Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2026
Authors
Marit Jørgensen Ragnhild Borchsenius Ellen Elverland Frøydis Gillund Khaled Murad Agha Kauê de Sousa Ievina SturiteAbstract
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Authors
Jirata Shiferaw Abosse Bekele Megersa Feleke Zewge Ståle Haaland Samuel Assefa Fasil Ejigu EregnoAbstract
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Abstract
Longitudinal analysis of a patient’s screening history is fundamental to mammography interpretation. Yet existing deep learning models struggle with the irregular, continuous-time nature of screening data, where patient histories involve unevenly timed multi-view exams paired with evolving textual reports. To address this gap, we introduce Dynamo, a novel multi-modal pretraining dynamic framework for longitudinal mammography with two key innovations to model exam timelines as latent trajectories, i.e., a coarse-fine grained exam-level temporal encoder based on Neural Controlled Differential Equations (Neural CDEs) and a Temporal Visual Question Answering (TVQA) loss for query-driven masked token prediction conditioned on temporal context. We perform comprehensive evaluations on downstream tasks including risk prediction using large-scale datasets (EMBED and CSAW-CC), BI-RADS assessment, and breast density classification. Across all benchmarks, Dynamo achieves overall gains over state-of-the-art vision-only and CLIP-style models, improving both calibration and temporal reasoning. On the EMBED dataset, representative gains include 11.81% relative reduction in Risk Prediction’s Brier Score, 3.59% relative improvement in BI-RADS ϰ, and 2.39% relative improvement in BI-RADS AUC in zero-shot settings. Our code is available at this URL.
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Authors
Ingrid Nesheim Julia Szulecka Anne-Grete Buseth Blankenberg Natalja Čerkasova Rozalija Cvejić Joana Eichenberger Caroline Enge Raimonds Ernšteins Marie Anne Eurie Forio Petr Fučík Marek Giełczewski Agota Horel Kinga Farkas-Iványi Ilona Kása Piroska Kassai Gregor Kramberger Dominika Krzeminska Tatenda Lemann Peter Molnar Federica Monaco Michael Strauch Brigitta Szabó Felix WitingAbstract
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Authors
Xiaoyu Xu Yuang Cao Suli Zhi Jiahua Liu Cheryl Marie Cordeiro Erik Sindhøj Han Wang Keqiang ZhangAbstract
Microalgae exhibit unique advantages in ARG removal, yet their growth and efficacy are often constrained by complex organic matter and microorganisms in wastewater. To address this issue, this study employed chemical pretreatment to synergistically enhance microalgal treatment and, for the first time, developed a novel coupled process to tackle ARGs in livestock wastewater. The results indicate that low-chlorine (1 mg/L) pretreatment combined with the indigenous filamentous alga (S2) significantly removed pollutants (TN: 81.50%, COD: 70.71%) and reduced the total abundance of ARGs by 81.73%. The core mechanism lies in low-chlorine pretreatment shaping a mutually beneficial algae-bacteria system, which achieves efficient ARG control by altering the host bacterial. The study formalized the operating condition with a multi-objective desirability index combining nutrient removal, ARG reduction, and algal growth, which identified 1 mg/L as the overall optimum. The combined treatment process at a low chlorine dosage demonstrated both high efficiency and feasibility, providing an innovative strategy for livestock wastewater treatment.
Authors
Yuang Cao Zhuowu Li Xiaoyu Xu Cheryl Marie Cordeiro Jiahua Liu Keqiang Zhang Lianzhu Du Suli ZhiAbstract
The spread of antibiotic resistance genes (ARGs) in livestock and poultry wastewater poses a serious threat to the ecological environment and public health. This study compared the effects of biochar (BC), ferrous sulfate (FS), ferrous sulfate-modified biochar (FC), a physical mixture of ferrous sulfate and biochar (F_C), and sulfuric acid (HS) on ARG dynamics and nitrogen metabolism during the 60-day storage and fermentation of pig manure slurry. The results showed that single treatments (BC or FS) had limited ARG-removal efficiency. Compared with the control, the F_C treatment maintained higher total nitrogen (TN) levels (up to 2.42 mg/g in F_C3) while contributing to ARG reduction; however, its ARG-removal performance was not consistently superior to that of all other treatments. Although HS inhibited some ARGs, strong acidification altered the microbial community structure and may have disrupted ecological stability. Metagenomic analysis revealed that multidrug, peptide, and glycopeptide ARGs were dominant (approximately 80%) and were significantly positively correlated with key nitrogen-metabolism genes (e.g., nxrAB and nasAB, p < 0.01), suggesting a link between nitrogen cycling and ARG dissemination. Overall, the physical mixing of biochar and ferrous sulfate shows potential as a practical strategy for jointly regulating ARG dynamics and nitrogen transformation during pig manure slurry storage and fermentation, but further optimisation and validation are needed before field-scale application.