Landscape Function Research Articles

Decoding multicellular interaction networks-a new horizon in tumor microenvironment research.

The tumor microenvironment (TME) milieu directs a plethora of tumor-modulating functions. Recent years have seen pivotal breakthroughs in our understanding of the TME's role in tumor initiation and progression, with tangible clinical applications. Individual components of the TME exert their function predominantly by cell-cell crosstalk, both in the form of physical interaction and secreted factors. Notably, different spatial niches represent exclusive signaling hubs in the TME, propagating pro- or antitumoral functions. The exploration of these interactions has been vastly facilitated by novel molecular technologies, each of which provides a different perspective on this intricate intercellular communication network. Together, these complementary methods paint a detailed, high-resolution map of the TME's interaction landscape. In this viewpoint, we explore how cellular interactions can unlock a new level of understanding of TME complexity, and discuss the promises and challenges of characterizing tumors based on their cellular interaction footprint.

Fluvial fragmentation and functional quality of the Guadalquivir River’s right bank tributaries (Southern Spain)

ABSTRACT The fragmentation of fish populations caused by dams has been extensively studied within the scientific community. However, there is limited data regarding the impact of riverine fragmentation on the loss of the functional quality of ecosystems, a key aspect for the optimal development of environmental, ecological, and landscape functions within river systems. In this context, an assessment of fragmentation was conducted across the Sierra Morena river network in the Guadalquivir basin using the Dendritic Connectivity Index (DCI). This analysis revealed that 88.4% of the network was disconnected from its regional base level, with 64.6% exhibiting a high degree of fragmentation. Furthermore, hydrogeomorphological alterations were investigated in three rivers from the basin – Bembézar, Guadalbarcar, and Rivera de Huéznar – characterized by different levels of fragmentation. Hydrological data from the period 2010–2021 were analyzed, and geomorphological diagrams were created to compare the functional floodplains of the three rivers in both pre-dam (1956) and post-dam (2010) conditions. Preliminary results indicate a strong correlation between the fragmentation level and various parameters: the degree of hydrological regulation (r = 0.98), reduction in flow circulation (r = 0.86), sediment retention (r = 0.92), and the loss of floodplain functionality (r = 0.86). Moreover, a very strong correlation (r = 0.999) was found between fragmentation and the decline in functional quality across the studied rivers.

Tangible consumption data landscapes of teenagers

Teenagers are increasingly concerned about clean energy and play a crucial role in driving energy transition. In this study, I investigate how teenagers understand energy consumption, how tangible data mapping through anthropological theories may increase their awareness, and what ideas they may have about how to change habits. I present an approach for designing new data physicalizations, shaping how to facilitate data interaction sessions, and analyzing data physicalization and interactions based on Ingoldian concepts such as the Life of Lines and Imagining for Real. The empirical material includes 1-h sessions in two secondary education schools with 30 teenagers and video-recorded explanations about their consumption habits. The teenagers completed a questionnaire, drew a layout drawing of their home, and built a physical model of their home with products and rooms representing their energy consumption. The data landscapes encouraged participants to explore the perceptual integrity of energy-consuming habits and identify ways to change them. The self-expressive sessions demonstrate that the three data-mapping techniques enable teenagers to recount their daily consumption patterns. In particular, the tangible mapping of Consumption Data Landscapes can open avenues for engagement to increase their awareness of energy consumption and spawn responsibility for a change of habits.

Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction.

Using one of the most comprehensive LAIV datasets compiled to date, immunaut , an integrative machine learning framework, identifies distinct immunophenotypic responder groups shaped by baseline immune landscapes, advancing precision vaccinology and guiding more effective, personalized immunization strategies. Immunaut , an automated framework for mapping and predicting vaccine response immunotypes. Step 1 outlines the identification of vaccine response outcomes using pre- and post-vaccination data integration across immune features, including antibodies, flu-specific T-cells, and immunophenotyping at mucosal and systemic sites. Clustering methods define the vaccine response landscape, stability, and validation through t-SNE-based visualization. Step 2 leverages an automated machine learning modeling approach, to enhance the accuracy and interpretability of vaccine response predictions, enabling stratification and targeted intervention strategies for personalized vaccine immunogenicity.

Looking for Marai ‘on-the-fly’: mobile more-than-human methods for cultural landscape mapping with Gajerrong Country

At the intersections of rapid socio-ecological transformation, Indigenous people in Northern Australia are articulating their aspirations for land management. However, while people reconcile goals, knowledges and practices to care for Country, the available toolkit that might facilitate planning and activities has often constrained ideas because technologies and systems have tended to be both inaccessible and incompatible with non-Western representations of place. In this paper, we report on a cultural landscape mapping project with Marralam Community of the Northern Territory, Australia, undertaken to analyze, advocate and communicate community ideas, knowledge and visions for managing Country. Prioritizing culturally respectful ways of working, we articulate mobile mapping methods that speak to the experiential, emergent and performative, and political aspects of how our research collaboration is mapping together with Country. Harnessing the innovations of qualitative GIS, mapping with Country ‘on-the-fly’ facilitates what may otherwise be conceived of as competing demands and interests, highlighting Indigenous expertise in navigating the complex terrain of natural resource management and knowledge sharing. Mapping with Country foreshadows lively possibilities for mapping as more-than-human collaborative research practice.

A map of the rubisco biochemical landscape.

Rubisco is the primary CO2-fixing enzyme of the biosphere1, yet it has slow kinetics2. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated3,4. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed5, although recent results indicate that the functional potential of rubisco has a wider scope than previously known6. Here we developed a massively parallel assay, using an engineered Escherichia coli7 in which enzyme activity is coupled to growth, to systematically map the sequence-function landscape of rubisco. Composite assay of more than 99% of single-amino acid mutants versus CO2 concentration enabled inference of enzyme velocity and apparent CO2 affinity parameters for thousands of substitutions. This approach identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data indicate that non-trivial biochemical changes are readily accessible and that the functional distance between rubiscos from diverse organisms can be traversed, laying the groundwork for further enzyme engineering efforts.

Comparison of Tree Typologies Mapping Using Random Forest Classifier Algorithm of PRISMA and Sentinel-2 Products in Different Areas of Central Italy

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping.

Identification of promising SARS-CoV-2 main protease inhibitor through molecular docking, dynamics simulation, and ADMET analysis

The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a major challenge to global health. Targeting the main protease of the virus (Mpro), which is essential for viral replication and transcription, offers a promising approach for therapeutic intervention. In this study, advanced computational techniques such as molecular docking and molecular dynamics simulations were used to screen a series of antiviral compounds for their potential inhibitory effect on the SARS-CoV-2 Mpro. A comprehensive analysis of compounds from the ChemDiv and PubChem databases was performed. The physicochemical properties, pharmacokinetics, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles were evaluated to determine drug similarity and safety. Compound 4896 − 4038 proved to be the most promising candidate. It exhibited a favorable balance between molecular weight (491.06) and lipophilicity (logP 3.957), high intestinal absorption (92.119%), and broad tissue distribution (VDss of 0.529), indicating good oral bioavailability and therapeutic potential. Molecular docking studies showed that 4896 − 4038 has a strong binding affinity to the active site of Mpro and forms key interactions, such as hydrogen bonds, carbon-hydrogen bonds, pi-sulfur, and multiple van der Waals and pi-pi stacked bonds. The binding energy was comparable to that of the reference drug X77, indicating potential efficacy. Molecular dynamics simulations over 300 ns confirmed the stability of the Mpro/4896 − 4038 complex of protein-ligand. Free energy landscape mapping and MM/PBSA calculations further substantiated the favorable binding and stability of the complex. Importantly, 4896 − 4038 exhibited a comparatively favorable safety profile. In summary, compound 4896 − 4038 shows significant potential as a potent SARS-CoV-2 Mpro inhibitor, combining potent inhibitory activity with favorable pharmacokinetic and safety profiles. These results support the further development of 4896 − 4038 as a promising therapeutic agent in the fight against COVID-19 that warrants experimental validation and clinical investigation.

MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins.

Intrinsically disordered regions are found in most eukaryotic proteins and are enriched with positively and negatively charged residues. While it is often convenient to assume that these residues follow their model-compound pKa values, recent work has shown that local charge effects (charge regulation) can upshift or downshift side chain pKa values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions. The number of potential charge microstates that can be populated through acid/base regulation of a given number of ionizable residues in a sequence, N, scales as ∼2N. This exponential scaling makes the assessment of the full charge landscape of most proteins computationally intractable. To address this problem, we developed "multisite extent of deprotonation originating from context" (MEDOC) to determine the degree of protonation of a protein based on the local sequence context of each ionizable residue. We show that we can drastically reduce the number of parameters necessary to determine the full, analytical Boltzmann partition function of the charge landscape at both global and site-specific levels. Our algorithm applies the structure of the q-canonical ensemble, combined with novel strategies to rapidly obtain the minimal set of parameters, thereby circumventing the combinatorial explosion of the number of charge microstates even for proteins containing a large number of ionizable amino acids. We apply MEDOC to several sequences, including a global analysis of the distribution of pKa values across the entire DisProt database. Our results show differences in the distribution of predicted pKa values for different amino acids and good agreement with NMR-measured pKa values in proteins.

Marine landscape mapping for ecologically relevant habitat suitability modelling

Marine landscape mapping for ecologically relevant habitat suitability modelling

Landscape diversity promotes landscape functioning in North America

Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. Here, we show that landscape-level diversity, measured as number of land-cover types (different ecosystems) per 250×250 m, is positively related to landscape-wide remotely-sensed primary production across all of North America, covering 16 of 18 ecoregions of Earth. At higher landscape diversity, productivity was temporally more stable, and 20-year greening trends were accelerated. These effects occurred independent of local species diversity, suggesting emergent mechanisms at hitherto neglected levels of biological organization. Specifically, mechanisms related to interactions among land-cover types unfold at the scale of entire landscapes, similar to, but not necessarily resulting from, interactions between species within single ecosystems.

Lithic analysis in African archaeology: Advances and key themes

AbstractStone artifacts (lithics) preserve for extended periods; thus they are key evidence for probing the evolution of human technological behaviors. Africa boasts the oldest record of stone artifacts, spanning 3.3 Ma, rare instances of ethnographic stone tool‐making, and stone tool archives from diverse ecological settings, making it an anchor for research on the long‐term temporal and spatial trends in human evolution. This paper reviews the application of scientific methods for studying African stone artifacts and highlights several popular research themes on the continent, including the origins of flaked stone technology, hunter‐gatherer mobility and landscape use, technological variability, function, biocultural evolution, and ancient human cognition. We conclude by outlining some key challenges to future lithic research in Africa.

From Antipsychotic to Neuroprotective: Computational Repurposing of Fluspirilene as a Potential PDE5 Inhibitor for Alzheimer's Disease.

Phosphodiesterase 5 (PDE5) inhibitors have shown great potential in treating Alzheimer's disease by improving memory and cognitive function. In this study, we evaluated fluspirilene, a drug commonly used to treat schizophrenia, as a potential PDE5 inhibitor using computational methods. Molecular docking revealed that fluspirilene binds strongly to PDE5, supported by hydrophobic and aromatic interactions. Molecular dynamics simulations confirmed that the fluspirilene-PDE5 complex is stable and maintains its structural integrity over time. Binding energy calculations further highlighted favorable interactions, indicating that the drug forms a strong and stable bond with PDE5. Additional analyses, including studies of protein dynamics and energy landscape mapping, revealed how the drug interacts dynamically with PDE5, adapting to different conformations and maintaining stability. These findings suggest that fluspirilene may modulate PDE5 activity, potentially offering therapeutic benefits for Alzheimer's disease. This study provides strong evidence for repurposing fluspirilene as a treatment for Alzheimer's and lays the foundation for further experimental and clinical investigations.

Mapping of Bacterial Landscape of Neonatal Septicaemia, Tripoli-Libya

Purpose: The objective of this study is to investigate the distribution and types of bacteria causing septicemia in newborns at Aljala Hospital in Tripoli, aiming to enhance understanding for better prevention and treatment of neonatal sepsis Method: This retrospective study gathered data from newborns with sepsis at Aljala Hospital in Tripoli, Libya, from 2014 to 2023, It focused on various factors, including the type of delivery, the causes of sepsis, and the types of microorganisms involved in the infections. Results The findings reveal a concerning predominance of Gram-negative bacteria, which constitute 63% of the identified pathogens, compared to 37% of Gram-positive bacteria. Notably, Staphylococcus aureus and Escherichia coli emerged as the most prevalent bacterial species associated with neonatal septicemia. Conclusion: This research underscores the urgent need for enhanced infection control measures and targeted therapeutic strategies to mitigate the impact of neonatal sepsis in such vulnerable populations.

Global scientific performance and research trends of the interrelation between climate change and economic growth

This paper conducts a comprehensive bibliometric analysis to explore the existing literature on the interrelation between climate change and economic growth through the prism of environmental policies. Utilising both VOSviewer and Bibliometrix software, we analyse 2,131 articles from the Web of Science database between 2000 and 2024, focusing on the fields of Economics, Social Sciences, and Environmental Science. Our investigation identifies key trends, research hotspots, patterns, and gaps in the literature, highlighting the significant impact of climate change on the macroeconomic environment. Bibliometric networks created on account of keywords such as “climate change risks,” “macroeconomy,” and “environmental policy” reveal strong interconnections among research clusters, particularly emphasising the role of public policy in managing climate-related risks. Notably, our findings evidence the geographical distribution of research on this topic, which is more prominent in non-EU countries despite the European Union’s recent policy focus on sustainability. This paper underscores the necessity for targeted research and policy measures to address the economic implications of climate change, emphasising the role of consumer behaviour, uncertainty, and expectations with a country-specific focus. Our research contributes to the existing literature by providing a detailed mapping of the intellectual landscape and suggesting directions for future research to support effective climate policies.

Le milieu universitaire comme instrument contextuel dans l'apprentissage du français professionnel

The linguistic landscape of the university environment is an interesting object for study in the field of linguodidactics. The territory of international universities or universities with a large number of foreign students usually represents a special linguistic educational and ecological space. The functions of the linguistic landscape, according to researchers-theorists, depends on the educational context and changes according it. Being the part of the academic environment, as educative programs, the linguistic landscape is also aimed at formation of desired linguistic knowledge of students. As it was noted by several researchers the linguistic landscape is the semiotic space of the language in which communication is carried out and where the language works and is being perceived by the participants in communication. Students, immersed in the linguistic landscape of the university, learn the language faster and more effectively, as the present study shows. This is especially efficient for professional vocabulary. Visual perception of public written texts develops students' visual memory, enriches their vocabulary and expands their range of knowledge in the professional field.

Mapping of US tobacco industry: Companies, products, histories, and market shares.

Data visualization can communicate information clearly and effectively through graphical means. We developed an industry landscape map to help tobacco regulatory scientists and policymakers understand a high-level overview of the US tobacco industry. This kind of mapping of the market data and deep visualization of companies and their products benefits regulatory science and public health policy in supporting potential knowledge gaps in the regulated industry.

The landscapes of the Aral physical-geographical region and their zoning experiments

In this article, new lands formed in the drainage zone of the Aral Sea and areas where marine deposits are widespread in the Eastern Aral Sea area were identified as a separate physical geographical unit – the Aral physical-geographical region. The article also describes the landscapes and physical-geographical districts of the Aral physical-geographical region. It has been studied that the units of physical-geographical zoning are classified differently by different researchers, and the units of zoning of the Aral physical-geographical region have been described. A map of landscapes and physical-geographic zoning of the Aral physical-geographical region with a medium scale (1: 500.000) has been compiled.

MAPPING THE LANDSCAPE OF DIGITAL INQUIRY-BASED LEARNING: A BIBLIOMETRIC ANALYSIS

This bibliometric analysis seeks to provide an in-depth examination of the academic landscape surrounding Digital Inquiry-Based Learning (DIBL). As digital technologies increasingly reshape educational practices, there is a pressing need for a comprehensive understanding of the research trends, key contributors, and thematic developments within this domain. The problem addressed by this study lies in the limited synthesis of the growing body of literature on DIBL, which hinders a nuanced understanding of its evolution and impact. To address this gap, we employed Scopus Analyzer and VOSviewer software to analyze a dataset comprising 1071 academic publications related to DIBL. The analysis identifies prevailing trends in publication volume, influential authors and institutions, and the key thematic clusters driving the field. Our findings indicate a marked increase in research output over recent years, with notable contributions from both the fields of educational technology and pedagogical research. The study further reveals distinct research clusters focused on the integration of digital tools into inquiry-based pedagogies, the effectiveness of such tools in enhancing student engagement, and their impact on learning outcomes. In conclusion, this bibliometric study provides a comprehensive mapping of the DIBL research landscape, offering critical insights into its current state and emerging directions. The findings contribute to a more informed understanding of how digital inquiry-based approaches are evolving and their implications for future educational practices.

Native cover crops enhance biodiversity and ecosystem services in hazelnut orchards

Abstract Agroecological restoration aims to restore biodiversity and ecosystem function in agricultural landscapes while sustaining crop production. Adopting native plants as cover crops may restore ecological value to cropping systems such as nut orchards. We focused on Oregon hazelnut orchards and compared how four seed mixes (native annuals, native perennials, conventional cover crops and unseeded controls) performed under three levels of orchard floor disturbance (flailing, flailing and scraping, and unmanaged/none) across three different orchard ages with corresponding differences in canopy shade over a 2‐year period. We evaluated cover crop performance by three criteria: the survival criteria (response to disturbance and shading), the production criteria (effects on weeds, erosion potential and soil moisture) and the ecological functioning criteria (abundance and diversity of native plants and pollinator visitations). We found that native species generally outperformed conventional cover crops and bare ground across these criteria. Plant survival was not affected by disturbance but shading reduced survival of most species. Native annuals had high cover in the first year, and native perennials had high cover across both years. Native perennial species provided the best weed reduction and erosion control while not reducing soil moisture, and hosted the highest pollinator abundances and diversity. Synthesis and applications. Our results suggest that agroecological restoration of orchards through native cover cropping is a viable strategy for improving ecological outcomes without compromising production needs.