Matrix Interactions Research Articles

Revealing the coupled evolution process of construction risks in mega hydropower engineering through textual semantics

Mega hydropower engineering (MHE) involves complex construction processes, harsh environments, and highly mobile personnel, equipment, and other resources, resulting in a high incidence of construction risks and significant difficulties in safety control. Previous studies extensively explored the identification and governance of risks in MHE. However, these studies did not adequately utilize accident report case texts, and the coupled evolutionary law of construction risks remains unclear. To address this gap, this study developed a Bert-based Global Pointer Network for Named Entity-Relation Joint Extraction (Bert-GPLinker) to extract risk factors and their relationships. Second, the extracted risk factors and their relationships were employed to construct a knowledge graph, integrating text matching and path inference algorithms to propose a coupled evolutionary path reasoning method for construction risks. Finally, the coupled interactions among the risk factors were considered, and the interaction matrix principle was utilized to quantitatively assess the importance of each risk factor in the knowledge graph. The results showed that (1) the F1 value of Bert-GPLinker on the self-built dataset was 91.90%, indicating better model performance; (2) the coupled evolutionary reasoning system can quickly deduce the evolutionary path with the highest probability of any risk node and the types of accidents that may result; and (3) among the top 25 risk factors in terms of importance, those in the management system category accounted for 52%, with higher importance mainly concentrated in inspection, supervision, and training.

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of tumor-immune interactions is unlikely in tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen evolve as a mechanism of immune escape, but the exact nature of immune-collagen interactions is poorly understood. Spatial data quantifying collagen fiber alignment in squamous cell carcinomas indicates that late-stage disease is associated with highly aligned fibers. Our computational modeling framework discriminates between two hypotheses: immune cell migration that moves (1) parallel or (2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-extracellular matrix interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. Here, computational modeling provides important mechanistic insights by defining a kernel cell-cell interaction function that considers a spectrum of local (cell-scale) to global (tumor-scale) spatial interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interaction kernels drives tumor growth and infiltration.

Integration of QFD, HOQ, Taguchi, and Kansei Engineering for Smart Desk Lamp Design

This research focuses on the analysis of smart desk lamps. To the authors’ knowledge, there is no previous study about Voice of Customer regarding smart desk lamps in Indonesia, to know the requirements for a smart desk lamp from Indonesian customer using the QFD and House of Quality (HOQ) The overall value of the interaction matrix with HOQ, the relative weight results for each customer and functional requirements are obtained. The highest technical requirement from HOQ is automatic on/off. The Taguchi method is to find out the best design for each Kansei Engineering that has been created. The Kansei Engineering that have been created are easy to use, adjustable lamp, affordable price, modern design, hi-tech. Each Kansei Engineering produces the required design according to the S/N Ratio (signal to noise ratio). Based on the QFD questionnaire and design using Kansei Engineering, one best design was obtained, namely hi-tech. The design consists of 3 parts, namely the top, middle and bottom. At the top, the slim rectangular shape is preferable with lights position in the middle. Pole position is round, upright position. At the bottom, it has a circular shape.

Obesity-driven changes in breast tissue exhibit a pro-angiogenic extracellular matrix signature

Obesity has reached epidemic proportions in the United States, emerging as a risk factor for the onset of breast cancer and a harbinger of unfavorable outcomes [1–3]. Despite limited understanding of the precise mechanisms, both obesity and breast cancer are associated with extracellular matrix (ECM) rewiring [4–6]. Utilizing total breast tissue proteomics, we analyzed normal-weight (18.5 to < 25 kg/m2), overweight (25 to < 30 kg/m2), and obese (≥30 kg/m2) individuals to identify potential ECM modifying proteins for cancer development and acceleration. Obese individuals exhibited substantial ECM alterations, marked by increased basement membrane deposition, angiogenic signatures, and ECM-modifying proteins. Notably, the collagen IV crosslinking enzyme peroxidasin (PXDN) emerged as a potential mediator of the ECM changes in individuals with an elevated body mass index (BMI), strongly correlating with angiogenic and basement membrane signatures. Furthermore, glycan-binding proteins galectin-1 (LGALS1) and galectin-3 (LGALS3), which play crucial roles in matrix interactions and angiogenesis, also strongly correlate with ECM modifications. In breast cancer, elevated PXDN, LGALS1, and LGALS3 correlate with reduced relapse-free and distant-metastatic-free survival. These proteins were significantly associated with mesenchymal stromal cell markers, indicating adipocytes and fibroblasts may be the primary contributors of the obesity-related ECM changes. Our findings unveil a pro-angiogenic ECM signature in obese breast tissue, offering potential targets to inhibit breast cancer development and progression.

Risk Assessment of Selected CCS Wells through Feature, Event, and Process Method and Comparison of the Barrier Effect.

To reduce the CO2 release in the atmosphere, the carbon capture and sequestration (CCS) technique presents a solution in which the CO2 is captured from the emitting source and injected into a suitable geological formation in the subsurface. For the CCS project to be successful, CO2 must be trapped underground for hundreds of years. In that respect, good integrity plays an important role as it ensures that the injected CO2 remains sequestrated in the subsurface. Hence, this study presents a risk assessment technique with the help of which critical elements that can compromise the integrity of the CCS well can be identified. The approach taken for the risk assessment is based on the feature, event, and process (FEP). However, this method gives a qualitative analysis, and to convert it to a semiquantitative one, FEP is integrated with an interaction matrix, incident potential matrix (IPM), and cause-effect plot diagram. In this paper, risk assessment was conducted on two fictitious wells with different well configurations. It was found that cement and casing were the most vulnerable components, while formation water and subsidence were the most problematic elements in the given well system. It was also concluded that the number of plugs and their location in the well could increase or decrease the intensity of the risk levels in the CCS wells.

A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C data.

Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared with classification-based methods, up to 20-fold acceleration compared with the SOTA kernel-based framework, and a fivefold acceleration compared with statistical algorithms. Furthermore, the proposed framework is capable of generalizing across various cell types, multiresolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for a single-cell contact map within 1 min and for a 900-cell-superimposed contact map within 3 min, enabling fast analysis of large-scale single-cell data.

Integrated bioinformatic analysis reveals the gene signatures, epigenetic roles, and regulatory networks in endometriosis

ObjectivesEndometriosis is a common gynecological disease with a significant economic burden. Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach. MethodsSix microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein–protein interaction (PPI) network of DEGs, miRNA, and lncRNA was constructed using STRING and Cytoscape, and then the top 15 hub genes in the PPI network were identified using CytoHubba. ResultsA total of 551 common DEGs were identified from four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA (5 upregulated and 11 downregulated) were identified from all studies, along with 12 lncRNA (10 upregulated and 2 downregulated) that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG. ConclusionsOur in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The role of identified hub genes, miRNA, and lncRNA may also have an opportunity to be explored as potential biomarkers for endometriosis diagnosis and prognosis.

Scalable imaging-free spatial genomics through computational reconstruction.

Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are limited in scale. Here, we developed an imaging-free spatial transcriptomics method that uses molecular diffusion patterns to computationally reconstruct spatial data. To do so, we utilize a simple experimental protocol on two dimensional barcode arrays to establish an interaction network between barcodes via molecular diffusion. Sequencing these interactions generates a high dimensional matrix of interactions between different spatial barcodes. Then, we perform dimensionality reduction to regenerate a two-dimensional manifold, which represents the spatial locations of the barcode arrays. Surprisingly, we found that the UMAP algorithm, with minimal modifications can faithfully successfully reconstruct the arrays. We demonstrated that this method is compatible with capture array based spatial transcriptomics/genomics methods, Slide-seq and Slide-tags, with high fidelity. We systematically explore the fidelity of the reconstruction through comparisons with experimentally derived ground truth data, and demonstrate that reconstruction generates high quality spatial genomics data. We also scaled this technique to reconstruct high-resolution spatial information over areas up to 1.2 centimeters. This computational reconstruction method effectively converts spatial genomics measurements to molecular biology, enabling spatial transcriptomics with high accessibility, and scalability.

Bridging Behavior of Palm Fiber in Cementitious Composite

This study addresses the growing need for sustainable construction materials by investigating the mechanical properties and behavior of palm fiber-reinforced cementitious composite (FRCC), a potential eco-friendly alternative to synthetic fiber reinforcements. Despite the promise of natural fibers in enhancing the mechanical performance of composites, challenges remain in optimizing fiber distribution, fiber–composite bonding mechanism, and its balance to matrix strength. To address these challenges, this study conducted extensive experimental programs using palm fiber as reinforcement, focusing on understanding the fiber–matrix interaction, determining the pullout load–slip relationship, and modeling fiber bridging behavior. The experimental program included density calculations and scanning electron microscope (SEM) analysis to examine the surface morphology and diameter of the fibers. Single fiber pullout tests were performed under varying conditions to assess the pullout load, slip behavior, and failure modes of the palm fiber, and a relationship between the pullout load and slip with the embedded length of the palm fiber was constructed. A trilinear model was developed to describe the pullout load–slip behavior of single fibers, and a corresponding palm-FRCC bridging model was constructed using the results from these tests. Section analysis was conducted to assess the adaptability of the modeled bridging law calculations, and the analysis result of the bending moment–curvature relationship shows a good agreement with the experimental results obtained from the four-point bending test of palm-FRCC. These findings demonstrate the potential of palm fibers in improving the mechanical performance of FRCC and contribute to the broader understanding of natural fiber reinforcement in cementitious composites.

Image-Based Visual Servoing of Manipulators With Unknown Depth: A Recurrent Neural Network Approach.

The image-based visual servoing (IBVS) of manipulators is important for intelligent manipulation using visual feedbacks. While the traditional IBVS methods for manipulators require the knowledge of the depth information in the interaction matrix, in this article, we propose a novel IBVS method for manipulators without depth estimation by leveraging the property of the associated image Jacobian. Because of a novel transformation, the IBVS problem is converted into a convex optimization problem subject to the kinematic constraint, joint constraints, and other constraints that are not explicitly related to the depth information. The problem is then solved by developing a recurrent neural network of global asymptotic convergence, and a dynamic neural control law without depth estimation emerges for the IBVS of manipulators. The theoretical guarantee and simulation results are provided to show the efficacy of the proposed method.

Spheroid-Hydrogel-Integrated Biomimetic System: A New Frontier in Advanced Three-Dimensional Cell Culture Technology.

Despite significant advances in three-dimensional (3D) cell culture technologies, creating accurate in vitro models that faithfully recapitulate complex in vivo environments remains a major challenge in biomedical research. Traditional culture methods often fail to simultaneously facilitate critical cell-cell and cell-extracellular matrix (ECM) interactions while providing control over mechanical and biochemical properties. This review introduces the spheroid-hydrogel-integrated biomimetic system (SHIBS), a groundbreaking approach that synergistically combines spheroid culture with tailored hydrogel technologies. SHIBS uniquely bridges the gap between traditional culture methods and physiological conditions by offering unprecedented control over both cellular interactions and environmental properties. We explore how SHIBS is revolutionizing fields ranging from drug discovery and disease modeling to regenerative medicine and basic biological research. The review discusses current challenges in SHIBS technology, including reproducibility, scalability, and high-resolution imaging, and outlines ongoing research addressing these issues. Furthermore, we envision the future evolution of SHIBS into more sophisticated organoid-hydrogel-integrated biomimetic systems and its integration with cutting-edge technologies such as microfluidics, 3D bioprinting, and artificial intelligence. SHIBS represents a paradigm shift in 3D cell culture technology, offering a unique solution to recreate complex in vivo environments. Its potential to accelerate the development of personalized therapies across various biomedical fields is significant. While challenges persist, the ongoing advancements in SHIBS technology promise to overcome current limitations, paving the way for more accurate and reliable in vitro models. The future integration of SHIBS with emerging technologies may revolutionize biomimetic modeling, potentially reducing the need for animal testing and expediting drug discovery processes. This comprehensive review provides researchers and clinicians with a holistic understanding of SHIBS technology, its current capabilities, and its future prospects in advancing biomedical research and therapeutic innovations.

Synthesis parameters and formulation of metakaolin based geopolymer matrix composites for high‐temperature applications (1150°C)

AbstractGeopolymer matrix composites (GMCs) are actively being developed for high‐temperature applications. In this work, we investigated different composite preparation parameters, such as the elaboration conditions (time, temperature and pressure) and composite dimensions (number of folds and surface area). In addition, we studied the effect of alkali cations (NaK or K), on metakaolin based geopolymer matrix composition, and textile type (N312, N610) of the composites on fiber–matrix interactions in high‐temperature geopolymer composites and their flexural behavior. The process we ultimately selected was as follows: an 8.5‐fold composite of surface area 100 cm2 was pressed for 2 h at 120°C. After thermal treatment at 1150°C, composites containing the N312 textile with B2O3 showed greater heterogeneity, viscous flow and lower flexural strength (7 MPa) due to fiber–matrix interactions. In contrast, above 1150°C, composites with the N610 textile had no fiber–matrix interactions and showed greater flexural strength (51 MPa). The specific flexural strength correlated well with the overmodifying element ratio based on the composite composition. In fact, the presence of viscous flow suggested that boron and some aluminum atoms could act as network modifiers. Thus, the two ratios were or depending on the presence of viscous flow.

Paramyxovirus matrix proteins modulate host cell translation via exon-junction complex interactions in the cytoplasm.

Viruses have evolved myriad strategies to exploit the translation machinery of host cells to potentiate their replication. However, how paramyxovirus (PMVs) modulate cellular translation for their own benefit has not been systematically examined. Utilizing puromycylation labeling, overexpression of individual viral genes, and infection with wild-type virus versus its gene-deleted counterpart, we found that PMVs significantly inhibit host cells' nascent peptide synthesis during infection, with the viral matrix being the primary contributor to this effect. Using the rNiV-NPL replicon system, we discovered that the viral matrix enhances viral protein translation without affecting viral mRNA transcription and suppresses host protein expression at the translational level. Polysome profile analysis revealed that the HPIV3 matrix promotes the association of viral mRNAs with ribosomes, thereby enhancing their translation efficiency during infection. Intriguingly, our NiV-Matrix interactome identified the core exon-junction complex (cEJC), critical for mRNA biogenesis, as a significant component that interacts with the paramyxoviral matrix predominantly in the cytoplasm. siRNA knockdown of eIF4AIII simulated the restriction of cellular functions by the viral matrix, leading to enhanced viral gene translation and a reduction in host protein synthesis. Moreover, siRNA depletion of cEJC resulted in a 2-3 log enhancement in infectious virus titer for various PMVs but not SARS-CoV-2, enterovirus D68, or influenza virus. Our findings characterize a host translational interference mechanism mediated by viral matrix and host cEJC interactions. We propose that the PMV matrix redirects ribosomes to translate viral mRNAs at the expense of host cell transcripts, enhancing viral replication, and thereby enhancing viral replication. These insights provide a deeper understanding of the molecular interactions between paramyxoviruses and host cells, highlighting potential targets for antiviral strategies.

Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition

Abstract Social interactions determine many economic behaviours, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are globally identified if networks are constant over time. We also provide an extension of the method for time-varying networks. We then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the adaptive elastic net Generalized Method of Moments. We employ the method to study tax competition across U.S. states. The identified social interactions matrix implies that tax competition differs markedly from the common assumption of competition between geographically neighbouring states, providing further insights into the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behaviour across states. Most broadly, our identification and application show that the analysis of social interactions can be extended to economic realms where no network data exist.

Bio-Pathological Functions of Posttranslational Modifications of Histological Biomarkers in Breast Cancer.

Proteins are the most common types of biomarkers used in breast cancer (BC) theranostics and management. By definition, a biomarker must be a relevant, objective, stable, and quantifiable biomolecule or other parameter, but proteins are known to exhibit the most variate and profound structural and functional variation. Thus, the proteome is highly dynamic and permanently reshaped and readapted, according to changing microenvironments, to maintain the local cell and tissue homeostasis. It is known that protein posttranslational modifications (PTMs) can affect all aspects of protein function. In this review, we focused our analysis on the different types of PTMs of histological biomarkers in BC. Thus, we analyzed the most common PTMs, including phosphorylation, acetylation, methylation, ubiquitination, SUMOylation, neddylation, palmitoylation, myristoylation, and glycosylation/sialylation/fucosylation of transcription factors, proliferation marker Ki-67, plasma membrane proteins, and histone modifications. Most of these PTMs occur in the presence of cellular stress. We emphasized that these PTMs interfere with these biomarkers maintenance, turnover and lifespan, nuclear or subcellular localization, structure and function, stabilization or inactivation, initiation or silencing of genomic and non-genomic pathways, including transcriptional activities or signaling pathways, mitosis, proteostasis, cell-cell and cell-extracellular matrix (ECM) interactions, membrane trafficking, and PPIs. Moreover, PTMs of these biomarkers orchestrate all hallmark pathways that are dysregulated in BC, playing both pro- and/or antitumoral and context-specific roles in DNA damage, repair and genomic stability, inactivation/activation of tumor-suppressor genes and oncogenes, phenotypic plasticity, epigenetic regulation of gene expression and non-mutational reprogramming, proliferative signaling, endocytosis, cell death, dysregulated TME, invasion and metastasis, including epithelial-mesenchymal/mesenchymal-epithelial transition (EMT/MET), and resistance to therapy or reversal of multidrug therapy resistance. PTMs occur in the nucleus but also at the plasma membrane and cytoplasmic level and induce biomarker translocation with opposite effects. Analysis of protein PTMs allows for the discovery and validation of new biomarkers in BC, mainly for early diagnosis, like extracellular vesicle glycosylation, which may be considered as a potential source of circulating cancer biomarkers.

Identifying interaction boundary of inverter-based generation in assessing system strength of power grids using relative electrical distance concept

The increasing use of inverter-based generation (IBG) in power grids raises concern about system strength. This is partly due to the inherent interactions among multiple IBGs in close proximity to one another. This paper proposes an approach to identifying the existential boundary of interaction in a network using the relative electrical distance (RED) concept. The mathematical formulation of the RED concept to address the interaction problem among the IBGs involved utilising the power system network’s admittance matrix to capture its structural characteristics. An interaction matrix derived from the RED values of all IBG pairs was then developed to identify the interacting IBG groups. The proposed approach was demonstrated using the IEEE 39-bus system and a practical 72-bus Nigerian power grid. Results showed that RED values effectively group interacting IBGs, with values closer to 0 signifying higher interaction levels, values closer to 1 indicating lower interaction, and a value of 1 denoting no interaction. Time-domain simulations confirmed the accuracy of the approach, demonstrating that the effect of control interaction propagates proportionally to neighbouring IBGs based on RED values. However, fault currents can influence the impact of control interactions. This approach, which requires less computational effort, provides a quick identification tool for potential areas of concern based on the degree of interaction, enhancing the reliability of power grids with high IBG penetration.

ECM Microenvironment in Vascular Homeostasis: New Targets for Atherosclerosis.

Alterations in vascular extracellular matrix (ECM) components, interactions, and mechanical properties influence both the formation and stability of atherosclerotic plaques. This review discusses the contribution of the ECM microenvironment in vascular homeostasis and remodeling in atherosclerosis, highlighting Cartilage oligomeric matrix protein (COMP) and its degrading enzyme ADAMTS7 as examples, and proposes potential avenues for future research aimed at identifying novel therapeutic targets for atherosclerosis based on the ECM microenvironment.

Activated Endothelium Changes The Activity Of Multipotent Mesenchymal Stromal Cells During Physiological Hypoxia Or Short Hypoxic Stress In Vitro

Multipotent mesenchymal stromal cells (MSCs) are used for supplemental therapy of ischemic and inflammatory diseases. After systemic administration, transmigration of MSCs to the target tissue is accompanied by interaction with activated endothelial cells (ECs) at the site of injury. In this study, we investigated the influence of TNF-α-activated ECs on the functions of MSCs under different levels of hypoxia. For this purpose, MSCs and TNF-α activated ECs were cocultured in a direct cell-to-cell setting for a short period of time. MSCs retained their stromal phenotype and multilineage differentiation potential after interaction with activated ECs. At the same time, changes in molecules involved in MSC-cell and MSC-extracellular matrix interaction were detected. The paracrine activity of MSCs and activated ECs after interaction was demonstrated by both upregulated transcription and increased levels of pleiotropic IL-6 and IL-8. Proteases/antiproteases profiles were also altered after interaction. These data suggest that short-term interaction of MSCs with activated ECs may play an important role in tissue repair and remodeling processes. In particular, it may promote the migratory phenotype of MSCs. In comparison to physiological hypoxia – 5% O2, acute hypoxic stress (0.1% O2, 24 h) attenuated the stimulatory effects of ECs on MSCs.

Vascularized platforms for investigating cell communication via extracellular vesicles.

The vascular network plays an essential role in the maintenance of all organs in the body via the regulated delivery of oxygen and nutrients, as well as tissue communication via the transfer of various biological signaling molecules. It also serves as a route for drug administration and affects pharmacokinetics. Due to this importance, engineers have sought to create physiologically relevant and reproducible vascular systems in tissue, considering cell-cell and extracellular matrix interaction with structural and physical conditions in the microenvironment. Extracellular vesicles (EVs) have recently emerged as important carriers for transferring proteins and genetic material between cells and organs, as well as for drug delivery. Vascularized platforms can be an ideal system for studying interactions between blood vessels and EVs, which are crucial for understanding EV-mediated substance transfer in various biological situations. This review summarizes recent advances in vascularized platforms, standard and microfluidic-based techniques for EV isolation and characterization, and studies of EVs in vascularized platforms. It provides insights into EV-related (patho)physiological regulations and facilitates the development of EV-based therapeutics.

Energy conservation strategies on academic buildings using interpretive structural modeling to develop energy sustainability

Effective energy conservation strategies are required to be implemented in academic buildings as they consume significant energy while considering the convenience and function of the buildings. However, the influencing factors are interrelated and complex, requiring an appropriate approach to unravel the complexity. Therefore, this study aims to identify, analyse, and map the interaction relationship between factors affecting energy conservation in academic buildings. This study used Interpretive Structural Modeling (ISM) procedure starting from developing the Structural Self Interaction Matrix (SSIM), converting SSIM into a Reachability Matrix, revising the matrix, and categorizing the factors by using MICMAC analysis. This study involved 9 factors, including architectural design, illumination technology, education and awareness, energy monitoring and management, renewable energy use, efficient HVAC system, energy-saving equipment, institutional policies, and campus community participation. The study found that renewable energy use at level 3 was the factor that was not influenced by and did not interact with any other factors. Meanwhile, the illumination technology was a factor that interacted with the efficient HVAC System factor which was at level 1 where these two factors were influenced by other seven factors. This study aligns with current developments in energy conservation, including an increased focus on renewable energy and energy efficiency in academic buildings, supported by global and national policies aimed at achieving sustainability goals. It provides a comprehensive understanding on developing sustainable energy conservation strategies in academic buildings.