dataesr/scientific-paragraphs-categorization huggingface dataset:
a. -not applicable. with the most recent relevant taxonomic literature. For animals we used the Fauna Europaea 10 as baseline and adapted recent changes. ORIGIN was completed and corrected by the database managers. Two rounds of data quality checks involving the data providers were included in the data acquisition procedure. We calculated simple descriptive statistics for every country to determine the number of entries per non-native tree, the number cases where for a tree occurring in a country (based on NNEXT information 2,11 ) we did not have any biotic threats entries in the database and the number of entries per organism group and non-native tree (compare Table  3 ). In online-only Table  1  we provide the number of entries per tree species per country. The pre-analysis helped to indicate missing or biased information.
A short, country specific report on these findings (stating i. e. whether there was a considerable and unexpected imbalance between insect pests and pathogens and for which non-native trees occurring in the country no records had been provided) was sent to the data providers. The report was accompanied with a formal request to (i) provide a personal assessment of the quality and completeness of the data (bias assessment), (ii) to name options to improve the data quality, (iii) to add new entries and (iv) to fill missing information in existing database entries. For the assessment of a potential bias in the number of entries and level of detail provided for different tree species for example due to missing experts or lacking economic value of a tree species resulting limited recording and knowledge on attacks, we offered the following categories: 1 -'The data well reflect the situation of the pest/pathogen impact.
There is no bias due to prioritization of certain tree species and/or lack of experts'; 2 -'The data on pest/pathogen impact have some bias. The bias due to prioritization of certain tree species and/or lack of experts is, however, minor'; 3 -'The data on pest/pathogen impact have major bias. Due to prioritization of certain tree species and/ or lack of experts the data does not reflect the complete situation in the country and thus should not be used in a cross-country analysis'; This call for data quality check and completion was successful and led to a large number of new and completed entries. In a second round of quality check, the data providers were given a final chance to update the database and give a final assessment of the data quality/bias (Table  4 ).
NNT -Number of non-native tree species out of the 23 investigated tree species known to occur in a country; NA -Number of cases where non-native trees are known to occur in a country, but are without biotic threats entry in our database; Number of database entries for pathogens, insects and other organisms groups per country and total number of entries per country. Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia, Thuja plicata) in 27 European countries. The table contains 2304 entries. The database is available at Figshare 8 in a comma separated values (. csv) format. The data table consists of 28 columns: ID, NNT (non-native tree species), COUNTRY (for which the data are provided), ORGANISM_GROUP, ORDER, FAMILY, GENUS, NAME (scientific name), AUTHOR (author of the scientific name), ORIGIN (continent of origin of the biotic threat), 1ST_OBSERVATION (year of 1 st observation), YEAR_ADDED (1 indicates where the year in 1ST_OBSERVATION was filled based on the reference year of publication), eight columns for the different types of damage (BUDS_SHOOTS, FOLIAGE, BARK_CAMBIUM_PHLOEM, WOOD_XYLEM, ROOT, SEEDLINGS_SAPLINGS), LEVEL (level of impact on an individual tree), MAX_AREA (maximum continuous area impacted), AGE_CLASS (tree cohort where impact occurs), CONFIDENCE (confidence level for the provided impact information), SPECIALISATION, RELATIVE_ORIGIN, REF (references), DATA_PROVIDER (name and email address).
acknowledgements This study is based upon work from  COST Action FP1403 (NNEXT)  "Non-native tree species for European forestsexperiences, risks and opportunities" supported by  COST (European Cooperation in Science and Technology)  (www. cost. eu). M. Z. acknowledges funding by the  Ministry of education, science and technological development of the Republic of Serbia , project number:  451-03-68/2020-14/200197. We express our gratitude to additional contributors of the study,  Thomas Cech ,  Milic Curovic ,  Ecaterina Fodor ,  Bo Karlsson ,  Thomas Kirisits ,  Johan Kroon ,  Leopold Poljakovic-Pajnik ,  Vytautas Tamutis ,  Hermann Voglmayr  and  Nikolay Zafirov. We also thank  Siniša Andrašev ,  Oscar Godoy ,  Jean-Marc Henin ,  Krasimira Petkova  and  Emil Popov  for helping with organising the data collection.
We present MISTRA-v9. 0, a one dimensional (1D) atmospheric chemistry model. The model includes a detailed particle description with regards to the microphysics, gas-particle interactions, and liquid phase chemistry within particles. Version 9. 0 is the first release of MISTRA as an open-source community model. A major review of the code has been performed along with this public version release to improve the user-friendliness and platform-independence of the model. In the past 20 years, MISTRA has been used in over 25 studies to address a wide range of scientific questions. The purpose of this public release is to maximise the benefit of MISTRA to the community by making the model freely available and easier to use and develop. This paper presents a thorough description of the model characteristics and components.
In turn, the results obtained with such models can be used to develop parameterisations for use in 3D models. Whatever the model, a crucial step is the validation based on field measurements. Balloon or flight surveys provided valuable data for this purpose, but the number of investigated parameters is necessarily limited, with many uncertainties and unknown values. Another useful tool for atmospheric chemistry and physic understanding is the atmospheric simulation chambers (see for instance https://www. eurochamp. org, last accessed 01-July-2021). These platforms enable the simultaneous measurement of a large number of chemical species and associated physical characteristics, in a constrained volume. The resulting datasets are highly valuable to validate models. In turn, numerical models are complementary tools to help understand and interpret measured results.
The radiation code used in MISTRA, called PIFM1, was updated by  Loughlin et al. (1997)  and the new radiation code, PIFM2, was evaluated. The collision-coalescence process was implemented in MISTRA by  Bott (2000 Bott ( , 2001)). Bott (1999a)  adapted the chemistry module from  Bott and Carmichael (1993)  in MISTRA, with special emphasis on sulfur chemistry, and studied the retroaction of cloud processing over the microphysics in  Bott (1999b). Meanwhile, von Glasow (2000)  developed another chemistry module for MISTRA, with special emphasis on halogen chemistry, and presented the results for cloud free  (von Glasow et al. , 2002a)  and cloudy  MBLs (von Glasow et al. , 2002b). Our paper develops the branch of MISTRA based on von  Glasow (2000). From the early 2000s to the mid 2010s, MISTRA was regularly improved with respect to the chemistry and the gas-particle interactions, and was used in several studies, many of them with a focus on halogen chemistry.
A major improvement was the introduction of a module for aerosol nucleation which improved the iodine chemistry  (Pechtl et al. , 2006 (Pechtl et al. , , 2007)). The gas-phase chemical mechanism was updated by  Sommariva and von Glasow (2012). Over the years, numerous modelling studies were performed using MISTRA  (von Glasow and Crutzen, 2004; Pechtl and von Glasow, 2007; Lawler et al. , 2009; Jones et al. , 2010; Joyce et al. , 2014)  including alternative model configurations where the chemistry was computed in a zero-dimension (0D) atmospheric box mode  (Buys et al. , 2013) , and a 0D chamber mode  (Buxmann et al. , 2015). MISTRA was also adapted to model specific environments such as volcanic plumes  (Aiuppa et al. , 2007; Bobrowski et al. , 2007 Bobrowski et al. , , 2015) )  polar conditions  (Piot and von Glasow, 2008, 2009; Buys et al.
, 2013) , and was coupled with an ocean model for studies over the Dead Sea  (Smoydzin and von Glasow, 2007, 2009). A module for firn chemistry was developed and coupled to MISTRA to specifically address the influence of chemical reactions occurring in the snowpack on the oxidative capacity of the atmosphere in snow covered regions  (Thomas et al. , 2011 (Thomas et al. , , 2012)). In this study, we present a selection of a few specific model settings reproducing previous work, to compare the original results with those obtained with MISTRA-v9. 0.
Recent developments and public release The previous (non-public) version of MISTRA (v7. 4. 1) included the update of the gas-phase chemical mechanism by  Sommariva and von Glasow (2012). A version 8 featuring an alternative chemical bin definition was partly developed but not completed, thus explaining the current version number. More information about past versions of MISTRA can be found in the preface of the manual (https://github. com/Mistra-UEA/Mistra/blob/master/doc/manual_v9. 0. pdf). Since 2015, significant efforts have been devoted to release MISTRA as an open-source model, including major technical improvements. The original code, written in Fortran77, has been updated to Fortran90 to ease future maintenance and developments. To improve robustness and portability of the code, intensive controls throughout the code have been performed to track issues, fix bugs, and conform to strict coding rules  (Metcalf et al.
, 2004). This was achieved with the help of the Fortran analyser Forcheck (v14. 6, no longer distributed), as well as standard code check options of compilers. Computing efficiency has also been improved by factorising parts of code, and re-indexing arrays to respect column-major order in Fortran (i. e. innermost doloops should be leftmost indexes). The chemical "Kinetic PreProcessor" (KPP) has been updated to the latest version 2. 2. 3 (https://people. cs. vt. edu/~asandu/Software/Kpp/ last accessed 23 June 2021). Overall, several technical developments have been implemented to make the model as user-friendly as possible, and easier to adopt. The model code of MISTRA-v9. 0 now has improved readability, documentation, and is available under licence EUPL-v1. 1 on https://github. com/Mistra-UEA.
Overview of the model components MISTRA is a one-dimensional model of the MBL. Figure  1  shows schematically the most important processes that are included in the model for a cloudy MBL. The meteorological and microphysical part consists of the boundary layer model MISTRA described in detail by  Bott et al. (1996)  and  Bott (1997). The most important processes are turbulent mixing, condensation, evaporation and radiative heating. Apart from dynamics and thermodynamics, MISTRA includes a detailed microphysical module that calculates particle growth explicitly and includes feedbacks between radiation and particles. The radiative-transfer parameterisation is a standard two-stream code using 6 spectral bands for visible and 12 bands for infrared radiation  (Loughlin et al. , 1997). A chemistry module computes the atmospheric chemistry in the gas phase and in the particles.
Get the code The model is provided on GitHub on the following repository: https://github. com/Mistra-UEA/Mistra. It is released under the European Union Public Licence (EUPL) v1. 1, which permits free commercial and private use and unrestricted distribution, but requires that future developments of MISTRA are shared under the same licence. The version of KPP adapted for MISTRA is provided along with the distribution, and is released under its own licence.
System requirements and installation A Fortran compiler is mandatory to compile the model code. During the recent development stages, MISTRA has been regularly 315 compiled using either GNU Fortran (gfortran) or Intel Fortran (ifort). New users are advised to choose one of those compilers. The implementation of KPP output files into MISTRA is done with bash and csh scripts, thus any change in the chemical mechanism will require these shells. Plotting scripts provided as example are written for ferret and NCL (NCL), but neither are necessary to run the model. Only KPP needs to be installed on the user system and the instructions to do so are in the readme file of the KPP distribution package. Preprocessed files using the current chemical mechanism are provided in the distribution, so the installation of KPP can be skipped until the user needs to modify the chemical mechanism.
Consistency with previous versions In this section, we present a series of example runs that have been performed to evaluate the model. All the examples provided here reproduce the settings of previous studies carried out with previous versions of MISTRA. For that purpose, several namelists have been introduced to hold all relevant parameters in order to reproduce the same simulation scenarios as in the original publications. These namelists, as well as the scripts used to produce the plots presented here, are available in the MISTRA repository.
The radiative code used by  Bott et al. (1996)  has been updated from PIFM1 to PIFM2 by  Loughlin et al. (1997) , and the simulation settings were the same in both papers. For this reason, we compare figures from the study of  Loughlin et al. (1997)  with the current output of MISTRA. Both Fig. 3  and Fig. 4  show very similar model output between the 1996 version of MISTRA and the current version. The runs are similar, qualitatively and quantitatively (the maximum LWC is 6 % higher, the maximum TKE is 2. 5 % higher in MISTRA-v9. 0 than in the 1996 version), without changing the findings and conclusions of the original study. Bott et al. (1996)  also shown the distribution of particles in the 2D grid, and we used the same graph format in Fig. 5. Qualitatively, the two simulations are similar.
however, this happens only for a minority of the particles ('plus' signs in Fig. 5  denotes bins where particle concentration is less than half the maximum particle concentration, for each dry radius class). As stated previously, the 1996 version of MISTRA used by  Bott et al. (1996)  included the first version of the radiative code PIFM1, now updated to PIFM2. The differences between both radiation schemes are likely the reason for this slightly different particle distributions observed for small dry particle radius. This figure also highlights the microphysics properties and dynamics of particle within the cloud, with the activation of particles occurring when the supersaturation (not shown) is high enough, which is the case in the upper part of the cloud. Conversely, in the middle and bottom parts of the cloud most of the particles are found below the critical radius, even if some particles grow to above their respective critical radius, since the supersaturation is not high enough.
Chemistry in 1D simulations A namelist reproducing the settings of the study by  Joyce et al. (2014)  is provided as namelist. Joyce14bc. In this study, MISTRA was used to simulate an urban pollution plume from Fairbanks, Alaska. The model was thus used in an alternative configuration, with surface covered by snow (with the relevant physical properties). An emission scenario of NO x was defined, and the evolution of gas and aqueous phase species was evaluated. In such configuration, the meteorological parameters have a strong influence over the stability of the atmosphere, thus in turn over the vertical exchange of chemical species. In Fig. 7 , key meteorological variables are presented for both the original study and the new runs obtained with MISTRA-v9. 0. As expected, there is excellent agreement between both versions, which shows that the recent code developments did not alter the model with regards to the plotted variables.
Code availability. The code of the MISTRA-v9. 0 model, the code of KPP-v2. 2. 3 tuned for MISTRA (referred to as v2. 2. 4), the additional 420 example namelists and param files, and all NCL scripts developed to produce the Figures in this article are available on https://github. com/Mistra-UEA/Mistra. The archives of code releases are also available on Zenodo: https://doi. org/10. 5281/zenodo. 5110025 and http: //doi. org/10. 5281/zenodo. 5109913 for  respectively.
Silent Speech Interfaces Two approaches have been proposed to decode speech from silent, speech-related biosignals  [14] : silent speech-to-text and direct speech synthesis. In the first approach, automatic speech recognition (ASR) algorithms trained on silent speech data are used to decode speech from the input data. Text-to-speech (TTS) software can then be used to synthesize speech from the decoded text if required. Several works have shown promising results on silent speech-to-text for a variety of methods. For instance, in  [24] , a EMG-based silent speech recognizer was proposed. The system was evaluated on a corpus of phoneticallyrich sentences recorded by n = 8 healthy persons, achieving a word error rate (WER) of 16. 8%. In  [25] , EMG-based silent speech recognition was evaluated as assistive communication device for n = 8 laryngectomized patients, achieving an average WER of 10.
The third scenario is particularly relevant to SP2 given the difficulty of recording speech for paralyzed patients. • Finally, a practical SSI must be able to generate audio from silent speech data in close to real-time (latency <100 ms), so its user can receive synchronous acoustic feedback while speaking and can adapt her/his articulation style to improve the output. We will accomplish these goals by taking advantage of the background work on speech synthesis and SSIs of both groups and by recording large datasets, which in turn will foster the use of cutting edge deep learning techniques to improve the performance beyond the state-of-the-art. The real-time system will play a central role during the evaluation phase to assess the performance of the SSI in terms of speech intelligibility, quality, and naturalness.
This system will also pave the way for studies of user-in-the-loop strategies, where both the user and the system co-adapt themselves to optimize the output. Summarizing, the specific objectives of the coordinated project are: 1. To explore the paths and advances in the application of state-of-the-art deep generative neural network architectures to improve the present quality and intelligibility of current SSIs using EMG and ECoG. 2. To develop corpus, databases, protocols and best practices for research on SSI in Spanish language. 3. To establish a new research line and, consequently, a research infrastructure for SSI in Spain. 4. To strengthen the links between two of the most consolidated research groups on speech technologies at the national level: Aholab at UPV/EHU and SiGMAT at UGR. WP0 and WP5 are transverse work packages dedicated to the project management and dissemination/communication activities, respectively.
WP1 is dedicated to the experimental part of the project (i. e. equipment setup, stimuli definition, participant recruitment and data acquisition). Data recorded in WP1 will be used by WP 2 and 3, where the foundational algorithms for direct speech synthesis from silent speech data will be developed. In particular, WP2 deals with training DNN architectures in a supervised fashion using parallel data. WP3, in contrast, is dedicated to training with non-parallel data. WP4 is dedicated to the evaluation of the algorithms and the user tests, which will provide continuous input, assessment, and improvement requests to the technical work packages. Our development model is iterative, with frequent interactions between work packages and partners. Most work packages run for the entire length of the project; yet research is structured by the subordinate tasks within the work packages.
Acknowledgments This work has been funded by the  Agencia Estatal de Investigación ref. PID2019-108040RB-C21/AEI/10. 13039/501100011033  and  PID2019-108040RA-C22/AEI/10. 13039/501100011033. Jose A. Gonzalez-Lopez  holds a  Juan de la Cierva-Incorporation Fellowship  from the  Spanish Ministry of Science, Innovation and Universities  ( IJCI-2017-32926 ).
Cohorts: The exploratory analysis of age and genetic changes was undertaken in the TCGA dataset for metastatic patients age >59 vs <=55  (Akbani et al. 2015; Weinstein et al. 2014)  and (https://www. cancer. gov/about-nci/organization/ccg/research/structural-genomics/tcga). Primary melanomas >59yo were from A cohort (n=109 patients) and B cohort (n=30 patients, Table  1 , Supplementary Table  S1 ). The effect of mutations and tumor mutation burden in immunotherapy-treated patients was investigated in the MSK-IMPACT study (n=181 patients >59yo  (Samstein et al. ) ). The experimental age cut-offs age >59 or <=55 were decided a priori to obtain more clear-cut young and old age categories. To ensure this did not introduce bias we then analysed the data using a simple <59 vs >60 cut-off and a wide interval of <50 vs >70.
To validate the findings we studied clinical and sequencing data collected in cohort A: La Timone University Hospital, (Aix Marseillle, France) and cohort B: Instituto Valenciano de Oncología (Valencia, Spain)  (Gaudy-Marqueste et al. 2014). Relevant institutional review boards and ethics committees at both institutions in both countries approved molecular research and all human participants gave written informed consent. The primary cohorts are prospectively collected and unselected (supplemental table S1), subjected to targeted deep sequencing at a depth of x100 of 40 melanoma genes. The sequencing panel includes genes mutated in >10% of TCGA samples or mutated in less than <10% of samples but known cancer drivers. The 4-gene signature is significantly less frequent in the primary cohorts (p= 0.
05) (supplemental table S2). To explore the effects of variant count in the model we ran the above sequence initially with variants as a log continuous variable, and then as a binary (high/low) variable with a series of cut-off values  (25, 50, 100, 110, 150, 150, 500). Of these only 100 and 110 showed a significant impact in the final model and both were less significant than age in those cases. We analysed the mutation data using the oncodriveCLUST algorithm  (Tamborero et al. 2013)  as implemented in the maftools package in R to discover key driver mutations. OncodriveCLUST considers the background mutation rate, gene length and the clustering characteristics of mutations identified in order to derive a confidence value for labelling a gene as a driver as opposed to a passenger.
Gene expression: For the TCGA data, differential gene expression analysis was performed using the DESeq2 (version. 28. 1)  (Love et al. 2014 ) package in R (version 4. 0. 0, RStudio v1. 2. 5003, RStudio Inc). RNA-seq count data for the TCGA SKCM cohort was acquired using the TCGABiolinks package (version 2. 16. 0  (Colaprico et al. 2015) ). The count dataset was filtered to remove genes with less than 1000 counts across all samples. Differential expression analysis was run between long and short-term survivors in the aged (>59 years) metastatic cohort. Long-term survivors were defined as patients with overall survival time 2000 days or greater, compared to patients who died <2000 days. For pathway enrichment analysis genes that were significantly differentially expressed (FDR p-value < 0. 1) were compared against the Reactome Database (Jassal et al.