dataesr/scientific-paragraphs-categorization huggingface dataset:
The ability of 4 to accommodate guests was investigated through binding studies carried out in dichloromethane with DABCO as a bidentate base. The complexations were monitored by UV-visible spectrophotometric titration in CH 2 Cl 2. The association constant (K) between tweezer 4 and DABCO was calculated from the UV-visible spectroscopic data and a value of 3. 9 × 10 5 M -1 (log K = 5. 6) was found. This association constant is increased by one order of magnitude, when compared to the association constant for the binding of the same bidentate ligands with a reference Zn(II) 5,10,15,20-tetra-di-tert-butyl-phenyl-porphyrin A 4 (2. 5 × 10 4 M -1 , log K = 4. 4). The enhanced stability of the complex is ascribed to the pre-organization of the bis-porphyrinic tweezer 4. Molecules 2017, 22, 1112 3 of 14 The ability of tweezers 1, 2 and 3 to accommodate guests was investigated through binding studies carried out in dichloromethane with DABCO as a bidentate base.
The ability of 4 to accommodate guests was investigated through binding studies carried out in dichloromethane with DABCO as a bidentate base. The complexations were monitored by UV-visible spectrophotometric titration in CH2Cl2. The association constant (K) between tweezer 4 and DABCO was calculated from the UV-visible spectroscopic data and a value of 3. 9 × 10 5 M -1 (log K = 5. 6) was found. This association constant is increased by one order of magnitude, when compared to the association constant for the binding of the same bidentate ligands with a reference Zn(II) 5,10,15,20-tetra-di-tert-butyl-phenyl-porphyrin A4 (2. 5 × 10 4 M -1 , log K = 4. 4). The enhanced stability of the complex is ascribed to the pre-organization of the bis-porphyrinic tweezer 4. 4.
Data obtained by UV-visible titrations were analyzed by using the values of the absorbance at fixed wavelengths, a method that is exclusively valid for the complexes with stoichiometry 1:1  [46] , and by simultaneous fitting of the whole series of spectra collected at 1 nm intervals using the software SPECFIT (TgK Scientific, Bradford-on-Avon, UK)  [47, 48]. After examining the complexation of the simple porphyrin with DABCO, we studied the binding of the same base to dimer 5, the bis-porphyrinic tweezer appended to an appropriate position of the flexible uridinic scaffold by the rigid acetylene group. The titration of the dimer with DABCO was measured by using the coordination shift of the Soret absorption as well as the Q-bands. The Soret band absorption was measured at the concentration of 1.
The architecture of multi-porphyrinic systems is important because the covalent connectivity between the interacting centers commands the geometry of resulting assembly, and the relative orientation of chromophores dictates the strength of the interaction with ligands. As previously noted, the geometry of porphyrins in a supramolecular assembly is crucial in the design of artificial light-harvesting complexes  [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]. Data obtained by UV-visible titrations were analyzed by using the values of the absorbance at fixed wavelengths, a method that is exclusively valid for the complexes with stoichiometry 1:1  [46] , and by simultaneous fitting of the whole series of spectra collected at 1 nm intervals using the software SPECFIT (TgK Scientific, Bradford-on-Avon, UK)  [47, 48].
Value of the data The present dataset depicts the comparative proteome analysis of four strains representative of different Mucor species ("technological", "contaminant" and non-cheese related) between two contrasting culture conditions, PDA (synthetic medium) and cheese agar (cheese mimicking medium). Differential morphologies and growth capacities among species and media were confirmed and a total of 289 proteins differentially accumulated were identified. Data reported here deepened our understanding on Mucor species metabolism and strongly sug- gested that adaptative response is species-specific.
Gel image analysis A minimum of 6 gels per condition were analyzed. 16-bit tif images of the gels obtained using G: BOX (SynGene) were aligned and spots were detected and quantified using the Progenesis SameSpots software (Nonlinear dynamics, v. 4. 5, Newcastle upon Tyne, UK) using the automated algorithm. ANOVA statistical analyses using the statistics module of SameSpot were done for spot selection by comparing abundance on PDA versus CA. Significantly differentially expressed proteins at P r0. 02 with an absolute fold change Z1. 6 were targeted.
Protein identification Mass spectrometry data were analyzed using the Mascot 2. 4. 0 program (Matrix Science Ltd. ). The variable modifications allowed were as follows: C-Carbamidomethyl, K-acetylation, methionine oxidation, and dioxidation. "Trypsin" was selected as enzyme, and 3 miscleavages were also allowed. Proportion (%) of over-accumulated proteins is given per family. Each protein was assigned to a single functional category that was considered as the best assignment according to data mining using Blast2Go. Mass accuracy was set to 300 ppm and 0. 6 Da for MS and MS/MS mode respectively. A public database (M. circinelloides assembly v2, 97356 entries) or unpublished genome data available in our laboratory (M. lanceolatus, 17940 entries; M. racemosus, 3380 entries) were used. Protein identification was considered as unambiguous when a minimum of two peptides matched.
All protein sequences were subjected to homology searches using the Blast algorithm available on NCBI (www. ncbi. nlm. nih. gov) and Uniprot (http://www. uniprot. org), with default parameters. Assignment of information about the biological function of over-accumulated proteins identified was done using Blast2Go (https://www. blast2go. com, version 3. 2. 7). A network analysis was then performed using the known and predicted protein interaction STRING database (www. string-db. org, version 10. 0) which includes both direct (physical) and indirect (functional) associations  [1]. Because none Mucor or other early diverging fungi data were present in this database, the analysis was carried out using the interactions known in Saccharomyces cerevisiae.
Reads were aligned to hg19 genome using the Torrent Mapping Alignment Program (TMAP). TMAP is a tool combining multiple mapping algorithms for Ion Torrent ® data. It uses three core algorithms: Burrows-Wheeler Alignment (BWA) tool for short read  [39] , BWA for long read  [40]  and a modified Sequence Search and Alignment by Hashing Algorithm (SSAHA) version  [41]. TMAP follows a two-stage approach, with a set of algorithms and associated settings for each stage. If there is no mapping for a read by applying the algorithms in the first stage, then the algorithms in the second stage are applied. Furthermore, TMAP allows applying penalties using flowgram information, making it particularly efficient in regions enriched with homopolymers. Only non-synonymous single nucleotide variations (SNV) with a quality score >22 were retained as acquired somatic mutations.
The limit of detection (LOD) and the limit of quantification (LOQ) were at 1% of MAF, as set by the manufacturer. Copy number variation (CNV) analysis was performed using the ONCOCNV package  [42]. Blood samples were obtained postoperatively and before the first-line treatment. Plasma was extracted from EDTA-anticoagulated blood samples by centrifuging once at 2000 g (10 min) at 4˚C and stored at -80°C until use. cfDNA was extracted from 1 ml of EDTA plasma using the QIAamp® Circulating Nucleic Acid Kit (Qiagen®, Hilden, Germany) with the QIAvac 24 Plus vacuum manifold, following the manufacturer's instructions. Extracted cfDNA from each plasma sample was eluted in a volume of 40 μl. cfDNA concentrations were measured using a fluorometric assay (Qubit® dsDNA HS Assay Kit, Life Technologies®, Carlsbad, California, United States of America).
Secondary objectives MSKCC score, tumor volume, type of neurosurgery, percentage of tumor cells, deep brain involvement and cfDNA concentration in plasma were compared between patients with ctDNA detected in plasma and patients without detected ctDNA. Tumor volume was estimated using postcontrast T1-weighted MRI. Section thickness was between 3 and 5 mm. The tumor area, defined as contrast enhancement, was delineated on each slide in the axial section. The volume was estimated using image-processing software (OsiriX ® , Pixmeo, Geneva, Switzerland). Deep brain involvement was defined by tumor involvement of subcortical structures on T1weighted MRI. Finally, an exploratory survival analysis was performed. We investigated three risk factors for overall survival (OS): the MSKCC score, MYD88 c.
However, GEP alone is limited and must be integrated with innovative approaches that use biological regulatory networks to extract biological information relative to gene expression datasets to provide significant clues about the etiology of myeloma. During the past decade, many methods of so-called pathway analysis or active pathways detection have been developed. These methods use as a knowledge base a biological pathway or regulatory network, that compiles a series of molecular phenomena that lead to activation (or inhibition) of gene expression, a cell product such as a hormone, or a physical modification of the cell. Regulatory network information is currently available through databases such as Gene Ontology (GO)  10  , the Kyoto Encyclopedia of Genes and Genomes (KEGG)  11  , the Pathway Interaction Database (PID)  12  , Wikipathway  13  , Transfac  14  , and Causal Biological Networks (CBN)  15.
The main objective of pathway analysis methods is to confront or integrate GEP data with regulatory networks or pathways to distinguish two or more classes of cells (e. g. healthy vs ill) from GEP data by inferring a specific signature for each class. We can identify three principal categories of approaches that have been used to associate GEP with specific pathways  16. The Over-Representation Analysis (ORA) group of approaches  17, 18  includes approaches that are based on differentially expressed (DE) genes. These approaches score single pathways based on the proportion of DE genes (identified with statistical tests or with a threshold) contained in each pathway. In most cases, these methods use a hyper-geometric test  17  to score each pathway. Moreover, the majority of ORA approaches that use functional annotation (GO) or pathway maps (KEGG) consider the consequences of the DE genes (leading to the differentially expression of proteins) in the associations between gene and pathway.
Martin et al. 19  called this type of reasoning forward assumption compared to the backward assumption  18  , which considers the causes of those DE genes in the gene-pathway association. The Functional Class Scoring (FCS) group of approaches uses the full datasets without any pre-selection, allowing integration of the effects of low gene expression variations in the identification of the pathways involved. FCS approaches can use forward  20, 21  or backward  22, 23  reasoning. Although these methods improve the problem of genes selection, the pathways in which individual genes are involved are still studied independently. Moreover, the position of the genes in the topology is not used in the analysis. The Pathway Topology (PT) approaches are very similar to the FCS approaches, but in addition, they score genes according to the pathways to which they belong.
This graph connects signaling pathways to the transcription of the genes in the GEP dataset. We then integrate the graph with the expression data by reasoning on its logic using IGGY  29  , a tool based on logic programming (Answer Set Programming) that confronts a node coloring (GEP) with labeled and directed graphs. Our combined approach could be considered to fall within the PT category since it takes into account the causality and activation/inhibition logic of graph edges. However, unlike previous methods, it uses a global logic to analyze experimental and pathway data. In this formalism, both forward and backward modes are included as reasoning modes (causes-consequences). IGGY allows us to check the consistency of the information and to generate predictions based upon automatic repairs for upstream non-measured species.
It uses DE data as well as the identically expressed genes across classes (invariant genes) in its analysis. The proposed method does not correlate protein activation with gene expression; the two entities are identified separately in the graph. The non-measured protein activations necessary to satisfy the GEP according to the entire pathway database topology are used later to propose a signature for each dataset profile. This global signature can be used to characterize the dataset classes. Moreover, our model also allows us to in silico quantify the effect of perturbations on this global pathway for each single patient. We show how this type of method, which combines large-scale information in terms of number of patients, the complete GEP, and the entire compendium database, can be applied to identify new specificities of MM disease compared to normal cells.
The study fulfilled the requirements of the Declaration of Helsinki. Gene expression profiling. RNA was extracted using the AllPrep DNA/RNA MiniKit or the RNeasy Micro kit (QIAGEN, Valencia, CA, USA) in accordance with the manufacturer's instructions. RNA quality and quantity were assessed using Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) and a Nanodrop Spectrophotometer (NanoDrop Technologies, DE, USA), respectively. MM samples from which 50 ng of total RNA was available were processed according to manufacturer's instructions (NuGEN, San Carlos, CA, USA) before labeling and hybridization onto an Affymetrix Human Exon1. 0 chip according to the manufacturer's instructions (Affymetrix, Santa Clara, CA). Data were analyzed and log 2 normalized with Expression Console Affymetrix software v1.
1 using an RMA algorithm. Data discretization. Since our graph coloring model requires invariant genes, classical discretization methods cannot be used in our study. To identify over-/underexpressed and invariant gene expression for each profile, we used two thresholds: k 1 for invariant genes and k 2 for variant genes. For each gene g, we computed a vector, p g , composed of its differential expression, p i g , in each dataset expression profile i. A profile i refers to each of the 611 cells of type MC or NPC considered in this study. p i g values were computed by subtracting the mean expression of g in the NPC set from its gene expression level in MC (Supplementary Material, Figure  S1 ). We then discretized the values of p i g using two thresholds k 1 and k 2. -< < , g was considered invariant for i.
If p k i g 2 > , By choosing different combinations of values for k 1 and k 2 (see Supplementary Material), we obtained 150 sets of vectors that contain the discrete overexpressed (+), underexpressed (-), or invariant (0) values for all the genes expressed in each dataset. We discarded combinations of values leading to p g vectors with a sign proportion greater than 50%. Each k 1k 2 combination was used to test the precision of our approach. We did this 100 times by using 50% of the discretized {+, -, 0} genes' expression to predict the other 50% for each MC dataset, after which we comparing the measured and predicted data using a precision matrix (Supplementary Material, Table  S1 ). The thresholds leading to the best precision of 43% (IC 95%: ±3%) were k 1 = 0. 03 and k 2 = 0. 2; these thresholds were used in the remainder of the study to select the variant and invariant genes.
Graph generation. We used the 2012 version of the complete pathways database PID-NCI (Pathway Interaction Database)  12  and downloaded it in PID-XML format. This database is specialized to include regulatory pathways involved in cancer. The complete graph contains 17,932 nodes (proteins, complexes, genes, transcription or protein modification events) and 27,976 edges (activation or inhibition). To orient our analysis to the expression profiles and to the biological problem at hand, we built a subgraph with signed edges by extracting the downstream events from three signaling pathways (IL6/IL6-R, IGF1/IGF1-R and CD40), all of which are known to include cellular receptors involved in MM  31  , to the over-and underexpressed variant genes from all datasets by the shortest paths. This cycled, directed subgraph was then filtered by deleting all nodes that are not observed and with one predecessor or one successor  32.
This filtering step involves no loss of information with respect to the graph coloring model and allowed us to reduce the complexity of the analysis while maintaining the dependencies between the nodes. Sign consistency modeling framework. In Fig. 1 , we illustrate the input (network and transcriptomic data) and output (sign projections) information obtained when the sign consistency modeling is applied. In the following sections, we describe in detail the main modeling steps of this framework. Graph coloring model. Assuming a directed graph G(V, E, α) in which V is the set of nodes, E is the set of edges and α is a function labeling the edges as α:E → {+, -}, let β be a set of observed data with β:V → {+, -, 0}. In our case, β is obtained from GEP and labels only the nodes that are preceded by a "transcription" event as reported by the PID-NCI database.
Nodes perturbation. In this analysis, we quantified the effectiveness of a node perturbation (Supplementary Material, Figure  S6 ) to simulate in silico the activation or inhibition of a protein. The quantification of these in silico perturbations was performed in two steps. First, we considered the set of assignments in M (see previous section), where = M 1 ij s expresses that node i is predicted to be of sign s in profile j, with s ∈ {+, -}. For each assignment M ij s , we generated a new dataset of observations β ij s identical to the original dataset of profile j (β j ) except that we added an observation on node i fixed to s ∈ {+, -}. We then computed the SCENFIT score SF ij s between the graph G and ij s β. The second step consisted of computing the Top Perturbation Score (TPS) for each assignment (i, s) according to its SF ij s across all GEP j, as follows: ∑ = = TPS N f i s j 1 ( , , ), i s C C j N , 1 C where f i s j SF top SF k V Dom ( , , ) 1, if ( ), \ ( ) 0, otherwise ij s kj s ij s β =       ≥ ∀ ∈.
Software and tools. For the sign consistency analysis, we used IGGY  29  , which makes use of an ASP  35  description of the consistency problem. The graph generation and the mapping of predictions to the couples node-sign were implemented with Python 2. 7 using the package NetworkX  36. The learning and statistical analysis was conducted using R  37. The computation associated with testing the consistency of the 611 GEP required 5 minutes on a standard machine. All the calculations of nodes perturbations were conducted using the BIRD infrastructure (www. pf-bird. univ-nantes. fr) with 320 nodes and 1. 3To RAM.
Data discretization and graph generation. The NCI-PID integration allowed us to find 634 genes (a protein preceded by a transcription event). Independently, our discretization method (Supplementary Material, Figure  S1 ) proposed observations {+, -, 0} on microarray probes corresponding to 15,418 proteins identified in Uniprot. Merging both lists allowed us to identify 557 genes present in the NCI-PID and observed as over/underexpressed or invariant in our GEPs. Variant and invariant genes are distributed across the MC and NPC datasets (Table  1 ). By extracting the downstream events from three signaling pathways (IL6/IL6-R, IGF1/IGF1-R and CD40)  31  to the variant genes, we generated an induced subgraph from NCI-PID containing 2,269 nodes, 2,683 edges and connecting 529 variant genes. This graph was then compacted to a new graph with 596 nodes and 960 edges (Fig.
Validation of predictions. The confrontation between the data and the graph topology allowed us to predict the node signs for each dataset (Table  1 ). To validate our predictions, we compared the precision of the predictions with that of the randomized data. In this case, we used 50% of the measured genes {+, -, 0} to predict the other half of the genes for each sample; we performed the same process after randomizing the data and repeated this computation up to 1000 times. We obtained two sets of precisions (Fig. 3 ; Supplementary Material, Table  S1 ). A two-tailed t-test yielded a p-value lower than 2. 2e-16. This shows the efficiency of our prediction method in comparison with random precision.
Similar results were obtained using a random forest classification (Supplementary Material, Figure  S7 ). To characterize the shared specificity for all MC, we computed the frequency scores (FS) for our predictions (see Methods section). The complete list of the FS obtained is shown in Table  S2  of the Supplementary Material. In Table  2 , we show the 5 best p-values associated with a Fisher test with FS MC > FS NPC. For these Figure  2. Representation of the subgraph obtained from the PID-NCI database. CD40, IL6 and IGF1 (the nodes in the top portion of the graph) are the 3 queried pathways. The 529 genes that are differentially expressed across all profiles are merged for this representation in the node "Gene set". The 5 top-ranked nodes according to their FS are labeled in bold type and colored in yellow.
We used the same syntax for all nodes in this study. The edges from the "Gene set" node to proteins have been deleted for the sake of clarity. Table  1. Observed and predicted data repartition between NPC and MC. Observed data are the data extracted from the gene expression profiles. Predicted data are the sign projections predicted after the confrontation between the observed data and the PID-NCI graph. In the last row, we show the total observations and predictions across all profiles.
Signs assignments, we checked the number of input/output variant genes connected to each node in the graph (Table  2 , column connectivity). We observe that inhibition of the complex JUN/FOS[n] is predicted for 95. 6% of MC. The activity levels of FOXM1*[c] and STAT6*[c] were predicted to decrease. This decrease, in terms of protein activity, is correlated with the level of gene expression in 76% and 93%, respectively, of the MC datasets. The FS classification identified the presence of (Src*, +) as an interesting marker for MC datasets. Interestingly, the decision tree approach identified the absence of (Src*, -) as distinguish MC datasets   JUN/FOS activity as specific marker. The FOS and JUN proteins form a heterodimer complex that is responsible for AP-1 activity. This activity is known to play a role in tumorigenesis because it has been implicated in the induction of apoptosis, in the promotion of cell survival and in proliferation.
Node perturbation. From the computation of all in silico node perturbations (see Methods section), we evaluated the impact of perturbing the key nodes found with the FS method (Table  4 ). A unilateral Fisher test allowed us to evaluate the significance of each perturbation compared to the NPC datasets. We can see that the activation of JUN/FOS generates a top-ranked (10% top) score of conflicts and therefore repairs 74. 6% of the MC datasets, whereas it repairs only 22. 2% of the NPC datasets. Interestingly, in vitro JUN overexpression in MM cell lines results in cell death and growth inhibition  41. A similar tendency (more conflicts in MC than in NPC) is observed when FOXM1 is activated, but the difference cannot be considered significant. Nonetheless, we note that of the 36. 4% of profiles in which the activation of FOXM1 is top-ranked, 96.