dataesr/scientific-paragraphs-categorization huggingface dataset:
RRNPP_detector reveals thousands of candidate RRNPP QSSs in complete bacterial chromosomes, plasmids and phage genomes To illustrate RRNPP_detector, we launched it against the 4974 complete genomes of Firmicutes and the 32919 complete genomes of Viruses Firmicutes available on the NCBI Assembly Database (Fig. 2 ). We describe how such analyses can be easily done with the practical example of Viruses   S1 ). In addition, we report the identification of 3529 'conservative' candidate RRNPP QSSs that we hereafter characterize in more depth (Table  S2 ). We first used the Phaster API  (40)  for each chromosome or plasmid carrying at least one QSS to identify whether the genomic coordinates of the QSS(s) fell within prophage regions (genomes of lysogenic phages inserted within host genomes) and should be thus considered as viral instead of bacterial.
Post-processing functional prediction of candidate RRNPP QSS reveals 50 putative QSSregulated biosynthetic gene clusters QSSs with a one component receptor module tend to be located nearby their target genes  (47) (48) (49)  and RRNPP QSSs are no exceptions to this trend, especially not MGE-encoded RRNPP QSSs (as shown in Fig. 1 ). antimicrobial compounds, because only a collective production may bring such molecules to the concentration levels required to exert a significant effect on the microbial community  (50). Consistently, many natural product biosynthetic gene clusters (BGCs) have been demonstrated to be controlled by a QSS located in their genomic vicinity, be it a molecule-based QSS  (48, 49)  or a peptide-based QSS (Fig. 5A )  (24, 25). To illustrate how the output of RRNPP_detector can be coupled with functional insights, we chose to focus on this practical example of putative QSSregulated BGCs.
CONCLUSION AND FUTURE PERSPECTIVES RRNPP_detector is able to predict a wide range of candidate RRNPP QSSs in chromosomes or in mobile genetic elements (e. g. plasmids, phages) of gram-positive bacteria (e. g. Firmicutes). Because RRNPP_detector does not rely on sequence homology to known QSS components to detect candidate QSS gene families, it can detect novel families of RRNPP QSSs, as exemplified in this article. Moreover, as RRNPP QSSs tend to regulate adjacent genes in a density-dependent manner, post-processing genomic context analyses can couple RRNPP_detector output with functional insights, as shown in this article with the specific example of putative QSS-regulated BGCs. RRNPP_detector presents a first step towards the large-scale identification of peptide-based QSSs. However, this software is dedicated to the sole detection RRNPP QSSs and we already anticipate to expand its usage to peptide-based QSSs with two-component systems.
We believe that our work will unlock new biological knowledge regarding peptide-based biocommunication and will reveal many novel density-dependent decision-making processes in bacteria, plasmids and bacteriophages that could likely transform our views of microbial adaptation and bacteria-MGE coevolution. Importantly, Firmicutes represent with Bacteroidetes the most prevalent phyla in human gut microbiomes  (54) , which makes the use of RRNPP_detector particularly relevant against human-associated metagenomics-assembled genomes or mobile genetic elements (e. g. from the human MGE database  (55)  or the Gut Phage Database (  56 )) to infer density-dependent behaviors that may take place within human intestinal microbiomes, plasmidomes and viromes. Each genomic context corresponds to the representative QSS of an experimentally characterized RRNPP-type QSS family.
/github. com/TeamAIRE/RRNPP_detector/readme. md. We report the identification of 6467 'permissive' candidate RRNPP QSSs, distributed across 1074 different taxa (TableS1). Some receptors of these 'permissive' candidate QSSs notably include the RopB regulator of virulence in in the readme file of RRNPP_detector: https:/Streptococcus pyogenes (27), as well as additional members of the AimR, AloR13, NprR, PlcR, PrgX, QsrB, RapA and Rgg2 experimentally-validated families that did not pass the 'conservative' thresholds of RRNPP_detector (Table S1). Interestingly, 105 'permissive' RRNPP QSSs have been detected in 97 isolated genomes of viruses, of which 89 bacteriophages and 8 giant eukaryotic viruses of the Mimiviridae taxonomic family. Accordingly, some bacteriophage genomes are found to encode multiple 'permissive' candidate QSSs (up to 4 in Brevibacillus phage SecTim467) (Table S1).
Outline and contributions Our study makes the following contributions: • We present the first parallel MapReduce algorithm to efficiently derive a set of short distinguishing sequences for all pairs of states of a nondeterministic FSM. • We provide a theoretical analysis of the communication cost of proposed methods in MapReduce model through a grounded theory. • We evaluate the performance of the proposed algorithms through extensive experiments using a variety of large-scale FSMs datasets. The remainder of the paper is structured as follows. Section 2 includes the necessary technical definitions and a brief introduction to the MapReduce computational model. Section 3 presents the related works as well as a survey of the Exact Algorithm for deriving DSs from nondeterministic FSMs. Section 4 presents and analyses the proposed MapReduce version of the Exact Algorithm to derive a set of shortest DSs for all pairs of states.
MapReduce model of computation Works based on MapReduce were recently introduced as an optimal parallel model to compute the intersection  [49]  and the composition  [50]  operations of FSMs. In this work, we implement the first MapReduce version of the EA to derive a shortest DS for each pair of states if exists from a large-scale complete observable nondeterministic FSM. MapReduce  [51]  is considered as one of the most prominent programming models for processing scalable problems. Nowadays, Hadoop  [52]  offers the most popular opensource framework written in Java for implementing MapReduce algorithms. Authored by Apache Software Foundation, the Hadoop project includes modules enabling reliable and scalable distributed computing. It presents several advantages, such as scalability, flexibility, cost-effectiveness, organized architecture, and resilience to failure.
Data generation method We randomly generated a large variety of FSMs data sets in two phases based on different combinations of the input alphabet size |I|, the output alphabet size |O|, the number of states |S|, the degree of nondeterminism D and the range R. First, we randomly generated complete deterministic finite automata using the method described in Abbadingo One competition (see http:// abbad ingo. cs. nuim. ie). Then, we randomly selected {(D × |S| × |I|)} transitions where |D| equals 20, 40, 60 or 80. Finally, we add to the obtained FSM nondeterministic the observability property, by generating randomly ( R×|O| 100 ) number of replications for each selected transition where the observ- ability range R is 20-30, 40-50, 60-70 or 80-90. Table  1  summarizes all the datasets used in our experiments along with their respective properties.
Communication cost analysis The communication cost is equal to the total number of key-value pairs sent from the map phase to the reduce phase. It can be optimized by minimizing the replication rate parameter i. e. the number of input copies sent to the reducers. The following table summarizes the relationship between the FSM size and the communication cost in the MapReduce algorithm for different datasets: Since we have introduced three mapping methods in the intersection step of our approach, we present the communication cost for the considered datasets in Fig. 5. In Fig. 5 , the obtained results show clearly that the mapping based on input alphabet outperforms the other mapping methods in terms of the communication cost. This is due to the fact that the number of the transition copies sent to the reducers using this method is less than the other ones as proved formally in Proposition 4.
The comparison of the three MapReduce methods proposed previously in the intersection step of our solution with a multi-threads parallel implementation based on OpenMP of the sequential Exact Algorithm (EA) step on a multicore CPU  [66]  using OMP4J  [67]. OMP4J is an open-source implementation of OpenMP and is used as a preprocessor for Java. The experiments confirm previous results, in Fig. 11  depicts the speedup for different datasets from Table  2. It can be seen that the mapping method based on input alphabets (Symbol) is the best and effective for different datasets. Figure  12  shows the speedup according to the ranges of nondeterminism R 50-60, 60-70, 70-80consistent performance gains that the speedup of the alphabets-based method is better than the three other methods under different circumstances.
Next, the quality of the library was assessed using the Agilent High Sensitivity DNA Kit on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, United States). Quantification was done using Qubit (Qubit dsDNA High Sensitivity Assay Kit, Invitrogen, Carlsbad, United States). Sequencing was performed at 6 pM with 20% phiX on the Illumina MiSeq platform at 2 × 301 bp paired-end V3 chemistry according to the manufacturer's specifications. Sequencing data determined in this study are available at NCBI BioProject PRJNA668126 at https://www. ncbi. nlm. nih. gov/bioproject/PRJNA668126. nifH Amplicon Sequencing Data Analysis Pipeline Quality control of MiSeq nifH amplicon paired reads was done using the TaxADivA (TAXonomy Assignment and DIVersity Assessment) perl wrapper script  (Gaby et al.
, 2018) , as detailed in  Lesser et al. (2018)  with the following settings: -y -k -r 26 -l 29 --keepc4 -g 500. Subsequent to TaxADivA-based read merging, quality control and filtering, samples with <500 sequences were removed from subsequent analyses  (Lesser et al. , 2018). The remaining 41 samples (including the negative control) were combined into one fasta file and minimum entropy decomposition (MED) was employed at default settings to cluster reads into oligotypes in an unsupervised fashion  (Eren et al. , 2015). Representative sequences of the resultant 197 final MED nodes were translated to the protein level using FrameBot  (Wang et al. , 2013). As a reference dataset, the "nifh_prot_ref. fasta" file included with the FrameBot program was used. To separate nifH-like genes belonging to clusters IV and V, from true functional nifH genes belonging to clusters I-III  (Raymond et al.
, 2004) , we employed a threecriteria model sensu  (Angel et al. , 2018). First, after alignment in MAFFT  (Katoh et al. , 2002) , representative sequences for each final MED node were analyzed using a Classification and Regression Tree (CART) model  (Frank et al. , 2016). Second, we constructed an Evolutionary Placement Algorithm (EPA) Tree  (Berger et al. , 2011)  using the corresponding RaxML implementation  (Stamatakis, 2014). Minimum entropy decomposition node representatives were aligned with a reference dataset from the NifMAP pipeline  (Angel et al. , 2018)  that included representatives of all nifH-like gene clusters. Query sequences within this combined reference alignment were then placed on the reference tree ("RAxML_bipartitions. MultipleOriginal. tree" also from the NifMAP pipeline).
Third, using node representative sequences as queries, a BLASTP  (Camacho et al. , 2009)  search was conducted against the nifH gene database taken from the Zehr lab website ( 1 accessed October 2 nd 2020). In addition, we looked for matches in the NCBI non-redundant (nr) protein database (excluding uncultured/environmental sample sequences and automatically generated model annotations) to further confirm the results of the initial BLASTP search. In case of mismatching results but similar percentages of protein identity between the two queries, the result to the curated Zehr nifH database was preferred. In order to determine the cluster designation for the representative sequences of all final MED nodes, a node was considered to be a true nifH-representative (i. e. , belonging to clusters I-III) if at least two of the three criteria produced consistent results.
A maximum likelihood phylogenetic tree of all final OTUs was constructed using RAxML from 20 starting trees using the PROTGAMMAAUTO method to determine an optimal substitution model (LG + G in this case). Additionally, 1,000 bootstrap trees were calculated to support the bipartitions of the best ML tree. Trees were visualized using the R package ggtree  (Yu et al. , 2017). Statistical analyses within and between sites and species as well as plotting were done in R using the packages phyloseq, ape, and vegan (R Development Core  Team, 2015). Pairwise PERMANOVA tests within and between coral host species for a given sampling site were done using the custom R function "pairwise_adonis. R" 2  based on a Bray-Curtis dissimilarity matrix. Comparisons of Shannon diversity, inverse Simpson's Index, and the Chao1 estimator between the two sampling sites over all species were based on Wilcoxon rank sum tests.
damicornis, and for samples of G. fascicularis. Taxon-Specific nifH Quantitative PCR Patterns of relative abundance based on nifH amplicon sequencing data were confirmed using qPCR (quantitative polymerase chain reaction). For this, OTU_31 was chosen due to its pervasive presence among P. damicornis samples. A specific primer pair for OTU_31 was designed based on its MED representative sequence using Primer-BLAST  (Ye et al. , 2012). The OTU_31 primer pair (forward: 5 -TTGCGATCCCAAAGCTGACT-3 ; reverse: 5 -GCCGGACTCAACACAGAGAA-3 ) amplifies a 157 bp long region of the nifH amplicon determined from the MiSeq sequencing. Primer products were checked on a 1. 2% agarose gel for single bands after PCR amplification using the following protocol: 90 • C for 10 min, followed by 40 cycles of 95 • C, 57 • C, and 72 • C for 30 s each, and a final extension step of 72 • C for 10 min.
, 2017)  (Figure  2  and Supplementary Data 1). The majority of Gammaproteobacteria OTUs were identified as Alteromonadales, some closely matched Pseudomonadales and Vibrionales (Figure  2 ). All Alphaproteobacteria were annotated as Rhodobacterales according to BLAST in discordance with the Zehr database that annotated all OTUs except OTU_46 as Sphingomonadales. Notably, two OTUs belonging to the order of Rhizobiales were detected in the negative extraction (OTU_70: 65. 52% of sequences in negative control, OTU_77: 27. 19% of sequences in negative control; Supplementary Data 1), thus considered contaminants (red font annotation in Figure  2 ), and removed, leaving 23 cluster I OTUs for downstream analyses. Only one OTU (OTU_21) was assigned to nifH cluster II (anaerobe Mo-Fe nitrogenases), a bacterium of the class Opitutae.
We assayed all samples of P. damicornis using the OTU_31 specific primer pair and the 16S rRNA gene primer pair (see Methods). The comparison of fold change values relative to 16S and the relative abundance in the nifH sequencing dataset showed high correlation between qPCR and amplicon sequences (Figure  4 ) with a Pearson correlation coefficient of r = 0. 87 (p < 0. 001).
, 2004) , we found that only 14. 1% of sequences could be considered a functional nitrogenase gene in the present dataset. This is in line with what  (Lesser et al. , 2018)  and  (Angel et al. , 2018)  observed using the same primers. Notably, we found that the amount of functional nifH sequences was highly variable across individual samples, which varied widely in their overall read depths as well as the read depths of cluster I-III nifH sequences. After removal of all cluster IV/V homologs of nifH from the initial 88 OTUs, 27 of them were identified as belonging to true, functional nitrogenase genes. Most of these nitrogenase sequences belonged to cluster I Mo-Fe nitrogenases, while cluster II and III were only represented by one OTU each. These cluster I-III nifH OTUs covered a wide range of phylogenetic backgrounds, with the commonly found groups of diazotroph associates of corals being represented by Cyanobacteria as well as Alpha-and Gamma proteobacteria.
ACKNOWLEDGMENTS We would like to kindly thank the  KAUST BioScience Core Laboratory (BCL)  for assistance with sequencing. We are grateful to the local populations for access to their reef, and to the  National Research Institute , the  Milne Bay Provincial Research Committee , the  New Ireland Provincial Administration , and the  Conservation and Environment Protection Authority of Papua New Guinea  and the  University of Papua New Guinea  for permits. Thanks to the crew of the  R/V Alis  and of the diving boat Chertan.
The classification algorithm running on each sensor consists of (1) a deep neural network and (2) a distributed consensus protocol that merges the classification results of neighboring nodes and outputs a final label to the events occurring in real-time. The long-term objective of the work is to physically implement the proposed approach in the Eixample district in the city center of Barcelona to be able to identify different noise sources, which would allow an objective survey of the acoustic comfort of the citizens in the street. Initially, the system was tested using the UrbanSound 8k dataset under laboratory conditions and, in this contribution, it has been validated with data from real operation simultaneous recordings obtained in the city center of Barcelona. Experimental results validate the feasibility of the proposal and justify the usage of physical redundancy to improve the classification accuracy of acoustic events.
The study was conducted on the PATH train that runs between New Jersey and New York City. Four data collection methods were used: focus groups, in-depth interviews, and online and on-site surveys. Findings indicate that the reasons why people use headphones vary depending on how they use them-whether they play audio or wear them without playing audio. A majority of respondents begin using headphones when they depart from their homes and discontinue using headphones when they reach their homes or places of employment. While using headphones, people engage in various activities, including relaxing and observing surroundings. These activities vary depending on whether people are playing audio or not. Respondents reported that the quality of their experiences on transit improves when they listen to audio and declines when they do not.
g. , from acousticians, artists or sociologists). A portion of this course will be illustrated through hands-on activities during the symposium. The lockdown enacted by governments worldwide following the SARS-CoV-2 pandemic caused a substantial decrease in mobility accompanied by anecdotes about a quieting of the city and an increase of natural sounds. We evaluated the change of the urban acoustic environment, comparing SPL and ecoacoustic indices before and during lockdown. We analyzed data from 22 automated sound recording devices located in different land use types in Bochum, Germany. In addition to the L Aeq,24h , we explored the normalized difference soundscape (NDSI) and bioacoustic (BIO) indices. BIO measures the total sound activities of biological sources 2-8 kHz, while the NDSI quantifies the proportion of anthrophonic to biophonic sounds.
David Moore, Chris Cogswell and Steve McDaniel Skyhub, CA, United States Sky Hub is an open-source citizen science project with the goal of studying atmospheric phenomena through observational science. It employs a network of globally distributed "Tracker" nodes built using readily available off-the-shelf hardware. A Tracker combines an Nvidia Jetson-embedded computer, a range of sensors, imaging equipment and sophisticated open-source software to intelligently monitor and identify atmospheric events. A key part of the Sky Hub system going forward will be the incorporation of an acoustic sensor suite to monitor the local soundscape. Data from the sensors will be fused to provide a detailed picture of activity. The goal with the acoustic sensor suite is to provide the capability to cover as wide a frequency range as possible.
g. , horizontal vegetation (low growing plants on ground surface, land use information) and vertical vegetation ("green façades"). This simplified model (regarding other expert models such as PE, TLM, FDTD, etc. ) is dedicated to engineering purposes (i. e. , impact studies) and is an open-source tool (calculation method + GUI software). It is called NoiseModelling. Thus, this poster presents the basic principles and steps for using NoiseModelling (issued from easily reachable documentation), next introduces a case study (on a district of Nantes city, France) and then gives some examples of results in terms of an acoustic Key Performance Indicator (KPI), i. e. , L den indicator expressed in global dB(A) and for some specific third octave bands (e. g. , L den @500 Hz and @8000 Hz in this study).
Gustave Eiffel University, France In light of the growing concern about the adverse effects of noise pollution on health, a better understanding is needed of the relationships between urban transport and individual exposure. To improve the scientific community's modeling capabilities specific to this issue, we are proposing a noise exposure modeling framework that uses agent-based activity, multi-agent travel simulation and a European standardized noise emission and propagation model. Based on two open-source software packages, MATSim and NoiseModelling, this framework aims to simulate the spatiotemporal distributions of daily individual activity and road traffic noise. The proposed approach makes it possible to use all the tools and methods proposed in the NoiseModelling software by importing MATSim outputs, therefore, taking full advantage of the development work carried out within the two communities.
Acknowledgments: The Organizing Committee and the Scientific Committee are grateful to all participants and volunteers of the  Urban Sound Symposium 2021  and in particular to the volunteers who supported the development of the online event and side activities:  Cleopatra Moshona ,  Christopher Trudeau ,  Valentin Le Bescond ,  Edda Bild ,  Nicola Di Croce. The Organizing Committee also would like to thank all the panelists who contributed to the panel discussions during the Symposium:  Brigitte Schulte-Fortkamp ,  Francesco Aletta ,  Juan Bello ,  Catherine Lavandier ,  Susanne Moebus ,  Maryse Lavoie ,  Andy Chung ,  Efstathios Margaritis ,  Trond Maag ,  Oscar Breugelmans ,  Judicael Picaut ,  Trevor Cox ,  Papatya Nur Dökmeci Yörüko glu ,  Christine Kerrigan.
To argue and support this affirmation, a literature review was carried out. The adopted method follows the guidelines proposed in Durach, Kembro, and Wieland, 2017. It consists of four steps: (1) determine inclusion/exclusion criteria, (2) select databases, (3) define keywords and construct database queries, and finally (4) select pertinent literature and report the results. These steps are detailed in what follows. Step 1. First of all, we consider a set of well-defined inclusion and exclusion criteria described below. -Inclusion criteria: (i) papers investigating our research problem: logistics modeling and control, (ii) High-quality articles published in peer-reviewed academic journals, and (iii) papers published over the past fifty years (i. e. , from 1970 to 2020). -Exclusion criteria: papers that are written in a language other than English (i.