dataesr/scientific-paragraphs-categorization huggingface dataset:
As independent validation sets, we used Swedish schizophrenia  28  and bipolar GWAS data  31  and the GENRED2 major depressive disorder dataset collected by the same methods as reported for the GENRED1 dataset. 32 SNPs in the validation data were processed through the same stringent quality control as the discovery data. The Swedish schizophrenia data were imputed with HapMap3 as reference. The bipolar disorder data and major depressive disorder data were imputed with the 1000 Genomes Project data as reference. Post-imputation quality control was applied to exclude poorly imputed SNPs from the validation sets. Finally, we selected SNPs that matched those in the discovery set. The number of SNPs in each validation set is shown in Table  2. Individuals were removed from the validation datasets if they had relatedness >0.
05 to any one of the individuals in the discovery set. Table  2  gives the numbers of case and control subjects in the independent validation datasets before and after excluding related individuals. In the discovery set, we obtained SNP solutions by applying SNP-BLUP (Equation  3 ) and then projected the SNP solution to the genotypes of the validation individuals (Equation  2 ). For assessing predictive performance in the independent validation, the correlation and regression coefficients were used as measures of prediction accuracy and biasedness, respectively, similar to the internal validation. A likelihood ratio test (LRT) was used to test for differences in prediction accuracy between methods comparing the likelihood of a logistic regression fitting the STGBLUP to that of a logistic regression fitting the MTGBLUP and STGBLUP jointly.
In the logistic regression models, case-control status was used as the dependent variable. In the validation datasets, all phenotypes were controlled for cohort, sex, and the first 20 principal components just as in the discovery dataset. This external validation confirmed the superior performance of MTGBLUP over STGBLUP (Table  3 ). From the LRT to test differences in prediction accuracy, the model including MTGBLUP fitted the data significantly better (p ¼ 2. 4 3 10 À24 for schizophrenia, 6. 6 3 10 À16 for bipolar disorder, and 0. 010 for major depressive disorder) (Table  4 ). We further tested the two-components model fitting similarity matrices based on SNPs annotated in CNS genes and/or SNPs localized elsewhere (MTGBLUP-CNS and STGBLUP-CNS). Including the CNS component resulted in increased prediction accuracy for schizophrenia and bipolar disorder (Tables  3  and 4 ).
g. , MDD) or to heterogeneity between the discovery and validation sample. In order to assess population differences, we calculated ancestry principal components from the POPRES reference sample  33, 34  and projected them into the discovery and validation samples and found ancestral differences between them for each disorder (Figure  S3 ). We estimated that the SNP correlation  21  between the discovery and validation datasets was significantly different from 1 for schizophrenia and bipolar disorder (Table  S4 ; the point estimate was lower for major depressive disorder but the small sample size generated a large standard error so it was not significantly different from 1). To explore whether the found heterogeneity reflects real population differences or is caused by other factors that lead to differences between the discovery and validation samples such as batch effects, we looked for evidence of heterogeneity within PGC discovery samples for schizophrenia, bipolar disorder, and major depressive disorder (Appendix B).
Using recent results on prediction accuracy of polygenic scores derived from quantitative genetic theory,  11, 38  we inferred the sample sizes required to achieve the accuracies observed by the methods (Figure  2 ). We assumed prevalence of 1% for schizophrenia, 1% for bipolar disorder, and 15% for major depressive disorder. The proportion of cases in the sample was based on the real structure of the discovery data (59% for schizophrenia, 64% for bipolar disorder, and 57% for major depressive disorder). The effective number of SNPs was assumed to be 69,748 calculated with a weighted SNP method. 39 The observed accuracy was within the theoretical expectation for schizophrenia and bipolar disorder, but not for major depressive disorder where the actual predictive power was lower. Accuracy of risk prediction for individual traits benefited from including the correlated disorders.
The gain in accuracy of MTGBLUP compared to STGBLUP was equivalent to increasing the sample size for schizophrenia, bipolar disorder, and major depressive disorder by ~4,660 (95% confidence interval: 3,110-6,270), ~5,560 (2,830-8,640), and ~10,940 (730-24,440) individuals, respectively (Figure  2 ). Gains in accuracy were even greater with the CNS annotation model (Table  S6 ). The 95% confidence interval was obtained according to the sampling error of the difference between the prediction accuracies (Appendix C). In order to test how sensitive our results on prediction are against population stratification, we re-estimated the prediction accuracy (correlation), removing potential outliers that were 56 SD, 2 SD, 1. 75 SD, 1. 5 SD, 1. 25 SD, or 1 SD away from the mean of the first and second principal component in the validation dataset (Figure  S6 ).
The accuracy of MTGBLUP and STGBLUP remained stable in all three diseases for which independent datasets were available. Restricting the samples to individuals whose values of the first and second principal component lay within one SD of the mean retained between 51% and 70% of the samples (Figure  S6 ). This shows that the prediction accuracy was not substantially affected by ancestry outliers in the validation dataset. We compared the performance of MTGBLUP with that of bivariate GBLUP (a special case of MTGBLUP). The accuracy of MTGBLUP was significantly higher than bivariate GBLUP except for a major depressive disorder risk prediction where the accuracy of MTGBLUP and that of the bivariate model involving schizophrenia and major depressive disorder was not significantly different (Table  S7  and S8 ).
Here, we have developed a multivariate method that can combine data from an arbitrary number of genetically correlated diseases, resulting in better estimates of the disease-specific SNP effects and thus generating more accurate predictors of individual risk. Our results demonstrate a significant advantage of incorporating data from The vertical error bars denote 95% CI. We note that the estimates for the different methods are highly correlated, and therefore the vertical error bars cannot be used to infer significance of difference between the methods (see Appendix C). multiple correlated diseases compared to single-trait analyses. Our estimates of pairwise genetic correlations obtained in independent datasets reconfirm previous results regarding the extent of genetic correlations between the five psychiatric disorders.
4, 5 hou and Stephens  40  recently introduced a multivariate linear mixed model algorithm that is particularly suited for genome-wide association studies. Their method requires that multiple traits are measured on the same individual or that the level of missingness is sufficiently small so that missing phenotypes can be imputed. However, this algorithm is not useful when phenotypes are collected from independent datasets as in the PGC data where dependent variables are totally missing for the other four traits as is typical of disease-ascertained cohorts. Moreover, the efficiency of Zhou and Stephens' algorithm substantially decreases when fitting multiple random effects (e. g. , the annotation model). Korte et al. 41  proposed a similar model to MTGREML using ASReml  42  that is as flexible as our method in that it can handle partial overlapping or disjoint sets of phenotypes.
However, our algorithm is different from that used in ASReml and is much more efficient when using genomic data  20  (see Appendix A). Moreover, Korte et al. did not explore their method with respect to improvements in risk prediction. Even though sensitivity and specificity of genetic diagnostics to predict an individual's risk of psychiatric disorders are generally low, genetic risk scores can still be a valuable tool for research to stratify a heterogeneous population in groups with shared ''genomic'' characteristics. 8] [19]  Our proposed multivariate approach with the annotation model is a flexible and powerful tool for such stratification. The MTGREML and MTGBLUP package and documentation are publicly available online, which we anticipate will be implemented into the GCTA package. 22 Using a CPU running at 2.
2 GHz, analyzing 58,128 samples with 5 disjoint sets of phenotypes (e. g. , the PGC data) takes ~7 hr per each iteration in MTGREML. Convergence is usually achieved within 10 iterations. The virtual memory required for such data is ~45 GB. Good starting values (probably from single-trait GREML  41  ) can reduce the number of iterations to convergence and our software has the option to provide starting values. The computational time increases cubically with sample size, e. g. , analyzing sample size of 10,000 takes a few minutes per each iteration. Our software provides a parallelization option that can reduce computational burden substantially; The increase from MTGBLUP equates to ~4,660 samples for schizophrenia, ~5,550 samples for bipolar disorder, and ~10,940 for major depressive disorder.
Acknowledgments This study was supported by the  Australian Research Council  ( FT0991360  and  DE130100614 ) and the  National Health and Medical Research Council  ( 613608 ,  1011506 ,  1047956 , and  1080157 ). The  Psychiatric Genomics Consortium  is supported by  National Institute of Mental Health (NIMH)  grant  U01 MH085520. We acknowledge the funding that supported the  Swedish schizophrenia study  ( NIMH R01 MH077139 ), the  Stanley Center for Psychiatric Research , the  Sylvan Herman Foundation , the  Karolinska Institutet ,  Karolinska University Hospital , the  Swedish Research Council , the  Stockholm County Council , the  So ¨derstro ¨m Ko ¨nigska Foundation , and the  Netherlands Scientific Organization  ( NWO 645-000-003 ). Statistical analyses were carried out on the Genetic Cluster Computer, which is financially supported by the  Netherlands Scientific Organization (NOW ;  480-05-003 ).
Energy resolution of CUORE bolometers Over the course of TeO 2 crystal production, the collaboration conducted ten CUORE Crystal Validation Runs (CCVRs) in which we operated a small fraction of the newly manufactured crystals as bolometers in order to check their radiopurity and performance  [24]. These monthlong test runs also served as excellent R&D platforms for testing post-Cuoricino improvements to the PTFE and copper frame designs, for characterizing NTD thermistors, and for studying the energy resolutions of the bolometers at different working temperatures. For each CCVR we randomly selected four crystals from the latest production batch received at LNGS (typically consisting of ∼ 100 crystals) and assembled them into a module equivalent to a single floor of a CUORE tower (Fig. 1b ).
CUORE prototype: CUORE-0 In order to commission the CUORE detector assembly line, confirm the effectiveness of our copper-surface-cleaning technique, and validate post-Cuoricino improvements to the tower design, we built CUORE-0, a single CUORE-type tower containing 52 TeO 2 bolometer modules (Figure  4a ), and have been operating it since March 2013  [35]. CUORE-0 is the first tower produced using CUORE assembly techniques and materials, including surface-cleaned copper. The detector's total mass is 39 kg, with 11 kg of 130 Te isotope. In this section we review the construction and commissioning of CUORE-0, its performance and background measurements, and its potential physics reach.
In addition, one heater could not be bonded during assembly, and the connection to another heater was lost during the initial detector cooldown. We considered this situation acceptable and proceeded with data taking. The DAQ hardware includes front-end preamplifiers, six-pole low-pass Bessel filters, and highprecision 18-bit National Instruments digitizers operating at 125 S/s  [37, 38]. On the software side we use the Apollo suite developed for CUORE. We record both the continuous data stream and software-triggered data samples; each bolometer module is triggered independently with a threshold in the range 50-100 keV. The detectors are calibrated using gamma lines from two thoriated tungsten strings which are lowered into guide tubes between the cryostat and external lead shield once per month.
Electronics and data acquisition hardware The CUORE electronics will provide an effective low-noise system for reading and monitoring the detectors. The main boards consist of 8-layer 233 × 280 mm 2 PCBs which accommodate 6 channels each. Each channel consists of a preamplifier and a programmable-gain amplifier, load resistors, a detector biasing system, as well as a number of other circuits that enable the DC characterization of each thermistor and the monitoring of many voltage nodes. The anti-aliasing Bessel filter boards have programmable cutoff frequencies in the range 15-120 Hz, allowing for optimal analog filtering when used in conjunction with the new DAQ system that has higher sampling rates than the system that was used for Cuoricino and which is currently being used for CUORE-0.
The production, characterization, and calibration of the electronics are in progress. We are in the process of procuring all CUORE data acquisition (DAQ) hardware, including National Instruments NI-628x-series high-precision 18-bit digitizers. A small DAQ system, based on a single chassis, will be used for upcoming commissioning tests of the CUORE cryostat while we configure and test the full DAQ. A Faraday cage will be needed to shield the signal links between the detectors and the frontend electronics from disturbances coming from the main power line (50 Hz), the cryogenic pumps, and any other electromagnetic interferences which could be injected from outside. The cage will be located atop the cryostat, on the second floor of the CUORE building, and have a volume of ∼ 6 × 6 × 3 m 3 and a total surface area of ∼ 150 m 2.
The design specification is for a 60 dB attenuation at 50 Hz. The CUORE slow control system will use LabVIEW for the instrumentation drivers, while the network layer will use one of the standard protocols available within LabVIEW and will store data in a schema-less database  [43]  as well as in the CUORE SQL analysis database. Custom packages will be used for high-level user interfaces, including web-based clients for monitoring and alarms. The slow control system is currently under development.
Data acquisition and analysis software CUORE will use a custom-built DAQ software package named Apollo that has been designed to read signals from ∼ 1000 bolometers. Apollo will digitize the analog waveforms, run trigger algorithms, and store data for offline analysis. Data are saved in two formats: triggered bolometer pulses are saved to ROOT files  [44]  while the continuous waveforms are saved to compressed ASCII files. Detector parameters and run configurations are stored in an SQL database which is also used for offline data analysis. Apollo provides graphical user interfaces for run control and monitoring, and a slow-control system for interacting with the front-end electronics. It also includes tools for the automated detector characterization to be performed in the start-up phase of the experiment.
Apollo has been tested extensively in our experimental setups at Hall A and Hall C, most recently during CUORE-0 data taking. For data analysis we use a custom-built software framework named Diana, which was developed using a plugin architecture in C++. Diana has been used extensively as our standard tool for analyzing data from R&D runs, Cuoricino, and CUORE-0. As we analyze the CUORE-0 data we are continuing to build upon the standard analysis procedure (see Sec. 3. 2), developing new features such as noise decorrelation  [45]  and a web-based data quality monitoring system.
The successful commissioning of CUORE-0 and its promising background rates represent a significant milestone for CUORE. The average energy resolutions of the CUORE-0 bolometers are on par with Cuoricino and are among the best seen in large-mass bolometer arrays, even before optimization and while running at a suboptimal working temperature. We find the background rates in the α continuum region and the ROI are lower by a factor of 6 and 2, respectively, with respect to Cuoricino. This success in reducing the background has confirmed the efficacy of our copper-cleaning techniques and detector-assembly methods. We intend to continue operating CUORE-0 until CUORE begins data taking, by which time CUORE-0 should have become the most sensitive experiment searching for 0νββ decay of 130 Te. CUORE is now in an advanced state of construction, making steady progress in all respects.
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).
Results In order to automatically identify incorrectly positioned duplications, we investigated a method that relies on the confidence score, a measure between 0 and 1 introduced by TreeBest that is assigned to each duplication node. This score reflects the ratio between the number of species with a duplicated gene and the total number of species derived from this node. A well-supported duplication will thus have a score closer to 1. With our method, if a duplication node is considered to be poorly supported it is replaced by a speciation node, and the duplication is moved to the following node which is tested using the same method. If the new duplication node passes the test, the duplication is maintained at this new position in the tree. To test our method comprehensively, we ran it on all 20194 phylogenetic trees available in the Ensembl compara database version 71.
The resulting 20194 new edited gene trees were then compared with the original Ensembl gene trees by feeding both databases to AGORA  [2] , an algorithm developed in our laboratory to reconstruct ancestral gene orders. This tool allowed us to assess the quality of the new gene trees as its performances are very sensitive to the quality of the input gene trees, in particular because the length of the reconstructed ancestral chromosomal regions varies substantially depending on the quality of the input gene trees. With the Ensembl gene trees, the number of ancestral genes increases and decreases rapidly during time, whereas with edited gene trees, the number of genes is more constant (Figure  1 ), which is more likely from an evolutionary perspective. Additionally, in some cases the number of ancestral genes is more reasonable.
Conclusions We find that using the confidence score method significantly improves the positions of duplications within gene trees when compared to the initial Ensembl gene tree database. The optimal value is obtained with a threshold score of 0. 3, at which 39% of the 197 894 duplication nodes of the Ensembl gene tree database are edited, resulting in an increase in the N50 length for the ancestral reconstruction of the 58 vertebrate ancestors. These results suggest that our improved gene trees are more reliable. Published: 13 February 2015.
The GW community fully expects to detect and study GWs with these detectors. In parallel, techniques for detecting and analyzing the detected waveforms have been under development for more than two decades  [6]. Many of these methods have been employed to search for GWs in Initial LIGO and Virgo data, which are dominated by instrumental noise and which contain significant non-Gaussian and non-stationary noise fluctuations. Recent efforts have focused on optimal and statistically rigorous extraction of astrophysical parameters from detected waveforms, and tests of General Relativity with these detections. The Amaldi 10 Parallel Session C2 on GW search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and over 30 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, corecollapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background.
We heard a comprehensive status report on searches for continuous waves from rotating neutron stars, including several innovative analyses in progress (Sect. 2. 1). There was an overview of current and future searches for cosmological and astrophysical stochastic background signals, including a warning about correlated instrumental noise between detector sites in the network due to global Schumann resonances (Sect. 2. 7). There were presentations on results from LIGO and Virgo on searches for binary black hole mergers, using matched filtering techniques to target systems with total mass in the range of tens of solar masses (Sect. 2. 6), and unmodeled burst techniques for systems of up to 1,000 solar masses, using anticipated data from the advanced detectors (Sect. 2. 2). We also heard about a search for GW bursts from cosmic string cusps (Sect.
2. 11, 2. 12). The effects of a massive graviton with non-GR polarizations can be detected in the stochastic GW background (Sect. 2. 8). The analysis techniques continue to grow in scope, sophistication, power, variety and generality, in preparation for the advanced detector network. It was evident from both the talks and the many relevant posters that the GW community is transitioning from the setting of upper limits using initial LIGO and Virgo data, to preparations for discovery and detailed study of GW signals with data from the advanced detectors. Attendees got the strong sense that the community is ready for those data and those discoveries, and are eager to extract as much physics and astrophysics from the observations as possible. This made for a very stimulating and forward-looking session! In Sect.
Statistical methods used in the data analysis and to calculate the upper limits on the gravitational waves in the initial phase of LIGO and Virgo projects were briefly described. The presentation of recent results include beating the spin-down limit for the Crab and Vela pulsars  [8, 10] ; the search for a coherent signal from the direction of the Cas A supernova remnant  [7] , as well as Sco-X1 binary, SN1987A and the Galactic center with the cross-correlation method  [9] ; and the all-sky searches for signals of unknown position and frequency, with the use of the Poweflux  [11]  and Einstein@Home  [12]  pipelines.