The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area.

The rapid evolution of AI has facilitated innovative solutions in analysing different biomedical imaging modalities. By leveraging the complementary information from each modality, multimodal AI solutions have shown a huge potential to go beyond human capabilities and offer advances in bioimaging. At the same time, new foundation models and transformer-based architectures are now poised to address unsolved challenges in this field. This review aims to explore and discuss the state-of-the-art AI techniques applied in multimodal biomedical imaging, presenting the key challenges and future directions. We discuss several integration strategies to combine multiple biomedical imaging data types. We also focus on methods to overcome the open challenges related to data quality, model interpretability, and ethical implications.

Due to tumour heterogeneity, cellular metabolic activities and tumour proliferation rates can vary across patients, limiting the predictive and prognostic power of population-based metabolic biomarkers. Genome-scale metabolic models have been developed to address this challenge. These models can simulate patient-specific metabolic reaction rates and mechanistically elucidate cellular metabolic alterations. Simultaneously, phenotypes and other omics data can be integrated to provide valuable insights into specific tumour behaviour and molecular mechanisms underlying cancer biology. However, integrative approaches that combine multiple data modalities with metabolic modelling remain largely undeveloped. We propose a unified framework based on interpretable multi-modal machine learning, integrating different data modalities, including transcriptomics, clinical data, and genome-scale metabolic modelling, to stratify breast cancer patients. By integrating validation analysis across data scales, from bulk to single-cell and spatial data, our framework combines mechanistic knowledge from metabolic modelling with machine learning to characterise molecular and metabolic dysregulations across breast cancer risk groups. This unique mechanistic interpretation approach, combining data-driven and biologically-driven knowledge, provides a comprehensive understanding of breast cancer biology to improve personalised cancer therapy and reveal key biomarkers across multiple scales, including patient-specific, cell-specific, and spot-specific prognostic signatures.

Science is losing knowledge it cannot afford to lose. Negative results go unpublished, hard-won expertise walks out the door with departing researchers, and preservation efforts remain fragmented. The consequences are wasted resources, duplicated effort, and missed discoveries. In this perspective, we argue that the research community can act now by embracing alternative dissemination channels, improving documentation best practices, and building sustainable digital infrastructure. We envision moderated platforms for sharing null results and practical know-how, community-driven standards, and AI-powered tools that lower barriers to implementation. With coordinated effort, science can become more open, efficient, and resilient for future generations.

Single-cell RNA sequencing enables high-resolution characterization of transcriptional heterogeneity, but provides only a partial view of the regulatory and metabolic processes associated with cellular states. Several computational methods infer transcription factor (TF) activity and metabolic features directly from RNA, yielding complementary functional representations of cellular organisation. Here, we use a latent factor organisational strategy to jointly model four transcriptome-derived functional projections—gene expression, TF regulon activity, metabolite-level features and predicted metabolic fluxes. Although all layers originate from the same measurement, each captures distinct regulatory or metabolic programs. The resulting latent space organizes these inferred programs into coordinated axes of variation guided by complementary regulatory and metabolic constraints, facilitating functional interpretation beyond gene expression alone. When applied to breast cancer cell line data, the proposed framework organises transcriptome-derived signals into distinct functional programs, including proliferative, oxidative-metabolic and stress-associated axes, that are only partially resolved in RNA-only analyses. Overall, we demonstrate that regulatory and metabolic programs inferred from scRNA-seq can be structured into an interpretable latent representation, supporting a more coherent functional characterization of cellular states from transcriptome-derived functional projections.

Background : T-cell acute lymphoblastic leukemia (T-ALL) and peripheral T-cell lymphoma (PTCL) are aggressive hematological malignancies requiring novel therapeutic strategies. The majority of T-ALL and PTCL tumors display metabolic activation and addiction to endogenous serine/glycine synthesis (SSP), providing opportunities for targeted therapy with the clinically used antidepressant sertraline, inhibiting SSP enzymes SHMT1/2. However, sertraline monotherapy only induces cell cycle arrest and has limited efficacy to suppress disease progression in vivo . Methods : Drug synergy of sertraline combined with clinically used proteasome inhibitors carfilzomib and bortezomib was evaluated. Drug effects on cell cycle, proliferation and apoptosis were assessed in T-ALL, PTCL and healthy blood cells using flow cytometry assays. Proteomic, lipidomic and metabolic analyses on drug treated T-ALL cells were performed to elucidate the molecular mechanisms underlying drug synergy, followed by validation of changes of interest, metabolic rescues and shRNA-knockdown of SSP enzymes in T-ALL cells. In vivo therapeutic efficacy and immune remodelling were evaluated in an immunocompetent MYCN-overexpressing PTCL mouse model. Results : Sertraline acted synergistically with clinically used proteasome inhibitor carfilzomib to induce cell cycle arrest and apoptosis in T-ALL and PTCL cells with SSP activity, with minimal effects on SSP-inactive T-ALL cells or healthy blood cells. Adding carfilzomib also enhanced the therapeutic efficacy of sertraline in an aggressive MYCN PTCL model. Sertraline rewired cell metabolism towards increased cholesterol uptake and biosynthesis in SSP-active T-ALL cells, and this effect was not obtained by other means of SSP inhibition. In contrast to sertraline, carfilzomib promoted cholesterol efflux. Moreover, carfilzomib reduced total lipid levels, further restricting nutrients in sertraline-carfilzomib treated cells. Additionally, the drug combination impaired mitochondrial respiration and elevated reactive oxygen species (ROS) levels and DNA damage in SSP-active tumor cells, which was rescued by citrate supplementation. Interestingly, these metabolic changes were associated with microenvironmental changes in our mouse model, where the drug combination elevated natural killer T-cells, neutrophils and eosinophils. Conclusions : Our study identifies synergy of sertraline-carfilzomib combination treatment mediated through metabolic impairment and is associated with remodelling of the immune microenvironment. This invites for further clinical investigation of this drug combination as a therapeutic strategy for SSP-active T-cell malignancies.