dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples of these updates are the replacement of the MERRA reanalysis by MERRA-2 of the Global Modelling and Assimilation Office (GMAO)  [11]  and the replacement of the popular ERA-Interim reanalysis by ERA5 produced by European Centre for Medium-Range Weather Forecast Reanalysis (ECMWF)  [13]. In this work, the ability of three reanalyses (MERRA-2, ERA5, and GLDAS) to represent the surface wind in southern Patagonia is studied. The main objective is to analyze the variability over the last decades of the strong winds in the present-day core of the SWW (51 • S). To this, the degree of agreement between the surface winds of the reanalyses and direct measurements of the few meteorological stations available in the area of study is first evaluated. In a second step, the reanalyses that best fit the data will be used to extend the time series of meteorological data in the past beyond the implementation of the meteorological stations, which will make it possible to analyze the wind variability on wider (monthly to interdecadal) timescale.
This paper is organized as follows. The next section (Section 2) describes the SWW and the area of study, the methodology, and the datasets used. Section 3 shows the time series of meteorological stations and reanalysis data. It also shows the results of the temporal variability of strong winds determined from statistical analysis. This information is used in Section 4 to discuss the consistency of the different reanalyses and to analyze the variability of the wind. Finally, the last section summarizes the results and presents the main conclusions of this work.
Direct Observations In the study area there are only three meteorological stations with open access records of near-surface wind speed: Teniente Gallardo, Cerro Castillo, and Torres del Paine. Information on these stations and limits of their periods of measurements is provided in Table  1  and Figure  2. The data of the first station were obtained from the Dirección Meteorológica de Chile (DMC) (https://www. meteochile. cl/, accessed on 20 April 2021). The data for the second station were obtained from Instituto de Investigaciones Agropecuarias (INIA) (https://agrometeorologia. cl/, accessed on 20 April 2021). Finally, the data from the Torres del Paine station were facilitated by Dirección General de Aguas (DGA) (https://dga. mop. gob. cl/, accessed on 20 April 2021). At the latter station, the measurements were performed manually for a short duration and are not continued.
Data were downloaded from the Copernicus Climate Data Store (https://cds. climate. copernicus. eu/, accessed on 30 April 2021). MERRA-2 of the Global Modelling and Assimilation Office (GMAO 2015) is an upto-date reanalysis for the satellite era (from 1980 onward). The spatial resolution of the Although short, these historical records are important as they serve to validate the data series of the reanalysis used in this work.
Data were downloaded from the Copernicus Climate Data Store (https://cds. climate. copernicus. eu/, accessed on 30 April 2021). MERRA-2 of the Global Modelling and Assimilation Office (GMAO 2015) is an upto-date reanalysis for the satellite era (from 1980 onward). The spatial resolution of the model is 0. 5 • latitude x 0. 625 • longitude  [11]  and provides data series with hourly temporal resolution (Table  2 ). The datasets, corresponding to near-surface (10 m above ground), wind speed were extracted using the Giovanni NASA interface (https://giovanni. gsfc. nasa. gov/, accessed on 20 April 2021). GLDAS is a system jointly developed by scientists at the National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA).
The system aims to ingest satellite and ground-based observational data products and generate optimal fields of land-surface states and flows  [29]. The spatial resolution is 1. 0 • x 1. 0 • with daily data for the 1948-2014 period (Table  2 ). These datasets are available at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) as well as via Giovanni NASA (https://giovanni. gsfc. nasa. gov/, accessed on 20 April 2021).
Analysis of Wind Time Series First, the coherence between the reanalyses data and the in-situ wind measurements is analyzed because possible discrepancies were expected due to the complex topography of Patagonia. High (hourly) and medium (daily) resolution data were used for the study and statistical indicators such as the frequency of occurrence of strong winds in a given month or year were calculated to quantify the high-resolution temporal variability of the regional winds. The focus was set on the strongest winds because they are those responsible for the transport of particles on the surface  [10]. For evaluating the performance of the reanalysis in the study area the following time series of the grids closest to the meteorological station were chosen: (-73. 125, -51. 5, -72. 5, -51) for MERRA-2, (-72.
25, -51. 25, -72. 5, -51) for ERA5, and (-73. 375, -51. 875, -72. 375, -50. 875) for GLDAS (Figure  1 ). Observation wind time series were then compared with data measured at hourly (for MERRA-2 and ERA5) and daily (for GLDAS, MERRA-2, and ERA5) resolution. Because the GLDAS period (1948-2014) does not overlap the period recorded at the Cerro Castillo and Teniente Gallardo stations (2016-2020; Figure  2 ) but presents an interestingly long time series of data, its consistency with ERA5 and MERRA-2 was evaluated. To do this, statistical parameters such as the bias, root mean squared error (RMSE) and correlation coefficient (R) were used to quantify the differences between reanalyses data and in-situ wind measurements. Finally, as indicated above, the occurrence of strong winds in the study area was studied considering that only winds above the saltation threshold (u*t) can effectively mobilize sediments (aeolian particles) over the surface  [10]  and are of interest for paleoclimate reconstructions.
Local Wind Variability The synchronicity of the measurements performed after 2016 at Cerro Castillo and Teniente Gallardo allows checking the consistency of the data collected at the two stations. In spite of the distance (ca. 55 km. ) separating them, the time series of daily averaged winds (Figure  3a ) are quite strongly correlated (R = 0. 87, Figure  3b ). This shows that the measurements performed at any which one of the two stations are not of purely local interest but are representative of a much larger area including the region of study. The main differences between Cerro Castillo and Teniente Gallardo are observed in the direction of the wind (Figure  3c ). Even though both stations have fundamentally winds from the west, the Teniente Gallardo station has a significant north component in its measurements (Figure  3.
(c1)). Atmosphere 2022, 13, x FOR PEER REVIEW 7 of 19 In the following, it was arbitrarily chosen to adopt the measurements of the Cerro Castillo station as a reference for the regional situation. The analysis of these data shows that maximum wind speed (6->12 ms -1 ) are mainly from the west whereas minimum speeds (0-4 ms -1 ) come mainly from the northwest (Figure  3 (c2)). At the annual timescale, the measurements reveal a significant seasonality in wind speed characterized by strong winds during the austral summer months (December-January-February; DJF), and weak winds in the austral winter (June-July-August; JJA) (Figure  3a ). Seasonality is also observed in wind direction (Figure  4 ). During the austral summer and spring, the strongest winds come from the west (Figure  4a, d ), whereas they come from the west and northwest during the austral fall and winter (Figure  4b, c ).
The statistical distribution of hourly winds speeds for the period 2016-2020 shows an asymmetric distribution with data shifted towards weaker winds (Figure  5a ). Most of the data are concentrated between 0 and 2 ms -1 , while winds between 2 and 8 ms -1 are distributed in two main modes, one between 2 and 4 ms -1 , and the other between 6 and 8 ms -1 Finally, winds greater than 8 ms -1 present lower frequencies (<3%). In the following, it was arbitrarily chosen to adopt the measurements of the Cerro Castillo station as a reference for the regional situation. The analysis of these data shows that maximum wind speed (6->12 ms -1 ) are mainly from the west whereas minimum speeds (0-4 ms -1 ) come mainly from the northwest (Figure  3.
Reanalysis Validation As indicated in Section 2. 2, the validation was carried out in two steps: first, the data of the Cerro Castillo meteorological station were used to assess the quality of the MERRA-2 and ERA5 wind products, then GLDAS was compared with the other reanalyses to assess the possibility of extending the time series towards a more distant past (i. e. , before 1980). Table  3  summarizes the error metrics related to the comparison between the daily wind of Cerro Castillo station and those of ERA5 and MERRA-2. Table  4  shows the parameters for the comparison between GLDAS and the other two reanalyses. The statistical distribution of hourly winds speeds for the period 2016-2020 shows an asymmetric distribution with data shifted towards weaker winds (Figure  5a ). Most of the data are concentrated between 0 and 2 ms -1 , while winds between 2 and 8 ms -1 are distributed in two main modes, one between 2 and 4 ms -1 , and the other between 6 and 8 ms -1 Finally, winds greater than 8 ms -1 present lower frequencies (<3%).
Reanalysis Validation As indicated in Section 2. 2, the validation was carried out in two steps: first, the data of the Cerro Castillo meteorological station were used to assess the quality of the MERRA-2 and ERA5 wind products, then GLDAS was compared with the other reanalyses to assess the possibility of extending the time series towards a more distant past (i. e. , before 1980). Table  3  summarizes the error metrics related to the comparison between the daily wind of Cerro Castillo station and those of ERA5 and MERRA-2. Table  4  shows the parameters for the comparison between GLDAS and the other two reanalyses. On a daily scale, it can be observed that winds are generally more intense during the afternoon, mainly between 12. 00 a. m and 18. 00 p. m, while at night, the intensity of the winds decreases (Figure  5b ).
Reanalysis Validation As indicated in Section 2. 2, the validation was carried out in two steps: first, the data of the Cerro Castillo meteorological station were used to assess the quality of the MERRA-2 and ERA5 wind products, then GLDAS was compared with the other reanalyses to assess the possibility of extending the time series towards a more distant past (i. e. , before 1980). Table  3  summarizes the error metrics related to the comparison between the daily wind of Cerro Castillo station and those of ERA5 and MERRA-2. Table  4  shows the parameters for the comparison between GLDAS and the other two reanalyses. The annual R ranging between 0. 86 and 0. 88, and the low associated error indicate a high level of agreement between the temporal variations of the daily wind data of both MERRA-2 and ERA5 and those of the measurements.
ERA5 vs. Cerro Castillo The daily covariation between the measurements and data yielded by the reanalysis are shown in Figure  8. In general, it can be observed that the reanalysis presents the same variability of wind speed as that measured at the Cerro Castillo station but with a lower amplitude, underestimating in-situ measurements most of the year (Bias ANN = -1. 28 ms -1 , Table  3 ). During the austral summer (DJF) and spring (SON) months this difference increases (-1. 66 and -1. 7 ms -1, respectively, Table  3 ) while in fall (MAM) and austral winter (JJA) the underestimation decreases (-0. 88 and -0. 89 ms -1 , respectively, Table  3 ).
ERA5 vs. Cerro Castillo The daily covariation between the measurements and data yielded by the reanalysis are shown in Figure  8. In general, it can be observed that the reanalysis presents the same variability of wind speed as that measured at the Cerro Castillo station but with a lower amplitude, underestimating in-situ measurements most of the year (BiasANN = -1. 28 ms -1 , Table  3 ). During the austral summer (DJF) and spring (SON) months this difference increases (-1. 66 and -1. 7 ms -1, respectively, Table  3 ) while in fall (MAM) and austral winter (JJA) the underestimation decreases (-0. 88 and -0. 89 ms -1 , respectively, Table  3 ). At daily time scale, the reanalysis presents a strong correlation (RANN = 0. 88 and RMSEANN = 1. 87, Table  3 ) with the data measured at Cerro Castillo.
Acknowledgments: This research was funded by  Fondecyt  grant no. 1191942  from  National Agency for Research and Development (ANID)  and support of IRD. V. F. -A. acknowledges the support from  IRD  through  CHARISMA  Project (JE0ECCHARI). C. G. -F. and V. F. -A. would like to thank  C. Gonzalez  for his help in obtaining reanalysis data.
Data Availability Statement: The meteorological station analyzed in this study were obtained from the Dirección Meteorológica de Chile (DMC) (https://www. meteochile. cl/ accessed on 30 April 2021), from Instituto de Investigaciones Agropecuarias (INIA) (https://agrometeorologia. cl/ accessed on 30 April 2021), and the data from the Torres del Paine station were facilitated by Dirección General de Aguas (DGA) (https://dga. mop. gob. cl/ accessed on 30 April 2021). Also, the data of ERA5 is available online at (https://cds. climate. copernicus. eu/ accessed on 30 April 2021), while dataset of MERRA-2 and GLDAS are available online at (https://giovanni. gsfc. nasa. gov/ accessed on 30 April 2021).
Determining the extent of any differences in mental health related to work-from-home status has clear health equity implications for employers and policy makers to ensure best practices throughout the ongoing COVID-19 pandemic, as well as for future public health crises. As COVID-19 risks continue to the present day-particularly with risks such as long-COVID and unmitigated Omicron infection-it has become important to understand mental health differences according to where participants are working. This study used survey data collected during the third wave of the COVID-19 pandemic in Canada  [36]  to examine whether there were any differences in self-rated mental health based on work setting and if so, what contributes to these differences? The dataset provided a unique opportunity to explore the nuances of self-rated mental health, and thus, bivariable and multivariable logistic regression models were used to test the hypothesis that mental health status is poorer among individuals who are not working from home.
Study Data The study utilized the Canadian Social Connection Survey (CSCS) dataset, which collected data from 21 April to 1 June 2021. The survey was circulated on the internet using paid advertising on Facebook, Twitter, Instagram, and Google. Participants were eligible if they were Canadian residents and 16 years of age or older. Ethics approval was granted by the University of Victoria Research Ethics Board (Ethics Protocol Number 21-0115)  [36]. All participants provided informed consent and were able to complete the questionnaire in English or French. Given the need to determine mental health effects in various work settings, the dataset allows for a comprehensive exploration. Inclusion for the current study was conditional on whether a respondent indicated that they were working during the COVID-19 pandemic.
Primary Explanatory Variable Work setting (listed as work_from_home in the dataset) was the primary explanatory variable for the study. The variable measured how often participants worked from home ("Not Working During COVID", "Not at all", "Very little of the time", "Some of the time", "Most of the time", and "All of the time"). The levels "Very little of the time", "Some of the time", and "Most of the time" were collapsed into a single level-"Hybrid". "Not at all" was recoded as "Do Not Work from Home" and "All of the time" was recoded as "Work from Home Only". These levels allowed for a continuum of working from home to be represented. Participants who reported not working during COVID-19 were removed from analyses as our goal was to explore the effects among Canadian workers who were currently employed.
Statistical Analysis All statistical analyses were performed using R Statistical Software version 4. 1. 1 (R Foundation for Statistical Computing, Vienna, Austria)  [42] ; DescTools and regclass packages were used to assist in model assessment and fitting  [43, 44] ; the mice package was used for multiple imputations of missing observations  [45] ; and the mediation package was used for mediation analysis  [46]. Missing observations on the remaining variables were imputed using multiple imputation in the mice package  [45]. An initial multivariable binary logistic regression model (Supplementary Materials File S1), with the outcome variable of self-rated mental health and primary explanatory variable of work setting, was constructed with 30 confounding variables. The final multivariable model was developed by running a backwards selection process favouring the model with lowest Akaike Information Criterion  [47].
Sample Overview 2286 respondents were initially included. However, 370 indicated they were not currently employed and of the remaining 1916 employed respondents, 340 were missing data on our primary measures. This resulted in 1576 participants eligible for analysis. Descriptive statistics, stratified by self-rated mental health, are presented in Table  1. The study sample predominantly reported positive self-rated mental health (80. 7%) with the majority of participants in both outcome groups responding that they work both from home and in person (hybrid); however, a greater proportion (46%) of those not working from home reported negative self-rated mental health compared to those in other work setting configurations (Figure  1 ). In terms of demographics, 41. 8% were 18 to 29 years-old; 49.
depression). Qualitative research could also be used to better understand specific pathways of poor mental health for those working exclusively from home or in-person. Given limitations in measurement, the results of the current study must be interpreted with caution when considering specific psychological disorders. As well, the dataset over-represented (77. 2%) individuals who work in hybrid arrangements, compared to the other two groups (exclusively working from home and exclusively working in-person). Caution should therefore be taken in interpretation, as this drastically departs from the range of Canadian workers working the majority of their hours from home-40. 5% in April 2020 to 26. 5% in June 2021  [65]. Lastly, as the CSCS did not include questions assessing individuals' worry about COVID-19 exposure at work, nor how well their workplace implemented protection protocols, we were not able to account for the nuance of psychological distress related to COVID-19 infection.
The measures we use to assess compliance are global and not work specific. As such, our mediation models should be interpreted as preliminary. Likewise, some measures need refined assessment in future studies. For example, to measure income, participants' household incomes were collected in increments of $10,000 CAD. Bins of $30,000 CAD were selected with consideration of classifying individuals according to approximate thresholds for low-(e. g. , Approx. $30,000 per households) and median income (approx. $90,000 per household) in Canada. As household size and cost-of living values varied, a more nuanced measure of income would have been preferred by was not available in this secondary data analysis. Personal income, adjusted for cost of living, could provide a more nuanced insight into working condition and types of work engaged in, as these parameters are undoubtedly important for understanding worker health.
For optimizing the operating parameters for MSI experiments, acquisitions of approximately 300 pixels were recorded from interweaved regions on the same section of liver homogenate with a single parameter varied per acquisition. Interweaving was performed by using the same region drawn in flexImaging with 160 µm pixel size displaced by successive 40 µm increments in X and Y directions. Lipid standard data was acquired as a single region per polarity covering one of each spots on a Bruker MTP 384 target plate with 60 µm raster size. Brain tissue imaging experiments were performed at spatial resolutions of 20 or 30 µm. TIMS-MSI data was acquired across a 1/K0 range of 1. 15-1. 60 using 1000 ms ramp time. Further acquisition parameters are provided in Supporting Information Table  S1. For liver homogenate, a sliding intensity cutoff was used to pick approximately 2,500 features (m/z values) from each combined set, and an additional list of theoretical mass values for lipid ions was imported as a target list.
MS/MS Regions of Interest for each individual lipid standard spot were manually drawn in SCiLS and averaged spectra exported and used for analysis. For mouse brain acquisitions, ion images were produced in SCiLS Lab using 6 or 8 ppm tolerance, total ion current normalization and 99% quantile hot-spot removal, unless otherwise specified. Mobility-m/z heatmaps were exported as a full-dataset average from DataAnalysis. Lipid Nomenclature: We endeavour to adopt recommendations for lipid annotations and fragment descriptions based on current guidelines,  39, 40  albeit by using the common convention of enclosing carbon chain information in brackets (e. g. [PE(40:6)+H] + ) for reading clarity when specifying fragments/adducts. We will use "Diox" in a manner similar to lipid classes for the series of dioxolane fragments which would otherwise be labelled as.
To further understand the detection and in-source fragmentation pathways of different lipid classes, we also analyzed a series of individual lipid standards, the results of which are provided in Supporting Information Figures  S3  & S4. Here we will focus on several key observations. First, in the positive-ion mode, all standards exhibited some in-source fragmentation to an extent and nature that varied dramatically between different classes. For all glycerophospholipid classes, dioxolane species were observed as a major fragmentation product. Intact [M+H] + ions were observed only at low intensity for PG and PI standards, and unobserved for PS. Consistent with data shown in Figure  3a , PE exhibited the least fragmentation, with the intact protonated species dominating the signal. Galactosylceramide Generally consistent with data in Figure  3 , negative mode analysis of PE, PG, PI and PS yielded [M-H] -ions with varying intensities, while the PE was also observed as [M+NO3] - and the GalCer was essentially exclusively observed as the abundant nitrate adduct (Supporting Information Figure  S4 ).
Mass Spectrometry Imaging We next explored the application of the AP-MALDI-PPI-equipped timsTOF for lipid imaging. Figure  4  shows positive-ion mode MSI data obtained from mouse brain tissue at a pixel size of 20 µm. At the laser spot sizes achieved, this corresponds to slight oversampling. Representative single-pixel spectra are provided in Figure  S5  in the Supporting Information. The averaged mass spectrum is shown in Figure  4a  and reveals the detection of numerous lipid species with excellent signal-to-noise, and sharing similarities with data obtained using MALDI-2 and other post-ionization methods where protonated PE and HexCer lipids produce the most intense signals  27, 30, 34. When analyzed using the METASPACE annotation engine  49  against the LIPID MAPS database, 351 m/z values with distributions specific to on-tissue regions could be assigned to potential lipid species with a 10% false-discovery rate and < 10% off tissue probability (295 peak are annotated at < 1% off tissue probability).