dataesr/scientific-paragraphs-categorization huggingface dataset:
TB is a leading cause of infection-related deaths worldwide  [26] , with high incidences in Sub-Saharan Africa, Asia, and Eastern Europe. It is also the most prevalent AIDS-defining disease and is overrepresented among PLWH [27]. Although TB was not part of the initial focus of RESPOND, the TB:HIV network  [28]  has now established links within RESPOND with the consortium's dynamic framework, enabling the formation of a TB-focused substudy and the inclusion of basic TB-related variables into the latest RESPOND data collection. The SIG plans to examine the risk factors for TB, the use of newer antimycobacterial treatments, and the frequency of multiresistant TB strains, generating valuable knowledge about HIV/TB coinfections from a real-life perspective. While these substudies do not provide an exhaustive list of co-infections for the population of PLWH, they show the ability of RESPOND to incorporate the standardized data collection of newly proposed data items across the network.
Subsequent studies will seek to validate this tool. The methodology could also play a role in assessing the TB and HCV disease burdens and treatment programs in locations where surveillance data is currently lacking. The linked pilot PrEP and Resistance project (PrEPaRE; separate ethic approval: H-19043630, 13 November 2019)  [30] , which is also part of the Public Health SIG's agenda, will investigate the frequency of prior PrEP use in individuals newly diagnosed with HIV. The project has initiated collaborations with more than 70 centers and, depending on the feasibility of reaching the enrollment numbers, follow-up studies will assess possible resistance patterns and correlate these to the drugs taken as part of PrEP. The ability to quickly utilize the network within and around RESPOND is in part possible due to RESPOND's widely used HICDEP-based data collection.
Limitations As RESPOND is a non-randomized observational collaboration, the inherent limitations of possible missing data, selection bias, and unmeasured confounding apply for all analyses. To address the data quality and completeness, RESPOND employs a comprehensive and rigorous quality assurance system consisting of both automated and manual checks to ensure a high degree of data completeness and quality. In addition, the size of the study makes it possible to adjust for a wide range of possible confounding factors and perform adequately powered interaction and/or subgroup analyses. In addition, RESPOND will explore modern statistical methods such as causal inference, bioinformatics, and artificial intelligence. At present RESPOND does not systematically collect data on resistance or HIV-subtype-both of which are subject to considerable heterogeneity in our study population  [32, 33] , nor does the study collect data on use of therapeutics that have not yet been approved by the European Medicines Agency.
Conclusions In conclusion, RESPOND is a newly formed, flexible, and open consortium based on a well-established infrastructure and dedicated to research related to HIV and other infectious diseases. RESPOND has a geographically diverse study population; has a large data pool containing key HIV-related and study-specific information; employs a rigorous data quality program; and includes a collection of standardized, centrally validated clinical outcomes. These qualities place the consortium in a unique position to respond to a wide range of modern challenges for PLWH.
Acknowledgments:  AIDS Therapy Evaluation in The Netherlands Cohort  (ATHENA):  F Wit ,  P Reiss , M (*Chair). RESPOND Scientific Steering Committee:  J Lundgren *,  H Günthard *,  J Kowalska ,  D Raben ,  L Ryom ,  A Mocroft ,  J Rockstroh ,  L Peters ,  A Volny Anne ,  N Dedes ,  ED Williams ,  N Chkhartishvili ,  R Zangerle ,  M Law ,  F Wit ,  C Necsoi ,  G Wandeler ,  C Stephan ,  C Pradier ,  A D'Arminio Monforte ,  C Mussini ,  A Bruguera ,  H Bucher ,  A Sönnerborg ,  JJ Vehreschild ,  JC Wasmuth ,  C Smith ,  A Castagna ,  F Rogatto ,  R Haubrich ,  V Vannappagari ,  H Garges  (*Chairs). HIV Community Representatives:  Alain Volny-Anne ,  Nikos Dedes ,  Luis Mendao  ( European AIDS Treatment Group ),  Esther Dixon Williams  ( UK ). RESPOND Staff:  D Raben ,  L Peters ,  L Ryom ,  B Neesgaard ,  JF Larsen ,  ML Jakobsen ,  T Bruun ,  A Bojesen ,  EV Hansen ,  TW Elsing ,  D Kristensen ,  S Thomsen ,  T Weide ,  O Valdenmaiier ,  A Mocroft ,  L Greenberg.
Western Ghats data The Western Ghats Mountains in southern India, along with Sri Lanka, are one of 36 global biodiversity hotspots (https:// www. cepf. net/our-work/biodiversity-hotspots). Native tropical forests of the Western Ghats underwent extensive loss (40%) and fragmentation (80% decrease in patch sizes and 400% increase in patch numbers) during 1920-1990, largely due to the expansion of agriculture and coffee plantations  (Menon & Bawa, 1997). While around 12% of the Western Ghats is protected by the State as nature reserves, remnant forests with less formal protection situated in surrounding HDLs are crucial for conserving biodiversity  (Das et al. , 2006) , and sustain significant carbon stocks  (Osuri et al. , 2014). Our study is based on plot datasets from HDLs in the central Western Ghats (CWG) and the Anamalai Hills (AH) in the southern Western Ghats (Figure  1 ).
Both landscapes comprise fragmented tropical rainforests interspersed among settled agriculture and/or horticulture plantations that date back at least two centuries. Forests in both landscapes range in anthropogenic disturbance from relatively undisturbed areas (e. g. , within or abutting State-protected forests) to degraded primary forests that are fragmented and subject to extractive resource use-that is, for fuelwood, and occasionally for timber  (Muthuramkumar et al. , 2006; Ramesh et al. , 2010b). The CWG dataset comprised sixty-eight 1-ha plots, including 33 plots in structurally and compositionally intact wet-evergreen forests, as classified by the dataset authors, and 35 plots in wet-evergreen forests that were more fragmented, and structurally and/or compositionally degraded. (Ramesh et al.
, 2010a). Degradation was assessed by the authors of the dataset based on indicators such as decreased canopy cover (structure), and prevalence of deciduous and/or disturbanceadapted species within tree communities (composition)  (Ramesh et al. , 2010a). The AH dataset comprised ninety 0. 04-ha plots, including 50 relatively unfragmented intact forest plots, and 40 plots in more degraded forest fragments  (Muthuramkumar et al. , 2006; Osuri, Chakravarthy, & Mudappa, 2017). Plot classification and sampling details are available in  Ramesh et al. (2010a)  and  Muthuramkumar et al. (2006). The former dataset reported species names and girth at breast height (gbh) of all trees over 10 cm gbh (3. 18 cm diameter at breast height [dbh]), and was filtered to retain trees ≥10 cm dbh only. The latter recorded species names and gbh of all trees over 30 cm gbh (9.
Multiregion data We searched the scientific literature for data on TSR, composition, and forest structure in tropical HDLs. We searched for keywords and phrases such as "tropical forest tree fragment," "tropical forest secondary," and "tropical forest recovery" on the ISI Web of Science (www. webofknowledge. com), within the Dryad data repository (https://datadryad. org/), and the PREDICTS database  (Hudson et al. , 2017) , for relevant datasets. We also examined literature cited by, and citing, studies identified through our primary searches. Given the focus on HDLs, we only considered studies from landscapes featuring sustained human occupation and land use, that is, fragmented primary or secondary forests in landscapes with settled or shifting agriculture, or pastures, were included, but studies in remote logging concessions without sustained human presence were not.
Variables assessed Tree species richness (species/plot), tree density (trees/plot), basal area (m 2 /plot), community-weighted average wood density (g/cm 3 ), and aboveground carbon storage (Mg/plot) were assessed in the Western Ghats datasets. Community-weighted average wood density was estimated at the plot level as ΣR i. W i , where R i and W i are relative abundance and wood density, respectively, of each species i. TSR, tree density (TD), basal area (BA) and community-weighted average wood density (WD) were included in the meta-analysis. Species wood densities were obtained from primary sources (AMO, SM, JR, MS and colleagues, unpublished data from the Western Ghats), and from the Global Wood Density Database  (Zanne, Lopez-Gonzalez, & Coomes, 2009). In cases of species wood density data being unavailable, genus averages constrained by continent were used  (Chave et al.
Western Ghats plots We assessed the relationship of overall TSR, and richness of endemic tree species, with ACS, TD, BA, and WD in the CWG (1. 0 ha) and AH (0. 04 ha) datasets using Pearson's correlation coefficients. Log transformations were applied to ACS and BA on account of the skewed distributions of these responses. We used a bootstrapping approach to examine how correlations of TSR with ACS, TD, BA, and WD vary in relation to forest disturbance in the 1. 0-ha and 0. 04-ha datasets. Starting with the pool of relatively intact forests in each dataset, we iteratively replaced randomly selected intact forest plots with randomly selected disturbed forest plots, such that the total number of plots remained constant (thirty-three 1-ha and forty 0. 04-ha plots). At every increasing level of disturbed plot prevalence, we estimated TSR-ACS, TSR-TD, TSR-BA, and TSR-WD correlations.
RESULTS In the Western Ghats, TSR, ACS, TD, and BA were consistently lower in disturbed forests than in intact forests in both landscapes, while disturbed forests in the AH (0. 04 ha plots) also had lower WD (Table  1 ). ACS correlated positively with overall TSR (Pearson's r =. 43 and. 35 [p <. 01] among 1. 0 ha and 0. 04 ha plots, respectively), and with endemic species richness (Pearson's r =. 51 and. 59, p <. 01) in both landscapes (Figure  2 ). TSR was positively correlated with TD (Pearson's r =. 61 and. 66, p <. 01) and with BA (Pearson's r =. 50 and. 36, p <. 01) in both landscapes (Figures  3a, b, d, e ), and positively correlated with WD across 0. 04 ha plots in AH (Pearson's r =. 43, p <. 01; Figure  3f ), but not the 1. 0 ha CWG plots (Figure  3c ). Further, using the unfiltered CWG dataset (all trees ≥ 3.
ACKNOWLEDGMENTS AND DATA AMO received fellowships from the  Science and Engineering Research Board  (SERB)  National Postdoctoral Fellowship  ( PDF/2016/000104 ),  NatureNet Science Fellows and Earth Institute Fellows  programs. Fieldwork was funded by a  SERB research grant  ( SERB/SR/SO/PS/78/2012 ) to MS. We thank  Susan Cook-Patton  for suggestions that improved the manuscript. All data and the R script are provided in Appendix A.
[33] lthough many aspects in the translation of implantable soft bioelectronics remain unsolved challenges, this work shows that an integrated methodology, from design to in vivo evaluation, can uncover needs for technical advances, and help projecting labbased innovation toward translational and ultimately clinical use. electrodes). d) Diagrams and function of the muscles activated by epidural electrical stimulation. e) Electromyographic signals recorded in the biceps, triceps, and abductor pollicis following stimulation using electrodes E2, E5, and E7. The color code for the curves corresponds to the different current amplitudes used for EES. f) Recruitment level based on the electromyographic activity recorded on the activated muscles (each data point corresponds to an individual trial out of a total of four trials per current amplitude per electrode).
Acknowledgements F. F. , X. K. , G. C. and M. C. contributed equally to this work. The authors would like to acknowledge financial support from the  Bertarelli Foundation , the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement no. 665667  to G. S. , the  Wyss Center for Bio-and Neuroengineering  [WCP008], a Whitaker foundation fellowship to I. S. , and the  Swiss National Science Foundation  including a Sinergia subside  CRSII3_160696 , a  Sino-Swiss Science and Technology Cooperation subside [IZLCZ3_156331] , an  Ambizione Fellowship  (No. 167912  to M. C. ), and the  National Center of Competence in Research (NCCR) in Robotics. The authors would like to thank  Prof. Eric Rouiller  for his support at the  Platform of Neuroscience of the University of Fribourg ,  Prof.
This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000)(2001)(2002)(2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling-Gupta Efficiency (KGE) changes at the local scale and the Pdataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v. 2. 2 and CHIRPS v. 2 for daily and monthly timestep, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (i.
e day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v. 7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i. e. GSMaP-RT v. 6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.
Regardless of the technique employed, precipitation data collection at the regional scale usually includes potential conflicts of interest in water resource management between neighbouring countries. In this context, gridded precipitation datasets (P-dataset) at an almost global scale offer an unprecedented alternative. Over remote regions, P-datasets have already shown promising A recurrent drawback of assessment studies on P-dataset reliability is the consideration of a limited number of P-datasets. A comprehensive reliability overview of available P-datasets, as listed in Table  1 , can be achieved only by backcrossing the results from different P-dataset assessment studies. However, the studies are conducted over distinct regions and are based on different statistical indices, spatial and temporal scales, and periods, thus creating difficulties in intercomparing P-dataset reliability assessments.
For example, when comparing TMPA, CMORPH, and PERSIANN P-datasets with reference gauge estimates, CMORPH was shown to have the most reliable P-datasets in Pakistan, China, Bali, and Indonesia  (Hussain et al. , 2017; Rahmawati and Lubczynski, 2017; Su et al. , 2017; Zeng et al. , 2018). However, TMPA was the most reliable in India, Guyana, Chile, and the South American Andean plateau  (Prakash et al. , 2014a; Ringard et al. , 2015; Satgé et al. , 2016; Zambrano-Bigiarini et al. , 2017). Hence, P-dataset reliability for a given region should not be determined from results reported for other regions. In this context, it is decisive to consider the most representative P-dataset sample to insure a consistent report on P-dataset reliability across the considered region.
The need for assessing P-datasets over West Africa Africa is particularly affected by climate changes threatening rainfed agriculture, which represents its main agricultural and economic activity  (Sultan et al. , 2013). However, owing to the socio-economic context, the available gauge network is limited by many spatial and temporal gaps which prevent efficient water management. According to the World Meteorological Organisation (WMO), the African continent requires uniform distribution of at least 3000 stations (ideally 10,000); however, only 744 stations are present. Moreover, only one quarter of the 744 stations conform to international standards. Because they provide precipitation information on a regular grid at the global scale, Pdatasets offer a unique opportunity for complementing traditional precipitation measurements and optimising population adaption to the ongoing changes.
However, as previously mentioned, P-dataset estimates are indirect measurements with spatial and temporal uncertainties which need to be reported to evaluate their reliability. Some authors have already initiated this effort over West Africa. In 2012, seven P-datasets were tested over the basin of la Volta including CMORPH, GPROF-v6, GSMaP-MVK v5, RFE-2. 0, TMPA-v6, PERSIANN, and ERA-Interim  (Thiemig et al. , 2012). In 2013, nine P-datasets including CMORPH, EPSAT-SG, GPCP, GSMaP-MVK, GSMaP-RT, RFE-2, TMPA-v6, TMPA-RT v6, and PERSIANN and seven P-datasets including PERSIANN, CMORPH, TMPA-RT v. 6, TMPA-Adj v. 6, GSMaP-MVK, GCPC-1dd, and RFE-2 were tested in Benin and Niger for hydrological  (Gosset et al. , 2013)  and agriculture applications  (Ramarohetra et al. , 2013) , respectively. Both studies found that their use could introduce large biases in crop or hydrological modelling framework.
More recently, six P-datasets including ARC-2, CMORPH, GSMaP-MVK, PERSIANN, TAMSAT, and TMPA-v. 6 were compared with gauge measurements data over the entire African continent  (Awange et al. , 2016). All of the aforementioned studies focus mainly on P-datasets regularly updated by their developers to enhance the precipitation estimates. Since then, updated versions of the considered products have been made available with more accurate precipitation estimates. For example, the benefits brought by the new TMPA-v. 7 in comparison to its previous version (TMPA-v. 6 ) has been reported in many regions (e. g. Anjum et al. , 2016; Prakash et al. , 2014b; Satgé et al. , 2016). Additionally, most of the tested P-datasets originate from the TRMM-era constellation which has limited temporal coverage from 1998 to the present.
In this context, new studies have reported on recently released P-dataset versions with larger temporal coverage. For example, in 2016 over Burkina, seven P-datasets including ARC-2, CHIRPS v. 2,  RFE v. 2, TAMSAT v. 2, TMPA v. 7, and TMPA RT v. 7  were assessed at the daily, decadal, and monthly timescales  (Dembélé and Zwart, 2016). In 2017, TAMSAT v. 3 was introduced and compared with its previous version (TAMSAT v. 2) and with six P-datasets including  ARC v. 2, CHIRP v. 2, CHIRPS v. 2, CMORPH v. 1, RFE and TMPA v. 7  over West Africa, specifically Nigeria and Niger; Uganda; Zambia; and Mozambique  (Maidment et al. , 2017). In 2017, 10 P-datasets including CFSR, CHIRPS, CMORPH v. 1 RAW and CRT, PERSIANN-CDR,  TAMSAT v. 2, TMPA v. 7, , and GPCC were assessed over six watersheds located in Burkina, Nigeria, and Ghana  (Poméon et al.
, 2017). Nonetheless, the reported studies indicate that the results are mostly limited in space (country or basin scale) and in terms of considered P-datasets sample. To our knowledge, only one study has reported on P-datasets at the regional West African scale with a limited sample of P-datasets including  TMPA v. 7, UDEL v. 3. 1, CRU v. 3, and ARC v. 2 (Akinsanola et al. , 2016).
Objectives According to the previously described context, the present study aims to compare the accuracy of 23 P-datasets in reproducing the characteristics of rain gauge measurements across West Africa, which is an unprecedented comparison. The consideration of a P-dataset sample ,as large as possible, aims to provide a robust overview of P-dataset performance over West Africa. The analysis is conducted at both daily and monthly time steps. This study provides important feedback to P-dataset developers for enhancing the algorithms for next-generation P-datasets and to potential users to support their P-datasets selection.
6, and MERRA-2 use CPC daily data. The gridded precipitation estimates merged with satellite precipitation estimates are (i) the CHPclim dataset with a 0. 05° grid-cell size  (Funk et al. , 2015)  and (ii) the GPCP dataset with a 2. 5° grid-cell size  (Adler et al. , 2003 (Adler et al. , , 2012) )  and (iii) the WorldClim 2 dataset with a 1km grid-cell size  (Fick and Hijmans, 2017). CHPclim and WorldClim 2 use satellite observations as predictors to improve the interpolation from point gauge records, whereas GPCP uses the gauge record to adjust the precipitation fields derived from satellite observations. Further details are reported elsewhere  (Adler et al. , 2003; Funk et al. , 2015). Among the considered P-datasets, CHIRPS v. 2 use the CHPclim dataset, MSWEP v. 2. 2 use the WorldClim 2 dataset and PERSIANN-CDR uses the GPCP dataset.
CHIRPS v. 2 also includes punctual precipitation estimates from various public data streams, private archives, and national meteorological agencies, while MSWEP v. 2. 2 incorporates monthly GPCC and daily CPC gridded precipitation datasets. Another difference between the P-datasets is the time latency for their availability. The Pdatasets are generally available in (i) a few days or (ii) a few months after the observation (Table  1 ). Some are in near real time (NRT) latency of one to three days and are more adapted for flood or landslides forecasting, water resource management, and agriculture, while the others are more adapted for retrospective climatic studies. Figure  2  shows the mean annual precipitation patterns retrieved from all P-datasets. Except for CPC v. 1 and the P-datasets, which use CPC v.
1 for post adjustment processing (ARC-2, CMORPH-CRT v. 1, CMORPH-BLD v. 1, GSMaP-Adj v. 6 and MERRA-2), all P-datasets represent the typical south-north precipitation gradient with two precipitation hotspots located over the southwest and south region. It should be noted that SM2Rain-CCI v. 2 estimates are based on soil moisture estimates, which are strongly attenuated by the vegetation canopy; this results in significant gaps over areas with moderate to dense vegetation, as observed over the southern region (Fig. 2 )  (Dorigo et al. , 2015). Additionally, a sensor failure in the ERS-2 gyroscope from January 2001 to June 2003 accentuated these gaps and explains the gaps observed over the central and northern regions (Fig. 2 )  (Dorigo et al. , 2015).
P-dataset pre-processing The P-datasets available at a sub-daily time step (Table  1 ) were aggregated to obtain daily time step records matching the local gauge observations (8 h to 8 h local time). It is worth mentioning that Pdatasets delivered at daily time scales (Table  1 ) use time-windows different from those of the gauge, which can compromise the comparison at the daily scale. This point is further discussed in section 4. 1. Moreover, the P-datasets differ in terms of grid-cell size, ranging from 0. 0375° for TAMSAT v. 3 to 1° for GPCC v. 7. To enable consistent comparison, all P-datasets were resampled to the 0. 1° grid-cell size. Bilinear averaging (interpolation) are used for P-datasets with grid-cell size < 0. 1° (> 0. 1°)  (Beck et al. , 2019).
Reference precipitation dataset A database of 1,440 gauges were made available by several African national meteorological and hydrological centres. The stations are distributed onto 952 0. 1° grid-cells. For each grid-cell, a reference daily precipitation series was obtained averaging the gauges included in the grid-cell. To ensure consistent analysis, only grid-cells with more than 80% of daily records were considered. The four-year period of 2000-2003 was finally retained to consider the largest number of 0. 1° grid-cells (187) grid-cells.