dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis To assess for the presence or absence of gene expression, the "Presence-Absence calls with Negative Probesets" algorithm was used  [59]. Differences in clinical parameters and cytogenetic analyses and between defined groups were investigated by exact Wilcoxon rank-sum test. Correlation was assessed using Pearson's correlation or Kendall's tau (for categorical variables) tests. The relationship between categorical variables was assessed using Fisher's exact test. Differential gene expression was assessed using empirical Bayes statistics in linear models for microarray data  [60]. P-values were adjusted for multiple testing controlling the false discovery rate as defined by Benjamini and Hochberg  [61]. All computations were performed using R 2. 14. 1 (http:// www. r-project.
org/), and Bioconductor 2. 9  [62]. EFS and OS were investigated using Cox's proportional hazard model as previously published  [53]. For EFS and OS, cutoffs were calculated as the mean cut-off from EFS and OS, respectively. Gene expression-based assessment of risk and proliferation as well as classifications of myeloma was performed as previously published  [2]. Findings were confirmed using the same strategy on the independent group of 701 patients treated within the total therapy 2 and 3 protocols, respectively  [63, 64]. Microarray data are available in the public databases under accession numbers E-TABM-1138 and GSE24080. An effect was considered as statistically significant if the P-value of its corresponding statistical test was not higher than 5 %.
Isobologram analysis For combination studies, data from CellTiter Glo™ assays were converted into values representing the fraction of growth affected (FA) in drug-treated versus untreated cells and analyzed using the CalcuSyn software program (Biosoft, Ferguson, MO) based on the Chou-Talalay method. A combination index (CI) < 0. 9 indicates synergism, whereas 0. 9 to 1. 1 indicates additive effects.
Gene expression analysis U-266 cells were treated with YM155 at concentrations of 4 nM and 50 nM for 6 and 12 hours as well as 36 hours (only 4nM). Control cells were cultivated with solvent only (DMSO, Merck, Darmstadt, Germany). Total RNA isolation using the RNeasy Kit (Qiagen, Hilden, Germany) was repeated independently and both biological replicates were used for microarray analysis using the Illumina chip HumanHT-12 v4 (Illumina, San Diego, CA). Data were analyzed using Ingenuity Pathway Analysis (Ingenuity Systems, Redwood City, CA). Only genes with a significant fold change (Benjamini-Hochberg adjusted p-value < 0. 05) were included in the analysis.
ACKNOWLEDGEMENTS This work was supported in part by the  Max-Eder-Program of Deutsche Krebshilfe (M. S. R) , the  Dietmar-Hopp Foundation  (D. H. , A. S. , M. S. R. , H. G. ), the  German Ministry of Education and Science (CAMPSIMM)  (D. H. , A. S. , H. G. ), the  Deutsche Forschungsgemeinschaft (SFB/Transregio   TRR79  (D. H. , A. S. , H. G. ), the  ARC  ( SL220110603450 ,  Paris, France)  and the  ANR  ( 2021-109/087437 ) (B. K. ), and the  European Community  ( FP7-OverMyR ) (D. H. , B. K. ).
To date, the genome sequences of only three T. equigenitalis strains have been reported  (6, 7)  and the genome sequences of numerous T. equigenitalis sequence types (STs) remain to be characterized. We herein report the genome sequence of T. equigenitalis MCE529, which was isolated in 2009 from the urethral fossa of a 15 year old asymptomatic carrier, Belgian Warmblood stallion from a stud farm in Lower Normandy (France). Sequence typing of this strain using the MLST database Taylorella (http://pubmlst. org/taylorella/), revealed its membership in the previously non sequenced ST16 of the clonal complex 2  (5). The genome of T. equigenitalis strain MCE529 was sequenced by Ion Torrent technology (Life Technologies). The library was constructed using the Ion Xpress Plus fragment library kit, with size selection by electrophoresis.
ncbi. nlm. nih. gov/genome /annotation_prok/), yielding 1,564 open reading frames (ORFs) and 36 tRNAs. The average GϩC content of the draft genome sequences is 37. 4%. One clustered regularly interspaced short palindromic repeat (CRISPR)/Cas loci and one restriction/modification system were detected. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. JRMO00000000. The version described in this paper is the first version JRMO01000000.
We perform an independent analysis of data from the CoGeNT direct detection experiment to quantify the evidence for dark matter recoils. We critically re-examine the assumptions that enter the analysis, focusing specifically on the separation of bulk and surface events, the latter of which constitute a large background. This separation is performed using the event rise-time, with the surface events being slower on average. We fit the rise-time distributions for the bulk and surface events with a log-normal and Pareto distribution (which gives a better fit to the tail in the bulk population at high rise-times) and account for the energy-dependence of the bulk fraction using a cubic spline. Using Bayesian and frequentist techniques and additionally investigating the effect of varying the rise-time cut, the bulk background spectrum and bin-sizes, we conclude that the CoGeNT data show a preference for light dark matter recoils at less than 1σ.
The CoGeNT collaboration have publicly released their 1129 live-days dataset, which contains events spanning the period between 4th December 2009 and 23rd April 2013  [5]. This dataset shows an exponential rise in the number of events at energies below 1 keV ee , where a low-mass DM recoil signal is expected to occur. The CoGeNT dataset also shows ∼ 2. 2σ evidence for the expected ∼ 10% annual modulation in the event rate induced by DM recoils  [17, 18]. This may also be consistent with a DM recoil signal, but appears to require significant departures from a Maxwellian velocity distribution  [5, [19] [20] [21] [22] [23]. In light of this, in this paper we focus only on the unmodulated data, which exhibits the low-energy exponential rise, and leave the analysis of the much smaller annual modulation signal to future work.
Our aim here is to quantify the evidence for a DM signal in the unmodulated dataset. A limitation of the CoGeNT experiment is that the data contains both bulk and surface events; the surface events constitute a background that mimics a DM recoil signal at low energy so should be removed before searching for a DM signal. In section 2 we discuss how the bulk and surface events can be distinguished by their 'rise-time' since surface events typically have a longer rise-time than bulk events. Unfortunately, the populations of bulk and surface events overlap significantly at low-energy because the difference between the bulk and surface events rise-time is small. This means that in the low-energy region of interest for DM searches, only a fraction of the observed events are bulk events; the fraction of bulk events (the 'bulk fraction') must be determined before the evidence for DM can be quantified.
In section 3 we characterise the bulk fraction as a function of energy using cubic splines (owing to the absence of a theoretically motivated function). This allows us to robustly incorporate the bulk fraction uncertainties in our Bayesian and frequentist analyses. Following previous analyses  [4, 5, 24]  we first model the bulk event population with a log-normal distribution. We then investigate fits with a Pareto distribution that gives a better fit to pulser data (which mimic bulk events) at larger values of the rise-time. We also test the robustness of our conclusions by considering variations in the DM search region (by investigating alternative rise-time cuts), the bulk background spectrum and bin-sizes. As our conclusions differ from the results of the CoGeNT analysis in ref. [4] , in section 4 we reanalyse the older 807 live-days dataset used in that analysis.
This is a problem because the signal from elastically scattering DM particles also has a characteristic rise at low energies so surface events can mimic a DM signal. The transition region is only ∼ 1 mm thick compared to the cm scale bulk region so the volume of the transition region is much smaller than that of the bulk. Since DM particles Figure  1. Bulk (red solid line) and surface (blue solid line) event distributions as a function of rise-time for energies between 0. 5 keV ee and 0. 9 keV ee. The pulser data (cyan points) mimics the behaviour of bulk events. In the upper panel both populations are fit with log-normal distributions, while in the lower panel the bulk population is fit with a Pareto distribution. Both the log-normal and Pareto bulk distributions (when summed with the surface distribution) give good fits to the CoGeNT data (black points) but the slower fall off of the Pareto distribution at large rise-time is better able to fit the pulser data.
Analysis of the full 1129 live-days dataset Having demonstrated how to separate bulk events from surface events (which are dominantly background events that rise at low-energy), in this section we discuss our analysis of the full 1129 live-days CoGeNT dataset. Initially, we describe our procedure to remove the surface events leaving the bulk-only event spectrum. Performing a fit to this spectrum will allow us to quantify the evidence for DM in the CoGeNT data. We begin by assuming that the bulk population is modelled by a log-normal distribution and take an energy-independent rise-time cut τ cut = 5 µs. This value is conservative as it captures the majority of the total number of events in the 0. 5 to 0. 9 keV ee energy bin, where the DM signal is maximal (cf. figure  1 ). Our analysis procedure with these assumptions is presented in sections 3.
Analysis of the 807 live-days dataset All of the analyses in the previous section showed that the preference for a DM signal in the full CoGeNT dataset is non-existent or small; the Bayes factors indicated a slight preference for the background only model while the p-value indicated a preference for DM at less than 1σ significance. This seems to be at odds with previous results presented by the CoGeNT collaboration, where an irreducible low-energy excess was found even after correcting for the surface event contamination below the rise-time cut. This low-energy excess was interpreted as evidence for DM with mass 7-10 GeV and cross-section ∼ 3 × 10 -41 cm 2  [4]. In order to show why our results differs from theirs, we also analyse the CoGeNT 807 live-days dataset. Unlike in previous sections, we now use the bulk fraction R determined directly by the CoGeNT collaboration in ref.
[4]. We have reproduced these values in figure  10. In their analysis, the CoGeNT collaboration employed an energy-dependent rise-time cut that results in a smoother drop-off in the bulk fraction at low energy (cf. figures 2 and 10). To derive their 'region of interest', the CoGeNT collaboration fitted a one-parameter exponential of the form R CoGeNT (E) = 1 -exp[-α • E] to the bulk-fraction data R, obtaining α = 1. 21 ± 0. 11. The purple dashed and dotted lines show the best-fit exponential and its error bars in figure  10. The one-parameter fit is determined by a fit to 12 data points but the fit is dominated by the small error bars at higher-energy. This means that the range of The lines in the central panel show the resulting bulk-only spectrum for each of these functions, together with the background estimate (black) and the number of events expected from a DM signal+background (green).
A 'low-energy excess' is produced for a limited choice of the fits consistent with R. For the cubic splines, the excess is either reduced or has vanished relative to the CoGeNT exponential (central panel). The right-hand panel shows that other functional choices give much lower evidence for a DM signal, than the exponential used by CoGeNT. When marginalising over all possible spline choices (black solid line), the signal is washed out leaving vanishing evidence for DM recoils in the CoGeNT 807 live-days data. allowed values from the exponential fit is several factors smaller than the errors on the data at the lowest energy, precisely the energy range relevant for an 8 GeV DM particle. As there is no a priori reason to fit the data with an exponential function over another function, we instead use a cubic spline.
The result of this fit together with the 1σ uncertainty is indicated by the green-band in figure  10. The cubic spline fit allows the error on R(E) to grow at lower energies, matching more closely the behaviour of the data points. To examine the extent to which this choice of exponential function inclines the analysis towards a DM recoil fit, we repeat the analysis of section 3. 3 using the 807 live-days data using the bulk-fraction data shown in figure  10. We follow the analysis cut scheme described in  [4]. The results of the analysis for CoGeNT's exponential fit and two alternative cubic spline fits are shown in figure  11. The central panel shows that the CoGeNT exponential fit results in a low energy excess above the background estimate. This excess is consistent with a DM particle with a mass 8 GeV and cross-section σ = 2.
Summary of results Table  2  summarises the results of our Bayesian and frequentist analyses of the 1129 live-days dataset performed in section 3. In these analyses, we modelled the bulk population by a lognormal and Pareto distribution and considered three variations in the rise-time cut τ cut. The results in the 'Log-normal' column correspond to the analyses in sections 3. 3, 3. 5 and 3. 6, where τ cut = 5 µs, τ cut = 0. 4 µs and was energy-dependent respectively. The first result in the 'Pareto' column corresponds to the analysis in section 3. 4, where τ cut = 5 µs. For completeness, we also list the results for the Pareto distribution when τ cut = 0. 4 µs and when it is energy-dependent. These analyses were not shown in section 3 but the methodology is the same as the other analyses. All of these analyses give consistent results for the p-values and Bayes factor B from the CoGeNT 1129 live-days dataset.
CoGeNT exponential (this work) 807 days CoGeNT limit (this work) 1136 days CoGeNT limit (this work) LUX SuperCDMS Figure  12. The red contour shows the 90% best-fit contour from the 807 live-days data, obtained using CoGeNT's exponential fit to the bulk-fraction, which biases the analysis. The purple line is the 90% limit from the 807 live-days data after marginalising over all cubic spline fits. The blue line is the 90% limit from the full 1129 live-days dataset after marginalising over all cubic splines, and assuming a log-normal distribution for the bulk population and τ cut = 5 µs. Also shown for comparison are the 90% limits from LUX and SuperCDMS. CoGeNT sets the strongest limit in the range 4. 5 GeV to 6. 5 GeV. dashed line shows the 90% upper limit from this data when marginalising over all cubic spline fits to R(E).
The solid blue line shows the 90% upper limit from our analysis of the 1129 live-days data after marginalising over all cubic spline fits to R(E). We model the bulk population with a log-normal distribution and impose τ cut = 5 µs. This limit is competitive with the low-mass limits from the SuperCDMS and LUX experiments (orange and green curves respectively). The 1129 live-days CoGeNT limit is the strongest limit in the narrow window between 4. 5 GeV and 6. 5 GeV.
Conclusions We have presented an independent analysis of the most recent 1129 live-days dataset from the CoGeNT experiment. The data contain both bulk and surface events; DM events are dominantly bulk events while the surface events constitute a background which mimics a DM recoil signal at low energy, so should be removed. Surface and bulk events can be discriminated by their rise-time (see figure  1 ). This allows the surface events to be subtracted by using the bulk fraction R(E) (see figure  3 ). Owing to the absence of any theoretically motivated functional form for R(E), we parameterised the energy dependence of R(E) with cubic splines (see figure  2 ). Using these cubic splines, we found that an excess above background arises only for specific choices of the bulk fraction R(E), while other equally plausible spline choices give no excess.
2σ significance for an annual modulation also present in this dataset  [5]. We note only that if this annual modulation is attributed to DM recoils, then the apparent lack of consistency with the unmodulated data must be explained (see  [23] ). In conclusion, we find that there is at most a 1σ evidence for a DM recoil signal in CoGeNT data. Using the same dataset to derive a limit on the DM-nucleon scattering crosssection, we found that the CoGeNT limit is competitive with those from other experiments (see figure  12 ) and sets the strongest limit in the mass range 4. 5 GeV to 6. 5 GeV. This means that spline 2 and 3 make a greater contribution to the marginalised posterior than in the analyses in section 3, which results in a very small peak in the marginalised posterior. Despite this, the data prefer a DM signal at less than 1.
B. 2 Dark matter We generate the DM recoil spectrum f (m, σ) using the following expression, f (m, σ) ≡ dR dE = (E nr ) σ(E nr ) 2mµ 2 ρη(E nr ), (B. 1) where σ(E nr ) is the DM-nucleus cross-section as a function of nuclear-recoil energy E nr , is an efficiency factor accounting for the CoGeNT cut-acceptance  [25, 28]  (for the 807 live-days dataset we follow the cut scheme in  [4] ), µ is the DM-nucleus reduced mass, ρ = 0. 3 GeVcm -3 is the local dark matter density and η(E nr ) = ∞ v min d 3 v f (v+ue) v is the DM mean velocity. The mean velocity is integrated over the distribution of DM particle velocities in the galaxy f (v) boosted into the reference frame of the Earth by u e  [29, 30]. The lower limit of the integration is v min (E nr ), which is the minimum DM velocity required to induce a recoil of energy E nr.
We assume the standard halo model such that f (v) is given by a Maxwell-Boltzmann distribution cut off at an escape velocity of v esc = 537 kms -1  [31] ; in this case, there is an analytic form for η(E nr )  [32]. We assume that DM particles interact identically with protons and neutrons so that σ(E nr ) = σ (µ/µ p ) 2 A 2 F 2 (E nr ), where σ is the zero-momentum DM-nucleon cross-section, A is the atomic mass of germanium, µ p is the DM-proton reduced mass and F (E nr ) is the Helm nuclear form factor  [33]. We convert nuclear-recoil energy E nr into electron-equivalent energy E using the relation E = 0. 2E 1. 12  nr  [4]. Figure  16. Analysis of 1129 live-days of CoGeNT data when the bin size used to calculate R is 0. 2 keV ee. In this case, the fall in the bulk fraction at low-energy is even more pronounced (cf.
We show the impact this has on our analysis in figure  16. Owing to the more rapid drop at low energy, splines 1 and 3 are now further disfavoured, which means their prior values are suppressed to a greater degree than for the case considered in section 3. 3. This can be seen in the right-panel, where the product P(d E |m, σ, R)P(d R |R) is now even more suppressed for splines 1 and 3 while even spline 2 gives too many events at low-energy. Hence, using smaller energy bins to calculate R further disfavours a DM interpretation of the CoGeNT data. Now the splines that result in a low-energy excess give an even worse fit to the data for the bulk fraction. We therefore find that our conclusions are robust against decreasing the size of the energy-bins used to calculate R(E).
E Marginalisation procedure and other technical details This section provides details of our Bayesian method to marginalise over the bulk fraction R(E). We start with equation (3. 4) for the marginalised posterior. This is calculated by comparing CoGeNT data to the bulk background plus a potential DM recoil for every possible form of the bulk fraction R(E). The marginalisation is then achieved by summing up all of these likelihoods, multiplied by a prior for m, σ and R(E). The latter prior provides a weight for each spline in the marginalisation integral. We now describe how we incorporated the information from the values of R into our marginalisation procedure. We model R(E) as a cubic spline, parameterised by a set of knots with positions x i = (E i , R i ) on the E-R axis, as shown for example in figure  2.
To assess the percentage of cells in which damage occurred, we used immunofluorescence. These experiments showed that gH2AX foci were detectable in 39% of RBBP6-depleted cells, but in only 4% of control HeLa cells (Figure  1B ). A similar proportion of these cells scored positively for the two other DNA damage markers that we tested: 53BP1 and Phospho-S1524-BRCA1 (Figures  1C  and S1E ). Finally, the effect of RBBP6 was not restricted to cancer cell lines, as the depletion of RBBP6 caused the spontaneous appearance of DNA damage in normal human fibroblasts as well  (Figures 1D and S1F). These data indicate that RBBP6 function is required to prevent DNA damage.
Thus, our data indicate that ZBTB38 accumulation represses MCM10 transcription, causing a subsequent decrease in MCM10 protein level. We then asked whether ZBTB38 directly repressed the expression of MCM10. First, we used luciferase reporter constructs. In this assay, ZBTB38 repressed transcription from the MCM10 promoter, but did not affect control promoters (Figure  7D ). Second, we showed by ChIP that the MCM10 promoter was bound by ZBTB38 in cells (Figure  7E ). Third, we overexpressed ZBTB38 in HeLa cells and monitored endogenous MCM10 mRNA and protein expression. We observed that expression of ZBTB38 was sufficient to cause a decrease in MCM10 mRNA and protein expression (Figures  7F  and 7G ) These results establish a direct and causal link between elevated ZBTB38 levels, MCM10 downregulation, and the appearance of DNA damage (model presented in Figures  S7C-S7E ).
DNA Combing Analysis Molecular combing and immunodetection were performed as previously described  (Techer et al. , 2013). Gene Expression Analysis mRNA was isolated using the TRIZOL reagent (Invitrogen) according to a standard protocol. Reverse transcription was performed using the Superscript III reverse transcriptase enzyme (Invitrogen) as recommended by the manufacturer. The primers used for quantitative PCR (qPCR) analysis are available in the PrimerBank database  (Spandidos et al. , 2010). Additional experimental procedures are available in Supplemental Experimental Procedures.
ACKNOWLEDGMENTS For the gift of reagents, we are grateful to S. Ait-si-ali,  R. Everett ,  R. Fahraeus ,  E. Matsuda ,  P. Pasero ,  O. Sordet ,  S. Saxena ,  B. Vogelstein ,  K. Yoshida , and  E. Soutoglou. For insightful comments on the manuscript, we thank  G. Almouzni ,  A. Bardin ,  C. Francastel ,  Z. Herceg ,  E. Julien ,  S. Khochbin ,  S. Polo ,  J. Sage , and  M. L. Schmitz. M. C. was supported by a fellowship from the  City of Paris. B. M. was supported by the  Ligue contre le Cancer (Comitede Paris)  and a  Marie Curie IRG fellowship  ( PIRG07-GA-2010-268448 ). P. A. D. is funded by the  Association pour la Recherche contre le Cancer (ARC) ,  Institut National du Cancer (INCa) ,  Groupement des Entreprises Francaises dans la Lutte contre le Cancer (GEFLUC) , and  Agence Nationale de la Recherche  ( ANR ;  ANR-11-LABX-0071  under program  ANR-11-IDEX-0005-01 ).
D. P. received financial support from the  Medical Research Council of South Africa  and the  National Research Foundation of South Africa. H. M. S. received funding from the  Medical Research Council of South Africa. L. Z. 's lab was supported partially by the  National Basic Research Program  ( 2012CB910702  and  2011CB910802 ) and the  National Natural Science Foundation  Project ( 31125010  and  81221004 ). Work in the lab of M. D. is supported by the  INCa ,  ANR , and  ARC. Received: October 29, 2012 Revised: January 17, 2014 Accepted: March 9, 2014 Published: April 10, 2014.