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- Laj, P., et al.
(författare)
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Measuring Atmospheric Composition Change
- 2009
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1873-2844 .- 1352-2310. ; 43:33, s. 5351-5414
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Tidskriftsartikel (refereegranskat)abstract
- Scientific findings from the last decades have clearly highlighted the need for a more comprehensive approach to atmospheric change processes. In fact, observation of atmospheric composition variables has been an important activity of atmospheric research that has developed instrumental tools (advanced analytical techniques) and platforms (instrumented passenger aircrafts, ground-based in-situ and remote sensing stations, earth observation satellite instruments) providing essential information on the composition of the atmosphere. The variability of the atmospheric system and the extreme complexity of the atmospheric cycles for short-lived gaseous and aerosol species have led to the development of complex models to interpret observations, test our theoretical understanding of atmospheric chemistry and predict future atmospheric composition. The validation of numerical models requires accurate information concerning the variability of atmospheric composition for targeted species via comparison with observations and measurements. In this paper, we provide an overview of recent advances in instrumentation and methodologies for measuring atmospheric composition changes from space, aircraft and the surface as well as recent improvements in laboratory techniques that permitted scientific advance in the field of atmospheric chemistry. Emphasis is given to the most promising and innovative technologies that will become operational in the near future to improve knowledge of atmospheric composition. Our current observation capacity, however, is not satisfactory to understand and predict future atmospheric composition changes, in relation to predicted climate warming. Based on the limitation of the current European observing system, we address the major gaps in a second part of the paper to explain why further developments in current observation strategies are still needed to strengthen and optimise an observing system not only capable of responding to the requirements of atmospheric services but also to newly open scientific questions.
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| 5. |
- Ameen, Carly, et al.
(författare)
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Specialized sledge dogs accompanied Inuit dispersal across the North American Arctic
- 2019
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Ingår i: Proceedings of the Royal Society of London. Biological Sciences. - : The Royal Society. - 0962-8452 .- 1471-2954. ; 286:1916
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Tidskriftsartikel (refereegranskat)abstract
- Domestic dogs have been central to life in the North American Arctic for millennia. The ancestors of the Inuit were the first to introduce the widespread usage of dog sledge transportation technology to the Americas, but whether the Inuit adopted local Palaeo-Inuit dogs or introduced a new dog population to the region remains unknown. To test these hypotheses, we generated mitochondrial DNA and geometric morphometric data of skull and dental elements from a total of 922 North American Arctic dogs and wolves spanning over 4500 years. Our analyses revealed that dogs from Inuit sites dating from 2000 BP possess morphological and genetic signatures that distinguish them from earlier Palaeo-Inuit dogs, and identified a novel mitochondrial clade in eastern Siberia and Alaska. The genetic legacy of these Inuit dogs survives today in modern Arctic sledge dogs despite phenotypic differences between archaeological and modern Arctic dogs. Together, our data reveal that Inuit dogs derive from a secondary pre-contact migration of dogs distinct from Palaeo-Inuit dogs, and probably aided the Inuit expansion across the North American Arctic beginning around 1000 BP.
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| 6. |
- Madonna, G, et al.
(författare)
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Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:16
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Tidskriftsartikel (refereegranskat)abstract
- The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.
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