dataesr/scientific-paragraphs-categorization huggingface dataset:
The Venus atmosphere is of significant interest yet only rudimentary solid data has been gathered about its composition and chemistry. These measurements are scattered through time and place and are limited by parameters such as resolution and error margins as well as reinterpretations. This paper presents an extensive compilation of published in situ data for the atmospheric composition of Venus. It also includes remotely gathered measurements and some extrapolated and modeled data for the lower atmosphere. The composition tables are divided in four categories: noble gases, reactive gases, noble and non-noble isotopes. These tables were first presented in 2016 within the scientific heritage appendix of the Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission proposal.
Introduction Venus is our nearest planetary neighbor, excluding the Moon, yet we only have rudimentary knowledge of its deep atmosphere and geology. This is not for the lack of trying. Several spacecraft have been sent to Venus, either as orbiters, atmospheric probes, or landers, with differing levels of success. There have been opportunistic observations of Venus using instruments on spacecraft 'flybys' that were headed elsewhere as well as numerous Venus observations conducted from Earth-based telescopes or made with the naked-eye. The motivation for this paper was to create an easily accessible manuscript that contains the sum total of all previous measured and modeled Venus composition data that could be used as a straightforward reference. Specifically, the renewed interest in sending spacecraft to Venus, as demonstrated by the recent NASA Discovery and New Frontiers Mission opportunities, justified such a compilation.
As a result, it is not uncommon for some extrapolated/modeled data to be quoted as 'fact', particularly for the deep atmosphere (below the clouds to the surface). There exist a large variety of Venus data compilations and presentations but an inescapable fact is that the in situ compositional data of the deep atmosphere, originates from the Venera, VeGa and Pioneer Venus missions. The last of these missions, VeGa 2, completed its task in June 1985. It should be noted that none of the entry/descending probes of the Venera or Pioneer missions were able to collect composition data until the probes became sub-sonic which typically occurred below 64km in almost all of the cases. More recent data was obtained through remote Earth-based, fly-by or orbiter observations. Tables  1a  and 1b  lists the spacecraft and Earth/space-based facilities that have gathered data about Venus and whose data is reflected in this compilation.
Overview of Previous Venus Atmospheric Data Compilation Studies The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission proposal included a science heritage table within its appendix on the atmospheric composition of Venus. While conducting background research for this mission, it was evident that there needed to be one comprehensive, clear-cut table of the existing measured Venus atmospheric data. Limaye et al. (2018)  present a comprehensive review of Venus thermal structure and radiative balance results but not the composition. Some previous compilations, although unquestionably excellent such as the Venus International Reference Atmosphere, aka VIRA,  (Kliore et al. 1985) , are now outdated while others were unclear or did not explain how values were derived.
Imagine the frustration when significant literature digging was required to determine whether a value was an extrapolation, or has been accepted as 'fact' because the original source receded far into citation history, or had been derived solely from models or from well-studied estimates. This additional research was, unsurprisingly, neither straightforward nor swift due to difficulty in obtaining original sources, if cited at all. It was also easy to fall into a false sense of knowledge regarding the breadth of data because, at first, the data appear comprehensive but upon closer inspection, the data is limited in scope either by vertical resolution, massive error bars, or having no measurements whatsoever, i. e. , oxygen at depth. This paper includes the aforementioned Venus data compilation from the DAVINCI proposal and recently obtained remote data.
Acknowledgements: Many thanks are extended to those who preceded this work. Apologies are given to Venus modelers whose work is not included. This compilation would not have come to pass without the encouragement and support of the  DAVINCI Principle Investigator Dr. Lori Glaze , the  DAVINCI Team , and the  Astrochemistry Lab Branch/691  at  Goddard Space Flight Center. The reviewers were instrumental in clarifying the discussion and provided valuable insight. All the data presented within this manuscript are accessible in the published literature through the cited references.
Visual responses of hippocampal neurons in an image memorization taskThis simulation was inspired by the study of Jutras and Buffalo (2010a) in which a large set of novel visual stimuli was presented to monkeys on a computer screen. Neuronal activity in the hippocampal formation in response to the visual stimuli was recorded. One of the results of this study suggested that hippocampal neurons encode stimulus novelty in their firing rates. To simulate this result, we presented to the model 100 novel stimuli randomly chosen from the dataset retrieved from http://www. vision. caltech. edu/Image_Datasets/Caltech101). The stimuli (resized to 160 × 100 pixels) were shown to the model successively in one continuous session (500ms stimulus presentation time + 1000ms inter-trial interval with no stimuli) and the activities of the hippocampal neurons during learning were recorded.
At each of these locations, 9 successive views were taken. Reorientation performance was assessed in two ways: (i) only the first view at each location was used for reorientation; (ii) successive views accumulated over 60 successive positions were used for reorientation. Simulation 6: Memory-based visual searchIn this simulation we used a dataset of visual images used in the study by Fiehler et al. (2014). This dataset consists of 18 image sets corresponding to 18 different arrangements of the same 6 objects (mug, plate, egg, jam, butter, espresso cooker). Each set includes a control image (all objects on the table in their initial positions) and images in which one of the objects is missing (target object) and one or more other objects displaced to the left or to the right. In the simulation we used only a subset of all images in a set that included either 1, 3 or 5 of the objects mentioned above displaced either to the left or to the right (referred to as "local" condition in Fiehler et al.
Simulation 4 : 4 Visual responses of hippocampal neurons in an image memorization taskThis simulation was inspired by the study of Jutras and Buffalo (2010a) in which a large set of novel visual stimuli was presented to monkeys on a computer screen. Neuronal activity in the hippocampal formation in response to the visual stimuli was recorded. One of the results of this study suggested that hippocampal neurons encode stimulus novelty in their firing rates. To simulate this result, we presented to the model 100 novel stimuli randomly chosen from the dataset retrieved from http://www. vision. caltech. edu/Image_Datasets/Caltech101). The stimuli (resized to 160 × 100 pixels) were shown to the model successively in one continuous session (500ms stimulus presentation time + 1000ms inter-trial interval with no stimuli) and the activities of the hippocampal neurons during learning were recorded.
Introduction The terahertz spectral region (in particular the 10-650 cm -1 interval) offers great potential for astronomy and atmospheric science. The region contains rotational transitions of many minor and major constituents which are significant for atmospheric chemistry and planetary climate. This frequency range covers the major part of the pure rotational band of the water molecule. Although being a minor constituent (≈0. 3% of the atmospheric mass), water vapor is the dominant contributor to the atmospheric absorption in this range. In general, the present accuracy of water line parameters (center frequency, intensity and broadening coefficients) presented in spectroscopic database  [Gordon2016]  fulfills most of the requirements of modern remote sensing experiments. In addition to rotational and rovibrational absorption lines, water vapor exhibits a continuum absorption which has been measured in several infrared regions and in the microwave, but which has been poorly studied in the far IR.
The experimental transmittance, ) ( / ) ( 0 ν ν I I , can be approximated by app l f e ⊗ - ) (ν α where ⍺(v) is the total absorption coefficient and f app is the apparatus function of the considered recording. ⍺(v) is the sum of the contributions of the monomer resonance lines, α res , and of the continuum, α con : ) ( ) ( ) ( ν α ν α ν α con res + = (1) Considering the smooth frequency dependence of the continuum, the transmittance can be expressed as app l l f e e res con ⊗ × - - ) ( ) ( ν α ν α. The apparatus function includes the effect of the finite spectral resolution and finite field of view which were modeled by a sinc and boxcar function, respectively. The resonance absorption coefficient was calculated as the sum of lines with parameters taken from the HITRAN database  [Gordon2016] , except for a number of lines, for which more accurate experimental data on collisional broadening and shifting parameters were preferred [Golubiatnikov2005, Golubiatnikov2006, Golubiatnikov2008, Koshelev2007, Koshelev2011a, Tretyakov2013]: ) ( ) ( ν ν α i i i res Φ S ∑ = , ( 2 ) where i is the line index and i S and ) (ν i Φ the corresponding line intensity and normalized line profile, respectively.
At the chosen spectral points, the monomer contribution (up to 50% of the total measured absorption) was simulated on the basis of the HITRAN database and subtracted. As illustrated in Fig. 7  for a few of the chosen spectral points, a very good quadratic pressure dependence of the resulting absorption continuum is achieved. Self-continuum crosssections and their statistical uncertainty were derived from a linear fit of the continuum absorption coefficient versus pressure squared (Eq. 6). The derived values for H 2 16 O (118 points) and H 2 18 O (127 points) are provided as Supplementary Material and plotted in Fig. 8. This data treatment was applied to the spectra recorded at resolution of 0. 02 cm -1 for which about six pressure conditions were at disposal. The continuum cross-sections were also derived from the spectrum recorded at the highest resolution at a single pressure of 6 mbar and 4 mbar for H 2 16 O and H 2 18 O, respectively.
Two mechanisms may contribute: (i) the uncertainty of line shape parameters taken from the HITRAN database (mostly the intensity and the self-broadening parameter) and (ii) inadequate resonance line shape modeling of the intermediate wings (at about 5-10 cm -1 from the line center). In the following, we analyze the potential impact of both mechanisms. In our region, the HITRAN database provides resonance line intensities with uncertainty ranging from 1 to 20%. However, it is believed that these error bars are conservative estimates and that the real uncertainty is about 1%  [Conway2018, Birk2017] , at least for the most intense lines. Such error bars have a marginal impact on the resonance absorption simulation and thus can hardly explain the observed non-smooth frequency dependence. On the contrary, the HITRAN uncertainty of the self-broadening parameter can be large (20%), even for some of the most intense lines.
Possible impact of this uncertainty on the retrieved continuum was estimated from the following procedure: (i) self-broadening parameter of all water lines were independently and randomly varied within the HITRAN uncertainty range and 80 sets of broadenings were generated for our line list; (ii) water vapor absorption was simulated as the sum of the continuum and the resonance absorption, the first one was modeled as a smooth curve, that roughly reproduce our experimental data (blue curve in Fig. 10 ), and the second one was modeled as a line by line sum using their parameters from HITRAN database except for self-broadening parameters for which values as described in (i) were used; (iii) the continuum spectra were determined by subtraction of the resonance absorption modeled using unperturbed HITRAN values from water vapor absorption data simulated at previous step (ii); (iv) maximum and minimum values of the continuum obtained from the 80 statistically spread data at each selected absorption micro-window frequency were found.
Acknowledgements This work became possible due to the Project No 20180347 supported by  SOLEIL Synchrotron Team. TAO, AOZ  and  MYT acknowledge Russian Foundation for Basic Research. The sub-THz range data treatment and the continuum retrieval was supported by project  № 18-05-00698 ; the FIR data analysis was performed within the project №  18-02-00705 ; water dimer contribution in sub-THz range was analyzed within  RAS program  №  0035-2018-00002. PR and OP aknowledge partial support from  CNRS-RFBR  project  18-55_16006. The authors are grateful to  C. Leforestier  for numerical data related to ab initio calculations of water dimer absorption.
Patients All patients admitted between 2010 and 2015 with a diagnosis of AP were retrospectively identified from the local clinical database. Diagnostic of AP was based on the presence of at least two of the three following items: (i), a serum amylase three times the limit of normal, (ii), radiological evidence of AP, and (iii), characteristic pancreatic upper abdominal pain. A total of 123 patients with AP who underwent CECT examination within 48-72 hours following the onset of symptoms were initially included. After exclusion of 11 patients, the study population consisted of 112 patients. There were 68 mean and 44 women, with a mean age of 56. 3 ± 21. 6 (SD) years (range: 19-98 years). Figure  1  is a flow chart of patients who were considered for this study. Baseline demographics and biological data including age, body mass index (BMI) and lipase serum level were collected for all patients.
Image analysis Visceral fat (VF) measurements were assessed using a semi-automatic process. An open source image analysis software (Image J, National Institute of Health), which is a Javabased image processing program was used to calculate VF from CECT images by segmentation of abdominal tissues based on Hounsfield unit (HU) value. An attenuation range of -190 to -30 HU was used  [12]. Measurements were performed on CECT image obtained in the axial plane passing by the fourth or fifth lumbar vertebrae (L4/L5) and at the level of the umbilicus (Figure  2 ). The regions of interest (ROIs) automatically drawn by the segmentation software were corrected manually when needed. The software then calculated the cross-sectional area of the VF. All CECT examinations were retrieved from the local picture archiving and communication system and studied under a DICOM format.
Statistical analysis SAS release 9. 4 analysis software (SAS Institute) was used for statistical analysis. Quantitative data were expressed as mean ± standard deviation (SD) or median. Univariate comparisons according to AP severity as defined above were made using the Mann-Whitney test. Multivariate logistic regression analysis was performed to identify independent variables associated with AP severity. Due to a skewed distribution, VF areas were log-transformed before fitting the logistic model. Correlation between variables were searched for using Spearman rank correlation coefficient. A two-sided P value < 0. 05 was regarded as significant.
Solution-based approaches in monitoring, assessment and management of risks of complex chemical mixtures and multiple stressors While current evaluations of chemical pollution in European surface waters focus on problem description and water quality classification, the projects SOLUTIONS, MARS and GLOBAQUA put emphasis on early exploration of prevention and abatement options considering the remedial space within the Drivers-Pressure-State-Impacts-Response (DPSIR) causal approach  [32]. To facilitate solution-focused risk assessment  [37] , a conceptual framework has been developed  [16]. The early consideration of possible responses is supported by a database on technical abatement options  [44]  and a systematic evaluation of non-technical abatement options. SOLUTIONS-focused assessment of multiple stressors is supported by the SOLUTIONS' Tools and Services for River Basin Toxicants Assessment and Management accessible through the web-based guidance tool RiBa-Tox (https ://solut ions.
marvi n. vito. be/ [29] ) and by a living database architecture for the exchange of chemical and effect-based monitoring data  [30]. Moreover, a scenario analysis tool developed by MARS provides indications on how stressor intensity and ecological status will develop under given scenarios of human impact at the European scale and broken down to more than 100,000 sub-catchments in Europe (https ://mars-proje ct-sat. shiny apps. io/mars-sat). In addition, a diagnostic tool developed by MARS assists water managers to identify the main stressors affecting the ecological status, and to derive appropriate management measures (http:// fresh water platf orm. eu/index. php/mars-diagn ostic -tools. html). GLOBAQUA developed ESPRES (Efficient Strategies for anthropogenic Pressure Reduction in European waterSheds, http://www.
To anticipate the effectiveness of interventions, the perspectives of the water cycle and the chemical life cycle were connected by providing a mitigation database coupled to hydrological models  [44]. River basin scale case studies were instrumental to benchmark performance of modelling and measurement tools for water contamination assessment, provided data necessary to identify river basin-specific pollutants, demonstrated the benefits of the technical upgrade of wastewater treatment plants, specified the potential for targeted remediation of pollution sources, and demonstrated the interactions between contamination and situations of water scarcity that need to be acknowledged. In particular, we sought to conceptually provide links for bridging between chemical and ecological water status measures  [49] [50] [51].
Funding This article has been prepared as an outcome of the projects SOLUTIONS (European Union's  Seventh Framework Programme  for research, technological development and demonstration under Grant Agreement No. 603437 ), GLOBAQUA (European Union's  Seventh Framework Programme  for research, technological development and demonstration under Grant Agreement No. 603629 ) and  MARS (European Union 's  Seventh Framework Programme  for research, technological development and demonstration under Grant Agreement No. 603378 ).
RT-qPCR were performed using Applied Biosystems™ SYBR™ Green master mixes (5 µL Sybergreen 5X, 0. 5 µL of each primer, 2. 5 µL cDNA and sterile water to 10 µL). Primer sequences are given in Table  S2. The reactions were performed on a LightCycler® 480 System (Roche), using the following program: 95°C for 10 min followed by 45 cycles of (95°C for 10 s, 60°C for 10 s, 72°C for 20 s). Melting curves were established (65°C-97°C) to check the specificity of the PCR products. The Av-IGFBP-rP1 expression levels were analysed relatively to the ribosomal protein RbL8 expression  (Chevalier et al. , 2012) , using LightCycler® 480 Software. Statistics on the tissue and development RT-qPCR data were calculated using the Kruskal-Wallis test, complemented by Dunn's post-hoc tests implemented in the R package PMCMR  (Pohlert, 2014).
Sequencing The 26 IGFBP-rP1 sequences amplified by RT-PCR were treated with 2 µL of EXOAnP solution (0. 05 µL of exonuclease I (1 u), 0. 1 µL of AnP (0. 5 u) and 1. 85 µL of sterile water) at 37°C for 1 h. Enzymes deactivation was achieved at 80°C for 20 minutes. New PCR using the BigDye® Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems) were then performed (3 µL of PCR product, 0. 5 µL of BDT, 0. 32 µL of primer, 3 µL of 5X buffer and sterile water to 15 µL). The products of the PCR were precipitated with a mix of 50 µL ethanol (100%), 2. 2 µL NaOAc (3M, pH 5. 2) and 10 µL water. Pellets were washed with 70% ethanol and solubilized in deionized formamide. Finally, DNAs were sequenced on both strands using Sanger method. The final 68 IGFBP-rP1 sequences were deposited in GenBank under accession numbers [pending submission].
Bioinformatics The assembly of both newly generated and already published reads was achieved on our servers (Table  S1 ). The raw reads were first filtered with Trimmomatic (leading and trailing quality of 5, minimum length of 36 bp, Illumina adapters removed)  (Bolger et al. , 2014). The trimmed data were then assembled with IDBA-tran assembler (maximum k-mer size of 100 bp)  (Peng et al. , 2013). The IGFBP-rP1 sequences were searched locally with TBLASTX, using first Cq-IGFBP and then Av-IGFBP-rP1 as queries. The post-translational modifications were predicted using CBS prediction servers (SignalP 4. 1  (Petersen et al. , 2011) , NetPhos 3. 1  (Blom et al. , 1999) , ProP 1. 0  (Duckert et al. , 2004) ). The conserved domains of the IGFBP-rP1 sequences were obtained using the Conserved Domain Search service (https://www.
1. 9 (Darriba et al. , 2012; Guindon and Gascuel, 2003). Branch support was estimated with both the non parametric Shimodaira-Hasegawa-like approximate Likelihood-Ratio Test (SHlike aLRT)  (Anisimova and Gascuel, 2006; Guindon et al. , 2010)  and the bootstrap proportions (1000 repetitions)  (Felsenstein, 1985). Branch supports were further checked with a bayesian method using BEAST 1. 8. 4  (Drummond et al. , 2012) , using the same model of evolution, an uncorrelated relaxed clock (log normal) and a run of 50,000,000 generations (log sampling every 1000 generations). Posterior probabilities (PP) were retrieved using TreeAnnotator. Evidence for natural selection in our IGFBP-rP1 dataset was investigated using PAML  (Yang, 1997 (Yang, , 2007 ). An overall dN/dS value was calculated with codeml (seqtype=1, model=0).
However, considering the very high conservation of these domains (e. g. half of the amino acids in the Ig domain strictly conserved in Isopoda), the resolution of these trees was rather poor (data not shown). To confirm the apparent good conservation of the isopod IGFBP-rP1, we calculated the dN/dS ratios for the whole sequence set and then for each site of the coding sequence. The overall dN/dS ratio of the isopod IGFBP-rP1 alignment was between 0. 12 (codeml, model 0) and 0. 17 (SLAC and REL), implying a strong purifying selection on this sequence. This was further confirmed by SLAC, FEL and IFEL methods that predicted 177 (73%), 191 (79%) and 173 (72%) negatively selected codons, respectively, 166 (69%) of these codons being found under purifying selection by these three methods.
Acknowledgements We are thankful to  Alexandra Lafitte , who is responsible for the laboratory husbandries and to  Daniel Guyonnet , who is in charge of the Sanger sequencing platform. This study was supported by the  French National Centre for Scientific Research (CNRS) , the  University of Poitiers (France) , the 2015-2020 State-Region Planning Contract and the  European Regional Development Fund. The work of B. H. is funded by a PhD fellowship from the  French Research Ministry.