Our Work

Resilience in the Basin / Otpornost u slivu An Educational Package on Wetland Management and Climate Resilience for Young Adults (Working Draft / Radna verzija   This interdisciplinary education…

Resilience in the Basin / Otpornost u slivu An Educational Package on Wetland Management and Climate Resilience for Young Adults (Working Draft / Radna verzija   This interdisciplinary educational package was created in the scope of the ClimEmpower project specifically for use by OBZ-AZP (Javna ustanova Agencija za upravljanje zaštićenim prirodnim vrijednostima na području Osječko-baranjske županije). Designed primarily for secondary school students (young adults) and interested members of the public, the package provides practical tools to understand wetland management, natural flood mitigation, and climate adaptation in the Osijek-Baranja region (Croatia). By combining ecological engineering with climate psychology, the curriculum guides learners from technical environmental literacy to active community engagement. The curriculum covers two main thematic pillars: Basin Hydrology & Nature-Based Solutions (NbS): Contrasting artificial river channelisation ("pipes") with natural floodplain restoration ("sponges"), supported by regional case studies (such as the Lower Danube Green Corridor and the Drava–Danube basin). Climate Psychology & The ACTIVATE Framework: Addressing cognitive biases (Distant Threat Bias, Cognitive Dissonance, The Collective Silence) and providing concrete coping strategies (strategije prevladavanja) to foster constructive communication and local resilience.  

Chimeric RNAs are a diverse class of molecules defined as RNA transcripts containing genetic information for multiple genes that arise from both DNA-level and RNA-level fusion events. While their expr…

Chimeric RNAs are a diverse class of molecules defined as RNA transcripts containing genetic information for multiple genes that arise from both DNA-level and RNA-level fusion events. While their expression patterns across various tissue types and disease states have been well-documented, the biological function of these transcripts remains poorly understood. In this study, we developed an integrative prediction pipeline to establish a comprehensive atlas of 569,640 chimeric RNAs across 1,019 cancer cell lines, providing the high-resolution landscape required for functional interrogation. Leveraging this resource, we performed a systematic functional dependency screen by integrating our predictions with large-scale RNAi datasets from The Cancer Dependency Map (DepMap) project. This approach moves beyond simple chimeric RNA prediction to identify high-confidence chimeric RNAs essential for cell viability, including a previously uncharacterized class of recurrent chimeric transcripts likely generated by cis-splicing of adjacent genes (cis-SAGe) found across wide varieties of tissue types and disease states. We demonstrate that depletion of selected candidate chimeras disrupts cell cycle progression and cellular fitness and that these transcripts are frequently dysregulated in primary breast tumors, highlighting their potential biological relevance and clinical significance. Together, these results demonstrate that chimeric RNAs constitute a previously underappreciated layer of gene expression, effectively expanding the repertoire of the functional genome beyond conventional genetics. By prioritizing function, this study establishes a framework for investigating biologically relevant chimeric transcripts and highlights their potential as therapeutic targets and biomarkers in cancer.

Chimeric RNAs are a diverse class of molecules defined as RNA transcripts containing genetic information for multiple genes that arise from both DNA-level and RNA-level fusion events. While their expr…

Chimeric RNAs are a diverse class of molecules defined as RNA transcripts containing genetic information for multiple genes that arise from both DNA-level and RNA-level fusion events. While their expression patterns across various tissue types and disease states have been well-documented, the biological function of these transcripts remains poorly understood. In this study, we developed an integrative prediction pipeline to establish a comprehensive atlas of 569,640 chimeric RNAs across 1,019 cancer cell lines, providing the high-resolution landscape required for functional interrogation. Leveraging this resource, we performed a systematic functional dependency screen by integrating our predictions with large-scale RNAi datasets from The Cancer Dependency Map (DepMap) project. This approach moves beyond simple chimeric RNA prediction to identify high-confidence chimeric RNAs essential for cell viability, including a previously uncharacterized class of recurrent chimeric transcripts likely generated by cis-splicing of adjacent genes (cis-SAGe) found across wide varieties of tissue types and disease states. We demonstrate that depletion of selected candidate chimeras disrupts cell cycle progression and cellular fitness and that these transcripts are frequently dysregulated in primary breast tumors, highlighting their potential biological relevance and clinical significance. Together, these results demonstrate that chimeric RNAs constitute a previously underappreciated layer of gene expression, effectively expanding the repertoire of the functional genome beyond conventional genetics. By prioritizing function, this study establishes a framework for investigating biologically relevant chimeric transcripts and highlights their potential as therapeutic targets and biomarkers in cancer.

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Structurally diverse series of novel pyrido[2,3-d]pyrimidine derivatives was designed and synthesized using a click-chemistry-based approach to identify promising anticancer candidates. The structures…

Structurally diverse series of novel pyrido[2,3-d]pyrimidine derivatives was designed and synthesized using a click-chemistry-based approach to identify promising anticancer candidates. The structures and purity of the synthesized compounds were established through comprehensive spectral characterization. Their antiproliferative potential was investigated by an in vitro MTT assay against a panel of human cancer cell lines. Most derivatives exhibited measurable cytotoxic activity, with compounds 11m and 11e emerging as the most promising candidates. Compound 11m, bearing an 8-bis(4-fluorophenyl)-4-oxo-2-phenyl-substituted pyrido[2,3-d]pyrimidine framework, demonstrated the strongest activity against MDA-MB-231, HeLa, and MCF-7 cells, with IC₅₀ values of 1.29 ± 0.18, 1.34 ± 0.02, and 1.57 ± 0.12 μM, respectively. Compound 11e, containing 5-amino, 3-chlorophenyl, 4-fluorophenyl, and 4-oxo-2-phenyl pharmacophoric features, also displayed pronounced cytotoxicity, producing IC₅₀ values of 1.42 ± 0.12, 1.54 ± 0.13, and 1.85 ± 0.23 μM against the corresponding cell lines. Molecular-docking investigations revealed favourable accommodation of the active derivatives within the binding pockets of the selected biological targets and identified stabilizing interactions consistent with their observed cytotoxic effects. The collective structure–activity and molecular-modelling findings indicate that appropriately substituted pyrido[2,3-d]pyrimidines represent a valuable scaffold for developing potent anticancer agents. In particular, compounds 11m and 11e warrant further mechanistic, selectivity, and in vivo investigations as promising lead candidates for anticancer drug development.

Background: Clear aligner therapy (CAT) has become increasingly popular as an aesthetic alternative to fixed appliances, yet its ability to achieve predicted tooth movement remains debated. The predic…

Background: Clear aligner therapy (CAT) has become increasingly popular as an aesthetic alternative to fixed appliances, yet its ability to achieve predicted tooth movement remains debated. The predictability of CAT the agreement between ClinCheck-predicted and clinically achieved tooth positions varies substantially across movement types and tooth types. Objective: This systematic review and meta-analysis aimed to quantify the predictability of tooth movement with clear aligners across different movement types (rotation, extrusion, bodily translation, and torque) and to compare treatment outcomes between CAT and fixed appliances.  Methods: PubMed, Embase, Scopus, Cochrane Library, and Web of Science searched from inception to January 2026. Eligible studies compared predicted versus achieved tooth movement with clear aligners and reported quantitative accuracy outcomes. Random-effects meta-analyses pooled mean differences and accuracy percentages. Risk of bias was assessed using ROBINS-I and RoB 2. Certainty of evidence evaluated using GRADE. Results: Twenty-seven studies (N=2,847 patients) were included. Overall predictability varied significantly by movement type. Rotation was the least predictable movement (pooled accuracy 51.2%; 95% CI:44.8-57.6%), with canine rotation particularly unreliable (42.3%). Extrusion showed moderate predictability (58.9%; 95% CI:52.1-65.7%). Bodily translation demonstrated higher accuracy (68.4%; 95% CI:61.2-75.6%), though posterior movements were less predictable than anterior movements. Torque control showed substantial variability (prediction interval 31.2-72.8%). No significant difference was found in overall American Board of Orthodontics Objective Grading System scores between CAT and fixed appliances (mean difference 1.87; 95% CI:-2.32 to 6.06; p=0.38), though heterogeneity was high (I²=90%). Conclusion: Clear aligner therapy demonstrates movement-specific predictability, with rotation being least predictable and anterior bodily movement most predictable. Clinicians should consider overcorrection strategies for rotation and extrusion and anticipate refinement needs. The certainty of evidence is low to moderate, highlighting the need for standardized prospective studies.

IMPORTANT DEADLINESSubmission of Full Paper: until January 31, 2027Initial decision sent to authors (desk review): by February 15, 2027Double-blind review results: April 15, 2027Deadline for authors t…

IMPORTANT DEADLINESSubmission of Full Paper: until January 31, 2027Initial decision sent to authors (desk review): by February 15, 2027Double-blind review results: April 15, 2027Deadline for authors to submit the revised papers (R1): May 15, 2027Double-blind review results (R2): until June 15, 2027Deadline for submission of the final versions: July 15, 2027Final acceptance notification: until July 31, 2027Special Issue publication (expected): July-September 2027 GUEST EDITORSKettrin Farias Bem Maracajá – Universidade Federal de Campina Grande (UFCG), Brazil (kettrin.farias@uaac.ufcg.edu.br)Adriana Fumi Chim Miki – Universidade Federal de Campina Grande (UFCG), Brazil and Universidade dos Açores, Portugal (adriana.fc.miki@uac.pt)Melise de Lima Pereira - Universidade Federal do Paraná (UFPR), Brazil (melisepereira@ufpr.br) BAR EDITOR-IN-CHIEFRicardo Limongi – Universidade Federal de Goiás, Brazil (ricardolimongi@ufg.br)

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Wie verändert sich unser Blick auf religiöse Bildung, wenn Jugendlicheihre Lebenswelt zunächst selbst deuten und die daraus entstehendenFragen anschließend mit einer kundigen j&uu…

Wie verändert sich unser Blick auf religiöse Bildung, wenn Jugendlicheihre Lebenswelt zunächst selbst deuten und die daraus entstehendenFragen anschließend mit einer kundigen jüdischen Gesprächspartnerinweitergeführt werden? Im Sommersemester 2021 beschäftigten sich Lehramtsstudierende im Seminar Bildungund Religion mit Selbstdeutungen charedischer Kinder und Jugendlicher in Israel.Ausgangspunkt war eine Folge des israelischen Fernsehformats Sliḥa al ha-she’ela –Yeladim ( סליחה על השאלה ילדים ). Die Gruppe sah das hebräischsprachige Interviewgemeinsam; der Dozierende übersetzte fortlaufend und klärte erste Rückfragen. Ausder intensiven Begegnung mit den dargestellten Lebenswelten entstanden Fragen, diein der folgenden Sitzung mit Zippora Lankin besprochen wurden. Lankin ist orthodoxjüdischeReligionslehrerin und Künstlerin und mit charedischen Milieus sozial engvertraut. Die Spannweite des Materials gehört zu seinem besonderen didaktischen Reiz: Gemara,Kaschrut und religiöse Normen stehen neben Minecraft, Musical.ly, Freizeit,Berufswünschen und den im Gespräch auftauchenden „heiligen Berufen“. Die vorliegendeDokumentation arbeitet die studentische Protokollierung des Gesprächs lesbaraus und ergänzt sie um Hinweise zum Ausgangsvideo, jüdische Begriffe und ausgewähltehistorische, halachische und gesellschaftliche Kontexte. Im Mittelpunkt steht,wie religiöse Bildung und Sozialisation in konkreten jugendlichen Selbstdeutungensichtbar werden und wie Lehramtsstudierende lernen können, solche Perspektivenaufmerksam zu erschließen.

The rapid development of artificial intelligence (AI), machine learning, process mining, and low-code development platforms is transforming the methods used to analyze and optimize organizational proc…

The rapid development of artificial intelligence (AI), machine learning, process mining, and low-code development platforms is transforming the methods used to analyze and optimize organizational processes. Traditional process analysis generally depends on manually constructed process models, expert knowledge, and retrospective examination of operational indicators. By contrast, intelligent process analysis combines event-log data, artificial intelligence algorithms, process mining techniques, predictive analytics, and visual low-code environments to support automated discovery, diagnosis, prediction, and improvement of business processes. This article examines the conceptual and technological foundations of intelligent process analysis based on AI and low-code platforms. The research is based on a structured analysis of scientific literature, systematic reviews, established process-mining research, artificial intelligence governance frameworks, and contemporary studies of low-code development. Particular attention is given to process discovery, conformance checking, predictive process monitoring, machine learning, explainable artificial intelligence, automated machine learning, and human-centered AI. The analysis demonstrates that low-code platforms can reduce the technical barrier to implementing AI-supported process-analysis solutions by providing visual modeling, reusable components, integration mechanisms, and automated workflows. However, low-code environments do not eliminate fundamental requirements concerning data quality, model validation, security, explainability, governance, and human oversight. Recent systematic research also indicates that the benefits of low-code development are context-dependent and that security, complexity, and governance remain important challenges. The article proposes an integrated conceptual architecture in which low-code platforms function as the implementation and orchestration layer, while AI and process-mining technologies provide analytical intelligence. Such an approach can contribute to faster process diagnosis, predictive decision support, continuous monitoring, and evidence-based process improvement.