dataesr/scientific-paragraphs-categorization huggingface dataset:
Cellular pathways shared between diseases Information on the involvement of the proteins in cellular pathways was obtained from the BioCarta database  [38]  and mapped onto the FIN. The number of cellular pathways with more than 2 edges in the FIN was 273. We measured the fraction of edges rather than the fraction of nodes (i. e. proteins) because we considered that edges better represented the influence of the network connectivity. We calculated how similarly two diseases dis1 and dis2 perturbed a cellular pathway p by means of a Jaccard index, which we called Functional Similarity (FSim): FSim p dis1dis2 ¼ je p dis1 \ e p dis2 j je p dis1 [ e p dis2 j where e p dis1 is the number of edges associated to the pathway p and disease 1, and e p dis2 is the number of edges associated to the pathway p and disease 2.
Predicting multimorbidity-associated proteins In the previous steps, we described the mechanisms of multimorbidity of asthma, eczema and rhinitis based on known protein-disease associations. In this section, we wished to use the information contained in the FIN to predict novel proteins potentially involved in the multimorbidity process. To do so, we employed the NetZcore algorithm of the GUILD package  [40]. This algorithm requires an initial group of nodes, called seeds, to be weighted with a score, which will be iteratively assigned to those nodes in the network depending on their connectivity. We generated a scored list of potential disease-associated proteins for each disease independently, using a seed score = 1 for known disease-associated proteins, and 0 otherwise. In order to compute z-scores, 10 3 random networks were generated by randomly exchanging edges between pairs of nodes  [41].
Furthermore, it is worth mentioning that, for simplicity, our network is undirected and independent of differential expression patterns or external factors (e. g. allergens). However, some studies have shown that the current interaction databases do allow the systematic investigation of disease mechanisms  [23]. Although we applied the usual procedure of multiple testing correction in our statistical analysis to avoid false positives (type I errors), some authors have argued against its use on the grounds that these corrections inflate the number of false negatives (type II errors)  [47]. Finally, it must be taken into account that our gene and protein selection is subject to some restrictions. First, we selected only curated disease-protein associations from several databases, so the quality of our dataset depends on their criteria as to establish what disease-protein associations are the most reliable.
Consequently, we chose three manually-curated databases (CTD, OMIM and Ensembl Variation) that are widely employed in computational genotypephenotype association studies. Part of our associations were GWAS-derived, and there is a debate on the use of this kind of data as a means for characterization of risk factors, based mainly on concerns about its statistical processing and the fact that, without additional functional information, it is often difficult in GWAS analysis to implicate a specific gene in a genotype-phenotype association  [48]. However, when integrated with other gene-disease association methods, and particularly when applied to pathway and network analysis, it has been shown to increase statistical power and provide valid results. These results give us additional confidence on the soundness of our results.
Recently, one kallikrein gene family member (KLKB1) was found to regulate soluble cleaved urokinase plasminogen activator receptor  [85]. KLKB1 cleaves UPAR and negates soluble cleaved UPAR effects on primary human bronchial epithelial cells. Interestingly, scu-PAR is encoded by UPAR, a gene found by positional cloning in asthma families  [85]. Other proteins predicted to be related to multimorbidity are KIF3A, already known to be associated to asthma, eczema and the atopic march  [86] , and predicted to be implicated also in rhinitis. Interestingly, seven KIF3A SNPs were actually reported to be associated with rhinitis in one study that was not incorporated to the databases used in this study  [87] , thus supporting the reliability of the prediction method. Lastly, PHF11, a transcriptional activator of the Th1 effectors interleukin-2 (IL2) and interferon-γ (IFNG), is predicted to be associated to eczema.
It has to be noted, though, that the identification of an association between a protein and a disease depends on the variable criteria used in different databases. It is thus possible that some of the predicted proteins in our study are reported as actually associated to the diseases according to some other databases or studies. Furthermore, the association test performed (Table  4 ) excluded predicted proteins from PubMed abstracts (see Methods). This minimized the number of false positives when comparing these literature-based protein sets to our predictions. However, because we used a keyword-based search, it is possible that some false positives may have been produced because of the diverse wording employed in abstracts. Also, excluding the words predicted and prediction (see Methods) might have introduced some false negatives by removing genes Applying systems medicine to the understanding of allergic diseases Asthma, eczema and rhinitis are salient examples of complex multimorbid allergic diseases.
Simulations details To study effects resulting from computing RDFs from simulation of finite and closed systems, we examine a binary mixture interacting using the WCA potential  [32]  where the Lennard-Jones (LJ) potential is shifted and truncated at 2 1/6 σ. The WCA mixture is simulated in the NVT ensemble using the Nose-Hoover thermostat  [39]. The MD package LAMMPS  [40]  is used to perform the simulations, with 1 million initialization timesteps and 5 million integration timesteps for each run. A timestep of 0. 001 in LJ reduced units, which are the units used for other variables, are used. All simulations were performed for the same system properties with: σ 11 = σ 12 = σ 22 = 1. 0, 11 = 22 = 1. 0, and 12 = 0. 1. Additionally, the same thermodynamic condition was maintained for all system sizes.
Computational Details Initially, atomic coordinates for all 14 synthesized compounds and the three inhibitors of chitin synthase 2 (linderone, methyllinderone and kanakugiol) were built using the Avogadro software  [47]. Subsequently, a fast geometry relaxation in vacuum was performed with the HF-3c method  [48]  implemented in Orca 3. 0  [49]  and then, a most exhaustive geometry optimization and frequency calculations were performed with the ωB97X-D functional and the 6-31G(d,p) basis set in Gaussian 09  [50]. For linderone, calculations were performed for both the keto and enol forms and the further analysis were done using the lowest energy isomer, which was the enol form. Next, the energy for the highest occupied molecular orbital (E HOMO ) and the energy for lowest unoccupied molecular orbital (E LUMO ) were obtained directly from these DFT calculations.
Global reactivity descriptors as chemical hardness (η), electronegativity (χ), electronic chemical potential (µ), chemical softness (S) and the electrophilicity index (ω) were obtained from the ionization potential (I) and the electron affinity (A) of the molecules following the Koopmans' theorem  [39]  as: I ≈ -E HOMO and A ≈ -E LUMO (1) η ≈ (I -A) ≈ (E LUMO -E HOMO ) (2) χ = -µ ≈ 1 2 (I + A) ≈ - 1 2 (E HOMO + E LUMO ) (3) S = 1 η (4) ω = µ 2 2η (5) Condensed-to-atoms Fukui functions (f α k , α = +,or 0) at the kth atom were computed using the frontier molecular orbital (FMO) method implemented in the UCA-FUKUI software  [51]  as follows: f - k = ∑ v∈k |C vH | 2 + C νH ∑ χ =ν C χH S χν (electrophilic attack) (6) f + k = ∑ v∈k |C vL | 2 + C νL ∑ χ =ν C χL S χν (nucleophilic attack) (7) f 0 k = 1 2 f + k + f - k (radicalary attack) (8) where C νH and C νL are the HOMO and LUMO frontier orbital coefficients, respectively, while S χν are the atomic orbital overlap matrix elements.
Acknowledgments: The authors gratefully acknowledge financial support from  COLCIENCIAS  and  Universidad del Valle  and the  Agencia de Promoción Cientifica y Tecnológica de Argentina  PICT  2014-1170. Maximiliano Sortino  is a member of the CONICET career. Susana Zacchino  and  Maximiliano Sortino are Professors of Farmacognosia de la Facultad de Ciencias Bioquímicas y Farmacéuticas ,  Universidad Nacional de Rosario ,  Argentina. Joel José Montalvo-Acosta  thanks  Diego Gomes  for useful discussions and advice on preparing high-quality figures.
The results were processed using the corresponding Agilent software. The SEC analyses of the poly(vinyl acetate) samples were carried out in filtered THF (flow rate: 1 mL min -1 ) at 35 • C on a 300 × 7. 5 mm PL gel 5 µm mixed-D column (Polymer laboratories) equipped with multi-angle light-scattering (Mini Dawn Wyatt) and refractive index (RI2000, Sopares or Wyatt Optilab Rex) detectors. The results were processed using Astra 6 software, using a dn/dc value of 0. 085.
Acknowledgments: The authors thank  Tosoh F-Tech Company (Shunan, Japan ) for providing MAF-TBE. Financial support from the  French National Agency (ANR  grant  FLUPOL , No. ANR-14-CE07-0012-02 ) is greatly acknowledged. Christophe Detrembleur  and  Antoine Debuigne  are Research Director and Research Associate by  F. R. S. -FNRS , respectively, and thank the  FNRS  for financial support.
Image analysis was carried out using a LGC homemade program for particles counting (density estimation) and average diameter measurement developed using MatLab image processing toolbox software. Pb underpotential deposition (UPD) experiments were conducted in a 0. 01 M HClO 4 solution containing 1 mM Pb(NO 3 ) 2. A potential step of -0. 4 V was applied for 10 s before stripping from -0. 35 V to 0. 3 V at 50 mV s -1.
5g] The present work reports novel results obtained in this family of uranyl complexes with mixed ligands. Although many carboxylic and sulfonic acids have been tried in the present as well as in previous experiments, most did not give any exploitable crystalline material, and the results presented here involve some very different ligands which allowed to grow crystals suitable for X-ray crystallography. These ligands are oxalic acid (generated in situ), 1,3-adamantanedicarboxylic acid (H2adc), 1,3-adamantanediacetic acid (H2adac), and the di-potassium salt of methanedisulfonic acid (K2mds). Oxalate is a very common ligand for uranyl ions, with more than 150 crystal structures reported in the Cambridge Structural Database (CSD, Version 5. 38),  [8]  but the adamantane-based dicarboxylates have been the subject of few reports.
71073 Å). The crystals were introduced into glass capillaries with a protective coating of Paratone-N oil (Hampton Research). The unit cell parameters were determined from ten frames, then refined on all data. The data (combinations of ϕ-and ω-scans with a minimum redundancy of 4 for 90% of the reflections) were processed with HKL2000. [21] Absorption effects were corrected empirically with the program SCALEPACK. [21] The structures were solved either by Patterson map interpretation (3) or direct methods (4) with SHELXS,  [22]  or by intrinsic phasing with SHELXT  [23]  (1 and 2), expanded by subsequent difference Fourier synthesis and refined by full-matrix least-squares on F 2 with SHELXL. [22] All non-hydrogen atoms were refined with anisotropic displacement parameters. In compound 1, one water molecule (O18) is disordered over two positions which were refined with occupancy parameters constrained to sum to unity, and another one (O19) was given an occupancy factor of 0.
Apparatus and Separation Conditions The determination of the drugs was performed using a Capillary Electrophoresis Agilent model G1600AX equipped with diode array detector (DAD). The storage and processing of capillary electrophoresis (CE) with DAD detection (CE-DAD) system data was performed using the Chemstation software. The separations were carried out with a fused silica capillary of 62 cm total length (54 cm effective length), 75 µm inner diameter and 375 µm outer diameter. Analyses were performed at 20 kV and 25 • C. Naproxen detection was performed at 228 nm, while the ketoprofen and clofibric acid detection was performed at 203 nm. Samples were introduced into the anodic end of the capillary by hydrodynamic injection at 50 mbar for 50 s. The background electrophoretic solution used for the separation was composed of 31.
Design of Experiment In order to obtain optimized conditions for extraction, a fractional factorial experimental design was used to evaluate the preliminary significance of the variables, as well as the interactions between them. The variables investigated were: sample volume, salt concentration; sorbent type, stirring speed, extraction temperature, extraction time and desorption time. All variables were evaluated at two levels, low (denoted as -1) and high (denoted as +1). The data were processed using the Statgraphics Centurion XVI computer program. The levels for the experimental design are summarized in Table  1. The concentration of the analytes was kept constant during the experiments at 37. 5, 50. 0 and 75. 0 µg•L -1 for ketoprofen, naproxen and clofibric acid, respectively. The significant variables indicated by the Pareto chart were optimized using a Box-Behnken design, which is a response surface methodology, based on a three-level factorial design.
Optimization of Electrophoretic Separation According to the literature, the electrophoretic solutions most used for the analysis of nonsteroidal anti-inflammatory drugs have been borate, acetate and phosphate, in a pH range of 7 to 9, since acidic drugs are found as anions thanks to their pKa values between 3 and 7 under these conditions  [15, 18]. Therefore, the electrophoretic separation of ketoprofen, naproxen and clofibric acid (with pKa of 4. 0, 4. 2 and 4. 4, respectively) was performed using phosphate buffer as background electrolyte. The separation was investigated in a pH range spanning from 7. 8 to 9. 2 and in a phosphate buffer concentration range between 25. 0 and 62. 5 mM (data not shown). All acidic drugs were baseline separated using phosphate buffer at pH 8. 75. At this pH, the analytes are negatively charged and migrate in the opposite direction of electroosmotic flow.
The levels of each factor were chosen according to the results obtained during preliminary studies carried out by univariate optimization (data not shown). The levels of the factors and the related experimental domain are given in Table  1 , the type of fiber being a qualitative variable, -1 represents C 18 fiber, and +1 the Porapak Q one. The responses for each experiment are the surface areas measured for each peak assigned to analytes. Optimization aims at obtaining the larger surface areas. The mathematical correlation between analytical response and the variables was performed by analysis of variance (ANOVA). The Pareto chart that compare the effect of each factor (Figure  3 ) shows that the variables that were significant (p-value < 0. 05) in the response of clofibric acid were extraction time, stirring speed, type of fiber and salt concentration, while, for ketoprofen and naproxen, significant variables were extraction time, stirring speed and type of fiber.
Acknowledgments The authors thank the  Universit e de Strasbourg , the  CNRS , the  Universit e de Haute-Alsace , the  ANR  (project  DYNAMITE   ANR-2010-BLAN-704 ), and the  R egion Alsace. We are grateful to  Dr. Didier Le Nou€ en  ( Universit e de Haute-Alsace ) for his assistance in NMR experiments.
Although other mutations, primarily within helix 3 of the BAG domain, also inhibited the binding of Bag-1L and AR, these sites additionally interacted with Hsp70/ Hsc70. The region in AR most affected by binding to Bag-1L maps to the partially folded region within tau-5, which strongly overlaps the region affected by EPI-001 binding  (De Mol et al. , 2016). EPI-001 is a recently developed experimental drug that targets the AR NTD  (Andersen et al. , 2010). A derivative of this compound (EPI-506) is currently being employed in clinical trials for CRPC patients resistant to abiraterone and/or enzalutamide (ClinicalTrails. gov Identifier: NCT02606123). However, EPI-001 and iodo-EPI-002 were not able to suppress Bag-1L function in a mammalian-1H assay. Given our findings that the BAG domain of Bag-1L binds and regulates AR (tau-5) with high specificity, targeting the Bag-1L:AR interaction might be an alternative approach for the treatment of PCa.
0) for 18 min at 800 W). The Bag-1 antibody was diluted (1:250) in Dako REAL diluent (Agilent Technologies, Santa Clara, CA) and tissue was incubated for 1 hr. After washes, the bound antibody was visualized using the Dako REAL EnVision Detection System (Agilent Technologies). Sections were counterstained with hematoxylin. AR protein expression was determined using the AR mouse monoclonal antibody (AR441, Agilent Technologies), as previously described  (Welti et al. , 2016). Nuclear and cytoplasmic Bag-1 and AR expression was determined for each case by author D. N. R in a blinded fashion using the modified H-score (HS) method using formula: [(% of weak staining) x 1] + [(% of moderate staining) x 2] + [(% of strong staining x 3) to provide a range from 0 to 300  (Detre et al. , 1995). HS data was reported as median values with interquartile range (IQR).
Demographic and clinical data for each patient were collected retrospectively from the hospital electronic patient record system. These characteristics were compared by Bag-1 status at HSPC using Fisher's exact test for categorical characteristics, the Chi-squared test for trend for ordinal characteristics and either an unpaired t-test for continuous data, if normally distributed (e. g. hemoglobin and albumin), or a Mann-Whitney test. The characteristics were then compared by Bag-1 HS value at CRPC biopsy using linear regression models and either a t-test or Walt test. PSA progression was defined as an increase in the PSA level of 25% or more above the nadir (and by !2 ng/ml); patients who stopped abiraterone without PSA progression were censored. Radiological progression was defined as any radiological imaging reporting disease progression; patients who stopped abiraterone without radiological progression were censored.
Chromatin immunoprecipitation (ChIP) and ChIP-seq ChIP was carried out as described  (Jehle et al. , 2014) , using anti-AR antibody (N-20; Santa-Cruz Biotechnology). Following primers were utilized for directed ChIP qPCR. KLK3 (AREIII) primers were previously published  (Jehle et al. , 2014). TMPRSS2 Fwd: 5'-GCTCACACAGGATCAGAGCA-3' TMPRSS2 Rev: 5'-TGCTCGTTAGTGGCACATTC-3' NKX3. 1 Fwd: 5'-TTTGGGCCACCCTGTAAATA-3' NKX3. 1 Rev: 5'-GGGTGGGAGGAGATGAAAAT-3' ChIP-seq libraries were generated using the ThruPLEX DNA-seq kit (Rubicon Genomics, Ann Arbor, MI) and were sequenced on the Illumina NextSeq 500 platform at the Molecular Biology Core Facility (Dana-Farber Cancer Institute). ChIP-seq data was processed using ChiLin2  (Qin et al. , 2016). All ChIP-seq data have been deposited at the GEO depository under accession number GSE89939.
RNA-sequencing Total RNA was extracted from cells using innuPREP RNA Mini (Analytic Jena AG, Jena, Germany), following the manufacturer's instruction. mRNA libraries were generated using the Illumina TruSeq stranded mRNA sample kit and 1 mg of total RNA per sample. Library preparation, sequencing on a HiSeq1500 Illumina platform, and data analysis were carried out at the NGS facility of the Institute of Toxicology and Genetics (KIT). Fastq files were processed with CASAVA and mapped against the human reference genome GRCh37 using TopHat 2. 0. 11 (Trapnell et al. , 2009). Reads were quantified with HTSeq  (Anders et al. , 2015) , using the reference gene annotation from Ensembl. Differential expression analysis was performed using DESeq2  (Love et al. , 2014). All RNA-seq data have been deposited at the GEO depository under accession number GSE89939.
6 Da, first mass = 110, CE = 30%, rapid scan, target = 5e3, max fill time = 50 ms, electron multiplier detection) and HCD (quadrupole isolation, width 1. 6 Da, first mass = 110, CE = 30%, target = 5e4, max fill time = 50 ms, image current detection with 15K resolution). Mascot was used to search peak lists (. mgf) generated by multiplierz  (Askenazi et al. , 2009; Parikh et al. , 2009)  against a forward-reverse database of human proteins (NCBI refseq). Search parameters specified SILAC quantitation (K + 8, R + 10), variable methionine oxidation, and fixed carbamidomethylation of cysteine residues, as well as a precursor mass tolerance of 10 ppm and product ion mass tolerances of 0. 6 Da and 25 mmu for CID and HCD spectra. After filtering results to 1% FDR, SILAC quantitation was performed using multiplierz scripts for genes or gene groups represented by at least 2 unique peptides.
SILAC data were then normalized according to Bag-1L expression levels (CMut vs. WT). For protein products detected in 'forward' (WT light, CMut heavy) and 'reverse' (WT heavy, CMut light) SILAC experiments, ratios were averaged to provide an aggregate CMut/WT ratio. Search results were each filtered to 1% FDR, combined and further filtered to yield proteins represented by 2 or more unique peptides. Proteins detected in the Bag-1L RIME experiment were considered background and excluded from further analysis unless total summed peptide signal intensities were !3 fold higher than those in the control experiment. Genes or gene groups were considered regulated by the mutation of Bag-1L if ratios deviated by more than 2 standard deviations from median-normalized IgG control ratios. Data were plotted using R version 3.
The spectrometer was equipped with a broadband triple resonance probe head with 4 scans per increment and a total of 128 increments in the indirect dimension. For chemical shift calibration and to compare relative signal intensities, 0. 2 mM DSS (2,2 Dimethyl-2-silpentane-5-sulfonic acid) was added. Data were processed with NMRPipe  (Delaglio et al. , 1995)  and analyzed using NMR VIEW  (Johnson, 2004). Expression, purification and nuclear magnetic resonance assignment of isotopically labeled 15 N-AF-1 has been previously described  (De Mol et al. , 2016). [ 1 H, 15 N]-HSQC spectra for AR-AF-1 alone (at a 25 mM concentration), at a 1:5 molar ratio with GST (as a control) or with unlabeled GST-BAG (wild-type or mutant) were acquired at 278K on a Bruker 800 MHz spectrometer. Data was processed using NMRPipe and NMRDraw, and analyzed using CcpNmr Analysis  (Vranken et al.
Acknowledgements We thank  Anna C Groner ,  Jin Zhao ,  Jutta Stober  and  Rebecca Seeger  for technical help and fruitful advice and assistance. A big thanks to  Noriko Uetani  for graphical support. We also thank the  Molecular Biology Core Facility  ( Dana-Farber Cancer Institute), particularly Zach Herbert , for help with Illumina sequencing. This research was support by the  Claudia Adams Barr Foundation (LC) , the  Prostate Cancer Foundation  (LC, AS, JB, ACBC, MB) and the  Deutsche Krebshilfe (ACBC). AS holds a joint  Prostate Cancer UK  and  Academy of Medical Sciences  Award and is supported by the  Medical Research Council. LY was supported by a research grant from the  KIT BioInterfaces International Graduate School (BIF-IGS ) to ACBC and GUN. EAN is supported by a joined scholarship from the  Government of Ghana  and the  DAAD (Germany).