-susceptible (HS, n=6) Jamunapari goats. Physiological metrics, such as rectal temperature, respiratory rate, and heart rate, were meticulously monitored under extreme environmental conditions (Temperature Humidity Index >92) to effectively classify goats based on their distinct heat stress responses. Samples of blood were obtained, and peripheral blood mononuclear cells (PBMCs) were extracted for subsequent RNA extraction. RNA-Seq analysis revealed a sum of 734 differentially expressed genes (DEGs), comprising 251 upregulated and 483 downregulated genes in HT goats compared to their HS counterparts. The WGCNA revealed three key modules, darkorange (tolerance), paleturquoise (respiration rate), and darkmagenta (heart rate). Moreover, functional enrichment analysis revealed that DEGs within these modules played intricate roles in crucial biological processes and pathways, including mitochondrial function, cardiac function, immune response, genomic stability, and metabolic regulation. This research notably enhances our comprehension of the genetic underpinnings of thermo-tolerance in goats and provides invaluable guidance for formulating breeding strategies aimed at bolstering livestock resilience against the challenges of climate change.

RIS-Assisted Receive Generalized Space-Shift Keying and Receive Generalized Spatial Modulation

RIS-Assisted Receive Generalized Space-Shift Keying and Receive Generalized Spatial Modulation

Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach.

Background/Objectives: Despite current treatments extending the lifespan of Glioblastoma (GBM) patients, the average survival time is around 15-18 months, underscoring the fatality of GBM. This study aims to investigate the impact of sample heterogeneity on gene expression in GBM, identify key metabolic pathways and gene modules, and explore potential therapeutic targets. Methods: In this study, we analysed GBM transcriptome data derived from The Cancer Genome Atlas (TCGA) using genome-scale metabolic models (GEMs) and co-expression networks. We examine transcriptome data incorporating tumour purity scores (TPSs), allowing us to assess the impact of sample heterogeneity on gene expression profiles. We analysed the metabolic profile of GBM by generating condition-specific GEMs based on the TPS group. Results: Our findings revealed that over 90% of genes showing brain and glioma specificity in RNA expression demonstrate a high positive correlation, underscoring their expression is dominated by glioma cells. Conversely, negatively correlated genes are strongly associated with immune responses, indicating a complex interaction between glioma and immune pathways and non-tumorigenic cell dominance on gene expression. TPS-based metabolic profile analysis was supported by reporter metabolite analysis, highlighting several metabolic pathways, including arachidonic acid, kynurenine and NAD pathway. Through co-expression network analysis, we identified modules that significantly overlap with TPS-correlated genes. Notably, SOX11 and GSX1 are upregulated in High TPS, show a high correlation with TPS, and emerged as promising therapeutic targets. Additionally, NCAM1 exhibits a high centrality score within the co-expression module, which shows a positive correlation with TPS. Moreover, LILRB4, an immune-related gene expressed in the brain, showed a negative correlation and upregulated in Low TPS, highlighting the importance of modulating immune responses in the GBM mechanism. Conclusions: Our study uncovers sample heterogeneity's impact on gene expression and the molecular mechanisms driving GBM, and it identifies potential therapeutic targets for developing effective treatments for GBM patients.

Exploring thermal effects of advanced backside power delivery network beyond 3 nm node

Backside Power Delivery Networks (BSPDNs) address scaling issues by relocating power, boosting efficiency and density. However, thermal effects pose challenges. In this work, a comprehensive thermal analysis of BSPDN is performed to elaborate the key modulation factors and possible optimization approaches, where the specific backside metal layers are constructed to investigate the impacts of operating conditions, via distribution and materials on thermal effects. To improve the simulation efficiency, the effective thermal conductivity is employed to simplify the Nanosheet (NSH) FET based front-end-of-line (FEOL) and other layers at the 3 nm technology node. Results show that non-uniform via distribution in BSPDN causes temperature fluctuations, but augmenting Backside Via counts effectively mitigates local peak temperature increases from higher power. For BSPDN, backside cooling solutions outperform frontside in efficiency, particularly at high via densities. Using high thermal conductivity inter-metal dielectric (IMD) materials significantly reduces global temperature rise and fluctuations from non-uniform vias in BSPDN, enhancing PDN design flexibility.