dataesr/scientific-paragraphs-categorization huggingface dataset:
The same definition is used in  Davis et al. (2016)  and ensures that the distribution of percent difference at each pressure level is not skewed toward positive values larger than 100 % (since water vapor values are constrained to be positive). In addition, this facilitates the comparison with the study of  Davis et al. (2016)    tween the 2004-2015 MLS water vapor data record and both routine monitoring and field campaign frost-point hygrometer balloon soundings at various stations around the world. Several factors could explain why a dry bias exists between the mean MLS profile and CFH convolved profile on 3 March 2017. First, the 3 km deep wet layer observed in March 2017 in the CFH profile will not be well captured by MLS with a 2-3 km vertical resolution in the upper troposphere. In addition, the CFH launch on 3 March 2017 at 18:00 UTC was planned using FLEXPART Lagrangian trajectory analysis and satellite images in the days prior to the launch to sample the convective detrainment of TC Enawo.
Temperature anomaly The hypothesis of a potential influence of convection on the CFH water vapor profile is further tested by analyzing the profile of the temperature anomaly. A seasonal mean (December-March) temperature profile is computed for the period 1997-2017 using the NDACC/SHADOZ dataset. The weekly NDACC/SHADOZ launch is performed at the airport in the north part of the island (Roland Garros, 20 m a. s. l. ). The flying distance between the Maïdo Observatory and the airport is ∼ 20 km, so while boundary-layer temperature values will differ for the two sites, free troposphere and TTL temperature distributions can be compared as they are less influenced by topography. The seasonal mean CPT height is 17. 31 km for the period December-March with a mean CPT temperature of 193. 90 K (Table  1 ).
Ray and Rosenlof (2007)   of the cyclones by up to 50 % above monthly mean water vapor mixing ratios. Therefore, our estimate of UT water vapor increases of 20 % to 100 % using CFH and MLS data for TS Corentin (category 1 hurricane at its peak intensity) and TC Enawo (category 4 hurricane at its peak intensity) are in broad agreement with our estimates based on the 2004-2017 MLS data and the study of  Ray and Rosenlof (2007). At 100 hPa (within layer L2), both MLS and CFH data are 20 % (-0. 7 ppmv) below the climatological monthly mean values on 25 January 2016. This would be consistent with the near tropopause cooling observed in Fig. 5  and the presence of deep convection around Réunion Island. In addition, TTL cirrus clouds were observed north of the island on both dates (Fig. 5 ). Convectively generated or in situ cirrus clouds in the TTL can dehydrate the tropopause region.
Jensen et al. (1996)  showed that ice clouds formed by large-scale vertical motions can result in the depletion of the water vapor mixing ratio by about 0. 4 ppmv. Chae et al. (2011)  investigated temperature and water vapor changes due to clouds in the TTL using MLS, CALIPSO and CloudSat datasets. They noted that generally clouds humidify the environment near 16 km (∼ 100 hPa) or lower but dehydrate the TTL above 16 km. On 25 January 2016, CFH and MLS data are 11 % (+0. 4 ppmv) and 18 % (+0. 7 ppmv) moister than the climatological values at 68 hPa (within layer L3) above the tropopause. Observational and modeling studies have indicated that overshooting convection can moisten the lower stratosphere by injecting water vapor or ice crystals directly above the overshooting clouds (e. g. ,  Danielsen, 1993; Corti et al.
High-resolution accurate observations of water vapor are needed to document the impact of tropical cyclones and deep convection in general on the TTL. The impact of tropical cyclones on the TTL water vapor budget will be analyzed in a more quantitative way using MLS data and tropical cyclone best tracks from 2004 to 2017 in a subsequent paper. In addition, the impact of deep convection and overshooting clouds within TC Enawo on the water vapor budget of the TTL will be analyzed using a highresolution (2 km) mesoscale simulation of TC Enawo.
Acknowledgements. We thank the  Aura science team  for the MLS data (https://mls. jpl. nasa. gov/,last access: 7 September 2020) and the  CALIPSO science team  for the L1B lidar data (https://opendap. larc. nasa. gov/opendap/CALIPSO/LID_L1-Standard-V4-10/contents. html, last access: 7 September 2020). OPAR ( Observatoire de Physique de l'Atmosphère à La Réunion , including Maïdo Observatory) is part of  OSU-R (Observatoire des Sciences de l'Univers à La Réunion ) which is being funded by  Université de la Réunion, CNRS-INSU, Météo-France  and the  French research infrastructure ACTRIS-France (Aerosols, Clouds and Trace Gases Research Infrastructure ). OPAR's water vapor lidar and ozone radiosounding belong to the international network  NDACC (Network for the Detection of Atmospheric Composition Change ).
Data availability. MLS water vapor data used in this study are available at https://doi. org/10. 5067/Aura/MLS/DATA2009 (Lambert et al. , 2015)  and CALIPSO L1B lidar data are available at https://opendap. larc. nasa. gov/opendap/CALIPSO/ LID_L1-Standard-V4-10/contents. html  (Trepte, 2020). The NDACC/SHADOZ ozone measurements for Réunion Island are available at https://tropo. gsfc. nasa. gov/shadoz/Reunion. html  (Posny, 2020). The SWOOSH dataset is available at https://data. nodc. noaa. gov/cgi-bin/iso?id=gov. noaa. ncdc:C00958 (Davis, 2020). The CFH and lidar water vapor data are available from the authors (Stephanie Evan, Valentin Duflot, Philippe Keckhut) upon request. The FLEXPART Lagrangian trajectories can be requested from the corresponding author Stephanie Evan (stephanie. evan@univ-reunion.
Method The aim of this study is to prove that the ML estimation of the cell ID in NB-IoT system can be carried out through a simple DFT without loss of performance compared with the exhaustive search. The paper includes a theoretical development that describes the DFT-based ML cell ID estimator and simulation results that show the relevance of the suggested method. The theoretical study does not require any specific material, and the simulations have been carried out with MatLab R2016a.
Simulations results The simulation results have obtained using MatLab, and 10 5 independent runs per point have been performed. The channel coefficient obeys a zero-mean complex Gaussian distribution with unitary variance. The signal to noise ratio (SNR) is defined as SNR = E{ αd 2 } E{ w 2 }. In the simulations, the chirp-z algorithm  [15]  has been used, but we remind that other fast DFT techniques could be applied. Note that for simplicity matter, we use a descriptive shortcut in all the following comments. Thus, we mention the "ML cell ID estimation" whereas we refer to the ML estimations of (u, q, θ f ) (e. g. , in (  9 ) or (  15 )). The actual cell ID estimation is obtained from the estimate ( û, q) using (  6 ), i. e. , NNcell ID = 126q + û -3.
The Pinter Histories & Legacies project home page is https://pinterlegacies. com 2. The database website link will be posted on the project's home page once it is publicly available. 3. The BFI Pinter season ran from July and August 2018 and included films scripted by Pinter, film and TV adaptations of his theatre plays and his original TV dramas. 4. BFI Pinter at the BBC, five DVD set, 2019: Tea Party (Charles Jarrott, 1965), A Slight Ache (Christopher Morahan, 1967), A Night Out (Christopher Morahan, 1967), The Basement (Charles Jarrott, 1967), Monologue (Christopher Morahan, 1972), Old Times (Christopher Morahan, 1975), The Hothouse (Harold Pinter, 1982), Landscape (Kenneth Ives, 1983), The Birthday Party (Kenneth Ives, 1987),.
British Stage  and  Screen' , which ran from 2017-20 and was funded by the  Arts and Humanities Research Council of England. documents every professional production of Pinter's plays in the UK, as well as Pinter's output on television, radio and film. 2 This public database underpins and sits alongside more expected kinds of academic output like film and television material for the season also facilitated the launch of a new DVD boxed set of Pinter's television work for the BBC.
Notes 1. 1 The Pinter Histories & Legacies project home page is https://pinterlegacies. com 2. The database website link will be posted on the project's home page once it is publicly available. 3. The BFI Pinter season ran from July and August 2018 and included films scripted by Pinter, film and TV adaptations of his theatre plays and his original TV dramas. 4. BFI Pinter at the BBC, five DVD set, 2019: Tea Party (Charles Jarrott, 1965), A Slight Ache (Christopher Morahan, 1967), A Night Out (Christopher Morahan, 1967), The Basement (Charles Jarrott, 1967), Monologue (Christopher Morahan, 1972), Old Times (Christopher Morahan, 1975), The Hothouse (Harold Pinter, 1982), Landscape (Kenneth Ives, 1983), The Birthday Party (Kenneth Ives, 1987),.
Results Identification of circRNAs generated from key β-cell genes. Taking advantage of a dedicated microarray platform containing probes spanning over the predicted circular junctions of annotated transcripts, we previously identified more than 3000 circRNAs in pancreatic islets  6. A major limitation of this methodology is that it can only detect circRNAs that are already annotated in other datasets. To circumvent this problem and obtain a comprehensive picture of all circRNAs present in islet cells, we used a two-algorithm computational approach to de novo annotate potential circular transcripts detectable in highthroughput RNA-sequencing data from mouse islets (GEO accession GSE92602)  10. This computational approach led to the prediction of 15,925 putative circRNAs (file provided in the GEO accession GSE134699), which included circRNAs generated from key β-cell genes such as Ins1, Ins2, Chga, Chgb, Gck, Glp1r, Pcsk1, Pcsk2, and Slc30a8  5, 11  (Supplementary Table  1 ).
RNA sequencing of rat islet cells transfected with the ci-Ins2 gapmeR revealed the decreased expression of 425 genes and an increase in the level of 90 genes (GEO accession GSE134699). Consistent with the observed phenotype, the downregulated mRNAs were enriched for transcripts originating from genes involved in insulin secretion. In contrast, upregulated genes were mainly related to the p53 signaling pathway (GEO accession GSE134699). Altered mRNA levels of several key genes involved in insulin secretion from glycolysis to exocytosis  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  were validated by qPCR (Fig. 6a, b ). These included mRNAs coding for Ca 2+ sensors (Syt4, Syt7, and Pclo), Ca 2+ channel subunits (Cacna1d), Na + /K + ATPase subunits (Atp1a1 and Atp1b3), and components involved in small GTPase signaling (Rapgef4 and Rims2) and in the recruitment of insulin granules for exocytosis (Unc13a).
4a ), we found that inhibition of ci-Ins2 reduced the quantity of newly synthesized Ins2 mRNA (Fig. 6e ). The discrepancy between these two observations may be explained by the high stability (half-life ≥29 h)  35  and abundance of the Ins2 mRNA (second most expressed gene in islets, GEO accession GSE134699). Therefore, we assessed whether silencing of ci-Ins2 affects the amounts of newly synthesized insulin. For this purpose, newly synthesized proteins of rat islet cells transfected with control or ci-Ins2 gapmeRs were labeled with L-Azidohomoalanine (AHA). Although we observed a tendency for a decrease in newly synthesized insulin upon ci-Ins2 silencing, the effect did not reach statistical significance (Fig. 6f ). This suggests that the reduction in insulin mRNA level may be insufficient to significantly impact on insulin content.
To assess whether the blunted insulin secretion observed in our model is mediated by the interaction with TDP-43, we compared expression changes upon ci-Ins2 down-regulation in rat islets (GEO accession GSE134699) with those in MIN6 cells depleted from TDP-43 (GEO accession GSE125424). We identified 18 orthologous mouse-rat pairs whose expression change in the same direction in both models, including Cacna1c, Pclo Pld1, and Rapgef4 (Supplementary Table  8 ). Of note, genes consistently differentially expressed between experiments are enriched in calcium ion regulated exocytosis (p = 0. 015) and insulin secretion (p < 0. 03) (Supplementary Table  9 ). To experimentally validate the predicted interaction with TDP-43, we immunoprecipitated the RNAs associated with this RBP from MIN6B1 cell lysates and found a significant enrichment of ci-Ins2.
Indeed, while incubation of total RNA with RNase R degrades linear transcripts and favors the detection of circRNAs, this treatment may decrease the chances to detect lowly abundant circRNAs and overestimate the level of intron-derived circRNAs due to lariats being resistant to the treatment. Thus, to obtain a comprehensive view of all potential circRNAs expressed in islets, we elected to perform data-analysis on untreated samples. The circularity and the relative abundance of circRNAs were then confirmed by RNase R treatment and qPCR. Recently, the presence of exonic circRNAs produced from CHGA, PCSK2, and SLC30A8 genes was also predicted by the annotation of RNA-sequencing data from human αand β-cells by Kaur et al. 47. However, intronic cir-cRNAs were not investigated in the Kaur study.
Methods CircRNA annotation. circRNAs were annotated in high-throughput RNAsequencing data from mouse islets (GEO accession GSE92602) 10 by computational detection using two separate circRNA algorithms: CIRI2 56 and find_circ  57. Only circRNAs detected by both algorithms were considered and two or more backsplice spanning reads were required to call circRNAs in individual samples. Animals. Male Wistar rats and C57Bl/6N mice aged 12 weeks were acquired from Janvier Labs, male C57BL/KsJ db/db, C57BL/6J ob/ob, and their respective control db +/and ob -/mice aged 13-16 weeks from the Garvan Institute breeding colonies, and female Goto-Kakizaki (GK/MolTac) and control Wistar rats aged 8 weeks from Taconic Europe. Animals were housed under standard temperature (20-24 °C) and humidity (40-70%) conditions on a 12-h light/dark cycle with free access to chow diet and water.
5. 3a) and quantified with RSEM  63  (1. 3. 0). To identify differentially expressed genes, reads were mapped to the rat genome (rn6) using HISAT  64  (2. 1. 0) and gene counts for rat genes (ENSEMBL 92, rn6) estimated using HTseq  65  (0. 10. 0). Normalization and differential gene expression (FDR < 0. 05) were performed using DESeq2 66 (v. 1. 12. 4). Gene enrichment analysis for significantly up or down-regulated genes relative to all islet expressed genes was carried out using the DAVID webserver  67   (6. 8). The overlap and direction of changes between genes differentially expressed upon ci-Ins2 knockdown (GEO accession GSE134699) were compared to those of TDP-43 knockdown in MIN6 cells (GEO accession GSE125424 42 ) (Supplementary Table  8 ). The Biological Process GO term for the genes that change consistency between the two experiments (Supplementary Table  9 ) were analyzed using the DAVID webserver  68, 69   (6.
8). Subcellular fractionation. Nuclear and cytosolic fractions of MIN6B1 cells were obtained using the PARIS Kit (Thermo Fisher) followed by RNA extraction, reverse transcription and qPCR as previously described. Binding site prediction and enrichment analysis. The lariat 5′-3′ genomic sequences of mouse and rat ci-Ins2 and of human ci-INS (Source Data Fig. 1b ) were used to predict putative binding sites of miRNAs, RBPs, and transcription factors (TFs) in ci-Ins2 and ci-INS. For miRNAs expressed in β-cells  6  , a custom python script based on a sliding window was utilized to detect a perfect match of miRNA seeds (6 nt) and their position relative to the 5′-end of ci-Ins2 or ci-INS sequences. For RBPs and TFs, the motif scanning tool FIMO  70  (5. 1. 0) was used together with the ATtRACT  71  and Jaspar 72 motif databases, and the relative position, p value (<10 -4 ) and sequence of each match are reported.
The samples, including a MIN6B1 input cell lysate, were run on a SDS-PAGE gel (Bio-Rad, 4561086) and transferred onto a nitrocellulose membrane (Bio-Rad, 1620117) which was subsequently blocked for 1 h in 5% BSA in TBS-T and incubated overnight at 4 °C with the anti-TDP43 antibody (1:1000 in blocking solution). Afterwards, the membrane was incubated for 1 h with the HRP-conjugated goat anti-rabbit IgG (H + L, Jackson Immu-noResearch 211-035-109, 1:10000 in blocking solution) and subsequently revealed using the ECL substrate (Pierce, 32106). For qPCR analysis, RNA molecules bound to immunoprecipitated proteins were eluted in Qiazol followed by RNA isolation, reverse transcription and qPCR as previously described. Statistical analysis. Data are represented as the mean or as individual values and mean ± standard deviation (s.
d. ). GraphPad Prism 7 and IBM SPSS Statistics 25 software were used to plot and analyze data with suitable statistical tests. Data normality was determined with a Shapiro-Wilk test, and homogeneity of variances with a Levene test. A two-tailed one-sample t-test was used to compare a data set to a control value settled to 1, and two-tailed paired or unpaired t-tests for comparing two data sets. One-or two-way ANOVA followed by Tukey, Dunnett, or Games-Howell post hoc tests were used to compare multiple data sets. Correlation of data sets was assessed by a two-tailed Pearson test. Differences between data sets were considered significant whenever p values were inferior to 0. 05. Reporting summary. Additional information on the design of this study is available in the Nature Research Reporting Summary of this article.
Acknowledgements We thank  D. Ross Laybutt  and  Kailun Lee  for providing islet RNA samples from db and ob mice. This work was supported by the  310030-169480  grant from the  Swiss National Science Foundation (RR) , the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreements number  115881  (RHAPSODY) and number  115797  (INNODIA) (P. M. ), which include financial contributions from  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  Horizon 2020 research and innovation programme ,  EFPIA ,  JDRF , the  Leona M. and Harry B. Helmsley Charitable Trust , and the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  16. 0097  (P. M. ), the  Placide Nicod foundation  (J. S. ), the  Swedish Research Council  ( 2017-01090  E.
Data availability All data supporting the findings of this study are available within the article and its supplementary information files or from the corresponding author upon request. RNA-sequencing data and annotated circRNAs are available under the Gene Expression Omnibus (GEO) accession code GSE134699. The source data underlying Supplementary Fig. 2  (ab1 raw sequencing files) are provided as a Supplementary Data compressed archive file. Source data are provided with this paper.
Reliable roosting information was not available for one of the Great Hornbills, whose tag was programmed to shut down at sunset and turn on at sunrise. For all the other birds the tag was programmed to shut down at least 45 min after sunset (~ 19:00 hr IST) and turn on at least two hours before sunrise (~ 03:10 hr IST), which allowed us to extract information on hornbill roosting reliably. Based on our field observations, hornbills arrive at roosts latest by 17:00-18:00 hr (IST) in June when the days are longest. Additional details on the methods and the study area can be found in  (Naniwadekar et al. 2019a, b). The GPS data for this study can be accessed from  Naniwadekar et al. (2019c). We monitored one roost site for 45 days between April to June (breeding season) in 2015 and two roost sites (including the one monitored in 2015) for a total of 211 roost watch days (190 unique days on 21 days both roosts were observed) across breeding and nonbreeding season in 2016.
Analysis To determine roosts of hornbills, we calculated mean displacement distances between consecutive time points (for every 15-min interval) between 03:15-19:00 hr and found that displacement distances were least for 19:00 hr for the five hornbills (< 32 m; range across individuals: 17. 9-31. 2 m). Therefore, we used the location of hornbills at 19:00 hr as the roost location for the day. Due to GPS tag errors, location data was not available for 19:00 hr (and often for few periods before that) for all days for all birds. Mean displacement distances were higher for time intervals before 18:30 hr. Days for which we had obtained data at 18:30 hr, we had received data at 19:00 hr also. Therefore, we used the data only for the days on which we had location information at 19:00 hr. We used the roost data to calculate the mean displacement distances between roosts on consecutive days to determine how far, on average, were the roosts located on successive days for the different hornbill individuals.
Acknowledgments This work was supported by the  Department of Science and Technology (Govt. of India)  under Grant No:  SB/S0/AS-124/2012  and  International Foundation for Science (Sweden)  under Grant No:  D/5136-2. We are grateful to  Mr. Tana Tapi  (Field Director, Pakke Tiger Reserve) and his staff for support. We are grateful to the  Thailand Hornbill  Project team:  Pilai Poonswad ,  Vijak Chimchome ,  Phitaya Chuailua ,  Ittiphon Buathong , and  Vichai Klinkai  for training. We are grateful to  Tsongjing Thonger , our field staff  Khem Thapa ,  Tali Nabam ,  Turuk Brah ,  Kumar Thapa ,  Bharat Chiri ,  Narayan Mogar  and  Sital Dako , volunteers  Malaysri Bhattacharya ,  Pranjal Mahananda ,  Gombu Tachang  and  Sathya Chandra Sagar  for assistance in data collection. We thank  Jahnavi Joshi ,  Anand Osuri ,  Amruta Rane ,  Sartaj Ghuman , and  Soumya Prasad  for discussions and help.
Data sharing The data that support the findings of this study are openly available in [Movebank Data     Repository] at http://doi. org/10. 5441/001/1. 14sm8k1d. The complete citation of the data is: Naniwadekar R, Rathore A, Shukla U, Chaplod S, Datta A (2019) Data from: How far do Asian forest hornbills disperse seeds? Movebank Data Repository. doi:10. 5441/001/1. 14sm8k1d.
RNA-seq assay Overexpression line OE #18 and down-expression line RNAi #10 were used for RNA-seq assay, with wild type used as control. Total RNA was extracted (RNeasy kit, QIAGEN, Germany) from mature green fruit (0d), mature green fruit treated 14 days at 5 °C (CI 14d) and mature green fruit treated 14 days at 5 °C then replaced at 25 °C for 14 days (CI 14d + RT 14d). cDNA libraries (Illumina) were then constructed for sequencing on the BGI-Seq 500 system (BGI Inc. , China). For each sample, the summary of sequencing data is shown in Data S2-S4, respectively. HISAT was used to map clean reads to the reference genome of Solanum lycopersicum L. in the Tomato Sol Genomic Network database (http://solgenomics. net/), and Bowtie2 was used to map clean reads to reference gene. And the homogenized data were used to calculate gene expression levels with RSEM.
For 0d samples, differentially expressed genes (DEGs) were detected with DEGseq with the following parameters: fold change 2. 00 and adjusted P-value (Q-value) 0. 001. For the samples of CI 14d and CI 14d + RT 14d, DEGs were detected with NOIseq with the following parameters: fold change 2. 00 and probability 0. 8. Sequence data of RNA-seq from this article can be found in the Sequence Read Archive (SRA) database under accession number PRJNA594085.