dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because where patient data can be anonymised, Ipsen will share all individual participant data that underlie the results reported in this article with qualified researchers who provide a valid research question. Study documents, such as the study protocol and clinical study report, are not always available. Proposals should be submitted to DataSharing@Ipsen. com and will be assessed by a scientific review board. Data are available beginning six months and ending five years after publication; after this time, only raw data may be available. Requests to access the datasets should be directed to DataSharing@Ipsen. com.
15 The manufacturer software of Zeiss Angioplex uses the term "vessel density" to quantify the total length of skeletonized perfused vasculature per unit area in a region of measurement (unit: U/mm), whereas Optovue uses the term "vessel density" to describe the total area of perfused vasculature per unit area in a region of measurement (unit: %). In contrast, the Zeiss software uses the term "perfusion density" to describe the latter assessment. In addition to this inconsistency, OCTA manufacturer software does not allow the homogenous assessment of 1 single quantitative OCTA parameter across different machines. Although Zeiss Angioplex offers the analysis of perfusion density, vessel density, and different foveal avascular zone (FAZ) parameters such as circularity, size, and area, Optovue enables the assessment of the vessel density, flow, and nonflow area assessment and different FAZ parameters such as FAZ area, perimeter, and AI/FD (circularity index/FD-300 [vessel density 300 mm from the fovea]).
Other commercial software only allows the manual assessment of the FAZ area so far. Until now, the FAZ area is the most consistently assessable parameter across all OCTA software. However, some devices allow FAZ area assessment only in the superficial slab, whereas others enable assessment only in the whole retina slab. This inhomogeneity leads to the use of nonvalidated third-party software such as Image J. In addition, various parameters must be manually adjusted for each software version, such as threshold, contrast, sharpening, color balance, and denoising, which makes the comparison among different studies and assessments unreliable. Variable segmentation strategies of the superficial and deep capillary plexus add to the heterogeneity. Although some algorithms use the middle of the inner nuclear layer as the boundary (e.
g. , Optovue), others use the top of the inner nuclear layer (e. g. , Topcon). 16 ll these issues make the homogenous description and collection of OCTA data impossible. Consensus terminology would not only help simplify and consolidate these terms but also improve accuracy of the measurements and quantification. It is our aim to establish a consensus nomenclature for OCTA findings in the field of retinal vascular diseases. This article describes the first step to develop the latter using a Delphi Method and presents the consensus results of an electronic survey carried out among the members of the Japanese Retina and Vitreous Society, The Retina Society, and the European Society of Retina Specialists. It will be followed by expert Delphi rounds, which aim to reach consensus for parameters and questions in which no initial majority agreement could be reached.
Methods We conducted a literature search via PubMed database for articles written in English in the field of retinal vascular diseases and OCTA. [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  This was the basis of an electronic survey consisting of 33 questions and 5 main categories. This survey was compiled via an online platform (Survey Monkey) and included general questions on OCTA and wide-field OCTA imaging, OCTA terminology of vascular flow alterations, terminology for retinal neovascularization (NV), the use of OCTA in retinal vascular diseases management and artifacts terminology in en face OCTA, and cross-sectional OCTA B-scans. 28 The complete survey is shown in Table  S1  (available at www. ophthalmologyretina. org). Institutional Review Board was not required. All research adhered to the tenets of the Declaration of Helsinki.
Measurement and Terms of Decreased Flow Signal Different methods have been used to measure areas of decreased flow on OCTA in retinal vascular diseases. Tan et al  14  used a custom MATLAB algorithm to calculate retinal perfusion density, capillary perfusion density, and capillary dropout density in DR using 12 Â 12-mm OCTA scans. Seknazi et al 40 used the AngioAnalytics software to binarize and calculate the relative density of flow as a percentage of the total area. They ordinally graded capillary dropout on OCTA slabs ranging from 0 to 8 (0 being no capillary dropout, and 8 being extensive capillary dropout). 40 Kim et al 8 used a custom semiautomated algorithm to assess skeleton density, vessel density, fractal dimension, and vessel diameter index. To convert OCTA into binary images, a 3-step method consisting of a global threshold, hessian filter, and adaptive threshold in MATLAB was used.
Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a 'blinded diagnosis' as likely 'ALS' or 'control'. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.
White matter parameters have been used in a multitude of categorisation frameworks including support-vector machines, discriminant function analyses, logistic regression, and random forest models with varying diagnostic classification accuracy. [18] [19] [20] [21] [22] [23]  The pitfalls of building models on smaller training datasets are well recognised, chief of which is the overfitting of such models to the training data which results in poor generalisability. [24]  While the description of group-level white matter signatures and stereotyped propagation patterns is academically relevant, the pragmatic demands of clinical imaging are markedly different. In clinical practice, the characterisation of shared imaging traits among many patients is a lower priority compared to the precision interpretation of MR data from single subjects.
Magnetic resonance imaging Diffusion tensor imaging (DTI) data were acquired on a 3 Tesla Philips Achieva Magnetic resonance (MR) platform using a spin-echo echo planar imaging (SE-EPI) pulse sequence with a 32-direction Stejskal-Tanner diffusion encoding scheme, FOV = 245 × 245 × 150 mm, 60 slices with no interslice gaps, spatial resolution = 2. 5 mm 3 , TR/ TE = 7639 / 59 ms, b-values = 0, 1100 s/mm 2 , SENSE factor = 2. 5, acquisition time: 5 min 41 s, dynamic stabilisation and spectral presaturation with inversion recovery (SPIR) fat suppression. For clinical interpretation, FLAIR images of each participant were also reviewed. FLAIR imaging took place in axial orientation using an Inversion Recovery Turbo Spin Echo (IR-TSE) sequence: TR/TE = 11,000 / 125 ms, TI = 2800 ms, FOV = 230 × 183 × 150 mm, spatial resolution = 0.
Diffusion-weighted (DW) data processing Pre-processing of DW data was performed within MRtrix3  [25]   [26, 27]  removal of Gibb's Ringing Artifacts  [28] , correction for motion and eddy currents  [29]  and bias field correction using the N4 method as implemented in the ANTs software. [30]  DW data were then co-registered to align with the T1-weighted data using a linear registration scheme with six degrees of freedom, where the b = 0 image served as a source and the raw T1-weighted image as the target data. Subsequently, the diffusion tensor model was fitted to the preprocessed DW-images  [31]  and for each subject, fractional anisotropy (FA) and radial diffusivity (RD) maps were extracted. T1-weighted images were skull-stripped and biasfield-corrected within FSL. [32, 33]  Version 6. 0 of FMRIB'S FSL was used.
z-score based interpretation of individual datasets To test each individual subject WM microstructure alterations with respect to healthy controls, bundle-specific tractograms were first generated for each subject based on the estimated peaks of the spherical harmonic function. [37]  DW data were segmented using TractSeg into 50 anatomically labelled tracts and FA / RD were estimated along 100 points of each tract. These estimates were averaged along each tract to generate a single proxy of WM microstructure integrity per subject and tract. Data from HCs were used as normative data, building distributions of the averaged and z-scored WM metrics separately for male and female patients. Normative data were used to test whether individual subjects' WM profile differed from the sex-matched 'physiological' range.
Discussion Our findings indicate that diffusion data from single subjects may be interpreted if a moderate sized control dataset is available. In ALS, the visual inspection of white matter changes on FLAIR and T2 datasets offers limited insights. Qualitative cues, such as hyperintensities along the corticospinal tracts and thinning of the corpus callosum have been observed in ALS, but the specificity and sensitivity of these findings are relatively limited and may depend on pulse sequence settings and disease duration. [41, 42]  The visual inspection of diffusion tensor data offers no meaningful information with regards to pathological changes and is merely suitable for the assessment of gross imaging artifacts pertaining to movement, susceptibility-relation distortions, or eddy currents. The visual evaluation of T1-and T2-weighted data may suggest cortical grey matter atrophy qualitatively, but one cannot comment on white matter atrophy based on these pulse sequences.
This study is merely a demonstration of how a single DTI data set may be interpreted in a low-incidence neurodegenerative condition. This is a proof-of-concept study of white matter interpretation and is not meant as a fully developed diagnostic framework. A number of limitations need to be acknowledged which need further optimisation for the concept to be transferable to clinical practice. First and foremost, a larger reference dataset is required, ideally also stratified for age and education. This method examines the integrity of tracts along their entire course and does not take segmental changes into consideration. The assessment of blinded data is not a validation of the approach, merely a pilot analysis, modelling clinical scenarios. A shortcoming of the labelling of blinded datasets is the binary categorised into 'ALS' or 'HC' instead of also considering a tertiary label of 'alternative diagnosis'.
Conclusions Amyotrophic lateral sclerosis is not only associated with corpus callosum and corticospinal tract degeneration, but the cerebellar peduncles, arcuate fasciculus and long association fibres are also commonly involved. These changes can be readily detected in single patients and a list of affected fibre bundles can be generated if normative datasets have been previously compiled. The presented approach may also be successfully applied to patients in their peri-diagnostic phase. While the description of group-level signatures is of academic interest, future imaging studies should focus on the development and validation of methods to classify single datasets into diagnostic and prognostic groups.
However, a number of recent papers in the choice experiment literature have considered it as one of the more viable income smoothing strategies  (Prasada (2020)  and references herein). We followed a D-efficient design approach to construct the choice sets, using prior information we had about the sign and relative values of the design attributes. 5 We used secondary data to construct prior values for the true parameters of the model. The value chosen for technical assistance was 600 BRL, which is the average price for hiring a specialist for one day in Minas Gerais region. The value chosen for weather insurance was 175 BRL for insuring one hectare of eucalypt plantation (after the 50% subsidy) and 35 BRL for the value of the equivalent of one meter cubic of wood. The design was generated with the software package Ngene in order to produce 10 choice sets per respondent.
Data We collected original data from a total of 80 out-grower farmers living in the state of Minas Gerais. In practice, we had access to the registry database of all the farmers enrolled in the contract-farming scheme of CENIBRA, an eucalypt pulp and paper company located in Ipatinga, on the eastern part of Rio Doce basin (Fig. 1 ). There were several hundreds of farmers likely to participate in the experiment. Given the financial constraints of the research project, we targeted of them, focusing on their geographic location so that we have respondents from all the different zones of the study area. After contacting them by phone, we ended up with 80 farmers who agreed to participate in the experiment. In order to ensure that respondents would fully understand the questions and concepts used in the CE, the questionnaire was pre-tested with technical assistants from CENIBRA, who interacted frequently with the out-grower farmers.
We moreover provided plausible values for average annual incremental loss for each scenario, based on expert estimates. 9 The participants first answered survey questions about themselves, their farm and their environmental perceptions, and then participated in the CE. Data collection took place between March and April 2017 through face-toface interviews. Descriptive statistics of the farms owned or managed by the survey respondents as well as their main socioeconomic characteristics are shown in Table  2. The sample is mainly composed of male growers, who have on average three household members, a secondary education and less than 15 years' worth of experience growing eucalypt. The majority of farmers interviewed grew eucalypt as a complementary source of revenue (less than 30% of their income).
The rainy period extends from October to March and precipitation in this period varies from 800 to 1300 mm. The dry period extends from April to September, with the most severe droughts occurring from June to August. Precipitation during the dry period ranges between 150 and 250 mm (Agência Nacional de Águas, 2010). 11 We do not have quantitative data on those farmers who ultimately did not participate in the choice experiment. However, qualitative (informal) information collected from CENIBRA when the sample was drawn up suggests that the sample is representative in terms of zoning (the four geographical areas of CENIBRA farms are represented), but not in terms of farm size (participants in the survey are presumably larger on average than non-participants, according to CENIBRA staff). Moreover, as in any survey where respondents voluntarily choose to participate or not, we cannot exclude that participants differ from non-participants in terms of their allocation of leisure time or perhaps in terms of awareness of the likely impacts of climate change.
1ES 2322-409 1ES 2322-409 is a bright TeV emitter, serendipitously discovered by H. E. S. S. (see  Abdalla et al. 2019). The redshift of this source is uncertain; a value z = 0. 174 has been suggested on the basis of a low S/N 6dF spectrum  (Jones et al. 2004 (Jones et al. , 2009)). We note that the redshift measurement was mostly based on a weak feature at ∼ 6900 Å, in a region where there is strong telluric absorption. A higher S/N spectrum taken with the FOcal Reducer and low dispersion Spectrograph (FORS) at the Very Large Telescope was featureless  (Landoni et al. 2013). The spectrum is available on the ZBLLAC 16  database and is quite noisy. When the FORS spectrum was taken the source was at magnitude R= 15. 7  (Landoni et al. 2013) ; there is no information on the source magnitude during the 6dF observation.
Optical extension of the sources We had eight targets with extended counterparts: four of them have extended NIR counterparts in the 2MASS Extended Source Catalogue (2MASX)  (Jarrett et al. 2000). Of the remaining four, the extension has been detected for three of them in dedicated observations and the last one, WISE J154824. 39+145702. 8, is classified as extended in the SDSS database. The 2MASX sources are TXS 1515-273, PMN J1457-4642, MITG J184126+2910, and 1RXS J195815. 6-301119. We measured the redshift for all of them with values between 0. 1116 and 0. 288 and an average value <z(2MASX)> = 0. 162. This may be an indication that gamma-ray BL Lacs whose counterparts belong to the 2MASX catalogue are good candidates for redshift measurement provided a sufficient S/N is obtained. The low average value of the redshift may be due to the relatively low sensitivity of the 2MASX survey that can reliably detect the extension only for sources with J ≥ 15.
069 (see e. g. Fosbury & Disney 1976)  and we took this value as its redshift. -B3 0609+413: This source has no redshift in the 3FHL catalogue, but an MgII system at z = 1. 108 has been detected in his spectrum  (Shaw et al. 2013). We adopted this value as its redshift. -PMN J0622-2605: The redshift of PMN J0622-2605 in the 3FHL catalogue is z = 0. 414 obtained from a spectrum in the 6dF database  (Jones et al. 2004 (Jones et al. , 2009)). However an examination of the spectrum (see fig. A. 1, first row, right) reveals no convincing feature in the 6dF spectrum, and we consider its redshift unknown. -1ES 0647+250: This source has redshift z = 0. 203 in the 3FHL catalogue. This value  (Rector et al. 2003)  has been classified as tentative. Recent high S/N spectra  (Paiano et al. 2017a ) failed to show any features, and therefore we consider its redshift unknown.
2013). We therefore consider its redshift unknown. -SUMSS J144236-462302: This source has no redshift in the 3FHL catalogue, but its SDSS spectrum is that of an elliptical galaxy at z = 0. 1026 (Fig. A. 1, third row, right) , which we took as its redshift. -PKS 1440-389: The redshift of PKS 1440-389 in the 3FHL catalogue is z = 0. 069, obtained from a low S/N spectrum in the 6dF database  (Jones et al. 2004 (Jones et al. , 2009)). Subsequent high S/N spectra failed to show any features, and we considered its redshift as unknown in our simulations. In this paper we report that its redshift is z = 0. 1385 (see Section 6. 4 for details). -RBS 1432: The redshift of RBS 1432 is unknown in the 3FHL catalogue. An MgII system at z = 0. 739 has been detected in its SDSS spectrum (Fig. A. 1, fourth row, left), and we took this value as its redshift.
-PMN J1603-4904: This source has no redshift in 3FHL. Its redshift has been measured as z = 0. 232  (Goldoni et al. 2016), and  we used this value in our simulations. -PMN J1610-6649: The redshift of PMN J1610-6649 is unknown in the 3FHL catalogue. An MgII system at z = 0. 447 has been detected in its spectrum  (Shaw et al. 2013) , and we took this value as its redshift. -TXS 1902+556: Same as the source above, the MgII system is at z = 0. 727. -PMN J1918-4111: Same as the source above, the MgII system is at z = 1. 591. -RX J1931. 1+0937: Same as the source above, the MgII system is at z = 0. 476. -1RXS J195815. 6-301119: 1RXS J195815. 6-301119 has a redshift of z = 0. 119 in the 3FHL catalogue from a low S/N spectrum in the 6dF database (Fig. A. 1, fourth row, right). Its redshift value was considered unknown in our simulations, but we confirmed it at z = 0.
119 in this paper (see Section 6. 15 for details). -RGB J2243+203: This source has no redshift in the 3FHL catalogue. An MgII system at z = 0. 395 has been detected in its spectrum  (Shaw et al. 2013) , and we took this value as its redshift. -MITG J225517+2409: The redshift of MITG J225517+2409 is unknown in the 3FHL catalogue. An MgII system at z = 0. 864 has been detected in its SDSS spectrum (Fig. A. 1, fifth row, left) , and we took this value as its redshift. -1ES 2322-409: 1ES 2322-409 has redshift z = 0. 174 in the 3FHL catalogue from a low S/N spectrum in the 6dF database (Fig. A. 1, fifth row, right). Its redshift value was considered unknown in our simulations, but we measured it at z = 0. 1738 in this paper (see Section 6. 19 for details). Article number, page 19 of 21 A&A proofs: manuscript no.
Acknowledgements. This paper went through internal review by the  CTA consortium. We thank  D. Horan  and  M. Errando  who acted as internal CTA reviewers for comments that helped improve the quality of the paper. We thank an anonymous referee for his/her comments. We thank  Elisabete de Gouveia del Pino  for useful discussions. This research has made use of the CTA instrument response functions provided by the  CTA Consortium and Observatory , see https:// www. cta-observatory. org/science/cta-performance/(versionprod3b-v1) for more details. We gratefully acknowledge financial support from the agencies and organisations listed here: http://www. cta-observatory. org/consortium_acknowledgments and in particular from the  US National Science Foundation , award  PHY-1707454. The authors wish to recognise and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community.
We developed DNS (Direct Numerical Simulation) in the finite-element software COM-SOL, which proved to provide better predictions than the classical PT (Perturbation Theory) modeling, especially at relatively large forcing. Finally, the dependency of SEAS on fluid viscosity and acoustic frequency was also experimentally studied in  [32]. Following our earlier work, to fabricate a channel with an array of sharp edge structures should be effective to considerably enhance mixing. Although several experimental and PT-based simulation studies focusing on chip-scale SEAS mixing have been reported by Nama et al. [30]  and Ozcelik et al. [33]  and, more recently in  [34] , the hydrodynamic streaming mixing enhancement mechanism remains unclear. Namely, several critical questions remain unanswered: (i) What is the best geometrical sharp-edge configuration that enables the best mixing performance? (ii) From a micromixer application perspective, evaluation of micromixing performance of SEAS through parallel chemical reactions (mainly based on micromixing time), to the best of our knowledge, is still absent in the literature.
Statistical analyses All statistical analyses were conducted in IBM SPSS, version 26, using cohort weights to ensure representativeness of the sample. First, we described the COVID-19-related stressors and pre-existing vulnerabilities variables using counts and percentages. Second, we tested whether (a) a mean symptom score and (b) a severity category of depressive and anxiety symptoms changed from before to during the COVID-19 pandemic using paired t-tests. Third, we calculated change in depression and anxiety symptoms by subtracting the mean symptom score before the pandemic (20 years) to the mean score during the pandemic (22 years). We standardized the mean change score (change/SD of change) to ease interpretation; positive scores indicate an increase in depression and anxiety symptoms.
Then, we examined crude associations between COVID-19-related stressors and pre-existing vulnerabilities variables and change in depression and anxiety symptoms, using t-tests and ANOVAs. The double-sided p-value for significance was set at 0. 05. We used multiple imputation (MI) to generate 100 datasets to handle missing values on risk factors. Missing values ranged from 0. 9% (loss of employment) to 17. 9% (sexual minority); there was no missing data for COVID-19-related stressors.