dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge fieldwork support by  Josep Marmi  and  Mireia Plà , preparation of thin rock samples by  Luis Gordón  and  Marc Puigcerver  at  UAB , and XRD and XRF data acquisition and analysis by  Josep Elvira  and  Soledad Álvarez  at  ICTJA-CSIC. E. Mujal  acknowledges  N. -E. Jalil (MNHN) ,  S. Jiquel ,  B. Marandat ,  L. Marivaux ,  M. Vianey-Liaud ,  P. -O. Antoine  and all  ISE-M  staff their kind help in the collection visits. We acknowledge comments and suggestions from three anonymous reviewers that have improved a previous version of the manuscript, as well as the editor  Prof. Sierd Cloetingh  for handling the manuscript. E. Mujal obtained financial support from the  PIF  grant of the  Geology Department at UAB, and from the Erasmus + program  of the UAB performed at the  Palaeontology Department from the Institut des Sciencies de l'Evolution (Université de Montpellier, France ).
Follow-up and case and control selection Information on cancer incidence, tumour subtypes and vital status was obtained via record linkage to regional and national cancer registries, except in Germany and Greece where active follow-up was used and self-reported information was verified via health insurance or medical records and municipality-, hospital-and physician-based cancer and pathology registries and reports. Prostate cancer was defined as code C61 in the 10 th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), and cases were men diagnosed with prostate cancer after blood collection and prior to the end of follow-up. For these analyses, samples were available for cases from both the first round of centralisation of follow-up data and a later round of follow-up (with end of follow-up ranging between centres from 2001 to 2002 and from 2007 to 2008, respectively).
advanced stage and non-aggressive vs. aggressive disease), and heterogeneity by subgroups was tested (Additional file 1). In a sensitivity analysis, we excluded men who were also in a previous analysis conducted in EPIC-Heidelberg  [10]  (91 cases and 11 controls; personal communication, Tilman Kühn) and resulting in incomplete matched sets. This left 985 matched sets for this analysis. All tests of statistical significance were two-sided, and to account for multiple testing the false discovery rate was controlled to 0. 05 using the Benjamini-Hochberg method  [16]  (Additional file 1). All analyses were conducted in the Stata Statistical Software Package, version 14 (Stata Corporation, College Station, TX, USA).
VENOMICS aims to establish a new paradigm in venom science by first exploring the peptide content of venoms by transcriptomics and proteomics and then by building a large library of bioactive molecules for drug discovery by high-throughput synthesis and recombinant expression of animal venom peptides. For the recombinant production of oxidized peptides in E. coli; first, the consortium reproduced and benchmarked several production options published in the literature and then, after optimization, applied the best protocol to the production of 5000 recombinant animal venom peptides selected from the VENOMICS database. The optimization of the production protocol is detailed in this manuscript. The application of this new procedure for the production of 5000 toxins is described in the accompanying article.
The wells were washed twice with buffer A (Tris 50 mM, NaCl 300 mM, Imidazole 50 mM, pH 8). The recombinant fusion proteins were eluted from the resin beads with 500 µL of elution buffer (Tris 50 mM, NaCl 300 mM, Imidazole 250 mM, pH 8) into 96-deep-well plates. All protein purification steps were automated on a Tecan robot (Switzerland) containing a vacuum manifold. Analysis of the purified protein yields was performed on a Labchip GXII (Perkin Elmer, USA) microfluidic high throughput electrophoresis system. These analyses provided an estimation of the molecular weight, purity and concentration of the proteins. All the quantitative values given in this manuscript are based on the calculation made by the Labchip GXII software.
Before the cleavage an aliquots (20 µL) of the 96 uncleaved samples were blocked with caliper sample buffer following the manufacturer protocol. After the 18 h TEV cleavage, samples were acidified for 1 h with 5% ACN, 0. 1% formic acid. Precipitated material (TEV protease, fusion tags and misfolded peptides) was removed by centrifugation (10 min at 4100×g). Three Aliquots (20 µL) of the 96 cleaved samples were collected, two for the mass spectrometry analysis and one that was boiled with the caliper sample buffer. To check that the cleavage was successful and to calculate TEV cleavage yields, these samples were run side by side with the uncleaved controls on the caliper GXII system. The TEV cleavage efficiency was calculated using the Labchip quantification software. Because proteins below 5 kDa cannot be quantified by the software, the cleavage efficiency was only calculated by integrating and comparing the disappearance of the fusion-peptide species band on the labchip.
9, Applied Biosystems). The quantitative calculation of peptide yields were determined using automatic processing with Xcalibur software (ThermoScientific), by OD 280 nm measurement and peak areas integration. The peptide cleavage and recovery yield calculation was made by comparing the quantities of peptide present in the initial sample after Nickel purification (fusion-His-Toxin band on the caliper), given by the caliper GX II software versus the final yield of oxidized peptide quantified by the OD 280 nm measurement and peak areas integration by the LC system. To comfort these results, the second aliquot was sent to our VENOMICS collaborator Dr. L. Quinton (University of Liège, Belgium) toxin and mass spectrometry specialist who confirmed the correct oxydation and good cystein connectivity of these known toxins (data no shown).
However, created gene variants incorporated changes in DNA primary sequences which reflect the random sampling of codon selection and the overall freedom permitted by the algorithm used for gene design. Thus, the average pairwise DNA sequence identity was 79. 8% within the three variants of the 24 datasets. The 72 genes were synthesized and cloned using the Gateway system into pETG82A prokaryotic expression vector under the control of a T7 promoter and in fusion with the gene encoding the DsbC fusion tag for cytoplasmic expression. E. coli BL21 (DE3) pLys S were transformed with the 72 plasmids and grown in auto-induction media. Fusion proteins were purified and protein integrity and yield measured by Caliper Labchip GXII analysis (Fig. 1a ). Depending on the peptide of interest, the purified fractions run on the Caliper Labchip GXII mainly as a single band (peptides 1, 2, 3, 4…) or as a double band  (5, 8, 16, 18…).
The single band represents the good protein population (His-DsbC-peptide) while the lower band (around 29 kDa) corresponds to the His-DsbC protein alone after truncation/degradation of the target peptide. This lower band probably indicates that there is a portion of the peptide population that was not properly folded and was degraded during the expression or purification processes. The protein concentration depicted in Fig. 1b  has been calculated by integrating only the His-DsbCpeptide band using the Caliper software. The data, presented in Fig. 1b , revealed that yields of purified fusion protein varied from ∼1 mg/L (for fusion protein 14) to above 100 mg/L (for fusion proteins 4, 5, 8, 9, 10, 18 and 24). For the vast majority of targets (19/24) the quantities of fusion protein purified would allow the purification of milligram scale of target peptide per litre of culture (assuming a cleavage and purification yield around 100%) while for the remaining five peptides (6, 7, 11, 13 and 14) a larger volume of culture would be needed.
The final yield for the 96 constructs, presented in Fig. 6 , was expressed as absolute peptide final yield in mg/L culture or normalized to 100% for each peptide relative to the vector used for expression. The data (Fig. 6 ) revealed that all 16 peptides under study could be produced recombinantly, but at different levels. Similar to the yield obtained for the fusion variants (Fig. 4 ), a general trend suggests that the shorter peptides are easier to produce than the longer ones. The final venom peptide yield varied greatly with a 50-fold difference between the worst case (0. 3 mg/L, T10 in pHTP6) and the best case (17. 6 mg/L, T1 in pHTP4). The dataset also revealed that there was a significant drop in yields following fusion tag cleavage. This is probably due to the harsh recovery condition after cleavage (acidification in 5% acetonitrile, 0.
Acknowledgements This work was supported by The  VENOMICS  project, European project Grant  N° 278 , 346  through the  Seventh Framework Program  ( FP7 HEALTH 2011-2015 ). The  VENOMICS  project is a collaboration between several research institutions and companies in Europe: AFMB,  Aix-Marseille Université (France) ,  CEA Saclay (France) ,  NZYTech (Portugal) ,  Sistemas Genomicos (Spain) ,  University de Liege (Belgium)  and  Zealand Pharma (Denmark). The authors want to thank  Dr Luc Dedieu  for his contribution to the project. The authors want also to warmly thank  Dr Loic Quinton ,  Liege University, Laboratory of Mass Spectrometry, Liège 1-Belgium  for having conducted all the QC measurement by mass spectrometry to confirm the AFMB results. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
Ana Filipa Sequeira  was supported by  Fundação para a Ciência e a Tecnologia (Lisbon, Portugal)  through the  individual fellowship   SFRH/BD/51602/2011. This work was supported by The  VENOMICS  project,  European  project grant No  278346  through the  Seventh Framework Program  ( FP7 HEALTH 2011-2015 ) who paid all expenses including the salary of JT, NS, FP, LR, MB.
Data This dataset is a list of 39 microsatellite markers from the worldwide pathogenic species complex Colletotrichum gloeosporioides, including primers and basic information relative to diversity levels expected at each locus. Colletotrichum fungi are diversified  [1] , with species ranging from genuine endophytic commensals to biotrophic parasites or even saprophytic pathogens  [2]. Species of this genus are thus often associated with crop diseases, and especially anthracnose in plants  [3] [4] [5]. Taxonomic studies are currently investigating sequence based delineation of species (DNA barcoding, e. g. [6] [7] [8] ), but reaching consensus is still undergoing  [9]. Defining co-dominant and highly polymorphic molecular markers such as microsatellites available for diversity studies and cross geographical or ecological comparisons would be a valuable tool for the study of this species complex and would allow introducing genetic data complementary to the current genomic approaches  [9].
Data accessibility This manuscript (Table1), primers are also available from probe data bank @ NCBI (www. ncbi. nlm. nih. gov/probe/)Value of the dataLarge set of potentially polymorphic microsatellite markers in Colletotrichum gloeosporioides. Diversity and genetic structure analyses at both fine and broad geographic scales. Pathogenic strains genetic profiling. Table How data was ABI PRISM 3730XL automated sequencer (MACROGEN) acquired Data format Raw (primers information) and partially analyzed (diversity indices) Experimental Genomic DNA factors Experimental Isolation of microsatellite markers and amplification test features Data source Guadeloupe 4°44. 0694 0 N 53°46. 881 0 W location Further Colletotrichum gloeosporioides species delineation (complimentary to sequencing data).
4. The preparation was allowed to equilibrate for about 1 h. Spontaneous activity and locomotor-like activity were recorded from lumbar ventral roots (left/right L2 or L2 and L5) by means of glass suction electrodes. Fictive locomotion was elicited by an application of NMDA ⁄ 5-hydroxytryptamine creatinine sulfate (5-HT) for 30 min (NMDA, 5-10 lm; 5-HT, 10 lm; DA, 50 lm). Electrophysiological data were acquired through a Digidata 1440A interface using the clampex10 software (Molecular Devices, Sunnyvale, CA, USA) as previously described by  [34]. Data analysis consisted of rectifying and integrating (time constant 50 ms) the raw extracellular recordings from ventral roots (Clampfit 10. 0 software; Molecular Devices). The phase relationships were evaluated on 60-s recordings by means of cross-correlation analysis (Clampfit 10.
0 software; Molecular Devices). The quality of the locomotor-related alternations was assessed by the negative correlation coefficient at zero phase lag (center of the cross-correlogram). The mean correlation coefficient (R) value is the average of all r observed in each experiment for a given experimental condition. Spontaneous activity was analyzed with clampfit10. 0 software (Molecular Devices). Event detection was performed with the 'Threshold Search' followed by the 'Burst Analysis'. The frequency and the average duration of spontaneous bursts were calculated.
Rats are not pre-trained to cross the walkway. Following acclimation, the animals were removed from the box and completed 3 consecutive runs (averaging approximately 15 s/run. ). The CatWalk apparatus and goal box were cleaned between each animal. Runs with walking velocities >30% variation were excluded from analysis. The images from each trial were converted into digital signature and processed using CatWalk XT 9. 1 software. The mean scores from 3 consecutive trials (per animal/time points) were analyzed for statistical significance. Following the identification and labeling of each footprint, a wide range of gait data was generated including: i. the static parameters: relative spatial relationship between different paws (base of support and stride length), ii. Interlimb coordination (regularity index) and iii.
0 mM) in drinking water exposure during prenatal and postnatal as well as a latency to fall in the grip strength test  [48]. Earlier observations also showed that Pb-treated animals exhibited deficits in righting reflex and grip strength in rats, suggesting a delay in attaining these skills probably because of damage or poor development of the motor system and brain  [49]. In order to delineate the neurophysiological basis of the neurobehavioral abnormalities, we performed an electrophysiological study which showed an increased frequency of spontaneous activity recorded from the 5 th (L5) lumbar ventral roots indicating a hyper-exitability induced by Pb. In fact, previous data support this observation. Indeed, a study realized by Weinreich  [50]  showed that Pb exposition to different doses (0, 0.
After excitation at 640 nm, the donor signal was recovered on the 695-770 nm emission filter (donor channel) and the FRET signal was recovered on the 810-875 nm emission filter (acceptor channel). Acceptor signal was recovered on the 810-875 nm emission filter (acceptor channel) after excitation at 745 nm. Images were processed using the Living Image software (PerkinElmer, USA). For quantification, a region of interest was automatically drawn around (i) the wells of the 96-well plates (in vitro studies) or (ii) the mice liver (in vivo studies). Average radiant efficiency values (threshold of 5% and 30% for in vitro and in vivo images, respectively) have been used for quantification. The semi-quantitative measurement of the efficiency of the FRET signal (i. e. , FRET proximity ratio)  [14]  was calculated as A/(A+D) ratio.
Acknowledgements This work was supported by the  ERC   Consolidator grant BrightSens  (No. 648528 ), the  ERC Advanced grant Ternanomed  (No. 249835 ) and the  LabEX nanoSaclay grant  ( ANR-10-LABX-0035 ). BA was supported by  LabEx Chimie des Systèmes Complexes. The  CNRS  and the  French Ministry of Research  are also warmly acknowledged for financial support. The authors declare no conflict of interest. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Introduction Programming errors which cause non-termination can compromise software systems by making them irresponsive. Notorious examples are the Microsoft Zune Z2K bug  3  and the Microsoft Azure Storage service interruption  4. Termination bugs can also be exploited in denial-of-service attacks  5. Therefore, proving program termination is important for ensuring software reliability. The traditional method for proving termination is based on the synthesis of a ranking function, a well-founded metric which strictly decreases during the program execution. FuncTion  [36]  is a static analyzer which automatically infers ranking functions and sufficient precondition for program termination by means of abstract interpretation  [13]. The tool is based on the abstract interpretation framework for termination introduced by Cousot and Cousot  [14]  The underlying abstract domain of FuncTion is based on piecewise-defined ranking functions  [40] , which provide an upper bound on the number of program execution steps until termination as a function of the program variables.
Implementation and Experimental Evaluation The ranking functions abstract domain and the new parameterizations introduced in this paper are implemented in FuncTion  [36]  and are available online  7. The implementation is in OCaml and consists of around 3K lines of code. The current front-end of FuncTion accepts programs written in a (subset of) C, without struct and union types. It provides only a limited support for arrays, pointers, and recursion. The only basic data type are mathematical integers, deviating from the standard semantics of C. The abstract domain builds on the numerical abstract domains provided by the APRON library  [25]. The analysis proceeds by structural induction on the program syntax, iterating loops until a fixpoint is reached. In case of nested loops, a fixpoint on the inner loop is computed for each iteration of the outer loop, following  [4, 31].
It is also possible to refine the analysis by only considering the reachable states. Experimental Evaluation. The ranking functions abstract domain was evaluated on 242 terminating C programs collected from the 5th International Competition on Software Verification (SV-COMP 2016). Due to the limitations in the current front-end of FuncTion we were not able to analyze 47% of the test cases. The experiments were performed on a system with a 3. 20GHz 64-bit Dual-Core CPU (Intel i5-3470) and 6GB of RAM, running Ubuntu 16. 04. 1 LTS. We compared multiple configurations of parameters for the abstract domain. We report here the result obtained with the most relevant configurations. Unless otherwise specified, the common configuration of parameters uses the default strategy for handling variable assignments (cf.
Related Work In the recent past, termination analysis has benefited from many research advances and powerful termination provers have emerged over the years. AProVE  [34]  is probably the most mature tool in the field. Its underlying theory is the size-change termination approach  [27] , originated in the context of term rewriting systems, which consists in collecting a set of size-change graphs (representing function calls) and combining them into multipaths (representing program executions) in such a way that at least one variable is guaranteed to decrease. Compared to size-change termination, FuncTion avoids the exploration of the combinatorial space of multipaths by manipulating ordinals. Terminator  [10]  is based on the transition invariants method introduced in  [33]. More specifically, the tool iteratively constructs transition invariants by searching within a program for single paths representing potential counterexamples to termination, computing a ranking function for each one of them individually (as in  [32] ), and combining the obtained ranking functions into a single termination argument.
Its successor, T2  [6] , has abandoned the transition invariants approach in favor of lexicographic ranking functions  [11]  and has broadened its scope to a wide range of temporal properties  [7]. UAutomizer  [22]  is a software model checker based on an automata-theoretic approach to software verification  [23]. Similarly to Terminator, it reduces proving termination to proving that no program state is repeatedly visited (and it is not covered by the current termination argument), and composes termination arguments by repeatedly invoking a ranking function synthesis tool  [28]. In contrast, the approach recently implemented in the software model checker SeaHorn  [21]  systematically samples terminating program executions and extrapolates from these a ranking function  [38]  using an approach which resembles the value extrapolation of the widening operator implemented in FuncTion.
Cell population densities and cell viability determinations In all the different experiments, cell populations were counted using a Beckman-Coulter electronic particle counter. Cell viability was determined by flow cytometry using a C6 cytometer (Accuri, BD Biosciences): propidium iodide (PI) (Calbiochem) was added to the cell suspension (5 μL of a 0. 1 mg/mL solution), and the samples mixed by gentle shaking. PI is a fluorescent nucleic acid stain (excitation 488 nm, emission 575 nm) which cannot penetrate intact cell membranes. PI flow cytometry analysis was performed 15 min after staining. Fluorescence data for cells stained by PI were collected in channel FL3. Viability was determined as the percentage of intact and fragile cells among all cells  [12].
mL -1 in PBS), sonicated for 15 s in an ultrasonic bath (Branson Sonifier), incubated for 1 h at 50˚C and centrifuged. The pellet was resuspended in 500 mL PBS and stored on ice until analysis. Before analysis, 50 μL of cells were incubated with SYTOX 1 Green at 1 μmol. L -1 and directly observed using the flow cytometer. Fluorescence data for cells stained with SYTOX 1 Green were collected in channel FL1.
RNAs were hybridized on 8 x 15 k array Agilent standard Yeast V2 Gene Expression Microarrays (Agilent Technologies) for 17 h in a rotating oven at 65˚C following manufacturer's recommendations. A Genepix 4000B scanner was used for array digitalization: laser voltage was set to avoid signal saturation and data were extracted with GenePix 1 Pro 7 software (Molecular Devices).
Statistical analysis of microarray data The R3. 1. 3 software was used for statistical analyses  [17]. The raw microarray data were imported and normalized with the quantile method for normalization between arrays using the limma package  [18]. On the normalized data set, we analyzed gene expression changes over time using the maSigPro package and the single series approach. maSigPro is a regression-based approach to identify genes with temporal expression changes  [19]. For the first step of this method, we defined a binomial regression model for each gene expression over the 4 time-points: We adjusted this model by the least-squared technique for each gene and only genes with significant changes over time (i. e. with an adjusted p-value threshold of 0. 005 corrected by the Benjamini-Hochberg method) were selected.