dataesr/scientific-paragraphs-categorization huggingface dataset:
Presently CUORE is in the final phase of its construction (see Section 5) and it is expected to start taking data in 2015. TeO 2 low temperature detectors (or bolometers) have a long history in ββ0ν searches of 130 Te  [20, 21] , the best sensitivity being achieved by Cuoricino, a ∼ 40 kg tower of 62 bolometers (∼ 11 kg of 130 Te) operated at LNGS site in the years 2003-2008: after an exposure of 19. 75 kg y of 130 Te, Cuoricino set a lower limit on the decay half-life of this isotope of 2. 8 × 10 24 y (90% C. L. )  [21]. To improve that result, as already discussed (see 2), one needs to increase the experimental mass, improve the energy resolution and, what's even more tricky, reduce the radioactive background contribution in the energy region of interest (ROI -centered on Q ββ ). CUORE design goals, besides the increase of the detector mass of a factor of ∼ 20 with respect to Cuoricino, are a FWHM resolution of 5 keV at an energy around 2.
More details on the CUORE detector assembly can be found in  [25]. In order to check the effectiveness of all the actions undertaken to improve Cuoricino results, we decided to run the CUORE-0 experiment. CUORE-0 is one CUORE-like tower built using the same detector components, cleaning protocols and assembly procedures defined for CUORE. CUORE-0 is therefore a proof of concept of the CUORE detector at all stages, including data acquisition and data analysis frameworks. Besides that, CUORE-0 is an independent ββ0ν experiment on its own, which should extend the physics reach beyond Cuoricino while CUORE is being assembled.
CUORE-0 CUORE-0 is an array of 52 nat TeO 2 detectors of 750 g each, for a total mass of 39 kg (∼11 kg of 130 Te). It is operated in the same experimental setup that hosted Cuoricino, i. e. a 25 year-old cryostat located in Hall A of LNGS, with an external shielding made of lead and borated-polyethylene, and enclosed inside a Faraday cage  [26]. Also the front-end electronics and data acquisition hardware are the same used for Cuoricino. The waveforms coming from the detectors are amplified, filtered with a six-pole active Bessel filter, and then fed into a National Instrument PXI analog-to-digital converter (ADC) with 18-bit resolution and 125 S/s sampling frequency. All samples are continuously saved to disk and then processed with Apollo, the data acquisition software developed for CUORE, which identifies pulses with a constant fraction software trigger.
In addition, a heater pulse is injected every 300 s to each bolometer for stabilization monitoring, and baseline samples are recorded at 200 s intervals to keep under control the working temperature and noise conditions of each detector. The signals have a typical time evolution of ∼5 s, with rise times in the range 40 -80 ms and decay times in the range 100 -700 ms. The energy calibration of the detectors is obtained by inserting two thoriated tungsten wires in between the external shielding and the cryostat. Usually, 2-3 days of acquisition time are enough to independently calibrate each detector in the energy region between 511 and 2615 keV with 232 Th γ rays. Physics (or low background) runs are usually 3-4 weeks long. A CUORE-0 data set is composed by an initial calibration, a low background run, and a final calibration.
The raw data are then processed offline with Diana, the analysis software developed for CUORE. The analysis procedure is basically the same applied to the Cuoricino data  [21] , the main steps being amplitude evaluation, gain correction, energy calibration, and time coincidence analysis among the detectors. Especially this last point serves as active background rejection technique, which will be even more effective with the CUORE geometry: if any two or more detectors register signal pulses within 100 ms from each other, the events are tagged as coincident and discarded. In fact the coincidence events are mostly attributed to background interactions, such as Compton-scattered γ rays or α decays on the surface of nearby crystals, since the majority (87. 4±1. 1%  [21] ) of the ββ0ν decay electrons are expected to be fully contained in a single crystal.
Events are selected also on the basis of their shape: noisy pulses are discarded as a result of a comparison with a set of template signals. The last step is the production of the total energy spectra (calibration and background) by summing all the single detectors spectra for the entire acquired statistics. CUORE-0 data taking started in March 2013, though in not ideal conditions: the first results (Phase I) were published in summer 2013  [27, 28]. Then a long stop was needed for cryostat maintainance. The data taking resumed in November 2013, with strongly improved performance, and is still going on (Phase II). The total spectrum obtained by summing all calibration spectra of all active channels (49 out of 52) acquired during Phase II shows a FWHM resolution at 2. 615 MeV ( 208 Tl line) equal to 4.
Acknowledgments The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 ,  NSF-PHY-0902171 , and  NSF-PHY-0969852 ; the  Alfred P. Sloan Foundation ; the  University of Wisconsin Foundation ; and  Yale University. This material is also based upon work supported by the  US Department of Energy (DOE) Office of Science  under Contract Nos. DE-AC02-05CH11231  and  DE-AC52-07NA27344 ; and by the  DOE Office of Science, Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551  and  DEFG03-00ER41138. This research used resources of the  National Energy Research Scientific Computing Center (NERSC).
Literature Search Dataset compilation methods are described in detail in  Winternitz and Abbate (2015). Briefly, studies were compiled from the reviews by  Havlicek and Roberts (2009)  and  Setchell and Huchard (2010) , from the meta-analysis by  Kamiya et al. (2014) , and additional studies were identified up from 2009 to January 2015 via Web of Science using the topic "MHC" and "Major Histocompatibility Complex" and "mate choice" or "mate selection" or "mate preference" and searching within results for "human" and "primate". Studies were listed as testing for human or primate preferences, and for preferences for MHC dissimilarity or diversity/heterozygosity. Studies were included if MHC genotypes (or their approximations via single nucleotide polymorphisms (SNPs) e. g. , HapMap data) were obtained for the individuals tested.
Accepted Article This article is protected by copyright. All rights reserved. heterozygous) and occasionally continuously (mean heterozygosity over all loci considered). Test statistics other than measures of correlation were converted to r effect sizes following  (Nakagawa & Cuthill 2007) ; while there are limitations to converting r from summary statistics other than bivariate correlations  (Aloe 2015) , we stress that these limitations will introduce noise and not bias. Results from  Yang et al. (2014)  could not be directly converted to effect sizes so we calculated values for the data points (points were extracted from Figure  5  using software Datathief (Tummers 2006)) and fit correlation models to obtain effect size estimates in the desired scale. When studies provided multiple effect sizes that we could not independently evaluate with moderator variables (e.
The number of individuals rated (number of independent repeats) in the study was recorded to calculate the variance in effect size (variance = 1/(N study rated -3)). When weighted effect size means were calculated, we also recorded the mean number of individuals rated, and used this estimate to calculate the variance of the weighted mean. Raw data and converted effect sizes were checked by independent extraction (JA) and any inconsistency was discussed until a consensus was reached (between JW, JA, and LG). We converted effect sizes into Fisher's Z (Zr) to stabilize variance across effect sizes, and Zr and its variance (defined above) were used for meta-analyses. The full dataset, comprising 58 effect sizes from 30 studies across 7 species, and effect size extractions and conversions are provided in the electronic supplementary material.
Accepted Article This article is protected by copyright. All rights reserved. system strongly impacts individual mating strategies and population genetic structure  (Sugg et al. 1996)  and so could influence the expression of MHC-mediated mate choice  (Setchell & Huchard 2010). Unfortunately, background genetic measures of dissimilarity and diversity were not available for the majority of studies in our dataset, thus we could not include this potentially important moderator in our analyses. However, we were able to extract a limited number of effect sizes based on neutral markers, which were found to show positive but non-significant correlations with MHC-based effect sizes (MHC dissimilarity correlation (95% highest posterior density, HPD) = 0. 164 (-0. 421 to 0. 747), N=11; MHC diversity correlation (95% HPD) = 0.
Statistical Analyses Meta-analytic procedures There were three causes of non-independence in our datasets: 1) more than one effect size was extracted from a study, 2) multiple effect sizes were available for the same species, and 3) species share evolutionary history making effect sizes confounded by the phylogeny of species. We used phylogenetic mixed-effects modeling that includes random effects to account for non-independence caused by study-, species-, and phylogeny-specific effects  (Hadfield 2010; Hadfield & Nakagawa 2010; Nakagawa & Santos 2012). A phylogenetic tree was obtained by trimming the primate 10KTree  (Arnold et al. 2010)  using the drop. tip function from the ape package v3. 4  (Paradis et al. 2004 ). The Deviance Information Criterion (DIC) was computed for all models considering different random effect structure (Table  1 ), and top model selection was based on DIC values, where the lowest DIC is considered the best, models within 2 DIC units are considered equivalent, and a change in DIC of 4 or more significantly improves prediction  (Spiegelhalter et al.
Accepted Article This article is protected by copyright. All rights reserved. Nakagawa 2010; de Villemereuil & Nakagawa 2014) , equivalent to Pagel's λ  (Pagel 1999). Our datasets of MHC-dissimilarity and MHC-diversity were limited (Dissimilarity N = 41; Diversity N = 17) and were comprised of 28 and 11 studies, respectively, and 7 species each. Preliminary exploratory analysis of the data revealed that the factors study ID and species were strongly associated, with each study focusing on a single species. We compared models for all combinations of the random factors: study ID, species, and phylogeny. We found that including study ID greatly improved model fit, but models adding species, phylogeny, or both were all within 1 deviance information criterion (DIC) from the top model (Table  1 ) and essentially equivalent in terms of prediction  (Spiegelhalter et al.
2002). Therefore, to avoid potentially overfitting the models, we chose to include only study ID and phylogeny to control for phylogenetic pseudoreplication in multi-species models, and to include study ID in human-only models. Meta-analyses were conducted with generalized linear mixed-effect models with Markov Chain Monte Carlo techniques using the R package MCMCglmm  (Hadfield 2010 ). We present details on MCMCglmm model specification and diagnostics in the supplementary material. Briefly, all models were fit using an uninformative inverse gamma prior for all random effects and residuals, and were checked for sensitivity to prior specification and convergence across independent model runs (following  Wilson et al. 2010). Each model was run for 3 million iterations, sampling every 500 after discarding one-million, and this process was repeated for each model to confirm stability of results.
Accepted Article This article is protected by copyright. All rights reserved. test for significant correlations between effect size and year of publication. Third, to assess the impact of publication bias and test the robustness of our results, we used the nonparametric trim and fill method  (Duval & Tweedie 2000b, a)  in the metafor R package  (Viechtbauer 2010). This method adjusts the mixed-model intercept for potentially missing studies and the difference is added to the original metaanalysis model intercept (and credible interval) (following  Sutton et al. 2011). Fourth and finally, as bias for publications with significant results can rely more on the p-value than on the effect size, we used the p-curve method to test if the distribution of significant p-values, the 'p-curve', indicates that our studies have evidential value and are free from "p-hacking"  (Simonsohn et al.
Preference for MHC-similarity As a post-hoc investigation to explain the large residual heterogeneity in effect sizes among the human dissimilarity dataset (I 2 residual = 36%, Figure  4a ), we specifically tested for the effects of population ethnic heterogeneity on patterns of MHC-assortative mating among experimental and observational studies (predicted by  Rosenberg et al. 1983 and Havlicek & Roberts 2009 ). We ran mixed-effect models for the subset of dissimilarity effect sizes that were negative (indicating preferences for similarity). We found that preference for MHC-similarity was significant in ethnically heterogeneous population samples, but not among those that were homogeneous. This result was found only in observational (mate choice) studies, and not among those using experimental approaches (odor and facial preference combined) (Figure  5 , Table  S5 ).
Publication bias and power analysis We found no evidence for publication bias in the datasets. Egger's Regression tests indicated the intercepts for MHC-dissimilarity and diversity datasets were not significant at 90% confidence intervals (Table  S14 , Figure  S2  and S3 ). The slope for MHC dissimilarity was significantly negative, indicating that studies with larger sample sizes (and less variance) showed stronger preference for MHC similarity (Table  S14 , Figure  S2a ). This effect was largely due to human correlative studies that had large sample sizes but did not control for ethnicity and assortative mating biases. Trim and Fill analyses on mixedmodel residuals (following Nakagawa & Santos 2012) were non-significant and suggested there were no missing studies on the left-hand side of the funnel plots (Table  S14 , Figures  S2-3 ).
Accepted Article This article is protected by copyright. All rights reserved. year of publication (all p>0. 05, Table  S16 ). Finally, P-curve analyses for MHC-dissimilarity, similarity, and diversity datasets suggests that studies contained significant evidential value and showed no evidence of intense p-hacking (Table  S16 ). However, study sets in general had low likelihoods to detect a true effect (average power = 24% for dissimilarity, 57% for similarity, 33% for diversity). Power analyses on our meta-analytic results indicated that the combined primate and human datasets for MHC dissimilarity and diversity had high power (77. 2% -98. 5%) to detect a biologically significant mean effect of 0. 15 at an alpha value of 0. 05 across low, medium, and high heterogeneity values (Table  S17 ).
Power was lower for non-human primate datasets (dissimilarity = 43. 8%; diversity = 58. 2% power at high heterogeneity) and a total of 17 and 13 effect sizes, respectively, would be required to reach 80% power for an overall effect size of 0. 15 at alpha = 0. 05 at high heterogeneity (Table  S17 ). Splitting the human dataset by experimental choice cue category, we should have high power to detect a biologically meaningful effect size, if it were present, for mate choice and facial preference sample sizes even at high heterogeneity (98. 8 and 64. 2% power, respectively). This indicates our non-significant metaanalytic results for human mate choice and facial preference category were likely not due to sample size limitations. In contrast, given the observed variation in effect sizes, we would need 84 odor preference effect sizes to detect biological significance at alpha = 0.
2005 ) used a chi-squared design, but had also had a percentage of participants on birth control, so this effect size along with other effect sizes of pill-users was ultimately removed from the dataset and could not influence the results. Sources of heterogeneity can arise through study design, how the outcome is measured, and through real biological differences between populations. Yet we emphasize that studies that differ in their methodologies can be combined for a meaningful meta-analysis. The meta-analysis of Kamiya et al. (2014) is a case in point, which combined studies employing a diversity of methods across a range of vertebrates and found a clear general pattern for individuals to prefer MHC dissimilar and diverse mates. In fact, combining research based on different methodologies insures that the variance in effect-size patterns reflects this process and not any one methodological artifact  (Lajeunesse 2010).
Study limitations One potential source of type I (false positive) errors is biases in published effect sizes. Publication bias testing showed no evidence for p-hacking but did reveal that the average power of the studies analyzed was low, ranging between 24-57% between datasets, indicating that true effects may have gone undetected in those studies. Based on our mean effect sizes for dissimilarity (Zr=0. 044) and diversity (Zr=0. 153), we recommend study samples sizes of ~ 4051 and ~260 respectively, to detect true effects in primates 80% of the time. The magnitude of these mean effect sizes are typical for ecological data  (Møller & Jennions 2002)  but this translates to dissimilarity only explaining approximately 0. 2% and diversity 2. 3% of the variation in primate mating patterns. Clearly, MHC-mediated mate choice in humans and other primates is just one relatively small consideration of many involved in choosing a mate.
Accepted Article This article is protected by copyright. All rights reserved. Regarding type II (false negative) errors, retrospective power-analysis of our own results indicates we have sufficient sample sizes to have enough power detect an effect of Zr = 0. 15 (explaining ~2. 2% variation in MHC mating patterns) for human MHC-diversity model and models of all human MHC dissimilarity choice cue categories excluding odor preference studies. Sample sizes for non-human primates were too low to have a good chance of detecting true effects, but high power could easily be achieved with less than 20 additional effect sizes. Noise in the data may increase the risk of not finding biological effects. Therefore, we have tried to draw attention to models where power was limited and new data would be very helpful (i.
e. , human odor preference models, non-human primate models), to help advance the field of MHC-linked mate choice. Another challenge linked with interpreting meta-analyses conducted across heterogeneous samples and study designs is the issue of confounding factors. Confounding factors not accounted for in original studies pose a substantial problem for the interpretation of all meta-analytical approaches, as they can increase the risk of missing a true effect. Specifically, in our study, only four studies in our dataset had (i) controlled for Pill effects, (ii) used subjects of the same ethnicity, and (iii) conducted an experimental study to control for confounding factors. Two of these studies  (Wedekind et al. 1995; Wedekind & Furi 1997 ) investigated odor preferences and found positive effects of MHC dissimilarity  r=0.
Rationale for using the bold models is explained in the methods. Model ID refers to models from Table  1. from models using all data. We removed pill-user effect sizes from the combined primate and human dataset to control for its potentially confounding effect (see methods), and tested pill-use as a moderator for studies that investigated its affect or controlled for it. The meta-analytic mean is from the interceptonly model run with study ID and phylogeny as random effects (and study ID as the random effect for the human-data model). Dashed boxes highlight mean estimates with HPD intervals that do not overlap zero.
We removed pill-user effect sizes from the combined primate and human dataset to control for its potentially confounding effect (see methods), and tested pill-use as a moderator for studies that investigated its affect or controlled for it. The meta-analytic mean is from the interceptonly model run with study ID and phylogeny as random effects (and study ID as the random effect for the human-data model). Dashed boxes highlight mean estimates with HPD intervals that do not overlap zero. Vertical bars indicate significant contrasts between moderator categories for humans and all data (*pvalue < 0. 05, see TableS10).
Acknowledgements We thank  T. Lenz ,  M. Milinski , and the four anonymous reviewers for constructive feedback that substantially improved our final manuscript. Funding support for JW came from the  Alexander von Humboldt Foundation , the  European Social Fund (ESF)  and the state budget of Czech Republic through the  Operational Program Education for Competitiveness (OPEC) ,  Reg. No. CZ. 1. 07/2. 3. 00/30. 0048. Funding support for JA came from the  University of Berne. JH is supported by the  Czech Science Foundation  grant ( 14-02290S ). LZG was supported by funds from the  Ministry of Economy and Competitiveness in Spain  ( CGL2015-70639-P ), and by the  National Research, Development and Innovation Office in Hungary (NKFIH ,  K-115970 ).
4) Otherwise 0 < val(P ′ f ) < +∞, in which case val(P f ) = 1 val(P ′ f ). Let ((ᾱ, β, t), (ȳ, s, ū, v)) be the strict complementarity primal-dual solution computed by the solver. Then x = (C ᾱ + D β) t is a solution to (P f ) and this one is unique if and only if N (A) ∩ R(C I+ D J+ ) = {0}, where I + ∶= {i ∈ I ∶ ᾱi > 0} and J + ∶= {j ∈ J ∶ βj > 0}. This algorithm has been implemented in a small piece of software, written in Matlab, called pgr. It is available on the author's site. The examples given in this paper have been implemented and can be solved by pgr. It may look surprising that, in algorithm 4. 14 and the discussion that precedes, the index sets I + ⊂ I and J + ⊂ J are determined by solving problem (P ′ f ), while throughout the proofs (see proposition 4. 10 for instance), these index sets were supposed to be determined by solving the linear optimization in  (4.
Turbulent convective heat transfer of dilute Al 2 O 3 / water nanofluid through a circular pipe was studied experimentally by Fotukian and Nasr Esfahany. 14 The tests were performed on Al 2 O 3 /water nanofluid with 0. 03%, 0. 054%, and 0. 135% loading. The range of Reynolds number was from 6000 to 31,000. Data obtained from experiments illustrated that adding minor quantity of nanoparticles to base fluid considerably enhances the heat transfer. At Re = 10,000 and 0. 054 vol% of nanoparticles, 48% rise in the heat transfer coefficient was observed compared to pure water. Addition of further nanoparticles did not enhance heat transfer in turbulent regime. The relative heat transfer coefficient enhanced with increasing the Reynolds number. It was noted that at Re = 2000 and nanofluid volume concentration of 0.