dataesr/scientific-paragraphs-categorization huggingface dataset:
Association analyses of predicted gene expression with prostate cancer risk We used the following criteria to select prediction models with at least two predicting variants for the association analysis: 1) with a model prediction R 2 of ≥ 0. 01 in GTEx and a Spearman's correlation coefficient of ≥ 0. 1 between the predicted and measure gene expression in the external validation (Mayo Clinic or TCGA dataset), 2) with a prediction R 2 of ≥ 0. 04 in GTEx regardless of the performance in Mayo Clinic or TCGA dataset, 3) with a prediction R 2 of ≥ 0. 01 in GTEx but unable to be evaluated in Mayo Clinic or TCGA dataset. The second group of genes was selected because that the gene expression data of the Mayo Clinic dataset were derived from fresh frozen tissue obtained from patients with either radical prostatectomy or cystoprostatectomy, and it is expected that the expression patterns of some genes in these patients may be different from those in the healthy subjects included in GTEx; for TCGA, some gene expression levels might have changed in TCGA tumoradjacent normal tissues, and thus it is anticipated that some genes may show low prediction performance in TCGA data due to the influence of tumor growth  (38, 39).
For this study we estimated correlations between SNPs included in the prediction models using the phase 3, 1000 Genomes Project data focusing on European population. We and iCOGS (20,219 cases/20,440 controls). Logistic regression summary statistics were meta-analyzed using an inverse variance fixed effect approach using METAL. All participating studies were approved by their appropriate ethics review boards. The studies were conducted in accordance with Declaration of Helsinki. In each participating study, written informed consent was collected from the participants. This study was approved by the PRACTICAL/ELLIPSE Data Access Committee. For our primary analyses, a Bonferroni corrected p threshold of 2. 61 × 10 -6 (0. 05/19,169) was used to determine a statistically significant association.
Gene expression prediction models Of the prostate tissue models built for 11,172 genes, 7,893 demonstrated a prediction performance (R 2 ) of at least 0. 01 (≥ 10% correlation) (Supplementary Table  4 ). The crosstissue models were built for 18,961 genes, of which 14,153 showed a prediction performance (R 2 ) of at least 0. 01 (Supplementary Table  4 ). We externally validated our models using Mayo Clinic and TCGA datasets. The correlations of two sets of R 2 s (external prediction performance and internal prediction performance) are shown in Supplementary Figures  1  and 2. Overall, models that predict gene expression well in GTEx data performed well in predicting gene expression in both Mayo Clinic and TCGA data sets, while models that predict gene expression poorly in GTEx showed lower external validity.
The correlation coefficients between internal performance R 2 of GTEx models and external performance R 2 derived from the Mayo Clinic dataset were 0. 60 for prostate tissue models (0. 43 after removing outliers) and 0. 68 for cross-tissue models (0. 68 after removing outliers), which were higher than the corresponding correlation coefficients of 0. 48 (0. 28 after removing outliers) and 0. 54 (0. 43 after removing outliers) obtained using TCGA data for external validation. We prioritized 6,390 prostate-specific models and 12,779 cross-tissue models for association analyses based on their performance in GTEx, Mayo Clinic and TCGA datasets.
Based on our external validation using both Mayo Clinic and TCGA data, the prostate tissue models and cross-tissue models built in this study demonstrated reasonable prediction performance, overall. The sample size for association analyses in this study was large, which provides high statistical power to detect a large number of prostate cancer susceptibility gene candidates. On the other hand, the sample size for building prostate tissue specific expression prediction models was relatively small (n=73), which may affect the precision of estimated model parameters. Given that the regulatory mechanisms for most genes are similar across most human tissues  (25, 26) , we also built cross-tissue models using gene expression data generated in all tissues from 369 European descendants to increase the statistical power.
The cross-tissue models are expected to have improved power for genes whose regulatory mechanisms are similar across most tissues. In comparison, prostate tissue models are likely to be more appropriate for genes whose regulatory mechanisms are specific to prostate tissue. With that being said, for genes that we could build both prostate tissue model and cross-tissue model, their associations with prostate cancer risk were, in general, consistent with each other (Supplementary Table  8 ). Not all genes could be evaluated in our study due to their various hereditary components in expression regulation. For example, previous studies suggested an important role of genes ASCL2(8), C10orf32  (8, 9) , COL2A1(8), DBIL5P(8), EBF2  (11) , and GJB1  (8)  in the etiology of prostate cancer. However, expression of these genes cannot be predicted well using data currently available in the GTEx project which has precluded us from including them in the association analyses.
Acknowledgements The authors thank  Jing He ,  Wanqing Wen ,  Hui Cai  and  Bingshan Li  of  Vanderbilt University School of Medicine  for their help with this study. The authors also would like to thank all the individuals for their participation in the parent studies and all the researchers, clinicians, technicians and administrative staff for their contribution to the studies. We are also grateful to  Hae Kyung Im of University of Chicago  for her help. The data analyses were conducted using the  Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University. This project at  Vanderbilt University Medical Center  was supported in part by funds from  Anne Potter Wilson  endowment. Lang Wu  was supported by  NCI   K99 CA218892  and the  Vanderbilt Molecular and Genetic Epidemiology of Cancer (MAGEC) training program (U.
S. NCI  grant  R25 CA160056 ). Joshua A. Bauer  was supported by  1R50CA211206. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The GTEx data used for the analyses described in this manuscript were obtained from dbGaP accession number phs000424. v6. p1. A full description of funding and acknowledgments for PRACTICAL consortium,  CRUK ,  BPC3 ,  CAPS , PEGASUS are included in the Supplementary Note.
Data availability The GTEx data are publically available via dbGaP (www. ncbi. nlm. nih. gov/gap; dbGaP Study Accession: phs000424. v6. p1). The Mayo Clinic study data are available via dbGaP (Accession: phs000985. v1. p1). TCGA data are available via the National Cancer Institute's Genomic Data Commons Data Portal (https://gdc. cancer. gov/). The OncoArray genotype data and relevant covariate information (i. e. ethnicity, country, principal components, etc. ) for prostate cancer study are deposited into dbGAP (Accession #: phs001391. v1. p1). In total 47 of the 52 OncoArray studies, encompassing nearly 90% of the individual samples, are available. The previous meta-analysis summary results and genotype data currently are available in dbGaP (Accession #: phs001081. v1. p1).
This response originates from the modulation of n, and a possible contribution to the observed signal from the the electric-field dependence of the STO dielectric constant  [27, 33, 34]  is discussed and ruled out in the Supplementary Material, Sec. 4. Similarly to the analysis reported in Fig. 2 , we calculate the voltage dependence of n at different temperatures using Eq. (  2 ). Here, we base our analysis on the third mode (m 3), which shows the best visibility over the whole space of parameters, while a complete dataset of the first five modes is reported in the Supplementary Material, Sec. 5. The phase diagram of the superfluid density reported Fig. 3(c ) is characterized by a dome-like shape that becomes progressively lower and narrower for increasing temperatures. It peaks at about -12.
(grey dataset of Fig. 3  in Ref. [15] ) and ascribed to inhomogeneities that locally suppress n in the over-doped regime. In our case this interpretation is at variance with the results from Fig. 2(c) , where the the critical exponent γ≈2 indicates low disorder. The data presented in this work allows one to view the superconducting phase as the ground state of a Josephson junction array. For zero gate voltage, Fig. 2(d)  shows that increasing the current I in the device produces dissipation above a temperature-dependent threshold. Upon further increase of I, one observes a steep rise in the resistance R beyond a second threshold, and R ultimately levels off to its normal state value. In the Josephson junction language a BKT-like transition takes place at the lower threshold value I c1 E J /(ε v Φ 0 ), where I c1 represents the typical maximum supercurrent of a junction, E J is the Josephson coupling and Φ 0 h/(2e) is the flux quantum  [38].
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the European Union's  H2020 programme/ERC Grant  Agreement No. 677458 , the project  Quantox of QuantERA ERA-NET Cofund in Quantum Technologies , the  Netherlands Organisation for Scientific Research (NWO/OCW)  as part of the  Frontiers of Nanoscience program  (NanoFront), and by the  Dutch Foundation for Fundamental Research on Matter (FOM).
The last extended time period when climate may have been warmer than today was during the Last Interglacial (LIG; ca. 129 to 120 thousand years ago). However, a global view of LIG precipitation is lacking. Here, seven new LIG climate models are compared to the first global database of proxies for LIG precipitation. In this way, models are assessed in their ability to capture important hydroclimatic processes during a different climate. The models can reproduce the proxy-based positive precipitation anomalies from the preindustrial period over much of the boreal continents. Over the Southern Hemisphere, proxy-model agreement is partial. In models, LIG boreal monsoons have 42% wider area than in the preindustrial and produce 55% more precipitation and 50% more extreme precipitation. Austral monsoons are weaker.
Yet, because proxy evidence of LIG precipitation is much less abundant than for more recent climates  (26) , a large-scale comparison was never attempted. This study aims to (i) provide the first empirical global view of LIG precipitation; (ii) use this to quantify the ability of state-of-the-art climate models to reproduce precipitation in a different climate; and, with these models, (iii) study the seasonal response of LIG precipitation and its extremes. We compiled the existing empirical evidence from studies covering decades of geological fieldwork into the first LIG precipitation database. Parallel to this, we simulated the LIG within the framework of lig127k experiment of the Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4)  (27) , which is part of the Coupled Model Intercomparison Project phase 6 (CMIP6)  (28) , and of the piControl experiment of CMIP6.
Annual global precipitation from proxies and models Our new database includes 138 published records, with near-global coverage (see Methods and Fig. 2 ). We selected proxy signals at ca. 127 ka to ensure comparability with the model simulations. From these records, we extracted semiquantitative anomalies between the LIG and the present/PI. Some of these records reflect precipitation amount [e. g. , loess deposits; speleothem  18 O according to some interpretations (29)], while others reflect the net balance between precipitation and evapotranspiration (e. g. , pollen and lake levels). As shown in our models, there is a tight linear relationship between anomalies in precipitation and precipitation minus evapotranspiration (fig. S2 ); therefore, in the remainder of this paper, we refer to "precipitation proxies" for simplicity.
A clear picture emerges from the proxy database of a wetter LIG over most parts of the boreal continents (Fig. 2 ): over Central and Northern Africa, the Arabian Peninsula and the Levant (where also some drier LIG anomalies are present), south and northeastern Asia, southern and eastern Europe, the western midlatitudes and the northwestern part of North America, and the northernmost part of South America. Over the austral continents, about 60% of the records indicate positive anomalies (i. e. , LIG wetter than PI), and 30% indicate negative anomalies (i. e. , LIG drier than PI). The south and northeast of Australia are wetter during the LIG; austral South America is wetter at its western coast over regions corresponding to central Peru and northern Chile and mostly drier elsewhere; and southern Africa has a spatially varying signal with both wetter and drier climates.
The Southern Hemisphere tropics are drier in the LIG, with a maximum negative zonal anomaly of ca. -300 mm/year around 20°S. All austral land receives less precipitation in the LIG, except the western part of Central Africa, parts of Borneo and New Guinea, and New Zealand. For all these regional anomalies, all seven models agree on the sign of change. Most proxies in our database reflect terrestrial precipitation in its annual mean, so we compare them with the annual mean of the model ensemble over land. Overall, modeled precipitation anomalies agree in sign with 64% of the proxies. This hit rate is significantly higher than in a comparison with randomized data with the same probability distribution (see Methods). It also far exceeds the hit rate calculated for the proxy dataset versus anomalies between the PI and an idealized simulation in which wet areas simply become wetter and dry areas become drier.
Stronger LIG North African monsoon is the clearest from both proxies and models. While this has been suggested for a long time (32), we provide here the most robust and geographically extensive evidence yet. The modeled domain of the LIG monsoon expands by 88% (Fig. 3 ), to the north and to the east, and encompasses the Arabian Peninsula, while the summer tropical rain belt shifts north over the continent west of 30°E (fig. S6 ). Average summer precipitation and extreme summer precipitation are much stronger by 123 and 138%, respectively. Abundant proxy evidence in our database supports the north-and westward expansion of an area of higher precipitation. As in previous simulations  (24) , wind anomalies of our models indicate that the moisture for monsoon precipitation over the Arabian Peninsula comes from North Africa.
The Asian monsoon is also unambiguously stronger from our proxy database and models. Proxies document increased LIG annual precipitation over eastern China, also specifically for summer (Supplementary text S2) [e. g. ,  (20) ], and over the Indian subcontinent. Average and extreme simulated monsoon precipitation increase by 40 and 29%, respectively, with very narrow intermodel spread. Increase is greatest over the Himalayas and Bangladesh/Myanmar and less prominent over the south of India. This is potentially linked to a more northern LIG position of the prevailing JJA westerlies over the northwest Indian Ocean and an attendant northward shift of the intertropical convergence zone (fig. S6 ). The modeled monsoon domain is 26% larger; its northward expansion is supported by a wet phase of the Kesang cave record in western China (see reference in external database S1).
Our ensemble shows higher average precipitation (+34%) and extreme precipitation (+26%) of the LIG North American monsoon. The spatial extent and the intensity of the precipitation anomaly are  larger than those found in a recent model study  (22)  and are particularly clear over northern South America. There, half of the proxies in our database document positive annual anomaly, and marine sediments from Cariaco Basin suggest a more northern intertropical convergence zone (see reference in external database S1). However, the anomaly for this monsoon is the smallest among NH monsoons, with a LIG domain only 14% larger and with considerable intermodel spread. The Australian monsoon is much weaker in our LIG simulations, with reductions in domain area (-36%) and in average (-49%) and extreme (-38%) monsoon precipitation.
Drivers of precipitation anomalies At the large scale, atmospheric circulation follows changes in temperature gradients, in turn affecting precipitation patterns. Precipitation is also strongly affected by thermodynamic processes. Our new proxy database indicates widespread, although not ubiquitous, increase in boreal precipitation, concurrent with warming of all land masses north of ca. 45°N  (8). The most robust empirical knowledge about large-scale LIG temperature patterns is that surface air was considerably warmer in the boreal high latitudes. This is also seen in our model ensemble (Fig. 1  and fig. S7 ). The causes of this are highlatitude albedo and water vapor feedbacks triggered by higher boreal insolation from April to August  (34) , the months of perihelion during the LIG (fig.
8 and 600 mm, respectively, to obtain reasonable areas for the PI simulation. We applied the angular calendar to define monsoon months. Ensemble means were calculated after regridding the results of each model by bilinear interpolation. Ensemble median values are negligibly different from the mean. For the ensemble, the bias of the PI simulation was assessed via comparison of its ensemble mean precipitation with the climatological average of the NOAA-CIRES 20th Century Reanalysis V2c dataset for the period of 1851-1880 (closer in time to the PI) and for the period of 1900-1929 (for which the confidence on the product is higher) from and with the climatological average of the ERA-20C Reanalysis for the period of 1900-1929 (fig. S11 ). The bias is very similar when calculated with the two periods of the NOAA-CIRES product.
Proxy database and model/proxy comparison We compiled the evidence for LIG precipitation at 127 ka from the existing peer-reviewed literature. The database comprises 138 records of proxies based on the following types of paleoclimatic archives: 38 based on pollen, 28 based on lacustrine or marine sediment composition, 21 based on speleothems, 20 are multiproxy, 10 based on landscape features, 7 based on fossils other than pollen, 5 based on loess deposits, and 9 based on other types of archives. In the database (Annex 1), we included precipitation anomalies (between the LIG and the present/PI climates), as explicitly stated in the original publication and as interpreted by the authors' expert judgment. Unlike for temperature, most proxies for precipitation in the literature are not quantitative.
The significance of overall hit rate of 64% between proxies and models was tested as follows. (i) On the basis of the frequency distributions of anomaly values in the proxy database, we calculated the probability with which values fell within the ranges established by the above definition of the hit rate; we calculated probabilities in the same for values from the model ensemble average map (considering only grid cells on land). (ii) We generated one random value to represent the proxy anomaly and one random value to represent the model anomaly using the probabilities estimated in step 1 as weights. (iii) We considered the comparison between the randomly generated proxy value to the randomly generated model value a hit if the conditions described above were met. (iv) We repeated steps 2 and 3 138 times, corresponding to the actual number of proxy-to-model comparisons in this study and thus obtained a synthetic hit rate.
(v) We repeated step 4 100,000 times. The average synthetic hit rate was 50. 4%; a hit rate of 64% was obtained in only 0. 06% of the iterations (P = 0. 0006) and, as such, was very unlikely to be a result of chance. We did not account for spatial correlation in the proxy database because proxy locations, in general, are not clustered and are nearly randomly distributed in space with considerable distances from each other. We also calculated the hit rate between the proxies and a hypothetic result of a model simulation in which, based on PI climate, "wet gets wetter and dry gets drier," i. e. , areas that received below (above) a given threshold of precipitation in the PI were assigned a positive (negative) anomaly value. For plausible thresholds of 500, 800, and 1000 mm/year, the proxy/model hit rate was 42.
NHS England has established 13 Genomic Medicine Centers to identify, acquire consent from, and enroll participants in the project; collect high-quality DNA samples, including the establishment of new pathways for processing fresh and fresh frozen tumor DNA; provide clinical information to facilitate data analysis; and be responsible for the interpretation and clinical actionability of final results. Genomic data are linked to health records in partnership with NHS Digital and are available to researchers and industry through the Genomics England Clinical Interpretation Partnership (GeCIP) and the Discovery Forum. Genomic-medicine initiatives have been funded in Scotland, Wales, and Northern Ireland (»6M [US$8M], »6. 8M [US$9M], and »3. 3M [US$4. 6M], respectively) to establish local clinical and laboratory genomics infrastructure and recruit participants for the 100,000 Genomes Project.
The 2016 annual report of England's Chief Medical Officer (''Generation Genome'') called for the transformation of patient care through the systematic use of genomics and made 24 recommendations addressing further changes needed in NHS infrastructure, data sharing, governance, research, and clinician training. 7 The NHS Genomic Medicine Service (GMS) was launched in October 2018 with a mandated test directory linking WGS for defined rare diseases and cancers to reimbursement. A new national network of Genomic Laboratory Hubs is being established, and WGS provision, data, and informatics infrastructure are delivered in partnership with GEL. The recent UK Life Sciences Sector deal, the »65M (US$92. 5M) investment by Health Data Research UK in a UK-wide collaborative network to facilitate the integration of health and data science, and the recent government announcement of plans to sequence 5,000,000 genomes in the next 5 years in the clinical and research environments are expected to further strengthen UK's leadership in genomics.
France France has a healthcare system based on government-funded national health insurance. The French Plan for Genomic Medicine 2025 (Plan France Medecine Genomique 2025) was commissioned by the prime minister in 2015 and developed by Aviesan (the French National Alliance for Life Sciences and Health) in 2016. It aims to integrate genomic medicine into healthcare and establish a national genomic-medicine industry that promotes innovation and economic growth. Of the V670M (US$822M) invested in the first 5 years, around V230M (US$282M) will come from industry. Genome sequencing will be performed by 12 ultra-high-throughput services, two of which will be launched in 2018. A national data-analysis facility (Collecteur Analyseur de Donnees) will interpret and store data and interface with other national and international databases.