Trends in cell biology · 2024
Na Zhang、Yuanxin Yang、Daichao Xu
Pyroptosis is a lytic, proinflammatory type of programmed cell death crucial for the immune response to pathogen infections and internal danger signals. Gasdermin D (GSDMD) acts as the pore-forming protein in pyroptosis following inflammasome activation. While recent research has improved our understanding of pyroptosis activation and execution, many aspects regarding the molecular mechanisms controlling inflammasome and GSDMD activation remain to be elucidated. A growing body of literature has shown that S-palmitoylation, a reversible post-translational modification (PTM) that attaches palmitate to cysteine residues, contributes to multi-layered regulation of pyroptosis. This review summarizes the emerging roles of S-palmitoylation in pyroptosis research with a focus on mechanisms that regulate NLRP3 inflammasome and GSDMD activation.
American International Journal of Computer Science and Technology · 2024
Parameswara Reddy Nangi、Chaithanya Kumar Reddy Nala Obannagari
Modern enterprises increasingly operate across heterogeneous computing environments encompassing public cloud platforms (e.g., AWS, Azure, and GCP), private clouds, on-premises data centers, Kubernetes clusters, and edge infrastructures. While this architectural diversity enables scalability and agility, it also significantly expands the attack surface and complicates the enforcement of consistent security and regulatory controls. Traditional perimeter-based security models and static compliance mechanisms are insufficient to address the dynamic, distributed, and identity-centric nature of these environments. This paper proposes a multi-layered Zero Trust–driven cybersecurity framework that unifies identity-centric access control, continuous verification, and adaptive policy enforcement across heterogeneous enterprise clouds. The framework integrates deep learning–based behavioral analytics to enable real-time threat detection, risk scoring, and anomaly identification across users, workloads, and services. In parallel, an automated compliance engine implements compliance-as-code principles, continuously validating security posture against regulatory requirements and dynamically enforcing policies across multi-cloud and containerized environments. The proposed architecture is evaluated using a representative enterprise multi-cloud deployment, incorporating public cloud services, Kubernetes workloads, and on-premises resources. Experimental results demonstrate improved threat detection accuracy, reduced mean time to detection and response, and higher compliance adherence compared with conventional rule-based and perimeter-centric approaches, while maintaining acceptable system overhead. The key contribution of this work lies in delivering a unified, scalable, and intelligent Zero Trust framework that tightly couples deep learning–driven security analytics with automated compliance enforcement, providing a practical and extensible foundation for securing modern heterogeneous enterprise cloud ecosystems.
Immune network · 2024
Jin Soo Joo、Dongeun Lee、Jun Young Hong
Pregnancy represents an immunological paradox where the maternal immune system must tolerate the semi-allogeneic fetus expressing paternally-derived Ags. Accumulating evidence over decades has revealed that successful pregnancy requires the active development of robust immune tolerance mechanisms. This review outlines the multi-layered processes that establish fetomaternal tolerance, including the physical barrier of the placenta, restricted chemokine-mediated leukocyte trafficking, lack of sufficient alloantigen presentation, the presence of immunosuppressive regulatory T cells and tolerogenic decidual natural killer cells, expression of immune checkpoint molecules, specific glycosylation patterns conferring immune evasion, and unique metabolic/hormonal modulations. Interestingly, many of the strategies that enable fetal tolerance parallel those employed by cancer cells to promote angiogenesis, invasion, and immune escape. As such, further elucidating the mechanistic underpinnings of fetal-maternal tolerance may reciprocally provide insights into developing novel cancer immunotherapies as well as understanding the pathogenesis of gestational complications linked to dysregulated tolerance processes.
Nature Communications · 2025
Xue-Jian Cui、Qi-Jin Yin、Zijing Gao 等
Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are predominantly sequence-based and typically focus on individual CRE types, limiting insights into their cell-type-specific functions and regulatory dynamics. Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. CREATE integrates genomic sequences, chromatin accessibility, and chromatin interaction data to generate discrete CRE embeddings, enabling accurate multi-class classification and robust characterization of CREs. CREATE excels in identifying cell-type-specific CREs, and provides quantitative and interpretable insights into CRE-specific features, uncovering the underlying regulatory codes. By facilitating large-scale prediction of CREs in specific cell types, CREATE enhances the recognition of disease- or phenotype-associated biological variabilities of CREs, thus advancing our understanding of gene regulatory landscapes and their roles in health and disease. Cui et al. present CREATE, a platform for the identification of multi-class cell-type-specific CREs by integrating multi-omics data. CREATE interpretably extracts discrete CRE embeddings, quantitatively unveils CRE-specific features, and effectively enables large scale prediction of CREs.
Journal of experimental botany · 2018
Hye Ryun Woo、Céline Masclaux-Daubresse、Pyung Ok Lim
暂无摘要
Molecular Omics · 2025
F. V. M. Nunes、Luiza Marques Prates Behrens、R. D. Weimer 等
The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment. Deep learning has shown great promise in addressing these issues through neural network-based frameworks, including variational autoencoders (VAEs) and graph neural networks (GNNs). In this Review, we examine cutting-edge deep learning methodologies for integrating single-cell multimodal data, discussing their architectures, applications, and limitations. We highlight key tools such as sciCAN, scJoint, and scMaui, which use deep learning techniques to harmonize various omics layers, improve feature extraction, and improve downstream biological analyses. Despite significant advancements, it remains challenging to ensure model interpretability, scalability, and generalizability across different datasets. Future directions of research in this field include the development of self-supervised learning strategies, transformer-based architectures, and federated learning frameworks to enhance the robustness and reproducibility of single-cell multi-omics integration.
The EMBO journal · 2026
Runhan Li、Jingyun Zhang、Kehang Mao 等
Skin aging, the most visible and accessible manifestation of organismal aging, reflects systemic physiological decline, compromising barrier integrity, immune defense, and regenerative capacity-functions essential for overall tissue homeostasis and longevity. Understanding why and how the skin ages offers crucial insights into tissue homeostasis and systemic aging. Here, we dissect the multi-layered mechanisms of skin aging across the epidermis, dermis, and appendages, highlighting how intrinsic cellular senescence, disrupted inter-compartmental communication, and dysregulation of the skin microbiome and hormonal signaling collectively undermine epithelial structure and function. We also summarize advances in quantitative evaluation of skin aging, from molecular signatures to morphological, microbial, and phenotypic indices, enabling objective assessment of biological age and intervention efficacy. Finally, we highlight rejuvenation strategies, encompassing rewiring of gene expression programs, metabolic modulation, microenvironmental remodeling, microbiome modulation, and hormone regulation, offering a framework for precision interventions and next-generation regenerative therapies.
BMC neuroscience · 2004
Giulio Tononi
BACKGROUND: Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? PRESENTATION OF THE HYPOTHESIS: This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation - the availability of a very large number of conscious experiences; and integration - the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Phi value of a complex of elements. Phi is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Phi>0 that is not part of a subset of higher Phi. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex. TESTING THE HYPOTHESIS: The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness. IMPLICATIONS OF THE HYPOTHESIS: The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.
International Research Journal of Modernization in Engineering Technology and Science · 2025
Kalyan chakravarthy Thatikonda
Deep learning technologies have revolutionized regulatory compliance automation in cloud-native architectures, addressing the increasing complexity of managing multiple compliance frameworks across distributed systems. The integration of deep reinforcement learning, convolutional neural networks, and natural language processing enables dynamic assessment and enforcement of compliance requirements at both infrastructure and application layers. These advanced systems demonstrate exceptional capabilities in anomaly detection, policy inference, and real-time monitoring of cloud resources, automatically identifying non-compliant configurations and implementing remediation actions. The three-layer architecture incorporating data collection, processing and analysis, and automated remediation has proven highly effective across diverse enterprise environments. Through continuous monitoring and adaptive learning mechanisms, the system successfully handles complex microservices architectures, rapidly evolving regulatory requirements, and intricate framework interactions. The automated system enhances regulatory compliance through proactive violation detection and rapid remediation capabilities, while significantly reducing manual oversight requirements. The framework demonstrates particular effectiveness in managing GDPR and HIPAA compliance requirements, with notable improvements in detection accuracy and response times. The implementation of federated learning and explainable AI techniques further enhances the system's ability to maintain compliance across multi-cloud environments while providing transparent decision justification for audit processes. This comprehensive approach to compliance automation addresses key challenges in modern cloud environments, including configuration drift, service dependency management, and regulatory framework conflicts, while maintaining high levels of accuracy and operational efficiency across diverse deployment scenarios.
The Pan African medical journal · 2018
Ibrahim Elaraoud、Erica Damato
暂无摘要
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