dataesr/scientific-paragraphs-categorization huggingface dataset:
The effects of this second hurricane on the LFDP forest area were less acute than those of Hugo  (Uriarte et al. , 2019) , and it had a limited effect on forest structure and composition  (Uriarte et al. , 2009 (Uriarte et al. , , 2019)). Basal area was estimated to average 36. 7 m 2 ha -1 at the time of Hurricane Hugo, 30. 85 m 2 ha -1 at the time Georges struck, and 38. 37 m 2 ha -1 in 2016. Density of stems ≥ 10 cm DBH has remained stable at c. 1000 ha -1 , whereas the density of small stems (1-10 cm DBH) ranged between 6841 in 1993 and 2444 ha -1 in 2016, reflecting continuous stem thinning through post-hurricane succession (Supporting Information Fig. S1 ). The forest has not been censused since the passage of Hurricane Maria in 2017, so we use census data collected through 2016. Altogether, our 13 target species represent c.
To do so, we used the bench dry method as described by  Tyree & Hammel (1972)  and the protocol developed by  Sack & Pasquet-Kok (2011). We let the branch rehydrate overnight, excised a mature fully expanded healthy leaf from the branch with a sharp razor blade, and immediately sealed it in a Whirl-Pak bag. As leaves dehydrated, we periodically measured water potential Ψ with a Pressure Chamber Instrument (PMS Instrument Co. , Albany, OR, USA) and leaf weight. We continued to measure the leaves until 1/Ψ and water loss became linear. Finally, we scanned the leaves, then oven-dried them at 65°C for at least 72 h and weighed them to obtain leaf dry mass. We calculated leaf area with IMAGEJ software (National Institutes of Health, New York, NY, USA) and used the Sack & Pasquet-Kok (2011) spreadsheet tool to construct pressure-volume curves from which we extracted leaf turgor loss point Ψ tlp and leaf capacitance at full turgor C ft.
This pattern was particularly marked in C. schreberiana, D. excelsa, and P. acuminata var. montana. Our results coincide with previous findings of a synthesis of data from multiple sites that found a trade-off Ó 2022 The Authors New Phytologist Ó 2022 New Phytologist Foundation New Phytologist (2022) www. newphytologist. com between capacitance and P 50 for tropical species  (Christoffersen et al. , 2016) ; here, we show this pattern operates at the community level. Environmental conditions after a hurricane favor fast-growing species that take advantage of the increase in light availability  (Guzman-Grajales & Walker, 1991; Burslem et al. , 2000; Uriarte et al. , 2004 Uriarte et al. , , 2005;; Comita et al. , 2009). Fastgrowing species also tend to have higher hydraulic conductance  (Markesteijn et al.
005 to 15 ha in Brussels, 0. 001-63 ha in Luxembourg, and 0. 001 ha-50 ha in Rouen. For sampling control, survey respondents also reported UGS use frequency. In case of a mismatch, i. e. a UGS located but 'never' reported as a frequency, respondents were removed. Based on each pair of residential and UGS points, an objective walking distance along the network was computed using the Google routing algorithm (R-package ggmap implementation  (Kahle & Wickham, 2013) ). While the algorithm may under-perform for pedestrian travel compared to driving, it is better than Euclidean at such a micro-scale and comparable across cases. Alternative algorithms based on Open Street Map for example might be prone to important data differences for pedestrian tracks and sidewalks. Origin and destination coordinates precision was obtained directly from respondents, e.
H3. Local UGS provision The provision of UGS around home is measured subjectively and objectively. Respondents were asked how satisfied they are with the quality of UGS within 5 min walking distance from home (3 categorical responses: not at all; somewhat; very). As an objective measure, we compute the share of UGS within a 400 m walking distance around each home (in line with e. g. Van Herzele & Wiedemann, 2003; Schindler et al. , 2018)  using OSM data (OpenStreetMap contributors, 2016). In addition, we compute the network walking distance to the nearest UGS for each residential location. We expect low objective or subjective UGS provision to act as constraint and require respondents to travel further to use UGS.
Model specification We apply a multi-level model with random slopes and intercepts to account for the hierarchical (spatial) structure of our data, i. e. distributed across the three case study cities. The framework allows for varying explanatory effects for each city, and quantifying heterogeneity of effects across cases is one of our explicit goal. Various sets of simpler regression models were run but failed to account for the spatial variability within our dataset. Despite the low number of cities we concluded a multi-level modelling approach is appropriate given our goal and along with relevant multi-level modelling literature  (Gelman & Hill, 2007; Huang, 2018). Our model is estimated using the 'lme4' package of the R statistical software  (Bates et al. , 2015). In our final model, the travelled distance to the most used UGS is logged to account for expected non-linearities and the skewed distri-bution (see results).
Methods We performed a retrospective study on medical records in the department of Obstetrics and Gynecology of Louis-Mourier Hospital, a university hospital center of the Assistance Publique-Hôpitaux de Paris (APHP), in the outskirts of Paris, France. All consecutive cases with a diagnosis of cervical cancer were identi ed over the period 2006 to 2019 from pathology reports on cervical biopsies or surgical samples. The data were extracted from multidisciplinary tumor board les, computerized medical records and paper archives.
The study was approved by the French database security commission (Commission Nationale Informatique et Liberté) and by the Institutional Review Board (Comité d'Evaluation de l'éthique des projets de Recherche Biomédicale, IRB00006477: N°2019-038 January 31, 2020). The patients were classi ed into two groups: those diagnosed with cervical cancer at a localized stage (FIGO stage IA1 to IB2) (group A) and those diagnosed with locally advanced or advanced cancer (FIGO stage IB3 to IV) (group B). We chose this classi cation because the rst line of treatment changes beyond stage 1B2 7,8. Patients whose les were partly or completely unavailable were excluded, as well as patients who refused access to their data for research purposes.
Subjects Thirty expert meditation practitioners (52±7. 8 years old, 13 females, right-handed, 3±2. 62 years of higher education, all white Caucasians) and thirty-six control healthy participants (52±7. 6 years old, 17 females, right-handed, 3±2. 33 years of higher education, all white Caucasians) participated in the study. Control participants (hereafter referred to as "novices") were recruited locally through flyers and posters in public places, mailing lists, social media and notifications to research participant databases. Novices underwent a weekend-long introduction to meditation and practiced at home before participating in the experiment (see section 2. 2 for details). Expert practitioners (> 10. 000 hours of practice and at least one 3-years intensive retreat) were recruited from multiple meditation centers, predominantly in France but also internationally, with the help of a long-term practitioner with extensive contacts within the Nyingma and Kagyü communities of Tibetan Buddhism.
EEG recordings EEG was recorded with the ActiveTwo system (BioSemi, Amsterdam, Netherlands) and sampled at 512 Hz. The recording system consisted of 64 active electrodes positioned on a cap following the standard 10/20 coordinates. The horizontal and vertical EOG was measured by placing flat electrodes on the outer canthi and above and below the subject's left eye. Electrodes were kept within an offset of 50 mV (+/-25 mV) using the Biosemi ActiView data acquisition system for measuring signal quality. Recordings were performed in a highly shielded Faraday chamber.
EEG Data preprocessing EEGlab  (Delorme & Makeig, 2004)  and in-house Matlab scripts (version R2015a) were used for data pre-processing. Raw recordings were downsampled to 250Hz and re-referenced offline based on the electrodes placed on the mastoids (average activity of the two channels). Bad channels (affected by muscle-related activity over long periods of time) were identified by means of visual inspection and subsequently marked for being interpolated. We applied Independent Component Analysis (ICA) to the continuous data using the Runica algorithm  (Makeig et al. , 2002) , distinctly for the two recording sessions. Data that underwent ICA were manually cleared of large movement-related artifacts following examination of the raw EEG (performed by the first author of the manuscript, who had previous experience in EEG data processing), did not include channels previously marked for interpolation and were filtered between 1 and 20Hz (using eegfiltnew function, which applies a Hamming windowed sinc FIR filter.
Alpha spectral power For spectral analysis, we limited the analysis to two spatial regions of interest (ROI), frontal and occipital, defined similarly to  Fucci et al. , 2018  (see Figure  S1  for a description of channels included in the ROIs. Available online at osf. io/3f9e7). Cleaned epoched data were imported into MNE-Python  (Gramfort et al. , 2013)  and power spectral density (PSD) was computed for each state (FA, OP and VD) and epoch with the multitapers method  (Percival et al. , 1993) , using defaults parameters of the psd_multitapper function from MNE-Python (version 0. 22. 1). PSDs were then averaged across all channels of a ROI and alpha absolute power was computed as the integral approximation of the spectrum between 8 and 12 Hz using Simpson's rule (simps function from scipy 1.
Bayesian Statistics Model structure. The MMN amplitude was reanalyzed in the Bayesian framework using the R package rstanarm (v2. 21. 1; Goodrich et al. , 2020). Data from the original experiment  (Fucci et al. , 2018)  and the present study were modeled together within a single statistical model, the same used for frequentist analysis (see section above), with the minor addition of a fixed main effect of Experiment to account for differences in recorded MMN amplitude due to differences in stimuli properties and recording devices. Interestingly, the Bayes factors obtained from a model combining two experiments is equal to the product of the Bayes factor obtained from the first experiment, by the Bayes factor obtained from the second experiment where the posterior distributions of the first experiment are used as priors of the replication  (Ly et al.
, 2019) : BF(exp1,exp2) = BF(exp1) x BF(exp2|exp1) Prior distributions. We followed the principled Bayesian workflow proposed by Schad et al. 2021 to define priors that are compatible with domain expertise (see Supplementary Information 2 at osf. io/3f9e7). Prior predictive checks revealed that the default, weakly informative priors of rstanarm were unrealistic as they were compatible with highly implausible datasets. Therefore, we used an alternative set of narrower priors that remain broad enough to reflect very general and uncontroversial elements of domain expertise regarding the MMN component, i. e. , knowledge about the likely characteristics of the MMN component obtained from previous research such as plausible ranges of amplitude and effect sizes. Notably, all priors for the regression coefficients were normally distributed, centered on 0 (except the prior for the intercept, which was centered on the mean of the data).
Standard deviations of normally distributed priors (regression coefficients) and rates of exponentially distributed priors (random intercept and residual variance) were conveniently set to 2 and 1µV, respectively. Statistical inference. 10,000 samples were drawn from every posterior distribution. Contrast samples were extracted using the R package emmeans  (Lenth et al. , 2020). Bayes factors and support intervals were calculated using the R package bayestestR (v0. 11. 0  Makowski et al. , 2019).
Moreover, participants underwent short oddball sequences embedded in safe or threat periods. Previous studies on MMN modulation by meditation states and traits did not explore this marker in the context of mood induction. Here, we excluded data acquired during periods of threat, which were separated from neutral periods by a pause of several seconds. In a separate report  (Fucci et al. , 2019) , we describe subjective and electrophysiological responses to the different periods in novice practitioners during a control state, showing how they can be independently characterized and how threat periods do not seem to influence the MMN amplitude. However, it is possible that emotional contexts masked effects that would otherwise be present in a neutral situation through a lingering effect. Secondly, our studies made use of the classical, simple oddball task and our conclusions are restricted to that specific class of experimental designs.
Acknowledgments This study was supported by a European Research Council grant ERC-Consolidator 617739-BRAINandMINDFULNESS to Antoine Lutz. The authors have no conflict of interests. We thank the numerous students who have been involved in the data acquisition and data analysis processes during their internships. In particular Kelly Assouly, Alix Arnas, Arthur Sevennec, Maximilien Metaireau and Gansheng Tan. We thank an anonymous reviewer for numerous detailed and constructive feedback.
e. , tablet and web browser) through several used web-based measures that could likely be used on several platforms (e. g. , web, smartphone, tablet). In total, six studies included remote measures that were completed in a laboratory setting, 23 were done remotely, and two used both settings. The upper circles of the logic model summarize reported reliability, sensitivity/specificity, construct validity, and criterion validity of the reviewed measures, detailed in Supplementary Data. For each cognitive domain, we report the number of times a given psychometric was evaluated over the total number of times it was measured across studies. Next to each total, we summarize the reported psychometric properties as either low (L), moderate (M), or high (H) and invite the reader to consult Supplementary Data for detailed findings.
First, it is a broad scoping review of remote measures of both cognitive capacity and cognitive biases in severe mental illness designed to address an urgent need given the COVID-19 pandemic. Second, it involves rigorous methodological procedures including randomization, repeated inter-rater reliability, extensive quality control, and iterative data synthesis. Third, the search was updated after six and nine months given the rapidly evolving literature in this domain. Finally, data extraction was comprehensive and included several characteristics (e. g. , diagnosis, setting, researcher presence, platform, duration, alternate forms, licensing, cognitive domain, psychometric properties) that will assist researchers and clinicians in their choice of remote measures. A potential limitation of this study is that the search strategy, which was focused on severe mental illness, may not have captured all articles assessing remote cognition in other psychiatric disorders, though several were identified, and reference lists were also checked.
Selected articles were then retrieved for full-text screening and data extraction of included articles. Given that articles were screened by different raters, rather than by all raters, we assessed inter-rater reliability (IRR) by having all raters assign ratings to three samples of 100 articles at the start, midpoint, and end of article selection, as in previous research  73. IRR was calculated using Gwet's AC 1 statistic  74  via the R AgreeStat package to account for the kappa paradox, in which unusually low agreement statistics are produced when there is a skewed distribution of ratings (e. g. , many excluded articles)  [75] [76] [77]  (see Supplementary Table  1  for a demonstration with the current data). Following each IRR timepoint, raters produced a consensus for any inconsistent ratings.
Data extraction Data extraction was performed on selected articles according to a pre-developed form, which was tested and fine-tuned with one exemplar article by the lead author. Articles were randomized for data extraction across three independent raters. Data extraction was quality controlled by authors K. M. L. , G. S. , and D. R. -C. by randomly selecting six articles (10% of articles originally extracted) and re-extracting the data. Data extraction included the following predetermined variables: bibliographic data (authors, year, title, abstract), study characteristics (aims, design, country, setting, researcher presence/title, sample size, psychiatric diagnosis, mean age, age range, sex/gender ratio), description of remote assessment methods (remote/comparison measure(s), battery, remote platform, developer, language, duration, alternate forms, availability of norms), main findings, sex/gender findings, psychometric properties (reliability, sensitivity/specificity, construct validity, criterion validity), facilitators, barriers, and future directions.
A comprehensive literature search was conducted on May 11, 2020 and updated on November 11, 2020, and February 4, 2021 using OVID (MEDLINE, PsycInfo, and EMBASE) and EBSCO (CINAHL) databases. The following keywords were used: (schizophreni* OR psychosis OR psychoses OR psychotic* OR severe mental illness) AND (cogniti* OR neuropsych* OR bias* OR reason*) AND (remote* OR online* OR mobile* OR digital*) AND (assessment OR evaluat* OR test* OR measure*). The search was limited to articles in either English or French from any publication year.
ACKNOWLEDGEMENTS The authors would like to thank  Philippine Hollander ,  Marianne Khalil , and  Vanessa McGrory  for their invaluable help during article selection and data extraction. This research was funded by a knowledge synthesis grant from the  Canadian Institutes of Health Research  COVID-19 rapid research funding opportunity in mental health and substance abuse (FRN:  171710 ). Initial reports are available online at: https://cihr-irsc. gc. ca/e/52066. html.
2D Fluorescence reflectance imaging (FRI). In vivo FRI is an innovative and non-intrusive technique for fluorescence detection on live animals. This technique was tested for the first time on fish. In vivo FRI quantifies the fluorescence signals of live animals that are imaged under excitation and emission filter sets. Depth of penetration for FRI light in tissue is several centimeters, allowing for whole body imaging of small animals. The FRI analyses were conducted at Vivoptic platform (ANR-11-INBS-006, Univ. Bordeaux, CNRS, INSERM, TBM-Core, UMS 3427, US 5, F-33000 Bordeaux). Marked (n = 8) and control live glass eels (n = 8) were anaesthetised with an eugenol solution (0. 03 mL. L -1 ) before being placed one by one into a slot of an holder (a dish drainer, Sticks, Lékué, Espagne). The holder with live glass eels (4 marked, 4 control glass eels × 2) were placed into Lumina LT optical system (Perkin Elmer Inc.
, Boston, USA) equipped with a CDD camera (maximal field of view in the machine: 12. 5 × 12. 5 cm). All alive glass eels were imaged by FRI (1 s time of exposition) with two sets of excitation and emission wavelength filter: 1) 450 nm-480 nm at excitation, 515-575 nm at emission; 2) 485 nm-515 nm at excitation, 515-575 nm at emission. The imaging of glass eels was photographed (100 ms time of acquisition) and the 2D images were analysed with Living Image software (Perkin Elmer Inc. , Boston, USA). The glass eels awaken were then transferred to the INRAE laboratory for the samplings. Prior to the analyses, an alizarin drop (alizarin red S, 150 ppm, SigmaAldrich, China) was imaged (Supplementary Fig. S2b ) to select the wavelengths of the excitation and emission filters. The holder was imaged with the filters selected and no autofluorescence was detected (Supplementary Fig.
Epifluorescence microscopy (EPI). EPI analyses were conducted at INRAE. The glass slides of pectoral (n = 45) and caudal (n = 45) fin tips of marked (n = 30) and control (n = 15) glass eels (n total = 45) were photographed with an epifluorescence binocular magnifier (SMZ25, Nikon, Japan; camera: DS-Ri2, Nikon, Japan) equipped with a B-2A fluorescence filter (Nikon, excitation bandpass: 450-490 nm; dichromatic mirror cuton: 500 nm longpass; barrier filter: 515 nm longpass). The fluorescence of each photograph was analysed using NIS-Elements BR software (version 5. 02). The fluorescence intensity observed on the photographs was assessed using a scale of 0-3 (0, no fluorescence; 1, weak autofluorescence around fin rays and at the edge of the fin as described in  15  ; 2, bright and heterogeneous fluorescence of alizarin stain on fin rays and tissue; 3, very bright and homogeneous fluorescence of alizarin stain on fin tip) (Fig.
Acknowledgements We are grateful to our close collaborators for the funds and the collect of glass eels for this research which are the DDTM, the  French Ministry for the Biodiversity, regional and provincial councils of "Région Nouvelle-Aquitaine '' and " Conseil Général Gironde ", CRPMEM, ARA, FDAAPPMA33, MIGADO. The authors would like to special thank  Malin and Aguirrebarrena  for providing batches of marked and unmarked glass eels,  K. Menu Courey  for the English revision,  C. Gazaux ,  M. Wash ,  G. Lefrevre ,  R. Le Barh ,  T. Deweulf ,  J. Philippe ,  L. Jacob ,  F. Polez ,  P. Chèvre ,  J. Gauthier ,  B. Henri ,  A. Bismuth  for rearing and technical assistance. Authors are particularly grateful to the editor and the two reviewers for their valuable comments. This work was supported by the  European regional development Fund (ERDF)  and  FSE Aquitain  (n° 3536310).
In the tube including the nucleotide complementary to the allele on the genome, the MIP probe is ligated and becomes circular. This structure is selected using exonucleases, and linearized. The products are ampli ed and hybridized onto an Affymetrix microarray for product identi cation. At the time we performed the analysis, OncoScan® Assay searched for 541 somatic mutations speci c to cancer, with a coverage of over 200 tumor suppressors and oncogenes, and a median spacing of 1 probe per 0. 5 kb for the top 10 "actionable" tumor suppressor genes, a median spacing of 1 probe per 2 kb for the top 190-plus actionable oncogenes, and a median backbone spacing of 1 probe per 9 kb. Data analyses were performed on Nexus-7 software (BioDiscovery,USA).
). A postampli cation melting curve program was initiated by heating to 98°C for 10 min. and then cooling to 12°C. Each PCR run included a no-template control. The results for ddPCR were generated using QX100 Droplet Reader (Biorad) and analysed using QuantaSoft software (Biorad). The ratio of NOTCH1-positive droplets to RnaseP-positive droplets was calculated. We calculated the mutant allele frequency using the mathematical formula Mmu/ MDNAconc, as previously reported, Mmu being the number of mutant copies per droplet and MDNAconc being the DNA concentration in the reaction. Mmu and MDNAconc are calculated as follows: Mmu = -ln (1-(nmu/n)) , where nmu = number of droplets positive for mutant NOTCH1 probe and n = total number of droplets. MDNAconc = -ln (1-(nDNAcon/n)), where nDNAconc = number of droplets positive for wild-type NOTCH1 probe and/or mutant NOTCH1 probe and n = total number of droplets  [15].