dataesr/scientific-paragraphs-categorization huggingface dataset:
Left to right: (1) knowledge integration using a variety of data sources, including public databases, text publication, shared assay data, clinical and preclinical disease model results, using both automated and manual data curation; (2) network construction links biological knowledge of signaling pathways and biological processes, with genes involved thereinthis step links genes in pathways and transcriptional regulatory networks to biological processes of interest, judged as relevant to a particular pathology; (3) pathway reporter genes assembled into an experimental panel to be subject to quantification using highly sensitive amplicon-based mRNA transcript quantification (see  [74] ); and (4) the molecular phenotype allows reporter pathway genes and signaling networks therein to be effectively correlated with cell-based (high-content microscopy) assay readouts, allowing drug effects to be better assessed, screened, and modeled according to their likely therapeutic outcome  [76].
It will be interesting to see how such systems perform on highly multiplexed labeling of protein networks  [84] , and the transcriptome  [85]  visualized at mesoscopic scales. Such approaches could allow high-fidelity interactome analysis of host-cell/pathogen specific responses obviating background signal from noninfected cells. In combination with deep-learning and machine-learning automation, frameworks like ANNA and HRMAn might help to assure unprecedented genotype-phenotype classification, annotation, and clustering that is currently incomplete for many infectious disease and microbial paradigms. Realizing opportunities from image-based morphological cell profiling highlights the imperative need for community coordination and collaborative efforts at every level from open-source software to open-access data and protocol sharing  [71].
Specifically, in parasitology, it is the clinical need in the field that is driving the shift toward screening campaign paradigms using phenotypic approaches  [86]. For Chagas' disease, T. cruzi drug-discovery campaigns have, for several years, employed computational approaches using data from several public whole-cell, phenotypic high-throughput screens made available by the Broad Institute (including a single screen of over 300 000 molecules)  [87]. Based on compiling and curating relevant biological and chemical compound screening data 584 compounds with activity data against T. cruzi were identified and made publicly available in the CDD database (Collaborative Drug Discovery Inc. Burlingame, CA, USA). Nonetheless, while providing a powerful proof-ofconcept and tool in hand for future screening efforts, there is still a need to advance the field and facilitate better data annotation, curation, and public sharing therein.
Acknowledgments We are grateful for support from the  Seventh Framework Program of the European Commission  ( LEISHDRUG  project,  223414 ), the  French Government [L'Agence nationale de la recherche (ANR) ] programs:  Investissements d'Avenir programme ('Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases' ; grant  ANR-10-LABX-62-IBEID  and ' Laboratoire Revive' ; grant  ANR 10-LBX-73-REVIVE );  France BioImaging  ( FBI ; grant  ANR-10-INSB-04-01 )), the  Région Ile de France  (Domaine d'intêret majeur Innovative technologies for life sciences, DIM ELICIT; and DIM1Health); the  GIS IBiSA (Infrastructures en biologie santé et agronomie ); the  National Research Foundation (NRF) of Korea  ( NRF-2017M3A9G6068254   Global Network and Research Infrastructure to Infectious Diseases ); the  Gyeonggi Provincial Government ; the  Institut Pasteur Korea, and Institut Pasteur Paris.
Introduction The opening of the modern North Atlantic Ocean represents the final dispersal and end of the Laurasia continental amalgamation that formed the northern portion of the Pangaea supercontinent  (Gaina et al. , 2009; Hansen et al. , 2009; Frizon De Lamotte et al. , 2015) (Fig. 1 ). The ocean is divided into two main spreading branches; the Northeast Atlantic between Greenland and Europe, and the Labrador Sea -Baffin Bay system between Greenland and North America  (Srivastava, 1978; Beniest et al. , 2017) , that form a complex junction with the northeast Atlantic to the north of the Charlie-Gibbs Fracture Zone (CGFZ)  (Gaina et al. , 2009). In addition to the primary breakup axes, complex styles of deformation occurred on the continental margins, including the preservation of relatively undeformed continental fragments  (Peron-Pinvidic and Manatschal, 2010; Peron-Pinvidic et al.
General model setup We use the GPlates software (version 2. 1) to conduct this investigation (e. g. ,  Williams et al. , 2012b; Müller et al. , 2018). This open source plate tectonic modelling platform allows for a variety of investigations using plate tectonic reconstructions (e. g. ,  Phethean et al. , 2016; Gion et al. , 2017)  including deformable models  (Gurnis et al. , 2018; Welford et al. , 2018). Within GPlates 'features' (i. e. points, polylines or polygons) can be defined from geological and geophysical observations  (Gurnis et al. , 2018). Such features, optionally including their time of appearance and/or disappearance, can be combined into a topological network (Fig. 3 ). A topological network is a feature whose spatio-temporal evolution is defined by the features comprising the network  (Müller et al.
Porcupine Bank and Basin Previous work has shown that the Porcupine Basin has a complex rift and post-rift history  (Jones et al. , 2001; Reston et al. , 2004; Naylor and Shannon, 2005; O'Reilly et al. , 2006; Yang, 2012; Chen et al. , 2018; Prada et al. , 2018; Watremez et al. , 2018). This complex rift history is depicted in the results of the deformable plate tectonic models, including the prediction of hyperextension. Resultant crustal thicknesses derived through deformable models show general similarity with the predictions of gravity inversion (Fig. 15 ). However, crustal thickness, both in the Porcupine Basin and the Porcupine Bank, are largely over predicted in the models, which means beta factors are intrinsically under predicted. The crust in the Porcupine Basin has been documented to be locally as low as 2 km thick based on wide-angle seismic data  (O'Reilly et al.
Acknowledgements Alexander  L. Peace 's postdoctoral fellowship at  Memorial University of Newfoundland  was funded by the  Hibernia Project Geophysics Support Fund and Innovate NL. We would like to thank the GPlates development and maintenance team and the members of the  MAGRiT group at Memorial University of Newfoundland  for valuable scientific discussions. We would also like to acknowledge  TGS  for the generous provision of the seismic reflection data from the  Orphan Basin. To prevent visual distortion of the data, most figures in this manuscript use perceptually uniform colour palettes from the Scientific Colour Maps collection for which we acknowledge Crameri (2018). Finally, we would like to thank the two anonymous reviewers for their constructive comments that greatly helped to improve this manuscript.
Nonetheless, it seems useful for the intraoperative evaluation of flap perfusion and immediate assessment of the quality of the microvascular anastomosis  [8, 9]. Indeed, some authors reported that flap areas with poor clinical aspect on intraoperative angiography were those where perfusion-related complications occurred  [7, [10] [11] [12] [13]. The SPY Elite System is the most widely used device for indocyanine green-based angiography in breast reconstructive surgery  [13]. The proprietary SPY-Q software (Novadaq, Mississauga, Canada) can perform various analysis of fluorescence angiography data. Several perfusion parameters can be determined and compared between different anatomical regions. Some authors have suggested that SPY-Q software analysis could be used to evaluate the risk of postoperative necrosis after mastectomy with immediate prosthetic breast reconstruction  [6, 7, 14, 15].
Videos selection and data collection We collected all fluorescence angiography videos recorded during unilateral DIEP flap breast reconstruction procedures during the study period. For this study, we selected videos recorded before the clamping of the epigastric vessels at their origin, after the main perforator had been fully dissected. The SPY Elite System was used for detection. It was placed over the abdominal flap and kept stationary. Each patient received a peripheral intravenous injection of 5 mg Infracyanine (indocyanine green) immediately followed by a peripheral intravenous injection of 10 mL of saline serum. Video recording was initiated at the time of Infracyanine injection and was stopped when the senior surgeon considered fluorescence of the flap to be stable. We included all patients undergoing unilateral DIEP flap breast reconstruction for whom the video recorded were of sufficient quality for analysis.
The quality criteria for video selection were as follows: film duration greater than 20 seconds, shooting centered on the abdominal flap, no camera movements, no additional image in the field of view (e. g. surgical instruments or other tissues), no previous background fluorescence. Videos must also have been recorded with SPY Elite System default recording parameters. For each patient included, clinical data were gathered in the computerized medical file: age, body mass index (BMI), menopausal status, cardiovascular risk factors (hypertension, dyslipidemia, diabetes mellitus, smoking status (divided into "former smoker", when ancient smoking was stopped less than three years before surgery, or "non-smoker". Current smoking was a contraindication for DIEP procedure)), treatment by radiotherapy, chemotherapy or hormone therapy (tamoxifen or aromatase inhibitor).
Surgical data were searched for in the surgical report validated by the senior surgeon in charge of the patient: time of breast reconstruction, intraoperative surgical difficulties (difficult dissection or wound of the main perforator, immediate refection of anastomosis, intraoperative partial resection of the flap due to abnormal poor tissular perfusion or any difficulties explicitely described in surgery report). Postoperative outcomes (reoperation, prolonged local care, duration of local care) were also gathered in the medical file.
Video analysis using SPY-Q software Video recordings were analyzed using proprietary SPY-Q software. Authors verified that initial recording parameters were similar among selected videos (especially ambient light correction). First, all videos were cut 20 seconds after the beginning of the recording to have all videos the same length and enable comparisons. On each video, a dynamic color analysis was first performed with the "Overview" function. After that, a quantitative analysis was performed, first on the whole flap, and then, on four regions of interest. Ingress and ingress rate were the two quantitative perfusion parameters selected for the analysis of flap perfusion. Ingress (in absolute perfusion units, APU) is the absolute difference between initial mean fluorescence in a region of interest and its maximal value.
A new accurate and objective tool to assess DIEP flap perfusion The main objective of this exploratory study was to describe the use of SPY-Q proprietary software for DIEP flap perfusion assessment. To date, this is the first study to describe the type of analysis that can be performed on indocyanine green based-angiography using SPY device in DIEP flap procedure: 1/color intra operative dynamic analysis, 2/quantitative whole flap or pre-defined region of interest perfusion analysis. This is the largest serie of patients reported with SPY scanning of DIEP flaps in breast reconstruction. It is also the first report of the use of SPY-Q proprietary software in this setting. We here propose a new approach for DIEP flap perfusion assessment. We show that flap perfusion can be both qualitatively and quantitatively evaluated quite easily.
Indeed, real-time color analysis can facilitate indocyanine green-based angiography interpretation and be useful for intra operative flap harvest and design. Precise quantitative analysis of flap perfusion is possible with SPY-Q software. Although it seems of little interest in intra operative setting or pre operative perforator mapping, we suggest that it could be an interesting tool to use when studying DIEP flap perfusion. In many reported studies, DIEP flap perfusion is generally clinically assessed. We think that quantitative analysis with SPY-Q proprietary software, using parameters such as ingress and ingress rate, could refine and precise the results. Our DIEP flap perfusion analysis using selected quantitative parameters (ingress and ingress rate) confirm that zone I is the best perfused zone and that zone IV is the less perfused one.
That can be explained by a lack of power due to the small number of patients. Another part of the analysis confirms that quantitative analysis with SPY-Q software is an accurate objective assessment of clinical flap perfusion. Dynamic color analysis showed three patterns of flap perfusion: the type 1 pattern, in which perfusion is homogeneous but limited in the hemi-abdomen in which the main perforator emerges; the type 2 pattern, in which perfusion is good around the emergence of the main perforator and poor elsewhere in the flap; and the type 3 pattern, in which flap perfusion is homogeneous across the midline. In type 3 pattern, the perforator emergence seems generally lower than in other patterns. The results of the quantitative analysis were consistent with those of the color analysis, which means that ingress and ingress rate are well related to clinical assessment of flap perfusion.
The ZigBee protocol has detailed procedures to establish multihop routes between nodes. In fact, ZigBee contains procedures specifically tailored for many-to-one communications  [4] , in which several nodes transmit data to a single concentrator node, which is a typical scenario in wireless sensor networks. Although there are many products already using the ZigBee protocol, the ZigBee Alliance is still actively developing future versions  [5, 6] , and there is still interest on ZigBee from a research perspective  [7] [8] [9] [10] [11] [12]. In this paper, we focus on one particular aspect that the ZigBee specifications do not fully specify: the estimation of the probability of successful transmission on a link. Such an estimation is important because it is used by nodes to select multihop routes.
In ZigBee, such an indicator is the link quality indicator (LQI) that the IEEE 802. 15. 4 medium access layer (MAC) includes in every packet received and forwarded to the network layer; and the ZigBee's network layer averages the received LQIs and maps the result into a probability of successful transmission to determine the cost of a link. As discussed in Section 3. 1, previous authors have shown that estimating link costs from beacons, such as ZigBee's LS packets, is less accurate than estimating them from unicast data packets and have proposed alternative procedures to improve route selection  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28]. Although these procedures are valuable and would improve link cost estimation and route selection, their implementation in ZigBee devices would require significant changes in the ZigBee specifications, protocol, and frame formats.
Overview of ZigBee ZigBee is different from protocols such as IEEE 802. 11, in that it specifies the use of a complete set of protocols specifically designed for device-to-device communication  [1, 2]. In its application layer, ZigBee defines application profiles that facilitate the communication between applications in the various devices and defines procedures for network discovery and connection establishment. ZigBee also defines the application support sub-layer (APS), which manages ongoing connections and has transport layer functionalities for end-to-end reliable data transfer with acknowledgments, retransmissions, and rejection of duplicate packets. In its network layer, ZigBee defines procedures to establish multihop communication routes between devices, defining procedures for route discovery and routing algorithms.
Assigning markers to linkage groups To localize markers we used Lep-MAP2  (Rastas et al. 2013 (Rastas et al. , 2016)) , first converting the Stacks genotypes output file -containing progeny and parents -to the required LINKAGE pedigree format  (Lathrop et al. 1984). Within Lep-MAP2 we first used the Filtering module to remove loci showing segregation distortion (dataTolerance = 0. 01) in the two recombinant backcrosses, and to remove loci/individuals with limited genotyping data. Different filtering criteria were employed for each backcross to maintain similar levels of missing data across the three sets of genotypes. For the KS-BC we retained 8,132 markers and 339 individuals, for the FL-BC we retained 5,970 markers and 313 individuals, and for the KS-SG we retained 13,295 markers and 198 individuals.
We assigned markers to linkage groups independently for each of the three mapping populations using the SeparateChromosomes Lep-MAP2 module, requiring a minimum LOD (logarithm of odds) score of 20, and a minimum of 20 markers per linkage group. To establish linkage group homology across populations we compared the assignment of markers to linkage groups in each map. Eight (0. 2%) of the 3,204 overlapping markers between KS-BC and FL-BC were removed entirely from the dataset because they were inconsistent in their placement, i. e. , a marker was placed on different linkage groups in the two maps. Based on the remaining overlapping markers, linkage groups in KS-BC and FL-BC were renamed to reflect the respective linkage groups in KS-SG. For the pair of recombinant backcross populations, markers associated with each linkage group were then separately ordered with OrderMarkers using the Kosambi function (useKosambi = 1;  Kosambi 1943 ), taking into account the achiasmatic meiosis in females (initRecombination = 0.
The cross type was set to 'bc' for all power calculations. Genome sequencing data collection DNA was isolated from multiple males from the KS A. grisella inbred line  (Gleason et al. 2016)  and pooled. We employed males, the homogametic (ZZ) sex, to ensure that the Z had similar coverage to each autosome in subsequent sequencing. Two Illumina sequencing libraries were generated (Cofactor Genomics, Inc. ); a short insert size paired end (hereafter "PE") library with 280-504bp inserts, and a long insert size mate pair (hereafter "MP") library with 3-5kb inserts. Each library was sequenced on three lanes of an Illumina HiSeq2500 instrument, one lane at Cofactor Genomics, Inc. and two lanes at the KU Genome Sequencing Core. We obtained 291 million and 405 million 101-bp read pairs for the PE and MP library, respectively (Table  S1 ).
Genome sequencing data processing Read quality has a major effect on the result of de novo genome assembly  (Salzberg et al. 2012 ), therefore we first preprocessed the raw FASTQ files. Initially, we removed adaptor sequences using Scythe (https:// github. com/vsbuffalo/scythe) and quality trimmed reads via Sickle (https://github. com/najoshi/sickle), eliminating any reads containing uncalled positions (i. e. , "N" bases), or with a trimmed length below 80-bp. Any reads whose pairs were discarded in the trimming process were saved in a separate FASTQ file which was used alongside the pairs in downstream analyses. Subsequently, we used bowtie2  (Langmead and Salzberg 2012)  with default settings to align preprocessed reads to the PhiX (Escherichia virus phiX174) reference genome (NCBI Reference Sequence: NC_001422.
Genome assembly and evaluation To assemble processed reads into scaffolds we used the de Bruijn assembler ABySS  (Simpson et al. 2009) , selecting this software both due to its low error rate when assembling a human chromosome  (Salzberg et al. 2012) , and its parallel processing ability and low memory requirements  (Simpson et al. 2009). ABySS uses PE reads to assemble contigs, and then MP reads to construct scaffolds. In common with most de Bruijn assemblers  (Pevzner et al. 2001)  ABySS requires that the user specify a k-mer size, where the optimal k depends on the repetitiveness of the genome, its heterozygosity, and technology-specific error rates  (Chikhi and Medvedev 2014). We used KmerGenie  (Chikhi and Medvedev 2014)  to estimate the appropriate k-mer as 93, then ran ABySS with this value, otherwise employing the default parameters, on a single cluster node with 16 processors and 32GB of RAM.
RNAseq and transcriptome assembly To assist with genome annotation, we collected RNAseq data and assembled transcripts from A. grisella. Total RNA was individually isolated from two pupae from strain "Louisiana line 112"  (Zhou et al. 2008). A poly-A selected, unstranded TruSeq Illumina RNAseq library was constructed for each individual, and the pair of libraries were sequenced over a single lane of a HiSeq2500 instrument to generate around 175 million paired-end 100-bp reads (KU Genome Sequencing Core). Quality trimming via Sickle (https://github. com/najoshi/sickle, window-wise quality threshold parameter q = 40, minimum read length post-trimming = 50-bp) retained around 96% of the reads. All qualitytrimmed reads were merged into a single FASTA file. To assemble transcripts we used Trinity  (Grabherr et al.
Genome annotation We annotated our de novo genome assembly using MAKER2  (Holt and Yandell 2011). We provided MAKER2 with the Trinity-assembled A. grisella transcripts, protein databases from the lepidopterans Heliconius melpomene  (Davey et al. 2016) , Danaus plexippus (OGS2. 0; http://monarchbase. umassmed. edu/), and Bombyx mori  (Xia et al. 2004) , and a repeat database generated by RepeatModeler  (Smit and Hubley 2008-2015)  that contains short and long interspersed nuclear elements, long terminal repeat elements, small RNAs, and other unclassified repeats. Within MAKER2 we used two gene predictors, Augustus  (Stanke and Morgenstern 2005)  and SNAP  (Korf 2004). For Augustus, we employed a publicly-available parameter set developed for H. melpomene, which is distributed with the software.
Associating genome scaffolds with linkage groups We created a nucleotide database from the genome assembly using makeblastdb on a local BLAST  (Altschul et al. 1990 ) installation. The sequences of markers that had been placed on linkage groups were then extracted from the Stacks cstacks catalog file, and formatted as a FASTA file, adding the linkage group for each marker to the sequence header. Each marker sequence was then mapped to the assembled genome using BLASTN (2evalue 1e-30), and 9,746 markers hit just one scaffold (59 markers with significant hits to more than one scaffold were ignored), enabling us to place scaffolds onto linkage groups. Similarly, we placed the sequences of the 75 EST-derived markers used by  Gleason et al. (2016)  onto scaffolds. This allowed us to translate among the linkage group identifiers assigned in our two studies (Table  S2 ).
When multiple markers from the present study were on one of these scaffolds we assigned a consensus genotype call for each individual, assigning a no-call in the case that marker-specific calls were not all identical. Associating A. grisella linkage groups with chromosomes from sequenced lepidopterans The genome of B. mori (version Jan. 2017) was downloaded from SilkBase (http://silkbase. ab. a. u-tokyo. ac. jp/cgi-bin/index. cgi), and the genome of H. melpomene (version 2. 5), along with the accompanying AGP file (A Golden Path file, which is a description of the assembly), was downloaded from the Butterfly Genome Database (http://butterflygenome. org/). We generated a BLASTable database for each genome, and subsequently mapped repeat-masked A. grisella scaffolds (with repetitive sequence replaced by runs of "N" bases) against each database using BLAST+ with default parameters  (Camacho et al.