dataesr/scientific-paragraphs-categorization huggingface dataset:
Betrie et al. (2011), Gessesse et al. (2015)C, PESERA are used by less than 1. 5% of studies/catchments included in the database. If one compares the USLE and RUSLE models, the (R)evised USLE model receives, on average, 0. 8 more normalized citations per year than the original version. It should also be noted that the SWAT model is relatively widely used (i. e. , in approximately 6% of papers in the database) and on average articles using this model receive more citations than the RUSLE and USLE models (Table3). Moreover,Borrelli et al. (2021)showed that SWAT has become more popular among the soil erosion modellers in the last years. Papers with the highest number of annual normalized citations (i. e. > 13) using the SWAT model areBetrie et al. (2011), Gessesse et al. (2015)and Yesuf.
Funding Nejc Bezak  and  Matjaž Mikoš  would like to acknowledge the support of the  Slovenian Research Agency  through grant  P2-0180. Diana Vieira  is funded by national funds (OE), through  FCT -Fundação para a Ciência e a Tecnologia, I. P. , in the scope of the framework contract foreseen - DL57/2016  ( CDL-CTTRI-97-ARH/2018 -REF. 191-97-ARH/2018 ), and acknowledges CESAM financial support of through ( UIDP/50017/ 2020+UIDB/50017/2020 ). Jae E. Yang  and  Pasquale Borrelli  are funded by the  EcoSSSoil Project ,  Korea Environmental Industry & Technology Institute (KEITI) ,  Korea  (Grant No. 2019002820004 ). Walter Chen  is funded by the  Ministry of Science and Technology (Taiwan) Research Project  (Grant Number  MOST 109-2121-M-027-001 ).
5 the study. Unknown USPED EPIC 1. 3 3. 2 3. 8 8. 6 14. 8 11. 7 1. 2 <1 <1 Investigated continent/area Mean normalized number of citations [per year] Percentage of entries in the database [%] RUSLE2 4 11. 3 <1 Global 17. 5 0. 8 PESERA 3. 7 10. 4 <1 Europe 3. 8 30. 7 EROSION 3D 3. 9 9. 1 <1 Africa 2. 5 8. 2 GeoWEPP 1. 9 4. 2 <1 North America 2. 5 20. 5 RUSLE/SEDD 0. 8 2. 7 <1 Oceania 2. 3 3. 5 EPM 1. 2 2 <1 South America 2. 1 4. 2 STREAM 6. 2 8. 6 <1 Asia 2. 0 32. 1.
Although numerous rapid reviews have been produced, their varied methodologies can diversely impact research results. 23 A rapid review protocol was compiled based on the 'framework of rapid review methods'. 24 he databases queried were PubMed, ScienceDirect, Cochrane Library and Cairn. A first exploratory phase of these databases helped refine the keywords. The search equations are presented in table  1. The following were the inclusion criteria: ► Studies published in English or French. ► Studies published since 1 January 1999 (in France, the development of quality procedures follows the Open access ordinance of 24 April 1996 establishing an accreditation procedure implemented since 1999  25  ). ► All types of studies. The following were the exclusion criteria: ► Title and abstract published in language other than English and French.
All simulations were performed with Brian 2  15. The code is available at http://doi. org/10. 5281/zenodo. 4315011  16. It allows for reproducing the results presented in Figure  4 , Figure  5  and Figure  6. To demonstrate that the details of the neuron model do not matter for the results presented here, the provided code can also be run with a simpler leaky integrate-and-fire model.
So which costs are reduced by using dendrites? Could you explain? We now insist on this point by providing a concrete example: To implement the D-AND when n=1001 a SLTU needs a single synaptic contact for the dominant input while a LTU requires a thousand. We want to thank again the reviewer: her question and remarks made our article clearer. In this modelling work, Caze and Stimberg propose that dendrites can efficiently decrease the weight resolution required to perform linear separable functions, something that is difficult to achieve using an LTU. The code that generates the model/data shown in figures is available and can be executed smoothly. All in all, the paper is a good fit for the F1000 Research. The results have the potential to advance the implementation of ANNs and neuromorphic hardware.
January 2021 https://doi. org/10. 5256/f1000research. 50405. r77774 © 2021 Zeldenrust F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fleur Zeldenrust Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands I appreciate the work the authors put in rewriting the text and reorganising the figures. I still think it is a nice and relevant story. I only have a few textual clarifications left.
Acknowledgements This project started a long time ago within a team directed by  Alain Destexhe  and stems from multiple discussions with B. Teleńczuk. We also want to thank  F. Danneville  and  C. Loyez  for the stimulant discussions,  Prof. A. Cappy  for his remarks on the method, and  M. Humphries  for numerous valuable comments. Finally we want to thank  A. Foust  for making our scientific English easier to read and  Ms Marini-Audouard  for the proof-reading before submission.
Reviewer Report 29 29 January 2021 https://doi. org/10. 5256/f1000research. 50405. r77774 © 2021 Zeldenrust F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fleur Zeldenrust Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands I appreciate the work the authors put in rewriting the text and reorganising the figures. I still think it is a nice and relevant story. I only have a few textual clarifications left.
Potential Functional Pathways of Gut Microbiota Are Modified by Metformin Treatment in Mice Infected with H. pylori Functional analysis of the gut microbiota was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software based on closed-reference selection of operational taxonomic units (OTUs). We examined 159 pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) reference database. Only KEGG pathways with a relative abundance > 0. 001% were considered; these represented 124, 119, and 132 KEGG pathways in stool, cecal, and intestinal samples, respectively. Pathways with a significantly different abundance between the metformin and control groups were identified for all three different digestive sites (p < 0. 05, after Bonferroni correction).
These differentially enriched KEGG pathways are shown in Figure  5  and Figure  S3. Beta diversity analysis according to the predicted functional pathways for each group was performed. A comparison of the microbiota of the metformin and control groups based on metabolic function showed differential bacterial profiles on 2D PCoA plots (Figure  6a ). Adonis statistical tests performed on these data showed significant differences among Bray-Curtis distances in all sample types (p = 0. 001). In stool samples, five predicted KEGG pathways were significantly more abundant in the metformin group, compared with eight KEGG pathways in the control group. The differential KEGG pathways between the metformin and control groups represented 10. 5% and 26. 9% of all pathways examined for stool and cecal content, respectively.
Adonis statistical tests performed on these data showed significant differences among Bray-Curtis distances in all sample types (p = 0. 001). In stool samples, five predicted KEGG pathways were significantly more abundant in the metformin group, compared with eight KEGG pathways in the control group. The differential KEGG pathways between the metformin and control groups represented 10. 5% and 26. 9% of all pathways examined for stool and cecal content, respectively. Finally, nitrate and nitrite-reducing bacterial species were specifically studied in intestinal content by focusing on the KEGG gene expression of k02575, k00370, k00363, and k03385, which code for nitrate or nitrite-reductase enzymes. Intestinal bacteria in the microbiota of metformin-treated mice showed significantly decreased nitrate and nitritereductase functions (p < 0,05, Mann-Whitney U test) (Figure  6c ).
pylori-infected mice in response to metformin treatment. Functional features of the microbiota of mice in this study were examined using the PICRUSt software  [36]. The resulting bacterial predicted profiles showed that the metformin and control groups had distinct metabolic functional signatures. The intestinal microbiota showed the highest expression among differential metabolic KEGG pathways between groups, indicating that metformin treatment leads to significant modification of the functional properties of the digestive microbiota, particularly in intestinal sites. Specifically, metformin treatment decreased nitrate and nitrite reductase functions in intestinal bacteria. The nitrate-reducing bacterial pathway was analyzed because it has been suggested to participate in the increase of intragastric concentrations of nitrite and N-nitroso-compounds  [37].
With this method, 10 mg/mouse/day corresponds to 2. 4 g of metformin/day for a human adult. The maximal dosage of metformin used to treat Type 2 diabetes patients is 3 g/day. Each group received a daily treatment (0. 1 mL) for 12 days by gavage. During this period all mice had access to water ad libitum and a normal diet. Stool samples were collected before infection with H. pylori and after 12 days of treatment. Weight was controlled during the study. After the treatment, no differences were observed in mice weight between the two groups (data not shown). At the end of the treatment, mice were euthanized by cervical dislocation. Cecum and intestine were aseptically taken to collect cecal and intestinal content separately. Intestinal content corresponds to the entire content of mice intestine, no specific region in intestine was selected.
Gastric samples were not available for use in this study. All collected samples were immediately stored in a sterile tube at -80 • C. Mouse experiments were performed in level 2 animal facilities at Bordeaux University with the approval of the local Ethical Committee, and in conformity with the French Ministry of Agriculture (approval no. 4608). Initially, alpha and beta diversity were analyzed using mouse stool samples collected from both groups prior to infection and treatment. This analysis confirmed that the groups were comparable and presented no differences in alpha or beta diversity (Figure  S1 ). Same analysis was also performed on stools collected after infection with H. pylori and before any treatment with the same results (data not shown).
DNA Extraction and rRNA Gene Sequencing DNA was extracted from samples using the QIAamp PowerFecal Pro DNA kit with a PowerLyzer (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Quantification, sequencing of the V3-V4 region of 16S rRNA, and assembly were performed by Genoscreen (Lille, France; further details provided in Supplementary material S1). The 16S rRNA sequencing datasets generated in this study can be found in the SRA database (http://www. ncbi. nlm. nih. gov/bioproject/701274, accessed on 3 April 2021).
Functional Metagenome Predictions Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) 1. 1. 0 software was used to predict virtual metagenomes for each sample using the 16S rRNA gene sequencing results  [36]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used as a reference database. Based on the predicted metagenomes, the relative abundance of KEGG genes or KEGG pathways (ko) within each sample was determined.
Potential Functional Pathways of Gut Microbiota are Modified by Metformin Treatment in Mice Infected with H. pylori Functional analysis of the gut microbiota was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software based on closed-reference selection of operational taxonomic units (OTUs). We examined 159 pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) reference database. Only KEGG pathways with a relative abundance >0. 001% were considered; these represented 124, 119, and 132 KEGG pathways in stool, cecal, and intestinal samples, respectively. Pathways with a significantly different abundance between the metformin and control groups were identified for all three different digestive sites (p < 0. 05, after Bonferroni correction).
2. 5. 5 Potential Functional Pathways of Gut Microbiota are Modified by Metformin Treatment in Mice Infected with H. pylori Functional analysis of the gut microbiota was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software based on closed-reference selection of operational taxonomic units (OTUs). We examined 159 pathways based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) reference database. Only KEGG pathways with a relative abundance >0. 001% were considered; these represented 124, 119, and 132 KEGG pathways in stool, cecal, and intestinal samples, respectively. Pathways with a significantly different abundance between the metformin and control groups were identified for all three different digestive sites (p < 0.
Microarray Analysis Whole-cell RNA from one million cells of AML derivates or peripheral blood mononuclear cells of patient were purified using RNeasy Mini Kit (QIAGEN). Microarray were performed at the core facility of Institut de Recherche Saint Louis of Université de Paris (Paris, France). Details can be found in Supplementary Materials and Methods. Primary data are accessible in ArrayExpress (accession ID: E-MTAB-10397). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025507.
Statistical Analysis GraphPad Prism software was used to perform multiple unpaired two-tailed t test to determine the P values and look for significant changes in the data generated from the isogenic cell lines and different drug treatment in vivo or ex vivo. All data are expressed as mean ± SD of technical replicates from a representative experiment of the performed ones, as indicated. For all graphs, *, P = 0. 01-0. 05; **, P = 0. 001-0. 01; ***, P < 0. 001. For the experiment shown in Supplementary Fig. S3F , ROS and inflammation-related gene changes from AML2-expressing NPM1c versus NPM1 or AML2-expressing NPM1 treated with ActD versus DMSO were selected to perform linear regression analysis using GraphPad Prism software.
Acknowledgments We warmly thank  P. G. Pelicci  for sharing mouse leukemia models,  V. Montcuquet  for the animal husbandry,  N. Setterblad  and  S. Duchez  for the imaging and cytometry platforms,  C. Vallot  for bioinformatic advice,  C. Bally  for help with the patient samples, the  3P5 proteomic facility of Institut Cochin , the imaging ORION platform of  Collège de France  for decisive help in PML NBs and mitochondrial alteration quantification,  A. Mourier  for discussions and advice,  A. Rötig  for DNA of mitochondria-depleted cells,  M. H. Verlhac ,  E. Gilson ,  U. Sahin ,  J. Ablain ,  R. Rodriguez ,  C. Esnault , and other lab members for advice and comments on the manuscript. This work is dedicated to the first author's late father  Jung-Mao Wu  and all those loved ones lost to COVID-19.
Here, we argue that this may be one reason behind the poor performance of current sequence-to-sequence models in generating absent keyphrases  (Gallina et al. , 2020). In this paper, we advocate for a stricter definition of absent keyphrases and propose a finegrained categorization scheme that reflects how many new words are introduced within each keyphrase. Through this scheme, we shed new light on the effect of absent keyphrases on document retrieval effectiveness, and provide insights as to why current models for keyphrase generation are unable to accurately produce absent keyphrases. As a by-product, we introduce a new benchmark dataset for scientific document retrieval through the task of context-aware citation recommendation, that is composed of 169 manually extracted queries with relevance judgments and a collection of over 100K documents on topics related to IR.
Experimental settings Experiments in ad-hoc document retrieval are carried out on the NTCIR-2 test collection  (Kando, 2001)  which is, to our knowledge, the only available benchmark dataset for that task. It includes 322,058 scientific abstracts in English annotated with author-assigned keyphrases (4. 8 per doc. on avg. ), and 49 search topics (queries) with relevance judgments. Documents cover a wide range of domains from pure science to humanities, although half of the documents are about computer science. Given the rather limited size of the NTCIR-2 test collection, we conducted additional experiments in context-aware citation recommendation  (He et al. , 2010)  which is the task of retrieving citations (documents) for a given text (query). Since no publicly available keyphrase-annotated collection exists for that task, we created one by collecting documents (BIBT E X entries) from the ACM Digital Library.
Our dataset contains 102,411 documents in English on topics related to IR 1  , most of which (69. 2%) have author-assigned keyphrases (4. 5 per doc. on avg. ). We then followed the methodology proposed in  (Roy, 2017) , and selected 30 open-access sci-entific papers 2  from which we manually extracted 169 citation contexts (queries) and 481 cited references (relevant documents). The resulting dataset, named ACM-CR, is publicly available 3. For both retrieval tasks, we rank documents against queries using the standard BM25 model implemented in the Anserini 4  open-source IR toolkit  (Yang et al. , 2017) , on top of which we apply the RM3 query expansion technique  (Abdul-Jaleel et al. , 2004)  to achieve strong, near state-of-theart retrieval results  (Lin, 2019; Yang et al. , 2019). For all models, we use Anserini's default parameters.
We evaluate retrieval effectiveness in terms of mean average precision (mAP) on the top 1,000 retrieved documents for ad-hoc document retrieval, and in terms of recall at 10 retrieved documents for context-aware citation recommendation as recommended in  (Färber and Jatowt, 2020). We use the Student's paired t-test to assess statistical significance of our retrieval results at p < 0. 05  (Smucker et al. , 2007). Table  1 : Proportion of Present, Reordered, Mixed and Unseen keyphrases in datasets. We also report the ratio of unique, unseen words in M+U keyphrases (%uw).
Distribution of gold-standard keyphrases under the PRMU scheme Table  1  shows the proportion of gold-standard, author-assigned keyphrases for each category in the different datasets. We also report results for the KP20k dataset  (Meng et al. , 2017)  should provide some form of semantic expansion. To have a precise idea of how many new words are actually added when indexing absent keyphrases, we compute the ratio (%uw) of unique words from keyphrases that do not occur in their corresponding documents. We find that only about 20% of the words included in keyphrases contribute to expanding documents. This surprisingly low percentage indicates that absent keyphrases play a much smaller role on document expansion than previously thought. Yet, as we will see next, this small fraction of new words is behind much of the gains observed in retrieval effectiveness.
Effect of indexing PRMU keyphrases on retrieval effectiveness Table  2  presents the results of retrieval models on documents supplemented with keyphrases from PRMU categories. We see that adding keyphrases systematically improves retrieval effectiveness on both datasets, but a closer look reveals that the largest gains are obtained with Mixed and Unseen keyphrases. This observation, combined with the fact that the number of Mixed and Unseen keyphrases is comparatively small (less than one on average), demonstrate that expanding documents is more effective than highlighting salient phrases for improving document retrieval performance. The higher scores achieved when combining Mixed and Unseen keyphrases, compared to when combining Present and Reordered keyphrases, further confirm this conclusion.
We examine a representative sample of 327 cities across 28 EU countries (total number of EU MS at the time of data collection) and their climate change mitigation policies. Data, i. e. the availability of LCPs with contents on mitigation (M-LCPs hereafter) was updated between November 2018 and December 2019. This study advances the quantification of the climate ambition of European cities and provides an analysis of contextual factors that relate to it. More importantly, our work aims to understand the spatial levels at which climate policy is being shaped within the EU and to assist in identifying the extent to which the climate planning process is moving at different speeds in different parts of Europe. In summary the paper addresses three issues, it: 1) analyses ambitions according to the type of plan, city size, the combination of adaptation and mitigation aspects in a single strategy and membership of climate networks, 2) performs a comparative assessment of these aspects across cities, European countries and regions, and 3) identifies whether the climate targets of this representative sample of EU cities are ambitious enough to reach the Paris Agreement.
2. 1 The city sample This study is based on a sample of 327 core cities within the EU-28 (the UK was still a member of the EU when we collected our data), covered by the Cities Statistics database of the European Statistics Office (Eurostat), formerly known as "Urban Audit" (UA)  [39]. The UA database was developed with the aim of comparing data across European urban areas. The UA sample is a representative sample of currently more than a thousand core cities, larger urban zones and metropolitan areas across the EU-28. The sample has fluctuated slightly while steadily increasing over the past years and the chosen sample of 327 core cities is mentioned in the Eurostat Regional Yearbook 2011  [40]. It has been chosen because of its smaller size (as compared to the 885 cities currently belonging to the whole Cities Statistics database selection) and its representativeness.
The UA city sample is representative in size and regional distribution per country as it uses the following criteria  [41] : (i) at least 20% of the population is covered in each country, (ii) at least one city from each NUTS3 Region is included to ensure a balanced geographical distribution, and (iii) varied city sizes are considered by including large and medium (above 50,000 inhabitants) as well as small cities (below 50,000 inhabitants). Considering the most recent data on EU-28 population (513. 5 million inhabitants  [42] ), the city sample covers 128. 8 million inhabitants, that is 25. 1% of the total EU-28 population, ranging from 14. 3% in Portugal to 55. 9% in Malta. Data collection for the City Statistics is a joint effort by the Directorate-General for Regional and Urban Policy (DG REGIO), the National Statistical Institutes and Eurostat.
More information on this database can be found in the Cities Statistics database itself  [39]  and in supporting documents  [41] [42] [43]. Figure  1  presents a map of the location of the 327 core cities considered in this paper. Figure  1 : Map of the location of the 327 core cities of the Eurostat Urban Audit database  [39]  underlying this study, showing the share of larger cities per country as country shading. Larger city is defined as above 250,000 inhabitants. Data: resident population as of 1 st January of latest year available (2011 to 2017), depending on city and country from Eurostat (2019), except Fareham (UK), Spijkenisse (NL), and Croatia (Wikipedia; no data on Eurostat at the respective time of download).