dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis and peptide clustering A central feature of MEMHDX lies in its ability to classify peptides based on their respective HDX behaviors. MEMHDX automatically summarizes and displays the statistical results by means of a 'Logit' representation. Two adjusted P-values are calculated by the software and define the position of each peptide in the 'Logit' plot (Fig. 2A ). On one hand, the magnitude of DD-associated P-value defines the magnitude of difference of deuterium uptake between the two states investigated, while on the other hand, the change in dynamicsassociated P-value describes the timewise evolution of HDX behavior between states. Considering the magnitude of DD only, the significance increases (i. e. P-value decreases) as the peptide position moves from right to left on the plot, due to the greater magnitude of change observed between states.
Similarly, the significance of change in dynamics increases as the peptide position moves from top to bottom. Deuterium uptake curves for selected peptides are displayed in Figure  2B  to illustrate the clustering performed by the software. Statistically non-significant peptides are clustered in the top righthand corner (e. g. peptides 349-365). Statistically significant peptides for the magnitude of DD only are found at the top of the plot (e. g. peptides 395-401), whereas those significant for the change in dynamics only are plotted on the far right (not applicable to this dataset). Those peptides which are significant for both states are located in the center of the plot. Comparing peptides  395-401, 499-506 and 246-260  allows us to easily visualize the effect of a change in the magnitude of DD-or the change in dynamics-associated P-value.
Peptides 395-401 and 499-506 possess the same magnitude of DDassociated P-value, but are separated by their differing change in dynamics-associated P-value. This difference is directly linked to the change of HDX behavior observed in peptides 499-506 in the presence of calcium, which is not observed in the other peptide (Fig. 2B ). Conversely, peptides 499-506 and 246-260 differ only by their magnitude of DD-associated P-value because the magnitude of difference between states is less for the former than the latter. MEMHDX enables the user to color code all peptides in each state based on their respective HDX behavior (Fig. 2A ). Dynamic peptides in two states are colored in gray (e. g. peptide 587-594), whereas those which are dynamic in one or the other state are colored in red or blue [Apo-RD (e.
g. peptides 633-639) in red, and Holo-RD (e. g. peptides 499-506) in blue herein]. Non-dynamic peptides are colored in green (e. g. peptide 395-401). Results generated with MEMHDX were compared with those manually obtained for RD  (O'Brien et al. , 2015). Excellent agreement was observed between the automated software and the manual approach. However, the speed of analysis was greatly increased by MEMHDX and the validation completed in one full day (10-15 s to generate the statistical results by the software, followed by several hours of user interpretation), compared with up to 2 weeks by manual analysis. Visually speaking, the difference in deuterium uptake between the two states was too small to be considered as significant in the manual analysis of some peptides. With the introduction of two independent P-values, the sensitivity of MEMHDX over the manual approach is enhanced.
This is the case for peptides 203-209 of RD, which incorporates more deuterium in the Apo-state than in the Holo-state, indicative of a calcium-induced protective effect (Fig. 3B ). In addition, the statistical results are reported on the differential uptake plot. Statistically non-significant peptides are highlighted in white, whereas those displaying significant P-values in gray. The second visualization tool allows the mapping of the HDX results on the 3D structure of the protein (Fig. 3C  and D ). The user can either download a PDB file or enter the Protein DataBank identifier of the protein. In this scenario, MEMHDX will automatically retrieve and display the structure from the protein databank archive. Differential exchange behaviors will be color-coded to distinguish modified from unmodified regions (Fig.
Conclusions MEMHDX is a statistical tool designed to aid in the analysis of large HDX-MS datasets. The initial concept of the software was to complement the HDX-MS solution provided by Waters Corporation, which is to-date, the only complete automated pipeline commercially available. The use of two distinct P-values in the handling of HDX-MS data introduces a novel way to interpret and classify HDX results. This was successfully demonstrated here with the analysis of the RD protein from the adenylate cyclase toxin. In this regard, the validation of the complete RD dataset was significantly expedited, taking only 1 day with MEMHDX, compared with up to 2 weeks when performed manually. The current version of the software allows for the comparison of two unique conditions using only one unique charge state.
Funding This work was supported by the  Institut Pasteur (Projet Transversal de Recherche , PTR# 451  and PasteurInnov TransCyaA 2015), the  Centre national de la Recherche Scientifique (CNRS UMR 3528, Biologie Structurale des Processus Cellulaires et Maladies Infectieuses ;  CNRS USR 3756 ) and funding from the  Investissements d'Avenir  through the  CACSICE project.
Related Work Practical solutions for rule-based MWE identification include tools like jMWE  (Kulkarni and Finlayson, 2011) , a library for direct lexicon projection based on preexisting MWE lists. Finite-state transducers can also be used to take into account the internal morphology of component words and perform efficient tokenization based on MWE dictionaries  (Savary, 2009). The problem of MWE identification has also been modeled using supervised machine learning. Probabilistic MWE taggers usually encode the data using a begin-inside-outside scheme and learn CRF-like taggers on it  (Constant and Sigogne, 2011; Schneider et al. , 2014). The mwetoolkit  (Ramisch, 2015)  provides command-line programs that allow one to discover new MWE candidate lists, filter them and project them back on text according to some parameters.
MWE Identification Our MWE identification algorithm uses 6 different rule configurations, targeting different MWE classes. Three of these are based on data from the training corpus, while the other three are unsupervised. The parameters of each configuration are optimized on a held-out development set, consisting of 1 ⁄9 of the training corpus. The final system is the union of all configurations. 1  For the 3 supervised configurations, annotated MWEs are extracted from the training data and then filtered: we only keep combinations that have been annotated often enough in the training corpus. In 1 When there is an overlap, we favor longer MWEs. other words, we keep MWE candidates whose proportion of annotated instances with respect to all occurrences in the training corpus is above a threshold t, discarding the rest.
DET_N errors 8 false negatives were compositional time adjuncts (e. g. this morning, this season). False positives are mainly cases that seem inconsistent between training and test data concerning frequent quantifiers (e. g. a lot, a bit, a couple). Noun compounds (two or more Ns in a row) account for a significant proportion of MWEs in the training corpus ( 601 /4232 = 14%) and an even larger amount of the testing corpus ( 203 /837 = 24%). The NOUN 2 rule sets were essential to obtaining good results. If we remove NOUN 2 from our system, its global performance would drop to a fuzzy F 1 = 33. 79%. The domain of the corpus does not seem to have a great influence on our method's performance. Our lowest performance is on the Reviews subcorpus (fuzzy F 1 = 49. 57%) and our best performance is on TED (fuzzy F 1 = 56.
Stative verbs correspond to the large majority of verbs in the dataset. This is magnified by the nature of the corpus: reviews tend to use stative verbs to talk about product characteristics, tweets often use them to describe the state of the author. While very frequent, stative verbs are also difficult to disambiguate: most false negatives were tagged as change verbs while most false positives were tagged as social verbs. Some distinctions seem extremely hard to make, specially for less frequent supersense tags like contact/motion and perception/cognition.
Conclusions and Future Work We developed a simple rule-based system that was able to obtain competitive results. Its main advantage is that it was very quick to implement in the context of the generic framework of the mwetoolkit. The system is freely available as part of the official mwetoolkit release. 5 The main limitation of our system is that it cannot properly take unseen MWEs into account and generalize from seen instances. Moreover, most of our rule sets are highly language dependent. Ideas for future improvements include:   • Adding specific rules for verb-particle constructions, probably based on a lexicon of idiomatic combinations. Gold tag P E R S A R T I C O M M A C T G R O U T I M E C O G N A T T R L O C A E V E N s k i p p • Replacing the CONTIG method by a sequence tagger for contiguous MWEs (e.
Acknowledgments This work has been funded by the  French Agence Nationale pour la Recherche  through projects  PARSEME-FR  ( ANR-14-CERA-0001 ) and  ORFEO  ( ANR-12-CORP-0005 ), and by  French-Brazilian cooperation projects CAMELEON  ( CAPES-COFECUB #707/11 ) and  AIM-WEST  ( FAPERGS-INRIA 1706-2551/13-7 ). Part of the results presented in this paper were obtained through research on a project titled " Simplificação Textual de Expressões Complexas ", sponsored by  Samsung Eletrônica da Amazônia Ltda. under the terms of Brazilian federal law No. 8. 248/91.
The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/ RNA isolation, taxa characterization, and data visualization.
However, integrating the many disparate types of data generated from entire cities requires an interdisciplinary approach bringing together experts in engineering, public health, medicine, architecture, microbiology, metagenomics, bioinformatics, biochemistry, data science, functional genomics, virology, architectural design, and the built environment. Thus, in order to bridge these disciplines and work across cities with global standards and approaches, in 2015, we initiated the Metagenomics and Metadesign of Subways and Urban Biomes (MetaSUB) International Consortium. Beyond the taxonomic classification and stratification of known and novel species that span a city, these data can be mined for other purposes. This includes characterizing novel markers for antimicrobial resistance (AMR), as well as biosynthetic gene clusters (BGCs), which can discern and validate the small molecules encoded by these organisms' genomes and dynamically regulated transcriptomes  [19, 20].
inaugural meeting of the MetaSUB Consortium The Inaugural MetaSUB Meeting was sponsored by the Alfred P. Sloan Foundation and held on June 20, 2015, at the New York Genome Center (NYGC), following the Microbes in the City Conference on June 19, 2015, at the New York Academy of Sciences. This represented the first gathering and open meeting of the MetaSUB International Consortium. We had 30 speakers representing a wide array of expertise and disciplines, from microbiology and genomics to building/subway design and metadata collection. The meeting had 139 registrants from over 14 countries, and many speakers and attendants noted that this represented the "coming out of the shadows" of the microbes in our cities and the beginning of using these data to make cities quantified and more integrated  [24, 25].
The meeting also featured a key discussion about the promises and pitfalls of metagenomics analysis, including a discussion of some of the first metagenomic data collected in NYC, Hong Kong, and Boston subways  [1] [2] [3] 26]. To organize the goals of the Consortium, five working groups convened, led by five moderators. Genome Center. The summaries of these discussions have been outlined below and are also posted on the study's website (www. metasub. org). The results of these working group discussions have built the foundations of MetaSUB, as each working group dealt with a key challenge the MetaSUB consortium will have to address with this global study. These working groups will evolve into committees that members of the consortium can sit on and lead. All the work by these committees will be reviewed by an external advisory board (EAB) made up of experts in the fields of bioinformatics, virology, microbiology, immunology, genomics, and mass transit.
Sample collection and metadata Any large-scale collection effort requires a detailed protocol and test of best practices, which was a key focus of the meeting. The discussion highlighted a number of challenges and suggestions related to sampling methods, standardization of protocols for data collection and processing, and validation and comparability of metadata. Also, some of the questions regarding MetaSUB collections spanned a range of unknown aspects of urban microbiomes. This ranged from the regularity of metagenomic species compositions (across time and space), the sensitivity of a surface to harboring bacteria or DNA in the context of weather, temperature, humidity, usage, and other metadata, the thresholds for persistence, the biochemical and biological functions of organisms as a function of their location, and the different methods for air vs.
Bioinformatics analytics With the advent of citizen science, crowdsourcing, and participatory international coordination of sampling, the ability to collect large metagenomic datasets from our surroundings is no longer the limiting factor in scientific discovery and exploration of the microbial landscape in urban environments  [35]. As the tide has shifted, key questions about ideal methods to analyze and process the data have become paramount, and multiple analytical challenges have arisen for computing, processing, and sharing of metagenomic data  [12]. Addressing these analytical challenges has implications for how we understand and interpret the diversity and complexity of urban biomes. The bioinformatics working group discussed current analytical challenges facing the consortium and suggests protocol adaptations as technologies improve.
These reference sequence sets provide ideal test cases for understanding technical issues with sequencing data or algorithms (given the known proportions of various bacteria) and supply useful benchmarks for consortium members during the development of new tools  [1]. More importantly, these references serve as standards for developing clear metrics on how to evaluate and interpret results from metagenomic analyses from large numbers of people  [35]. Fig. 1  Performance of new metagenomics enzyme cocktail. We used replicate samples for a range of different extraction kits with (red) and without (blue) the polyzyme mixture (x-axis) and examined the yield of DNA from the extraction (y-axis). Samples include Halobacillus (Hal), Escherichia coli (EC), soil samples, yeast, sea ice, and a wine trub for the Omega Mullusc Kit (OM), Norgen Soil Kit (N), and the Standard CTAB-Phenol protocol (ST).
Code sharing and transparency are important features of reproducibility, and open source tools such as R and Bioconductor exist for creating processing pipelines. It is important to create transparent workflows that can be cloned and deployed on remote machines so the analyses can be reproduced with minimal effort  [37]. Furthermore, electronic notebooks with protocols can be linked with publications. Having version control or Docker-style tracking encourages collaboration and enables best practices to spread through the community of developers and scientists. Other large-scale consortiums such as The Cancer Genome Atlas (TCGA) and Human Microbiome Project (HMP) have successfully navigated these issues and provided a model for creating accessible data portals with community-based tools  [38, 39].
Also, consideration of other logistical challenges related to the interpretation and release of the data and analysis are required, regarding city, transit, and health authorities in each city. Some cities may wait until data are published before deciding to comment, but nonetheless, all data and manuscripts should be shared with city officials beforehand, and this has been the standard applied thus far  [1]. Also, three new guidelines have been implemented as part of MetaSUB: all data and sequences collected will be given to the local authorities for a "Right to First Review," before any publication or presentation of these results to the public, due to the potential sensitivity of some of the species that may be discovered. Protocols will follow internationally recognized standards for quality control and sequencing rigor from the US Food and Drug Administration's (FDA) Sequencing Quality Control Consortium (SEQC) and the Earth Microbiome Project (EMP) as outlined above.
Collection Establishing a coordinated, global data collection is slated to begin on June 21, 2016, to match and parallel the Global Ocean Sampling Day (OSD)  [46, 47]. The will begin the seasonal of cities around the world for the next five years, matching at least the once-a-year frequency of (OSD), but each season if possible for each city. Notably, this time frame overlaps both the Brazilian and Japanese Olympics, generating the profile of a city's "olympiome," representing a first-ever sampling of cities before, during, and after a global human migration event. Sampling will be done to include: air in public parks, surfaces in subway or transit system kiosks, park water fountains, and adjacent ocean water (through OSD). Also, a subset of 50 samples will undergo some single-cell and cross-linked read capture (Hi-C), and long-read sequencing for improved species resolution.
Analysis There will be ongoing work for testing, sharing, and advancing computational methods. Also, we will link to and curate a global database of detected BGCs as well as antimicrobial resistance (AMR) markers. We will also use rarefaction plots and Shannon diversity indices to create cross-kingdom (plant, animal, bacterial, viral) measures of diversity between climates and cities. Finally, we will look for any evidence of horizontal gene transfer (HGT) in the samples when comparing to newly sequenced genomes from local areas.
Design These methods of collection that characterize many types of surfaces may have an impact on future designs and types of transit systems. There, collections include samples from many types of surfaces, including plastic, cloth, metal, ceramic, glass, and stone. In addition, we will collect metadata about temperature, humidity, volatile organic carbons (VOCs), air components, and other environmental parameters. A long-term goal of the consortium would be to design surfaces to enhance the "good bacteria" present such that they could outcomplete the "bad bacteria" and make the surfaces better for human occupancy and transit.
Education Using our methods for outreach, education, and handson training is one of the key components of the consortium. We have already engaged hundreds of students in cities associated with the MetaSUB Consortium study, and we intend to maintain this educational component. This will include some citizen science outreach for high school, college, graduate, and medical students, as well as credits for a related course (microbiology, ecology, genetics, genomics) during the sampling expeditions ("swabventure"). Also, we have started a study abroad and lab exchange program so that members of the Consortium can visit each other's labs and sites to learn about genomics, informatics, or architecture. Indeed, we already have three artists in residence for the Consortium, all of whom work to visualize the microscopic and metagenomic world around us.
Conclusion Working together, we are building an unprecedented, global metagenomics dataset and molecular portrait of the urban microbiomes that we all share. Our collective efforts aim to help current and future work in city planning, urban design and architecture, transit systems, public health, ecological studies, genome technologies, and improved understanding of cities. We aim to use the lessons of the preliminary studies to highlight the richness of the microbial ecosystems of cities, train new students in best practices and methods for metagenomics and microbiome analysis, and ensure the greatest utility and benefit of these data. These data will also provide a novel resource to discover new biochemical pathways, sources of antimicrobial resistance, new methods of metagenomic design, and new antibiotics that are created by the ecosystem of microbes that have evolved to live among us (and we among them).
Acknowledgements We would like to thank the  Alfred P. Sloan Foundation  ( 2015-13964 ) and in particular,  Paula Olsiewski , for her insightful guidance during the founding of the  Consortium, and for the Alfred P. Sloan Foundation 's generous support in funding the MetaSUB planning meetings and conferences. Moreover, the  Bill and Melinda Gates Foundation's Grand Challenges Exploration  grant helped generate the sequence data for the first global city sampling day (CSD). We also wanted to thank  Jeff Zhu  and the  Clinical and Translational Science Center (CTSC). We would also like to thank the  Irma T. Hirschl  and  Monique Weill-Caulier Charitable Trusts , the  WorldQuant Foundation , the  Bert L and N Kuggie Vallee Foundation , the  STARR Consortium  ( I7-A765 ,  I9-A9-071 ), and support from the  National Institutes of Health  ( F31GM111053 ,  R01NS076465 , and  R25EB020393 ).
Ecosystem service-support tools are commonly used to guide natural resource management. Often, empirically based models are preferred due to low data requirements, simplicity and clarity. Yet, uncertainty produced by local context or parameter estimation remains poorly quantified and documented. We assessed model uncertainty of the Revised Universal Soil Loss Equation -RUSLE developed mainly from US data. RUSLE is the most commonly applied model to assess watershed-level soil loss. We performed a global sensitivity analysis (GSA) on RUSLE with four dissimilar datasets to understand uncertainty and to provide recommendations for data collection and model parameterization. The datasets cover varying spatial levels (plot, watershed and continental) and environmental conditions (temperate and tropical).
RUSLE is a deterministic and empiricalbased model built with parameter estimates from mostly the US-based erosion studies, which might not be appropriate if applied outside the range of original estimates or using non-plot-level data (e. g. remotely sensed data)  (Wischmeier & Smith 1978; Renard et al. 1997; Nearing et al. 2000). Despite these limitations, practitioners and researchers increasingly apply RUSLE at larger spatial scales and under different environmental conditions than its original intent (e. g. Biesemans et al. 2000; Lu et al. 2004; Bewket & Teferi 2009; Falk et al. 2009; Ligonja & Shrestha 2013; Galdino et al. 2015; Naipal et al. 2015; Panagos, Borrelli, Poesen et al. 2015)  and it has been incorporated into ecosystem services decision-support tools, such as InVEST  (Sharp et al.