dataesr/scientific-paragraphs-categorization huggingface dataset:
Scotoma and Metamorphopsia Detection with Paper Amsler Grid and OdySight At least one eye with metamorphopsia was detected in 50 eyes using the paper Amsler grid and in 50 eyes using the OdySight module. At least one scotoma was detected in 50 eyes using the paper Amsler grid and in 50 eyes using the OdySight module. There was only one participant for whom the OdySight app did not detect metamorphopsia while the paper Amsler grid did (McNemar test, p value = 1), and also one participant for whom the paper Amsler grid did not detect metamorphopsia while the OdySight app did (McNemar test, p value = 1) (Fig. 9 ). For these cases, similar results were noted in the detection of scotoma.
DISCUSSION OdySight is based on standardized visual tests which are well accepted and used in the ophthalmology community world-wide. The purpose of this study was to demonstrate that there is good agreement between the results of tests performed by the patient using the modules of OdySight and those obtained using The B&A analysis for near visual acuity using the standard 40-cm Sloan ETDRS and the Ody-Sight module demonstrated a low mean difference (bias), with the value very close to zero; these results show there is no disagreement between the two methods. According to these results, the OdySight application overestimated visual acuity by 0. 53 letters as compared to the Sloan EDTRS method. The LoA are outside the limits considered to be acceptable, according to the literature, but very close to nine letters (difference of either ? 9 or -9 letters).
The B&A analysis for visual acuity using the standard 4-m ETDRS and the OdySight module demonstrated a low mean difference (bias), with the value very close to zero; these results show there is no disagreement between the two methods. According to the results, the OdySight Sloan ETDRS]) and the mean visual acuity measured ([40cm Sloan ETDRS ? OdySight)]2). For an explanation of cohorts, please see footnote to Table  1. LoA Limits of agreement, s standard deviation  For visual acuity, in addition to the primary objectives of the study, an analysis of the variation between two standardized tools used to measure visual acuity, namely, the Sloan ETDRS (at 40 cm) and the ETDRS at 4 m, was also performed. The B&A analysis showed a small difference in the measurements between the two standardized methods: a mean of -2.
06 and a LoA of between -12. 60 and 8. 48 letters. Yet, both methods are currently used, independently, to monitor visual acuity. Overall, this study reveals that the results from the OdySight modules for visual acuity are close to those obtained by the standardized tests (Sloan ETDRS and 4-m EDRS). For the contrast sensitivity module, the bias is non-negligible as it represents more than three letters, which is equivalent to more than one line on a Pelli-Robson chart. The results Fig. 9  Example of corresponding results for two versions of the Amsler grid. Left: paper 10 9 10-cm Amsler grid completed with a felt tip pen. Right: digital Amsler grid completed on the OdySight app indicate that the application underestimated contrast sensitivity by 0. 16 logCS as compared to the Pelli-Robson method.
Furthermore, the interval for the LoA is quite large. Therefore, results using OdySight may not be truly comparable with those using the Pelli-Robson chart. For the Amsler grid module, the study showed no difference in the detection of scotoma and/or metamorphopsia using OdySight compared to the paper Amsler grid. A review of the comment section of the study electronic case exit report forms highlighted two comments on particular difficulties encountered by some participants: (1) ''the scotoma of the left eye bothered the participant during the contrast test on OdySight. When the opening of the ''C'' was on the left, he clicked on the button ''I don't know''. It was not related to the contrast of the letter but to the location of the opening. '' (2) ''trembling hands and difficulty to understand the instructions.
Indeed, when addressing an elderly population, we might expect some hand-eye coordination issues when using a smartphone, (such as tremors) that need to be taken into consideration. '' Even if the OdySight medical modules were created to be equivalent to the standardized tests (in terms of design and calculation) they obviously are not identical to these tests. The platforms vary and at times the distances also vary (e. g. , Pelli-Robson test is done at 1 m). Any differences observed in this study between the OdySight modules and the standardized tests may be due to measurement errors, misuse of the app, and/or other elements which are participant-dependent. In addition, the participant's condition (severely reduced visual fields, tremors, or other difficulties with hand-eye coordination), the novelty of smartphones and tablets, as well as other challenges might explain a slight difference in results.
As with all scientific methods, these methods are not 100% reliable and have a degree of error. Agreement between two methods must always be assessed alongside the clinical status of the eye in question. There are some limitations to this study. First, each patient was asked to perform each test once per eye. More data points would have been collected had the parameters been tested on each patient multiple times. Also, this study is not a real-world evaluation of OdySight, as the distance to screen and ambient light features were disabled. However, those features were tested and validated through an independent Human Factors study. Furthermore, during the study, all tests were performed on one specific type of smartphone only.
CONCLUSION The results of this study demonstrate good agreement for the measurements taken by two of the OdySight medical modules (visual acuity and Amsler grid) compared to currently used gold standards (ETDRS Sloan near visual and distance ETDRS charts and paper Amsler Grid, respectively) for most people who participated in the study. Regarding the contrast sensitivity module, variability of the results and the absence of consensus on the appropriate limit precludes a definitive conclusion. OdySight, for remote health monitoring, is a tool that challenges standard ophthalmic tools for measuring visual acuity and detecting scotoma and metamorphopsia.
Real World Use OdySight received CE Marking as a class 1 medical device in the EU in May 2018 and, as of this manuscript submission, more than 40 ophthalmologists in France are prescribing it to their patients. Physicians and their clinical staff propose the application to those patients with chronic eye diseases who may benefit from remote monitoring. A starter kit composed of the basic information to install the game and perform the tests, as well as a stand to hold a smartphone, are provided to each patient. Patients then download OdySight from the iOS or Android store onto one of 3000? compatible smartphones and tablets, play the puzzle game, and perform the eye tests on a regular basis, between clinic visits, from their home or other remote locations. At the end of each test, the data are transmitted via a secure server to a dashboard in the clinic.
, 2014; Rodriguez et al. , 2008; Spatz and Theckes, 2013). Previous finite element analysis studies have been limited to small numbers of trees due to the difficulty of manually mapping tree geometry  (Moore and Maguire, 2008; Sellier et al. , 2006). However, terrestrial laser scanning (TLS) data offers the potential for dramatic advances in this area. Recent developments in the extraction of accurate 3D tree models from TLS data can provide detailed geometrical information for a scanned tree in a matter of minutes  (Raumonen et al. , 2015; Calders et al. , 2015; Gonzalez de et al. , 2018). We used 3D tree models derived from TLS data as the basis of a finite element analysis, applied using the engineering software Abaqus  (Simulia Software Company, 2017). This approach allowed us to simulate the dynamics of broadleaf trees without the need to manually map their geometry.
In addition to its application in natural forests, this approach could be useful in testing proposed interventions on urban amenity trees, or valuable veteran trees, in a model environment before implementation  (Reiland et al. , 2015). This paper tests the hypothesis that finite element models developed using TLS data can be successfully used to simulate the behaviour of broadleaved trees under applied wind loading. We describe the method and discuss the assumptions involved. We also provide software to implement this technique. We test the model against field data collected in Wytham Woods, UK, for which we have both TLS data and field measurements of strain in the tree stem and wind speed. This validation takes three forms: 1 We predict the exact time-series of strain produced during a fiveminute period based on high time-resolution wind data.
Study site and field data Wytham Woods (51°46′27. 2″ N 1°20′20. 1″ W) is a mature temperate deciduous woodland near Oxford, UK. The woodland contains approximately 950 trees per hectare and the dominant species are sycamore (Acer pseudoplatanus), ash (Fraxinus excelsior), beech (Fagus sylvatica) and English oak (Quercus robur). We measured the bending strains produced on 21 trees (eight ash, nine sycamore, four birch (Betula spp. ) and one oak) ranging from 12 to 24 m tall. One of the sycamore trees had four main stems below 1. 3 m, each of which are treated as separate trees in the forest census and were instrumented as such in this study, we therefore refer to them as separate trees in this paper. Seven of the trees were located near a canopy walkway structure, and the remaining 14 trees were located 230 m away.
Average canopy height was 21-23 m in both locations. Strain was measured using the method developed by  Moore et al. , (2005) : pairs of strain transducers were fixed to the trees at approximately 1. 3 m and perpendicular to each other. These transducers were then connected to Campbell Scientific (Logan, UT, USA) data loggers and measured at 4 Hz (two CR23xs and three CR1000s). The limited storage capacity of the CR23x data loggers was supplemented using a Raspberry Pi (Cambridge, UK). In the field, strain gauge resistances drift with environmental factors. We partially corrected these in situ using a variable resistor within a wheat-stone bridge circuit. We further accounted for this drift by subtracting a 5000 point (20 min) running mode from the signal. This is based on the assumption that the tree will regularly pass through its equilibrium position.
This same technique was used in  Wellpott (2008)  and  Hale et al. (2012 Hale et al. ( , 2015) )  and we found our results to be relatively insensitive to this window length (SI-1). Wind speed was measured in winter using three cup anemometers (A100LK/5 M, Vector Instruments, Rhyl, Wales) mounted at 5 m, 10 m and 15 m heights above ground level on a canopy walkway next to the focal trees and supplemented, notably during the 5-minute high-resolution validation period, with a 3D sonic anemometer (Campbell Scientific CSAT3) mounted at 15 m. In summer, we used a Gill (Gosport, UK) Sonic-1 anemometer recording at 1 Hz mounted at 15 m. Local climate data are available through a meteorological station approximately 1 km away, just outside the forest, operated by the Environmental Change Network  (ECN, www.
ecn. ac. uk). This station records mean hourly wind speed as well as the maximum 5 s mean within that hour (5 s maximum gust speed). The wind data taken from anemometers within the canopy were considered unrepresentative, especially in summer, because of localized sheltering effects. We therefore used the ECN meteorological station's hourly maximum wind speeds for all critical wind speed analysis and select the maximum strain on each tree within each hour. A comparison of the different measures of wind speed is given in SI-2.
Dynamic simulation The dynamic simulation of a tree's response to wind forcing was conducted using the Abaqus Aqua module (Simulia Software Company, 2017). Each tree was modelled individually, meaning that the dynamic response does not include crown clashing which is thought to be significant in some forest structures  (Rudnicki et al. , 2003 (Rudnicki et al. , , 2001)). In all simulations, we used a non-linear solution method to account for large displacements in the tree structure. The equation of motion takes the form + + = + Mq Dq Kq G F ¨D (1) where q q , and q ¨are the nodal displacement, velocity and acceleration vectors. M, D and K are the mass, damping and stiffness matrices respectively, and G and F D represent the gravitational and wind induced forces. The output variable, strain, is calculated internally by Abaqus and we use the strain at the approximate position of the strain gauges (1.
Hourly maximum strain validation In this validation, we used the five trees from time series validation as well as twelve additional trees situated approximately 230 m away. Two of these additional trees were excluded due to a poor connection between the tree and the strain gauge. We used hourly maximum wind speed measurements taken from the ECN meteorological station. For each hour we extracted the maximum strain value for every tree. All but four trees had over 500 h of data available, and for these four trees we had 165 h of data. We tested a robust maximum selection technique, using the mode of the maxima of subsections of the hours data, but found no increase in coefficient of determination nor reduction in root mean squared error. Details of the strain data processing and reference to the software and data repository to repeat this analysis are given in SI-1.
Hourly maximum strain validation This section examines the model's accuracy in predicting the strains produced by wind speeds of 5 and 15 ms -1 , and in extrapolating up to the critical wind speed (Fig. 4 ). It is important to note that, in the case of CWS, both the x and y axes are estimates, since the CWS was estimated from field data by extrapolating beyond the available measurements. Therefore, this is not a straightforward validation but rather a demonstration that predictions from field and model approaches are correlated. We used hourly maximum wind speeds (highest 5 s mean per hour) measured at the nearby meteorological station, since this avoids the need for a multiplication factor (gust factor). A more detailed discussion of the data analysis choices relating to the wind data is given in SI-2.
Acknowledgements We would like to thank the  Oxford Department of Engineering , especially  Mike Stockford , for the use of their workshop and access to  Abaqus. Also ,  Clint Davies-Taylor  for his expert advice on Abaqus scripting. This research was supported by a  NERC  studentship to TJ on the  DTP Environmental Research  ( NE/L0021612/1 ). The TLS data collection was part-funded through the  Metrology for Earth Observation  and  Climate project (MetEOC-2 ), grant number  ENV55  within the  European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the  European Union. MD was supported in part by  NERC  Standard  GrantsNE/N00373X/1  and  NE/P011780/1 , and via the  NERC National Centre for Earth Observation  for capital purchase of lidar equipment.
AS and YM are supported by  NERC  GrantNE /P012337/1  (to YM). YM is also supported by the  Frank Jackson Foundation. Part of this study has been funded in the frame of the 3DForMod project,  ERA-NET FACCE ERA-GAS  ( ANR-17-EGAS-0002-01 ). FACCE ERA-GAS  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  696356.
Data availability All the field data collected for this study is available online (https:// doi. org/10. 5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc)as are the data processing scripts (https://github. com/TobyDJackson/ WindAndTrees_Wytham). The matlab scripts that convert QSMs to Abaqus input files are also available online (https://doi. org/http:// doi. org/10. 5281/zenodo. 894543). We used the commercial finite element analysis software Abaqus (Simulia Software Company, 2017), but a student version can be freely obtained on which a more limited analysis can be run -this is clearly explained in the software readme files.
All responses were filtered at 0. 3-500 Hz and signal averaging was used. Single nucleotide polymorphism (Snp) array analysis. Whole genome SNP analysis was performed using 10K/250K Affymetrix system and 6K Illumina (human genome build 37/hg19). A region of homozygosity was determined by a minimal length of 5 cM. Mutation analysis. Primers were designed using PRIMER3 software (available in the public domain at http://bioinfo. ut. ee/primer3-0. 4. 0/, Supplementary Table  1 ). PCR was performed in 30 µl reaction with 35 cycles according to classical protocols. Mutation analysis was performed by Sanger sequencing of PCR products.
Results prevalence and genetics of cSnB in israeli and palestinian patients. Aiming to estimate the prevalence of CSNB in the vicinity of Jerusalem, we created a database that contains clinical information from the only two centers that perform ERG in the vicinity of Jerusalem: Hadassah-Hebrew University Medical Center and St. John Eye Hospital. Data were collected and verified for patients who reside in the vicinity of Jerusalem only, to reliably assess disease prevalence. Our database contains a total of 161 CSNB patients representing the minimal number of CSNB patients in the vicinity of Jerusalem with an estimated population size of 1,000,000 individuals (based on the Israeli, https://www. cbs. gov. il/EN/pages/default. aspx, and Palestinian, https://www. pcbs. gov. ps/ default. aspx, Bureau of Statistics).
Light-adapted cone responses were within normal limits or slightly below lower limit of normal in all but three patients (MOL0611-1, SJ0016-1 and RD356-1). Our cohort included mainly two groups of patients who are homozygous for a TRPM1 mutation for whom clinical data was available for at least one parameter: 29 patients were homozygous for c. 880A>T and 11 patients for chr15: 31355203-31391647del. Most clinical parameters did not show a significant difference between the  two major groups, however, a single parameter (mixed a-wave response) was lower in the chr15: 31355203-31391647del group (139. 2 ± 54. 2 μV versus 200. 9 ± 96. 4 μV, p = 0. 035 for the c. 880A>T group).
Additional lineages of archaea with an MCR or MCR-like complex To identify previously undescribed lineages of potential methanogens, anaerobic methanotrophs and short-chain alkane oxidizers, we probed available metagenomes from the JGI/IMG database for McrA homologues and identified sequences distantly related to well characterized lineages (Methods). Ten MAGs were reconstructed from the corresponding metagenomes sourced from a wide range of anoxic environments including an inland petroleum reservoir from Brazil, oil seeps from USA25, soda lake sediments from Russia, and hot-springs from China and USA (Table  1 ). Nine of the ten MAGs had an estimated completeness ranging from 78. 4 to 94. 4%, and one was only 51. 5% complete. Estimated contamination (without strain heterogeneity) ranged from 0 to 3.
Metagenomic database probing and contig binning Contigs of 6108 metagenomes publicly available on the IMG/JGI database in April 2017 were screened for the presence of COG4058, corresponding to McrA, using search tools of the database. 819 contigs containing an McrA sequence with a minimal length of 750 bp were downloaded. The McrA sequences present on these contigs were aligned on those of 188 published genomes by using Mafft56 (mafft-linsi) and were trimmed with BMGE57 (BLOSUM30). A maximum likelihood (ML) phylogeny was calculated in IQTree58 with the TEST option for best model selection and 100 bootstrap replicates. Metagenomes containing one or several contigs coding for an McrA homologue that was only distantly related to know lineages or belonged to undersampled lineages were downloaded from the IMG database.
These metagenomes were assembled with MetaSPAdes59, IDBA-UD60 and Newbler (Roche) (see Supplementary Table  4  for details). The contigs were binned with ESOM, MetaBAT61, ABAWACA 1. 07 (http://ggkbase. berkeley. edu/), MaxBin 2. 062 and CONCOT63 (Supplementary Table  4 ). An in-house pipeline (Let-it-bin, https://github. com/ QuentinLetourneur/Let-it-bin) was used for read trimming, assembly, and contig binning. Two of the MAGs were refined using DAS_Tool64. Completeness and contamination of the assembled MAGs were estimated with CheckM65.
Phylogenomic analyses A reference archaeal phylogeny was built from a concatenation of 40 phylogenetic markers corresponding to the 36 proteins of the Phylosift dataset66, plus the alpha and beta subunits of the RNA polymerase and two universal ribosomal proteins (L30, S4) (Supplementary Table  5 ). We used a subset of the genomes available for each order/class/phylum level lineages (Supplementary Table  6 ) to minimize biased associated with uneven distribution of taxa among them (e. g. >100 taxa in Halobacteriales vs. 3 taxa in Methanocellales). The 147 genomes were chosen because they were the most complete and the most distant to each other within each lineage. Two phylogenies were built from a concatenation of McrABG and of six co-localized markers (m4 to m9) specific to genomes encoding an MCR/MCR-like complex.
Sequences used for these trees were searched by HMM in the ten MAGs obtained in this study and in genomes present in the NCBI or IMG-databases, aligned with Mafft56 (mafft-linsi), trimmed with BMGE57 (BLOSUM30) and concatenated with an in-house script. Before concatenation, the genes of the two datasets (McrABG and m4 to m9) were tested for congruence using the Internode Certainty (IC)67 test in RaxML68. Maximum likelihood phylogenies for each gene and blind concatenations were calculated in IQTree58 with the TEST option for best model selection and 100 bootstrap replicates. Sequences causing strong incongruences (with a bootstrap>=80%) at high taxonomic ranks (order to phylum as applicable) were removed, and the procedure was repeated until no further incongruence was found. Bayesian phylogenies were constructed in PhyloBayes69 under the CAT+GTR+Γ4 model.
Acknowledgements We thank  Rudolf Thauer  for feedback on an earlier version of the manuscript. G. B. acknowledges support from the  Institut Pasteur  through  a Roux-Cantarini  fellowship. P. S. A. is supported by a PhD fellowship from  Paris Diderot University  and by funds from the  PhD Program  " Frontières du Vivant (FdV)-Programme Bettencourt ". S. G. acknowledges funding from the  French National Agency for   Research Grant ArchEvol  ( ANR-16-CE02-0005-01 ). This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris. S. J. H. acknowledges support from the  US Department of Energy (DOE) JGI  supported by the  Office of Science of US DOE  Contract  DE-AC02-05CH11231 , the  Natural Sciences and Engineering Research Council (NSERC) of Canada, Genome British Columbia ,  Genome Canada ,  Canada Foundation for Innovation (CFI) , and the  Tula Foundation.
I. N. S. G. and V. M. O. are grateful to  São Paulo Research Foundation -FAPESP  (process numbers  2011/14501-6  and  2013/20436-8 ) and  Petrobras  for financial support and to  Dr. Neil Gray  and  Dr. Ian Head  from the  School of Civil Engineering and Geosciences at Newcastle University  for lab facilities. W-J. L. was supported by  Key Projects of Ministry of Science and Technology (MOST)  (Nos. 2013DFA31980 ,  2015FY110100 ). G. M. was supported by the  ERC Advanced Grant PARASOL  (No. 322551 ). L. J. M. appreciates funding from the  NASA   Postdoctoral Program  through the  NASA Astrobiology Institute  and W. P. I. was supported by the  Montana Agricultural Experiment Station  (Project  911300 ).