dataesr/scientific-paragraphs-categorization huggingface dataset:
The tuning offset with respect to the CO(1-0) sky frequency for BzK-21000 was chosen to cover the CO lines for 4 galaxies with the same narrow bandpass (for details see  Hodge et al. 2012 Hodge et al. , 2013)). Similarly, the pointing center was selected to cover all 4 galaxies, such that the flux scaling multiplicative factor needed for primary beam correction at the location of BzK-21000 was 1. 33. The nearby quasar J1302+5748 was used for gain and pointing calibration and the source J1331+3030 (3C286) served as flux and bandpass calibrator. The data was calibrated using the Astronomical Image Processing System (AIPS;  Greisen 2003)  and the Common Astronomy Software Application (CASA) package  (McMullin et al. 2007 ). Time ranges with poor visibilities as well as edge channels where the bandpass deteriorated (at each edge) were flagged.
The calibrators and correlator configuration used for the D-array data were the same as those used to observe BzK-21000. For the BzK-4171, the SPWs were centered at 46. 740 and 46. 822 GHz, while for BzK-16000, these were centered at 45. 647 and 45. 765 GHz, for a total bandwidth of 228 MHz. Similarly to the case of BzK-21000, the difference in bandwidth with respect to the nominal 256 MHz is due to overlapping channels to improve the bandpass. For the C-array data, the observations were performed with the WIDAR correlator but using its expanded capabilities. In this case, two basebands (AC and BD) of 8 contiguous SPWs each were used. Baseband AC covered the SPWs 0-7 while baseband BD covered SPWs 8-15. Each SPW was set to have 64 channels and 2 MHz per channel resolution. Both basebands were configured to overlap, with an offset of 44 channels with respect to each other and making an effective bandwidth of ∼ 1.
112 GHz. With this setup, the SPWs 5 and 12 covered the redshifted CO(1-0) line from BzK-16000. Hereafter, we focus on the data obtained in these SPWs. Both Dand C-array visibility datasets were concatenated and imaged accordingly. Data calibration and flagging were done with AIPS, and imaging was performed using CASA. After trying several weighting schemes, we chose Briggs weighting with a robust parameter of 1. 0. This approach minimized negative noise peaks in the image when averaging channels across the CO line, while retaining good sensitivity. In the case of BzK-4171, we applied a further taper of 1. 7 ′′ to increase the sensitivity toward extended sources (although the actual rms of the map is higher). We used a box around the source of ∼ 5 ′′ in size, cleaning down to 2σ. The obtained sensitivities and beam sizes are summarized in Table  1.
Finding clumps in CO data: BzK-21000 The high resolution achieved in the observations of BzK-21000 enables us to look for bright compact CO clumps. While the map tapered to 1. 1 ′′ resolution only reaches a significance of ∼ 4. 5σ for the integrated CO emission of the galaxy, compact clumps with larger brightness temperatures (TB) might be identified in the high resolution CO data cube. We searched for clumps using the AIPS task SERCH, which carries out a matched-filter analysis using a Gaussian kernel to convolve the data cube along the frequency axis with an expected input line width, and thereby searches for regions with a significance over a specified limit. This method has been used for source finding in a number of studies (e. g. ,  Hodge et al. 2012). The expected size of the molecular clumps range from 0.
, 2008)  with a version of the Greengenes 13_5 database  (McDonald et al. , 2012)  trimmed to the V6 region. OTUs were analyzed with QIIME v1. 7  (Caporaso et al. , 2010)  and the R package phyloseq  (McMurdie and Holmes, 2013). Data sets were rarefied to the lowest number of sequences per sample (142,916 reads). Bray-Curtis distance was used for Principal Coordinates Analysis (PCoA) on a relative abundance matrix comprising all OTUs belonging to the orders listed in Table  1. LEfSe  (Segata et al. , 2011)  was used on the same matrix to determine indicator bacterial groups with a logarithmic linear discriminant analysis (LDA) score threshold of 2.
, 2010). Alignments were generated with MAFFT  (Katoh, 2002)  and refined manually in MEGA5  (Tamura et al. , 2011). Final alignments comprised 206 and 460 amino acid positions for soxB and rdsrAB, respectively. Phylogenetic trees were constructed with the maximum likelihood method in MEGA5. The sequence data have been submitted to the GenBank database under accession No. KF910786-KF910914 (soxB) and KF910915-KF910961 (rdsrAB).
QUANTITATIVE PCR (qPCR) The AlleleID 7. 75 software (PREMIER Biosoft) was used on alignments of soxB and rdsrAB sequences to design a suite of primers targeting divergent phylotypes (Table  Supp1 ). Primers were checked against the nr/nt database using Primer-BLAST on NCBI server  (Ye et al. , 2012). For each target phylotype, DNA standards were prepared by linearizing plasmids from one representative clone. qPCR reactions were performed on a Mx3005P thermocycler (Stratagene) using the Maxima SYBR Green qPCR Master Mix containing 0. 01 μM ROX (Thermo Scientific). For each primer set, qPCR conditions were optimized on serial dilution of the respective standard clone (10-10 5 copies), to ensure satisfying specificity and efficiency above 80%. Reactions were denatured 10 min at 95 • C, followed by 40 cycles of 15 s at 95 • C, 30 s at the specific annealing temperature and 30 s at 72 • C.
ACKNOWLEDGMENTS This work was supported by  NSF  Ecosystems Studies grants  DEB-1050557  ( Stefan M. Sievert ) and  DEB-1050713  ( Zoe G. Cardon ,  Anne E. Giblin ). Logistical support was provided by the  PIE-LTER (NSF  grant  OCE-1238212 ). We thank  Suzanne Thomas  and  Jérôme Dabin  for assistance in the field, as well as  Joseph Vineis  and  Hilary Morrison  for their advice and help on the HiSeq sequencing.
EXPERIMENTAL PROCEDURES Chemistry and Analytical Data Sulforaphane was purchased from Sigma-Aldrich (S6317). PMI (1-(3-iodophenyl)-4-(3-nitrophenyl)-1,2,3-triazole) was synthesized from commercially available starting materials in four solution-phase synthetic steps. 1 H and 13 C nuclear magnetic resonance (NMR) spectra were recorded on a Bruker Avance 400 instrument using solvent residuals as internal references.
After further washing in TBST, blots were developed using an ECL Plus western blotting detection kit  (Fisher Scientific, 12316992). Immunoreactive bands were analyzed by performing densitometry with ImageJ software. Blots from Hepa1c1c7 cells were obtained as follows. Following treatment with 10 mM PMI, cells were harvested at different time intervals. Nuclear and cytosolic fractions were obtained using NE-PER Nuclear and Cytoplasmic Extraction Reagents  (Thermo Scientific, 78833). Proteins were separated by electrophoresis on 10% SDS-PAGE gels and transferred onto nitrocellulose membranes. The blots were blocked with 1% skimmed milk and probed overnight with the primary antibodies: Heme Oxygenase 1 (Santa Cruz Biotechnology, sc-10789) polyclonal antibody, NQO1 (Santa Cruz Biotechnology, sc-271116) monoclonal antibody, or b-actin (Santa Cruz Biotechnology, sc-130657) polyclonal antibody.
A volume of 2 ml of the cDNA product from the reverse transcription reaction was used in a total volume of 20 ml SYBR green detection (QIAGEN). The SYBR green PCR reaction mix constituted of a final concentration of 2. 5 mM MgCl 2 , 0. 1 mM of each primer with the reaction consisting of the following: an initial 15 min denaturation step at 95 C followed by denaturation at 94 C for 15 s, annealing at 55 C for 25 s, and extension at 72 C for 15 s repeated for 40 cycles. The DNA standards and samples were run in the same 96-well PCR plate and the average of each duplicate value was used for subsequent statistical analyses. All PCR products were checked for specificity and purity from a melting curve profile performed by the lightcycler software at the end of each run.
ACKNOWLEDGMENTS The research work of this manuscript was funded by the  BBSRC New   Investigator Award Grant  to M. C. ( BB/I013695/1 ), which has D. A. E. employed as a Post Doctoral Research Assistant; the  PPCT Research Grant  to M. C. ; the  Bloomsbury Colleges Consortium PhD Studentship  to G. W. and M. C. ; and  Cancer Research UK  ( C9344/A10268 ) to G. W. We also thank  Bigboy Simbi  ( RVC ) for his technical assistance with the qRT-PCR. We thank  Prof. Michael C. Wooten  of the  Auburn University  for the p62 À/À MEF cell line,  Prof. Andrey Abramov  ( UCL ) for the pink1 À/À SH-SY5Y line, and  Albena Dinkova-Kostova  ( University of Dundee ) for the Nrf2 À/À MEF line.
There are different software packages that can generate rules including ROSETTA  [12] , and WEKA  [13]. Rulebased classifiers are non-linear and the identified rules may describe important features and interactions in the data. An intuitive heuristic to identify putative interactions from a set of rules is to search the rules for combinations of conditions that occur frequently in them. However, a classifier typically contains a large number of rules, which sometimes may be very complex with five to ten, or even more, conditions. Thus, new tools are needed to support the visualization and interpretation of the rules. Most attempts to visualize rules have concerned association rules. For an overview of such visualization techniques, see for example  [14, 15]. Software previously developed for this task includes the R package arulesViz  [16]  that uses a two-dimensional matrix in which similar rules are clustered.
However, most methods scale poorly with an increased number of rules. We were impressed by the readability of the circular graphs produced by the Circos software  [17]  and decided to use it for rule visualization. To our knowledge, the only attempt to visualize rules in a circular layout was done for association rules by  [18]. We therefore present Ciruvis: a web-based tool  [19]  for the visualization of conditions that are associated in the rules using a circular layout. It relies on a scoring system previously introduced by  [20]  for which we now provided a free-to-use web-based implementation. The tool may produce both separate rule networks for each decision outcome and a combined network. In this study we focused on the detection of interaction effects in those networks, although they may also be valuable solely for visualization purposes.
Generation of simulated data We used simulated data to test the ability to detect interactions using the networks. The dataset was constructed to contain both noise, features correlated to the decision, and pairs of interacting features. The interacting features were defined so that they together were predictive for the decision but that each of them was uncorrelated to it. Translated into a real-world situation, this could represent a situation with SNPs of which some lack marginal effects on the outcome, but have an interaction effect caused by gene-gene interactions or epistasis. For each data set we defined five correlated features with expected correlation c = X*i/4, where X was the maximal correlation for that data set and i = 0, 1, …, 4. Each correlated variable was named after the index i and its correlation c according to Ci_c.
Similarly, we defined five pairs of interacting features which, when taken together, were predictive for the outcome with the probability p = Y*i/4, where Y was the maximal value for that data set and i = 0, 1, …, 4. The features of the pairs were named Ri_ p and Si_ p where i was an index 0 < = i < = 4, and p was their probability of being predictive. Each choice of the parameters X and Y thereby represented one data set with 15 features. In order to generate datasets with different properties, we allowed X and Y to take all values in {0. 00, 0. 05, 0. 10, …, 0. 95, 1. 00}, which defined 21*21 = 441 datasets. In each dataset 1000 objects were generated using the algorithm below. Note that the Random() function returns only discrete values and thus, that both the decision and the features are discrete.
Rule-based classification using ROSETTA The rule-based classifiers were constructed using the ROSETTA toolkit for analysis of tabular data  [12, 21]. ROSETTA is a mathematical framework capable of deriving IF-THEN rules from a set of training examples. Boolean reasoning is used to compute minimal sets of features, called reducts, able to discriminate between the training examples equally well using all features. Based on the feature values in the training data, the reducts are transformed into rules that describe minimal sets of feature conditions associated with a particular decision class. Combined, these rules may be used to classify previously unseen objects. Algorithms and parameters are described shortly in the results section and in more detail in the Supplementary methods (Additional file 3: Supplementary methods).
Detection of correlated versus interacting features in simulated data To investigate how well the rule networks from Ciruvis could detect feature interactions, we first tested it using simulated data. The data contained both features correlated to the decision and pairs of interacting features predictive for the decision. The level of correlation and pairwise predictability was determined by two parameters that defined a maximum level for the most predictive feature/pair in the dataset. The maximum level of correlation, X, and of interaction, Y, was varied between 0 and 1. Then, for each data set the number of correctly classified objects was counted (Additional file 4: Figure  S2 ). As expected, there were usually more correctly classified objects when the features were more predictive (as measured by higher X and/or Y).
Next, we investigated the behavior of the rule networks for different datasets (Figure  1 ). Since both the features and the decision were binary only the networks for outcome "0" are presented. Based on the data generation algorithm opposite values of the R and S variables were expected to predict the "0" decision, e. g. , IF R = 0 AND S = 1 THEN DEC = 0, whereas equal values predict the "1" decision. The aim was to observe how small interactions could still be detected and to learn about their properties; for instance, whether they would be masked by features strongly correlated to the decision. Using X = 0. 00 and Y = 0. 10 we could identify visible connections between pairs with an interaction level at 10, 8, and 5% (Figure  1A ). The connections between "R4_10" and "S4_10" were the two strongest in the figure demonstrating that very weak interactions may be detected in the Ciruvis networks even in the presence of very noisy data.
This particular example also illustrated that the rules from a classifier may be informative, even when the quality of classification is essentially not better than "random guessing". In the following runs we processed datasets with a small background correlation, X = 0. 15 (Figure  1B-H ). With Y = 0. 10 the pair with a 10% chance of interaction was barely visible, and not among the highest scored connections in the figure (Figure  1B ). As Y was increased the two (or three) highest scored pairs became step-by-step more visible (Figure  1C-E ) and when Y was set to 0. 55 or higher the three most interacting pairs were by far the strongest connections (Figure  1F-G ), with the exception of Y = 1. 00 when the third pair (R2 + S2) was masked by the more predictive pairs (Figure  1H ). Similarly, when the best interaction was 100% predictive (Y = 1.
00) and with higher correlation (X = 0. 35 or X = 0. 45, respectively), the strongest interacting pair was highly visible and the second pair had indeed a visible connection, but it was on the same level as some of the noise (Figure  1I-J ). Although it is useful to know that stronger rules may mask weaker ones, masking caused by perfect correlation would normally not be expected in a real data set. When the dataset had both a high level of correlation and interactions, the connections for the two strongest interacting pairs were visible, but not the strongest connections (Figure  1K-L ). However, the true interactions are shown as connections from conditions with otherwise few and weak connections, while connections that are artifacts caused by combinations of correlated features origin from conditions with a lot of strong connections.
Comparison to other methods using real data In order to compare the interaction detection to other methods, and to apply the methodology to real data, we used the California Housing  [22]  dataset downloaded from  [23]. This dataset was chosen as it had previously been subject for interactions detection  [24]. California Housing describes housing value based on 1990 census data in California. The decision is the median value of a block group (medianHouseValue) and there are 8 features. We discretized the decision into three groups; one group of houses valued ≥500 000 which was encoded as '2', the remaining houses were split at their median into the intervals 0-173 600 and 173 601-499 999 (encoded as '0' and '1', respectively). We used the features longitude, latitude, housingMedianAge, total Rooms, population, and median Income previously selected by  [24]  to build a rulebased model using ROSETTA.
Direct comparison between the networks was therefore difficult, since the same width would relate to a different number of rules. Instead we studied the strongest connections in each network. For this dataset both networks were quite simple, with all connections supported by only one high-quality rule. For ALL the highest scoring connections were based on any pair of the following conditions: SPTAN1 = high, PTX3 = low, and CFP = low; the conditions SPTAN1 = high and CFP = low were the most frequent in other rules as well. Had the set of patients been larger, noiseless relationships would likely have been harder to identify and Ciruvis might have helped us identify the most important pairs out of more complicated rules. The AML network showed the same property, with a large number of connections based on only one rule with a pair of conditions.
Using simulated data, we showed that the ROSETTA software may be used to construct rules that describe interactions even if the features lack marginal effects. Yet the rule detection may be biased towards features strongly correlated to the decision. We modeled different trade-offs between correlated and interacting features, and demonstrated to what degree stronger associations mask weaker ones. The masking is a consequence of the classification algorithm, which is biased towards using the most predictive features for classification, omitting weaker but still predictive features or feature combinations. The problem arises when the interpretation of the classifier is important. To detect masking features, correlations between each feature and the decision may be computed or Ciruvis may be used to identify nodes with connections to almost all other nodes.
Surprisingly so far, although the number of fully sequenced bacterial genomes is approximately two thousand, functional TALE proteins have been identified and characterized only from two plant pathogens namely Xanthomonas spp. 8 and Ralstonia solanacearum  11. The constant release of new genomic data, notably from metagenomic samples, prompted us to search for new modular DNA binding proteins with potentially unique biochemical, biophysical and sequence/structural features. In this study, using publicly available databases, we first carried out a bioinformatic search for putative DNA binding domains showing a few desired sequence/structural characteristics including: (i) the possibility to assemble the new putative DNA binding domain as modules, (ii) the possibility to design the specificity of the new putative DNA binding domain towards for individual nucleotides, and as a novel feature (iii) a certain degree of polymorphism in the primary amino acid sequence (Fig.
Results Identification of new modular DNA targeting scaffolds. In order to identify new modular DNA targeting domain we firstly queried structural based databases such as SCOP  12  and Cath 13 together with Pfam 14 (sequence based database based on Hidden Markov Model method) using the keywords ''repeats 1DNA''. This type of inquiring allowed us to extract 10 SCOP families and 27 CATH domains and 40 Pfam families. To refine our search we queried Pfam using the primary amino acid sequence of TAL effector PthXo1 15 retrieving the PFAM family PF03377, composed of 801 sequences, 12 architectures (sequence domains organization) and 45 species (such as Metazoa, Firmiticus, Proteobacteria and others). We next inspected the multiple sequence alignment (composed of the sequence motives of the PF03377 family), to be able to extract the complete sequence of the most promising hits (sequences composed of repeat motives having a certain degree of polymorphism).
The best candidates were subsequently characterized for: prediction of secondary structure elements, protein localization (PSORT  16  , TargetP  17  ) and prediction of function (Go 18 and KEGG  19  ). A few hits were then used as query for hmmsearch software (biosequence analysis software using hidden Markov model  20  ) to retrieve possible hits in the metagenomic sequence repository. Altogether, we identified two new families of putative modular DNA binding domains (M3BD for Modular Base-per-Base Binding Domain) (Table  1 ). The first family is composed of four protein fragments (containing 4 to 9 modules) from a metagenomic sample. The second family is composed of three proteins (containing 6, 20 and 27 modules) belonging to the proteome of Burkholderia rhizoxinica, a symbiotic bacterium living in the cytosol of Rhizopus microspores (Table  1  and Supplementary Fig.
1a, b, c ). We have focused our further studies on one of the three proteins from Burkholderia rhizoxinica, identified under the accession number E5AV36 (UniProt  21  ). This new scaffold protein will be further named BurrH, Fig. 2a. A prediction of secondary structure elements (SSE) for the BurrH primary sequence revealed a high content of a-helices organized in a tight stretch of 20 supersecondary elements (modules) consisting of three a-helices linked by short loops (Fig. 2b ). Globally, the predicted BurrH fold resembled the solenoid protein families such as tetratricopeptide (TPR), pentatricopeptide (PPR) and Sel1-like (SLR) repeat  22. All these families share similar a-helical conformations but no conservation of primary sequence and superhelical topologies. Functionally they are mainly involved in protein-protein inter-actions but also in signal transduction pathways and polynucleotide recognition such as for TALE  22.
The structural coordinate of a TALE (PDB code 3UGM, TPR fold  15  ) was used to build a 3D model of the BurrH modules, Interestingly, we found notable differences between them (Fig. 2a, c ). The BurrH modules revealed high polymorphism levels and key amino acid differences (Fig. 2b, c ). Notably, the first position of a module was occupied by a large aromatic residue in BurrH (phenylalanine), whereas this was an aliphatic and more hydrophobic leucine in TALE (AvrBs3 TALE protein named AvrBs3-TALE  8  , was chosen as reference for comparison). Furthermore, clear inversions of polarity were revealed at several positions. In detail, positions 8 and 16, which surrounded the putative DNA binding loop, were occupied respectively by a lysine and an alanine in BurrH, but were inverted in AvrBs3-TALE (alanine in position 8 and lysine in position 16).