dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction of Regulatory Networks A crucial step in our integrated model is the reconstruction of the transcriptional regulatory network. Nowadays, the large amount of information related to gene regulatory interactions has been stored in various biological databases  [28] [29] [30]. Nevertheless, to more efficiently exploit such knowledge, there is a need to exchange and integrate this information from several database resources in an automated fashion. In fact, our main idea is to retrieve an exhaustive overview of possible regulators (TFs) associated with a subset of target genes of interest. Web semantic technologies that have a significant impact on knowledge integration is an efficient way for this purpose  [31]. These technologies are now available to interconnect various and heterogeneous sources of data.
They have been applied for biological knowledge management by integrating and querying disparate biological databases. In order to build the structure of a transcriptional regulatory network (Figure  2 ), we first extract a set of relevant metabolic target genes. For this purpose, we performed a single reaction analysis  [32]  to identify important reactions of which the removal drastically reduces biomass production. Next, we select all the enzymes that catalyze these reactions and consequently the genes that regulate these enzymes. Despite these correspondences between reactions, proteins and genes are not always available in the metabolic model; it is often possible to retrieve it by automatically querying the literature databases. Considering only a set of relevant genes, denoted later by ξ, allows us to reduce the size of the network and thus the computational complexity of the remaining steps of the BayRegMet framework.
The second step of the reconstruction process consists of using semantic web technologies in order to reconstruct the upstream regulatory network. The main idea behind these technologies is to describe data in the Resource Description Format (RDF) and to query them using the SPARQL language  [33]. Therefore, to perform exploratory searches of TF-target gene interactions, Pathway Commons database  [34]  is iteratively interrogated by performing SPARQL queries on RDF data in order to identify all the known regulators of a given set of genes. The following SPARQL query (Algorithm 1) is the basis of the GetTFcontrollers(g) that aims to return the regulatory ancestors of a given gene. The basic steps of the gene network reconstruction are described in the algorithm below Algorithm 2. As a result, we obtain a directed graph encoding the regulatory effects that impact the activity of genes in the set ξ.
A binary discretization is performed on all transcription levels of normalized RNA-seq dataset reflecting whether a gene is activated or repressed in a given sample. Moreover, the first component of BNs is a directed acyclic graph that contains only directed edges and no cycles. Therefore, we need to ensure that the resulting TRN structure does not involve any directed cycles. For this task, we refer to the greedy method to reconstruct the BN structure from the original TRN resulting from SPARQL queries. TF-target interactions are iteratively added to the DAG if and only if there is no feedback loop or path between a regulated gene and their TFs  [35]. Once we have the structure of the BN that determines the set of TFs for each gene, the out task now is to learn the BN parameters that define the CPDs for this structure.
Thus, given the dependency structure and the discrete training data, we follow the standard approach of maximum likelihood estimation to estimate the parameters of the different CPDs. Since the exact inference of marginal probabilities over the variables of interest quickly becomes infeasible for our network, we used approximation techniques based on statistical sampling  [36]. This part has been implemented in Python by using the pgmpy library  [37]  that enables us to handle large-scale probabilistic graphical models. Next, we compute the probability for a given gene to be active or inactive in the absence of stimuli, which in this case corresponds to a computation of the marginal probabilities. Complementary to this, it is also possible to compute the probability for the same gene to be active or inactive under a given stimulus that consists in assuming that several genes that are known to be targeted by a given drug are active or not.
This restricted overview of the gene regulatory network may discard genes that can be considered as potential therapeutic targets. For this purpose, by using our BayRegMet tool, the single reaction deletion analysis is extended to simulate the effect of the inhibition of genes that are not directly related to the produced enzyme. Note that the metabolic analysis in this case study is based on simulating growth phenotype in order to predict the biomass growth of the tumor cell under diverse drug effects. To achieve this challenge, we need a functional reconstructed metabolic network that can be downloaded from different model repositories like Human Metabolic Atlas  [43]  and Virtual Metabolic Human  [44]  for instance. As shown in Figure  3 , once we have the metabolic model, the single reaction deletion analysis step is performed to identify which reactions have an impact on the cell growth and therefore define the set ξ of relevant target genes that encode metabolic enzymes.
Then, a reverse-engineering technique based on SPARQL querying language is used to identify functional relationships between transcription factors and target genes in ξ. The resulting network is transformed into a directed acyclic graph (DAG) that can be explored using a bayesian network framework. However, complementary to the graph structure, a set of transcriptomic data is needed to calculate marginal likelihood distribution through all nodes in the DAG in order to predict the posterior probability of the enzyme activity in a given context. In our study, RNA-Seq data for liver cancer, are obtained from the TCGA data portal. They are emerged from the treatment of the BAM alignment files  [45]  to generate a read count matrix, which has genes along the rows and samples along the columns. The entries in the matrix give how many reads for a given gene in a given sample can be uniquely aligned.
Based on the metadata file associated with counts files, we divided samples into four phenotypic groups: (male/female) × (primary tumor tissues/healthy tissues). Once we have raw counts for these samples, a normalization step is performed in order to remove systematic technical differences between samples and to thus reduce the impact of technical bias on the results. For this, the DESeq normalization method from the DESeq Bioconductor package  [46]  is performed on the htseq-counts data. The next step consists in identifying the active/inactive states of genes. For this, binary discretization was performed on normalized data based on each log gene's transcription level mean over all 268 samples. Thus, all genes were assigned two states: 0 was assigned to genes with number of reads less than the mean and 1 otherwise.
The main metabolic task in this study case is the biomass production; thus, according to the biomass objective function defined in the HCC metabolic model, the optimal growth rate of tumor cells while taking into account the model stoichiometry, steady-state assumption, and the predicted activities of enzymes under drug effect is thus obtained by solving a linear optimization problem as described in the section above. Potential drugs and their targets are available in diverse databases such as KEGG DRUG  [28]  and DrugBank  [47]. We have listed two approved drugs with known effects on HCC cancer: Sorafenib(USAN/INN) and Regorafenib(USAN/INN). To predict the growth rate of HCC tumor cells under the effect of those drugs, BayRegNet systematically knocked out all the TFs that control the transcription of known drug target genes.
The main contribution in our study is the way in which RNA-seq data is exploited to quantify the interactions between genes and their regulators. Using a Bayesian network for this end reveals some limitations, given the fact that the number of samples is small relative to the number of parameters to be inferred. This limitation raises interesting perspectives that can be addressed either by reducing the size of the reconstructed TRN or by using logic networks, for instance, that are more qualitative but require less data. It is important to note that existing databases that provide information about the key genomic changes occurring in human tumor cells present certain limitations in terms of the lack of knowledge related to isoenzymes  [49]  that may have a crucial function on regulating human cancer cell.
Results and Discussion The proposed real-time comfort management algorithm has been implemented and applied to the system model, in the MATLAB ® environment. Extensive simulations were run for a large number of scenarios with different weather conditions, traffics, and initial battery's state-of-charge. This section presents the results and a statistical analysis is conducted in order to assess the capacity of the proposed method in adjusting the thermal comfort according to the amount of energy available for the HVAC system. Firstly, we verify that the HVAC system consumption estimated during the cooling phase using the look-up table G corresponds to its actual consumption, calculated afterwards using the system dynamic model. A good match justifies the relevance of using a precalculated static model in slowly varying conditions.
), the metabolism rate may be lower than 1. 4. We assume therefore that driving corresponds to a metabolism rate of 1 (58. 2 W/m ) , with an uncertainty of + 0. 2. Finally, the air speed in the cabin is assumed to be equal to 1 m/s. Five initial SOC of the battery are tested, each one corresponds to a given amount of energy available for thermal comfort. In summary, the benchmark built from the abovementioned scenarios corresponds to four given trips, which are carried out in 210 weather conditions, with five different levels of available energy. The total number of test cases reaches 4200. A summary of simulation data is reported in Table  3. This large number of situations allows assessing the capacity of the proposed real-time algorithm in adjusting the thermal comfort according to the available energy.
g. Schreiber et al. 2017). In order to test the galaxy evolution models, we need to obtain reliable number counts of well characterized DSFGs. In this work we present the band 6 (1. 2 mm) continuum observations obtained as part of the ALMA Spectroscopic Survey in the HUDF (ASPECS,  Walter et al. 2016). The HUDF, and specially the eXtreme Deep Field (XDF), is the deepest extragalactic fields observed by the Hubble space telescope  (Beckwith et al. 2006; Illingworth et al. 2013). ASPECS Large Program (ASPECS-LP) is an ALMA cycle 4 large program that represents an unparalleled three-dimensional survey in a contiguous ∼ 4 arcmin 2 region in the HUDF designed to trace the cosmic evolution of cool gas and dust. The first results using the band 3 observations were recently published  (González-López et al.
Survey design The data used in this work correspond to the band 6 observations from ASPECS-LP. The observational setups used in ASPECS-LP are the same as the ones used in ASPECS-Pilot observations presented in  Walter et al. (2016). We used eight spectral tunings that cover most of the ALMA band 6 (212-272 GHz, ≈ 94% of band 6). The mosaic consists of 85 pointings separated by 11 (≈ 51% of the half power beam width at the highest frequency setup) and is Nyquist-sampled at all frequencies. The ASPECS-LP observations were designed to obtain a sensitivity similar to that of the ASPECS-Pilot observations. The final data ended up being deeper than requested owing to several of the executions being obtained with excellent weather conditions and low precipitable water vapor (pwv < 1 mm).
Data reduction, calibration and imaging We processed the data with the CASA ALMA calibration pipeline  (v. 5. 1. 1; McMullin et al. 2007 ), using the calibration and flagging input provided by ALMA. We created the continuum images as well as the data cubes with CASA v. 5. 4. 0-70. That version of CASA fixed a couple of bugs that affected large mosaic imaging. We obtained the continuum images using the task tclean with natural weighting and multi-frequency synthesis (mfs) mode. We created the cleaned images by putting cleaning boxes on all the sources with S/N ≥ 5 and cleaning down to 20 µJy beam -1. The deepest region of the continuum image has a rms value of 9. 3 µJy beam -1 with a beam size of 1. 53 × 1. 08. We used the same procedure to obtain a tapered image with a rms value of 11. 3 µJy beam -1 with a beam size of 2.
Noise reference images We here present two independent approaches to obtain a noise reference image from a calibrated ALMA dataset. The first is based on the jackknife resampling used in single-dish bolometer observations  (Perera et al. 2008; Scott et al. 2008; Weiß et al. 2009 ). If the emission is stable with time, then the image produced by the combination of the jackknifed scans (where the amplitudes in every second scan are multiplied by -1) will have all the real emission removed, whereas the noise properties are conserved. This will provide a good representation of the noise properties of the observations. The same exact jackknife resampling can not be used with interferometric observations since different scans do not provide information from the same region on the sky. Each interferometric integration corresponds to one point in the uv-plane and is sensitive to one determined position and angular scale on the sky.
To bypass this limitation we jackknifed the observations by inverting (multiply the real and imaginary part by -1 or shift phase by 180 • ) every second visibility, which artificially creates destructive interference on the real sky emission. The second approach to obtain a noise reference image is also based on jackknife resampling but done in channel space of the data cube. In a sense, all continuum images made from ALMA observations are done by combining the information from multiple channels into one. Such combination can be done by fitting a continuum emission to all different channels, as done by tclean in mfs mode, or by collapsing the associated data cube. Based on this, we randomly inverted (multiply by -1) half of the channels in the dirty cube created for the band 6 ASPECS-LP data.
acknowledges support from the  National Science Foundation  under grant number  AST-1614213  and from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship for Experienced Researchers. RJA was supported by  FONDE-CYT  grant number  1191124. IRS acknowledges support from  STFC  ( ST/P000541/1 ). B. M. acknowledge support from the  Collaborative Research Centre 956 , sub-project  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG)  -project  ID   184018867. We acknowledge support from  CONICYT  grants  CATA-Basal AFB-170002  (FEB);  FONDECYT Regular 1190818 (FEB) ;  Chile's Ministry of Economy, Development, and Tourism's Millennium Science. Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics, MAS (FEB). Este trabajo cont ó con el apoyo de  CONICYT + PCI + INSTITUTO MAX PLANCK DE ASTRONOMIA MPG190030  (M.
26 When compared with two widely used methods on simulated and real datasets, 27 BAdabouM was faster, exhibited a similar accuracy with a good concordance on SVs 28 detected, and detected significantly more insertions. BAdabouM was more reproducible 29 to detect independently SVs across individuals, which is a clear advantage when 30 characterizing population polymorphism. Furthermore, BAdabouM demonstrated a 31 superior ability to detect breakpoints with a base pair resolution. 32 BAdabouM proved to be efficient, fast and accurate to detect SVs, and handle. BAdabouM 33 is a complementary method to be used for a more comprehensive detection of SVs, and 34 is especially suited for studying polymorphism for all types of SVs with a high accuracy.
SVs 57 possibly have a huge impact on individual's fitness, inducing phenotypical modifications, 58 diseases susceptibility or local adaptation  (Chain & Feulner, 2014). Despite these major 59 roles on individuals and populations, structural variations survey remains uncommon, 60 mainly due to the lack of standardized protocols to reliability detect them in whole 61 genome re--sequencing datasets. 62 The detection of SVs in WGS data is based on multiple signals. Reads aligned with a split 63 (split reads) allow to detect the breakpoints (i. e. SVs' start point and end point), while 64 abnormally mapped pairs (e. g. , read pairs with both reads aligned with the same 65 orientation or an unlikely long insert size), and uneven depth of coverage indicate the 66 presence of SVs and provide informations to infer their type  (Alkan, Coe, & Eichler, 67 2011).
68 69 Several tools are already available to detect SVs  (Lin, Smit, Bonnema, Sanchez--Perez, & 70 Ridder, 2014) , but they might be hard to install, have non--trivial settings, implies to use 71 arbitrary thresholds and should be run in multiple successive phases lengthening 72 calculation time. Moreover, their joint use on same datasets shows very high false 73 positive rate  (Sedlazeck et al. , 2018)  and low overlap, which justifies a multi tool 74 approach  (Pabinger et al. , 2014  Pre--processing and threshold settings: BAdabouM browses part of the file (100k first 90 reads, modifiable option) to auto evaluates experimental characteristics, i. e. read length, 91 library length and mean coverage. Read length and library length are then used to create 92 a sliding window divided in three parts (the first and third ones of the mean size of the 93 library fragments, and the middle one of the mean size of the reads) in order to browse 94 the whole file to detect abnormally mapped reads (respectively pairs of reads) as 95 indicators of SVs.
164 165 Application 166 167 To test BAdabouM's ability to detect SVs in both simulated and real datasets, we 168 compared it with two commonly used methods, Delly  (Rausch et al. , 2012)  and 169 Breakdancer (K. Chen et al. , 2009). Theses two softwares were selected for their ability 170 to detect a wide range of SVs. Delly detects deletions, inversions, duplication and inter--171 and intra chromosomal translocations based on abnormally mapped pairs, while 172 Breakdancer detects deletions, inversions, duplication and insertions based on both 173 abnormally mapped pairs and split read). They were also chosen because they are 174 widely used by the scientific community to analyse datasets alone  (Zhao et al. , 2016)  or 175 combined in pipelines  (Mimori et al. , 2013; Mohiyuddin et al. , 2015) , and also to 176 benchmark new softwares  (Layer, Chiang, Quinlan, & Hall, 2014; R.
Simulated dataset 181 182 Softwares were first benchmarked using a set of simulated genomes harbouring each 183 types of structural variant. This panel was composed to test the detection of Insertions, 184 Deletions, Inversions, Copy Number Variations, and Inter and Intra--chromosomal 185 translocation. For each type of SVs, 25 events distributed over five different sizes were 186 simulated (five event of each size: 100, 250, 500, 1000 and 5000 bp). These SVs were 187 simulated in the first 1M bp of the first two chromosomes of the sheep reference 188 genome OAR_v4, after removal of undefined nucleotides, i. e. N). 189 We then simulated resequencing data for one hundred homozygous and one hundred 190 heterozygous individuals, with a sequencing coverage of 20X and a library size of 300bp 191  (Huang, Li, Myers, & Marth, 2012).
More details about simulated data are available in 192 supplementary informations. Software were then evaluated based on (i) their ability to 193 detect the event and (ii) the precision of the detection (distance between predicted and 194 simulated breakpoints). 195 196 The results of the simulations (Table  1 ) show that softwares performances vary across 197 SVs types and size. Considering SVs discovery rates, the performances of BAdabouM are 198 similar to that of the two other softwares, except a lower discovery rate for inversions. 199 Alike, BadabouM had a lower ability to detect SVs at the heterozygous state than the two 200 other methods, inducing higher false negatives rate (Table  1 ). It's interesting to note that 201 BAdabouM was able to detect insertions much better than Breakdancer (Delly does not 202 detect insertions).
The softwares were clearly different in their precision in predicting 203 breakpoints. BAdabouM located SVs with very high accuracy (median distance between 204 predicted and real breakpoint ranging from 1 to 1. 5bp, except for 250bp inversion 205 where breakpoint precision was 28bp), while Delly was less accurate (median distance 206 ranging from 14bp to 60bp) and breakdancer was even less precise (median distance 207 ranging from 16bp to 286bp). 208 Combining discovery rate and precision, BAdabouM could detect all types of structural 209 variations at a single base pair resolution. The lower detection rate observed (Table  1 ) is 210 probably due to the fact that BAdabouM integrates multiple signals simultaneously. The 211 cost to pay for this precision is a higher rate of false negatives.
Real dataset 217 218 To benchmark the different methods on real data, we took advantage of a recently 219 published dataset of medium coverage whole genome sequences (about 12X) from 53 220 individuals from wild and domestic sheep (three Ovis species)  (Alberto et al. , 2018, 221  Supplementary table 1), The goals were to (i) test BAdabouM's ability to discover SVs 222 on real data, (ii) examine the concordances between BadabouM and the two other 223 methods and (iii) compare the computational resources needed by the three softwares. 224 225 A correspondence analysis (Fig. 1A ) showed that BadabouM was able, as the two other 226 methods, to detect the genetic signals that fully differentiate the Ovis species. 227 While 11,5 % of the whole set of variants discovered were detected by all three 228 methods, a greater part (32,5 %) was shared only by two methods, and the majority (56 229 %) was specific to one software (Fig.
1B ). This low concordance between methods 230 highlights the technical difficulty to detect SVs, a general concern that has already been 231 emphasised  (Pabinger et al. , 2014). However, we must consider that 80% of the events 232 detected by BAdabouM alone were insertions that are not detected by Delly, and for 233 which Breakdancer performs poorly as shown with the simulated dataset. Thus, if we 234 except insertion, a high concordance of 91. 1% was observed between BAdabouM and 235 the two other softwares. 236 Interestingly, variants detected by BAdabouM in an individual were more frequently 237 detected independently in another individual (i. e. , overlap Table  2 ) than with the two 238 other methods. The lower number of SVs globally detected and the higher overlap 239 between individuals reflects the BAdabouM's stringency, which relies on the 240 simultaneous detection of multiple signals for high confidence SVs.
Moreover, the 241 measurement of computation time ( BAdabouM is easy to install and handle, fast, and can detect all types of structural 247 variations in whole genome re--sequencing data. Moreover, it is more conservative than 248 other softwares by integrating all signals to detect SVs for maximising the avoidance of 249 false positives. It also performs better to detect a given SV across individuals with a very 250 high accuracy at single base pair resolution. All these properties are especially suited for 251 the characterization of SVs polymorphisms in population genomics approaches  (Luikart, 252 England, Tallmon, Jordan, & Taberlet, 2003)   :,Summary,of,the,simulations,used,to,benchmark,the,three,SVs,callers. ,For,each,type,of,SVs,,the,first,value,correspond,to,the,median,value,of,calling,rate,over,the,five,replicates,, while,the,value,between,parenthesis,correspond,to,the,median,distance,between,estimated,and,simulated,.