dataesr/scientific-paragraphs-categorization huggingface dataset:
Building the global pseudo-reaction requires finding the appropriate stoichiometric coefficients such that the intermediate compounds of the pathway cancel out. A linear optimization program (Eq. 1) can be set to find the stoichiometric coefficients. The program can be solved using SciPy  60  with a simplex algorithm. where c is the objective function, A the stoichiometric matrix, and x the unknown stoichiometric coefficient multipliers. As an example, let's consider the following 3-reaction set: Rxn 1 : MNXM188 + MNXM4 + MNXM6 + 3 MNXM1 ! CMPD4 + CMPD3 + MNXM13 + MNXM15 + MNXM5 Rxn 2 : MNXM4 + 2 CMPD3 ! 2 MNXM1 + TARGET Rxn 3 : MNXM4 + MNXM6 + 3 CMPD4 ! MNX M13 + MNX M5 where MNXM are species IDs from MetaNetX, CMPD are intermediate species within the heterologous pathway and not present in the chassis organism, and TARGET is the product of interest.
A clip reaction master mix was prepared by combining 3 µL of 10X NEB T4 DNA ligase buffer, 1 µL NEB BsaI-HF v2 (NEB #R3733), 1 µL T4 DNA Ligase (NEB M0202 (Micalis), or Promega M1804 (Imperial)) per 20 µL required for each reaction. The Opentrons OT-2 pipetted the 20 µL master mix for each reaction, plus 1 µL of Biolegio BASIC linkers and 1 µL of each DNA parts, together with sufficient H 2 O to give a total volume of 30 µL. Clip reactions were incubated in a thermocycler (Applied Biosystems) for 30 cycles (37 °C for 5 min, 16 °C for 5 min), followed by a 5 min incubation at 60 °C at Micalis. Clip reactions were incubated in Opentrons Thermocycler Module for 20 cycles (37 °C for 2 min, 20 °C for 1 min), followed by a 10 min incubation at 60 °C at Imperial. For clip reaction purification, 54 µL of Mag-Bind® TotalPure NGS magnetic beads (OMEGA BIO-TEK (Micalis) or AMPure XP (Imperial)) were added; 150 µl 70% ethanol was used during wash steps; following re-suspension in H 2 O, 40 µL of the eluent was transferred to a fresh well.
Constructs were assembled in volumes of 15 µL using 1. 5 µL of each purified clip reaction in a solution of 1X assembly buffer (CutSmart Buffer, NEB #B6004). Assembly reactions were incubated at 50 °C for 45 min in a thermocycler (Applied Biosystems (Micalis) or Opentrons Thermocycler Module (Imperial)). 20 µL of DH5-alpha Competent E. coli (NEB #C2987H, Micalis) or home-made DH5-alpha competent E. coli (Imperial) were distributed per well into 96-well plates; then, they were used for transformation reactions. In all, 5 µL of the assembly reactions were mixed with cells. Heat shock was conducted according to the manufacturer's instructions. SOC media (125 µL) was transferred to each assembly and the reaction incubated at 37 °C for 1 h with lids off. Transformation reactions were spotted on plates (Thermo Scientific™ OmniTray™ Single well) each containing 40 mL LB-agar supplemented with 17.
Machine learning global scoring The purpose of the machine learning model is to predict if a given predicted pathway is a valid one or not. To that end, we developed a classifier based on the XGBoost library  62. The classifier was trained on 7919 Galaxy-SynBioCAD generated pathways (comprising 43392 reactions) used during the expert validation trial where 754 pathways were labeled positive. The training set can be found in the Supplementary file 'Dataset 3'. The input features used by the classifier are given in the Table  3 , these were computed for each training pathway and each reaction within the pathways. XGBoost learn function was parameterized with a Maximum depth of a tree of 1000 and a step size shrinkage used in update to prevent overfitting of 0. 3 (default value). The classifier accuracies were recorded during 4-fold cross validation.
Acknowledgements M. d. L. and J. L. F. acknowledge funding provided by the infrastructure  IBISBA1. 0 (Horizon 2020  under grant agreement No  730976 ). J. H. , T. D. , K. B. K. , and J. L. F. funding provided by the project  ALADIN (ANR EQUIPEX+ ). T. D. , J. L. F. , and G. S. B. funding provided by  BioRoBoost (Horizon 2020  under Grant agreement No  820699 ) and  PC, NS  and JLF funding from the  Biotechnology and Biological Sciences Research Council (BBSRC)  and the  Engineering and Physical Sciences Research Council (EPSRC)  under grant ' Centre for synthetic biology of fine and specialty chemicals (SYNBIO-CHEM )' ( BB/M017702/1 ). N. S. acknowledges further funding from the  BBSRC  under grant 'GeneORator: a novel and high-throughput method for the synthetic biology-based improvement of any enzyme' ( BB/ S004955/1 ) and from the  University of Liverpool.
PC also acknowledges support from the  Universitat Politècnica de València Talento Programme. VZ was supported for this work by The  Edinburgh Genome Foundry  funded by the  BBSRC  ( BB/M025659/1 ,  BB/M025640/1 , and  BB/M00029X/1  to  Susan Rosser ) and the  BBSRC/MRC/EPSRC funded UK Centre for Mammalian Synthetic Biology  ( BB/M0101804/1  to  Susan Rosser ) as part of the  RCUK's Synthetic Biology  for Growth programme. M. K. acknowledges funding support from  INRAe's MICA department ,  U. Paris-Saclay ,  Ile-de-France  (IdF) region's DIM- RFSI , and  ANR DREAMY  ( ANR-21-CE48-003 ). G. S. B. and G. B. acknowledge the support of the  EPSRC  ( EP/R034915/1 ). We thank  Thomas Nowak  ( LISN, Université Paris-Saclay ) for the use of the Opentrons OT-2 robot (funded by project  ABIDE, CNRS) ,  Bikash Ranjan Samal and Bibal Shahin  ( Micalis Institute, U.
Data availability Source data of figures are provided in the Source Data file. The eQuilibrator database is available online at Zenodo (https://doi. org/10. 5281/ zenodo. 4128543) and it can be queried using the equilibrator-api python library which is available online at github (https://gitlab. com/ equilibrator/equilibrator-api). The LASER database is available online at bitbucket (https://bitbucket. org/jdwinkler/laser_release/src/master/). All other relevant data are included in the paper and in the Supplementary Text and Dataset files. Source data are provided with this paper.
A sensitivity analysis to the choice of ϕ described in Section 2. 1. 3. It is well established that perceived underlying abundance does not solely drive the distribution of fishing effort in time and space; economic 1 SEAPODYM is an integrated age-structured, ecosystem model with explicit spatial population dynamics modeled using advective and diffusive processes. These spatial dynamics are driven by age specific environmental preference functions, based on observed environmental covariates, estimated in a data assimilation process by fitting to geo-referenced WCPFC skipjack tuna catch, size-frequency, and tag-recapture data. Senina et al. (2020) ) showed that SEAPODYM is able to adequately replicate WCPO skipjack tuna dynamics including longitudinal shifts in distribution related to "El Niño" phases  (Senina et al.
Japan was chosen as the "home base" for the hypothetical fishing fleet and the great-circle distance from Tokyo, Japan (139. 692222 ∘ E, 35. 689722 ∘ N) to every spatial cell x was calculated in kilometers using the distHaversine function from R package geosphere  (Hijmans, 2019)  in R 3. 6. 2. In the Expansion scenario, fishing effort was constrained to a maximum distance of 1000 km from Japan for the first 15 time steps (i. e. the first 15 quarters) of the simulation (1/8 th of the total simulation time of 30 years or 120 quarters). Over the next 90 time-steps, the maximum distance was allowed to temporally vary according to a Brownian bridge which progressively relaxed the maximum distance to 10,000 km by the 105 th time-step in the simulation. All spatial cells x in the spatial sampling frame were able to be fished at this point, and this was maintained for the final 15 time steps of the simulation.
The Contraction scenario was created in the same way but with the pattern in time-varying maximum distance reversed. For each of the six fishing effort sampling patterns, a total of 60,000 observations were generated, and each time-step t had an equal probability of being sampled. Each combination of the six fishing effort sampling patterns and two catchability patterns (described in the following Section 2. 1. 2) were simulated 100 times, resulting in 1200 total data sets used to estimate abundance indices.
Estimating indices For each of the 1200 simulated data sets described in Section 2. 1. 1, four different configurations of a spatiotemporal delta-lognormal GLMM implemented using R package VAST release number 3. 2. 2 2  (Thorson, 2019a)  were employed to estimate abundance indices I t from the simulated logbook data Âi. A baseline configuration of the spatiotemporal model 3 consisted of specifying two sub-models, as in any delta model: one for modeling the encounter probability with a binomial error structure, and one for modeling positive catch component with a lognormal error structure  (Lo et al. , 1992). Each sub-model separately estimated spatial variation (spatial random effects) as well as spatiotemporal variation (a set of spatiotemporal random effects for each unique combination of spatial time-step) at 150 "knots" that were uniformly distributed across a 1 ∘ spatial extrapolation grid defined by the model's spatial domain.
For reference, positive temperature anomalies in the Niño 4, hereafter referred to as ENSO, box coincide with "El Niño" conditions or negative phases of the Southern Oscillation Index. Including the climate 2 Accompanying utility functions for VAST used in this analysis can be found in v1. 0. 0 of the R package ofp-sam-vast-utils: https://github. com/Pacifi cCommunity/ofp-sam-vast-utils/releases/tag/v1. 0. 0 3 A technical description of the spatiotemporal model can be found in the Supplemental Information section "VAST Technical Annex". index as an SVC in the spatiotemporal model results in the estimation of an additional set of mean zero random effects (corresponding to the aforementioned "knots"), and an extra fixed effect (the random effect variance term) for each component of the delta model.
Fishery application 2. 2. 1. Description Spatiotemporal delta-GLMMs were also applied to the analysis of catch records from a fishery where the spatial sampling has changed over time, in this case the JPPL fishery for skipjack tuna in the WCPO (Fig. 3 ). Daily catch observations from the JPPL fishery, where catch was recorded as metric tons (mt) of skipjack tuna caught per day, were available for the period of 1972-2018 from the National Research Institute of Far Seas Fisheries (NRIFSF) operational logbook. This corresponded to 1,088,659 daily catch records. Data were collected with a 1 ∘ spatial resolution, where the position recorded in the logbook was the noon position from that day's fishing activities. In addition to the catch, date and location of fishing activity; the logbook records also contained detailed information about the vessel including vessel class (OS or DW), GRT (vessel size), vessel identifier, the average number of poles fished that day, and in the case of DW vessels, the gear configuration/fishing technology employed (i.
Furthermore, the spatiotemporal delta-GLMM used in Kinoshita et al. (  2019 )) modified the VAST source code to apply a temporal-mask in the calculation of the standardized indices from the abundance estimated at all extrapolation grid cells. This mask functioned such that the predicted abundance from cells which were deemed to be "biologically unfeasible" did not factor into the spatial average to generate the estimated abundance index. In the case of the Kinoshita et al. (  2019 )) analysis, cells were excluded from the calculation if their average temperature was below a minimum temperature threshold (18 ∘ C for skipjack;  Kiyofuji et al. , 2019)  for the time-step in question.
, 2019). Additional means of dealing with spatially-imbalanced, fisheries-dependent data are the use of a "bias-correction estimator"  (Thorson and Kristensen, 2016) , and employing a first-order autoregressive structure across time for the spatiotemporal variation terms  (Thorson, 2019a). The very large number of data points and scenarios handled in the present study precluded us from implementing these two additional options within reasonable computation time, but we recommend that future studies working with smaller datasets consider them. The bias-correction estimator developed in  Thorson and Kristensen (2016) ) is useful to correct for the "retransformation bias" when one predicts a derived quantity that involves a non-linear transformation of the random effects. A first-order autoregressive structure across time for the spatiotemporal variation terms allows for the estimation of abundance in unfished areas based on predicted abundance for these areas in adjacent years rather than based on a long-term predicted average abundance for these areas  (Thorson, 2019a).
Logbook catch data for the JPPL fishery is recorded as metric tons caught per day, without mention of the number of schools encountered and/or the size of the schools fished. This format of data reporting could be intrinsically hyperstable by inadvertently obscuring potential effects of gear/vessel saturation  (ArreguinSanchez, 1996; Kuriyama et al. , 2019)  and searching behavior for schools  (Gaertner and Dreyfus-Leon, 2004). For instance, 10 mt harvested from the edge of a 100 mt school could imply something different in terms of the population health than 10 mt harvested by completely capturing 5 separate 2 mt schools. Information on the search time, school size, number of schools fished per day, or school association (i. e. associated with natural floating object or fish-aggregating device) could reduce the risk of hyperstability in the abundance index.
Acknowledgments The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of  National Oceanic and Atmospheric Administration (NOAA) , the  Department of Commerce, Pacific Community (SPC) , the  National Institute of Water and Atmospheric Research , the  University of Washington , and the  Japan Fisheries Research and Education Agency. The authors would like to thank those whose insightful discussion contributed to the manuscript:  J. Macdonald  (SPC), T. Vidal (SPC), and  J. Kinoshita  ( National Research Institute Far Seas Fisheries ; NRIFSF). Additionally, we gratefully appreciate the time given by the following reviewers for improving the quality of this manuscript with their comments:  P. Hamer  (SPC),  M.
, 2019) , we undertook the extensive sequencing of haploid drones. These data comprised samples from French conservatories and commercial breeders in addition to samples from several European countries each representing potentially pure  A. m. ligustica, A. m. carnica, A. m. mellifera and A. m. caucasia populations typically imported by French breeders. Finally, A. m. iberiensis, the Iberian subspecies only separated from the native French A. m. mellifera by the natural barrier of the Pyrenees was also studied. In total, 870 samples were sequenced for SNP detection and 629 were used for a detailed genetic analysis of present-day honey bee populations in France. The results are publicly available as a phased vcf file for high-quality SNP markers carefully filtered against sequencing and mapping artefacts, which can be used for imputation or for selecting already genotyped reference individuals to be used in future studies.
| Mapping and genotype calling Sequencing reads were mapped to the reference genome Amel_ HAv3. 1  (Wallberg et al. , 2019)  using bwa-mem (version 0. 7. 15)  (Li, 2013) , and duplicates marked with picard (version 2. 18. 2; http:// broad insti tute. github. io/picar d/). Libraries that were sequenced in multiple runs were merged with samtools (version 1. 8)  (Li et al. , 2009)  prior to marking duplicates. Local realignment and base quality score recalibration (BQSR) were performed using gatk (version 4. 1. 2. )  (McKenna et al. , 2010) , using SNPs called with gatk haplotypecaller as covariates for BQSR. Each drone was independently processed with the pipeline and genotyped independently with haplotypecaller. Although the drones sequenced are haploid, variant calling was performed using a diploid model to allow the detection and removal of SNPs for which heterozygous genotypes are called in >1% of samples, and that might have arisen for example as a result of short-tandem repeats (STRs) or could highlight copy number variants (CNVs) in the genome.
Individual gVCF files were combined with combinegvcfs and then jointly genotyped with genotypegvcfs, resulting in a single VCF file for the 870 samples containing 14,990,574 raw variants. After removing Indels with gatk selectvariants, 10,601,454 SNPs remained. Sequencing depth was estimated using mosdepth  (Pedersen & Quinlan, 2018). Further details are given in https:// github. com/avign al5/SeqAp iPop/blob/v1. 5/SeqAp iPop_1_Mappi ngCal ling. md.
Evanno method provided  (Evanno et al. , 2005). Tree summaries for the 100 runs per migration tested were performed with dendropy  (Sukumaran & Holder, 2010)  and drawn with figtree version 1. 4. 4 (http://tree. bio. ed. ac. uk/softw are/figtr ee/). Further details are given in https://github. com/avign al5/SeqAp iPop/blob/v1. 5/SeqAp iPop_5_TreeM ix. md. Local ancestry inference and positioning of haplotype switches were performed with rfmix version 2. 03-r0  (Maples et al. , 2013). Three main genetic backgrounds were considered for this analysis, corresponding to the three major groups highlighted in the PCA. Reference samples were selected as having >95% pure background. Although most diploid data were removed and the data were already phased, shapeit Version 2. 904  (Delaneau et al. , 2012)  was run to format the vcf files for rfmix.
rfmix was run using genetic maps generated from the data of  Liu et al. (2015). Briefly, reads from the project SRP043350 were retrieved from the Short Read Archive (SRA) (https://www. ncbi. nlm. nih. gov/sra), aligned to the reference genome for SNP detection and recombinants were detected with the custom script find_crossing_overs. py to produce a genetic map. Further details on the rfmix analysis are given in https://github. com/avign al5/SeqAp iPop/blob/v1. 5/ SeqAp iPop_6_RFMix. md.
| Migrations between populations Due to the commercial interest expressed by beekeepers for the Buckfast bees and the peculiar genetic composition observed in the Corsican population, we performed a population migration analysis with treemix  (Pickrell & Pritchard, 2012). All samples having more than 80% ancestry from one of the nine backgrounds detected in F I G U R E 3 Admixture analysis. (a) Estimation of cross-validation (CV) error for 50 runs of admixture for 3 ≤ K ≤ 16. (b) Major modes and modes with the lowest mean CV error for admixture runs. For each value of K ranging between 2 and 12, Q matrices from admixture runs were grouped by similarity in modes by using the pong software  (Behr et al. , 2016). Blue: number of runs in the major mode; orange: number of runs in the major or minor mode having the lowest mean CV value.
, 2019; Li et al. , 2021; Wasik et al. , 2021). Although other studies have used whole genome sequencing for honey bee population genomics, these either concerned workers, thus complicating phasing  (Dogantzis et al. , 2021; Wallberg et al. , 2014) , and/or were more limited in terms of the number of samples studied  (Henriques, Wallberg, et al. , 2018b; Parejo et al. , 2016 Parejo et al. , , 2020)). Moreover, to make practical use of these published results, the raw reads need to be downloaded from the short-read archive (SRA) and aligned to the genome reference prior to detection of variants. This is a highly demanding task, in terms of both labour and computing time. In contrast, our data set is much larger, is naturally phased, and genotypes for the samples and markers can be directly selected from the vcf file provided.
, 2018). PCA on all ~7 million markers indicate that our data set is structured into three main genetic types (Figure  S8 ). The first principal component, representing 10. 8% of the variance, separates two major groups corresponding respectively to subspecies from northwestern (M lineage) and southeastern Europe (C lineage). These two groups are represented by several populations, including the Savoy and Porquerolles conservatories from South-East France on one side, and bees that are not geographically far from Italy or Slovenia on the other. This large genetic distance, despite relatively close geographical proximity of the populations, supports the hypothesis of the colonization of Europe by honey bees via distinct western and eastern routes  (Estoup et al. , 1995; Han et al. , 2012; Ruttner, 1988; Whitfield et al.
3. Principal component analyses (PCAs) were performed with plink and the contributions of individual SNPs to the principal components were estimated using smartpca from the eigensoft package version 7. 2. 1 (Patterson et al. , 2006). The significance of the contribution of the SNPs to PC1 was evaluated with the r package pcadapt version 4. 3. 3 (Privé et al. , 2020)  and qvalues estimated with the r package qvalues version 2. 26. 0 (Storey et al. , 2022). Further details are given in https:// github. com/avign al5/SeqAp iPop/blob/v1. 5/SeqAp iPop_3_LDfil terAn dPCAs. md. Admixture analysis was performed with the program admixture version 1. 3. 0 (Alexander & Lange, 2011) , with values of K ranging from 2 to 16. Fifty runs were performed each time using a unique random seed. The pong software (Behr et al.
, 2016)  was used for aligning runs with different K values and for grouping results from runs into clustering modes, setting the similarity threshold to 0. 98. Further details are given in https://github. com/avign al5/SeqAp iPop/blob/v1. 5/SeqAp iPop_4_Admix ture. md. Analyses of population migration was performed with treemix (Pickrell & Pritchard, 2012) , with the option for grouping SNPs set to -k = 500, testing between 0 and 9 migrations and performing 100 runs per migration with a unique random seed. The optimum number of migrations was estimated with the r package optm (Fitak, R. R. : https://CRAN. R-proje ct. org/packa ge=OptM) using the.
17550998, 2022, 8, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/1755-0998. 13665 by Cochrane Portugal, Wiley Online Library on [13/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License These groups reflect the main evolutionary lineages present in the data set, being the M lineage (A. m. mellifera and A. m. iberiensis), C lineage (A. m. ligustica, A. m. carnica) and O lineage (A. m. caucasia). For K = 2, A. m. caucasia bees are considered as having the same genetic background as the A. m. ligustica and A. m. carnica bees, also reflecting the results from the PCA (Figure 2; Figure S19). Some admixture can be observed for a small proportion of the reference samples.
A considerable number of Internet of Things deployments are isolated from specific solutions, from devices to data platforms. Standardized data models were proposed to overcome the interoperability gap between these deployments. Next generation service interfaces-linked data (NGSI-LD) is one of the proposed platforms that exploits linked data and proposes an information model and an application programming interface (API) for easy use and standard management of context information. The NGSI-LD information model is based on JSON for Linked Data (JSON-LD) as a serialization format for context information. This efficiently exploits the potential of semantics and linked open data. However, the NGSI-LD graph API and query language are still theoretically defined and limited to some preliminary works.