Separating water-potential induced swelling and shrinking from measured radial stem variations reveals a cambial growth and osmotic concentration signal

The quantification of cambial growth over short time periods has been hampered by problems to discern between growth and the swelling and shrinking of a tree stem. This paper presents a model, which separates cambial growth and reversible water-potential induced diurnal changes from simultaneously measured whole stem and xylem radial variations, from field-measured Scots pine trees in Finland. The modelled growth, which includes osmotic concentration changes, was compared with (direct) dendrometer measurements and microcore samples. In addition, the relationship of modelled growth and dendrometer measurements to environmental factors was analysed. The results showed that the water-potential induced changes of tree radius were successfully separated from stem growth. Daily growth predicted by the model exhibited a high correlation with the modelled daily changes of osmotic concentration in phloem, and a temperature dependency in early summer. Late-summer growth saw higher dependency on water availability and temperature. Evaluation of the model against dendrometer measurements showed that the latter masked a true environmental signal in stem growth due to water-potential induced changes. The model provides better understanding of radial growth physiology and offers potential to examine growth dynamics and changes due to osmotic concentration, and how the environment affects growth.Peer reviewe

The Center of Excellence in Atmospheric Science (2002–2019) — from molecular and biological processes to the global climate

The study of atmospheric processes related to climate requires a multidisciplinary approach, encompassing physics, chemistry, meteorology, forest science, and environmental science. The Academy of Finland Centre of Excellence in atmospheric sciences (CoE ATM) responded to that need for 18 years and produced extensive research and eloquent results, which are summarized in this review. The work in the CoE ATM enhanced our understanding in biogeochemical cycles, ecosystem processes, dynamics of aerosols, ions and neutral clusters in the lower atmosphere, and cloud formation and their interactions and feedbacks. The CoE ATM combined continuous and comprehensive long-term in-situ observations in various environments, ecosystems and platforms, ground- and satellitebased remote sensing, targeted laboratory and field experiments, and advanced multi-scale modeling. This has enabled improved conceptual understanding and quantifications across relevant spatial and temporal scales. Overall, the CoE ATM served as a platform for the multidisciplinary research community to explore the interactions between the biosphere and atmosphere under a common and adaptive framework

Does the magic of BERT apply to medical code assignment? A quantitative study

| openaire: EC/H2020/101016775/EU//INTERVENEUnsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in downstream tasks. In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries. However, it is not clear if pretrained models are useful for medical code prediction without further architecture engineering. This paper conducts a comprehensive quantitative analysis of various contextualized language models' performances, pretrained in different domains, for medical code assignment from clinical notes. We propose a hierarchical fine-tuning architecture to capture interactions between distant words and adopt label-wise attention to exploit label information. Contrary to current trends, we demonstrate that a carefully trained classical CNN outperforms attention-based models on a MIMIC-III subset with frequent codes. Our empirical findings suggest directions for building robust medical code assignment models.Peer reviewe

Solution of linear differential equations in chemical kinetics through flow graph theory approach

A flow graph theory is a method for finding the analytical solution of linear differential equations which arise in chemical kinetics through Cramer's method of determinants. This article presents the applicability of flow graph theory for deriving the analytical solution of kinetic equations which arise in modeling of complex reaction system such as hydrocracking of heavy oils. A discrete lumped model for hydrocracking of heavy oils was developed and analytical solution for the governing model equations was derived using Laplace transforms earlier. In this work, a new method involving flow graph theory was used instead of Laplace transforms. The kinetic equations which describe the performance of a hydrocracker are governed by linear differential equations and a general analytical solution was successfully derived using flow graph theory. The analytical solution obtained through flow graph theory is similar with the reported solution using Laplace transforms for the kinetic equations of hydrocracking of heavy oils. Furthermore, the relative errors between the experimental data and model calculations using analytical solution of the three lump hydrocracker model are reasonable except for few data points