dataesr/scientific-paragraphs-categorization huggingface dataset:
These patterns were consistent for areas that experience both one and two dry seasons per year (Fig. 2d, e ). Further analyses revealed that the discrepancy of DSL among different precipitation datasets is much smaller than that among definitions (Fig. 2 ). DSL defined by P < P was the least sensitive to precipitation inputs, as the P self-adjusted for different precipitation datasets. We found that the inter-metric DSL difference varied according to regional mean annual precipitation (MAP), due to the relationship between Ep and E (Supplementary Fig. 6 ). Under arid conditions (that is, when P is much smaller than Ep), actual water consumption, E, is mainly limited by the available supply of surface water, which is much smaller than the Ep and converges toward the multi-year average rainfall P.
We next assessed long-term trends in dry season length and timing (DSL, DSA, and DSE) as well as the water deficit (WD, defined as the cumulative difference between P vs. Ep, E, or P) during the dry season using each of the three definitions. All the three definitions indicated more widespread drying than wetting over the tropics (Fig. 4 ). However, the extent of land area experiencing a drying trend varied considerably among these different definitions. The overall fraction of tropical land area with lengthening dry seasons (at least one significant drying trend, and no significant wetting trend, among different datasets) was largest when dry season was defined as P < E (48. 7%), followed by P < Ep (43. 1%), and was smallest for P < P (33. 7%). Accordingly, a smaller fraction of regions was probably experiencing shorter dry seasons (thus longer wet seasons), accounting for ~20.
4%, 17. 1%, and 19. 8% of tropical lands when dry season was defined as P < Ep, P < E, and P < P, respectively (Fig. 4a ). We also found that the extension of the dry season was accompanied by an increase in the cumulative WD during the dry season (Fig. 4b ). The percent of tropical land area experiencing increasing dry season water deficit (~55. 7%, 51. 7%, and 38. 0% for P < Ep, P < E, and P < P, respectively) was larger than that experiencing decreasing water deficit (~16. 8%, 17. 5%, 19. 2% for P < Ep, P < E, and P < P, respectively). Analyses using other two independent Ep products detected similar fraction of lengthening dry seasons (MERRA-2: 41. 5%; GLDASv2. 0: 42. 9%) and increasing water deficit (MERRA-2: 49. 9%; GLDASv2. 0: 52. 0%), indicating a robust drying trend among different Ep datasets (Supplementary Fig.
8 ). The three dry season definitions consistently identified some regions with robust dry season lengthening (more than four out of eight datasets agree), including southern Amazonia and central Africa (red rectangles in Fig. 4 ). The increased DSL in southern Amazonia (4. 81-10. 84, 3. 65-11. 96, and 5. 95-10. 53 d decade -1 when defined as P < Ep, P < E, and P < P, respectively, Supplementary Table  2 ) was mainly attributed to a delayed DSE (Supplementary Fig. 7 ). In the Congo Basin and southern central Africa, the length of the June-August dry season increased by 7. 62-11. 61, 6. 62-11. 65, and 8. 19-11. 93 d decade -1 when defined as P < Ep, P < E, and P < P, respectively (Fig. 4 , Supplementary Table  2 ). This dry season extension in central Africa was due to both a delayed DSE and an earlier DSA (Supplementary Fig.
7 ). For both southern Amazonia and central Africa, the dry season lengthening and associated increased water deficit were most robust under the definition of P < Ep, followed by P < E (Fig. 4 , Supplementary Table  2 ). In southern Africa and Sahel, different definitions suggested different trends (purple rectangles in Fig. 4 ). In southwestern Africa, for example, all eight datasets indicated dry season lengthening (5. 30-16. 51 d decade -1 ) due to delayed DSE, when defined as P < E (Fig. 4 , Supplementary Table  2 , Supplementary Fig. 7 ). By contrast, we found a shortening of DSL (-5. 66 to -9. 24 d decades -1 ) when dry season was defined as P < Ep, and The mean ratio of P amplitudes of the harmonics at frequencies of two and one cycle per year via Fourier Analysis of whole daily time series (1983-2016) for each grid box based on the eight precipitation datasets.
The blue line marks the boundaries with the ratio of 0. 75, inside which grid boxes have two wet seasons and two dry seasons per year. b, c Daily mean rainfall (light blue) for each day of the year, smoothed using a 30-day running window (blue), the multi-year average daily mean precipitation (red horizontal line), and cumulative P anomaly value (green) for the grid box centered at 11. 125°S, 57. 875°W (left, point 1 in a) and 1. 375°N, 12. 875°E (right, point 2 in a) according to the daily CHIRPS precipitation dataset for the period 1983-2016 (see Eq. 3). Red dots mark the arrival of dry seasons (DSA), while blue dots mark the end of dry seasons (DSE). The solid black shaded area represents the water deficit, calculated as the cumulative difference between P and P during the dry season. The two longest seasons are assumed to be the two seasons of interest for the biannual region (c).
5b ). The rising atmospheric water demand that driven by decreasing air humidity and rising temperature were also identified in the other two Ep datasets (Supplementary Fig. 12 ). Hence, compared with the P-only trend in DSL, the enhanced atmospheric water demand exacerbated the drying trend (Fig. 4 ). Towards the end of dry season when rainfall started to increase, Ep decreased simultaneously (Fig. 5a-d ). However, the thermodynamically-driven increase in water demand (i. e. , Ep) does not translate into similar growth in actual water loss (i. e. , E) during the dry season, which additionally depends on available water for evaporation. The persistent rainfall deficit and warming-induced depletion of soil moisture caused an ongoing decline in soil moisture (Fig. 5c ). Less available soil moisture and higher atmospheric water demand typically drove plants to partially close their stomata to reduce water loss and avoid critical xylem embolism, evidenced by a decrease in vegetation water content (measured by Vegetation Optical Depth in Fig.
Methods Climate and land cover data. Our study of tropical dry season dynamics required climatic variables with high temporal resolution (i. e. , daily) and full coverage of tropic regions. To reduce uncertainties associated with the choice of precipitation (P) and evapotranspiration (Ep or E) datasets, we used an ensemble of eight precipitation products, three reanalysis-based products for Ep, and one satellite-based land E product. These precipitation datasets were derived four gauge-based or satellite observation (CHIRPS  58  , GPCC  59  , CPC-U  60  and PERSIANN-CDR  61  ), three reanalyses (ERA-5  62  , MERRA-2  63  , and PGF  64  ) and a multi-source weighted ensemble product (MSWEP v2. 8  65  ). The potential evapotranspiration (Ep) was calculated using the FAO Penman-Monteith equation 66 (Eqs.
e. , monthly)  71  or short duration  72, 73. ET outputs of reanalysis products are not considered in our analysis, because the assimilation systems lack explicit representation of interannual variability of vegetation activities and thus may not fully capture hydrological response to vegetation changes  62, 63, 67. We used land cover maps for the year 2001 from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MCD12C1 C5  74  ) based on the IGBP classification scheme to exclude water-dominated and sparely-vegetated pixels (like Sahara, Arabian Peninsula). All climate and land cover datasets mentioned above were remapped to a common 0. 25°× 0. 25°grid and unified to daily resolution. The main characteristics of the datasets mentioned above are summarized in Supplementary Table  1. Outputs of CMIP6 simulations.
First, we calculated the mean P, Ep, and E for each day (j) of the calendar year (P j , Ep j , and E j ) and the daily mean rainfall P for all datasets for 1983-2016. To reduce the synoptic noise, we smoothed P, Ep, and E with a 30-day running window. Then, we calculated cumulative P -Ep, P -E, and P -P on day d, ranging from 1 Jan to 31 Dec, as: decrease when the daily precipitation is below the corresponding diagnostic criterion. We defined the day of maximum A(d), B(d), or C(d) as the arrival of the climatological dry season (DSA) and the day of minimum cumulative value as the end of the climatological dry season (DSE). For regions with two or more dry seasons per year, we detected all days of maximum and minimum in the cumulative curve (Fig. 1c ), but we used only the four days marking arrivals (dsa1, dsa2) and ends (dse1, dse2) of the two longest dry seasons for our analysis, usually a boreal summer (June-August) dry season and a boreal winter (December-February) dry season.
A d ð Þ ¼ ∑ d j¼1 P j À Ep j ð3Þ B d ð Þ ¼ ∑ d j¼1 P j À E j ð4Þ C d ð Þ ¼ ∑ d j¼1 P j À P ð5Þ A(d), B ( We calculated DSA, DSE, and DSL under each definition for each dataset (Supplementary Fig. 4 ), and we calculated the mean DSA, DSE, and DSL under each definition in Fig. 2  and Fig. 3. The mean annual precipitation values and the mean annual temperature values in Fig. 3  were derived from the ERA-5 datasets. We examined the uncertainty by calculating the standard deviation among all ensembles of P, Ep, and E under each definition. To assess temporal changes, we calculated the arrival and end dates individually for each year from 1983 to 2016. We calculated the cumulative A(d), B(d), and C(d) for each day from DSA -60 to DSE + 60 for each year instead of the entire calendar year from 1 Jan to 31 Dec, to ensure the correct season was captured.
In addition, we calculated the time series of DSL, DSA, DSE, WD, and meteorological variables at the regional aggregated level using area-weighted averaging over the southern Amazonia, northern and southern central Africa, and southwestern Africa, to maximize large-scale features while minimizing local-scale variability and noise  16. We estimated the linear trends at the regional level as at the grid level (Supplementary Table  2 ). Considering the inconsistency of trends across precipitation and evapotranspiration datasets, we judged the level of consistency with the following criterion  29  : "very likely" if the sign of the trend was the same and significant (P < 0. 05) in six to eight precipitation datasets and no significant changes in the others, "likely" if the sign of the trend was supported by four or five precipitation datasets, "probably" if the sign of the trend was supported by one to three, "uncertain" when conflict trends (i.
e. , both significant increase and decrease trends existed) were found among different precipitation data sources, and "no change" when no significant changes for all of the six datasets were detected. Arid and humid regions (solid gray shaded area in Fig. 4 ) were excluded when calculating the percent area, since there is no climatology wet or dry season, thus no trends calculated under definitions of P < Ep or P < E. Driving factors of Ep and E changes. To further illustrate the thermodynamic mechanism driving higher atmospheric water demand, we disaggregated the individual contributions of four meteorological variables (i. e. , T, RH, u 2 , and Rn) to the Ep trends. We derived the contribution of a certain meteorological variable I to Ep change (C I ) as the difference between the Ep calculated with all variables changing (i.
Daily VOD was derived from VODCA Ku-band  79  , but only available for the period 1987-2016. Data uncertainties. Due to the insufficient and unevenly distributed observation  80  in the rainfall data over tropics, we integrated daily meteorological station recode (Supplementary Fig. 13 ), gauge-based, satellite-combined, and reanalysis datasets to study the variations in precipitation and associated dry season change. Our analyses unravel an overall trend of tropic dry season lengthening and identifying some hotspot regions of changes. However, there are some discrepancies in regions like central Africa and Amazon Basin that may have resulted from data uncertainties and the different approaches used to generate homogeneous climate records. Gauge-based and satellite-combined datasets are quite sensitive to the number and density of observations used, but the observational station is sparse in these regions with large number of missing values in daily record (Supplementary Fig.
13 ). Different interpolation methods were adopted to fill data gaps and produce grid data, which might have generated errors in the rainfall products. For reanalysis, uncertainties are mainly caused by the biases in reanalyzing models, especially in regions with intricacy land surface process, such as Amazon rainforest. For the Ep datasets, we used three independent sets of reanalysis data to verify the changes of atmospheric water demand. Our analyses indicate consistent rising in dry-season water demand, which exacerbated the lengthening of tropical dry seasons, from all datasets in southern Amazonia and southern central Africa. However, only a single dataset was used for E, due to the limited data availability at daily intervals, so uncertainty in evapotranspiration estimation has not been fully considered.
Data availability All observational and reanalysis datasets that we used are publicly available. The daily CHIRPS precipitation datasets are available from the following location: ftp://chg-ftpout. geog. ucsb. edu/pub/org/chg/products/CHIRPS-latest/. The daily satellite-observed TRMM 3B42 precipitation datasets are available at https://disc2. gesdisc. eosdis. nasa. gov/ data/TRMM_L3/TRMM_3B42_Daily. 7/. The daily CPC-U precipitation data are available at https://psl. noaa. gov/data/gridded/data. cpc. globalprecip. html. The daily GPCC precipitation datasets are available at https://www. dwd. de/EN/ourservices/gpcc/ gpcc. html. The daily PERSIANN-CDR precipitation data are available at https://www. ncei. noaa. gov/data/precipitation-persiann/access/. The daily MSWEP v2. 8 precipitation data can be obtained from http://www.
gloh2o. org/mswep/. The Reanalysis products MERRA-2 are available at https://disc. gsfc. nasa. gov/datasets?project=MERRA-2. The climate variables of ERA-5 reanalysis are available at https://cds. climate. copernicus. eu/ cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. The PGF reanalysis precipitation product is available at http://hydrology. princeton. edu/data/pgf/v3/0. 25deg/ daily/. The GLEAM v3. 3a datasets are available at https://www. gleam. eu. The land cover product from MODIS (MCD12C1 C5) is available at ftp://ftp. mpic. de/Kaiser/MODIS_ land_cover/0. 05deg/. The VODCA products are open access at https://doi. org/10. 5281/ zenodo. 2575599.
Although the Wave 1 sampling protocol ensured national representativeness in terms of age, gender and region of residence, the remaining sample in Wave 6 may suffer from problems associated with selective attrition. We show in Table  A2  of the online Supplementary Information that selective attrition does not seem to a major problem here, as our results are unchanged when we weight our observations using the standard Inverse Probability Weighting method. The data from Wave 6 was collected during the course of June 2021, a period when the number of cases or deaths related to COVID-19 was at its lowest after the progression of the epidemic over the previous Winter. (1) VH ijt = β 1 D it + β 2 E it + β 3 H it + β 4 P it + β 5 Conf it-1 + β 6 COVID jt + t + µ j + ǫ ijt. This downward trend appeared in all six countries in our sample.
Funding This study was funded by the  French Agence Nationale de la Recherche  ( ANR-21-CO16-0002 ),  André Losch Fondation ,  Art2Cure ,  Cargolux, CINVEN Foundation  and COVID-19 Foundation, under the aegis of the  Fondation de Luxembourg , and the  Fonds National de la Recherche Luxembourg  (Grant  14840950-COME-HERE ).
ACKNOWLEDGMENTS Work supported by the  Spanish  grants  PID2020-113775GB-I00  ( AEI / 10. 13039/501100011033 ) and  PROMETEO/2018/165  ( Generalitat Valenciana ), in part by the  European Union   Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie  grant agreement No. 860881-HIDDeN , by the  LabEx P2IO  ( ANR-10-LABX-0038 -Project  " BSMNu") in the framework of the "Investissements d'Avenir"  ( ANR-11-IDEX-0003-01  ) managed by the  Agence Nationale de la Recherche (ANR), France , and by  CONACYT-Mexico  under grant  A1-S-23238. O. G. M. has been supported by  SNI  ( Sistema Nacional de Investigadores, Mexico ).
Design and recruitment This paper presents a monocentric (i. e. , patients were recruited from a single center, namely, the Nantes University Hospital in France), qualitative (i. e. , no quantitative data were collected) study exploring patients' perspectives, which was conducted by the CRESERC. Participation in the study consisted of a qualitative individual interview within 4 months of the final TPE session. During the individual educational diagnosis session, such participation was proposed to patients who were enrolled in two separate bipolar TPE programs that occurred simultaneously from 11 September 2020 to 11 November 2020. All patients were systematically invited to participate in the study, irrespective of whether they attended all the sessions. Following scrutiny of these participants' interviews, to ensure data saturation, it was decided to include four additional participants from two other bipolar TPE programs lasting from 28 January 2021 to 2 April 2021.
The reason for this addition was that new themes emerged during the analysis of the final initial interviews, which indicated that data saturation could not have been achieved using solely the initial interviews. Patients were included if they met the following criteria: diagnosis of bipolar disorder type 1 or 2 made by a psychiatrist and confirmed at the Mini International Neuropsychiatric Interview (MINI), age between 18 and 70 years, and current medical management of the bipolar disorder and treatment with a mood stabilizer. Subsequently, patients who were minors, who had poor French language skills or obvious cognitive impairment as measured by the Montreal Cognitive Assessment (MoCA), or who were under guardianship were excluded. Patients were informed of the study via an information letter, and their written non-opposition was provided.
Data collection Patients' perspectives were collected during semistructured interviews conducted by three professionals with different profiles: an advanced practice nurse who regularly conducts TPE sessions (7 interviews), a medical intern who was trained in TPE (5 interviews) and a sociologist who was not trained in TPE (4 interviews). The participants had no prior relationships with the interviewers. The three researchers followed an interview guide containing open-ended questions, which was created in collaboration with a medical doctor who was trained in qualitative analysis (Additional file 2). The questions were developed following a literature review that identified key themes related to the research question. The initial questions asked about the patient's experience of bipolar disorder in general and contextualized the contributions to and experiences of TPE among participants.
These interviews were distinct from the individual post-TPE evaluation sessions, which were an integral part of the TPE cycle. Each interview lasted between 30 min and 1 h and 15 min. All interviews were recorded and transcribed with the patient's consent. In addition, to describe our study sample in brief, sociodemographic data were collected at the time of inclusion: age, employment, family status, year of bipolar disorder diagnosis, year of most recent hospitalization and type of bipolar disorder.
All the themes were identified by the two researchers, and consensus among the members of the research team concerning the organization of the main themes was reached. Data saturation, which can be defined as a situation in which no new themes are identified in the analysis of the final interviews  [24] , was reached. No new themes related to the research question appeared in the coding of the final three interviews. The interviews were coded with the help of qualitative analysis software, i. e. NVivo version 12.
Strengths and limitations Data saturation seemed to be reached, with no new themes emerging from the final interviews. Semistructured interviews featuring open-ended questions minimized the risk of biasing the participants' discourse. One strength of our study is the fact that the interviewing and coding was carried out blindly by three trained health care professionals who had different profiles; despite this all the themes were identified by the two researchers. Furthermore, the interviews were analyzed inductively, without any preconceived notions regarding the potential results. All these factors ensure the reliability of the results of our study. In addition, the sample used in our study included individuals from different social situations, who were different ages and had very different durations of the progression of the disorder.
These participants were also drawn from four separate TPE cycles with different facilitators. The resulting sample improves the reproducibility of our results. Our study does face some methodological limitations. First, we could have increased the diversity of perspectives in the study by employing other data collection methods or by interviewing the facilitators  [28, 29]. In addition, the individual interviews were conducted approximately two months after the end of the TPE sessions. This delay allowed us to objectify the shortterm effects of TPE, but it is insufficient to attest to the long-term impact of TPE. Interviews at an even greater remove from the chenconclusion of the TPE would be necessary to evaluate the effects of the TPE over time.
In North America, CT-P13 SC received approval for the RA indication in Canada in 2021  [38]. The FDA is considering CT-P13 SC under the new drug pathway because of the difference in dose and administration route, as well as PK benefits, compared to its IV formulation  [14, 39]. Therefore, the FDA has requested that randomised, placebo-controlled efficacy studies are conducted. Studies are underway in patients with moderately to severely active CD (ClinicalTrials. gov Identifier NCT03945019) and UC (NCT04205643), with FDA approval anticipated in the second quarter of 2022  [39]. Given the new drug status, it is unclear whether the FDA will allow extrapolation of use to all indications or whether studies will be needed for each disease area  [39]. For CT-P13 SC, two pivotal studies demonstrated non-inferiority to CT-P13 IV in terms of efficacy (in part 2 of the phase I/III CT-P13 SC 3.
Compliance with Ethics Guidelines. This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors. Data Availability. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
ACKNOWLEDGEMENTS Funding. Open access funding was provided by  Medical University of Vienna. The development and publication of this article was funded by  Celltrion Healthcare Co. , Ltd (Incheon, Republic of Korea ). Medical Writing and/or Editorial Assistance. Medical writing support was provided by  Beatrice Tyrrell , DPhil, at  Aspire Scientific Limited (Bollington, UK). Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.