Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2024
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Abstracts from The 22nd Triennial conference of EAPR in Oslo 7-12 July 2024. The organizer of the conference was the Norwegian Institute of Bioeconomy Research (NIBIO).
Forfattere
Habtamu AlemSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Ragnhild Aabøe Inglingstad Tove Gulbrandsen Devold Nicola Damiano Anna Caroline Holene Nina Svartedal Irene Comi Tone Inger Eliassen Tora Asledottir Ellen Kathrine Ulleberg Gerd VegarudSammendrag
Six cattle breeds native to Norway, have for almost half a century been at risk of extinction. Due to their small population sizes, they have hardly been improved by breeding for many decades. Still, the endangered breeds represent a source of genetic diversity with special milk qualities compared to the modern breed, Norwegian red (NRF). This study reports for the first time a detailed overview of their milk composition. Milk from seven native breeds, in total 200 individuals, were included in the study. Rare genetic variants of αs1-and αs2-casein, and β-casein A1 and κ-casein B were more prevalent in milk form the endangered breeds compared to NRF. Moreover, milk from these six breeds showed better renneting properties and lower incidences of non-coagulating milk, compared to the NRF milk, which showed better acid coagulation properties. This study shows the potential for native breeds in small-scale production of high-quality rennet cheeses.
Sammendrag
With the intensification of global climate change and environmental stress, research on abiotic and biotic stress resistance in maize is particularly important. High temperatures and drought, low temperatures, heavy metals, salinization, and diseases are widespread stress factors that can reduce maize yields and are a focus of maize-breeding research. Molecular biology provides new opportunities for the study of maize and other plants. This article reviews the physiological and biochemical responses of maize to high temperatures and drought, low temperatures, heavy metals, salinization, and diseases, as well as the molecular mechanisms associated with them. Special attention is given to key transcription factors in signal transduction pathways and their roles in regulating maize stress adaptability. In addition, the application of transcriptomics, genome-wide association studies (GWAS), and QTL technology provides new strategies for the identification of molecular markers and genes for maize-stress-resistance traits. Crop genetic improvements through gene editing technologies such as the CRISPR/Cas system provide a new avenue for the development of new stress-resistant varieties. These studies not only help to understand the molecular basis of maize stress responses but also provide important scientific evidence for improving crop tolerance through molecular biological methods.
Forfattere
Rune Andreassen Berit Hansen Liya Pokrovskaya Vladimir Zhakov Daniel Kling Cornelya Klutsch Ida Marie Luna Fløystad Hans Geir Eiken Snorre HagenSammendrag
Det er ikke registrert sammendrag
Forfattere
Freya Maria Rosemarie Ziegler Vivien Rosenthal Jose G Vallarino Franziska Genzel Sarah Spettmann Łukasz Seliga Sylwia Keller-Przybyłkowicz Lucas Munnes Anita Sønsteby Sonia Osorio Björn UsadelSammendrag
Blackcurrant (Ribes nigrum L., family Grossulariaceae) is a perennial shrub that is widely cultivated for its edible berries. These are rich in antioxidants, vitamin C and anthocyanins, making them a valuable ingredient in the food and beverage industry. However, prolonged periods of drought during the fruiting season lead to drought stress, which has serious ecological and agricultural implications, inhibiting blackcurrant growth and reducing yields. To facilitate the analysis of underlying molecular processes, we present the first high-quality chromosome-scale and partially haplotype-resolved assembly of the blackcurrant genome (cv. Rosenthals Langtraubige), also the first in the family Grossulariaceae. We used this genomic reference to analyze the transcriptomic response of blackcurrant leaves and roots to drought stress, revealing differentially expressed genes with diverse functions, including those encoding the transcription factors bZIP, bHLH, MYB and WRKY, and tyrosine kinase-like kinases such as PERK and DUF26. Gene expression was correlated with the abundance of primary metabolites, revealing 14 with significant differences between stressed leaves and controls indicating a metabolic response to drought stress. Amino acids such as proline were more abundant under stress conditions, whereas organic acids were depleted. The genomic and transcriptomic data from this study can be used to develop more robust blackcurrant cultivars that thrive under drought stress conditions.
Sammendrag
Raman spectroscopy provides detailed information about the molecular composition of a sample. The classical identification of components in a multi-component sample typically involves comparing the preprocessed spectrum with a known reference stored in a database using various spectral matching or machine-learning techniques or relies on universal models based on a two-step analysis including first, the component identification, and then the decomposition of the mixed signal. However, although large databases and universal models cover a wide range of target materials, they may be not optimized to the variability required in a specific application. In this study, we propose a single-step method using deep learning (DL) modeling to decompose a simulated mixture of real measurements of Raman scattering into relevant individual components regardless of noise, baseline and the number of components involved and quantify their ratios. We hypothesize that training a custom DL model for applications with a fixed set of expected components may yield better results than applying a universal quantification model. To test this hypothesis, we simulated 12,000 Raman spectra by assigning random ratios to each component spectrum within a library containing 13 measured spectra of organic solvent samples. One of the DL methods, a fully connected network (FCN), was designed to work on the raw spectra directly and output the contribution of each component of the library to the input spectrum in form of a component ratio. The developed model was evaluated on 3600 testing spectra, which were simulated similarly to the training dataset. The average component identification accuracy of the FCN was 99.7%, which was significantly higher than that of the universal custom trained DeepRaman model, which was 83.1%. The average mean absolute error for component ratio quantification was 0.000562, over one order of magnitude smaller than that of a well-established non-negative elastic net (NN-EN), which was 0.00677. The predicted non-zero ratio values were further used for component identification. Under the assumption that the components of a mixture are from a fixed library, the proposed method preprocesses and decomposes the raw data in a single step, quantifying every component in a multicomponent mixture, accurately. Notably, the single-step FCN approach has not been implemented in the previously reported DL studies.
Forfattere
Frank T. Ndjomatchoua Ritter Atoundem Guimapi Luca Rossini Byliole S. Djouda Sansao A. PedroSammendrag
Life history traits have been studied under various environmental factors, but the ability to combine them into a simple function to assess pest response to climate is still lacking complete understanding. This study proposed a risk index derived by combining development, mortality, and fertility rates from a stage-structured dynamic mathematical model. The first part presents the theoretical framework behind the risk index. The second part of the study is concerned with the application of the index in two case studies of major economic pest: the brown planthopper (Nilaparvata lugens) and the spotted wing drosophila (Drosophila suzukii), pests of rice crops and soft fruits, respectively. The mathematical calculations provided a single function composed of the main thermal biodemographic rates. This function has a threshold value that determines the possibility of population increase as a function of temperature. The tests carried out on the two pest species showed the capability of the index to describe the range of favourable conditions. With this approach, we were able to identify areas where pests are tolerant to climatic conditions and to project them on a geospatial risk map. The theoretical background developed here provided a tool for understanding the biogeography of Nilaparvata lugens and Drosophila suzukii. It is flexible enough to deal with mathematically simple (N. lugens) and complex (D. Suzukii) case studies of crop insect pests. It produces biologically sound indices that behave like thermal performance curves. These theoretical results also provide a reasonable basis for addressing the challenge of pest management in the context of seasonal weather variations and climate change. This may help to improve monitoring and design management strategies to limit the spread of pests in invaded areas, as some non-invaded areas may be suitable for the species to develop.