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  • 1
    In: The Lancet Digital Health, Elsevier BV, Vol. 5, No. 4 ( 2023-04), p. e185-e193
    Type of Medium: Online Resource
    ISSN: 2589-7500
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2972368-1
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  • 2
    In: Blood Advances, American Society of Hematology, Vol. 7, No. 14 ( 2023-07-25), p. 3450-3457
    Abstract: Previous studies have indicated a possible cancer-protective effect of statins in solid cancers; however, this has never been investigated in myeloproliferative neoplasms (MPNs). We aimed to investigate the association between statin use and the risk of MPNs in a nested nationwide case-control study, using Danish national population registries. Information on statin use was obtained from the Danish National Prescription Registry, and patients diagnosed with MPNs between 2010 and 2018 were identified from the Danish National Chronic Myeloid Neoplasia Registry. The association between statin use and MPNs was estimated using age- and sex-adjusted odds ratios (ORs) and fully adjusted ORs (aORs), adjusting for prespecified confounders. The study population included 3816 cases with MPNs and 19 080 population controls (5:1) matched for age and sex using incidence density sampling. Overall, 34.9% of the cases and 33.5% of the controls ever used statins, resulting in an OR for MPN of 1.07 (95% confidence interval [CI], 0.99-1.16) and an aOR of 0.87 (95% CI, 0.80-0.96), respectively. 17.2% were categorized as long-term users (≥5 years) among the cases compared with 19.0% among controls, yielding an OR for MPN of 0.90 (95% CI, 0.81-1.00) and an aOR of 0.72 (95% CI, 0.64-0.81). Analysis of the effect of the cumulative duration of statin use revealed a dose-dependent response, and the association was consistent for sex, age, and MPN subgroups and across different statin types. Statin users were associated with significantly lower odds of being diagnosed with an MPN, indicating a possible cancer-preventive effect of statins. The retrospective design of this study precludes causal inferences.
    Type of Medium: Online Resource
    ISSN: 2473-9529 , 2473-9537
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2023
    detail.hit.zdb_id: 2876449-3
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Precision Agriculture Vol. 23, No. 1 ( 2022-02), p. 52-82
    In: Precision Agriculture, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2022-02), p. 52-82
    Abstract: Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.
    Type of Medium: Online Resource
    ISSN: 1385-2256 , 1573-1618
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2016333-2
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2020
    In:  Precision Agriculture Vol. 21, No. 4 ( 2020-08), p. 802-830
    In: Precision Agriculture, Springer Science and Business Media LLC, Vol. 21, No. 4 ( 2020-08), p. 802-830
    Abstract: Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.
    Type of Medium: Online Resource
    ISSN: 1385-2256 , 1573-1618
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2016333-2
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