dataesr/scientific-paragraphs-categorization huggingface dataset:
HSFA1b is one route for the transmission of environmental cues to a core set of stress-responsive and development-associated genes In Arabidopsis, genomic regions are occupied by multiple TFs and enriched for genes involved in development and stimulus responses  (Heyndrickx et al. , 2014). The notion of co-operation with other TFs is supported by the clear enrichment of co-occurring binding motifs in promoter regions of HSFA1b-bound genes under NS and HS conditions (Fig. 7A ). In the ChIP-seq data sets available to us from NS plants, we compared them with our NP:HSFA1b NS data only. This revealed 76 gene targets common to the eight TFs (Fig. 7B, C , E; Supplementary Data S9). These eight TF genes have all been implicated in the control of growth and development and responses to environmental stress.
HSFA1b and eight of its direct TF gene targets, BASIC LEUCINE ZIPPER28 (BZIP28), REVIELLE7 (RVE7), SALT INDUCED ZINC FINGER1 (SZF1), HSFB2b, G-BOX BINDING FACTOR3 (GBF3), HSFB2a, RVE1, and ETHYLENE RESPONSE FACTOR13 (ERF13; Fig. 5A) are represented in the Arabidopsis Cistrome Atlas (O'Malley et al. , 2016). The Cistrome Atlas is a database of genomewide TF-binding sites experimentally determined by DNA affinity purification sequencing (DAP-seq) (O'Malley.
Acknowledgements We are grateful to  Professor F. Schöffl  for the gift of the hsfA1a/hsfA1b mutant and  Drs Y. -Y Charng  and  H. -C. Liu  for the qKO mutant. The authors acknowledge the support of a  Kingdom of Saudi Arabia Studentship  and the  Biotechnology and Biological Sciences Research  (grant  BB/F005822/ ). This work was supported by the  'Laboratoire d'Excellence (LABEX) '  Saclay Plant Sciences (SPS ;  ANR-10-LABX-40 ) and the  ANR  grant SPLISIL, France.
Data S7. Genes whose expression is indirectly regulated by HSFA1b. Data S8. Target genomic loci and their cisNAT RNAs that show differential expression. Data S9. Shared binding sites between HSFA1b and HBI-1, PRR5, LFY, SEP3, PIF4, PIF5, and PRR7. Data S10. All significantly enriched GO terms of the genes targeted by HSFA1b and HBI-1, LFY, PRR5, PRR7, SEP3, PIF4, and PIF5. Cytoscape File S1. A Cytoscape file which allows an interactive view of Fig. 5D  but requires first that the open source program is downloaded from http://www. cytoscape. org/.
Data analyses Sequences were edited using Geneious 10. 1. 2 resulting in 167 sequences of length of 621-658 bp excluding primers. All sequences were deposited in GenBank with the accession numbers MG264740-MG264881, KY072917-KY072920 and MG521122-MG521157 (Suppl. material 1). Relevant voucher information, taxonomic classifications, and sequences are accessible through the public data sets "DS-AMPIA" (dx. doi. org/10. 5883/DS-AMPIA) and "DS-RHACHOTR" (https://doi. org/10. 5883/DS-RHACHOTR) on the Barcode of Life Data Systems (BOLD; www. boldsystems. org) (Ratnasingham and Hebert 2007). The sequences were aligned with MAFFT v7. 308 algorithm with default settings  (Katoh et al. 2002, Katoh and Standley 2013)  in Geneious 10. 1. 2 resulting in a 599 bp alignment used for further analyses. Uncorrected p-distance and the Kimura 2-parameter (K2P) model (Kimura1980) were used to determine sequence divergence in MEGA V7.
Results Among the 75 morphologically identified species, 81 Barcode Identity Numbers (BIN) were ascribed by BOLD (Figure  2 , Suppl. material 1). Fifty-eight of these are unique for the database, while 23 are shared with other studies. Within the second group, nine are held in private datasets and another nine are left identified at the order level. As a result, only five are public and are associated with known species names. In total, 94% of the BINs in the present study are published for the first time. The ABGD method allowed for recognition of 79 to 83 MOTUs when using K2P distance and 78-79 MOTUs for p-distance analysis. In the case of K2P the most stable division over a wide range of the prior maximum divergence values (P= 0. 004-0. 03) was 79 MOTUs and thus only this division is presented in Figure  2.
Recognizing amphipod species diversity in Icelandic waters Based on the material studied 81 BINs were recognized. Only five of the BINs are identified to the species level and publically available, while 94% are either unique, held in private datasets, or without detailed identification. That proportion indicates the extent to which knowledge of this important group of marine zoobenthos is still poorly known. In another barcoding study of Crustacea from Gulf of St. Lawrence (North Atlantic) new barcodes accounted for 75 percent of studied sequences  (Radulovici et al. 2009 ). In the eight years since the release of this earlier study, there is still large gaps in the knowledge of genetic diversity including the deeper parts of the ocean as demonstrated here for the Icelandic and adjacent waters in the North Atlantic.
hystrix complex. At present the complex is not supported by a morphological assessment which would allow comparison of the voucher specimens. Vouchered taxonomic identifications are essential for genetic studies, as once the mistake appear in barcoding database it is easily repeated by further users of the online genetic resources. Without currently published information on the morphology associated with these lineages, at present there can be no further comparison of this species complex as MOTUs (BINs) with the morphological concept of P. hystrix in the taxonomic literature. For Amphilochus manudens Spence Bate, 1862 it appears also, that the individual which was assigned to this species from IceAGE sampling represents a different BIN than the specimens collected from the North Sea and ascribed to the same taxon.
Conclusion DNA barcoding can help considerably in recognition of species diversity in the deep sea by indicating the existence of cryptic or pseudocryptic species and allowing the taxonomists to focus on the novel morphological and genetic incongruence. However, the accuracy of the taxonomic identification of records in molecular databases is crucial to make those databases reliable for further users. The current study of Amphipoda from Icelandic and adjacent water in the North Atlantic strongly recognises that molecular methods need to be supplemented by comprehensive taxonomical analysis of species morphology in order to provide an expert certified baseline for further biodiversity studies.
Acknowledgements We thank crew and captain of  R/V Meteor  and  R/V Poseidon  for their support the sampling during the IceAGE expeditions 2011 and 2013 in all weather and sea ice conditions. Sorting on higher taxon level took place at the DZMB in Hamburg. The material was sorted on family level during workshops organized with the financial support of the  Volkswagenstiftung "Forschung in Museen" given  to  Saskia Brix ,  Anne-Nina Lörz  and  Bente Stransky. Without the enthusiasm of all workshop participants, obtaining the specimens for barcoding would not have been possible. Thanks especially go to  Antje Fischer  for her help in entering all soring results into our project database and producing the background data for proper BOLD entries. Anna Jażdżewska  received the support from internal funds of  University of Lodz  to visit DZMB in Hamburg and discuss the present work.
They also found a second GnRH gene in C. milii, indicating the occurrence of both GnRH1 and GnRH2 genes in this species. Moreover, they reclassified the two GnRH forms previously identified in S. acanthias as GnRH2 and GnRH3, instead of GnRH1 and GnRH2. In an attempt to better understand the molecular evolution of the GnRH gene family in vertebrates, we searched the genome and/or transcriptome databases of two representative species of elasmobranchs, namely the common dogfish, now renamed small-spotted catshark, Scyliorhinus canicula  (Coolen et al. , 2008)  and the whale shark, Rhincodon typus  (Read et al. , 2017). We also took the opportunity to reexplore the genome and transcriptome databases of the holocephalan elephant shark C. milii, the first cartilaginous fish to have its full genome sequenced  (Venkatesh et al.
Identification of GnRH-Related Sequences in Cartilaginous Fish Catshark GnRH genes were sought by TBLASTN  (Altschul et al. , 1990)  using the little skate GnRH sequences  (Roch et al. , 2014a)  as queries against the catshark genome draft assembly (version 1) (unpublished data). Briefly, this assembly size is 3. 68 Gb (N50 = 9558 bp; %N = 7. 22) for an estimated genome size of 5. 3 Gb  (Stingo et al. , 1980). It was obtained from a 151× coverage using a combination of Illumina paired end and mate pair sequencing, generated by the Genoscope (France). Assembly was conducted using CLC (Bio, Qiagen) for assembly and scaffolding of paired end sequences, SSPACE  (Boetzer et al. , 2011)  and GapCloser  (Luo et al. , 2012)  for the introduction of mate pair sequences. Whale shark GnRH genes were sought by TBLASTN using the catshark GnRH cDNA sequences as queries against the whale genome assembly database  (Read et al.
, 2017)  via NCBI 1. Elephant shark genes were sought by TBLASTN using the catshark GnRH cDNA sequences as queries against the elephant shark genome assembly plus transcriptome data from 10 tissues (brain, gills, heart, intestine, kidney, liver, muscle, ovary, spleen, and testis; GenBank accession number SRA054255)  (Venkatesh et al. , 2014).
For the 5 amplifications, the primers used were GnRH Rev × Universal Primer A Mix (UPM) then GnRH Rev Nest × Nested Universal Primer A Mix (NUP), and for the 3 amplification, they were GnRH For × UPM then GnRH For Nest × NUP. The PCR products were subcloned into the pGEM-T vector (Promega, Charbonnières-les-Bains, France) and sequenced (Value Read Sequencing at MWG Biotech, Ebersberg, Germany). The coding sequence of the catshark GnRH cDNAs have been deposited in the GenBank database under the accession numbers MH468810, MH468811, and MH468812 for GnRH1, GnRH2, and GnRH3, respectively. 1 http://blast. ncbi. nlm. nih. gov/Blast. cgi.
Phylogenetic Analysis Amino acid sequences of 74 chordate GnRH precursors [73 sequences from vertebrate species  (Pérez Sirkin et al. , 2017)  including the eight complete chondrichthyan prepro-GnRH sequences characterized in the present study plus the amphioxus (Branchiostoma floridae) prepro-GnRH sequence  (Roch et al. , 2014b) , used as outgroup] were aligned using the MAFFT algorithm 2  then manually adjusted. The phylogenetic tree was built using PhyML  (Guindon and Gascuel, 2003)  via the Seaview version 4 software  (Gouy et al. , 2010). The best amino acid substitution model for the alignment was determined to be JTT+G+I using ProtTest  (Abascal et al. , 2005). The robustness of the tree was assessed by the bootstrap procedure with 1,000 replications. The GenBank accession numbers for all sequences used in the analysis are listed in Supplementary Data Sheet S1.
The alignments are presented in Supplementary Data Sheet S2. Phylogeny analysis was also performed specifically on chordate GAP sequences, including the eight chondrichthyan GAP sequences characterized in the present study, as previously described  (Pérez Sirkin et al. , 2017). GAP sequences were delimited between the dibasic site for proteolytic processing after the GnRH sequence and the stop codon of the open reading frame (ORF) (see Supplementary Data Sheet S3 for the alignment).
Synteny Analysis To generate synteny map, genes flanking the elephant shark, whale shark and catshark GnRH genes were identified by searching flanking sequence in batches of 10 kb non-overlapping fragments against NCBI NR database by BLASTX. Catshark GnRH1 and GnRH3 genes were not included in the study because they were found on very short scaffolds that did not contain any other genes. Genes flanking the GnRH genes in five osteichthyan species, namely human, chicken (Gallus gallus), western clawed frog (Xenopus tropicalis), spotted gar (Lepisosteus oculatus), and medaka (Oryzias latipes), were obtained from Genomicus  (Muffato et al. , 2010).
Prediction of GAP Three-Dimensional Protein Structure Secondary protein structures of GAP from the three pre-proGnRH variants present in catshark, elephant shark, and whale shark were modeled using the I-TASSER server, an automated protein-modeling server from the Zhang Lab at the University of Michigan 3  (Yang et al. , 2015). GAP sequences were delimited between the dibasic site for proteolytic processing after the GnRH sequence and the stop codon of the ORF. Only models with C-score between -4 and 2 were considered. Visualization of the predicted three-dimensional (3D) structures, as well as its orientation (N-terminal extreme to the left), were performed using the Jmol software 4.
In Whale Shark Using TBLASTN, we searched the whale shark genome assembly using the three catshark GnRH precursor sequences as queries. Three hits were found encoding three putative different GnRH peptides. Where possible, catshark GnRH precursor sequences were used as reference to predict the whale shark GAP-coding exons. For the GnRH1 gene, only a partial sequence (135 bp) could be retrieved corresponding to exon 1 (Supplementary Data Sheet S5). For the two other genes, GnRH2 and GnRH3, the complete coding DNA sequences (CDS) were retrieved split into three exons (Supplementary Data Sheet S5). GnRH2 CDS is a 258 bp sequence composed by a 138 bp exon 1, a 84 bp exon 2 and a 36 bp exon 3, while GnRH3 CDS is a 285 bp sequence composed by a 141 bp exon 1, a 90 bp exon 2, and a 54 bp exon 3 (Supplementary Data Sheet S5).
In Elephant Shark TBLASTN was used to search the elephant shark genome assembly using the three catshark prepro-GnRHs as queries. Three hits were found encoding three putative GnRH precursors, GnRH1a, GnRH1b, both containing the same GnRH1 peptide sequence plus GnRH2. Full length GnRH1a and GnRH2 cDNAs were retrieved from RNA-seq data (GenBank accession number SRA054255)  (Venkatesh et al. , 2014). Comparison of these cDNAs with the genomic sequences revealed the general organization of GnRH1a and GnRH2 genes. The GnRH1b CDS, which could not be retrieved from the RNA-seq data, was predicted using the GnRH1a sequence as a reference. The GnRH1a cDNA sequence contains a 486 bp 5 terminal UTR and a 235 bp 3 terminal UTR with the canonical polyadenylation signal sequence; an ORF of 252 bp encoding a 84 aa protein including a 24 residue potential signal sequence, the GnRH1 decapeptide, the 3 aa proteolytic cleavage site KKR and the 47 aa GAP (Supplementary Data Sheet S7).
ACKNOWLEDGMENTS We acknowledge  Drs. Daniel Sellos  and  Samuel Iglésias  ( Station de Biologie Marine de Concarneau, France ) for their cooperation in collecting dogfish and  Dr. Graeme Roch  ( University of Victoria, Canada ) for providing us unpublished sequences and information. The catshark genome sequencing project was conducted by the  Genoscope, CEA Evry, France. We thank  Dr. Patrick Wincker , and the  Genoscope  staff for this work. Finally, we are very grateful to  Mitchell Fleming  ( MNHN ) for English corrections.
Measure of the characteristic response distance The particle trajectories are recorded using a high spatial resolution camera JAI GO-5000M (2560 × 1700 px 2 ) at 60 frames per second. The camera was located above the flume, perpendicular to the flow. The time evolution of the particle position is determined with a standard image detection algorithm using Matlab R2017a R. Then, the model equation Eq. (  6 ) is fitted on the experimental trajectory by a root mean square method, using only λ as fitting parameter.
Data sources and statistical methods The data on the BU cases from Nigeria treated in the three CDTUBs were taken from the PNLLUB's Register BU 01, which is used to collect standard information for each BU patient. The data were supplemented with additional information from the patients' medical records kept in each CDTUB. The complete data were recorded with the software Microsoft Excel 2010 and then analysed with the statistical software IBM SPSS Statistics version 20. We only performed a descriptive analysis of the epidemiological, clinical, biological and therapeutic variables of the cases. The maps were drawn from free-access shapefiles obtained from DIVA-GIS (http://www. diva-gis. org/)with QGIS 1. 8. 0 and ArcView 3. 2 software.
The same results were obtained in the Pobè study  [35]  as well as by other authors who studied BU cases in Nigeria  [38, 40]. This could be explained by the patients' long delay before seeking medical attention, by which time the lesions have had sufficient time to extend. This falls short of the objectives set by WHO, which called upon countries endemic for BU to conduct community actions in order to reduce the proportion of ulcerative cases to < 60% and of Category III cases to < 30%  [45]. As the biological confirmation was not systematic at the CDTUB in Zagnanado, the majority of patients were not sampled. Because this facility has lengthy experience in managing the disease by the same team, patients are sampled only in case of doubt about their clinical diagnosis  [46] , However, PCR confirmation of all suspected BU cases is recommended in order to limit the inappropriate use of specific antibiotic and to ensure standardization of surveillance data across all treatment centres.
The laser sheet thickness was 2 mm. In all planes, the images were recorded through the glass side-wall of the flume with a high resolution camera (2k×2k pixels) and an acquisition frequency of 3 Hz. A telecentric lens was used to prevent any parallax effects since the optical path includes the transparent glass prisms in water. All PIV measurements were performed near the center of the flume with 10 000 decorrelated velocity fields and a spatial resolution of 0. 6 mm. This set-up allowed measurements along the whole flow depth, from z = 0 to z = h (position of the free surface). All PIV post-processing was done with the PIV software CPIV-IMFT developed at IMFT (a FFT cross-correlation method with peak-locking reduction schemes and parallelization). β = (h -k)/ the modified-relative-submergence.
To the best of our knowledge, multi-sensor fusion is one of the most suitable technologies to use when dealing with data from disparate sources  [3]. Following this approach, our research group previously present a wearable health sensor monitoring system based on a multi-sensor fusion approach  [4]. It consists of a chest-worn device that embeds a controller board, an electrocardiogram (ECG) sensor, a temperature sensor, an accelerometer, a vibration motor, a colour-changing light-emitting diode (LED) and a push-button. Biometric and medical monitoring applications can be performed by using this multi-sensor device. The embedded vibration motor makes it possible to actuate distinctive haptic feedback patterns according to the wearer's health state. In this way, the user can intuitively become self aware of his own health situation.
Encryption techniques can be used to transfer data securely and to consider privacy concerns. Data mining can be performed with the raw data and could be used in conjunction with methods from the fields of artificial intelligence, machine learning, statistics and database systems by using a knowledge base. The overall goal of the data mining process is to extract information from the data set and transform it into an understandable structure so that it is possible to find previously unknown information to make inferences regarding the user's health state. In the context of constantly aging populations, maintaining persons at home represents one of the most serious challenge to be considered. The proposed system may make it possible for elderly people to live longer in their own homes. The paper is organised as follows.
Several other technologies have been also introduced, including the MIThril by MIT  [8] , e-textile from Carnegie Mellon University, and wearable e-textile from Virginia Tech  [9]. Furthermore, the Lifeguard project of Stanford University is a physiological monitoring system comprising physiological sensors (ECG/respiration electrodes, pulse oximeter, blood pressure monitor, temperature probe), a wearable device with built-in accelerometers, and a base station. The crew physiologic observation device acquires and logs the physiological parameters measured by the sensors. More recently, Dong et al. [10]  proposed an elderly health-monitoring distributed platform, which collects health data through intelligent wearable sensors and then send them to a remote server through a smartphone. Using big data technology and parallel computing, the platform uses Spark Streaming technology to carry out the online data stream processing, so as to feedback the results to the designated emergency center, hospital, or family.
Client (chest-worn device) The wearable device is designed to work as a client and gather different biometric data. In particular, it is a chest-worn device that consists of a controller box attached to a belt. The device is worn by the user and it is powered by means of a polymer lithium ion battery. In the following, the components that are housed in the controller box are listed: • Controller board: an Arduino Uno board  [12]  based on the ATmega328 microcontroller is used as a client. Arduino is an open-source electronics prototyping platform based on flexible, easy-to-use hardware and software. Using Arduino boards simplifies the amount of hardware and software development needed to get a system running. On the software side, Arduino provides a number of libraries to make programming the micro-controller easier.
The choice of using Arduino boards makes the system framework easy to maintain and makes it possible to easily add new features in the future; • Communication module: to give communication capabilities to the proposed wearable device, an Arduino WiFi Shield  [12]  is stacked on top of the adopted controller board. In detail, the Arduino WiFi Shield allows the client to communicate with the server by using the 802. 11 wireless specification (WiFi). The Arduino WiFi Shield connects to an Arduino Uno board using long wire-wrap headers that extend through the shield. This keeps the pin layout intact and allows another shield to be stacked on top; • Memory Shield Module: a Secure Digital (SD) card reader module and an SD memory card are adopted to locally store data through the Serial Peripheral Interface (SPI) by using the file system driver.
In detail, the health state of each user is continuously monitored by a separate thread. This level can access both the lower Low-Level methods layer as well as the higher High-Level methods layer. Each monitoring thread continuously sends the current information status back to the corresponding client so that the vibration motor and the LED can be accordingly actuated; • the Low-Level methods layer includes the low-level functions that are used to write the acquired data to a local database (write), to read previously acquired data from a local database (read), to send the acquired data to the cloud (send), to receive previously acquired data from the cloud (receive), and to send data from the server to a knowledge base where the data can be analysed (analyse). From an implementation point of view, the server is implemented by using the Java programming language and the corresponding standard tools for multi-threaded programming.
Knowledge Base The proposed system can communicate with a knowledge base (KB). A KB may store complex structured and unstructured information collected from different sources such as public hospital databases and other health care facilities. KB technology and methods from the fields of artificial intelligence, machine learning, statistics and database systems can be employed to process the health measurement data and to look for unusual signs or trends. In this initial study, the interface that provides the possibility of exposing data from the server side to an hypothetical KB was developed. In the future, rules for execution by an inference mechanism of an automation program may be implemented  [15]. The rules must be based on the expertise of the health professionals. The rules would describe the range values of the health data that requires constant monitoring.