dataesr/scientific-paragraphs-categorization huggingface dataset:
Global Distribution of Mangroves (GDM) The Global Distribution of Mangroves (GDM) map was produced by the United States Geological Survey (USGS) team  (Giri et al. , 2011). The GDM map is based on the Global Land Survey (GLS) data, i. e. , Landsat images prepared in partnership between the USGS and the National Aeronautics and Space Administration (NASA). For this mapping, a global dataset of Landsat 5 Thematic Mapper (TM) with 30 m of spatial resolution acquired from 1997 to 2000  (Giri et al. , 2011)  was used. About a thousand Landsat images were interpreted using hybrid supervised and unsupervised digital classification techniques to estimate and map the total area of global mangrove forests  (Giri et al. , 2011). Moreover, the global mangrove database from  FAO (2007)  and national and local mangrove database were used as secondary data  (Giri et al.
Global Mangrove Watch (GMW) The Global Mangrove Watch (GMW) map is the most recent mapping initiative of mangrove ecosystems and is part of the Japan Aerospace Exploration Agency (JAXA) Kyoto & Carbon Initiative with the objective to generate a global map of mangroves for the year 2010. GMW takes full advantage of combining optical and SAR (Synthetic Aperture RADAR) images  (Bunting et al. , 2018)  and relies on ALOS PALSAR L-band SAR dual polarisation (HH + HV) backscatter data released in 1°× 1°mosaic tiles  (Shimada et al. , 2014)  to discriminate mangroves. Since some confusions with other wetland or forest types remained, the near infrared and shortwave infrared band of Landsat data (optical) were used to reduce the confusion between these land cover classes  (Bunting et al. , 2018). Currently, the GMW map is led by Aberystwyth University (U.
K. ) and Solo Earth Observation (Sweden). The GMW map was developed in collaboration with Wetlands International, the International Water Management Institute (Laos) and the United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (U. K. ). The methodology used to produce the GMW mangrove for 2010 involved a combination of ALOS PALSAR and optical satellite data from Landsat 5 Thematic Mapper (TM) and Landsat-7 Enhanced TM (ETM+). The authors used the composite images from ALOS PALSAR of the 2010 mosaic as a reference mainly because it was the most complete in terms of temporal consistency and spatial coverage. A composite was also generated using Landsat sensor data acquired for 2010 mainly. Bunting et al. (2018)  used four main methodological steps to produce the GMW mangrove extent which included (i) the extraction of a coastal water mask from the PALSAR data; (ii) generating a mangrove "habitat" layer that identified areas potentially able to support mangroves; (iii) generating an initial baseline classification using the PALSAR data only; and (iv) a refinement of the classification using Landsat sensor composites.
Mangrove range limits: latitudinal position and reference We identified the latitude for twenty-one mangrove range limits in the four global datasets (see Supplementary material). As a reference, we used the twenty-one mangrove range limit positions considered by  Quisthoudt et al. (2012)  because these latitudinal limits were gathered from the literature and verified through communication with local mangrove specialists. Also, their study focused on mangrove latitudinal limits of Rhizophora and Avicennia, the only two pantropical mangrove genera of which species are generally found at all mangrove range limits around the world. Hence, this approach allows comparing the latitudinal position of mangrove range limit sites globally. For each of the twenty-one mangrove range limit sites, we computed the latitudinal difference between the reference dataset  Quisthoudt et al.
Bioclimatic variables at mangrove range limits For each of the range limits presented in the different mangrove maps, we examined differences in the values of bioclimatic variables related to the distribution of mangroves. Environmental data were obtained from the WorldClim Version 2. 1 database  (Fick and Hijmans, 2017 ) freely available at: www. worldclim. org, which consists of spatially high-resolution (approximately 1 km 2 at equator) raster layers of climate and are the average for the years 1970-2000. This historical climate data can vary regarding the availability of each local meteorological station  (Fick and Hijmans, 2017). We focused on minimum air temperature of the coldest month (BIO6), annual precipitation (BIO12), and precipitation of the driest month (BIO14), since these three variables have been put forward as playing an important role in determining mangrove latitudinal limits  (Quisthoudt et al.
, 2012; Cavanaugh et al. , 2014; Ximenes et al. , 2016; Osland et al. , 2017a). The "Min temperature of coldest month" or BIO6 is a multi-decade average of the minima of the coldest months, which is therefore comparable to the "mean temperature of the coldest month"  (Hijmans et al. , 2005). In this study, we use the minimum temperature of the coldest month as a proxy of extreme cold events. However, the intensity, frequency, and duration of extreme events from hourly and/or daily climate measurements (i. e. , the intensity, duration, and frequency of the absolute coldest temperatures of the year) is not available or hardly accessible statistic in a global database. However, it is the extreme cold events, rather than the mean, which periodically halt the poleward expansion of mangroves  (Osland et al.
, 2017a). Bioclimatic variables from the WorldClim database were generated for the terrestrial realm, so that the variable files contain 'no data' in the marine realm. However, mangroves thrive at the ocean-land interface, and some mangrove patches are positioned in marine 'no data' locations. For this reason, a land-ocean mask was generated with the same size and resolution as the bioclimatic data, and range limit longitude-latitude information from all mangrove maps were updated to the center of the nearest land grid cell. The processing of the land-ocean mask and figures related to this part of the study were generated using MATLAB version R2020b (MathWorks, 2020). Subsequently, using the updated longitude-latitude information, corresponding bioclimatic data were extracted at the above mentioned twentyone mangrove range limits for all mangrove datasets.
Data extraction was performed using the QGIS 3. 14. 0 software (QGIS Development Team, 2020). Fig. 2. Global map showing the position of poleward mangrove range limits in 21 regions, as identified in four widely used global mangrove distribution maps: the World Atlas of Mangroves (WAM-1)  (Spalding et al. , 1997) , the World Atlas of Mangroves (WAM-2)  (Spalding et al. , 2010) , the Global Distribution of Mangroves (GDM)  (Giri et al. , 2011)  and the Global Mangrove Watch (GMW)  (Bunting et al. , 2018). Line symbols (grey) denote the position of these poleward range limits as identified by Quisthoudt et al. (  2012 ), which is considered as a reference map in our study.
Latitudinal comparison between mangrove range limits Comparison of the global mangrove maps reveals important discrepancies between range limit latitudes (Figs. 2  and 3 ). In all global mangrove maps, the southernmost range limit is found in East-Australia (38. 84°S ± 0. 06°). However, the location of the northernmost range limit differs between the different mangrove maps. While this range limit is found in Bermuda at 32. 30°N in the  Quisthoudt et al. (2012)  data, the northernmost global range limit is found in Japan in the WAM-1 and the GDM maps, at 31. 21°N and 30. 81°N, respectively, and in California at 33. 80°N in the WAM-2 and GMW maps (California mangroves were introduced; see further information in Section 4. 1). The latitudinal difference for range limits between the datasets is <1°f or eleven of the 21 range limit areas considered, but exceeds 4°, 5°, 7°a nd 11°for Japan, Western Baja California, West-Australia, and Northern West Africa, respectively (Fig.
Assessment of bioclimatic data at mangrove range limits The largest latitudinal discrepancies found in Japan, Western Baja California, West-Australia, and Northern West Africa, are associated with pronounced differences in minimum temperature of the coldest month (BIO6) of 11. 1 °C, 2. 1 °C, 3. 2 °C, and 3. 5 °C (Fig. 4 ), and differences in annual precipitation of 596 mm, 250 mm, 620 mm, and 195 mm, respectively (Fig. 5 ). Differences in precipitation of the driest month associated with latitudinal discrepancies are relatively small overall, and is most pronounced for the range limit in China (18 mm), Japan (24 mm) and Taiwan (29 mm) (Fig. 6 ). The lowest minimum temperature of the coldest month (BIO6) is found for the WAM-1 dataset (3. 5 °C), in Japan, the northernmost global range limit in that dataset, whereas the warmest minimum temperature of the coldest month is found for the Western South America mangrove range limit (19.
1 °C) in the WAM-2 and GMW datasets (Fig. 4 ). For annual precipitation (BIO12), the lowest (3 mm) and highest (3308 mm) values are found for the Northern East Africa range limit in the GDM dataset and the Taiwan range limit in the WAM-1 dataset, respectively (Fig. 5 ). Precipitation of the driest month (BIO14) was lowest (0 or 1 mm) at the range limits in California, West Africa, Northern East Africa, Saudi Arabia and Iran, in all datasets. The highest value for this environmental variable was found for the range limit in Taiwan in the GDM map (Fig. 6 ).
Uncertainty in mangrove distribution data also directly influences the results of predictive climate-driven biomass and soil carbon (C) models. Modeled estimates of global mangrove carbon stocks rely on these distribution data to scale up their estimates of carbon density, e. g. ,  Hutchison et al. (2013) ;  Sanders et al. (2016). As a result, global carbon estimates will only be as good as the underlying estimates of mangrove extent. Uncertainty in these estimates also has subsequent implications for C accounting and the quantification of C offsets. This is critical in an era where nature-based climate solutions are sought, whereby estimates of land cover accounting will require thorough verification. This is visualised in the Dataset Explorer application at www. mangroves4sdgs. com, which compiles all existing global mangrove maps of extent/cover, biomass, and soil carbon, including 3 of the 4 datasets used in this study.
, 2014). In the case of the global mangrove maps compared in this study, manual edition based on visual interpretation (as in WAM-1) turns the method operator-dependent and subjective, thus difficult to describe to the end-users. On the other hand, maps based on data-driven approaches using advanced supervised and unsupervised classification algorithms (as in GDM and GMW) are easier to describe. Moreover, machine learning algorithms should be taken into consideration for mangrove mapping  (Pham et al. , 2019). However, these approaches depend on the training and validation datasets, which is another point discussed by  Grekousis et al. (2015)  who recommends improving the collection of training and validation datasets. In addition, limitations of the imagery and methodology should be clearly described.
For example, it may not be possible to detect the small-stature mangroves found near many poleward range limits using moderate resolution imagery (10-30 m). Fourthly, refers to map accuracy. Global maps usually suffer from spatial heterogeneity, especially when training samples used in supervised classifiers are geographically unequally distributed. To overcome this issue,  Grekousis et al. (2015)  recommends developing pixel-based accuracy metrics. For instance, when using supervised classifiers, this could be done by releasing probabilities of the mangrove class to which the pixels belong. The research community must be aware of the limitations of their datasets before using them in additional studies. It is important that sources of error and uncertainty are understood and propagated appropriately in order to avoid the inappropriate use of a dataset or provide recommendations based on results which may have built-in yet hidden error.
Design and settings Aladdin is a cross-sectional study of NSHW working in all 39 structures of the AP-HP public hospital network which covers Paris and the surrounding area. It aimed to portray the situation of these workers during the COVID-19 pandemic by collecting sociodemographic and economic data, as well as data on work-related characteristics, perceived health, quality of working life, mental health, use of psychoactive substances and sleep quality  [22]. The study questionnaire was created using NetSurvey ® and was made available on personal and professional devices between 15 June and 15 September 2020  [23]. The Aladdin instrument has been tested for understanding through a cognitive debriefing  [24]  on a sample of NSHWs before the start of the survey. Several strategies were used to disseminate the study.
Participants Of the 12,000 NSHW in the AP-HP network, 1,585 (13. 2%) answered the Aladdin study questionnaire. Of these, 1,200 participants had complete data for mental health outcomes (HADS, IES-R and ISI scales) and constituted the study sample for the present analysis. Mean age of the study sample was 39. 4 years (standard deviation: 11. 8 years), 78. 2% [95% CI = 75. 7-80. 8] were women and 50. 7%  [47. 7-53. 6 ] had children. Nurses represented more than half (53. 6%) of the sample. The characteristics of the study sample are described in Table  1  and Additional file 1: Appendix 1.
Discussion Using data from the Aladdin survey, the present study documented the mental health of NSHW in the AP-HP public hospital network during the first wave of the COVID-19 pandemic in France, and explored the potential impact of two factors on mental health, namely perceived negative representations of nightwork, and COVID-19-related organizational changes. In our weighted sample, which was representative of the 12,000 NSHW in the network, in terms of sex, age, and professional category, 23% of respondents had at least one of the four severe mental health issues explored. Specifically, twenty percent of participants probably or definitely had depression, while 40% probably or definitely had anxiety. Seventy-five percent of respondents suffered from sleep disorders (light, moderate or severe), and almost 12% suffered from symptoms of post-traumatic distress.
At the beginning of the first COVID-19 wave in France, a systematic review was conducted to identify the risks factors of mental health deterioration in health workers  [50]. It included findings from previous epidemics (SARS-CoV-1, H1N1) and data from the first weeks of the COVID-19 pandemic. The review described healthcare providers' mental health vulnerability and higher risk of anxiety, depression, burnout, addiction and post-traumatic stress disorder. Consistently with our study, it identified several organizational factors as a source of anxiety, such as the lack of personal protective equipment and medical care equipment, the reassignment of one's work position, the lack of communication, and a high level of stress at work. Familial and social difficulties (especially in terms of daily life), a lack of support from family and friends, the fear of infecting a loved one, isolation, and negative representations all impacted anxiety levels.
Strengths and limitations The large sample of the study allowed us to identify several dimensions of mental health of NSHW in the AP-HP network after the first wave of COVID-19 ended in France. Although NSHW are essential for the continuity of care, few data exist on this population because they are difficult to reach. One reason for this is that they have little contact with their managers and work outside of normal working hours. A major novelty of our work is that we were able to explore the association between the negative representations of NSHW and this population's mental health. Furthermore, our results highlight the impact of the organizational upheavals caused by the COVID-19 pandemic on mental health and sleep quality in NSHW. An advantage of our work is that its results can have a concrete use and translation for public decision makers.
Indeed, by assessing and identifying the mental health disorders of NSHW and the factors that affect them, it is possible to set up programs for prevention, surveillance and early diagnosis of these disorders. The study also has limitations; it was only conducted with NSHW working in the public university hospitals of the AP-HP network in the region around Paris, and therefore cannot be representative of all NSHW. In addition, data concerning characteristics of NSHW who did not answer the survey questionnaire were lacking. However, the weighted analyses allowed us to ensure representativeness of the sample in terms of gender, age and professional category. Some professions such as midwifes and stretcher bearers were underrepresented as the number of these professionals who work at night is very low in France.
Acknowledgements We would like to thank all the study participants. Special thanks to  Isabelle Chavignaud  of the FIDES mission ( AP-HP ) for her active involvement in designing and implementing the Aladdin study. We also thank the medicine students who helped us recruit participants, the  AP-HP communication department  for its help in promoting the survey, and the  AP-HP Clinical research department  for its administrative support. Finally, our thanks to  Jude Sweeney  ( Milan, Italy ) for the English revision and copyediting of the manuscript.
These data are particularly important to aid in differentiating between BIR at the neuronal level versus limited BBB transport of insulin under various conditions in vivo studies. In sum, these studies show that the detection of INSR activation by circulating insulin is difficult, but perhaps clearer when insulin is injected directly into the brain circumventing BBB transport or when cell populations are independently assessed (i. e. , microvessels following carotid insulin injection or neuronal patch clamp studies). Supraphysiological doses or dietary modifications  [143]  are also often necessary to increase brain levels of insulin. However, supraphysiological doses may not accurately reflect what occurs under normal conditions. First, insulin BBB transport is saturable  [144, 145]. Therefore, increasing doses of insulin above the saturable rate will not result in further increases in brain insulin.
Second, the activation of Akt/GSK3β/Erk pathway is more frequently reported than the direct assessment of the phosphorylation of INSR/IRS-1. This issue limits data interpretation because while we can reasonably attribute a higher phosphorylation rate of INSR and IRS to the action of insulin, downstream kinases involved in the INSR cascade are shared by a wide variety of cell signaling pathways  [146] , which makes it more difficult to tightly link such endpoints to a specific response to insulin. Finally, studies are most often performed on brain homogenates, which do not provide cellular localization and may dilute the signal of INSR-activated cells, as will be discussed later. Keeping in mind these challenges, preclinical models offer suitable means to investigate the mechanism underlying BIR in vivo.
In vivo neuroimaging In vivo neuroimaging has the potential to provide measurements of a subset of the various physiological events characterizing BIR, with notable gaps and limitations. One of the most significant gaps in the neuroimaging-based assessment of BIR is the relative lack of techniques for assessing insulin transport across the BBB and binding to receptors in the brain. PET with a radiolabeled insulin ligand would appear to be ideal for this application because dynamic PET techniques, together with compartmental modeling techniques, have the ability to provide separate estimates of BBB exchange and receptor occupancy. However, to our knowledge, data from only one such radiolabeled insulin ligand has been published to date  [203] , and this data came from mice only. Lacking such insulin PET radioligands, PET radioligands that selectively bind to the insulin-like growth factor 1 (IGF-1) receptor, including radiolabeled IGF-1 itself, could be useful as proxy indicators of INSR binding due to the high level of cross-binding between these molecules and their respective receptors  [204] [205] [206].
This highlights the need to improve our methods for assessing BIR or combine tools to further understand BIR in the context of AD. Magnetic resonance spectroscopy (MRS) provides an alternative means for assessing brain glucose utilization responses to peripheral or central insulin challenges. Infusing a fuel substrate (such as glucose) labeled with 13carbon (13C), together with the collection of 13C MRS measurements, allows for the simultaneous and dynamic measurement of both the 13C-labeled fuel and the byproducts of its metabolism  [213]. The glucose metabolic flux estimates resulting from such measurements could potentially be compared between differing insulin states, although human data using 13C MRS with 13C-labeled glucose and insulin manipulation has been limited  [214]. Recent advances in 1-hydrogen (1H) MRS allow for measurement of the concentration of endogenous glucose in the brain with no need for a stable isotope infusion  [215] , although to date it also does not appear that comparisons of these measurements across differing insulin states have been made.
Additionally, in the 3xTg-AD mouse model, insulin-induced activation of the INSR at the BBB is blunted, beginning at 6 months of age  [41]. In female 3x-Tg-AD mice, pathological development of Aβ deposits, p-tau, and cognitive deficits typically appear by 6 months of age  [268]. These data support the link between BIR and AD hallmarks but also suggest that BBB insulin resistance may correlate with AD hallmarks as well. Other studies have tried to investigate the relationship of BIR with AD pathology. The A7-Tg AD mouse model deposits Aβ at approximately 12 months of age  [143]. At 3 months of age, prior to changes in the insoluble levels of Aβ, an experiment placed mice on a HFD. After just 2 months on this diet, the cerebral cortex INSR response to peripherally administered insulin was blunted, but insulin BBB transport was also decreased.
In post-mortem human brain microvessels, significant inverse correlations exist between INSRα-B levels and neuritic plaques, but not with parenchymal Aβ 40 or Aβ 42  [41]. Furthermore, there was a positive association between INSRβ and total soluble tau and an inverse relationship with neurofibrillary tangles. These data continue to support a relation not only between BIR and AD pathology but also between BBB insulin resistance and AD.
CNS cell specificity and regional localization Thus far, research surrounding BIR is predominantly approached from a whole brain or individual region point of view, although advancements in interrogating the impact of insulin signaling in sub-cellular populations are evident  [33]. A central question to consider is whether the brain is considered wholly an insulin-sensitive organ or whether insulin sensitivity varies across types of brain cells (e. g. , neurons, astrocytes, endothelial cells) and regions, dependent on the age, disease, or stage of disease, and other factors (e. g. , exposures). The very definition of BIR could also differ for each type of cell or region in the brain. However, with the recent advances in single-cell data and omics-driven data, researchers are more aware that specific cell populations may be drivers or initiators of a given pathology.