dataesr/scientific-paragraphs-categorization huggingface dataset:
Trend analysis The areas of TMF and disturbance classes are reported annually in 5-year intervals from 1990 to 2019, by country, subregion, and continent (Tables 1 to 10, Figs. 3  and 4 , fig. S11 , and see Supplementary Text on trend analysis), using the country limits from the Global Administrative Unit Layers dataset from the FAO  (57). Area measurements were also computed for 1° by 1° cells of a systematic latitude-longitude grid to delineate hot spot areas of deforestation and degradation for the three decades (Fig. 5 ). For the three most recent years of the considered period (i. e. , for 2017-2019), the proportions of disturbance types (degradation followed by deforestation, degradation not followed by deforestation, and direct deforestation) were calibrated with historical proportions (2005-2014) of the three types of disturbances.
Validation The performance of our classifier was assessed in term of errors of omission and commission at the pixel scale, and the uncertainties in the area estimates derived from the transition map were quantified (see Supplementary Text on validation). A stratified systematic sampling scheme was used to create a reference dataset of 5250 sample plots of 3 by 3 pixels (0. 81 ha of plot size) (fig. S8 ). For each sample plot, Landsat images on several dates were visually interpreted, together with the most recent HR images available from the Digital Globe or Bing collections, to create the reference dataset. The dates of the Landsat images to be interpreted were selected to optimize the assessment of the performance of our classifier as follows (fig. S9 ): (i) at least one random date within three successive key periods to verify the consistency of the temporal sequencing and the classifier performance across the main sensors (L5, L7, and L8); (ii) for the disturbed classes, the two dates corresponding to the first and last disruption observations were selected to assess the commission errors; and (iii) for the undisturbed forest class, at least one random date during the GFC loss year (if existing) to assess omission errors.
Comparison with the GFC dataset We compared our transition classes with the GFC dataset  (24)   A matrix of correspondences between the synthesized GFC map (four classes) and our reclassified transition map (eight classes) was then produced for each continent and for the pantropical region, where area estimates are compared (table  S1 ). This comparison shows that our annual change dataset depicts 138. 9 million ha of forest disturbances in the period 2001-2019 that are not depicted in the GFC map (representing 59% of the total area of our disturbances). This finding is corroborated by previous studies  (32, 33). In addition, 17. 6 million and 3. 2 million ha are depicted as a GFC loss, whereas it is classified as old deforestation and degradation, respectively, (before 2001) in our TMF dataset.
Among the disturbances that are not depicted by GFC, the greatest discrepancies concern the gradual processes such as degradation, the forest regrowth classes, and the deforestation that follows degradation. For these processes, the respective results showed that 75, 67, and 59% of our depicted areas are missing on the GFC map, whereas our direct deforestation class corresponds well with the GFC map (60%). The discrepancy between our dataset and the GFC map is even greater for the changes within mangroves, with an 83% difference. Mangroves are a key ecosystem within the TMF. We also observed a lower level of agreement for the disturbance classes in Africa (38% of our disturbances are depicted by GFC) compared with other continents (40. 9 and 43. 3% for Asia and Latin America, respectively).
A higher underestimation of GFC loss in Africa compared with other continents has also been observed by Tyukavina et al. (32)  using a sample-based analysis. We observe greater discrepancies between GFC and our study for shorter and lower-intensity events. We observe an average duration of 6. 7 years (visible impact) for the disturbances (both degradation and deforestation) detected only by our approach (and not by GFC) and an average duration of 9. 4 years for the disturbances captured by both approaches (GFC and our approach). We observe an average intensity (or total number of disruptions detected for each disturbance) of 9. 9 for the disturbances detected only by our approach compared with 32. 6 for the disturbances captured by both approaches. The evolution of the discrepancies over time shows major differences between the period (2001-2010) for which our annual change dataset depicts 61.
4% more deforested areas and the past decade (2010-2019) for which GFC losses include all our deforestation areas and 5. 7% of our degradation areas (Table  11  and Fig. 3 ). This change in the past decade has also been observed in another study  (58)  and can be explained (i) by the differences in processing applied by the GFC team before and after the year 2011 (https://earthenginepartners. appspot. com/science-2013-globalforest/download_v1. 3. html) and (ii) by the inclusion of burned areas in the GFC loss (particularly for the dry period of 2015-2016) that are mainly classified as degradation in our TMF dataset.
S16 ). To provide a more conservative estimate of the remaining undisturbed forested areas, we also produced another estimate of undisturbed forested areas, using a buffer zone with a threshold distance of 120 m from the detected disturbed pixels to exclude the potentially edge-affected forest areas. Further contextual spatial analysis would be needed to better estimate the characteristics of fragmented areas. At pantropical scale, with fine spatial resolution and annual frequency, detailed information about the historical forest area changes within the plantation concessions of oil palm and rubber are provided through to the combination of ancillary information and dedicated visual interpretation (see Supplementary Text on ancillary datasets). Although some confusion between forests and old plantations may remain (in particular, for plantations that are not included in the ancillary database of concessions or that cannot be easily identified visually in satellite imagery from a regular geometrical shape), these errors are expected to be limited because of the consideration of (i) a minimum duration for the initial period and (ii) a long observation period.
Classes of tree plantations do not include all commodities, such as coffee, tea, and coconut, that are detected as deforested land (if initially TMF and converted into commodity during the monitoring period) or other land cover (if the concession was already established during the initial period). Some isolated commission errors may remain in the bamboodominated TMF, wetlands, and semideciduous forests, as reference data were available on restricted areas (see Supplementary Text on specific tropical forest types). These will be continuously improved as the reference information layers improve and based on the feedback of users and national authorities. The L7 SLC-off issue may introduce some spatial inconsistencies owing to a higher number of valid observations outside the SLC-off stripes, allowing more disruptions to be captured and, potentially, leading to a different transition class.
Efforts have been made to classify disturbances based on their characteristics (timing, recurrence, and sequence) to fit with the land cover use. However, all metrics used in this study are made freely available to the end user, who may apply different decision criteria that would better match specific user needs and constraints, e. g. , the threshold applied to discriminate between deforestation and degradation may be different because of the selected definition of degradation. This approach can be applied to future Landsat data (from 2020) automatically and is intended to be adapted to Sentinel-2 data (available since 2015) for TMF monitoring with higher temporal frequency and finer spatial resolution.
Acknowledgments: The  USGS  and  NASA  provided the Landsat imagery. R. Moore  and team provided the  GEE. N. Gorelick ,  M. Dixon , and  C. Herwig  provided support in GEE. A. Cottam  provided support for the validation tool (adaptation from the water surface validation tool). A. Langner ,  H. -J. Stibig ,  R. Beuchle ,  A. Verhegghen ,  H. Eva ,  R. Grecci , and  B. Desclée  provided useful feedback on the legend and products in the framework of the  ReCaREDD (Reinforcement of Capacities  for REDD+) and REDDCopernicus projects. We thank  A. Marelli  and  C. Bourgoin  for preparing the TMF Explorer. We thank  A. Cescatti  and  P. Mayaux  for reviewing earlier versions of the manuscript. Funding: This study was funded by the  Directorate General for Climate Action of the European Commission (DG-CLIMA)  through the  Roadless-For pilot project  ( Making efficient use of EU climate finance: Using roads as an early performance indicator for REDD+  projects) and the  Lot 2 ("TroFoMo ," Tropical moist Forest Monitoring) of the  ForMonPol Administrative Arrangement  ( Forest Monitoring  for Policies).
ACKNOWLEDGMENTS This study was carried out within the framework of the  FUI 22 collaborative project  " Abysound ," funded by  Pôle Mer  M editerrann ee and  Banque Publique d'Investissement (BPI). The authors would like to thank all  Abysound  project partners: Naval Group, OSEAN  SAS, MicrodB, Semantic TS, l'Institut Francais de Recherche pour l'Exploitation de la Mer (IFREMER), Gipsa-Lab , the  Laboratoire de M ecanique et d'Acoustique (LMA) , and the  Laboratoire des Sciences de l'Information et des Systemes (LSIS). This work was performed within the framework of the  LABEX CeLyA  ( ANR-10-LABX-0060 ) of  Universit e de Lyon , within the  program Investissements d'Avenir  ( ANR-16-IDEX-0005 ) operated by the  French National Research Agency (ANR).
Evaluation of Automatic Procedures and Data Processing (Digital Image Analyses) Personal macros were written with the Fiji software  [19]. Fiji as a bundle is licensed (https://github. com/fiji/fiji/blob/master/LICENSES) under the GNU General Public License (Version 3, 29 June 2007) -as specified at http://www. gnu. org/licenses/gpl. txt-, with exceptions for plugins and other components.
Only structures with a circularity between 0. 50 and 1. 00 were kept. The final data that will be extracted by this procedure are the number of specifically stained nuclei (i. e. , TRA98-and CREM-positive) and the number of positive cells per 1000 µm 2 of tissue. A macro combining Background_Elimination_Stitching, Necrotic_Area_Elimination, and IHC_Tool procedures is available at https://github. com/LudovicDumont/IHC-Tool. git.
Stained_Nuclear_Area Procedure In order to have numerical data corresponding to the cells present on the sections provided, the nuclear 'diameter' (d ̄) of germ cells was manually measured at right angle in the section plane to quantify the area (approximated to π×d ̄2:4) of TRA98-and CREM-positive cell nuclei. A total number of 100 cell nuclei were counted per group (Supplementary Figure  1 ). To quantify the number of TRA98-and CREM-positive nuclei, the total intensity of DAB-stained elements was reported to the estimated size of the corresponding cells according to the staining (spermatogonia and leptotene-zygotene spermatocytes I for TRA98 or late pachytene spermatocytes I and round spermatids for CREM). The final extracted data are considered to be the number of positive cells per 1000 µm 2 of unaltered area.
Statistical Analyses Statistical analyses were performed with GraphPad Prism version 6. 00 (GraphPad Software, La Jolla, California). The Mann-Whitney test was used for unpaired rank comparisons and the nonparametric unilateral one-tailed Wilcoxon test was used for paired rank comparisons. The one-way ANOVA test was used for the evaluation of assays reproducibility, followed by a multiple comparison. The ordinary two-way ANOVA test was used for the evaluation of the testicular tissue analyses, followed by a Tukey's multiple comparison post-hoc test. Data are presented as means ± SEM and P<0. 05 was considered to be significant.
DISCUSSION Achieving a common consensus in the quantitative analysis of immunohistochemical images is still an issue in various clinical or translational researches  [21]. Testis biopsy with diagnostic and prognostic importance is a crucial assessment in reproductive practice in the evaluation of men at risk  [22]. For an accurate histological classification, proper tissue handling, fixation, preparation of the specimen and evaluation are needed  [23]. Re-evaluation of testis biopsies reported externally often reveals that the original report was deficient and that clinical care may have been improved by the use of a systematized approach. In the present study, we showed that manual examination is hardly reproducible. Herein, we focused on the development of a series of macros for an open-source image processing package based on ImageJ to quantify staining of IHC images, which requires minimal supervision for analysis.
In this study, with the IHC_Tool procedure at a ×200 magnification and a backup in TIFF format, we (i) reduced background and noise on brightfield images, (ii) generated a global stitched high-resolution representation of testicular tissue sections, (iii) extracted the proportion of necrotic area, and (iv) allowed an automated counting of the number of immunostained cells per 1000 µm 2 of unaltered area. These IHC evaluations were consistent with other studies carried out by our team on in vitro matured testicular explants for the number of immunostained nuclei  [17, [25] [26] [27] [28] [29] [30]  and the proportion of necrotic area  [17, 25]. The procedures developed in this study are running on Fiji software, which creates a 'profile analysis' of a digital IHC image. The selected procedure offers many advantages: We foresee that this method will minimize the inter-examiner variations and further help groups to use open-source software, providing a collaborative option for image analysis whereas commercial software provides more personalized image analysis choices.
Each option presents certain advantages and disadvantages; a comparison between the selected IHC_Tool procedure, a 'generic' commercial software, and the manual examination is available in Table  1. This tool can be used as a systematic procedure providing crucial clinical information on the status of highly heterogeneous immature human testicular tissues in the context of cryopreservation for fertility preservation (i. e. , absolute germ cell numbers per area)  [31]. Moreover, we provide the code of the macros validated herein, encouraging other researchers to adapt and change key elements to their specific needs like file format (e. g. , PNG, BMP), cell staining, magnification, morphological modifications, etc… In order to improve the IHC_Tool procedure, it will be possible to quantify the intensity of nuclear staining in individual cells in addition to their area.
The macro is available at https://github. com/LudovicDumont/Background_Elimination_Stitching. git. A flowchart schematizing the processing performed by the Background_Elimination_Stitching procedure is shown in Figure 1. To compare the different conditions evaluated in this article (i. e. , magnification, save-file format, procedure), a relative difference between experiments was measured. The formula for this type of percent error is: relative difference between condition A and B = │1 -(condition B)/(condition A)│ × 100.
Digital images can be analyzed sideways, freeing time to work on other tasks • Take about 10~15 min to analyze an image depending on the magnification, the save file format and the area studied • Easy to learn and user-friendly • Compatible (TIFF and JPEG) and extendable (e. g. , PNG, BMP, etc…) to various Original Images file formats • Runs on a multitude of operating systems (e. g. , Windows 32-or 64bits [Microsoft], macOS [Macintosh; Apple Inc. ], Linux 32-or 64bits, and even without Java Runtime Environment) • Allows images to be saved and analyzed a posteriori • Evolutive and sharable (encouraging other researchers to adapt and change key elements to their specific needs) • Possibility to remove a 'ROI' from the analysis (e. g. , necrotic area) • Does not yet have a morphometric analysis system • Cannot (as it stands) quantify fluorescent images • Can analyze, as it stands, only nuclear immunostaining • Results are saved on a local database and statistical analyses & plots have to be generated manually Commercial Software • Bias-free analysis • Digital images can be analyzed sideways, freeing time to work on other tasks • Take about several minutes to scan and analyze a 'ROI' on an image depending on the magnification and the area studied • Possibility to remove a 'ROI' from the analysis (e.
g. , necrotic area) • Most often allows to analyze the morphometry • Results are stored in a Workstation database and, sometimes, statistical analyses and plots can be automatically generated • Commercial (cost-effective) which often requires a whole slide scanner • Additional cost for purchasing apps to specific wishes • Settings often blocked or difficult to change • Specific images file formats (making recording and reading of images dependent on the private software • Runs on a specific operating system (e. g. , Windows [Microsoft] or macOS [Macintosh; Apple Inc. ]) • May require an expert opinion for some application • Requires dedicated learning time and customer support Manual examination • Requires no dedicated computer system and can be performed directly • Total time analysis >90 min • Long learning curve • Difficult to have good reproducibility (inter-and intra-individual).
ACKNOWLEDGEMENTS This work was supported by  Rouen University Hospital, Ligue contre le Cancer  [to N. R. ],  Agence de la Biomédecine  [to N. R. ],  Association Laurette Fugain  [to N. R. and C. R. ],  France Lymphome Espoir  [to N. R. ], and co-supported by  European Union  and  Région Normandie. Europe gets involved in Normandie with  European Regional Development Fund (ERDF).
g. , necrotic area) • Does not yet have a morphometric analysis system • Cannot (as it stands) quantify fluorescent images • Can analyze, as it stands, only nuclear immunostaining • Results are saved on a local database and statistical analyses & plots have to be generated manually Commercial Software • Bias-free analysis • Digital images can be analyzed sideways, freeing time to work on other tasks • Take about several minutes to scan and analyze a 'ROI' on an image depending on the magnification and the area studied • Possibility to remove a 'ROI' from the analysis (e. g. , necrotic area) • Most often allows to analyze the morphometry • Results are stored in a Workstation database and, sometimes, statistical analyses and plots can be automatically generated • Commercial (cost-effective) which often requires a whole slide scanner • Additional cost for purchasing apps to specific wishes • Settings often blocked or difficult to change • Specific images file formats (making recording and reading of images dependent on the private software • Runs on a specific operating system (e.
The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets ( N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network.
In this context, electro/magneto-encephalography (EEG/MEG) can be used to describe those fast (sub-millisecond) modularity-dependent fluctuations. Here, we hypothesized that the dynamic modular reorganization of the human brain at rest is characterized by a continuous process of separation and merging within and across different RSNs over time. To detect "modular brain states ", we use a recently developed framework that allows to precisely quantify the fluctuations of the time-varying 'states' (A. Kabbara et al. , 2019 ). Unlike other clustering algorithms, this framework detects the fast-transient changes in the brain modular structure and was shown to outperform other existing clustering algorithms in terms of spatiotemporal precision. We tested our hypothesis on three independent EEG/MEG datasets ( N = 568) for healthy subjects at rest, source-reconstructed to 68 regions across the entire cortex.
Dynamic brain networks were reconstructed using the EEG/MEG source connectivity technique using both power-and phase-couplings  ( Hassan and Wendling, 2018 ) , combined with a sliding window approach and an algorithm to detect modular states ( Fig. 1  ). Notably, our results revealed the presence of consistent network patterns for most participants and a splitting phenomenon of some of these networks, such as the default   ( Desikan et al. , 2006 ). Then, the regional time series of each subject were reconstructed using the weighted minimum norm estimate inverse solution (WMNE) for Datasets 1 and 2, and beamforming for Dataset 3. (C) Using a sliding window technique, the dynamic brain networks were computed. (D) The first step in the modularity-based algorithm was to parcellate each temporal network into communities.
Results We performed our analysis on three independent datasets: 1) Resting-state EEG data provided from the Healthy brain network biobank comprising 444 subjects; 2) Resting-state EEG data acquired from 56 healthy subjects; and 3) Resting state MEG data provided from the Human Connectome Project including 61 subjects. The dynamic functional connectivity networks were assessed for each subject using the EEG/MEG source connectivity method  ( Hassan and Wendling, 2018 ). For EEG datasets (datasets 1 and 2), we used the weighted Minimum Norm Estimate (wMNE) followed by phasecouplings as recommended by previous EEG studies  ( Hassan et al. , 2016 ; M. 2014 ). For the MEG dataset (dataset 3), we used the beamforming approach followed by envelope-couplings, as recommended in previous MEG studies  ( O'Neill et al.
12 states were identified for the second database According to the second dataset ( Fig. 3  ), 12 modules are extracted from the 57 subjects in the alpha frequency band. These modules are: POST-DMN (present in 98% of subjects), VIS (present in 94% of subjects), DAN (present in 91% of subjects), DMN (present in 84% of subjects), L-TEMP (present in 82% of subjects), ANT-DMN (present in 81% of subjects), SMN (present in 73% of subjects), AUD + VIS (present in 68% of subjects), DAN + VIS (present in 60% of subjects), TEMP (present in 45% of subjects), DMN + CCN (a module that combines DMN with cognitive control components, present in 32% of subjects) and SAN (present in 22% of subjects). Results were also consistent among several threshold values of functional connectivity matrices (see Table  S2 ) and within the beta frequency band (see Table  S5 ).