dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations. Urbanization is regarded as one of the most important contributors to the global obesity epidemic, but this perspective is based on limited data. We present the first comparable estimates of mean BMI for rural and urban populations worldwide over three decades using, to our knowledge, the largest and most comprehensive global database of human anthropometry with information on urban or rural place of residence. We used population-based measurement data from almost all countries, with information on participants' urban or rural place of residence for the majority of data sources. We maintained a high level of data quality through repeated checks of study characteristics against our inclusion and exclusion criteria, which were verified by NCD-RisC members, and did not use any self-reported data to avoid bias in height and weight.
Data were analysed according to a common protocol to obtain mean BMI by age, sex and place of residence. We used a statistical model that used all available data, while giving more weight to national data than sub-national and community studies and took into account the epidemiological features of BMI by using nonlinear time trends and age associations. The model used information on the urban-rural difference in BMI where available and estimated this difference hierarchically and temporally in the absence of stratified data. Despite our large-scale data collation effort, some countries and regions had fewer data sources, particularly the Caribbean, and Polynesia and Micronesia. There were also fewer data sources before 2000. This temporal and geographical sparsity of data led to wider uncertainty intervals for these countries, regions and years.
Acknowledgements This study was funded by the  Wellcome Trust. H. B. was supported by a  Medical Research Council   Doctoral Training Partnership Studentship , J. B. by a  Royal Society   Research Grant , and M. D. C. by an  Academy of Medical   Sciences Springboard Award. We thank  L. Jaacks ,  B. Popkin ,  S. Sundberg  and  W. Willett  for recommendations of relevant citations. The authors are responsible for the views expressed in this Letter and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated. Reviewer information Nature thanks  Miguel A. Martinez Beneito ,  Barry M. Popkin  and the other anonymous reviewer(s) for their contribution to the peer review of this work.
, 2015; Seixas Arnaldo et al. , 2011). However, no Agent-Based Model (ABM) model is available for T. pityocampa. In this article, a spatial and temporally explicit ABM called INSTAR is presented. INSTAR has been designed to simulate the population dynamics of T. pityocampa. This manuscript firstly provides an overview about the ecological background of the target species. The structure and functioning of INSTAR are then described in detail according to the ODD protocol ("Overview, Design concepts and Details") proposed by  Grimm et al. (2010). Finally, we present a consistency test of the submodules responsible for species development, constituted by a calibration analysis of the pest phenology and a stress test exploring the influence of extreme temperatures on the model.
Details ABMs often require a high number of parameters and detailed information is needed to fully parameterise the model (Kelly  (Letcher) et al. , 2013). Thus, as any other ABM, INSTAR parameterisation requires an exhaustive review of literature as well as testing. During last 20 years we produced and recorded data on T. pityocampa in Sierra Nevada and surrounding areas (SE Spain) regarding phenology, relationship with altitude, egg parasitoidism, or tree species selection, and therefore the model is based on these local data whenever possible. When unavailable, data were taken from scientific literature elsewhere. The parameters used by INSTAR are listed in Table  2   where h i is the number of hosts in cell i. This function assumes that the number of hosts per cell is a good surrogate of shaded surface, which affects soil moisture and temperature.
Afterwards, colonies are initialised. The number of colonies to be created is defined by the percentage of infected pines, which can be set by the user as a model input. In this study, real data from a defoliation monitoring programme within the Andalusia region (southern Spain) are used. This programme assigns a defoliation value from 0 (no defoliation) to 5 (complete defoliation) to each forest plot at the end of every defoliating season. These data are used to define the percentage of infected pines and thus the number of colonies present in the landscape. Colonies are then distributed among the hosts by weighted random selection based on moth suitableness: the higher the moth suitableness, the less probability for the host to be infected (i. e. to have colonies on it). Colonies are initialised as "egg" stage with days as egg corresponding to a random number between 0 and Egg min.
Input data INSTAR uses a set of input data as follows: • Map of elevation for the simulated area, obtained from the Andalusian Network of Environmental Information (REDIAM) in a raster format and 30 × 30 m resolution. • Daily maps of meteorological variables (maximum and minimum temperature) for the simulated area and period. These maps are raster files generated by a hydrometeorological model interpolating data from weather stations at 30 × 30 m resolution  (Herrero et al. , 2007 , see Appendix C for more details). In this study, the meteorological dataset corresponds to years 2008-2013 (see section 4 for more details). • Map of the pines present in the simulated forest. This map is a vector file derived from Airborne Laser Scanning data, which contains the location and height of trees (more details about this processing in Appendix D).
All identified trees are considered to be pines (i. e. hosts). • Initial percentage of infested pines. In this study, this percentage was based on defoliation data from Andalusian defoliation monitoring programme. This dataset provides an infestation rate based on observed defoliation for each plot within the region, on a 0-5 scale. The initial percentage of infested pines is calculated based on the defoliation assigned by the monitoring programme to the plot where the simulated area is located. This dataset is fully documented by  Ros-Candeira et al. (2019).
Model consistency test INSTAR has been written in NetLogo language  (Wilensky, 1999)  and reviewed in an iterative process by a multidisciplinary team consisting of computer scientists, modellers and ecologists. An independent implementation of each individual submodel has been used to check that the model agrees with the conceptual model initially proposed by Torres  Muros (2015)  or, in other words, for model verification in the sense of  Findeisen et al. (1978). The last INSTAR stable version can be freely downloaded from GitHub (http://sl. ugr. es/github_instar) and run under a NetLogo free licence (https://ccl. northwestern. edu/netlogo). Besides this, a demo execution of INSTAR can be run at https://vlab. geodab. eu, a virtual laboratory promoted by ECOPOTENTIAL project (https://ecopotential-project.
These experiments constitute a first internal validation of the development submodels (Fig. 2 ), since they can help with confirming hypothesis and observing unexpected model behaviours. After these tests, parameterisation and sensitivity analysis are needed before the model can be fully applied. A virtual experimental area was created for this analysis by using a combination of real and built-in datasets (see Appendix E for more details). This virtual area is 15 × 12 cells large, covering an area of 450 × 360 m 2 (16. 2 ha). The location is real: Sierra Nevada Protected Area (south-eastern Spain) at an elevation of 1706 ± 34 m. a. s. l. and contains 1128 pine trees, which have been extracted using an existing LIDAR dataset (see section 3. 3. 2). Finally, the climatic data needed to conduct the consistency analysis were generated by WiMMED hydrological model (see section 3.
INSTAR results were calibrated against field observations provided by the Sierra Nevada Global Change Observatory  (Bollullos and Aspizua, 2014). The observations consist on a dataset reporting hatching, procession and egg laying dates recorded for several plots within the Sierra Nevada mountain range during years 2009, 2010 and 2011. A summary of this dataset can be observed in Table  3. Calibration was performed following a stepwise approach: egg development calibration followed by larvae development calibration. In the first step, the parameter T egg_devel , which modulates how long the stage "egg" takes by adjusting days as egg, was calibrated in order to obtain simulated hatching dates within the range of the above-mentioned observations. For this analysis, a systematic execution of the model was performed with values of T egg_devel ranging from 15 to 25 °C in 1 °C steps.
Simulations were initialised with colonies at stage L1, to avoid noise coming from the previous stage. Each simulation was started on Julian day 226 of the corresponding year and lasted 9 months, therefore extending to the beginning of next year. Thus, input datasets correspond to years  2008/2009, 2009/2010 and 2010/2011. The initialisation date corresponds to the average hatching date derived from the previous experiment. For each combination of parameter values and input dataset, five replicates were run. Finally, to avoid noise coming from other submodels, mortality submodels based on host quality and quantity were disabled and the procession submodel was unconstrained (colonies could bury in all cells around them). In this case, the procession date of each colony was recorded in each simulation and the difference between observed and simulated average procession date was calculated as a measure of model fit.
0 days, is reached for T egg_devel equal to 25 °C. Considering these results, the selected value for T egg_devel corresponds to 22 °C. Once the egg phase was calibrated, larvae development calibration was carried out. Difference in average procession date between observations and simulations, which has been considered as the calibration metric for larvae development phase, was calculated for each combination of parameter values and seasons. The minimum difference across years corresponds to 2. 5 days and is found for T larvae_devel_high equal to 27 °C, T larvae_devel_internal equal to 25 °C, and T larvae_devel_low equal to -8 °C, followed by all other T larvae_devel_low tested values except for -5 °C (data not shown). Fig. 4  shows the mean difference for the three years along the tested values for parameters T larvae_devel_high and T larvae_devel_intern (T larvae_devel_low fixed to -8 °C).
Although larvae weight gain has been studied  (Avtzis, 1986) , no reference for biomass consumption has been found. This has avoided a more realistic modelisation of the host-pest interaction. Due to the interest on forest health, pests are commonly monitored by assessing their impact as defoliation or other stress signals on the host  (Trumbore et al. , 2015). In the case of the pine processionary moth, the number of colonies is also often monitored and reported. INSTAR uses this type of information for the calculation of the trees carrying capacity (see section 3. 3. 1 and Appendix A). However, data at a higher resolution (i. e. number of larvae per colony at different stages) is not available and such information would be particularly valuable to improve how the host-pest interaction is modelled and to calibrate the mortality submodels.
Author contributions María Suárez-Muñoz leaded the manuscript writing process as well as the INSTAR code review and testing. She also designed the experiments showed in the text. Francisco Bonet-García contributed to the initial conception of the model and to write the manuscript. He supervised the whole process and contributed to the funding acquisition. José A. Hódar contributed to the conceptual model and provided basic data and knowledge regarding pine processionary moth natural history and ecology. He also participated in the model implementation phase, writing of the manuscript and the funding acquisition. Javier Herrero designed and executed the hydrometeorological model and contributed to the manuscript. He also produced and provided the meteorological data from Guadalfeo Projec network.
Mihai Tanase pre-processed the LiDAR dataset and produced the LiDAR based metrics used in this study. He also contributed to the manuscript with the description of the LiDAR data analysis. Lucía Torres-Muros developed the initial conceptual model as well as to the collection of data needed to calibrate some INSTAR functions. The table shown below describes with more detail the specific contributions of each author. The authors' contribution to each parameter is depicted using a quantiative range from 0 (no contribution) to 5 (full responsibility).
Acknowledgements This work has been carried out within  Sierra Nevada Global Change Observatory  (LTER platform. South Spain). This research infrastructure has provided datasets to calibrate the model as well as hardware/ software support for the authors. Besides, the tasks described in this work are outcomes of the  H2020  project " ECOPOTENTIAL: Improving Future Ecosystem Benefits Through Earth Observations " (http://www. ecopotential-project. eu). The project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  641762. María Suárez-Muñoz  has been funded by the  European Social Fund  through the  Youth Employment Initiative  and the  Ministerio de.
We tested protein levels of Casq1, Casq2, and sarcalumenin by immunoblot analyses of whole muscle lysates. Sarcalumenin levels decreased significantly between control and dKO muscles, while Casq2 protein levels increased, and Casq1 remained unchanged (Fig. 5c , e, Supplementary Figs. 2g, 7c ). These data indicate changes to cellular calcium handling in dKO muscles, which may be reflected in defective excitation contraction coupling and muscle contraction. To investigate if these molecular changes alter muscle physiology, we studied EDL twitch parameters in all groups (Table  2 ). EDL muscles of obscurin-knockout and dKO mice displayed increased time-topeak (TtP) values compared to muscles from control and Obsl1 knockouts (Fig. 5f ). However, half-relaxation times remained unchanged (Fig. 5g ).
6a ). Moreover, the complete dataset for the transcriptome analysis of fibroblasts from 3M-growth syndrome patients is not readily available  40. However, there are several striking similarities between our proteome and the transcriptome and interactome datasets (Supplementary Fig. 5a, b ; Supplementary Data File 5). Specifically, both studies found significant deregulation of genes/proteins involved in cellular transcription (e. g. , Hnrnpf, Eef1a1, Ybx transcription factors), translation (small and large ribosomal subunits), and in genes involved in metabolic pathways downstream of IGF1 (insulin-like growth factor 1) and mTOR (mammalian target of rapamycin) signaling (Supplementary Fig. 5c ). Several proteins that form part of membrane and vesicle trafficking complexes show up in both large datasets, including coatomer protein complex subunit alpha (Copa) or clathrin (Cltc) (Supplementary Fig.
Deficiencies in several of these pathways have been implicated in growth retardation and developmental delays, including COP-related protein shuttling pathways  [49] [50] [51]  or clathrin-dependent trafficking  52. Another striking overlap between the datasets are proteins that are involved in metabolism, mitochondrial function (e. g. , electron transport chain complex proteins), and generation/scavenging of ROS). Specifically, we found deregulation of Gapdh and Prdx3, both identified as putative interaction partners for Obsl1  35  (Supplementary Figs. 5a, 6c ). Maoa was also found downregulated in both the transcriptome and our proteome datasets. with Maoa/Maob deficiency display loss of muscle tone and short statue  53, 54  , while global Maoa/Maob dKO mice show developmental delays, behavior, and locomotion problems  55.
If not stated otherwise, biological replicates were used for immunoblot analyses. Sample sizes for quantification of protein levels are given in the figure or figure legend. Uncropped blot images for all western blot data are presented in Supplementary Data File 6. For immunofluorescence, muscles were isolated, snap frozen while submerged in isobutane, and embedded into cryogenic molds using optimal cutting temperate (OCT) medium. Longitudinal or cross-sections of muscles were done by cryosectioning using a cryostat (Leica). Sections were collected on surface-treated microscopy glass slides (Colorfrost Plus, Fisher), dried, and stored at -80 °C until further use. Immunofluorescent staining of sections was done by fixing sections in ice-cold acetone (5 min at -20 °C), rehydration of tissue using 1× phosphatebuffered saline (PBS) (5 min), permeabilization (1× PBS, 0.
1% formic acid, and 0. 005% trifluoroacetic acid (TFA)) and Buffer B (100% ACN, 0. 1% formic acid, and 0. 005% TFA). MS/MS data were acquired in a data-dependent manner in which the MS1 data was acquired for 250 ms at m/z of 400 to 1250 Da and the MS/MS data was acquired from m/z of 50 to 2000 Da. Independent data acquisition parameters were as follows: MS1-TOF (mass spectrometry imagingtime of flight) acquisition time of 250 ms, followed by 50 MS2 events of 48 ms acquisition time for each event. The threshold to trigger MS2 event was set to 150 counts when the ion had the charge state +2, +3, and +4. The ion exclusion time was set to 4 s. The collision energy was set to iTRAQ experiment setting. Finally, the collected data were analyzed using Protein Pilot 5. 0 (ABSCIEX) for peptide identifications and Peaks  80.
Bioinformatic enrichment and pathway analysis was done using Metascape (http://metascape. org/)  81 , Morpheus (https://software. broadinstitute. org/morpheus/), the BioGRID (https://thebiogrid. org/)  82  , and Venny (http://bioinfogp. cnb. csic. es/tools/venny). Eukaryotic expression constructs, yeast two-hybrid screening, and coimmunoprecipitation. Constructs for green fluorescent protein (GFP)-tagged Obsl1 fragments and yeast two-hybrid screening were generated as described elsewhere  24. In short, coding sequences for human Obsl1 fragments (NCBI access no: NM_178884) were subcloned to enhanced GFP (EGFP) (Clontech) or pLex vectors to produce in-frame fusion between GFP or the DNA-binding domain and the insert. All expression constructs were sequenced to determine correct integration into the vector.
2% NP-40). Analysis of input and bound protein immunocomplexes was done by SDS-PAGE, followed by immunoblot analysis. Bioinformatics and statistical analysis. The following bioinformatics packages and programs were used to perform analyses in this manuscript: analysis of proteome data was done using Peaks Studio (http://www. bioinfor. com/peaks-studio/)  80 ; pathway enrichment analysis was done with the help of MetaScape (http:// metascape. org/)  81 ; hierarchical clustering of proteome data was done using Morpheus (https://software. broadinstitute. org/morpheus/); image analysis was done using ImageJ with the LOCI BioFormats and Image5D plugins (by the Open Microscopy Environment consortium and Joachim Walter, respectively); sequence alignments and domain structure analysis were done using NIH/NCBI BLAST (https://blast.
ncbi. nlm. nih. gov/Blast. cgi), SDSC Biology Workbench (http:// workbench. sdsc. edu/), and SMART (http://smart. embl-heidelberg. de/)  83. With the exception for built-in statistics by the Peaks Studio software, statistical analysis of all data was done by using analysis of variance comparison, followed by Dunnett's multiple comparisons test, or unpaired t test, performed using Excel (Microsoft) or GraphPad Prism version 7 for Mac (GraphPad Software, http://www. graphpad. com). Violin plots were generated using BoxPlotR (http://shiny. chemgrid. org/boxplotr/). Results are presented as means ± standard error. P values of p < 0. 05 were considered statistically significant. Sample sizes are indicated in the figure or figure legend. If not stated otherwise, both sexes were analyzed in the experimental procedures.
Acknowledgements This work was supported by grants from the  Muscular Dystrophy Association  to SL ( MDA68929 ) and JB (MDA515518), AHA undergraduate fellowships to MC and MW ( 17UFEL33520004 ), and  NIH  grants to SL ( HL128457 ,  HL107744 ). Parts of this project have been funded by a  Pilot Project grant  from the  San Diego Muscle Research Center (SDMRC) , supported by a grant from the  NIH  ( P30AR061303 ). We are thankful for technical assistance by  Dr. Jennifer Santini  and the  UC San Diego Microscopy Core  that is supported by an  NIH  grant ( NS047101 ). We are grateful to  Alexander Lun ,  Lauren Waller , and  Allyson Xie  for assistance with experimental procedures, as well as to  Dr. Julius Bogomolovas ,  Dr. Elisabeth Ehler , and  Dr. Mathias Gautel  for useful scientific discussions.
Data availability All relevant data described in the manuscript can be found in the figures, tables, and supplemental files, are deposited in public repositories, or can be requested from the authors. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  84, 85  with the dataset identifier PXD013008 and https://doi. org/10. 6019/PXD013008.
In order to detect low abundant microRNAs, a pre-amplification step was included. The pre-amplified product was loaded into the TLDA and amplification signal detection was performed in the 7900 FAST real time thermal cycler (ABI). Data were exported to the DataAssist software version 2. 0 (Life Technologies) and normalized using the small-nucleolar RNA RNU44. Mean relative quantity (RQ) was calculated and microRNAs differentially expressed between groups (SW-480 cells interacting with trophozoites vs. SW-480 cells alone) were defined as those with fold change >1. 5-and p-value < 0. 05.
Cells were analyzed in a FACS Calibur flow cytometer (BD Biosciences). The SW-480 cells cultured in DMEM or treated with 50 µM cisplatin for 24 h were used as controls. Assay was repeated three times in triplicate and data were expressed as mean ± standard deviation. Statistical analyses were performed using the Student's t-test and a p-value < 0. 05 was set as significant.
Actin antibodies were used as loading control. Immunodetected bands intensity was quantified by densitometry and normalized against actin. Numbers between panels represent the fold change values relative to control (time 0). interaction. Data showed that control SW-480 cells incubated without parasites form monolayers of well-spread cells with irregular microvilli (Figure  1A ). At early times of interaction between host cells and amoeba (15 min), we hardly observed morphological changes indicative of apoptosis (Figure  1B ). In contrast, after 30 and 45 min interaction with trophozoites, the SW-480 cells exhibited the typical early changes associated with apoptosis including a general cell-shrinkage, loss of microvilli, and nuclear chromatin condensation (Figures  1C, B, D ). Finally, after 75 min interaction we found extensive SW-480 cell death and monolayers destruction (Supplementary Figures  1, 2 ) representative of late stages of apoptosis and cell killing, thus we did not considered longer time for downstream molecular analysis.