dataesr/scientific-paragraphs-categorization huggingface dataset:
The RNA quality indicator (RQI) grading RNA from 10 (intact RNA) to 1 (degraded RNA) was calculated by Experion software for all samples. Possible presence of inhibitors in each RNA isolate was tested by calculating RT-qPCR reaction efficiencies from standard curves prepared by serial dilutions of respective cDNA samples (fivefold dilutions, 6 point-curve, conducted in duplicates). cDNA synthesis 1 lg of purified RNA from cell culture samples per reaction (20 ll) was reversely transcribed using Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Scientific), containing modified M-MuLV reverse transcriptase, RiboLock TM RNase inhibitor, and a mixture of oligo (dT) 18 and random hexamer primers, according to the manufacturer's protocol: 10 min incubation at 25 °C, 30 min incubation at 50 °C, and reaction termination by heating samples at 85 °C for 5 min, all in C1000 termocycler (BioRad).
RT-qPCR We evaluated the following HKG: ACTB, B2M, GAPDH, GUSB, HPRT1, IPO8, MRPL19, PGK1, PPIA, RPLP0, RPS23, SDHA, TBP, UBC, and YWHAZ. Full gene names, accession numbers as well as functions of encoded proteins and the sequences of specific, intron-spanning primers (designed and tested for specificity as previously described (manuscript submitted)) are listed in Table  1. Primers' efficiencies (Table  1 ) were determined with RT-qPCR and a mixture of DNA templates used in this experiment. Samples were assessed in three technical replicates (within the same run) and accompanied by respective no-RT controls as well as no template control. To minimize inter-run variation, the same gene was tested in the same analytical run on different samples; each cDNA was diluted from stock once, aliquoted, and stored at -80 °C; all genes were tested on a series of samples within 2-3 days to avoid prolonged storage of diluted cDNA.
For this, standard curves based on serial tenfold dilutions of CDKN1A, TP53, or MDK transcripts cloned into pJET1. 2 plasmid (10 9 to one copy per ml)   et al. 2004) , and (2) the average pairwise variation of a specific gene as compared with other genes, derived using geNorm utility in qbasePLUS version 2. 4 software (Biogazelle BE, Ghent, Belgium)  (Vandesompele et al. 2002). NormFinder generates a stability value for each gene, which is a direct measure for the estimated expression variation. It allows ranking genes according to the similarity of their expression profiles with lower values indicative of higher stability. Similarly, GeNorm generates M value for each gene with a lower value representative of increased gene stability across samples. GeNorm M value below 1. 5 is arbitrarily suggested to be acceptable expression stability.
GeNorm generates also V value, which is a pairwise stability measure to determine the benefit of adding extra reference genes for the normalization process with 0. 15 as an arbitrary cut-off. Data were uploaded as suggested by software designers: in an efficiency-corrected linearized form using the following expression: Eamp^-Cq, where Eamp = 10^(1/-slope of target standard curve) for NormFinder and as efficiency corrected Cq values for geNorm. Relative expression of target genes (CDKN1A, TP53, and MDK) was calculated using qbasePLUS. The effect of growth conditions (serum availability or time) on HKG expression in each cell line was tested on relative quantities, log-transformed if necessary, using paired t-test while the impact of line type with Kruskal-Wallis H test. Relative gene expression in isogenic cell lines was compared using unpaired t-test.
Data distribution was tested using Kolmogorov-Smirnov test and homogeneity of variances using Levene's test. All calculated probabilities were twotailed and p values B0. 05 were considered statistically significant. The analyses were performed using MedCalc Statistical Software version 12. 7. 5 (Med-Calc Software bvba, Ostend, Belgium; http://www. medcalc. org; 2013).
Effect of serum availability, length of culturing and line type on HKG expression: non-normalized data To evaluate the potential effect the growth conditions and line type might have upon HKG expression, we calculated inter-and intra-group variability using NormFinder algorithm. Across all evaluated cell lines, the highest inter-group variability was displayed by UBC (commonly down-regulated upon serum resupplementation) and by HPRT1 and MRPL19 (commonly up-regulated) (Fig. 1a ). Subsequently, we compared the relative quantities of these genes in individual cell lines using paired t-test. The analysis showed UBC down-regulation to be statistically significant in HT29 cells (p = 0. 004) and HPRT1 and MRPL19 up-regulation statistically significant in, respectively, HT29 (p = 0. 045) and SW480 (p = 0.
NormFinder found RPLP0 the most stably expressed single HKG, followed by PPIA and IPO8. However, the software suggested RPLP0 and SDHA, the fourth HKG in rank, as an optimal pair of normalizers. As shown by inter-group variability (Fig. 1a ), SDHA expression in the present sample set is rather up-regulated upon serum re-supplementation what would compensate RPLP0 down-regulation while the expressions of PPIA and IPO8 tend to be down-regulated as well. According to GeNorm, under study conditions, the average stability of evaluated HKG was medium with average M value [0. 5 but B1. Optimal number of genes to be used as normalizers in the studied set of samples was calculated to be three, namely PPIA, RPLP0, and SDHA. As depicted on Fig. 2 , there was significant improvement in normalization based on three than two HKG (GeNorm V2/3 value exceeded arbitrary cut-off of 0.
426) when normalized against YWHAZ/B2M and YWHAZ/ GUSB, respectively. However, the expression of UBC in HT29 cells upon serum induction remained over fourfold down-regulated (p = 0. 011) following normalization and that of HPRT1-1. 75-fold up-regulated (p = 0. 032). Yet, the twofold increase in MRPL19 transcripts in serum re-supplemented SW480 cells lost significance, whether normalization was based on geNorm (RPLP0/RPS23) or NormFinder (YWHAZ/RPS23) selected pairs of line-specific HKG (p = 0. 145 and p = 0. 259, respectively).
However, this phenomenon, particularly in case of ACTB and TBP, seems to result from the variability being hard to attribute to any specific factor rather than lack of thereof. As preanalyses are based on raw data, non-biological variation introduced during sample handling may contribute to observed differences. Accordingly, RPLP0 was no longer found significantly affected by length of culturing when data were normalized against linespecific normalizers. Consequently, normalization against ACTB/TBP/PPIA/IPO8 was suboptimal, failing to show significant down-regulation of MDK in DLD-1 cells, and was outperformed by HKG set devised by dedicated software from among all genes, without any exclusion. Regardless algorithm used, PPIA, RPLP0, and SDHA were ranked the most stable in the sample set investigated.
In many Ox proteins, a Rieske structure serves as the [2Fe-2S] center  [8, 9]. The Rieske is isolated from the ROC by a dense network of hydrophobic residues requiring transmission either via electron tunneling or proton coupled electron transfer (PCET)  [10] [11] [12] [13] [14] [15]  via a collimated network of hydrogen-bonded conjugated residues or aromatic residues. As a complex, where both the relatively sedentary dioxygenase (dOx) and the itinerant and elusive ferredoxin (Fdx) molecules are found joined together, only one select group of examples  [4]  from this family has been successfully crystalized and measured using X-ray crystallography. Specifically, this complex is an X-ray structure of the dOx molecule carbazole 1,9a-dioxygenase (e. g. , the oxidized structure PDB: 1WW9, Fig  1A )  [16]  and its Fdx partner (PDB: 1VCK, Fig  1B )  [3]  at 1.
9 Å resolution (PDB: 2DE5-7, Fig  1C ). This class of dOx molecules form crystal in a trimer structure, which is usually abbreviated as α 3  (Fig 1C). The Fdx binds to the α 3 complex reproducibly around the boundary region between any two adjoining dOx subunits in the trimer structure (Fig 1C, brown structures). A reduced structure (2DE6) and an oxidized structure containing the carbazole molecule in the redox chamber (2DE7) were also crystallized and measured. In the literature, the components of 2DE5-7 have been labeled CARDO-o for the carbazole 1,9a dOx and CARDO-f for the Fdx cognate  [3, [16] [17] [18] [19] [20]. In this study, these labels are used extensively; consequently, we have abbreviated these labels as CRDo (for CARDO-o) and CRDf (for CARDO-f),respectively. CRDo oxidizes carbazole at the 1,9a position (Fig  1D ).
In the high temperature regime (T > 40 K), the relaxation rate gradually is dominated by the Orbach relaxation process  [42] [43] [44]. In this regime, the exchange coupling jJj of the (Cys) 2 [2Fe-2S](Cys) 2 structure (4-Cys) is roughly twice the Rieske structure  [43, 45, 46]. Part of this appears to be due to a more observable antisymmetric exchange in the [2Fe-2S] 1+ cluster  [47] : characterized by the higher upward shift of the g-value in many Rieske structures. However, it has also been shown that the exchange coupling of the Rieske can be shifted toward the 4-Cys values by eliminating specific hydrogen bonds (H-bonds) between the protein and the sulfur atom in the bridge  [44]. In this study, we examine changes in the partial charges of the universally conserved Rieske-Asp-ROC complex found in all α 3 type Ox proteins, where the parts are first evaluated separately and then coupled via the conserved Asp (Asp180 in the case of this dOx, PDB ids 1WW9 and 2DE5-7).
Final geometry optimizations were done with g03 at the OPBE/cc-pVTZ level for the largest complexes in this work with a final SCF calculation carried out using g09. The final Rieske, 4-Cys, and ROC structures use g09 exclusively at the OPBE/cc-pVTZ level, though these were also calculated independently using g03. In the optimized Rieske, ROC, and complete complex, partial charges were analyzed using RESP in the Amber 10 package  [52]  to integrate these substructures into the Amber ff99SB force field. For both the ROC and [2Fe-2S], the individual Antechamber programs had to be run separately (manually) to build the force field files. Antichamber is not designed to assign "BOND" relationships between free molecules that associate with the principal structure of interest, particularly in the case of locally coordinating water molecules.
4-Cys-[2Fe-2S ] structure. The first step was to determine a level sufficient to ensure that the applied theory could obtain partial charges and force field parameters. To understand the performance, we needed to compare calculations with known experimental data whenever possible. Most of the experimentally obtained spectroscopic data for the [2Fe-2S] structure has been obtained from the 4-Cys: a structure of the form (Cys) 2 [2Fe-2S](Cys) 2. Therefore, we began by using the 4-Cys system as a benchmarking model. The Cys ligand was simplified to a thiomethyl anion (SMe -) resulting in the structure (SMe) 2 [2Fe-2S](SMe) 2 : one structure with D 2h (Fig 5A  and 5B ) and the other with D 2 symmetry (Fig 5C  and 5D ). The Fe ions in the [2Fe-2S] ring exhibit antiferromagnetic (AF) coupling between the electron spins in the ring.
Calculated frequencies are also down-shifted proportionally (Table  3 ). Indicative of this downshift in frequency in the reduced state is a corresponding extension of the bond length, particularly the Fe-Fe distance (2. 71-2. 72 Å) and the Fe-S(b) bond (2. 21-2. 26 Å); i. e. , the shorter the Fe-Fe distance, the higher the frequency. This can be seen in the raw data of the uncorrected AF spin coupling where the Fe-Fe bond length reduces to 2. 59 Å (see the Tables in S2 File). The correspondence between the observed and predicted normal mode frequencies is approximate, and there are also differences in the assignment symmetry. Some of the frequency assignments are strongly dependent on the configuration of the ligands in the structure where a minor rearrangement of the ligands can easily shift the modes in the range of 330-360 cm -1.
Although several configurations were verified, the most critical modes needed to determine the force constants for bond stretching (Fe-Fe and Fe-S(b)) and the related bending modes (S(b)-Fe-S(b), S(t)-Fe-S(b) and S(t)-Fe-S(t)) can be deduced from data of the normal modes. Table  4  shows the extrapolated bond stretching and bending constants for the oxidized state  [57, 60]. From previous studies, we realize that there is considerable variation in the experimental estimates obtained at different times by the same group  [57, 60]. This indicates the difficulty of determining a precise fit for these force constants. Because of the vast complexity of the interactions, it is expected that the parameters will have a wide range of values. For example, the [2Fe-2S] structure has out-of-plane bending, breathing modes, symmetric stretch, and antisymmetric stretch.
Additionally, further splitting of the Fe-S(b) is observed, due to localization of the additional negative charge on the His side of the structure. In the 4-Cys, this effect appears to be less detectable in the charge symmetry of the structure. The Mossbauer data shows that the unpaired electron tends to localize more on the His side of the Rieske structure  [9, 40] , and the exchange is typically weaker  [9, 40, 44, 47]  than the 4-Cys  [42, 43, 45, 68]. Localization is consistent with a zero formal charge of the His and charge delocalization of the electron within the His ligands, which would tend to encourage the electron to residue on the His side where it can transfer the electron to the ROC. Finally, it is important to reiterate that the asymmetric structure of the reduced Rieske in Fig 6C  and 6D  is the result of     Building this structure and optimizing the Fe 2+ state with S = 5 without any additional water molecules and without any constraints always resulted in a bidentate structure (Figs A and D in S1 File; panels b and a, respectively).
The configuration of the water molecules was optimized by running variations of the structure and allowing the structure to optimize in a variety of charge and spin states. The decomposition of important normal modes, excluding the numerous modes involving free molecular water molecules, is shown in Fig 8. The bond stretching and bending parameters, which were extracted from the optimized structure using frequency analysis, are shown in Tables  9  and 10. Unlike the Rieske normal mode data, there are no direct measurements of stretching or bending in these Fe-center structures. The main available bidentate data are those from catecholate structures  [69, 70] , and the main available monodentate data are those from tyrosine binding to Fe in a ligand structure  [70]. However, the main Fe-Nε and Fe-OH 2 interactions are not reported  [33, [69] [70] [71].
3 Å. The same optimization was also carried out for the Rieske structure using a fragment of 3GKQ (Fig  9 , left, red and Fig 10C  and 10D ). The results after a 4 ns simulation are compared with the original X-ray structure in the PDB file (Fig  10C ). Thus, a stable structure could be obtained with the force field parameters of this system, at least within a time period of 5 ns. A These are certainly very short simulations that are not able to test the long term stability of the force field. In fact, the force field has been tested in the full (dOx) 3 :(Fdx) 3 for stability up to 50 ns. These results will be reported elsewhere. S4 File provides a zipped folder containing an example of a completely prepared 2DE5 complex (2DE5o_fixed. pdb) and scripts for building the 2DE5 complex in a water box, carrying out the warmup cycle (from 0 K), equilibration cycle and then a full scale run for as long as the user desires.
The batch submission script must be modified to local system requirements or to operate on one's local machine. Using these scripts, we have carried out several simulations on these complexes out to 50 ns. The development of MD simulation parameters is largely an art where one must balance the practical realities of a doable simulation with the weakness of water and ion models, the massive simplification of normal modes and partial charges with QM, pH effect, etc  [84]. The task here was no different. Using the files and the scripts in the S4 File, one can see how we chose to achieve that balance. For example, the QM suggests that the water is more networked than the TIP3P water model supports. What we can safely say is that these parameters have been tested multiple times to 50 ns.
Structure construction and quantum chemistry methods To build the structures consistent with configurations observed in the protein environment, coordinates for the carbazole 1,9a-dioxygenase (CRDo) structure were obtained from the Protein Data Bank (PDB): accession numbers 2DE5, 2DE6, and 2DE7. Difficult optimizations were first carried out using Gaussian 03 (g03)  [92]  because they required weeks or even many months of wall clock time on a dedicated machine to converge. After this initial optimization, electrostatic potentials and force fields were later determined using Gaussian 09 (g09)  [92, 93]. All calculations used density functional theory (DFT)  [94]  at the BP86/6-311+G(d) and OPBE/6-311+G(d)  [95] [96] [97] [98] [99]  and OPBE/cc-pVTZ  [100] [101] [102]  levels. Selection of the basis set and the BP86 DFT  [103, 104]  approach were based on conclusions drawn from Refs  [6, 51]  and from testing and comparison of results.
Eventually, this cycling yielded a reproducible structure suggesting optimization (Fig  2B ). The details of this optimization process will be discussed elsewhere. Building and optimizing the Rieske-Asp180-ROC structure. A composite structure was built based on recent work on the water interactions and experimental studies of a related complex of dOx (PDB: 1Z01, 1Z02, and 1Z03) (Fig  3A  and 3B ). The complex demonstrated that the Asp residue (1Z01:Asp218) connected to His (1Z01:His108 and 1Z01:His221) when the Rieske domain is reduced (Fig  3B ). Thus, by building the complete complex and optimizing under minimal constraints, the difference in the partial charges can be tested for the oxidized and reduced states of the Rieske or the mononuclear Fe. The method for obtaining the proper spin in the Fe was achieved using GIFA with g03 to determine the orientation of the spin in the structure.
Further details about on constructing the vibrational parameters and dihedral angles can be found in Section 4. 4 in S1 File and the Amber frcmod files can be found in Listing 7 in S1 File. Some further information about the parameters in Listing 7 in S1 File are explained briefly in Listings 8 and 9 in S1 File. Example scripts and files for running Amber simulations can be found in Listings 10 and 11 in S1 File. Partial charges. Partial charges were determined using the RESP charge calculation in Antechamber (Amber 10 modeling toolkit  [52, 120] ) and CHelpG  [53, 54]. Because these partial charge calculations involve Fe ions and molecules of H 2 O surrounding the ROC chamber, both RESP and CHelpG were compared. To achieve RESP2 type charges, the CHelpG results for chemically equivalent atoms were averaged in the structure, e.
Acknowledgments For his master's thesis study,  Yu Inoue  first constructed a prototype version of the force field parameters for carrying out MD simulations on the dOx-Fdx protein complex. We thank  Prof Hideaki Nojiri  for helpful advice and insights on dioxygenase structures and crystallography. We also thank  Prof R Szilagyi  ( Montana State University ) for advice on using GIFA to set up the spin structure in Fig 4. Special thanks also to  Profs Shugo Nakamura ,  Kazuya Sumikoshi , and  Dr Mizuki Morita  of  Univ of Tokyo  for their advice and helpful discussions, and  Yucong Zhu  for encouragement.
disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a diseasespecific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge. The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes.
Coordination of research activities on different patient cohorts requires development of a Sickle Cell Ontology that provides a controlled and consistent vocabulary of various definitions of SCD in terms of clinical events, genetic and environmental modifiers, co-morbidities, therapeutics, psychosocial burden and quality of life. Such an ontology, ultimately linked to standard protocols for assessing phenotypes and interventions, will inform the standardized collection of data in large cohort studies, as well as surveillance systems which phenotype and follow up patients and families affected by SCD. Previous work in this area has focused on standardization of SCD complications or phenotypes  (Ballas et al. , 2010) , disregarding other aspects pertinent to this domain such as diagnostics. These attempts suffer from several limitations, which were pointed out by an independent commentary  (DeBaun, 2010)  and include lack of a reliable method to reach consensus agreements on terms and their descriptions, and imprecise definitions which failed to differentiate between similar but distinct phenotypes.
WebProtégé is an open-source Web application for designing and editing ontologies, which offers several features which permit collaborative development and editing within communities, and has a simple user-friendly interface  (Horridge et al. , 2014). Some groups chose to continue working in WebProtégé, others used spreadsheets, alternative electronic tools or flip charts. The workshop concluded with a presentation of the results achieved by each group, followed by a discussion of possible applications of the ontology, and future plans and projects for both the ontology and the working group. An existing SCD database was presented, along with REDCap (Research Electronic Data capture) as an option for an integrated SCD resource. REDCap  (Harris et al. , 2009)  is a secure, web-based application for managing online databases and surveys.
Workshop recommendations To finalize the SCD ontology, workshop participants agreed to continue reviewing terms independently after the workshop and make them accessible to their working group for final review. Skype meetings, Google docs and WebProtégé will be used to facilitate the collaborative work going forward. It was also agreed that a face-toface meeting would be essential to conclude a major section of the work. Once the ontology is finalized, which is expected to be by September 2016, existing SCD data which is comprehensively phenotyped from 2 to 3 different sites in Africa will be used to assess the ontology for coverage and accuracy. The future plan is also to create a standardized case reporting form (CRF) for SCD patient recruitment, which can be mapped to the ontology and to standard measures  (Hendershot et al.