Complex Ecosystem Research Articles

Single-cell RNA sequencing reveals the landscape of the cellular ecosystem of primary hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) cells, along with multiple nonmalignant stromal cells, such as fibroblasts, endothelial cells and immune cells, comprise an intricate cellular ecosystem, undergo dynamic phenotypic changes and present complicated cellular interactions, thus synergistically facilitating HCC initiation and progression and leading to treatment resistance. Clarifying the heterogeneity, cell plasticity and complexity of the cellular ecosystem of HCC will be highly beneficial for understanding HCC development and identifying novel therapeutic targets. Single-cell RNA sequencing (scRNA-seq) refers to profiling the transcriptome at single-cell resolution, and the development of scRNA-seq technology and analysis algorithms has greatly promoted the analysis of cell composition, cell subpopulation heterogeneity, development trajectory and cell-to-cell interactions in cell populations. In this review, we systematically summarized and discussed scRNA-seq in treatment-naive primary HCC and revealed the global cell composition of HCC; the widespread molecular heterogeneity of HCC cells; the molecular subtypes of fibroblasts; the cell composition, functional states and development trajectory of immune cells; and the frequent interactions between different cell types to systematically draw the landscape of the cellular ecosystem of primary HCC.

Single-cell transcriptomic landscape deciphers olfactory neuroblastoma subtypes and intra-tumoral heterogeneity.

Olfactory neuroblastoma (ONB) is a rare malignancy known to originate from the olfactory epithelium. The complex tumor ecosystem of this pathology remains unclear. Here, we explored the cellular components within ten ONB tumors and one olfactory mucosa sample based on single-cell RNA profiles. We showed the intra-tumoral heterogeneity by identifying five unique expression programs among malignant epithelial cells. A distinct three-classification system (neural, basal, mesenchymal) for ONB was established according to the distinguished gene expression patterns. Biomarkers for categorizing bulk tumors into uncharacterized subtypes were elucidated. Different responses towards certain chemotherapy regimens could be cautiously inferred according to the molecular features representing the three tumor types, thus helping with precision chemotherapy. We also analyzed subclusters of the tumor microenvironment (TME) and the interactions among different cell types within the TME. The relative abundance of immunosuppressive tumor-associated macrophages suggests potential benefits of immunotherapies targeting macrophages.

Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage

IntroductionTumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge.MethodsThis study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations.ResultsApplying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes—PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)—involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV.DiscussionThese genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies.

Advanced Cyber Threats and Cybersecurity Innovation - Strategic Approaches and Emerging Solutions

Given that the size and complexity of digital ecosystems are constantly growing, cybersecurity is constantly under attack by increasingly complex threats ranging from ransomware attacks to cyber-attacks based on artificial intelligence. Thus, this paper focuses on these new generation cyber threats and assesses the old and advanced security technologies that address them. This study addresses the future of advanced threats and emerging solutions deep diving into the application of AI, blockchain and the use of Zero Trust architectural concepts in mitigating cybersecurity risks. Moreover, a number of strategic measures – such as dynamic threat identification, multiple-tier protection systems, and sound data security programs – are discussed to enhance the organizational defense against possible weaknesses. Results show that it is crucial for companies to adopt an amalgamated model that incorporates innovative technologies together with active cybersecurity measures. In turn, this paper suggests directions for further research and policy implications for enhancing a current and relevant cybersecurity framework.

Stability in fecal metabolites amid a diverse gut microbiome composition: a one-month longitudinal study of variability in healthy individuals

ABSTRACT An extensive network of microbial-host interactions exists in the gut, making the gut microbiome a complex ecosystem to untangle. The microbial composition and the fecal metabolites are important readouts to investigate intricate microbiota-diet-host interplay. However, this ecosystem is dynamic, and it is of interest to understand the degree and timescale of changes occurring in the gut microbiota, during disease as well as in healthy individuals. Cross-sectional study design is often used to investigate the microbiome, but this design provides a static snapshot and cannot provide evidence on the dynamic nature of the gut microbiome. Longitudinal studies are better suited to extrapolate causation in a study or assess changes over time. This study investigates longitudinal change in the gut microbiome and fecal metabolites in 14 healthy individuals with weekly sampling over a period of one-month (four time points), to elucidate the temporal changes occurring in the gut microbiome composition and fecal metabolites. Utilizing 16S rRNA amplicon sequencing for microbiome analysis and NMR spectroscopy for fecal metabolite characterization, we assessed the stability of these two types of measurable parameters in fecal samples during the period of one month. Our results show that the gut microbiome display large variations between healthy individuals, but relatively lower within-individual variations, which makes it possible to uniquely identify individuals. The fecal metabolites showed higher stability over time compared to the microbiome and exhibited consistently smaller variations both within and between individuals. This relative higher stability of the fecal metabolites suggests a balanced, consistent output even amid individual’s differences in microbial composition and they can provide a viable complementary readout to better understand the microbiome activity.

TMOD-22. MULTIREGION BIOPSIES AND CORRESPONDING NEUROSPHERE CULTURES REVEAL SPATIAL DIVERGENCE IN GLIOBLASTOMA EVOLUTION

Abstract Spatial organization of normal tissues is key for their function, yet the role of distribution of distinct clones of cancer cells in tumor is not usually studied as a potential vulnerability in cancer. This is of particular interest in glioblastoma (GBM), in which extensive intratumor heterogeneity hampers effectiveness of treatment. To better understand tumor evolution and the complexity of cancer ecosystem in GBM, we performed profiling of biopsies collected from MRI-defined regions of the tumors, spanning the surface, tumor core and deep tumor margins, which drive local recurrence after surgery. To test how spatially distinct biopsies differ in their cancer cell composition and to assess the tumor microenvironment heterogeneity, we took advantage of 5-ALA metabolite fluorescence, which accumulates in GBM cells and allows the surgeon to visualize cancer under blue light. We used it to separate cancer cells (predominantly 5-ALAhigh) from tumor microenvironment (5-ALAlow) and subjected the fractions to bulk and single cell transcriptomics, respectively. We found a gradient of cellular states associated with location in the tumor, with the deep margin samples having more 5-ALAlow cancer cells and generally less defined phenotype. We have also developed neurosphere cultures from our collection of spatially distant biopsies. Despite the selection pressure of the in vitro culture conditions, cells from different regions of the tumor adopted different morphologies, suggesting retention of some of their differential features. The differences between cultures were reflected in metabolism, proliferation, and differential sensitivity to a drug panel. Whole exome sequencing and transcriptomic analysis were also performed to identify the selection bottleneck of culture conditions, establish evolutionary trajectory of the distinct regions and neurosphere cultures, and test how strongly our cultures represent both the 5-ALAhigh and 5-ALAlow subpopulations of GBM cells. Our study identified properties associated with different spatial locations within GBM tumors, which will help uncover the cellular and microenvironmental dependencies that could lead to novel therapeutic targets for this highly heterogeneous tumor.

How Emergent Vegetation Patch Shapes Affect Flow Structure in Open Channel Environments

ABSTRACTVegetation patches, which naturally occur in a variety of patterns, have an impact on the flow dynamics and geometry of rivers. There have been prior studies on vegetation patches, but none have examined the impact of differently shaped vegetation patch, i.e., circle, square and rectangle, and the comparison of these varying patch shapes from an ecological point of view. Hence, a computational fluid dynamics (CFD) technique using FLUENT has been employed in the present research to evaluate the flow dynamics and turbulence characteristics around emergent vegetation patches of various forms in an open channel. The results show that the shape of vegetation patches had a significant impact on the approaching flow, with circular patch offering the highest flow resistance among all other configurations. Flow shedding behaviour behind the patch was also prominent in the case of circular patch shape with a formation of large Kármán vortex street with vortex sizes up to 1.5 times the patch diameter. In the downstream region of vegetation patch, a reduction of 25%–30% in the mean streamwise velocity was observed for circular patch, whereas a 15% decrease was observed for rectangular and square patches. The outcomes of this study found that flow turbulence and stability in the open channel are significantly influenced by vegetation patch shape. This research reveals that the shape of vegetation patches, particularly circular configuration, significantly influences flow dynamics, thereby enhancing habitat complexity in riverine ecosystems. These findings are vital for developing informed river restoration and management strategies that support biodiversity and promote ecological resilience.

The Connection Between the Oral Microbiota and the Kynurenine Pathway: Insights into Oral and Certain Systemic Disorders

The oral microbiome, comprising bacteria, fungi, viruses, and protozoa, is essential for maintaining both oral and systemic health. This complex ecosystem includes over 700 bacterial species, such as Streptococcus mutans, which contributes to dental caries through acid production that demineralizes tooth enamel. Fungi like Candida and pathogens such as Porphyromonas gingivalis are also significant, as they can lead to periodontal diseases through inflammation and destruction of tooth-supporting structures. Dysbiosis, or microbial imbalance, is a key factor in the development of these oral diseases. Understanding the composition and functions of the oral microbiome is vital for creating targeted therapies for these conditions. Additionally, the kynurenine pathway, which processes the amino acid tryptophan, plays a crucial role in immune regulation, neuroprotection, and inflammation. Oral bacteria can metabolize tryptophan, influencing the production of kynurenine, kynurenic acid, and quinolinic acid, thereby affecting the kynurenine system. The balance of microbial species in the oral cavity can impact tryptophan levels and its metabolites. This narrative review aims to explore the relationship between the oral microbiome, oral diseases, and the kynurenine system in relation to certain systemic diseases.

Integrating historical approaches of university ecosystems: reviewing the literature streams and future directions

AbstractIt is practically consensual that universities contribute to economic and social growth; therefore, they nowadays need to play a more active role in answering the changing needs of society. In addition, universities hold the potential to promote regional innovation through their influence over the ecosystems they participate in. This article aims to identify, analyse and integrate the historical approaches of university ecosystems and university roles through a systematic literature review. The findings identify four different periods: (i) University-Industry collaboration (until 2000); (ii) changing the university model into a triple-helix model (2001–2010); (iii) from the triple to the quadruple-helix model in the knowledge society (2011–2020); and (iv) ecosystem complexity and the Neo Triple-Helix Model (from 2021). The study contributes to the literature on universities and the ecosystem by providing a unified perspective on the importance of the university’s role in the economic and social growth of the knowledge business and the evolution of the ecosystem. These results provide an overview of the trends in university ecosystems, including an integrative framework, research themes, and research questions able to guide future studies.

How Much Hatchery-Reared Brown Trout Move in a Large, Deep Subalpine Lake? An Acoustic Telemetry Study

Fish movement into large, deep lakes has been rarely investigated due to the complexity and extent of such ecosystems. Among the different monitoring methods available, acoustic telemetry enables the study of the spatial ecology and behavior of aquatic organisms in lentic environments. In this study, the movement of 69 hatchery-reared adult brown trout (size 43–61 cm) marked with acoustic transmitters was monitored in the large and deep subalpine Lake Lugano (Switzerland and Italy). Trout were tracked for six consecutive months by seven acoustic receivers (March–August 2022), positioned in a non-overlapping array. Trout movement was reconstructed using R packages specific for acoustic telemetry (actel and RSP), which also allowed us to translate tracking information into utilization distribution (UD) areas for each fish. The effects of different environmental variables (rainfall, water discharge of the two main tributaries of Lake Lugano, atmospheric pressure, cloud coverage, and moon phases) on trout movement were tested, but none of these variables seemed to significantly correlate with fish movement. After release, most of the tagged fish exhibited reiterative movements during the initial month, with some maintaining this behavior throughout the entire study period. This spatial behavior can be particularly evident in hatchery-reared fish due to their aggressive and bold attitude. The association of these behavioral traits, shaped by domestication, could expose hatchery-reared fish to high risks and post-release mortality in the wild. Indeed, within a few months after the release, most of the tagged fish were no longer detected by the acoustic receivers. In addition, 26% of the total tagged fish were caught by recreational or professional fishermen.

Analysis of landscape vegetation ecology and cellular structure in nature reserves based on an improved analytic hierarchy process

With the increasing severity of global environmental issues, natural reserves have become crucial areas for maintaining biodiversity and protecting natural ecosystems. The ecological status of landscape vegetation and the characteristics of cellular structures within these reserves have become focal points of research. However, traditional evaluation methods have limitations when dealing with the complex ecosystems of natural reserves, making it difficult to assess the relationship comprehensively and accurately between vegetation ecology and cellular structures. Therefore, this paper proposes an analysis of landscape vegetation ecology and cellular structure in natural reserves based on an improved Analytic Hierarchy Process (AHP). By incorporating fuzzy set theory and expert scoring mechanisms, the evaluation process is enhanced in terms of objectivity and precision. An evaluation index system for landscape vegetation ecology and cellular structure in natural reserves is constructed, covering aspects such as vegetation species richness, community structural complexity, and ecological functional stability. This systematic approach reflects the ecological status of vegetation and cellular structure characteristics, providing scientific support for targeted protection and management measures.

From synthetic communities to synthetic ecosystems: exploring causalities in plant-microbe-environment interactions.

The plant microbiota research field has rapidly shifted from efforts aimed at gaining a descriptive understanding of microbiota composition to a focus on acquiring mechanistic insights into microbiota functions and assembly rules. This evolution was driven by our ability to establish comprehensive collections of plant-associated microbes and to reconstruct meaningful microbial synthetic communities (SynComs). We argue that this powerful deconstruction-reconstruction strategy can be used to reconstitute increasingly complex synthetic ecosystems (SynEcos) and mechanistically understand high-level biological organization. The transitioning from simple to more advanced, fully tractable and programmable gnotobiotic SynEcos is ongoing and aims at rationally simplifying natural ecosystems by engineering them. Such reconstitution ecology approaches represent an untapped strategy for bridging the gap between ecology and functional biology and for unraveling plant-microbiota-environment mechanisms that modulate ecosystem health, assembly, and functioning.

MAGqual: a stand-alone pipeline to assess the quality of metagenome-assembled genomes

BackgroundMetagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted.ResultsTo address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards.ConclusionsThe MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual.EB_9RbKd3CYdEq3zCTHKaqVideo

Vegetation traits and biogeomorphic complexity shape the resilience of salt marshes to sea-level rise

The adaptive capacity of ecosystems, or their ability to function despite altered environmental conditions, is crucial for resilience to climate change. However, the role of landscape complexity or species traits on adaptive capacity remains unclear. Here, we combine field experiments and morphodynamic modelling to investigate how ecosystem complexity shapes the adaptive capacity of intertidal salt marshes. We focus on the importance of tidal channel network complexity for sediment accumulation, allowing vertical accretion to keep pace with sea-level rise. The model showed that landscape-scale ecosystem complexity, more than species traits, explained higher sediment accumulation rates, despite complexity arising from these traits. Landscape complexity, reflected in creek network morphology, also improved resilience to rising water levels. Comparing model outcomes with real-world tidal networks confirmed that flow concentration, sediment transport and deposition increase with drainage complexity. These findings emphasize that natural pattern development and persistence are crucial to preserve resilience to climate change.

The Impact of Big Data and Artificial Intelligence on Influencer Marketing

Influencer marketing has rapidly emerged as a cornerstone of digital advertising strategies, enabling brands to connect authentically with target audiences through trusted voices on social media platforms. However, measuring the effectiveness of these campaigns has long been a challenge due to the complexity of digital ecosystems and the sheer volume of data generated by user interactions. Big Data and Artificial Intelligence (AI) are revolutionizing the way brands evaluate influencer marketing campaigns, offering unprecedented insights into key performance metrics such as engagement, audience sentiment, and conversion rates. This paper explores how data analytics and AI tools are transforming the identification of influencers, the optimization of campaign performance, and the prediction of future trends. Furthermore, we address the ethical considerations surrounding data privacy and the transparency of AI-driven decisions in influencer marketing. Ultimately, this study highlights how Big Data and AI are not only enhancing the efficiency of influencer marketing but also reshaping the future of digital advertising as a whole.

Fish–Seascape Associations Within an Offshore Protected Area in the Arabian Gulf

ABSTRACTCoral reef ecosystems support high fish biodiversity through ecological interactions with structural complexity across multiple spatial scales including coral colony architecture and the surrounding seascape structure. In an era where the complexity of coral reef ecosystems is being diminished, understanding the importance of structural characteristics beyond single focal patches has the potential to better inform actions for protecting, restoring or creating habitat for reef‐associated species. A seascape ecology approach was applied to explore the associations between multiple scales of seascape structure and fish assemblage response variables within a small (49.6 km2) offshore no‐take MPA, Sir Bu Nair Island Protected Area, in Sharjah, United Arab Emirates. Fish–seascape associations were modelled with single regression trees. Both in situ and remote sensing–derived variables produced the best models with highest contributions from coral cover, amount of hard‐bottom habitat type and structural complexity of the seafloor terrain. Fish species richness was significantly higher where coral cover exceeded 35%. The hard‐bottom areas with coral supported diverse assemblages dominated by carnivorous and omnivorous fishes. The Sir Bu Nair Island Protected Area provides a critical refuge for threatened and regionally overexploited species including those with low resilience to fishing. The ecological success of this protected area is key to safeguarding regional marine biodiversity and recovering fish populations to enhance food security.

Binding properties of olfactory proteins to host volatiles, free fatty acids and cuticular hydrocarbons in the termite Reticulitermes aculabialis.

As eusocial insects prevalent in tropical and subtropical regions, termites are characterized by highly organized behaviors and exceptional adaptability, rooted in caste differentiation and chemical communication. These traits make them excellent models for studying insect social structures and ecological interactions. Investigating how termites use chemical signals to perceive and respond to their environment provides insights into their coordination and adaptation within complex ecosystems. This study delved into the chemosensory mechanisms of Reticulitermes aculabialis, examining the interactions of four olfactory proteins with 70 ligands, including host volatiles, cuticular hydrocarbons (CHCs), and free fatty acids (FFAs). Molecular docking simulations revealed varied affinities of the olfactory proteins for long-chain hydrocarbons (n-C23 to n-C28), suggesting a nuanced chemical communication system through specific hydrocarbon detection. RacuCSP1 and RacuCSP2 exhibited specific binding to linoleic acid and undecanoic acid, respectively, highlighting the significance of FFAs in the physiological and behavioral processes of termites. The four olfactory proteins showed a strong affinity for longifolene in fluorescence competitive binding experiments. Notably, RacuOBPs specifically exhibited unique affinities for terpenoid volatiles such as β-lonone and neocembrene, while RacuCSPs specifically bound with terpenoids like 3-carene, myrtenol, α-pinene oxide and β-pinene indicating their critical roles in host detection. Behavioral observations following gene silencing revealed that RacuOBP5 was essential for recognizing longifolene and α-lonone recognition, while RacuCSP1 was key for detecting α-pinene in termites. These findings enhance our understanding of the termite chemosensory system and offer insights for developing precise pest management strategies.

Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research.

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms

Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms

Bottom Trawling and Multi‐Marker eDNA Metabarcoding Surveys Reveal Highly Diverse Vertebrate and Crustacean Communities: A Case Study in an Urbanized Subtropical Estuary

ABSTRACTEstuarine habitats serve as critical feeding and nursery grounds for many aquatic species and support fisheries. However, monitoring these complex ecosystems using conventional trawling methods is destructive, costly, and labor‐intensive. This study compared trawling and a multi‐marker environmental DNA (eDNA) metabarcoding approach to monitor marine vertebrate and crustacean communities in an estuarine environment in subtropical Hong Kong. We analyzed 16 bottom trawl samples and the eDNA from 32 two‐liter water samples using primer sets specific to fishes and mammals (MiFish‐U, 12S‐V5, and Berry‐Fish) and decapod crustaceans (MiDeca). We found that the eDNA approach detected more pelagic and demersal fishes (237 vs. 106 in trawling) and elasmobranchs (6 vs. 3) than trawling. The eDNA approach was also more effective than trawling in detecting threatened vertebrates (16 vs. 4), including the Indo‐Pacific Finless Porpoise and the critically endangered Large Yellow Croaker. Among the detected fish at species level, 70 species were detected by both approaches, 32 species were detected by trawling only, and 142 species were detected by the eDNA approach only. Regarding crustaceans, the eDNA approach detected slightly fewer decapods (61 vs. 77) and stomatopods (5 vs. 8) than trawl surveys. However, the eDNA approach could be enhanced through the development of suitable decapod‐specific primers and the expansion of the local reference database. In addition, multivariate analyses of the eDNA data revealed spatial patterns of fish and crustacean assemblages that might be associated with sediment loading, oxygen, and nutrient levels. Furthermore, there was a positive correlation between eDNA read counts and trawl catch, but their correlation coefficient was low. We conclude that eDNA metabarcoding can provide high‐resolution detection of species, composition, and unravel community–environment relationships in estuarine ecosystems. Overall, integrating the non‐destructive eDNA approach can complement the conventional trawling method for better‐informed sustainable fishery management and conservation.