dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge financial support from the pea  CRB  project that allowed the SSR genotyping, from the  FP7- GLIP  project for RBIP genotyping, from the  FEDER-Région Bourgogne  project " Qualité de la graine de pois " for the field phenotyping of the collection and from the  ANR   Bioressources  2009  GENOPEA  project for SNP genotyping. We warmly thank  Hélène Charrel ,  Céline Rond ,  Hervé Houtin  and  Pascal Marget  as well as the team of  Domaine d'Epoisses  for their significant contribution to the collection phenotyping, and  Noel Ellis  and  Andy Flavell  for providing us with the RBIP data on the collection. We are grateful to  Mike Ambrose  for providing us with the core16 of the JIC, and to  X. Zong ,  M. Vishnyakova ,  C. Coyne  and  A. Ramos-Monreal  for providing us with some accessions from their national collections.
The resulting AbrB depletion is consequently followed by activation of AbrB repressed genes, which are often important for stationary phase processes. Notably, because of its role in the elaborate sporulation and competence decision making network  [26, 27] , AbrB has mainly been studied in the context of entry into stationary phase while much less is known about its exact role in the exponential growth phase. We selected putative sRNAs from a rich tiling array dataset of 1583 potentially regulatory RNAs  [9]. This selection was made for evolutionary conserved putative sRNAs with a high expression level on defined minimal medium. Deletion strains of these putative B. subtilis sRNAs were subsequently tested for growth phenotypes. One sRNA-RnaC/S1022-stood out since the mutant strain displayed a strongly increased final optical density on minimal medium with sucrose as the sole carbon source.
Statistical data analyis was performed with a one-sided Welch twosample t-test (H 1 : AbrB/abrB levels in ΔRnaC/S1022 > than in the parental strain and the RnaC/S1022 complementation strain). The respective p-values are either indicated, or marked with asterisks (* p-value <0. 05; ** p-value <0. 01). B) abrB Northern blot analysis. Equal amounts of RNA were loaded in each lane. The bar diagrams show the relative abrB mRNA levels, with the level in the parental strain (wt) set at 100%. Quantifications are based on minimally two independent experiments. Error bars represent the standard deviation between experiments. Data was analyzed for significance as in A. To test whether the AbrB levels were directly dependent on RnaC/S1022 levels, we placed the RnaC/S1022 complementation cassette in the amyE locus of the parental strain and used Western and Northern blotting to measure AbrB protein and mRNA levels.
The third option would be a double-negative feedback loop consisting of sRNA repression of AbrB levels and AbrB repression of sRNA levels, which would ultimately lead to an increase in AbrB protein expression noise. This would thus depend on repression of the RnaC/S1022 promoter by AbrB, in addition to the confirmed negative regulation of abrB by RnaC/S1022. Together this would lead to a decrease in AbrB protein levels in cells that start with belowthreshold AbrB levels. First of all, we found no indication for AbrB binding sites in the region upstream of RnaC/S1022 in the dataset of Chumsakul et al. , where the binding sites of AbrB were mapped genome-wide  [24]. To test whether the RnaC/S1022 promoter is indeed not directly controlled by AbrB, or possibly under indirect control of AbrB, we deleted the abrB gene from the above-mentioned P RnaC/S1022 promoter fusion strain.
Evolutionary conservation analysis of RnaC/S1022 targets In order to find predicted targets co-conserved with RnaC/S1022, we used the 62 Bacillus genomes available in Genbank (as of  January 31, 2013). On each of these genomes a BLAST search (Blastn v2. 2. 26 with default parameters) was conducted with the B. subtilis 168 RnaC/ S1022 sequence as identified in Nicolas et al. [9]. Genomes where a homologue of RnaC/S1022 (E-value < 0. 001) was found were then subjected to TargetRNA_v1 search with extended settings around the 5'UTR (-75 bp; +50 bp around the start codon and additional command line arguments "-z 250 -y 2 -l 6") using as query the sequence of the first high-scoring-pair of the first BLAST hit in that particular genome. A bidirectional best hit criterion (based on Blastp v2. 2. 26 with default parameters and E-value cut-off 0.
001) was used to compare the predicted targets in each genome with the predicted targets in the reference B. subtilis 168 genome (Genbank: AL009126-3). The data was tabulated and subsetted for B. subtilis 168 genes predicted for RnaC/S1022 in 8 or more genomes. The Bacillaceae phylogenetic tree was computed based on an alignment of the rpoB gene BLAST result from the same set of genomes mentioned above. RpoB was reported to be a better determinant of evolutionary relatedness for Bacillus species than 16S rRNA  [62].
Before loading on Novex nuPAGE 10% Bis-Tris gels (Life technologies), samples were boiled for 10 min and centrifuged to pellet the glass beads and cell debris. Equal OD units were loaded on gel and the intensity of the AbrB band was corrected with the intensity for the unrelated BdbD control. Data from Northern blots and Western blots were quantified ImageJ software (available via http://rsbweb. nih. gov/ij/).
Absorption spectra analysis. The absorption spectra of the device at different reverse bias voltages obtained from optical transmission measurements are reported in Fig. 5  for TE polarization. The data have been filtered and the baseline of each curve has been shifted by 200 cm -1 to better highlight the spectral features. From these measurements the absorption spectra in Ge/SiGe CQW show unique characteristics which differ from those previously recorded in standard non-coupled Ge/SiGe QW  14. A combination of increasing/decreasing absorption peaks is obtained when the electrical field varies from 10 kV/cm to 90 kV/cm. To understand this behavior, the measurements are compared with simulations performed within the envelope function approximation using the Nextnano software package  22. The simulated wavefunctions of cΓ 1 , cΓ 2 , HH 1 and HH 2 in the CQW as a function of the applied electric field are reported in Fig.
Acknowledgements The CARIPLO foundation is acknowledged for financial support through the grant  EIDOS 2011-038. The fabrication of the device was performed at the nano-center  CTU-IEF-Minerve , which is partially funded by the  'Conseil Général de l'Essonne '. This work was partly supported by the  French RENATECH network. D. M-M. acknowledges support by the  Institut Universitaire de France. Marie Curie International Outgoing Fellowships  through grant agreement PIOF-GA-2013-629292 MIDEX is partly acknowledged. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  N°639107-INsPIRE ).
Since its reclassification as a distinct disease entity, clinical research efforts have attempted to establish baseline characteristics and prognostic scoring systems for chronic myelomonocytic leukemia (CMML). Although existing data for baseline characteristics and CMML prognostication have been robustly developed and externally validated, these results have been limited by the small size of single-institution cohorts. We developed an international CMML data set that included 1832 cases across eight centers to establish the frequency of key clinical characteristics. Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7. 5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%.
Several prognostic models derived from smaller data sets have been developed to stratify CMML patients into groups that are predictive for overall survival (OS). 9-18 However, the validity of these models in a large international data set has never been investigated, and a consensus is not yet present that would standardize risk stratification. The incidence of CMML is estimated at 0. 4 per 100 000 based on several large epidemiologic studies. 19, 20 Given the apparent low incidence of CMML and its broad range complexity, detailed baseline characteristics describing the clinical heterogeneity have not been reported in a large data set. To examine CMML baseline characteristics and test the prognostic significance of clinical and genetic variables, as well as the relative power of existing prognostic models with sufficient resolution, we constructed a large international CMML database that merged CMML registries from eight tertiary cancer centers across three different countries.
MATERIALS AND METHODS Participating centers were identified via the International Working Group for MPNs and the Evans Foundation MDS Clinical Consortium. Data were abstracted from the first visit at each institution and deposited for central data review at the Moffitt Cancer Center. Internal Review Board approval was obtained at each respective institution. Manual central review of cases was performed to ensure data quality before analysis. Data curation and merging were performed to ensure that (1) data elements were uniformly recoded in all spreadsheets for accuracy and consistency; (2) data were centrally transformed into categorical variables for analysis; (3) descriptive cytogenetic information was uniformly categorized according to the International Prognostic Scoring System (IPSS), Revised (R)-IPSS, Mayo and Spanish prognostic models (CPSS);  [10] [11] [12]  (4) calculated scores for the different prognostic models in CMML were centrally reviewed and concordant with the methodology in their respective publications; and (5) baseline data were reflective of CMML that was strictly defined according to the WHO criteria.
The primary objective of this study was to establish an international CMML data set and validate the above models calculated at the time of presentation to each center. We validated and performed a detailed statistical comparison between the IPSS, 11 R-IPSS,  10  Global MD Anderson Scoring System,  18  MD Anderson Prognostic Score,  17  Dusseldorf Score (DS),  13  Mayo, 9 and CPSS. 12 All prognostic models were calculated as previously described. The Kaplan-Meier (KM) method was used to estimate the median OS and leukemia-free survival (LFS) and the log-rank test was used to compare KM survival estimates with SPSS version 21. 0 (IBM Corp. , Armonk, NY, USA). Random forest survival and receiver operator characteristic (ROC) analyses were carried out with R. Comparison of relative statistical power was performed with the Harrell's concordance index (C-index) and the area under the curve (AUC) of the ROC.
Patients who received allogeneic stem cell transplant (n = 129) were censored from all survival analysis. Genetic data were retrospectively collected from each institution. Although raw data were not centrally acquired, the genetic data merged in this data set were previously published from their respective cohorts or generated in a CLIA (Clinical Laboratories Improvement Act of 1988) environment using next-generation sequencing technology as part of the patient's permanent medical record. Published genetic data were generated by both Sanger and next-generation sequencing with amplicon-based target enrichment. ] 21  Role of the funding source The study sponsors had no role in the study design; no role in the collection, analysis and interpretation of data; no role in the writing of the report; and no role in the decision to submit the paper for publication.
Baseline characteristics Between July 1981 and June 2014, 1832 CMML patients were captured in the international CMML database. Each deposited CMML case contained up to 70 discrete data elements that could include genetic information. The median age at diagnosis was 70 (16-93) years, with a male (67%) predominance. Most patients were evenly subcategorized as MPN-CMML (49. 8%) versus MDS-CMML (50. 2%) by the French-American-British criteria. Splenomegaly was demonstrable in 25% of all cases. Most patients had favorable cytogenetics by IPSS (73%), R-IPSS (71%), CPSS (71%) and Mayo (71%) classification schemas. The mean bone marrow (BM) blast percentage was 5. 6 cellsx10 3 /dl (0-19), and mean monocyte count was 4. 85x10 3 /dl (1-120). Surprisingly, the majority of patients had CMML-1 (79. 9% vs 20.
5% is the optimal cut-point based on a likelihood ratio test splitting criteria. 23 To confirm 7. 5% as an optimal cut-point, the log-rank tests were calculated at every possible cut-point from 3 to 15%. This confirmed that the cut-point of 7. 5% had the highest log-rank test statistic (Supplementary Figure  S1b ). Our data suggest that a cut-point of 7. 5% blasts may be a more appropriate discriminator of prognosis in CMML. The median OS of the entire data set was 31 (22-64) months. At last follow-up, 1116 deaths (61%) were recorded and 380 leukemia transformations (21%) were observed. An extended description of baseline characteristics and their differences among contributing centers are present in Supplementary Table  S1.
Analysis of existing prognostic models To confirm that existing CMML prognostic models were valid in our merged database, we calculated the prognostic score for the IPSS (n = 1599), R-IPSS (n = 1618), MD Anderson Scoring System (n = 1297), MD Anderson Prognostic Score (n = 1584), Dusseldorf Score (n = 1234), Mayo (n = 1653) and CPSS (n = 1281) for each evaluable case. All tested prognostic models were valid and able to predict OS by the KM method and the log-rank test (P o 0. 0001) (Figure  1 ). Next, we compared the relative model performance using 1013 complete cases with sufficient data to calculate all risk models using ROC curves and their AUC. ROC curves were calculated for OS at 36 months. The C-index, which evaluates prognostic power across time points, was also used to orthogonally validate the relative prognostic power of each model.
KM survival analysis and log-rank test within our database demonstrated a significant OS difference among these risk groups at not reached (95% confidence interval: 53. 6-79. 2), 35. 1 months (95% confidence interval: 32. 6-38. 4) and 13. 8 months (95% confidence interval: 11. 7-15. 4), respectively (Po0. 0001). These results, and the statistically significant differences in LFS among groups (Po 0. 0001), are shown in Supplementary Figure  S2. Next, we tested the relative prognostic power of this novel model against other existing CMML models and found that it had the highest AUC at 0. 714 for OS and second highest AUC for LFS at 0. 709 (similar results for C-index). However, the difference in AUC between our novel model and existing models was not statistically significant, despite being compared within the data set for which the new model was developed (Figure  4 ).
DISCUSSION CMML is a rare hematologic neoplasm that has been confirmed to be distinctly different from MDS. However, much of standardization in CMML clinical practice remains based on the MDS data partially because, unlike MDS, large CMML data sets have not been available to generate evidence-based clinical recommendations. Our data set represents the largest international CMML-specific collection. This provided us sufficient resolution to accurately estimate frequencies of key clinical characteristics and interrogate the utility of existing CMML prognostic models. Of particular interest, our data demonstrated that the majority of CMML cases are CMML-1 (BM blasts o 10%) by the WHO classification schema. We were able to demonstrate that a cutpoint of 7. 5% BM blasts may provide improved prognostication, as cases with 5-9% blasts had a similar OS compared with those with 410% blasts.
The adverse prognosis associated with CMML cases with 5-9% BM blasts has been substantiated by a recent publication from the Dusseldorf registry, which was not part of this data set. 29 A new BM blast cut-point should therefore be validated under central pathology review and subsequently be considered as a revision to the current CMML classification schema. Our data set also allowed us to validate seven distinct prognostic models used in daily CMML clinical practice. Although all models were valid, it is notable that the prognostic significance of the IPSS and R-IPSS were valid in our entire data set because proliferative CMML cases were excluded in the development of both the IPSS and R-IPSS. Further, even when only proliferative CMML cases (MPN-CMML) were considered, the R-IPSS and IPSS remained valid, albeit with decreased prognostic power as measured by AUC and C-index (Figure  2 ).
Taken together, our data suggest that the prognostic power of clinical variables may have reached an asymptote and that novel prognostication strategies are needed to accurately estimate the OS of patients with CMML. To address this, we explored the prognostic impact of genetic data retrospectively annotated in our database. Although this genetic information was not centrally collected, we were able to demonstrate that frequencies, mutual exclusivities and the expected prognostic relevance of ASXL1 were maintained, supporting the use of this data set for future study. We were also able to identify the independent prognostic significance of CBL mutations in CMML, which had not previously been demonstrated. This is relevant given that our analysis exploring the relative prognostic power of models containing ASXL1 mutations identified no difference in prognostic power compared with other models, perhaps suggesting that combinations of mutations such as CBL and interrogation of RNA expression signatures may yield a more powerful molecular prognostic model.
Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in a separate appendix.
Introduction Systems biology is an emerging field aiming at the investigation and understanding of biology at a system and multi-scale level. After biological entities have been identified in a specific environment, it remains to elucidate how they interact with each other in order to carry out a particular biological function. Therefore the construction of mathematical and predictive models is a fundamental goal of this field. Cells respond to their environment by activating signaling networks that trigger processes such as growth, survival, apoptosis (cell death), and migration. Post-translational modifications, notably protein phosphorylation, play a key role in signaling. Nowadays, there exist public repositories such as Pathways Commons  [10] , Pathways Interaction Database  [53]  and KEGG  [35]  that contain curated knowledge about intracellular causal molecular interactions, from which canonical cell signaling networks can be retrieved  [29].
Nonetheless, stochastic search methods cannot characterize the models precisely: they are intrinsically unable not just to provide a complete set of solutions, but also to guarantee that an optimal solution is found. To overcome this limitation, approaches based on Integer Linear Programming (ILP)  [42, 55]  and Answer Set Programming (ASP)  [62]  have been applied, providing a proof of concept that a global optimum can be identified. ASP  [5, 21]  is a declarative problem solving paradigm, in which a problem is encoded as a logic program such that its answer sets (i. e. stable models) represent solutions to the problem. Moreover, modern ASP tools allow handling complex preferences and multi-objective optimization, guaranteeing the global optimum by reasoning over the complete solution space.
In fact, combinatorial optimization in computational biology has been reviewed in  [28]  from the perspective of mathematical programming pointing out the importance of exact methods in this subject. Further, multi-objective optimization in the context of bioinformatics and computational biology has been recently reviewed in  [31]  showing its increasing relevance in this field. In this context, ASP offers a unique pairing of declarativeness and performance to address combinatorial multi-objective optimization problems. Herein we extend our work in  [62]  as follows. First, we consider numerical datasets instead of only binary. Essentially, this allows us to simulate realistic experimental datasets and test our approach towards real-world data. Also, in order to cope with such numerical datasets, we introduce and compare several discretization schemes.
Clearly, we have elaborated upon our previous ASP encoding in order to cope with the mentioned extensions. That is, (1) numerical datasets, (2) lexicographic multi-objective optimization and (3) feedback-loops in the prior knowledge. We have validated our approach using a real-world prior knowledge network related to signaling events upon stimulation of cellular receptors in hepatocytes and in silico generated datasets. The paper is structured as follows: Section 2 recalls main biological hypotheses underlying the learning of Boolean logic models; Section 3 provides a formal characterization of our problem; Section 4 introduces ASP and shows how to learn Boolean logic models using it; Section 5 presents benchmarks evaluating the performance of our approach; Section 6 reviews related work; and Section 7 concludes.
Learning Boolean logic models Based on the assumptions and concepts described above, authors in  [52]  have proposed a method to learn from a prior knowledge network describing causal interactions (Fig. 1(a) ) and phosphorylation activities at a pseudo-steady state, Boolean logic models (Fig. 1(b )) fitting experimental data. In particular, given a network encoding our knowledge of signal transduction and a dataset measuring the activation of proteins (outputs) in this network upon various perturbations (inputs), one can derive from the network Boolean logic models fitting the data. By default, it is assumed a "resting" network (no stimuli present). Then, the value of a variable that has no mapping in φ is given by its default truth value, i. e. 0. On the other hand, the value of all other variables is overwritten by signals propagated from the inputs (stimuli and knock-outs) according to their mappings in φ.
1) indicates that the stimulus v is absent (resp. present), while if v ∈ V K , then ε(v) = 0 (resp. 1) indicates that the species v is inhibited (resp. not inhibited). Furthermore, the concept of an experimental observation under ε is captured by a partial mapping ω : V R → [0, 1]. That is, dom(ω) ⊆ V R denotes the set of observed readouts under the experimental condition ε. If v ∈ dom(ω), then ω(v) represents the phosphorylation activity of the readout v under ε. Since phosphorylation assays represents an average across a population of cells, the phosphorylation activity for each readout is usually normalized to [0, 1]. Finally, an experimental dataset ξ is a finite set of pairs (ε i , ω i ) with each ω i defined under ε i. Further, we denote with N ξ the size of ξ given by the number of observed readouts across all experiments, i.
e. , N ξ = n i=1 |dom(ω i )|. Let us illustrate the problem inputs with our toy example. Consider the PKN (V, E, σ) defined in Fig. 1(a). From the graph coloring, we have V S = {a, b, c}, V K = {d} and V R = {f, g}. Finally, let ξ = ((ε 1 , ω 1 ),. , (ε 4 , ω 4 )) be an example experimental dataset over (V, E, σ) defined by ε 1 = {a → 1, b → 0, c → 1, d → 1} ω 1 = {f → 0. 9, g → 0. 0} ε 2 = {a → 1, b → 0, c → 1, d → 0} ω 2 = {f → 0. 1, g → 0. 9} ε 3 = {a → 1, b → 0, c → 0, d → 1} ω 3 = {f → 0. 0, g → 0. 1} ε 4 = {a → 1, b → 1, c → 0, d → 1} ω 4 = {f → 1. 0, g → 0. 8}. (1).