dataesr/scientific-paragraphs-categorization huggingface dataset:
Severe droughts have the potential to reduce forest productivity and trigger tree mortality. Most trees face several drought events during their life and therefore resilience to dry conditions may be crucial to long-term survival. We assessed how growth resilience to severe droughts, including its components resistance and recovery, is related to the ability to survive future droughts by using a tree-ring database of surviving and now-dead trees from 118 sites (22 species, >3,500 trees). We found that, across the variety of regions and species sampled, trees that died during water shortages were less resilient to previous non-lethal droughts, relative to coexisting surviving trees of the same species. In angiosperms, drought-related mortality risk is associated with lower resistance (low capacity to reduce impact of the initial drought), while it is related to reduced recovery (low capacity to attain pre-drought growth rates) in gymnosperms.
Here we took advantage of a recently assembled pancontinental database  22  to study the relationship between past resilience to drought and mortality risk under subsequent drought events. The database contains tree-ring width (TRW) series for surviving and dead trees from 118 sites and >3500 individuals around the globe (mostly from the temperate, Mediterranean and boreal ecosystems of the Northern hemisphere), including 22 species (8 angiosperms and 14 gymnosperms; Fig. 1 , Supplementary Data 1). Because surviving and dead trees were sampled concurrently at the same sites (matched-pairs case-control study), this database is ideal to assess and compare growth patterns of trees before mortality  22. We quantified tree resilience to drought using three indices that were proposed by Lloret et al.
This is consistent with narrower hydraulic safety margins (i. e. , the buffer between minimum water potential experienced by the tree in the field and the empirical threshold of water potential for rapid loss of vascular function caused by embolism) in angiosperms compared to gymnosperms  39, 40. However, this difference must be interpreted with caution due to the difference in the critical percentage loss of hydraulic conductivity causing mortality in these two groups  41. Despite the The data are presented as modeladjusted, back-transformed least-square means ± 95% confidence intervals (Table  1 ). Resilience, resistance and recovery indices were computed from tree-ring width (TRW) series of surviving (grey squares) and now-dead (red squares) trees. Asterisks indicate significant pairwise differences in least square means between now-dead and surviving trees (t or χ 2 test in LMM: *P < 0.
05; **P < 0. 01; ***P < 0. 001). Panels are separated by taxonomic group only when differences between angiosperms and gymnosperms are significant (Table  1 ). Source data are available in Digital. CSIC repository (https://doi. org/10. 20350/digitalCSIC/10536). high recovery capacity of angiosperms, differences in their ability to resist previous droughts are carried over and eventually result in either death or survival when trees are exposed to an even more severe subsequent drought. In gymnosperms, negative effects on growth may persist beyond 4 years after the drought in now-dead trees, implying long-term legacy effects  22, 30. The gymnosperms that recovered less are more prone to die one or more decades after the severe droughts we studied (Fig. 3b , Supplementary Fig. 3 ). The greater importance of recovery in gymnosperms is consistent with the fact that their internal carbon reserves may be severely impacted under extreme drought  15.
Methods Growth data. We selected TRW (mm) data sets from the pancontinental database compiled by Cailleret et al. 22  , for which (i) both dying and surviving trees growing together at the same site were cored, (ii) all individual TRW series had been successfully cross-dated and (iii) mortality was mainly induced by drought, solely or in combination with other factors, such as bark beetles, fungi or mistletoes  6  (Fig. 1 , Supplementary Data 1). The database included 127 sites mostly located in the boreal, temperate and Mediterranean biomes of North America and Europe. We used two metrics of tree growth, TRW and BAI (mm 2 ). Although TRW and BAI show size and age effects, BAI reduces the pure geometric effect associated with the increase in tree DBH. Drought characterisation. To explore the effect of drought on tree growth, we used the SPEI as a drought metric  49.
handle. net/10261/ 104742). The SPEI has been used for large-scale and long-term studies because it allows the comparison among sites with contrasting climates and accounts for timing and duration of drought during the current and previous years  59. Selecting the SPEI time windows. Extreme drought events were detected by combining information from growth and SPEI time series. We selected the optimal time window of SPEI that maximised the goodness-of-fit of a linear model between SPEI and the residual chronology of standardised growth for each site. We considered different SPEI time windows for each site because species and biomes respond to drought at specific time scales  24, 59. First, we standardised the TRW series with the R package "dplR" 60 in the R environment  61. Each individual TRW series was fit to a cubic smoothing spline (the frequency response was set to 0.
g. Supplementary Fig. 1b ). After discarding 9 sites that did not meet the latter criteria, we considered 118 sites with 2456 and 1454 co-occurring, surviving and now-dead trees, respectively, of 22 species (8 angiosperms and 14 gymnosperms) for the subsequent analyses (Supplementary Fig. 1 , Supplementary Data 1 and Data 2). Computing indices of resistance, recovery and resilience. We computed resistance, recovery and resilience indices as proposed by Lloret et al. 33  , considering 4 years before and after the drought event in agreement with Anderegg et al. 30. Other periods around the studied drought (from 1-to 8-year period) were also analysed resulting in similar outcomes (Supplementary Fig. 3 ). The resilience indices were computed as: Resistance ¼ Dr=PreDr ð1Þ Recovery ¼ PostDr=Dr ð2Þ Resilience ¼ PostDr=PreDr ¼ resistance recovery ð3Þ where PreDr was defined as mean raw TRW of the preceding 4-year period, Dr as raw TRW of the drought year and PostDr as mean raw TRW of the subsequent 4year period.
All indices were also calculated using BAI as a measure of growth. Climate covariate. To account for climate diversity found across the study populations, we used the Global Aridity Index that provides high-resolution (30 arc-seconds) raster climate data for the 1970-2000 period  65. The Global Aridity Index is the ratio between mean annual precipitation and mean annual reference evapotranspiration, based upon the implementation of the Penman Monteith Evapotranspiration equation for reference crops and using the WorldClim 2. 0 data (http://worldclim. org/version2). The aridity index indicates rainfall over potential vegetation water demand (aggregated on an annual basis), and its value thus increases under more humid conditions and decreases with more arid conditions. Soil covariate. We accounted for the variation in soil properties by means of two high-resolution soil databases from the International Soil Reference and Information Centre (ISRIC-World Soil Information).
First, we used the WISE30sec database (WISE Soil Property Databases) that comprises a set of harmonised soil profiles at 30 arc seconds (~1 km at the equator), including 20 soil properties derived from statistical analyses of ca. 21,000 soil profiles (https://www. isric. org/ explore/wise-databases)  66. Second, we used SoilGrids, a global gridded soil information system at 250 m that provides global predictions for standard numeric soil properties at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm) and depth to bedrock based on ca. 150,000 soil profiles (https://www. isric. org/ explore/soilgrids)  67. We included ten soil characteristics to describe soil fertility in the study populations: organic carbon (g kg -1 ), total nitrogen (g kg -1 ), carbon/ nitrogen ratio, bulk density (kg dm -3 ) and available water capacity (from -33 to -1500 kPa; cm m -1 ) from WISE30sec database and absolute depth to bedrock (cm), pH measured at 200 cm and clay, silt and sand content (%) measured at 60 cm from the SoilGrids250m database.
We conducted an ordination analysis of these soil characteristics at the study sites using principal component analysis (PCA) with the "FactoMineR" R package  68  in the R environment  61. The first component of this PCA explained 55% of the variance and was positively associated with nitrogen concentration, organic content and available water capacity (Supplementary Fig. 5 ). This component was interpreted as indicator of soil fertility and included as an explanatory variable in our statistical models. Comparison between surviving and now-dead trees. We analysed growth differences between coexisting now-dead and surviving trees for the three different response variables (resistance, recovery and resilience) around a severe drought previous to the mortality event, using linear mixed models (LMMs).
2 ). In all cases, data exploration, model fitting, variance analyses and pairwise differences between the effects were computed with the R library "HighstatLib"  70  and the R packages "lme4"  71  , "car"  72  , "emmeans"  73  and "effects" 74 in the R environment  61. Additional models. We tested the effects of the source of mortality and the interaction between taxonomic group and aridity on resilience indices based on TRW. We used additional models separately from the main models for the sake of clarity.
Acknowledgements This article is based upon work from the  COST Action FP1106 STReESS , financially supported by  European Cooperation in Science and Technology (COST). L. DS. was funded by the  Fundação para a Ciência e a Tecnologia  ( SFRH/BPD/70632/2010 ) and by the  European Union (EU)  under a  Marie Skłodowska-Curie IF  (No. 797188 ); K. K. was supported by the  Dutch Ministry of Agriculture, Nature and Food-quality  ( KB-29-009-003 ); E. M. R. R. by the  Research Foundation -Flanders  ( FWO, Belgium ) and by the  EU  under a  Marie Skłodowska-Curie IF  (No. 659191 ); T. A. by the  Kone Foundation ; J. J. C. by the  Spanish Ministry of Science  ( CGL2015-69186-C2-1-R ); K. C. by the  Slovenian Research Agency ARRS  ( P4-0015 ); L. J. H. by the  USDA Forest Service-Forest Health Protection and Arkansas Agricultural Experiment Station ; V.
II. HUMAN-AWARE CONTROL APPROACH Fig. 1  shows an overview of the proposed control architecture, split in two main modules: a task planner and an action planner (which, in turn, integrates the motion planner). The Task Planner addresses human-robot coordination and task sequencing and assignment. It generates a suitable task sequence dealing with the temporal variance entailed by the human presence. The Task Planner sends the goals to a human operator, for example through a Human-Machine Interface (HMI). In addition, it receives feedback from the HMI, as operators can accept or discard the incoming tasks, and also inform the system about the outcome of the performed task (successfully finished or terminated with errors). This feedback is fundamental as the controller cannot assume the duration of human tasks but it needs to wait for a feedback.
Pursuing a Sense-Plan-Act approach, the task planner is capable of dynamically (re)planning the sequence of tasks according to the feedback and the (possible) refusal of task from the operator. The robot Action Planner computes the optimal sequence of motion plans that satisfies the operational constraint. Remarkably, the actual human position is considered as feedback when the motion plans are computed. Once the action is defined, the Action Planner sends the trajectory to be executed to the robot motion controller. The combination of these modules realizes flexible robot behaviors that can also be dynamically adapted according to the observed behaviors of a human operator, limiting negative effects on the production flow, when unforeseen events occur.
The Task Planner is implemented through PLATINUm  [12] , a timeline-based software framework for P&S able to deal with temporal uncertainty and already successfully deployed in real-world manufacturing scenarios  [2]. A planning synthesis module (Task Plan Synthesis in Fig. 1 ) synthesizes flexible behaviors of domain features (i. e. , the timelines). An executive module (Task Plan Execution in Fig. 1 ) dispatches the tasks composing the timelines and checks the consistency of a plan with respect to the actual state of the environment. PLATINUm pursues a general solving behavior can be tailored to the particular features of a planning problem through the use of specifically designed heuristics and search strategies. The former supports the solving algorithm in selecting the flaw to solve at each iteration.
B. Action Planning Module The Action Planner is in charge of converting high-level actions from the task planner into actual robot movements. Its general structure is represented in Fig. 1. It is equipped with a motion planning module used to solve motion planning queries during the action planning. It encodes a set of actions such as pick from, place in, assemble, which are automatically parsed into a sequence of motion instances and basic operations (e. g. , move to, grasp, release, wait). These basic operations represent the smallest level of granularity in the task decomposition hierarchy described in Fig. 2. For example, consider the case of a pick and place skill. The robot should perform the following operations: move to the picking station; approach the object; grasp it; move to the placing spot; approach the final pose; and release the object.
B. Action Planning Module Implementation The general structure of the Action Planner module described in Section II-B is implemented here for the case study. The general description of Fig. 2  is therefore tailored to the case study tasks and actions. Each pick-place operation is an action planning instance (yellow nodes in Fig. 2 ). A pick-place is then composed of several motion instances (green nodes in Fig. 2 ), namely: move to X, approach object, grasp object, move to Y, approach slot, release object. X and Y are symbolic labels that identify the color (orange, white, blue) of the picking cube and the coordinates of placing spots (A1, A2, etc. ) respectively. When an action from the task planning arrives (e. g. , P ickP lace J1 ), the motion planning module decodes the desired color and chooses the most suitable cube from the available ones.
C. Software Implementation The mock-up scenario shown in Fig. 4  is a simulation environment built in ROS. It is composed of an Universal Robot UR5 mounted on an linear axis. The mosaic should be assembled on a worktable parallel to the linear axis. The blue pieces are on a worktable (accessible to both the robot and the operator), the orange ones behind the robot, and white ones on a table near the operator. A human mannequin is integrated in the simulator 1. The Action Planner is implemented as a set of ROS action servers dedicated to pick or place tasks. Each action server implements a MoveIt! planner pipeline, allowing the use of all path planning algorithms available in ROS (e. g. , OMPL  [18]  and STOMP  [19] ). The Task Planner is implemented through PLATINUm (https://github. com/pstlab/PLATINUm) implementing a classical plan refinement solving procedure which consists in iterative refining the timelines of a plan by solving flaws concerning the completeness and the temporal validity of a plan (i.
Human tasks are not controllable according to Section III-A: their duration is not ensured to be finite, as well as the time from request to beginning (such time might even be infinite if the operator refuses to perform the given task). The ROS package rosbridge suite was used to set up the communication between PLATINUm and ROS. The feedback from the user have been simulated through the deployment of a simple random-delay trigger that sends the feedback to the Task Planner after the human task is completed. To simulate the effects of safety functions on the robot motion (i. e. , robot slowdown/stop when the human enters the collaborative space), we have implemented a Speed and Separation Monitoring function, according to ISO/TS 15066  [21]  using a simplified safety-zone approach that reduces the robot speed to: i) 50% of the nominal speed when the distance from the human to the worktable is less than 1 m; ii) 30% of the nominal speed when the distance is less than 0.
Bacterial Preparation Strains from the CSUR (Collection des souches de l'unitédes Rickettsies, IHU Mediterraneé infection, Marseille France) were used. Bacterial strains were identified by Maldi Toff mass spectrometry using the Bio Typer database (Bruker, Dresden, Germany). In a second time, they were cultured on 5% sheep blood-enriched Columbia agar (COS, BioMeŕieux, Marcy l'Etoile, France). After 18 h of incubation at 37°C, colonies were removed and suspended in NaCl at the required concentration. Three different strains of each species were used, thus Methicillin sensitive S. aureus (P6142, P2188 and P6141) and S. sanguinis (P8633, P760 and P2754). All strains were isolated from positive blood cultures.
ACKNOWLEDGMENTS We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara , yusuke  Ominami ,  Akiko Hisada , and all the  Hitashi Team of Japan  for the collaborative study that we are leading together between HHT and IHU, and for the installation and services on the TM4000 microscope in our facility. Authors would like to thank the  Algerian Ministry of Higher Education and Scientific Research  for supporting NH during his thesis in the IHU Mediterraneé infection.
Objective: Surgical management of deep endometriosis is associated with a high incidence of lower urinary tract dysfunction. The aim of the current systematic review and meta-analysis was to assess the rates of voiding dysfunction according to colorectal shaving, discoid excision, and segmental resection for deep endometriosis. Data Sources: We performed a systematic review using bibliographic citations from PubMed, Clinical Trials. gov, Embase, Cochrane Library, and Web of Science databases. Medical Subject Headings terms for colorectal endometriosis and voiding dysfunction were combined and restricted to the French and English languages. The final search was performed on August 28, 2019. The outcome measured was the occurrence of postoperative voiding dysfunction. Methods of Study Selection: Study Quality Assessment Tools were used to assess the quality of included studies.
Electronic Search The following databases were searched: PubMed, Clinical Trials. gov, Embase, Cochrane Library, and Web of Science databases. The major Medical Subject Headings search terms used were ("endometriosis" or "deep infiltrating endometriosis" or "deep endometriosis" or "deep pelvic endometriosis" or "colorectal endometriosis") and ("surgery for endometriosis" or "conservative management" or "radical management" or "colorectal resection" or "rectal shaving" or "full thickness resection" or "discoid excision") AND "treatment," "outcomes," "short-and long-term results," and "voiding dysfunction. " Studies in French or English published before April 1, 2019, were included. Abstracts were excluded if the authors did not provide adequate information when contacted. The final search of each database was performed on August 28, 2019.
Risk of Bias The risk of bias was assessed using the clarity of the statements, the eligibility criteria, size of the population, ethical considerations, representativeness, response rate, data collection, objective, definition of the objective, and consistency between research question and data reported. The quality of the included studies was evaluated with the Study Quality Assessment Tools  [14]. Studies were considered as "satisfactory" when at least 70% of 12 to 14 assessment criteria were met, "fair" when 50% were respected, and "poor" when fewer than 50% of the criteria were fulfilled.
If sufficient data were available, a predefined subgroup analyses for the primary outcome was undertaken. All statistical analysis was performed on Review Manager (RevMan; IOS, version 5. 3, Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2011). For the comparison of the NS techniques, the number of patients and percentages are presented. A Comparison was performed using Fisher exact test (qualitative variable) and p <. 05 was considered statistically significant. For this analysis, statistical analysis was performed using R 3. 6. 0 software (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) (http://lib. stat. cmu. edu/R/ CRAN/).
Indeed, identifying and sparing the pelvic nerves should be a standard practice in 2020, as it is accepted that the more nerves are preserved, the fewer sexual, rectal, and bladder disorders caused  [49]. The strengths of this review include the use of an exhaustive search strategy applied to 5 different databases. In addition, it seems to be the first to report the risk of postoperative voiding dysfunction comparing the 3 surgical approaches for DE with colorectal involvement. Methodologic limitations such as differences between the criteria used for defining the voiding dysfunction may limit the quality of the available evidence. Furthermore, the absence of taking preoperative voiding dysfunction into account should be considered as a limitation. However, most of the articles included in this review lacked data on preoperative voiding dysfunction and thus we were unable to realize if the postoperative dysfunction was due to operative damage.
, 2019). Combined with data on abiotic and ecological data, it can provide previously unattainable insights into spatial and temporal changes in species compositions and link environmental parameters with the occurrence of specific taxa  (Chariton et al. , 2015). This can generate novel ecological information for taxa such as gastrotrichs and microturbellarians that are small, difficult to identify, and may only be present as resting eggs or other propagules during certain times. However, metabarcoding and its applications in ecology are still in development and need to overcome several challenges, many of which apply to meiofauna in general and rouphozoans in particular (see  Ruppert et al. , 2019  for a review). DNA reference databases for gastrotrichs and microturbellarians are still poorly populated and need to be strengthened through global collaborations of taxonomic specialists.
As this is an ongoing and future effort, students and researchers will need to be trained in fundamental biodiversity research, including careful identification of individuals selected for building DNA barcode databases. Other well-known issues include PCR primer bias and design, marker choice, standardization of methods, and integration with ecological data  (Schenk & Fontaneto, 2019). U N C O R R E C T E D P R O O F U N C O R R E C T E D P R O O F 332 Methods for identification 333 Gastrotricha 334 The phylum Gastrotricha currently comprises over 335 850 free-living species widespread in aquatic ecosys-336 tems. The division into two classes, Macrodasyoidea 337 and Chaetonotidae, each including a single order 338 (Macrodasyida and Chaetonotida, respectively) dates 339 back to  Remane (1925) , and follows the evident 340 differences in morphology, biology, and ecology 341 between the two taxa  (Balsamo et al.