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Deciphering the mysteries of MEG3 LncRNA and its implications in genitourinary cancers
No full textMaternally expressed gene 3 (MEG3), a long non-coding RNA, plays a pivotal role in various biological processes, including tumorigenesis. Aberrant expression of MEG3 has been implicated in several cancers, including genitourinary malignancies. This comprehensive review explores the multifaceted functions of MEG3 in the context of genitourinary cancers through unravelling the molecular mechanisms underlying the influence of MEG3 on cellular proliferation, apoptosis, invasion, and metastasis. Additionally, we discuss the potential clinical implications of MEG3 as a biomarker and therapeutic target in genitourinary cancers. By unraveling the intricate role of MEG3 in these biological processes, this review aims to contribute to the development of novel strategies for the diagnosis and treatment of genitourinary malignancies
Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning
No full textAbstract
The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya’s key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the interactions between climate extremes and forest dynamics is essential for conservation planning, especially in the Mount Kenya Forest Ecosystem (MKFE), where rising temperatures and erratic rainfall are altering vegetation patterns, reducing forest resilience, and threatening both biodiversity and water security. This study integrates remote sensing and machine learning to assess historical vegetation changes and predict areas at risk in the future. Landsat imagery from 2000 to 2020 was used to derive vegetation indices comprising the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Bare Soil Index (BSI). Climate variables, including extreme precipitation and temperature indices, were extracted from CHIRPS and ERA5 datasets. Machine learning models, including Random Forest (RF), XGBoost, and Support Vector Machines (SVM), were trained to assess climate-vegetation relationships and predict future vegetation dynamics under the SSP245 climate scenario using Coupled Model Intercomparison Project Phase 6 (CMIP6) downscaled projections. The RF model achieved high accuracy ( R 2 = 0.82, RMSE = 0.15) in predicting the dynamics of vegetation conditions. Model projections show a 49–55% decline in EVI across forest areas by 2040, with the most pronounced losses likely in lower montane zones, which are more sensitive to climate-induced vegetation stress. Results emphasize the critical role of precipitation in sustaining forest health and highlight the urgent need for adaptive management strategies, including afforestation, sustainable land-use planning, and policy-driven conservation efforts. This study provides a scalable framework for modelling climate impacts on forest ecosystems globally and offers actionable insights for policymakers
Drivers of success when scaling innovations: insights from European agricultural and forestry co-innovation processes
No full textAbstract
Agriculture and forestry are facing numerous challenges, driven by a complex set of social, economic, and ecological factors. Innovation is a key to devising viable, resilient, and sustainable solutions to these challenges, but for innovations to have impact, they need to be “scaled.” The current policy context, in the European Union (EU) and elsewhere, encourages the use of the “interactive” model of innovation through the so-called “multi-actor” approach. In this study, we explore the dynamics of scaling in agricultural and forestry co-innovation partnerships. We ask whether such partnerships can be effective instruments to scale innovations and what factors play a role in the scaling process. Thus, the novelty of our paper is that it is the first published study of the dynamics of scaling within the current EU policy framework. Our analysis draws upon evidence from eight co-innovation case studies across Europe, encompassing varied contexts, scales, and funding mechanisms, and identifies three distinct forms of scaling: scaling out, up, and deep. The selection by co-innovation partnerships of strategies and enabling mechanisms in pursuit of scaling is dependent on factors such as funding conditions, contextual norms, and partnership objectives. Partnerships need to be clear about the type of scaling they aim to achieve, have an in-depth understanding of contextual complexities, and ensure that scaling is an integral part of the entire project cycle. Co-innovation partnerships can be effective catalysts for transformative change, provided scaling complexities are navigated, and enabling mechanisms leveraged adeptly. Our insights advance the understanding of scaling dynamics in co-innovation and offer evidence-based strategies for practitioners, policymakers, and researchers to bolster the impact of co-innovation initiatives in agriculture and forestry
Design principles for advancing higher education sustainability learning through transformative research
No full textAbstract
A growing number of transformative research practices that redefine the role of science in engaging with local–mostly urban–transformation processes have emerged in recent decades. However, while education is considered a key driver for sustainability transformations, higher education has been slow to develop and implement dedicated, appropriate and effective transformative education programmes and learning modules. In this paper, we present a framework of design principles for transformative learning modules in higher education. These principles are derived from two growing discourses: higher education sustainability learning , and transdisciplinary and transformative research —both of which are centrally anchored in the field of sustainable development and sustainability science. The principles presented provide guidance for course leaders in higher education to create learning modules aimed at enabling students to become engaged in transdisciplinary and transformative research that fosters sustainability transitions in local and urban contexts. We use the Transformative Innovation Lab (TIL)—a learning course developed and tested at two German universities—as an example of how the design principles can be applied. The module, which runs over two semesters, supports Masters students in their process of developing real-world laboratories and exploring urban sustainability transitions through collaborative experimentation with local practice partners. We discuss the factors that enable and limit the implementation of transformative learning modules and outline aspects of the novel roles adopted by lecturers in transformative teaching environments. Moreover, we highlight the need for both institutional change and transformative teaching formats that go beyond transformative research as key for driving universities to take responsibility for collaboratively fostering sustainability transitions in their local contexts
Untersuchung des Nachfragepotenzials für Workation-Angebote in der Metropolregion Berlin-Brandenburg mit Fokus auf Familien und der Förderung der Vereinbarkeit von Beruf und Kinderbetreuung
No full textDie fortschreitende Digitalisierung und die zunehmende räumliche Flexibilisierung von Arbeit haben den klassischen Büroarbeitsplatz an Bedeutung verlieren lassen. Vor diesem Hintergrund gewinnen mobile Arbeitsformen wie Workation an Relevanz. Unter Workation wird die zeitweilige Verbindung von beruflichen Tätigkeiten mit Aufenthalten an touristischen Destinationen verstanden. Ziel dieser Arbeit ist es, zu untersuchen, inwiefern Workation im Sinne der Work–Life Integration eine geeignete Möglichkeit zur besseren Vereinbarkeit von Beruf und Familie bietet und ob in der Metropolregion Berlin-Brandenburg grundsätzlich Interesse an familienorientierten Workation-Angeboten besteht. Analysiert werden dabei zentrale Bedürfnisse und Herausforderungen von Familien auf Grundlage einer Onlineumfrage mit 430 Teilnehmenden. Die Ergebnisse zeigen, dass Workation bislang wenig bekannt ist: Mehr als die Hälfte der Befragten hatte den Begriff zuvor nicht gehört, und nur ein kleiner Teil verfügt über praktische Erfahrung. Zugleich deutet sich Potenzial an, wenn spezifische Familienbedürfnisse berücksichtigt werden. Besonders wichtig sind familienfreundliche Unterkünfte, Freizeitangebote für Kinder, verlässliche Kinderbetreuung und separate Arbeitsbereiche. Bevorzugt werden Aufenthalte von ein bis zwei Wochen während der Schulferien. Insgesamt wird Workation von vielen Familien als Möglichkeit gesehen, Beruf und Familie besser zu vereinbaren, wenngleich die Umsetzbarkeit stark von individuellen Präferenzen und beruflichen Rahmenbedingungen abhängt
Examining the social-ecological dimensions of Philippine pangolin (Manis culionensis) conservation
No full textAbstract
Pangolins are the world’s most trafficked mammals due to the persistent international demand for their scales and meat. The critically endangered Philippine pangolin ( Manis culionensis ) is one of the eight extant pangolin species worldwide. It is endemic to Palawan Biosphere Reserve, a province in the Philippines which experiences several social and environmental challenges. We examine the state of Philippine pangolin research using Ostrom’s Social-Ecological Systems framework as a conceptual and analytical guide in this paper. By employing a systematic literature review, we analyse the emergent themes from existing research and gathered insights on the interactions and outcomes among habitat, resource use, institutions and policies, and diversity of actors involved in Philippine pangolin research and conservation. We discuss several knowledge gaps and offer recommendations for future research and conservation efforts. Finally, we call for implementing interdisciplinary and multistakeholder approaches to knowledge co-production on Philippine pangolin research and conservation
Evaluation zweier mit RISE DE erarbeiteten Forschungsdatenstrategien am Beispiel einer Universität und einer Hochschule für angewandte Wissenschaften
No full textDiese Präsentation zum Thema „Evaluation zweier mit RISE DE erarbeiteten Forschungsdatenstrategien am Beispiel einer Universität und einer Hochschule für angewandte Wissenschaften“ wurde auf den E-Science-Tagen in Heidelberg am 13.03.2025 gehalten
Zertifikatskurs "Forschungsdatenmanagement für Forschende und FDM-Verantwortliche" 2025 der Landesinitiative Forschungsdatenmanagement in Brandenburg
No full textDer Zertifikatskurs Forschungsdatenmanagement (FDM) für Forschende und FDM-Verantwortliche wurde im Rahmen des BMFTR- und MWFK-geförderten Projekts IN-FDM-BB entwickelt und fand zum zweiten Mal im Frühjahr 2025 als mehrwöchiger Modulkurs mit gut 30 Teilnehmenden aus brandenburgischen Hochschulen und außeruniversitären Forschungseinrichtungen statt. Der Umgang mit Forschungsdaten ist ein bedeutsamer Aspekt der guten wissenschaftlichen Praxis. Dazu gehören Themen wie Datenmanagementpläne, Metadaten, Langzeitarchivierung, Datenpublikation, rechtliche und ethische Aspekte, Kollaborationen, Standards u. v. m. Ein strukturiertes FDM bietet Vorteile wie Effizienz, Zeitersparnis und Transparenz und wird nicht zuletzt durch die Anforderungen der Forschungsförderer immer stärker zur Notwendigkeit.
Die rund 20 Lehrveranstaltungen des Kurses verteilten sich über einen Zeitraum von 7 Wochen und fanden in der Regel als 90-minütige Online-Veranstaltungen statt. Der kostenfreie Kurs bot die Möglichkeit, die erworbenen Kenntnisse über Microcredentials nachzuweisen.
Die hier zur Nachnutzung bereitgestellte Materialiensammlung beinhaltet:
(A) Das Modulhandbuch des Zertifikatskurses (eine .pdf-Datei)
(B) Die gesamten Lehrskripte (13 .odp-Dateien) mit folgenden Modulen (M):
M1: Open Science
M2: Wissenschaftliche Organisationen
M3: Datendokumentation, Metadaten
M4: Datenmanagementpläne (DMP)
M5: Aktives Datenmanagement
M6: Langzeitarchivierung (LZA)
M7: Datenpublikation
M8: Rechtliche Aspekte des FDM
M9: Ethische Aspekte des FDM
M10: Gute wissenschaftliche Praxis (GWP)
M11: Kollaborationen & Standards
M12: Fachspezifisches FDM
M13: FDM vermittel
Assessing the Effect of Field Disturbances On Biomass Estimation in Grasslands Using UAV-Derived Canopy Height Models
No full textAbstract
Accurate estimation of biomass in grasslands is essential for understanding ecosystem health and productivity. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools for biomass estimation using canopy height models derived from high-resolution imagery. However, the impact of field disturbances, such as lodging and molehills, on the accuracy of biomass estimation using UAV-derived canopy height models remains underexplored. This study aimed to assess the relationship between UAV-derived canopy height and both reference canopy height measurements and dry biomass, accounting for different management systems and disturbance scenarios. UAV data were collected using a multispectral camera, and ground-based measurements were obtained for validation. The results revealed that UAV-derived canopy height models remained accurate in estimating vegetation height, even in the presence of disturbances. However, the relationship between UAV-derived canopy height and dry biomass was affected by disturbances, leading to overestimation or underestimation of biomass depending on disturbance type and severity. The impact of disturbances on biomass estimation varied across cutting systems. These findings highlight the potential of UAV-derived canopy height models for estimating vegetation structure, but also underscore the need for caution in relying solely on these models for accurate biomass estimation in heterogeneous grasslands. Future research should explore strategies to enhance biomass estimation accuracy by integrating additional data sources and accounting for field disturbances
Facilitating Effective Reuse of Soil Research Data: The BonaRes Repository
No full textABSTRACT
Soil plays a paramount role in addressing complex challenges related to climate change, the agri‐food system, and ecosystem services. This importance makes soil research data highly relevant for meta‐analysis, research synthesis, modelling, and assessment. As data‐intensive techniques proliferate in studying global change impacts on agricultural systems, effective data management and reuse are essential. Repositories that adhere to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are crucial for maximizing the value and efficiency of research data. While publishing in an Open Access repository is necessary for data reusability, it alone is not sufficient. Specialized repositories enhance data reuse potential by addressing discipline‐specific needs through targeted metadata and technical frameworks. The BonaRes Repository was developed for agricultural soil research data and is guided by the FAIR principles, with a focus on data reusability. Here, we introduce the repository's infrastructures and services, including specialized tools for data quality assurance and the management of soil profile as well as long‐term field experiment data. We emphasize the ability of these infrastructures and services to promote data publication and reuse specifically in soil and agricultural sciences. We review examples of data reuse, highlighting their scientific contributions to the understanding of soil and agricultural systems. Finally, we discuss the remaining challenges in achieving FAIR and open soil data publication and reusability. From 2018 to date, the BonaRes Repository has facilitated 815 data publications; 62 papers have reused the published data. Reuse applications range widely—from extracting study site metadata or environmental covariates to reanalysing (meta)data in light of new research questions, to developing scenarios and conducting model calibration and evaluation. A key insight from our review of data reuse is that researchers frequently apply reused data to advance method development. Initiatives such as reciprocal metadata harvesting and integration into larger national and international research data infrastructure will further expand the scope and reuse of the repository's data, including in broader agrosystems science