BACKGROUND: Climate change is a pressing environmental and social challenge that demands effective monitoring of greenhouse gas (GHG) emissions. One widely adopted approach for this is quantifying the carbon footprint (CF). Given that agriculture is a major contributor to GHG emissions, we have developed a comprehensive framework for CF accounting at the farm level. This framework has been tested on 12 farms in the Czech Republic to assess both data availability and calculation accuracy. RESULTS: Our study examines how various farm characteristics, such as turnover, land area and number of employees, influence the overall CF and enable meaningful comparisons between farms. We found that absolute farm CFs are significantly influenced by the size effect, making them unsuitable for benchmarking purposes. By contrast, relative farm CFs (per turnover, per area and per employee) are not affected by the size effect, but can be affected by a scale effect. Additionally, we investigated whether a focus on animal husbandry leads to higher relative CFs. By calculating the share of animal husbandry (SoAH) in farm operations, we discovered a significant correlation between SoAH and relative CFs, with the strongest correlation observed for CF per turnover (0.87). CONCLUSION: We argue that farms with high shares of SoAH are unlikely to reduce their relative CFs to the levels of farms with zero or low SoAH. We therefore propose applying benchmarking to farms with similar SoAH. We also propose that further research should focus on defining and validating relevant reference values, comprising a benchmark set that reflects different farm types. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
- Klíčová slova
- Czech Republic, GHG emissions, agricultural farms, benchmarking, carbon footprint (CF),
- MeSH
- benchmarking MeSH
- chov zvířat MeSH
- farmy statistika a číselné údaje MeSH
- klimatické změny MeSH
- skleníkové plyny analýza metabolismus MeSH
- uhlíková stopa * statistika a číselné údaje MeSH
- zemědělství MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
- Názvy látek
- skleníkové plyny MeSH
The use of artificial intelligence (AI) is increasingly integral to the drug-discovery process, and among AI-driven methodologies, deep generative models stand out as one of the most promising approaches for hit identification and optimization. Here, we report a retrospective benchmarking analysis of a series of tubulin inhibitors, 3-aroyl-1,4-diarylpyrroles (ARDAP), using the deep-generative algorithm Molecule Optimization by Reinforcement Learning and Docking (MORLD) in combination with five docking software (QuickVina 2, AutoDock-GPU, PLANTS, GOLD, and Glide). Our results indicate that the performance of the MORLD/docking workflow is highly dependent on the availability of initial structural information; only the incorporation of a core constraint in Glide yields satisfactory predictions. To address this limitation, we developed a docking-free variant of MORLD that exploits receptor-derived shape similarity and pharmacophore alignment. Kernel-density estimation, convergence analysis, and SMARTS-based success-rate metrics confirmed that this Shape-Pharmacophore implementation autonomously generates chemically valid, SAR-consistent analogues of the reference compounds. Collectively, this work demonstrates a practical, structure-only driven paradigm for reinforcement-learning-based compound optimization, thereby extending the reach of AI-enabled drug design beyond traditional docking workflows.
- MeSH
- algoritmy MeSH
- benchmarking MeSH
- farmakofor MeSH
- modulátory tubulinu * chemie farmakologie metabolismus MeSH
- objevování léků MeSH
- pyrroly * chemie farmakologie metabolismus MeSH
- retrospektivní studie MeSH
- simulace molekulového dockingu * MeSH
- tubulin metabolismus chemie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- modulátory tubulinu * MeSH
- pyrroly * MeSH
- tubulin MeSH
The Covid 19 pandemic has caused real turmoil, impacting every aspect of society. Education is one of the most significant fields that was the most influenced by the pandemic. All levels of education had to change to online teaching for a significant amount of time. Teachers worldwide had to demonstrate flexibility and creativity to change not only the form of learning - from face-to-face to online teaching - but also to modify their course methods and materials to be equally efficient. The Intercultural Communication course, a compulsory component of the English language teacher training program at the university where this research was conducted, required extensive planning to ensure its effectiveness. Typically, the course accepts as many foreign Erasmus students as possible to foster a multicultural environment. However, the transition from face-to-face teaching in a multicultural setting to remote teaching via computer screens, without the personal contact and participation of Erasmus students, proved to be an immense challenge. Even though it seemed to be almost impossible to develop intercultural communicative competences within the online teaching format, we had to be creative and develop a new course that would fulfil the purpose. Participatory action research was employed to develop and implement a new online course Intercultural Communication, aimed at effectively fostering intercultural communicative competences of English language teacher trainees. The study addressed three research questions: 1. How did English language teacher trainees develop their intercultural communicative competences during the online teaching? 2. How successful was the structure of the online course of Intercultural communication? 3. How successful was the content concerning the development of intercultural communicative competences of English language teacher trainees in the online teaching? Data were collected through observation, self-reflecting journals, and open-ended surveys. The collected data were analysed using descriptive narrative and coding with the NVIVO qualitative data analysis software. The new online course comprised weekly assignments involving reading texts, watching videos, completing quizzes, and engaging in lectures and discussions during seminars both in break-out rooms and as a whole class. Findings from individual research methods were triangulated, and the newly developed course of Intercultural Communication was evaluated as highly successful.
- Klíčová slova
- English language teacher trainees, Intercultural communication, Intercultural communicative competence, Online course,
- MeSH
- COVID-19 epidemiologie MeSH
- distanční studium * organizace a řízení metody MeSH
- hodnocení programu MeSH
- jazyk (prostředek komunikace) MeSH
- komunikace * MeSH
- kulturní kompetence * výchova MeSH
- kulturní různorodost MeSH
- kurikulum MeSH
- lidé MeSH
- SARS-CoV-2 MeSH
- studenti MeSH
- univerzity organizace a řízení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
MOTIVATION: MicroRNAs (miRNAs) are crucial regulators of gene expression, but the precise mechanisms governing their binding to target sites remain unclear. A major contributing factor to this is the lack of unbiased experimental datasets for training accurate prediction models. While recent experimental advances have provided numerous miRNA-target interactions, these are solely positive interactions. Generating negative examples in silico is challenging and prone to introducing biases, such as the miRNA frequency class bias identified in this work. Biases within datasets can compromise model generalization, leading models to learn dataset-specific artifacts rather than true biological patterns. RESULTS: We introduce a novel methodology for negative sample generation that effectively mitigates the miRNA frequency class bias. Using this methodology, we curate several new, extensive datasets and benchmark several state-of-the-art methods on them. We find that a simple convolutional neural network model, retrained on some of these datasets, is able to outperform state-of-the-art methods reaching average precision scores between 0.81 and 0.86 in test datasets. This highlights the potential for leveraging unbiased datasets to achieve improved performance in miRNA binding site prediction. To facilitate further research and lower the barrier to entry for machine learning researchers, we provide an easily accessible Python package, miRBench, for dataset retrieval, sequence encoding, and the execution of state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: The miRBench Python package is accessible at https://github.com/katarinagresova/miRBench/releases/tag/v1.0.1.
- MeSH
- algoritmy MeSH
- benchmarking MeSH
- lidé MeSH
- mikro RNA * metabolismus genetika chemie MeSH
- neuronové sítě MeSH
- software * MeSH
- vazebná místa MeSH
- výpočetní biologie * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- mikro RNA * MeSH
BACKGROUND: Globally, most people with head and neck cancers (HNCs) are diagnosed with advanced-stage disease. HNC diagnostic stage has multifactorial explanations, with the role of health system factors not yet fully investigated. METHODS: HNC centres (n = 18) from the HEADSpAcE Consortium were surveyed via a bespoke health system questionnaire covering a range of factors. Centres were compared using the least square means for the presence/absence of each health system factor to their proportion of advanced-stage HNC. RESULTS: Health system factors associated with lower proportion in advanced-stage diagnosis were formal referral triaging (14%, 95% CI-0.26, -0.03), routine monitoring of time from referral to diagnosis (16%, 95% CI-0.27, -0.05), and fully publicly funded systems (17%, 95% CI-0.29, -0.06). Several health systems factors had no routinely available data. CONCLUSIONS: Through identifying and monitoring health systems factors associated with lower proportions of advanced stage HNC, interventions could be developed, and systems redesigned, to improve early diagnosis.
- Klíčová slova
- diagnostic pathway, head and neck cancer, health systems, stage at diagnosis,
- MeSH
- benchmarking * MeSH
- časná detekce nádoru MeSH
- konziliární vyšetření a konzultace statistika a číselné údaje MeSH
- lidé MeSH
- nádory hlavy a krku * patologie diagnóza MeSH
- průzkumy a dotazníky MeSH
- staging nádorů MeSH
- třídění pacientů MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
The Renal Expert in Vascular Access (REVAC) is one of the four modules of the Nephrology Partnership for Advancing Technology in Healthcare (N-PATH) project, the first European-wide advanced training course in diagnostics and interventional nephrology, funded by Erasmus+ Knowledge Alliance, a European Commission program. The N-PATH primary goal was to train 40 young European nephrologists in both theoretical knowledge and practical skills related to interventional nephrology. The REVAC module focused on the crucial aspects of vascular access (VA) care in nephrology practice, as a complementary training path to the actual residency program. The aim was to provide nephrology fellows with comprehensive knowledge and skills related to VA management. The methodology was based on face-to-face meetings and online learning, modern facilities, experienced tutors, cutting edge simulators, augmented reality tools by means of a multidisciplinary international faculty and hands-on-courses. A feedback survey reported the experience of fellows who attended the REVAC module, confirming the positive impact on their ongoing nephrology training. We are confident that this project will revitalize their nephrology careers and will help training the next generation of nephrologists; they will be able to manage VA needs with the help of multi-disciplinary teams to safely optimize the care of hemodialysis patients.
- Klíčová slova
- Vascular access, hands-on-training, interventional nephrology, nephrology education, patient safety, simulation training,
- MeSH
- arteriovenózní zkrat * výchova MeSH
- dialýza ledvin * MeSH
- hodnocení programu MeSH
- klinické kompetence MeSH
- kooperační chování MeSH
- kurikulum MeSH
- lidé MeSH
- nefrologie * výchova MeSH
- nefrologové * výchova MeSH
- stipendia MeSH
- studium lékařství specializační postgraduální * metody MeSH
- zdraví - znalosti, postoje, praxe MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
INTRODUCTION: Investigator-initiated trials (IITs) bridge the gap between applied clinical research and everyday clinical practice. However, they require the skilled multidisciplinary teams from different backgrounds but all with clinical trial training to ensure trials are designed, conducted and reported according to best practice and regulatory standards. The availability of trainings to fulfil these needs is limited. The CONSCIOUS II project facilitated to expand the supply of such programmes. The objective is to describe the curriculum designed for PhD students and early-career researchers, and evaluate participants' perceptions and feedback after completion of the training. METHODS: The curriculum was developed according to key principles that underpin building of competencies relevant to quality IITs and transdisciplinary skills. A multidisciplinary team created the curriculum, elaborated a comprehensive set of study materials, including the training platform. This team also conducted an international, collaborative pilot course. The effectiveness of the educational materials for the target audience was assessed through questionnaires administered after the pilot course. Additionally, all learning materials, including the video recordings of the pilot course, were externally evaluated. RESULTS: A 12-chapter thoroughly revised curriculum was developed for asynchronous preparation and served as a pre-class reading for a 3-month pilot course. The chapters, along with supplementary materials, and recordings of the pilot course are freely accessible on the CONSCIOUS II training platform. This platform facilitates the dissemination and implementation in the existing curricula. The feedback from both the pilot course participants and the stakeholders was uniformly positive across all survey aspects. CONCLUSION: This remote programme which combines asynchronous and synchronous components with international and interprofessional collaboration effectively addresses the gap in developing core competencies for the 21st -century clinical researchers. The implementation of this curriculum has the potential to improve the quality of IITs.
- Klíčová slova
- Curriculum, Early-career researchers, Investigator-initiated trials, Practice-oriented,
- MeSH
- biomedicínský výzkum * výchova MeSH
- hodnocení programu MeSH
- klinické kompetence MeSH
- klinické zkoušky jako téma * normy MeSH
- kurikulum * MeSH
- lidé MeSH
- pilotní projekty MeSH
- výzkumní pracovníci * výchova MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, despite the importance of SBDD to the field, the underlying methodologies and techniques have many limitations. In particular, binding pose and activity predictions (P-AP) are still not consistently reliable. We strongly believe that a limiting factor is the lack of a widely accepted and established community benchmarking process that independently assesses the performance and drives the development of methods, similar to the CASP benchmarking challenge for protein structure prediction. Here, we provide an overview of P-AP, unblinded benchmarking data sets, and blinded benchmarking initiatives (concluded and ongoing) and offer a perspective on learnings and the future of the field. To accelerate a breakthrough on the development of novel P-AP methods, it is necessary for the community to establish and support a long-term benchmark challenge that provides nonbiased training/test/validation sets, a systematic independent validation, and a forum for scientific discussions.
- MeSH
- benchmarking * MeSH
- konformace proteinů MeSH
- objevování léků metody MeSH
- proteiny * chemie metabolismus MeSH
- racionální návrh léčiv MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Názvy látek
- proteiny * MeSH
PURPOSE: STereotactic Arrhythmia Radioablation (STAR) showed promising results in patients with refractory ventricular tachycardia. However, clinical data are scarce and heterogeneous. The STOPSTORM.eu consortium was established to investigate and harmonize STAR in Europe. The primary goal of this benchmark study was to investigate current treatment planning practice within the STOPSTORM project as a baseline for future harmonization. METHODS AND MATERIALS: Planning target volumes (PTVs) overlapping extracardiac organs-at-risk and/or cardiac substructures were generated for 3 STAR cases. Participating centers were asked to create single-fraction treatment plans with 25 Gy dose prescriptions based on in-house clinical practice. All treatment plans were reviewed by an expert panel and quantitative crowd knowledge-based analysis was performed with independent software using descriptive statistics for International Commission on Radiation Units and Measurements report 91 relevant parameters and crowd dose-volume histograms. Thereafter, treatment planning consensus statements were established using a dual-stage voting process. RESULTS: Twenty centers submitted 67 treatment plans for this study. In most plans (75%) intensity modulated arc therapy with 6 MV flattening filter free beams was used. Dose prescription was mainly based on PTV D95% (49%) or D96%-100% (19%). Many participants preferred to spare close extracardiac organs-at-risk (75%) and cardiac substructures (50%) by PTV coverage reduction. PTV D0.035cm3 ranged from 25.5 to 34.6 Gy, demonstrating a large variety of dose inhomogeneity. Estimated treatment times without motion compensation or setup ranged from 2 to 80 minutes. For the consensus statements, a strong agreement was reached for beam technique planning, dose calculation, prescription methods, and trade-offs between target and extracardiac critical structures. No agreement was reached on cardiac substructure dose limitations and on desired dose inhomogeneity in the target. CONCLUSIONS: This STOPSTORM multicenter treatment planning benchmark study not only showed strong agreement on several aspects of STAR treatment planning, but also revealed disagreement on others. To standardize and harmonize STAR in the future, consensus statements were established; however, clinical data are urgently needed for actionable guidelines for treatment planning.
- MeSH
- benchmarking * MeSH
- celková dávka radioterapie MeSH
- komorová tachykardie chirurgie radioterapie MeSH
- konsensus * MeSH
- kritické orgány * účinky záření MeSH
- lidé MeSH
- plánování radioterapie pomocí počítače * normy metody MeSH
- radiochirurgie * normy metody MeSH
- radioterapie s modulovanou intenzitou metody normy MeSH
- srdce účinky záření MeSH
- srdeční arytmie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- konsensus - konference MeSH
- multicentrická studie MeSH
- Geografické názvy
- Evropa MeSH
