dataesr/scientific-paragraphs-categorization huggingface dataset:
The majority of these studies take a crowdsourcing-based approach-in which participants are invited to contribute by providing their own wearable data along with regular symptom reports and COVID-19 test results-as the main way of engaging individuals. The goal of the data collection process in these studies is to create big data sets to interrogate. In contrast, there have been limited efforts to engage personal scientists in cocreating such symptom tracking efforts. Personal science practices are largely done in isolation, and the Quantified Self movement has consequently accumulated limited knowledge so far  [8]. To fill this gap, we present a case study of Quantified Flu (QF), a project cocreated by a community of personal science practitioners in response to the COVID-19 pandemic.
Overview To provide users with easy ways to facilitate understanding of their own physiological data and potentially explore it in relation to their own symptom reports, QF used D3. js to create interactive visualizations. These visualizations present the evolution of the various physiological data points and put them into the context of their symptom reports where available. The QF platform provides personal science practitioners 2 main ways to explore their physiological wearable data in relation to infections-the retrospective analysis of prior events and an ongoing (prospective) analysis of symptom reporting.
Retrospective Analysis Users can select a given, historic date on which they fell sick or had specific symptoms, and QF will, if available, extract wearable data for the 3 weeks before that date and 2 weeks after the incident. This allows users to visualize sickness incidents that occurred before the launch of QF. Depending on the wearable (Table  1 ), users are given the option to display different physiological variables over that 5-week time period and explore how they change over time. To facilitate the interpretation of changes and outliers in the graphs, both the first and second SDs are presented as well (Figure  3 ). Although users can add comments to retrospective events, detailed symptom reports are absent in this mode, as most users do not have detailed records of the historic sickness events.
Principal Findings In this paper, we present QF, a cocreated web-based project that enables personal science practitioners to engage with their own wearable data and visualize it in the context of when they are experiencing symptoms of potential infection. The spark that led to this community deciding to cocreate a symptom tracking tool was the beginning of the global COVID-19 pandemic, along with population-wide studies that made individuals wonder how useful their own wearable data might be for them in such a pandemic context. With its focus on individual learning, QF stands in contrast to various population-level studies performed to evaluate the usefulness of wearable technology for the prediction of illness  [20, [24] [25] [26] [27]. At this individual scale, symptom tracking and health data more generally can offer support for individual sense-making on health experiences and conditions  [44] , which can be idiosyncratic and complex  [45].
QF was also distinguished by a cocreation approach that targeted the individual learning and research interests of a web-based community and involved the iterative development of a digital tool in response to feedback, resulting in a format that attracted increased and sustained participation from early users. One of the main aims of our work is to investigate the consequences of a cocreation approach that focuses on personal science: We observe that the initial QF prototype, which focused solely on retrospective symptom tracking, was rarely used. Only 24 users were engaged in this prototype. However, importantly, this initial version facilitated additional discussions about designing both the data collection protocol and extending the prototype (Figure  1 ), leading to the creation of the ongoing symptom reports as a feature launched in the next QF iteration.
Acknowledgments The authors would like to thank all participants of the  Open Humans community calls  and the  Quantified Self and OpenCovid19 communities  for their input and support. The authors would also like to thank all of the users of  Quantified Flu  for their support and feedback. The authors are also grateful for the suggestions and recommendations of the two anonymous reviewers who helped to substantially improve our manuscript. Thanks to the  Bettencourt Schueller Foundation 's long-term partnership, this work was partly supported by the  Center for Research and Interdisciplinarity Research Fellowship  (awarded to BGT). This work was also supported by a microgrant from the  OpenCovid19  initiative. KW was supported by the  H2020 WellCo Project (769765 ).
Strains isolation and identification Strain Marseille-P3248 was isolated from a urine sample of a 20month-old French girl with rotavirus gastroenteritis using culturomics  [10, 11]. The guardian of the girl supplied signed and informed consent. The study was authorized by the ethics committee of the Institut Federatif de Recherche IFR48 under agreement number 09-022. The sample was pre-incubated and treated as reported previously  [14]. Distinct and pure colonies of strain Marseille-P3248 were tested by matrix-assisted laser desorption/ionization time-offlight mass spectrometry (MALDI-TOF MS) (Bruker Daltonics, Bremen, Germany). No identification was obtained but the generated spectra were compared with those of a regularly updated local database (https://www. mediterranee-infection. com/urms-data-base).
The failed identification by MALDI-TOF MS led us to systematically sequence the 16S rRNA gene, as described previously  [15]. Then, CODONCODE ALIGNER software (http://www. codoncode. com) was used to assemble and correct the different short sequences to obtain a consensual and suitable sequence for comparison with the NCBI database using the BLASTn algorithm (https://blast. ncbi. nlm. nih. gov/Blast. cgi).
Extraction and sequencing genome On the basis of its phylogenetic position, the similarity of 16S rRNA and phenotypic differences with other close members of the Actinomycetaceae family, we decided to sequence strain Marseille-P3248. Currently, there are six available genomes  within the genus Arcanobacterium, among them the first genome of A. urinimassiliense sp. nov. Its GenBank accession number is FPJH00000000 and consists of 11 contigs. The genomic DNA of strain Marseille-P3248 was extracted with an EZ1 biorobot using an EZ1 DNA Tissue kit (Qiagen, Hilden, Germany), after being pretreated with lysozyme and incubated at 37°C for 2 hours. Genomic DNA of Arcanobacterium urinimassiliense strain Marseille-P3248 was sequenced on a MiSeq sequencer (Illumina, San Diego, CA, USA) using the mate-pair strategy.
Genome assembly, annotation and comparison The genome assembly of strain Marseille-P3248 was performed using pipeline softwares such as SPADES  [19] , VELVET  [20]  and SOAP DENOVO  [21]. Sequences recovered from Illumina MiSeq were trimmed with TRIMMOMATIC software  [22]  or untrimmed using MISEQ software. To minimize the gaps, we used GAPCLOSER software  [23]  to remove scaffolds <800 bp and scaffolds with a depth value less than 25% of the mean depth. Parameters such as number of scaffolds, N50, and number of N were chosen to obtain the best assembly. Finally, genome annotation was obtained through the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) v5. 0  [24]. The genomic comparison was carried out by evaluating the degrees of similarity of the genomic sequences between the studied genomes using the suitable tools.
Phylogenetic analysis An analysis of the sequences of the 16S rRNA gene of strain Marseille-P3248 showed that it presented nucleotide similarities of 94. 73% with Arcanobacterium pluranimalium strain M430/ 94/2 (GenBank accession number NR_028908. 1). This was also  the closest phylogenetic species with a validly published name (Fig. 1 ). Given that this percentage of similarity is much lower than the recommended threshold value  [13, 27]  for predicting a new bacterium, we consider that the strain Marseille-P3248 is potentially a new species belonging to the genus Arcanobacterium.
Conclusion Phenotypic criteria, morphological characteristics and genomic features of strain Marseille-P63248 led us to conclude that this strain is a new member of the Arcanobacterium genus. In addition, comparative analysis with the phylogenetically closest species with standing in nomenclature support formally the creation of Arcanobacterium urinimassiliense sp. nov. , of which its type strain is strain Marseille-P3248. The genome and the 16S rRNA sequences have been deposited in the GenBank database under accession numbers FPJH00000000 and LT598574, respectively. The type strain is strain Marseille-P3248 t , which was isolated from the urine sample of a child with rotavirus gastroenteritis.
The observations were followed by observations of the calibrator HD 124058. The reduction of this dataset was standard using the ESO GRAVITY pipeline 1  (Lapeyrere et al. 2014). The observations of the exoplanets used the dual-field on-axis mode. Dual-field means the fringe tracker and the science fibers observed different objects. Thanks to the splitter, the fringe tracker observed the star for phase referencing, and the science fiber observe the planet. Non-common path phase aberrations were calibrated by interleaving the observation of the planet with singlefield on-axis observations.
The plain DRIFT-PHOENIX model, in addition to being the most favored model for PDS 70 c, is the model with the second highest support for PDS 70 b. Adding modifications to the DRIFT-PHOENIX model did not improve the fit, resulting in lower Bayes factors. This can also be seen in the range of A V , T 2 , and R 2 parameters derived in the fits with modifications. The ranges of these parameters are typically consistent with the lower bounds of the priors for these parameters, implying they are minimally altering the DRIFT-PHOENIX spectrum. The BT-SETTL and Exo-REM models, on the other hand, are poor fits to the data without modifications, with a Bayes factor orders of magnitude worse than both the plain DRIFT-PHOENIX and blackbody models. However, adding some sort of extinction to change the overall 1-4 µm slope drastically improved their fit, pulling their Bayes factor to within a factor of 100 of the best fitting model.
The ISM extinction amplitude of 3. 9 < A V < 9. 4 mag for BT-SETTL fits to PDS 70 b corresponds to an 0. 23 < A K < 0. 54 mag, which is similar to the extinction values found for dusty brown dwarfs  (Marocco et al. 2014; Delorme et al. 2017). Switching from ISM extinction with one free parameter to a variable power-law dust extinction with three free parameters caused drops in the Bayes factor in nearly all cases, except for the Exo-REM model of PDS 70 c (although this model's B was too low to be considered adequate). We do not think this implies that we are seeing extinction from ISM-like grains, but rather that the current data are insufficient to constrain moreflexible extinction models. In all cases, we ruled out extreme size distributions with β dust < -5 that are dominated solely by small particles.
While the maximum dust size (a max ) is relatively unconstrained for PDS 70 b, most of our fits ruled out dust particles larger than about 1 µm for PDS 70 c. However, we note that only the DRIFT-PHOENIX model with power-law dust extinction has an adequate B for PDS 70 c, so it is unclear how robust this conclusion is. Rather, we are worried that the free parameters in the model are compensating for other model deficiencies. Overall, the lack of improvement in the B indicates the current data is unable to characterize the properties of any obscuring dust. The DRIFT-PHOENIX and Exo-REM models have free parameters to describe the composition of the atmosphere ([M/H] and C/O). In all the adequate fits to the data, these parameters are essentially unconstrained (e. g. , [M/H] spans the whole prior range for acceptable DRIFT-PHOENIX models of PDS 70 b).
The superscript and subscript denote the upper and lower bounds of that range. a Mode of posterior reached edge of model grid rior for PDS 70 b from Section 3. 2 as a prior on log(g), we obtained surface gravity values for PDS 70 b near the lower bound of all of the model grids, but none of the other atmospheric parameters changed significantly. As spectroscopic masses from surface gravity and radius have been shown to be unreliable for brown dwarf atmospheres of comparable temperatures (e. g. ,  Zhang et al. 2020) , we avoid overinterpreting the results on these protoplanetary photospheres. It appears that the 1-5 µm data alone does not provide significant evidence CPD emission. Evidence for a second blackbody component by itself is marginal in our fits. For both PDS 70 b and c, the models with a second blackbody component for both the blackbody and DRIFT-PHOENIX models have smaller B than the plain models.
Adding a second blackbody does improve the Bayes factor from the plain models for the BT-SETTL and Exo-REM models for both planets, but only the BT-SETTL model for PDS 70 b with a hot compact second component has an adequate Bayes factor. The addition of extinction to the BT-SETTL and Exo-REM atmospheric models combined with the second   6  except for PDS 70 c. To make it clearer to see by eye, both GRAVITY datasets have been binned together into a single spectrum (in red) using a weighted mean for each bin. 2. 27 +0. 17 Note-For each parameter, a 95% credible interval centered about the median is reported. The superscript and subscript denote the upper and lower bounds of that range. blackbody component generally improved the Bayes factor significantly more than the addition of the second blackbody component alone.
Such low opacities have been predicted to occur due to the accretion of dust grains that have undergone grain growth or by coagulation of grains after accretion onto the planet, resulting in a distribution of grain sizes that favors more larger-sized grains than typical ISM distributions  (Mordasini 2014; Piso et al. 2015). The plain DRIFT-PHOENIX model without any modifications has some of the highest Bayes factors for both planets. Given that these models were originally designed to fit dusty, but older, brown dwarfs, we investigated why they have the most support from the data we have obtained so far. We found that this is due to the fact the DRIFT-PHOENIX models do not reproduce the L-T transition by having the clouds clear up at lower effective temperatures, but rather produce thicker clouds at T eff < 1600 K that create a redder near-infrared spectrum  (Witte et al.
These extincted models also seek to redden the planetary atmosphere to better match the overall 1-5 µm SED of both planets. The similar extinction amplitudes with dusty brown dwarfs that are not actively accreting may be a coincidence due to large uncertainties in the extinction characteristics. The sedimentation timescale of the dust in these protoplanet atmospheres should be on the order of 10 years  (Wang et al. 2020) , so it is unlikely that we are seeing lingering dust from accretion in the field brown dwarfs. The formation of aerosols in the upper atmosphere through some undetermined process has been proposed to explain the dusty brown dwarf population instead  (Hiranaka et al. 2016). Overall, we interpret the fact that DRIFT-PHOENIX models and extincted BT-SETTL and Exo-REM models having the most support form the data to indicate that the planetary atmospheres are indeed significantly extincted by dust from the the planet formation process.
ACKNOWLEDGMENTS We thank  Dino Mesa  and  Michael Liu  for helpful discussions. This research has made use of the  Jean-Marie Mariotti Center Aspro service. J. J. W. , S. G. , P. G. , and S. B. acknowledge support from the  Heising-Simons Foundation , including grant  2019-1698. AV acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757561 ). T. H. , P. M and R. A-T acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Advanced Grant  Origins  83 24 28. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France ),  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zürich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with  ESO.
SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for  FP6  ( 2004-2008 ), grant number  226604  for  FP7  ( 2009-2012 ) and grant number  312430  for  FP7  ( 2013-2016 ). R. G. L. has received funding from  Science Foundation Ireland  under Grant No. 18/SIRG/5597. Facility: VLTI (GRAVITY) Software: orbitize!(Blunt et al. 2020), pyKLIP.
The error bars are relative errors. For the calculation of LPPs (Figure  4E ), bilayers not containing TG molecules were equilibrated for a variable length (from 200 to 1500 ns) and then run for additional 200 ns for the calculation of LPP. Stress components along z were obtained using the LAMMPS commands 'compute stress/ atom' and 'fix ave/chunch' during production and then converted to corresponding pressures using the script stress2press. py, available in the ELBA-LAMMPS toolkit (https://github. com/orsim/elbalammps/tree/master/tools). The LPP curves are reported as average and the error bars as standard deviation over three independent replicas. p CH values were obtained integrating LPP curves for p(z) values > 0, employing the Simpson's rule and using a bin size of 0. 01 Angstroms. The values of p CH and the relative percentage change with DOPC bilayers as reference are reported in Supplementary file 1.
Acknowledgements We thank  Vikram Reddy Ardham  and  Vineet Choudhary  for useful discussions. This work was supported by the  Swiss National Science Foundation  (grant # 163966 ). This work was supported by grants from the  Swiss National Supercomputing Centre (CSCS)  under project  ID  s726, s842 and s980. We acknowledge  PRACE  for awarding us access to  Piz Daint ,  ETH Zurich/CSCS, Switzerland. RS was supported by the  Swiss National Science Foundation  ( 31003A_173003 ) and the  Novartis Foundation  for medical-biological Research ( 19B140 ).
SimpleForest's QSMs can be conveniently extracted in csv format to be postprocessed with AMAPstudio  [22]  or R software  [23]. Ply format allows visual inspection with tools like CloudCompare, Meshlab or Blender. The latter approach is useful for exemplary quality assessment after computation when a larger number of plots is processed in the batch-mode of Computree. The SphereFollowing parameters are automatically searched for in higher dimensional space. This automatic search is especially user friendly for users without informatic knowledge but also did outperform us on expert level. In case wood vs. leaf separation is desired, separated tree clouds can be processed in a supervised machine learning manner. Remaining leaf fragments in the cloud lead to errors in QSMs which are afterwards automatically handled by filters relying on the WBE  [24]  theory.
Supporting information S1 Text. The SimpleForest UserGuide. • Over 100 pages of documentation are contained in the User Guide: https://gitlab. com/SimpleForest/computree/-/blob/master/bin/SimpleForestUserGuide. pdf, https://doi. org/10. 5281/zenodo. 5138255. All algorithms are described in detail with a special focus on the parameterization of the steps. S2 Software Online resources for S1 Software. • An homepage : https://www. simpleforest. org/. • A video tutorial channel: https://www. youtube. com/channel/UCq2dqxUF3IGmusX0ptk7xhw. • Support forum Computree: http://rdinnovation. onf. fr/projects/computree/boards. • SimpleF orest users help users list: https://lists. posteo. de/listinfo/simpleforest.
Results The Quercus petraea and denoised Pinus massioniana of S1 Dataset were corrected with the Tapering and Erythrophleum fordii were corrected with VV approach. In a second modeling run all QSMs were corrected with the RPRB filter. For S2 Dataset  [36]  we used the VV filter in one run and the RPRB filter in a second one. For S3 Dataset we applied Tapering firstly and then in a second run the RPRB filter, Fig 9.
Discussion SimpleForest utilizes an advanced parameter search for optimizing the SphereFollowing method compared to the brute force search published in earlier works  [7]  leading to better models. Nevertheless, as input data already overestimates the twig regions  [35]  a radius correction has to be applied. The choice of this correction influences the results significantly. On the S3 Dataset data set we can perform a direct comparison versus an earlier published approach and our RPRB filter performs very strong as can be seen in Fig  10 (a). We can also see, that even with the less well performing Tapering radius correction we can achieve better results, if we use more than one QSM tool. Outlier QSMs can be simply filtered out if the QSMs of different tools do not have similar volume predictions,  Fig 10 (b).
Future work The RPRB filter should not rely on an origin forced intercept, as twigs can be removed during leaf separation. Another downside of this filter is the fact, that the RPRB is same for all cylinders inside a segment  [7]. Notice, that we defined a segment in previous works as the list of all cylinders between two neighboring branch junctions. The VV filter does not have this downside and if the non linear fit can be performed in a more robust way even better filtering might be doable. Lastly we consider it beneficial to gather with our semi automatic leaf separation approaches training data for a full automatic leaf separation procedure. Tiny dnn  [44, 45]  is a deep learning header only library which allows easy integration into SimpleForest code base. A network architecture using FFPHs as input can be implemented fastly.
Data We will evaluate on 5 different data sets. Each data set is online available, in one repository: https://doi. org/10. 5281/zenodo. 5131717. S1 Dataset Hackenberg et al. 2015a data  [35]  12 Quercus petraea (leaf-off),  12  Erythrophleum fordii (leaf-on) and  12  Pinus massioniana (needle-on) tree scans are available with harvested ground truth volume data  [35]. The Quercus petraea trees have been overgrown with moss leading to larger overestimation of the Radius. • Erythrophleum fordii clouds raw: https://zenodo. org/record/5131717/files/hackenbergErythrophleumRawClouds. zip • Erythrophleum fordii clouds denoised: https://zenodo. org/record/5131717/files/hackenbergErythrophleumDenoisedCloudsCC. zip • Pinus massioniana clouds raw: https://zenodo. org/record/5131717/files/hackenbergErythrophleumRawClouds.
zip • Pinus massioniana clouds denoised: https://zenodo. org/record/5131717/files/hackenbergPinusDenoisedClouds. zip • Quercus petraea clouds denoised: https://zenodo. org/record/5131717/files/hackenbergQuercusDenoisedClouds. zip S2 Dataset De Tanago et al. 2017 data [36] 29 tropical buttresses trees have been scanned across sites in Peru, Indonesia and Guyana. Harvested reference data is included, [36]. All trees have been scanned in leaf-on condition. • Peru, Indonesia and Guyana clouds raw: https://zenodo. org/record/5131717/files/deTanagoRawClouds. zip • Peru, Indonesia and Guyana clouds denoised: https://zenodo. org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds. zip S3 Dataset Demol et al. 2021 data  [37]  65 trees in total of the species Fraxinus excelsior, Fagus sylvatica, Pinus sylvestris and Larix decidua have been scanned and published with reference data  [37].