dataesr/scientific-paragraphs-categorization huggingface dataset:
We selected all significantly lower expressed proteins annotated to the pathways  and created a heatmap displaying the log2-fold change for a detailed visualization of protein changes in the pathway. We included RNA expression data of the corresponding genes to check if the protein changes coincide with changes at the RNA level. Strikingly, most of these proteins are not changed on RNA level (Figure V in the Data Supplement). The top clusters of the higher expressed proteins are associated with the GO terms ECM organization, actin filament-based process, myofibril assembly, muscle contraction, post-translational protein modification, protein folding, and microtubule cytoskeleton organization (Figure  2B , more extensive list in Figure VI in the Data Supplement). The biggest cluster of more abundant proteins is ECM organization representing different collagens that are components of fibrotic tissue.
DOI: 10. 1161/CIRCHEARTFAILURE. 120. 007022 January 2021 47 Levels of tyrosinated and detyrosinated tubulin do not show any genotype-specific differences. Levels of tyrosinated and detyrosinated tubulin normalized to total αtubulin are depicted in Figure  XVB  and XVC in the Data Supplement. Our data show that high levels of tubulin in HCM SMP represent mostly the detyrosinated form. We also determined desmin protein levels by Western blot since this protein is associated with microtubules in cardiomyocytes. Accordingly, desmin levels were elevated in HCM with the largest increase in HCM SMP (Figure  4D ).
Evidence-based decision-making is most effective with comprehensive access to scientific studies. If studies face significant publication delays or barriers, the useful information they contain may not reach decision-makers in a timely manner. This represents a potential problem for mission-oriented disciplines where access to the latest data is required to ensure effective actions are undertaken. We sought to analyse the severity of publication delay in conservation science-a field that requires urgent action to prevent the loss of biodiversity. We used the Conservation Evidence database to assess the length of publication delay (time from finishing data collection to publication) in the literature that tests the effectiveness of conservation interventions. From 7,447 peer-reviewed and non-peer-reviewed studies of conservation interventions published over eleven decades, we find that the raw mean publication delay was 3.
How publication delay: (a) differs between different conservation subjects; (b) has changed over time; and (c) differs between different publication sources (i. e. , peer-reviewed and non-peerreviewed literature); 3. For species-focussed studies, how publication delay changes with the IUCN Red List (IUCN, 2020) status of the species on which interventions are tested; and 4. The typical length of manuscript processing at destination journals. We define publication delay as the time taken from finishing data collection for a study to when the study is published (either in a peer-reviewed scientific journal or in the non-peer-reviewed literature). We discuss the factors that could be driving publication delay, and provide suggestions on how the scientific community can work together to minimise them.
Calculating publication delay Using the Conservation Evidence database  (Sutherland et al. , 2019) , we examined the difference between the year that data collection ended for a study and the year that the study was published (either in a peer-reviewed scientific journal or in the non-peerreviewed literature). The Conservation Evidence database contained studies documenting 2,399 conservation interventions (as of December 2020; e. g. , sowing strips of wildflower seeds on farmland to benefit birds) across multiple 'synopses'. Synopses are used in the Conservation Evidence database to categorise studies into useful subject areas such as by species group, habitat, or related interventions (e. g. , 'Bird Conservation' or 'Management of Captive Animals'). To construct the Conservation Evidence database, publications were retrieved from the literature using a standardised protocol of manual searching through entire journals, and non-journal literature sources, for quantitative assessments of the effectiveness of a conservation intervention ('subject-wide evidence synthesis'; see  Sutherland et al.
, 2019  for details). The aim was to cover all the main general and specific journals; as of July 2021 this has involved reading 1,629,688 paper titles from 657 journals. We focused on the number of unique studies of an intervention within each Conservation Evidence synopsis. For example, if a publication reports studies of two different interventions (e. g. , supplementary feeding and provision of artificial nests), then these studies are counted separately. Using this classification of conceptually distinct studies, we were able to extract information on when 7,477 studies were published and when their data collection ended. Only approximately 1% of studies did not report dates (68 out of 8,209 in the entire database) and so were excluded from the analyses. The remaining 664 studies were reviews or meta-analyses, which were excluded as we were only interested in the publication delay of primary literature.
We extracted temporal information from the Conservation Evidence database (publication year) and a summary of each study that included information on the years during which the study was conducted. We defined the end year of a study as the year within which data collection ended. End years were extracted from Conservation Evidence summaries using regular expressions and text mining of the website (www. conservationevidence. com) with the XML package  (Lang, 2020a)  and RCurl package  (Lang, 2020b)  in R statistical software version 3. 5. 1  (R Core Team, 2020). This extraction was necessary because this information is not currently stored in the database. We checked the accuracy of text mining by reviewing data for a random sample of 382 studies (approximately 5% of the total number of studies analysed) and found that 94.
4% had the correct study end year. Although there were a small number of errors, these were mostly caused by assigning the publication year as the study end year, and therefore would yield an underestimate of publication delay. In addition, automating the extraction of dates from study summaries offered the most feasible and reproducible way to analyse the entire database, and avoided human error and unconscious bias that would affect manual extraction of dates from a large database  (Christie et al. , 2020 (Christie et al. , , 2021)). To determine publication delay, we subtracted the end year of each study from its publication year. For studies conducted and published within the same year, their length of publication delay was therefore zero years. The coarse temporal resolution of years would have caused us to overestimate publication delay for studies with a delay of a few months that run between calendar years (e.
The synopsis 'Bee Conservation' and non-peer reviewed category were set to the zero value of the beta slope and used as reference categories as these had the lowest mean publication delay values based on preliminary exploration of the data. We selected the best model structure using quasi-Akaike's Information Criterion (qAIC) (see results; Table  S3 ). We used the R package emmeans  (Lenth, 2021)  to calculate the Estimated Marginal Means (EMMs-or least squares means using the Tukey adjustment) and asymptotic 95% Confidence Intervals (using the Sidak adjustment) for different synopses and types of publication sources, as well as to test for significant differences between these categories based on the quasi-Poisson GLM. We used the EMMs (which average across the values of other explanatory variables or fix these variables at a constant value) and their associated 95% Confidence Intervals for different categories to produce most data visualisations and summary statistics, except for calculating publication delay for all studies combined for which we used a simple raw mean.
Since studies that are published more recently have a greater potential to suffer from a longer delay on a purely mathematical basis-i. e. , a study published in 2020 has theoretically had 10 more years over which it could have remained unpublished than a study published in 2010-we conducted a sensitivity analysis to check for this potential bias. We restricted the dataset we analysed in our original GLM to different time periods  : 1980-2020, 1990-2020, 2000-2020, and 2010-2020   and repeated GLMs (Table S4 ). To test for differences between studies published in the peer-reviewed and non-peerreviewed literature, we classified the publication type of studies in the Conservation Evidence database using two categories: (i) peer-reviewed sources; and (ii) non-peerreviewed sources. This differentiation of publication sources by peer-review allowed us to approximate the degree of publication delay that is due to the peer-review and editorial process in journals-assuming that similar studies are published in both types of literature sources.
We used the Scimago (2020) dataset to detect recognised peer-reviewed journals and then manually searched the websites and/or texts of any unrecognised publication sources to check whether they were peer-reviewed or not; if there was no explicit evidence of peer-review, we allocated studies to the 'non-peer-reviewed' literature category. Whilst some books, reports, and articles published outside of scientific journals may undergo a form of internal peer review (potentially within organisations), we focused on defining peer-review as that occurring within the context of a scientific journal or magazine where there was explicit evidence of a peer-review process. Therefore, a small number of publication sources that undertake some form of non-journal peerreview may have been allocated to the non-peer-reviewed category.
Testing for differences in publication delay between IUCN red list categories In a separate analysis, we tested for significant differences in publication delay between studies testing interventions on species with different IUCN Red List statuses. We first extracted data from the Conservation Evidence database on the species studied within five taxa-specific synopses (n = 3,941 studies in: Amphibian Conservation, Bird Conservation, Terrestrial Mammal Conservation, Primate Conservation, and Bat Conservation synopses), and the threat status of each species from the IUCN Red List (IUCN, 2020). We limited the analysis to these synopses as these taxa had been comprehensively assessed in the IUCN Red List. We ran separate quasi-Poisson GLMs on this reduced dataset, and then separately for each of the three broad taxa (amphibians = Amphibian Conservation; birds = Bird Conservation; mammals = Terrestrial Mammal Conservation, Primate Conservation, and Bat Conservation synopses).
We included explanatory variables of publication source, publication year, and IUCN Red List Status for our initial models (plus the term synopsis for mammals to maintain consistency with our main model as this taxon has studies split across three different synopses). We selected the best model structure for each subset of data using qAIC values (see results for model structures selected; Table  S5 )-if two models were within <2 units of qAIC, we selected the simplest model (according to the principle of parsimony). For these taxonomic GLMs, we only considered the most threatened IUCN Red List category (out of Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered) of all species for each published study. For example, if a study targeted multiple species, such as two that were listed as Least Concern and one listed as Endangered, we considered that as a study on an Endangered species.
To determine whether picking the most threatened species in each study affected our results, we conducted a sensitivity analysis through rerunning analyses and selecting a random species from each study (see results; Tables  S6  and S7 ). There were insufficient studies on species with IUCN Red List statuses of Data Deficient (four studies) or Extinct in the Wild (eight studies) and so we did not include these categories in our taxonomic analyses. Five mammal species could not be matched to the IUCN Red List (e. g. , domesticated or hybrid species) and so were not represented in the analysis (this only affected 18 studies or 0. 46% of studies in the taxonomic analysis). We were unable to obtain the IUCN Red List status of species at the time when each relevant study was conducted and therefore had to use the current status of species in the latest IUCN (2020) Red List update.
Investigating journal processing delays To investigate delays taking place during manuscript processing by journals (i. e. , from submission to a journal to online publication of the study), we extracted data on the number of days from receipt of studies to acceptance and the number of days from acceptance to online publication for any study published from 2015-2021 and indexed in PubMed, using code produced by  Himmelstein & Powell (2016) ; see also  Himmelstein (2016). We restricted the dates to between 2015-2021 to capture recent data on journal processing delays. We then filtered these data to only include journals present in our Conservation Evidence dataset, as well as those that contained the terms 'Wildlife', 'Environment', 'Ecology', 'Biology', 'Conservation', OR 'Zoology'-we also manually edited the list of retrieved journals by removing any journals that we believed were obviously not relevant venues for conservation science studies (e.
g. , Free Radical Biology and Medicine). Journals and studies for which data can be extracted is dependent on their deposition in PubMed-the number of studies at different journals for which delays could be calculated is therefore variable and sometimes small. This also only gives a general view of delays taking place at journals for any given study, rather than those specifically focusing on conservation interventions-however, we believe these data can still provide some general guidance on the publishing speed of journals relevant to researchers wishing to publish studies that test conservation interventions (Table  S14 ). R code to perform all analyses is available here: https://doi. org/10. 5281/zenodo. 4621310.
Journal processing times (objective 4) For journals indexed in PubMed that were included in our dataset, the time from submission to acceptance was on average 150 days (95% Confidence intervals =  [139, 161] ), and acceptance to publication was on average 55 days (95% CIs =  [46, 67] ). When adding the mean delays from submission to acceptance and acceptance to publication together for each journal, the mean destination journal delay (time from submission to publication; see 'Breaking it down: considering different components of publication delays') was 186 days (95% CIs =  [169, 205] ). For each category, summary statistics were obtained using Estimated Marginal Means (averaging over other explanatory variables) based on a quasi-Poisson Generalised Linear Model (GLM). For publication sources classified under each category, see Table  S1  Expanding the journals included in these estimates to those with a name containing the terms 'Wildlife', 'Environment', 'Ecology', 'Biology', 'Conservation', or 'Zoology' (also filtered manually for journals that have relevance to conservation science-see Methods), delays were relatively similar, albeit slightly shorter (time from submission to acceptance = 130 days (95% CIs =  [125, 136] )), time from acceptance to publication = 47 days (95% CIs =  [43, 52] ), and overall mean destination journal delay = 166 days (95% CIs =  [159, 175] ; see Table  S14  for delays by journal).
For example, for mammals only (D), studies on Least Concern species have a significantly shorter mean delay than studies on Endangered species (top row, second column is dark red with red diamond), but did not have a significantly shorter mean delay compared to studies on Vulnerable species (top row, third column is grey with black diamond). Post-hoc tests of differences between synopses were conducted using Estimated Marginal Means with Tukey adjustment in the R package emmeans  (Lenth, 2021). shorter delay than those published in the peer-reviewed literature. Although we found publication delay has increased since 1912, sensitivity analyses suggested that change post-2000s was weak. Finally, we found little evidence that studies focusing on species listed as more threatened by the IUCN Red List suffered from shorter delays, with results to the contrary for mammals and birds.
6 ). O'Donnell, Supp & Cobbold (2010) found a median submission delay of 696 days (1. 91 years), higher than the 402 days (1. 1 years) of destination journal delay they observed. We found that journals that have published tests of conservation interventions (based on our dataset) had an average destination journal delay (from submission to publication) of 186 days (0. 51 years) between 2015-2021. If these delays generally hold true for the studies testing conservation interventions that are published in peer-reviewed journals in our dataset (which based on our results have a mean total publication delay of 2. 95 years), a crude estimate would suggest that destination journal delay makes up only 17% of total publication delay, with submission delay accounting for the remaining 83% (i. e. , write-up and resubmission delay; Fig.
Results Data include APOE genotype and multiple measurements of BMI, lipids, and immune markers in 1266 Tsimane adults. Mixed effects multiple regression models were used to accommodate for multiple measurements per individual for some biomarkers, as well as community-level differences in pathogen exposures. Leveraging multiple observations per individual over time should better capture individuals' average levels, and minimize the potential of single or few outliers driving results. Models also include covariates which adjust for age, sex, seasonality, and current infection (WBC >12 × 10 9 /L) (see Materials and methods for details). The sample is 50 % female and includes individuals from 80 villages. The mean ± SD age of the sample is 54 ± 11 years (range: 21-93). Mean ± SD BMI is 24 ± 3 for both sexes; Tsimane adults are relatively short (women: 150.
Inversely, acute infections and systemic inflammation (e. g. acute phase reaction) are associated with a decrease in HDL and HDL remodeling that results in lower cholesterol efflux capacity and higher peripheral levels of LDL cholesterol  (Ronsein and Vaisar, 2017; Zimetti et al. , 2017). While we did not find differences in HDL by APOE status among the Tsimane, we cannot completely discount the possibility that HDL remodeling plays a role in the higher lipid levels, in addition to APOE allelic variation. Further, given the relative lack of, and difficulty in accessing, medical care facilities, it is difficult to assess degrees of infection severity, and thus it is also possible that APOE4 may mitigate infectious disease burden. However, the current data cannot provide evidence for either of these potentials, and further research is needed to disentangle the roles of APOE and lipids in infection.
Sampling design Biomarker data used in this paper were collected by the THLHP between 2004 and 2015 (see  Gurven et al. , 2017; Kraft et al. , 2020 for details). A Bolivian and Tsimane mobile medical team travel annually or biannually among study communities conducting clinical health assessments and collecting biochemical and anthropometric information from community members that want to participate. This sample includes all data from individuals for whom we have APOE genotyping and measurements of age, sex, and BMI -which is the base criteria for this study. Sample size varies by biomarker and over time for several reasons: sampling strategy varies by data type, absent or sick team personnel needed to collect data, the number of study villages and thus enrolled participants has increased over time, and the data types collected have changed over time (see  Kraft et al.
Data availability Working with indigenous populations requires special care and sensitivity, especially in regards to the public sharing of genetic data. A long history of abuse and mistrust between researchers and indigenous populations behooves us to prioritize the wishes of Tsimane. Given current ethical restrictions on sharing Tsimane genetic data, we cannot upload Tsimane genetic data to a public server at this time. Individuals interested in accessing the APOE genetic data should submit a request to the co-senior authors. Access will be granted for health focused research, subject to approval by THLHP co-Directors and Tsimane Gran Consejo (Tsimane governing body). All other de-identified data and code are openly accessible on Dryad through the following link: https:// datadryad. org/ stash/ share/ FqfE p93S dnqk lFbf bjBb enHsZh_ 10gulo8cf_ 9LMR3o While we are strong advocates of open science and believe that inclusion of diverse populations in genomics is critical for advancing the goals of public health and health equality, we also need to ensure that all research participants are protected.
It is both time-consuming and very expensive  [3]. In recent decades, single-beam echosounding and many other technologies such as multi-beam echosounding, Light Detection and Ranging (LIDAR), have been used to produce the bathymetry of coastal regions at different resolutions and accuracy  [3] [4] [5]. Classical studies of surveying coastal regions, generally use acoustical techniques which consist of measuring the distance between the device and the bottom of the sea. This technique offers a good accuracy but it is very slow and covers very limited areas. An alternative that can scan a wide area with a good spatial resolution is LIDAR. It uses infrared and green laser transmitter and post-flight data processing techniques to generate survey depths with high accuracy  [6, 7]. However, this technique is more expensive, especially when the study area is large.
In this study, 287 Sentinel 2A/B tiles cover the North Africa coastal zone (Figure  1 ) were used. First, we queried all available Sentinel 2A/B (S2-L1C) images between January 2015 and January 2020 in the PEPS collection of the Theia Land data center. For the analysis in this work we limit the computations to ten scenes of cloud-free Sentinel 2A/B images during a 5 year span from 2015 to 2020. A sensitivity analysis in the original method article  [18]  and for West-African hotspots  [19] , shows that the wave power (as a function of wave height and period) as well as directional spread of the wave field affect depth estimation. Generally, the more powerful, narrow-spread wave fields allow for a more accurate estimation of the water depth. Considering this, 10 cloud free images with most powerful waves are selected in this study.
Conclusions and Way Forward By positioning itself on a global coverage, S2SHORES covers different coastal zone environments around the world with a sensor resolution of 10 m (Sentinel 2A/B) over a variable band from the shore and down to a depth limit of about 50 m  [19]. The approach can be extended to other missions (Pleiades, World-View, SPOT6, etc. ) of higher resolution for high precision need at hot-spots  [20]. Sentinel 2A/B assets are the free data and the long-term nature of the Sentinel 2A/B program. Indeed, Sentinel-2A and B cover the periods 2015-2022 and 2017-2024, respectively, and their successors Sentinel-2 C and D to ensure the continuity of the mission are already scheduled for the next seven years.