dataesr/scientific-paragraphs-categorization huggingface dataset:
In turn, the reserve model predicts that training compensates for unfavorable initial conditions and that less able or less healthy individuals will benefit more from additional training, thereby reducing initial cognitive disadvantages. These two hypotheses will be examined here in relation to age, sex, education, dementia risk, and frailty. The goal of this exploratory study was to use the data from the MAPT study to model the relationship between dose and cognitive improvement in a multidomain intervention and determine the optimal dose. Dose was modeled separately for global cognition, delayed memory recall, and executive function measured with verbal fluency. Global cognition was the primary outcome in the MAPT efficacy study. Delayed memory recall and verbal fluency were found to predict progression from mild cognitive impairment to dementia.
Design The MAPT trial was a 3-year intervention multicenter randomized controlled trial with four parallel arms: multidomain intervention plus placebo, multidomain intervention plus polyunsaturated fatty acids, polyunsaturated fatty acids alone, and placebo alone. The initial study included 1680 participants, who were randomly assigned to one of the four conditions (1;1;1;1). The methods and design have been described in detail elsewhere  154  and registered in www. clinicaltrials. gov (NCT00672685). Cognitive status was assessed at baseline, at 6 months, and then annually at year 1, year 2, and year 3. Neuropsychologists conducting the evaluation were blinded to group assignment. Written consent was obtained from all participants. The study protocol was approved by the Toulouse Ethical Committee and authorized by the French health authority.
Because the present study evaluated the effect of dose for the multidomain intervention, we only used data from 749 participants randomized to this intervention (multidomain intervention plus placebo or multidomain intervention plus polyunsaturated fatty acids). We, therefore, summarized only the methodological elements related to the multidomain intervention and those related to the outcome analyses. Additional information on the other components (polyunsaturated fatty acids and placebo) and measures can be found in Vellas et al. 15  and Andrieu et al. 4.
We also examined group differences (t-tests for independent samples) before and after the optimal dose, defined in the main model for each cognitive domain. This determined whether group differences (in terms of effect size) remained present after the optimal dose. Because the intervention was delivered in two stages, which included a 2-month intensive period followed by distributed sessions, we re-ran the main models excluding the 73 participants, who completed only the 2-month intensive period. This allowed us to address whether delivering intensity makes a difference. Analyses were done using IBM SPSS Statistics 26 software.
Extraction of Information Article ID data, experimental design, treatment groups, size sample (n), dose and route of administration, anesthetic success index, anesthesia depth, anesthetic time, pain intensity, rescue analgesic intake, and adverse effects were obtained. Aksoy and Ege published two articles using local tramadol in patients with SIP in 2020. These reports were placed with a lowercase letter to identify them in this SRME ("Aksoy and Ege, 2020a"  [13]  and "Aksoy and Ege, 2020b"  [14] ). Two independent clinical researchers made the bias measurement and data extraction. The differences between them were decided by a third investigator, only when necessary.
Statistical Analysis The review management 5. 3 software for Windows from the Cochrane Library (London, UK) was used for data analysis. Both anesthetic success index and adverse effects of local tramadol versus placebo/local anesthetic were compared with the Mantel-Haenszel test and odds ratio (OR). Data inconsistency was analyzed with the I 2 test. Furthermore, the funnel plots were employed to assess the publication bias of the included clinical studies. A p-value ≤ of 5% (0. 05), with an OR ≥ 1 within a 95% confidence interval (CIs), was considered a statistical difference  [32, [35] [36] [37] [38].
In this SRME, 9/144 patients in the tramadol group had a significantly increased risk of adverse effects when compared to the control group. Nausea and dizziness were the adverse effects reported by patients receiving tramadol in the clinical trials included in this SRME. However, the upper ICs is wide, which could indicate that the significant data did not have great relevance, despite the OR obtained. The adherence to international guidelines for conducting this SRME, the use of studies with a low risk of bias, and the low heterogeneity of the data are the main strengths of this study. However, some weaknesses were detected, for example, the low number of studies detected, a relatively small sample size, the different doses of tramadol used, the different methods employed to assess the same variable (i.
Methods Song collection and transcription. Data were collected from two sites (Fig. 3 ): Peregian Beach on the coast of southeast Queensland, Australia (26°30' S, 153°05' E), and the southern lagoon of New Caledonia (22°28' S, 166°56' E). Passive acoustic recordings in east Australia were collected in 2009-2014 using two autonomous loggers (Acousonde 3A with external battery housings, Greenridge Sciences) and a fixed five-buoy hydrophone array (detailed in Allen, et al. 27  ). New Caledonian recordings were made in the Southern Lagoon using boat-based hydrophones (2010-2013, HiTech) and Zoom recorder (16 bit, sampling rate of 44 kHz, WAV files), and a single passive acoustic recorder (2014-2015, SM2M + Whalesong recorder, Wildlife Acoustics with a sampling rate of 22 kHz). The years selected for analyses were based on the availability of high-quality recordings and resources for the subsequent transcription of recordings.
The LSI matrix was clustered using average-linkage hierarchical clustering and bootstrapping using pvclust and hclust packages in R  57  and custom written code (available at https:// github. com/ ellen garla nd/ leven). Resulting dendrograms were assessed using the cophenetic correlation coefficient (CCC) to determine how well they represented associations within the data, with CCC > 0. 8 indicating good representation  58. Based on their respective patterns, LSI grouped the song types (Purple, Light Purple, Brown, Light Brown, Teal, Orange) onto separate major dendrogram branches, and were clearly distinguished from one another (Figs. S7  and 8 ). On each major branch (i. e. , each song type), the LSI further grouped each 'song cycle' (i. e. , one set of specific themes sung in a stereotyped order  13, 23  ) regardless of the recording's population of origin.
Acknowledgements We would like to acknowledge the  Quandamooka People on  who's country this research was conducted. We thank the staff and volunteers of the  Cetacean Ecology Group  for data collection, maintenance, and storage over the course of the study. East Australian data were collected during the  HARC  project (2009) and the  BRAHSS  project ( 2010-2012  and  2014 ). Thanks to  Douglas Cato  for his significant role in instigating and participating in all these field programmes. New Caledonian data were collected through Opération Cétacés with the assistance of the  Province Sud Gardes Nature. We are grateful to  Patrice Plichon ,  Veronique Pérard , and  Remi Dodemont  for their roles in this field data collection. We thank  Adam Pack  and  Christopher Templeton  for providing comments on a previous version of the manuscript.
Data analysis The extracted data was used to identify the vascular biomarkers studied in AO ophthalmic imaging. Given the different terms adopted in literature for the same vascular biomarkers, a normalization of these terms has been proposed, as shown in Table  1. All results are presented as mean plus or minus one standard deviation unless stated differently. A meta-analysis was executed when at least three articles did a comparable study (i. e. , the same biomarker applied to the same disease) using Please cite this article as: Elise Bakker, Felix Anne Dikland, Roan van Bakel et al. , Adaptive optics ophthalmoscopy: a systematic review of vascular biomarkers, Survey of Ophthalmology, https://doi. org/10. 1016/j. survophthal. 2021. 05. 012 the same modality. The software MedCalc® (MedCalc Software bv, Ostend, Belgium) was used to assess heterogeneity applying the I 2 test.
Results The search queries on PubMed and Scopus resulted in 217 unique articles. From those, 85 studies were considered eligible after screening the titles and abstracts. Full text screening resulted in 42 studies that met all inclusion criteria and hence were eligible for the data analysis (see Fig. 1  ). From the 42 included articles, 33 are cross-sectional, and 9 were longitudinal studies. The most commonly diseases studied by AO FIO were systemic hypertension in 10 articles and diabetes for AO SLO in 14 articles (see Fig. 2  ). The vascular biomarkers found for AO FIO and AO SLO are shown in Fig. 3  and summarized in Table  3. AO ophthalmoscopy is done using two modalities: either full-field illumination without optical sectioning (AO FIO) or confocal scanning laser, enabling optical sectioning, but occulting backscattered light (AO SLO).
Literature search A literature search was conducted in the PubMed and Scopus databases. The PubMed search query was: (biomarkers[MeSH Terms] OR "biomarker * ") OR (Retinal Vessels[MeSH Terms] OR "vessel * " OR "vascul * " OR "retinal art * ")) AND (adaptive optics[Title/Abstract]). The Scopus search query was: (("biomarker * " OR "retinal vessel * ") AND "adaptive optics"), searched in "abstract, title and keywords". Both searches were conducted on July 9, 2020. The following exclusion criteria were used: the study (1) has no accessible full text, (2) only has an available abstract, (3) is not written in English, (  4 ) is not performed on humans, (  5 ) is a review, (6) does not apply AO in FIO or SLO, (7) only includes healthy subjects, (8) does not describe a vascular biomarker in AO FIO or AO SLO images, and (9) does not describe a statistical or non-statistical relation between a biomarker and having a pathology or the severity of a pathology.
Measuring freshwater submerged aquatic (SAV) biomass at large spatial scales is challenging and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat-scuba diver technique, fast rake sampling and large scale echosounding. We found that the relationship between quadrat and rake biomass is moderately strong (R 2 = 0. 62, RMSECV = 2. 19 g/m2) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume10 and its error (R 2 = 0. 53, RMSECV = 5. 95 g/m 2 ), a biomass proxy derived from echosounding, at a resolution of 10 m radius from rake sampling point. However, the relationship was affected by SAV growth form, depth, acoustic data quality and wind conditions.
Materials and Procedures To compare, and integrate the different biomass assessment methods, three datasets were used: the quadrat-rake, the rake-echosounding and the validation datasets (Table  1 ). All datasets were collected in Lake Saint-Pierre (LSP), a ~300 km 2 widening of the Saint-Lawrence River in Quebec, Canada (Figure  2a, b ). Part of the quadrat and rake dataset was also collected in the nearby upstream fluvial lakes, Saint-Louis and Saint-François.
Quadrat-rake (Q-R) dataset The first dataset was used to assess the correspondence between biomass estimates derived from quadrat and rake samples (hereafter named the Q-R dataset), using one of three different collection strategies: systematic pairs, haphazard pairs or blocks (Figure  2c ). For the first two strategies, paired rake and quadrat samples were collected around the anchored boat, using either a systematic or a haphazard strategy. In the "systematic pair" strategy, a single quadrat (0. 25 m x 0. 25 m) was systematically collected to the upper right side of each rake sample (1 m x 0. 35 m). In the "haphazard pair" strategy, bigger quadrats (0. 40 m x 0. 60 m) were located in the vicinity of the rake sample site. In the block sampling strategy, a block, which consisted of two rake samples positioned on both sides of a row of 4 individual quadrats (0.
Rake and echosounding (R-E) dataset The second dataset was used to predict rake biomass from echosounding (hereafter named R-E dataset). Both acoustic and rake surveys were carried out in the southwest portion of LSP once a year from 2012 to 2017 during the moment of maximum biomass, but also once early in the growing season in June 2012  (Figure 2b, d). Acoustic surveys were conducted on 250 m-spaced transects perpendicular to the lake shore at low and constant speed (~1. 1 m/sec). Acoustic data were collected using a downward-looking BioSonics single beam transducer mounted on an Ocean Science riverboat fixed to the side of the vessel by a steel rod. This set-up allowed the transducer to be constantly immersed below 5 cm of the water surface. Floating and drifting vegetation frequently became entangled with the transducer, which consequently was regularly cleaned.
dddd) and mean depth (m) were exported. SAV biovolume, a potential proxy of SAV biomass, was computed as mean plant height × mean plant cover. Within one to two week of the acoustic surveys, rake samples were collected at 35 sites along the echosounding transects. Sites were positioned using a Trimble GeoXT (precision 0. 50 m) in 2012-2013, a SXBlue II GPS (precision 0. 65 m) in 2014-2015 and a Garmin 64S (precision 3 m) in 2016 and 2017. Water depth (z) was measured with a survey ruler and, to be comparable with depth during echosounding, was corrected using the equation z = zrake -lvldate rake + lvldate echo and water level (lvl) at station 15975 (Fisheries and Ocean Canada, www. isdm-gdsi. ca, accessed 2017/11/27). At each site, 3-5 rake replicates were collected around the anchored, 7-m long boat, using the same apparatus and dragged length as the Q-R dataset.
To ensure that a location was not raked twice, rakes were systematically collected in front and on each sides of the boat and as distant as possible when replicate number exceeded three. Plant material was similarly processed as described for the Q-E dataset, but plants were sorted by species in the lab and macrophyte biomass was measured on dried material. Total SAV biomass was computed from the species biomass and pooled by growth form. The same species as the Q-R dataset were found. All SAV biomass herein are reported as mean dry biomass in g/m 2.
Echosounding and quadrat validation dataset The third dataset was used to validate biomass estimation from echosounding and compare it to quadrat biomass (hereafter named the validation dataset). Acoustic and quadrat surveys were carried at the time of maximum biomass accumulation in 2013 and in 2016. In 2013, the survey was concentrated in a small (100 m x 100 m) area of high biomass in southwest LSP characterized by a narrow depth range (1. 4 -1. 5m, Figure  2b ). In 2016, the survey was conducted in both the southwest and the northern sector of LSP. The northern sector was characterized by greater depth range (0. 5 -7. 0 m) due to the crossing of the St. Lawrence navigation channel. Quadrat sampling in this sector was carried in 5 transects and the 250 m-spaced echosounding transects were perpendicular to the quadrat transects.
The southwest sector had shallower depths (< 2. 7 m) and echosounding transects were located following the same methodology as described for the R-E datasets. Processing of acoustic data and biomass samples were also conducted similarly to the R-E dataset, but aboveground biomass was collected in triplicates by scuba divers using a 0. 30 m x 0. 30 m quadrat in 2013, a 0. 25 m x 0. 25 m quadrat in 2016 and balance precision was 0. 0004 g. When SAV density was very low, divers evaluated the surface they patrolled without plants as a straight line or a circle around the anchor (up to 100 m 2 ) and collected the few plants they found.
Using the dataset with the determined resolution, we evaluated how the sampling conditions varied between field campaigns by applying analysis of variance or Kruskal-Wallis test when variance was not homogeneous across groups (previously analyzed using Bartlett test). Differences among groups were assessed using post-hoc parametric or non-   1 ). We then fitted rake biomass to the multiple potential predictors using partial least square regression (PLSR). This method is robust when there is high collinearity among many predictors and when numbers of observations are low  (Mevik and Wehrens 2007). We first selected components using the leave-one-out cross-validation and the one-sigma heuristic approach. We then selected variables using the filter method of selectivity ratio (SR), which is the ratio of the explained to the residual variance of the X variables on the y target projection.
We chose SR over the commonly used variable importance of the projection (VIP) because the former selects important variables using a F-test and performs well for prediction  (Farrés et al. 2015). To validate our intercalibration approach, we applied our models in two steps. First, from echosounding biovolumeD, we predicted rake biomass and from that estimation we predicted quadrat biomass. We propagated model error with Monte Carlo simulations and used the root mean square error of cross-validation (RMSECV) as the model error term. Second, we visually compared in space the quadrat-equivalent biomass predicted from echosounding to measured quadrat biomass. For this, we created spatially interpolated biomass maps from both quadrat and echosounding using kriging. To compare the effect of sample size and spatial coverage on whole-system biomass, we compared biomass estimate from point sampling to remote sensing using the R-E dataset over five summer campaigns.
Models were evaluated for their ability to predict biomass by minimizing the RMSECV and the mean absolute percentage error (MAPE) of cross-validation predictions. Data handling and statistical analyses were performed in R 3. 6. 3 (R core team 2020). Cross validation for OLS was computed using the package caret and PLSR with variable selection carried out using pls and plsVarSel  (Kuhn 2020 , Mevik et al. 2020 , Mehmood et al. 2012). Geospatial data was handled using the sp, sf, raster and concaveman packages  (Pebesma and Bivand 2005 , Pebesma 2018 , Hijmans 2020 , Gombin et al. 2020) , while geostatistical modelling and kriging was performed using gstat  (Pebesma 2004 ). Bootstrapping confidence intervals were computed using the boot package  (Canty and Ripley 2021).
Interannual variability of the paired echosounding and rake observations dataset The resulting 52 pairs of acoustic and rake observations were collected during 7 independent field campaigns (spring 2012 and summers 2012-2017) that differed markedly in terms of SAV abundance and community composition due to interannual variation in environmental conditions (SI Figure  S2 ). Of these 52 sites, 50 were used is subsequent analyses as 2 pairs were collected in depths too shallow for echosounding. This was found by using a 0. 5 m threshold to depth collected during rake collection  (Sabol et al. 2002) ; we chose rake depth since comparison to echosounding depth revealed the latter to be overestimated in shallow areas (<1. 5 m, SI Figure  S3 ). We also investigated how acoustic data collection and quality varied among campaigns (SI Figure  S4 ).
40 g/m 2 , medianpred = 98. 06 g/m2, medianobs = 107. 31 g/m 2 ). Second, given that most quadrat measurements in our dataset were located beyond 10 m from the echosounding track, we compared spatially interpolated measured quadrat biomass to interpolated predictions derived from echosounding (Figure  7 ). In the 2013 constant depth range survey, the quadrat and echosounding estimation were very similar and small local differences were potentially caused by the higher sampling effort and surveyed area of echosounding (Figure  7a, b ). In the deeper North sector of LSP, interpolated biomasses were similar for quadrat and echosounding above the SAV maximum colonization depth (3 m, Figure  7c, d ). Echosounding clearly enabled the delimitation of SAV spatially because of a better accountability of limits imposed by depths.
Discussion We successfully developed two intercalibrations that allows for the interchangeable use of quadrat, rake and echosounding techniques to estimate SAV biomass. We first predicted quadrat biomass from rake biomass and showed the effect of substrate and species growth form on the predictions. Using a resolution of 10 m radius, we predicted rake biomass from biovolume, a proxy of biomass derived from echosounding. We also showed that this prediction is affected by SAV growth form, depth, wind conditions and acoustic data quality. By sequentially applying both models to echosounding tracks, we were able to accurately predict quadrat biomass. By correcting its bias, rake thus has the potential to be a versatile ground truthing techniques for echosounding. In rugged and deeper bottoms, echosounding outperformed point sampling techniques in estimating biomass, but underestimated biomass in very shallow waters.
Comments and recommendations The choice to use one or both intercalibrations will depend on the study goals, resources available and time granted. If one only intends to use rake, the quadrat and rake calibration can be conducted using the simplest sampling strategy, where only a one-time calibration is needed, with sites chosen to maximize a biomass gradient and ensure divers security. Increased accuracy can be achieved by deriving species-and substrate-specific equations. For the rake and echosounding combination, an even more efficient intercalibration can be achieved than the one presented here. For example, despite having a rake dataset of 217 sites, we only had 52 sites that spatially matched the echosounding tracks at 10 m radius resolution. Since the two sampling techniques were not used simultaneously, sampling with both techniques had to be conducted as close as possible to an assigned geographic position.
The rake has the advantage of providing species information, but each sample entails considerable sample processing time. Using a faster semi-quantitative approach such as applying a visual abundance scale based on the degree of each species filling the rake teeth can help increase sample size and provide community information at the ecosystem scale  (Yin and Kreiling 2011) , which can be used to more accurately predict biomass. When different SAV communities display distinctive heights, for example understory charophytes and taller angiosperms, applying height threshold to echosounding data can provide functional group information at large spatial scales  (Bučas et al. 2016). Recent echosounders also provide information on substrate type that can increase accuracy when applying our two-step calibration approach  (Helminen et al.
Acknowledgements We thank  Stéphanie Massé ,  Caroline Chartier ,  Elena Neszvecsko ,  Geneviève Trottier ,  Magalie Noiseux-Laurin  and  François Messier  from  GRIL ,  Patricia Bolduc  from  UQTR  and all other students that conducted field work and processed samples. The technical support of  Jean-Pierre Amyot ,  Conrad Beauvais ,  Rollando Benjamin ,  Maude Lachapelle ,  Yasmina Remmal  and  Maxime Wauthy  from  Environment Canada  is acknowledged with thanks. Bruce Gray  and the  Technical Operations diving team (Environment Canada, Canada Centre for Inland Waters, Burlington, ON ) provided excellent field support for underwater sample collection. We also thank  Richard Carignan  and  Andrea Bertolo  for planning and troubleshooting during echosounding surveys. Sampling was made possible thanks to the guides from pourvoirie  Roger Gladu.