dataesr/scientific-paragraphs-categorization huggingface dataset:
These enzymes are responsible for the breakdown of b-1,3-glucans found as components of various fungi  [61]. Pectin degradation. Polysaccharide lyases (PLs) are enzymes that mainly degrade uronic acid containing polysaccharides such as glycosaminoglycans and pectin  [23]. They are currently classified into 23 families in the CAZy database. C. termitidis encodes a total of four genes which are all predicted to be localized extracellularly and belong to two PL families: family PL8 with three genes and family PL11 with one gene (Table  S3  in File S1 and Table  S6  in File S1). All members of C. termitidis PL8 have Table  2. Comparative analysis of the number of putative CAZy sequences in selected Clostridial species. three C-terminal SLH domains, which are putatively responsible for attachment to the cell wall.
2 SD above mean; lightgreen = deviation ,-2 SD below mean; dark orange =. 3 SD above mean; lightblue = deviation ,-3 SD below mean; red =. 4 SD above mean; blue = deviation ,-4 SD below mean; dark red =. 5 SD above mean; darkblue = deviation ,-5 SD below mean. Table  S6 ) Comparative analysis of predicted extracellular CA-Zymes, designated in the CAZy database, involved with lignocellulosic biomass hydrolysis within Clostridium species. Numbers below each family class indicate the number of members belonging to the specific family for the specific Clostridium specie. (XLSX).
Acknowledgments We would like to thank the reviewers for their help in improving the redaction of this paper and for pointing out Kendall's 'coupling from and into the past' method (named by Wilson). We further thank  Sandro Gallo ,  Antonio Galves , and  Florencia Leonardi  ( Numec, Sao Paulo ) for the stimulating discussions on chains of infinite memory. This work received support from  USP-COFECUB  (grant  2009. 1. 820. 45. 8 ).
Participants We recruited two groups of musician participants from the student pool at the Hanover Music University and young professionals. We furthermore recruited non-musicians in the same age range. Table  1  lists biographical and questionnaire data of each group. Participants reported no hearing impairment or neurological disorder, were aged between 18 and 40 years and right-handed. The musician participants were recruited in two groups: one group whose primary instrument was the piano (or who were professional pianists) and another group with a main instrument from the brass family (e. g. , trumpet, trombone or tuba). A further criterion for inclusion in the non-musician group was having received less than 1 year of musical training (apart from obligatory courses in primary or secondary schools).
Materials Keystroke-sound delay detection task. We used a USB keypad (Hama Slimline Keypad SK110) that interfaced through the HDI protocol with a python script. This script detected keystroke onsets and played a woodblock wave sound (duration: 63 msec) after a predetermined duration through headphones (Shure SRH440). The woodblock sound was chosen because of its relatively sharp sound onset and nevertheless being pleasant to hear. Anisochrony detection. We used a python-pygame graphical user interface that presented the sounds (using pyAudio) and the instructions. Instructions were given orally as well. The fivetone sequences were generated as follows (adapted from  [37, 41] ). The base sequence consisted of five isochronous sine wave tones of 100 ms presented with an inter-onset-interval (IOI) of 350 ms.
In some trials, the fourth tone was delayed by a certain amount but the fifth tone was always on time  [37, 41]. That is, when the tone was delayed by an amount d, the third interval was longer by d msec and the fourth interval was shorter by d msec. Synchronisation-Continuation Tapping. The synchronisation stimulus was generated offline as follows and saved to a wave file. First, we presented 4 finger snap sounds with an interonset-interval of 300 msec. Then 30 instances of the woodblock sound (the same as used during the learning part of the experiment) followed with an inter-onset-interval (IOI) of 600 msec. This was followed by a silence of 30*600 msec (the equivalent of 30 more taps). Finally, a high-pitched gong sound was used to signal the end of the trial. The sounds were played using a custom developed python experimental script, which also communicated through a HID-USB interface with the button box to register the responses.
Next, they performed a block of 10 trials, starting at a 600 ms keystroke-sound delay but then using MLP to determine the stimulus levels of the following trials. If the procedure was clear, we continued with 3 experimental blocks of 36 trials. Each experimental block consisted of 36 trials containing 6 catch trials. Catch trials are trials on which the delay was always 0 msec (regardless of the delay that was suggested by the MLP The maximum likelihood procedure was implemented in python. We made our source code freely available online on https://github. com/florisvanvugt/PythonMLP. The source code for the delay detection paradigm is furthermore available upon request (to the corresponding author). Anisochrony detection. Participants were seated comfortably and on each trial heard a sequence of five tones (see materials).
This work was supported by the  EBRAMUS ,  European Brain  and  Music ITN Grant  ( ITN MC FP7, GA 238157 ). The team '' Auditory cognition and psychoacoustics '' is part of the  LabEx CeLyA (''Centre Lyonnais d'Acoustique'' ,  ANR-10-LABX-60 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Consequently, TEs dramatically affect genome size and structure, as well as genome function; transposition has the potential to induce complex genomic rearrangements that detrimentally affect the host, but can also provide the raw material for adaptive evolution  [8] [9] [10] , for example, by creating new transcription factor binding sites  [11]  or otherwise affecting expression of nearby genes  [12]. Despite their biological importance, knowledge of TE dynamics is hindered by technical limitations resulting in the absence of certain TE families from genome assemblies. Many software packages for whole genome assembly use coverage-based heuristics, distinguishing putative unique regions from putative repetitive regions based on deviation from average coverage (e. g. , Celera  [13] , Velvet  [14] ).
TEs, as with other classes of repeats, may also induce misassembly. For example, TEs that lie in tandem may be erroneously collapsed, and unique interspersed sequences may be left out or appear as isolated contigs. Several studies have assessed the impact of repeat elements on de novo genome assembly. For example, Alkan et al. [17]  showed that the human assemblies are on average 16. 2% shorter than expected, mainly due to failure to assemble repeats, especially TEs and segmental duplications. A similar observation was made for the chicken genome, despite the fact that repeat density in this genome is lower than humans  [18]. In addition to coverage, current approaches to deal with repeats such as TEs generally rely on paired-end data  [17, 19, 20]. Paired-end reads can help resolve the orientation and distance between assembled flanking sequences, and repeat-containing reads can sometimes be placed based on uniquely anchored mates.
DNA from all wells is then pooled and sequenced on the Illumina HiSeq 2000 platform. Data from individual wells are demultiplexed in silico according to the barcode sequences. Synthetic long-reads are then assembled from the short reads using an assembly pipeline that accounts for properties of the molecular biology steps used in the library preparation (see Supplemental Materials in File S1). Because each well represents DNA from only *200 molecules, even identical repeats can be resolved into synthetic reads as long as they are not so abundant in the genome as to be represented multiple times within a single well. We applied TruSeq synthetic long-read technology to the fruit fly D. melanogaster, a model organism with a high-quality reference genome, including extensive repeat annotation  [29] [30] [31].
Assessment of assembly content and accuracy Assembly length and genome coverage metrics. To perform de novo assembly, we used the Celera Assembler (version 8. 1)  [13] , an overlap-layout-consensus assembler developed and used to reconstruct the first genome sequence of a multicellular organism, D. melanogaster  [25] , as well as one of the first diploid human genome sequences  [35]. Our Celera-generated assembly contained 6,617 contigs of lengths ranging from 1,506 bp to 567. 5 Kbp, with an N50 contig length of 64. 1 Kbp. Note that because the TruSeq synthetic long-read data are effectively singleend reads, only contig rather than scaffold metrics are reported. The total length of the assembly (i. e. the sum of all contig lengths) was 152. 2 Mbp, with a GC content of 42. 18% (compared to 41.
74% GC content in the reference genome). Upon aligning contigs to the reference genome with NUCmer  [36, 37] , we observed that the ends of several contigs overlapped with long stretches (w1 Kbp) of perfect sequence identity. We therefore used the assembly program Minimus2  [38]  to merge across these regions to generate supercontigs. All statistics in the following sections are based on this two-step assembly procedure combining Celera and Minimus2. The merging step resulted in the additional merging of 1,652 input contigs into 633 supercontigs, resulting in an improved assembly with a total of 5,598 contigs spanning a total of 147. 4 Mbp and an N50 contig length of 69. 7 Kbp (Table  1 ). We used the program QUAST  [39]  to evaluate the quality of our assembly based on alignment to the high-quality reference genome.
This program analyzes the NUCmer  [36, 37]  alignment to generate a reproducible summary report that quantifies alignment length and accuracy, as well as cataloging mis-assembly events for further investigation. Key results from the QUAST analysis are reported in Table  1 , while the mis-assembly event list is included as supplemental material in File S1. The NA50 (60. 1 Kbp; 63. 0 Kbp upon including heterochromatic reference scaffolds) is a key metric from this report that is analogous to N50, but considers lengths of alignments to the reference genome rather than the lengths of the contigs. Contigs are effectively broken at the locations of putative mis-assembly events, including translocations and relocations. As with the synthetic long-reads, the QUAST analysis revealed that indels and mismatches in the assembly are rare, each occurring fewer than an average of 10 times per 100 Kbp (Table  1 ).
To gain more insight about the alignment on a per-chromosome basis, we further investigated the NUCmer alignment of the 5,598 assembled contigs to the reference genome. Upon requiring high stringency alignment (w99% sequence identity and w1 Kbp aligned), there were 3,717 alignments of our contigs to the euchromatic portions of chromosomes X, 2, 3, and 4, covering a total of 116. 2 Mbp (96. 6%) of the euchromatin (Table  2 ; Figure  S2  in File S1). For the heterochromatic sequence (XHet, 2Het, 3Het, and YHet), there were 817 alignments at this same threshold, covering 8. 2 Mbp (79. 9%) of the reference (Table  2 ; Figure  S2  in File S1). QUAST also identified 179 fully-unaligned contigs ranging in size from 1,951 to 26,663 bp, which we investigated further by searching the NCBI nucleotide database with BLASTN  [40].
Impact of the coverage on assembly results The relationship between coverage and assembly quality is complex, as we expect a plateau in assembly quality at the point where the assembly is no longer limited by data quantity. To evaluate the impact of depth of synthetic long-read coverage on the quality of the resulting assembly, we randomly down-sampled the full *346 dataset to 206, 106, 56, and 2. 56. We then performed separate de novo assemblies for each of these downsampled datasets, evaluating and comparing assemblies using the same size and correctness metrics previously reported for the fullcoverage assembly. We observed an expected nonlinear pattern for several important assembly metrics, which begin to plateau as data quantity increases. NG50 contig length (analogous to N50, but normalized to the genome size of 180 Mb to facilitate comparison among assemblies) increases rapidly with coverage up to approximately 106, increasing only marginally at higher synthetic long-read coverage (Figure  4A ).
Assessment of synthetic long-read quality To estimate the degree of contamination of the D. melanogaster libraries prepared by Illumina, we used BLASTN (version 2. 2. 28z)  [40]  to compare the synthetic long-reads against reference sequences from the NCBI nucleotide database, selecting the target sequences with the lowest e-value for each query sequence. The TruSeq synthetic long-reads were then mapped to the D. melanogaster reference genome as single-end reads using BWA-MEM  [41]. Depth of coverage was estimated by applying the GATK DepthOfCoverage tool to the resulting alignment. To estimate error rates, we then parsed the BAM file to calculate position-dependent mismatch, insertion, and deletion profiles. Because a portion of this effect would result from accurate sequencing of genomes harboring residual heterozygosity, we used data from the Drosophila Genetic Reference Panel (DGRP)  [66]  to estimate both the rate of residual heterozygosity as well as a corrected error rate of the TruSeq synthetic long-reads.
Genome assembly Most recent approaches to de novo genome assembly are based on the de Bruijn graph paradigm, which offers a substantial computational advantage over overlap-layout-consensus (OLC) approaches when applied to large datasets. Nevertheless, for datasets with moderate sequencing depth (such as TruSeq synthetic long-read libraries), OLC approaches can be computationally tractable and tend to be less affected by both repeats and sequencing errors than de Bruijn graph-based algorithms. Likewise, many modern Bruijn graph-based assemblers simply do not permit reads exceeding arbitrary length cutoffs. We therefore elected to use the Celera Assembler  [13] , an OLC assembler developed and used to generate the first genome sequence of a multicellular organism, D. melanogaster  [25] , as well as one of the first diploid human genome sequences  [35].
As TruSeq synthetic long-reads share some characteristics with consensus-corrected PacBio reads, we applied Celera Assembler parameters recommended for these PacBio data to take advantage of the read length and low error rate  [23, 68]. In particular, we applied a different unitigger algorithm, a decreased unitig error rates (which is made possible by low synthetic long-read error rates and low rates of polymorphism), and an increased k-mer size to increase overlap specificity. Upon observing partially overlapping contigs among the output of the Celera Assembler, we decided to use the program Minimus2  [38]  to merge these contigs into supercontigs, reducing redundancy and improving assembly contiguity. Parameters used for both assembly programs are further described in the Supplemental Materials (File S1).
3) which again uses the NUCmer alignment to the reference genome to calculate the prevalence of mismatches, indels, and other mis-assembly events. The GLMM used to test the characteristics of TEs that affected accurate assembly were built using the lme package (version 1. 0-5)  [69]  within the R statistical computing environment  [70]. TE features (predictor variables) were available for all but the Y family of TEs, which was recently annotated (Release 5. 56). The response variable was represented by a binary indicator denoting whether or not the entire length of the TE was accurately assembled. This model assumed a binomial error distribution with a logit link function. TE copy length, GC content (including 1 Kbp flanking regions on each side), divergence (number of substitutions per base compared to the canonical sequence of the TE family), number of high-identity (v0.
The authors confirm that all data underlying the findings are fully available without restriction. Sequence data can be found under the NCBI BioProject: PRJNA235897, BioSample: SAMN02588592. Experiment SRX447481 references the synthetic long-read data, while experiment SRX503698 references the underlying short read data. The main genome assembly is available from FigShare at http://dx. doi. org/10. 6084/m9. figshare. 985645 and the QUAST contig report is available at http://dx. doi. org/10. 6084/m9. figshare. 985916. Scripts written to assess presence or absence of genomic features in the de novo assembly can be found in a GitHub repository at https://github. com/rmccoy7541/assess.
Data access Sequence data can be found under the NCBI BioProject: PRJNA235897, BioSample: SAMN02588592. Experiment SRX447481 references the synthetic long-read data, while experiment SRX503698 references the underlying short read data. The main genome assembly is available from FigShare at http://dx. doi. org/10. 6084/m9. figshare. 985645 and the QUAST contig report is available at http://dx. doi. org/10. 6084/m9. figshare. 985916. Scripts written to assess presence or absence of genomic features in the de novo assembly can be found in a GitHub repository at https://github. com/rmccoy7541/assessassembly while other analysis scripts, including those to reproduce down-sampled assemblies, can be found in a separate GitHub repository at https://github. com/rmccoy7541/dmel-longreadassembly. The parameter choices for various software packages are described in the Supplemental Materials (File S1).
With this as the back-drop, the Council's strategy then focused on its database to identify people would likely be active supporters. Focusing on empowering people who were ready and willing, they created a 'Big Blue Army' and generated over 42,000 public submissions to the government in support of MPAs. After the federal government said it planned to create one of the world's largest MPA networks, it received 70% support in a public opinion poll asking whether people supported the plan  (Essential Media Communications, 2012).
They are keen observers with every bit of love and enthusiasm for nature as marine scientists. In fact, given half a chance, they would gladly volunteer their time and resources to collect data and contribute to ocean observations and science. Based on an appreciation and understanding of this type of person, iNaturalist(www. inaturalist. org) and iSeahorse(www. iseahorse. org) are examples of citizen science projects that use a combination of a website, mobile apps and social media integration to record observations of species and then use crowd sourcing (i. e. other users of the website) to help validate and identify the sighting or species in question. Users can also post comments on individual sightings and projects, providing an interactive social media/feedback element to the site. Once validated the sighting records end up in a global database, which scientists can use for any research projects they are conducting.
With a large and very active user base, the websites are constantly being updated with new sightings, projects, and general "chatter" on the social media side. This keeps users engaged and gives good reasons to return. Building on this approach, some sites are using ICT to enhance observations. For example, the Whale Shark ID Library (www. whaleshark. org) and Manta Matcher (www. mantamatcher. org) are websites for recording sightings of specific species. Both species have unique markings so sightings can be matched against the database to identify and follow the individual animals. The projects are innovative in their use of image recognition and pattern matching to enhance citizen scientists' observations. Citizens are also able to get involved in monitoring marine protected areas (MPAs).
The free MPA Guardian app has an easy interface enabling citizens to report details, pictures or video of illegal activity, such as poaching (www. mpaguardian. org). Some caution needs to be noted about these advances. Just within the European Community, for instance, websites about seahorses are numerous, existing in France (www. peaubleue. org), Italy  (Goffredo et al. , 2004) , Malta (www. theseahorsetrust. org/malta-research-project. aspx), UK (www. theseahorsetrust. org, www. naturalengland. org. uk/regions/south_west/ourwork/seahorses. aspx), and Ireland (www. visitsealife. com/bray/seahorse-sanctuary). To the audience, it is important to see that all the efforts to get a better knowledge and a better protection of the seahorses and syngnathidae in general are connected. Too many websites and communications initiatives at local, regional, national, international levels could affect the general understanding about the reasons for the data collecting process and could disturb the interest of citizens to record their observations.
Case Study 3: We all live in a yellow submarine OpenROV. com is an online community of professional and amateur ROV (remotely operated vehicle) enthusiasts. OpenROV is one many open source robotic submarines designed to make underwater exploration possible for everyone. This technology, combined with new advanced photogrammetry and computer vision techniques (Underwater Programmetry Task Group, 2014) can be used for a variety of professional and recreational applications. It creates exciting possibilities about ocean exploration and discovery, not the least of which is the ability for citizens to interact more directly with the underwater world. Along the same lines, the use of Google Street View by the Papahānaumokuākea Marine National Monument has not only 'brought the place to the people' but has also resulted in data collection.
Case Study 4: Happy Appy The challenge of connecting fishermen, buyers, scientists and managers has been a vexing problem in countries with limited infrastructure and funds for science and management. Data is often difficult to collect, leading to poor understanding of the state of fish stocks. In the Solomon Islands, software developers set out to solve this problem. Hapi Fis, Hapi Pipol! (Happy Fish, Happy People!) is a mobile app accessible on cell phones. It gathers data about inshore fisheries and creates a real-time database that managers use to share information back with the fishing industry, helping manage and protect the country's seafood resources  (Nyberg, 2014).
Whomever the messenger, defining and understanding your audience is also a key lesson and theme, as demonstrated by the 'Blue Army' study. Building profiles and databases of different types of people and concentrating on groups where you think you can 'move the needle' most can help maximize and focus limited resources. Understanding audiences means being interested in them as human beings. What motivates them? What are their values, interests, passions, and knowledge levels? What barriers do they have to acting? How might you help them advance their interests and overcome the barriers? Reflecting with their values, interests, passions, and knowledge and addressing their barriers can build connection and response. In some cases, re-framing an intention or message based on their values will significantly enhance the effectiveness of communication and may lead to a better initiative.
Background: The neuropeptide Kiss and its receptor KissR are key-actors in the brain control of reproduction in mammals, where they are responsible for the stimulation of the activity of GnRH neurones. Investigation in other vertebrates revealed up to 3 Kiss and 4 KissR paralogs, originating from the two rounds of whole genome duplication in early vertebrates. In contrast, the absence of Kiss and KissR has been suggested in birds, as no homologs of these genes could be found in current genomic databases. This study aims at addressing the question of the existence, from an evolutionary perspective, of the Kisspeptin system in birds. It provides the first large-scale investigation of the Kisspeptin system in the sauropsid lineage, including ophidian, chelonian, crocodilian, and avian lineages.