dataesr/scientific-paragraphs-categorization huggingface dataset:
Details of use Installing the different programs or accessing the Web servers is not a difficult task. They all contain a ReadMe file or a manual that is easy to follow with no prior knowledge assumed. However, not all programs provide both Web server and source code. Table  1  lists the various RNA design programs according to four categories: general purpose programs, shape aware programs, adaptive sampling programs and specialized programs. It then indicates the availability of Web server or source code for each program, including extended features and general remarks that relate to their capability, use or strategy without providing more details on their specific methodology. From all programs listed in Table  1 , we picked five programs for further description on their details of use based on the following selection criteria: the programs have both a Web server and a source code available, and they were already used in the literature in a biological meaningful way by either a 'wet laboratory' experiment or the identification of a putative new noncoding RNAs.
These criteria are of interest to practitioners who are considering the use of RNA design programs. The selection yielded the programs RNAinverse, RNAiFold, NUPACK and incaRNAfbinv. In addition, the antaRNA program was added because it was published recently without a chance yet for practical use, but it is considered promising as can also be observed by the program comparisons provided in next section and its overall strategy that allows much flexibility. Finally, although Nanofolder could not be added because of a lack of source code and our recommendation for the interested user would be to contact its authors  [12] , it is worthwhile noting the significant practical experience that has been accumulated by Nanofolder as a specialized program for RNA nanostructure design. There are sequence design rules implemented in Nanofolder that have been formulated based on the concept of same-length sequence fragments called 'critons'  [12] , which have been successfully applied beforehand to the design of DNA nanostructures.
RNAinverse in detail RNAinverse  [1]  was the first program developed for RNA design. It is available as a Web server at http://rna. tbi. univie. ac. at/cgibin/RNAinverse. cgi and as a stand-alone version in the Vienna RNA package  [24]. The algorithm uses an adaptive random walk to minimize base pair distance. The distance is calculated by comparing the minimal energy folding of a mutated sequence (its predicted structure) with the target structure. To avoid folding the entire sequence, small substructures are optimized and then elongated. The algorithm also supports designing sequences, which are more probable based on the partition function. Those sequences may be more stable but mostly differ from the target structure. The server receives a secondary structure in dot-bracket notation. An optional start sequence can be inserted; any lower-case letter in the sequence will be conserved in the final result.
The server also supports multiple energy models, folding temperature and number of sequences to generate. Once the form is submitted, the result page will appear with a list of designed sequences and the calculated minimum energy for them. It may also show designed sequences that did not match the exact structure and their base pair distance away. Another set of results that will appear below that are designed sequences using the partition function if selected. The Web server also shows links to RNAfold  [24]  for extensive information on a specific result. The command line used to run the design in the stand-alone version is also written. The stand-alone version of RNAinverse is part of the Vienna RNA package. The package is a C code library that includes several stand-alone programs. This means RNAinverse can be accessed both from command line and through the package API.
RNAiFold in detail RNAiFold  [42, 43]  is a well-established program available as a Web server at http://bioinformatics. bc. edu/clotelab/RNAiFold/. It uses constraint programming and is available in two modes. RNA-CP design takes as simplest input a secondary structure and returns up to 50 sequences or the maximum found in 2 h, optimized for one of three different criteria: yielding the target as the minimum free energy (MFE) structure, the free energy or the ensemble defect. RNAiFold ensures the optimality of the solutions. Additional constraints can be provided such as a target amino acid sequence, and limits on the amount of GC, AU and GU base pairs as a list of admissible and forbidden base pair. The nucleotide distribution and energy model are customizable. A special mode, RNA Synthetic Design, leverages RFAM  [65]  to add constraints from sequence conservation.
A drop-down menu allows the selection of any family and automatic extraction of the constraints. The consensus structure can be automatically selected as target. The constraints from RNA-CP design are also available. The output presents for each resulting sequence a number of different statistics such as its GC content, energy and entropy and its amino acid sequence. It also provides a link to BLAST  [67] , the resulting sequence. Structure (2) was tested on the Web server. It took a short time before the query was allowed to run and returned 23 sequences after 2 h. A version can be downloaded in source code or binaries for Linux or Mac OS X; the source requires the Vienna Package as the open source Google optimization library OR-Tools. The binary was tested on Ubuntu 12. 04, while the Mac binaries still need to be updated for OS 10.
Remarks General AntaRNA  [44, 45]  RNAiFold  [42, 43]  Experience in biology 'wet laboratory' RNAinverse  [1]  First program developed; experience in biology 'wet laboratory' NUPACK  [35, 36]  Optional multistranded target structures; experience in biology 'wet laboratory' INFO-RNA  [33]  RNA-SSD  [34]  Frnakenstein  [39]  ERD  [40, 41]  MODENA  [38]  Shape aware IncaRNAfbinv  [52]  Fragment selection enabled; experience in RNA detection RNAfbinv  [51]  Fragment selection enabled RNAexinv  [48]  No user-selected fragment Adaptive sampling IncaRNAfbinv  [52]  Global-local approach; experience in RNA detection IncaRNAtion  [47]  Global-local approach RNA-ensign  [46]  Global approach Specialized Nanofolder  [12]  Nanostructures; multistranded RNA; experience in biology 'wet laboratory' CDSfold  [54]  Design of protein-coding sequence RNAdesign  [56]  EternaBot  [57]  Design rules set by Eterna players RNA-redesign  [58]  Three-dimensional: fixed backbone It uses ant colony optimization to allow the design of structures, allowing pseudoknotted structures using hardcoded energy parameters.
A range of temperature can additionally be provided for this step. A link is provided to download the MFE secondary structure representation and the base pair probabilities. NUPACK is also available as command line software and was tested on a MAC OS X 10. 11. 5. Some options are not available on the Web server, such as setting a seed sequence or specifying the concentration of salt and magnesium. A few parameters of the algorithm can also be modified from the command line as its random seed, which is necessary to generate different sequences.
IncaRNAfbinv in detail IncaRNAfbinv  [52]  is a recent program for fragment-based design. It is available as a Web server at https://www. cs. bgu. ac. il/ incaRNAfbinv/. The Web server combines two base applications: IncaRNAtion  [47]  and RNAfbinv  [51]. Both applications are available as a stand-alone client. RNAfbinv uses simulated annealing with a four-nucleotide look ahead local search function. The function includes biologically meaningful constraints such as sequence constraints, fragment-based design and a variety of optional features. The resulting sequences fit a coarsegrained tree graph shape of the original target structure, thus allowing for flexibility. IncaRNAtion augments the local search method. It uses a global sampling approach and weighted sampling techniques. The sequences generated by IncaRNAtion are used as seed sequences for the local search.
Finally, a link is available for an image of the designed structure. The stand-alone versions of both tools can be run locally; links can be found in the Web server. IncaRNAtion is a simple to run Python script, thus requiring Python installed and recommends adding the MPMATH library for long sequences. It receives as input a file containing the target structure and an optional multiple sequence alignment (MSA). The user is also required to enter a value between 0 and 1, where 1 means only to regard the structure and 0 the MSA. Additional variables are available for GC content control and specific sequence constraints. A single run will generate a large amount of sequences; a minimal value for the number of outputs can also be set as input. The output is a list of sequences separated by lines.
Discussion The programs listed in Table  1  have been developed in the past several years, following the first program named RNAinverse that was introduced >20 years ago, and offer some interesting prospects for RNA sequence design. They all in one way or the other rely at present on thermodynamic parameters corresponding to the nearest-neighbor model and therefore structures that are known to be well predicted by energy minimization, for example the secondary structure of the guanine-binding riboswitch aptamer that is illustrated in the second test case example of previous section and in Figure  1 , are the best to work with as inputs to these programs to achieve reliable results. Though exceptional cases exist, in general, the upper range estimate for the sequence length that these programs are useful for is around 150 nucleotides  [71].
It is expected that in future, having more experimental structures elucidated, the number of RNA sequences with a well-predicted secondary structure by energy minimization techniques will grow significantly, and more biological systems involving RNAs will be designed by the aid of these programs. Runtime can be a critical issue concerning the usage of these tools. A runtime comparison of six programs is provided in Table  2  for the two test case examples that were provided in the previous section in dot-bracket notation (the first is a toy problem and the second is the structure of the guanine-binding riboswitch aptamer). The times reported are in minutes. Standard parameter values were used in the comparison. Because runtimes are measured in downloadable source code and cannot be measured in programs that require user interactive intervention such as with incaRNAfbinv and RNAfbinv, we replaced incaRNAfbinv that was discussed in the previous section by the simpler and less developed program RNAexinv.
The justification is that RNAexinv is shape aware (preserving the same coarse-grain tree graph shape in the output as in the input) without the user's interactive selection of a fragment for preserving its secondary structure exactly like in incaRNAfbinv; therefore, RNAexinv contains the shape aware feature itself for inclusion in the comparison. RNAexinv is still much slower than the rest of the programs because it solves a more general inverse RNA folding problem that is shape aware. By our past experience, the programs RNAfbinv and incaRNAfbinv are about 10% more computationally expensive than RNAexinv. Additionally, we inserted the program INFO-RNA because it is known to be the most computationally efficient among all programs, as is also observed in Table  2. Correspondingly, for the two test cases measured in Table  2  by 1000 runs, a histogram is plotted in Figure  2  for the first test case, and in Figure  3 , for the second test case to examine how far away the predicted structures of the designed sequences are from the input structure that is initially given.
The distance between the two secondary structures was measured by the RNAdistance routine available in the Vienna RNA package that calculates by default the tree edit distance. Histogram comparison between the six selected programs is available in Table  2  for the example test case that is designated as (1) in the Details of Use Section and for which the runtimes are reported in the first column of Table  2. While it is impossible to draw conclusions from Table  2  and the associated Figures  2  and 3  as to which program is better for use, because a program such as RNAexinv belonging to the shape aware category (Table  1 ) is expected to be much slower along with histograms that are more widespread compared with the rest of the programs in a notably beneficial way for its purpose, some trends can be observed.
For example, INFO-RNA is the quickest as expected, but its histograms are more widespread compared with most of the other programs and this correlates with the known result that sequences designed with INFO-RNA tend to be more biased to high GC contents  [47]. In contrast, the newly introduced antaRNA program from the same laboratory is both relatively fast and achieving histograms with results that are close to the input structure. RNAiFold is also showing some fairly balanced outcomes between efficiency and proximity of the results to the input structure. Finally, RNAinverse shows impressively that although it was written >20 years ago and it features less constraints compared with the newer programs, it is still both fast and faithful to the input structure. The above comparison is by no means exhaustive and can be supplemented by the additional references  [43] [44] [45] 52].
These references could benefit a reader interested in the topic of runtime comparisons, design capabilities and properties of the output sequences produced by each method. The RNAiFold Web server article  [43]  contains a section on comparison with other software. Because this article does not include the most recent methods antaRNA and incaRNAfbinv, the interested reader can find more information about the performance of these algorithms in  [44, 45]  and  [52] , respectively. It should also be noted that the use of tree edit distance to the target structure in Figure  3  as a performance measure may not consider that some of the methods included do not necessarily use the same energy model and dangle treatment as used herein. The one used herein for computing the tree edit distance is the Turner 2004 model  [29]  included in the Vienna RNA package 2  [24].
Concluding remarks The various programs, especially the ones who are gaining experiences in biological meaningful problems and are being improved as a consequence by updated versions, should best be examined along with the constraints they allow and their   2  for the example test case that is designated as (2) in the Details of Use Section and for which the runtimes are reported in the second column of Table  2. orientation purposes. There are already several programs that were described in detail and offer both a Web server implementation and source-code availability, along with a proven experience in biological meaningful problems. Other programs should strive to achieve these goals. Practitioners should then select which program is more suitable for their needs according to the specific constraints and capabilities that are advertised in each one of the programs.
Funding The  Lynn and William Frankel Center for Computer Sciences at Ben-Gurion University  and  ISF  within the  ISF-UGC joint research program  framework (September 2014). An Azrieli  and  FQRNT postdoctoral fellowships  (to V. R. ). YP is supported by the  Austrian/French project  ' RNAlands ',  FWF-I-1804-N28  and  ANR-14-CE34-0011. JW is supported by a  NSERC   Discovery Grant  ( Rgpin 386596-10 ) and  NSERC Accelerator Supplement  ( Rgpas 477873-15 ).
Furthermore, some bug reproductions require more than adaptation tasks. This is the case of MongoDB NoSQL database bug 7974, where elasticity and consequently, adaptation tasks, happen in parallel with other events. In that case, adaptation tasks are not the unique cause of the bug, though they are still a requirement for its reproduction. Reproducing bugs several times is necessary to diagnose and to correct them. That is, bugs reported in a CBS bug tracking should be corrected by developers, who need to reproduce them, at rst. Moreover, during the development, regression tests should be run regularly  [Engstrom et al. , 2010] , requiring the design of ecient and deterministic tests. Making CBS testing reproducible is then an important concern. In this paper, we consider any bug that occurs in presence of elasticity as an elasticity-related bug.
CBS Case Studies MongoDB 2  is a NoSQL document database. MongoDB has three dierent components: the conguration server, MongoS and MongoD. The conguration server stores metadata and conguration settings. While MongoS instances are query routers, which ensure load balance, Mon-goD instances store and process data. Apache ZooKeeper 3  is a server that provides highly reliable distributed coordination. ZooKeeper is intended to be replicated over a set of nodes, called as an ensemble. Requests from ZooKeeper's clients are forwarded to a single node, the leader (which is elected using a distributed algorithm). The leader works as a proxy, distributing the request among other nodes called as followers. The followers keep a local copy of the conguration data to respond to requests.
ACKNOWLEDGMENTS This study was funded by the  Emirates Center for Wildlife Propagation (ECWP) , a project of the  International Fund for Houbara Conservation (IFHC). We are grateful to  H. H. Sheikh Mohammed  bin  Zayed Al Nahyan ,  Crown Prince  of  Abu Dhabi  and Chairman of the IFHC, and  H. E. Mohammed Al Bowardi ,  Deputy Chairman  of  IFHC , for their support. This study was conducted under the guidance of  RENECO International Wildlife Consultants LLC. , a consulting company managing ECWP. We are thankful to  Dr. F. Lacroix , managing director, and  G. Leveque , project director, for their supervision. We sincerely thank all ECWP staff from the  Ecology division  who participated in data collection. We are grateful to two anonymous reviewers who provided thorough and constructive comments and  Dr Tom Martin  for improving the English text.
For ECIS-based measurements, impedance was monitored with measurements taken at 160s intervals over a 500 Hz to 64 kHz frequency (f) range, for up to 96 h post-induction (described in detail below). We used the ECIS-Zθ built-in mathematical model, which scans impedance measurements through various frequencies to recognize current pathways, translating these into quantitative data on cell-cell junctions (Rb), cell-electrode adhesion (z-alpha) as well as the cell membrane capacitance (Cm).
Methods overview. A technical workflow, outlining the HepaRG-based model, and subsequent impedance spectral data deconvolution into biologically-relevant parameters of cell behaviour, are shown in Fig. 2  and Supplementary Fig. 1. Parallel assessment of cellular morphology, phenotype, protein expression, alone, or in the context of APAP hepatotoxicity in HepaRG cells, were conducted independently, alongside each ECIS experiment (see Supplementary Information). Furthermore, effects of both phorbol ester (Protein Kinase C (PKC)-activator which abrogates hepatic TJs integrity) and purified NAPQI, on HepaRG TJs were investigated. We also report in Supplementary Information, additional methodological details related to: HepaRG and primary human hepatocytes (PHHs) cell culture on ECIS microelectrodes; hepatotoxicity assays; flow cytometry analysis; immunocytochemistry (in vitro) and immunohistochemistry (*murine model of APAP-induced liver injury); morphological and ultrastructural assessment; and impedance spectral modeling data analysis.
Cells were imaged using a Zeiss LSM700 Confocal Microscope (Carl Zeiss, Cambridge, UK) and Zen software. Prestoblue live-cell viability and ATP-depletion endpoint hepatotoxicity assays. Following the 24 hour treatment with APAP, as described above, 10% (v/v) PrestoBlue ® (A-13262; Life Technologies Paisley, UK) was added to cell culture medium and incubated for 30 minutes, before the fluorescence signal was measured on a GloMax-Multi + Microplate Multimode Reader (Promega Southampton, UK), and data processed, according to the manufacturer's instructions. After performing the non-toxic PrestoBlue assay, remaining cells were lysed to determine total cellular ATP levels using the CellTiter-Glo ® Luminescent Cell Viability Assay (G7570; Promega), as per vendor's instructions. Bioluminescent signals were detected with the above plate Reader.
1% BSA and 0. 1% sodium azide. Unstained cells were included as controls, and dead cells and debris were excluded from the analysis, based on scatter characteristics. Data for at least 10,000 live events per sample were acquired using a MACSQuant Analyzer (Miltenyi Biotec) and analyzed using FlowJo version 9. 6. 7 software (Flowjo LLC). Data is expressed as relative mean fluorescence intensity (MFI), and % positive staining. In vivo mouse model of APAP-induced liver injury. Animal work was approved by the University of Edinburgh College of Medicine & Veterinary Medicine Animal Ethics Committee and in accordance with the Animals (Scientific Procedures) Act 1986 and UK Home Office regulations (licence PPL 60/4320). After an overnight fast (12 hours), male mice (C57/BL6) aged 12-14 weeks, received acetaminophen by intra-peritoneal (IP) injection.
In order to quantify the DAB stained percentage of each sample, three microscopic pictures were taken at x400 from an area adjacent to the central vein in randomly chosen lobules for each sample. In samples with centrilobular necrosis, this was not included in the picture, as cellular collapse resulted in concentration of positive staining (see results). A Fiji macro was developed for automatic DAB stain detection  70. This macro included a background substraction rolling ball algorithm with a radius of 150pixels  71  , followed by colour deconvolution calibrated to DAB and haematoxylin stains in single stained slides  72  , thresholding (0-170), and area measurement. Before final data collection, the macro was refined and validated in a randomly chosen subset of pictures for each experimental group.
Comparison of different parameter combinations was achieved by overlaying the measured area to the original picture  73. The amount of positive staining is expressed as the percentage area of positive stain relative to the total area of the picture (i. e. 225123 μ m 2 ). Differences between groups were assessed with Kruskal-Wallis, with a significance threshold of p < 0. 05. Morphological and ultrastructural assessment. To examine ultrastructural features of APAP toxicity using transmission electron microscopy (TEM), cells were cultured in 6-well plate format and treated for 24 hours (as above), then processed as previously described  28. Statistical analysis and data presentation. All data are represented as mean ± standard error. One-way Anova and Tukey-Kramer multicomparison tests were used to determine whether the groups under investigation were significantly different from each other.
The Scintillator Detector Array (SD) The goal of the particle detector (notably described in  [3] ) is to provide the arrival direction, the size, the core location of the atmospheric shower and an estimation of the energy of the incident cosmic ray. It also provides a master trigger, which can be broadcasted to any other system throughout the observatory, among others the compact array of antennas and a dedicated station holding the same GPS module as the radio stations described later, for time-stamping. The particle detector array is made of 13 scintillators located on a grid with a variable spacing (approximately 120 m on the diagonal and 170 m on the NS and EW directions). It covers a 340×340 m 2 area whose center roughly corresponds to the former cabled radio array center of CODALEMA-2.