dataesr/scientific-paragraphs-categorization huggingface dataset:
Characteristics of the dataset We obtained data from 18 longitudinal cohorts spanning 10 countries and four continents (table 1 and electronic supplementary material, table  S1 ). The combined dataset included data from 12 579 adults comprising 4678 current smokers and 7901 non-smokers. The mean age at baseline of the cohorts was 54. 8 + 15. 4 years (mean + s. d. ; range: 26. 0-80. 2). Given that tobacco use typically begins by age 16  [41] , smokers in the dataset are likely to have already been smoking for at least a decade at the time of the baseline telomere measurement. The mean follow-up interval was 8. 6 + 2. 2 years (mean + s. d. ; range: 5. 9-13. 1). Fourteen cohorts measured LTL using the quantitative polymerase chain reaction (qPCR) method and four used the terminal restriction fragment method (TRF; ADE, BHS, ERA and JLRCS; electronic supplementary material, table  S1 ).
61 bp (95% CI: 22. 62, 146. 61) shorter than non-smokers. There was significant heterogeneity among cohorts in the size of the association between smoking and LTL (table 2, model 5). Given that most smokers start smoking in their teenage years, the causal and mixed hypotheses both predict that the difference in LTL between smokers and non-smokers should increase as the mean age of the cohort increases. Our dataset allows a powerful test of this prediction, because mean age at baseline varies from 26 (in DMHDS) to over 80 years (in LBC1921; a far greater age range than the effect of follow-up interval analysed in model 4). Therefore, we added mean age at the midpoint between baseline and follow-up as a moderator to model 5. The slope of the resulting meta-regression was not significantly different from zero ( parameter estimate and 95% CI: ,0.
00 [20. 05, 0. 05], p ¼ 0. 9947). Second, to test whether the lack of an association between smoking and LTL attrition is explained by the inclusion of cohorts with high LTL measurement error in the dataset, we re-ran model 6 weighting The resulting parameter estimate for the association between smoking and telomere attrition was now slightly positive, but still not significantly different from zero (parameter estimate and 95% CI: 0. 08  [20. 20, 0. 36] , p ¼ 0. 5694). Third, we explored whether there was any evidence for bias in our selection of cohorts. We started with a systematic literature search, but due to the lack of suitable data in published articles, the final dataset assembled is an opportunity sample. For half of the cohorts included we had some idea of the effects of smoking expected due to information available in published articles  [17] , but for the remaining half we were blind to the expected findings at the time of requesting the data (table  1 ).
On the basis of this observation, Farzaneh-Far et al. [22]  have argued that any rapid initial shortening of telomeres caused by smoking is subsequently offset by a length-related decrease in attrition caused by this 'homeostatic' process. However, it has now been recognized that a more parsimonious explanation for the observed correlation between baseline LTL and attrition rate is regression to the mean resulting from measurement error  [29]. Thus, the apparent 'homeostatic' mechanism proposed to account for nonlinear attrition rates in smokers is largely a statistical artefact. Importantly, our analysis of the current dataset provided no evidence that the association between smoking and LTL attrition changed over time, as would be expected if an accelerating effect of smoking was present early on and subsequently diminished or reversed.
It is a limitation of our dataset that our youngest cohorts were already in their 20s, meaning that the majority of smokers are likely to have been smoking for at least a decade. It is therefore possible that we could have missed an effect of smoking on LTL attrition that occurred prior to the baseline telomere measurements. To decisively rule out a highly nonlinear effect of smoking that is completely restricted to the decade immediately after starting smoking, we need to know whether telomere length differences in childhood precede the initiation of smoking  [17]. Cohorts are currently becoming available in which it will be possible to test this prediction. Is the lack of an effect of smoking on telomere attrition a limitation of low power? Some authors have argued that longitudinal studies have low power for detecting effects of smoking on attrition due to their small sample sizes  [20, 21, 26].
While it is often the case that sample sizes are larger in cross-sectional than longitudinal studies, this is not true of the 18 cohorts included in the current analysis, where the same individuals were studied both cross-sectionally and longitudinally. Furthermore, longitudinal studies eliminate the substantial between-individual variation in LTL by measuring within-individual changes in LTL and are therefore much more powerful than cross-sectional studies of the same sample size for detecting effects of smoking on telomere dynamics  [44]. Importantly, our meta-analysis of 18 longitudinal datasets shows no significant heterogeneity among cohorts in the effect of smoking on telomere attrition rates. This suggests first, that it is valid to compute a summary estimate for the difference in attrition between smokers and non-smokers, and second, that the precision of the metaanalysis exceeds that of its constituent cohorts  [39].
Indeed, the weighted meta-analysis actually yielded a marginally lower rate of LTL attrition in smokers compared to non-smokers. This result strengthens the evidence against the causation hypothesis, because it implies that no amount of smoking could yield the observed crosssectional difference in LTL. Is it possible that some kind of bias is masking a true effect of smoking on telomere attrition? Restricting our dataset to participants that survived to follow-up undoubtedly introduces selection based on mortality (e. g. [26] ). Since both smoking and short LTL have been argued to cause earlier mortality, mortality is a collider variable in our analyses. Selection based on the value of a collider is usually discussed in the context of producing spurious associations between independent variables, so-called 'collider bias' (e.
parameter estimate and 95% CI: 20. 02, [20. 09, 0. 04], p ¼ 0. 4218), it was significant for royalsocietypublishing. org/journal/rsos R. Soc. open sci. 6: 190420 Results from random-effects meta-analytic models on data from 18 cohorts. 2,3and5-shorterLTLin smokers; model 4-stronger assn. betweensmoking and LTL at follow-up; model 6-faster attrition in smokers. Significant associations are shown in italics. royalsocietypublishing. org/journal/rsos R. Soc. open sci. 6: 190420 the subset of cohorts measured with TRF ( parameter estimate and 95% CI: 20. 03, [20. 05,,0. 00],p¼0. 0186). The estimated slope of the meta-regression was similar for both the whole dataset and the TRF subset, suggesting that although qPCR measurements are less precise, there is little evidence for bias. Using the parameter estimate for the effect of age on LTL obtained from model 1 we calculated the estimated decrease in LTL between baseline and follow-up as 330.
77bp (95% CI: 241. 55, 420. 00). This equates to age-related attrition of 38. 33 bp yr 21 (95% CI: 27. 99, 48. 67), a value not significantly different from estimates of annual attrition obtained from longitudinal studies of LTL in cohorts not included in the current dataset (e. g. 40. 2 bp yr21[42] and 31. 0 bp yr21.
Data accessibility. An R script that generates the figures and analyses presented in this paper and the required summary dataset (also presented in electronic supplementary material, table  S1 ) are publicly available at: doi:10. 5281/zenodo. 1240964. Individual participant-level data for CCS, HAS, LBC1921, LBC1936 and NSHD are available on request to bona fide researchers. For NSHD see http://www. nshd. mrc. ac. uk/data; doi:10. 5522/NSHD/Q101 and doi:10. 5522/NSHD/Q102).
77bp (95% CI: 241. 55, 420. 00). This equates to age-related attrition of 38. 33 bp yr 21 (95% CI: 27. 99, 48. 67), a value not significantly different from estimates of annual attrition obtained from longitudinal studies of LTL in cohorts not included in the current dataset (e. g. 40. 2 bp yr21[42] and 31. 0 bp yr21 forest plot electronic supplementary material, figure S2a figure 2a figure 2b figure 3a not shown figure 4a H 2 24. 31 1. 81 9. 78 10. 43 6. 24 1. 57 I 2 (%) 95. 89 44. 70 89. 78 90. 41 83. 96 36. 48 t 2 0. 07 0. 01 0. 06 0. 04 0. 03 0. 00 heterogeneity statistics Q 17 p-value 333. 73 ,0. 0001 26. 55 0. 0650 95. 13 ,0. 0001 73. 45 ,0. 0001 85. 62 ,0. 0001 24. 68 0. 1021 p-value ,0. 0001 0. 0003 0. 0312 0. 3884 0. 0075 0. 5311 summary SMD estimate a [95% CI] 20. 49 [20. 62, 20.
Introduction The upgrade of accelerators and experiments at the Large Hadron Collider (LHC) for high luminosity will result in multiple times higher event rates, which demands high data rate readout systems  [1]. For instance, the readout data requirement at the upgraded ATLAS silicon micro-strip tracker will be from 50 to 100 Tbps  [2]. The transfer rate for highly granular tracking detectors is limited by the available bandwidth in current readout links  [3]. Current readout systems typically use wired optical links which are immune to cross-talk but require large connector size and are sensitive to mechanical damage. Wireless data transmission using millimetre-wave technology has not yet been used in trackers for particle physics experiments. The wireless allowing data and power transmission (WADAPT) collaboration  [4]  was formed to study the feasibility of wireless technologies in high-energy physics (HEP) experiments.
The objective is to provide a common platform for research and development in order to optimise the effectiveness and cost, with the aim of designing and testing wireless demonstrators for large instrumentation systems. A strong motivation for using wireless data transmission is the absence of wires and connectors which will be an advantage in areas with low material budget. The availability of 9 GHz license-free bandwidth at 60 GHz (57-66 GHz) provides the opportunity to achieve multi-Gbps data rates for a single link. In addition, wireless transmission allows radial readout instead of current axial links, which opens for topological readout of tracking detectors. This can also reduce on-detector data volume using inter-layer communication, which will drastically reduce the readout time and latency  [5].
A modulation block diagram of the chip is shown in Fig. 1a. The carrier is generated in the transmitter subsystem by an on-chip voltage-controlled oscillator (VCO), which provides a differential sine-wave signal around 60. 5 GHz. This signal is amplified to provide a large sinewave to the modulator, while the amplification is controlled through feedback calibrated path. The amplified signal, along with the differential data from a line receiver, are fed to the OOK modulator. The output of the modulator is then transmitted through an antenna after amplification by the power amplifier (PA). In the receiver subsystem, the signal is passed through an on-chip low noise amplifier (LNA) and forwarded to non-coherent envelope detector. An adaptive comparator and baseband stages eliminate the duty cycle distortion and provide an error-free payload differential data stream at the output.
Simulations and calculations The software package FLUKA [http://www. fluka. org] has been used for simulations of particle transport and interactions with matter. As shown in the simulation setup in Fig. 3a , a proton beam is extracted from the cyclotron through a Havarfoil [http:// www. goodfellow. com/E/Havar-High-Strength-Non-Magnetic-Alloy-Foil. html] window. A collimator is used to restrict the beam to an area of 3 × 3 mm 2 to protect the surrounding electronics on the PCB. Both collimator and beam stop are made of lead (Pb). Given the unknown radiation hardness of the TRX chip, an initial fluence F of about 10 14 N eq / cm 2 was selected. Fig. 3b  shows the TID per incident proton while neutron distribution is shown in Fig. 3c. These neutrons are secondary particles that could potentially damage electronics surrounding the TRX chip.
Likewise, a power consumption comparison is presented in Table  3. The measurements were taken for an active transmission link (Data-ON) and without data transmission (Data-OFF). These results also show notable differences in pre-and post-irradiation measurements for the RX compared to the TX. The reduction in the power consumption can be caused by radiation induced traps in the SiO 2 /Si interfaces. It has been reported that the absolute value of the threshold voltage increases in both p-and n-channel MOSFETs due to creation of new interface traps  [9]. This increase in the threshold voltage is a possible reason of reduced power consumption, which in turn resulted a reduction in the TX output power and the RX gain by 1 and 4. 5 dB, respectively. Sufficient link budgeting can fully compensate the reduction in the TX power, while up to 2 dB loss in the RX gain can also be recovered.
In addition, the RX envelope detector and digital interface remained unaffected by the irradiation. A spectrum analyser [Keysight Technologies 11970V 50-75 GHz Series Harmonic Mixer] with external V-band mixer was used in the pre-, post-and during-irradiation characterisations to monitor the RF power level as a function of frequency. The normalised power spectrum of 5 Gbps data transmission is shown in Fig. 6. The transmitter was set on continuous wave mode and a small downshift of 10 MHz in the centre frequency was observed after the irradiation. This is in agreement with the results from RD-53 collaboration [14] at CERN. From their studies, we can expect higher doses to shift the central frequency even more. However, as the chip uses the envelope detection in RX mode, this shift will not affect the communication.
Acknowledgments The authors would like to thank  Pierre Busson ,  Nicolas Ricome  and  Frederic Lagrange from STMicroelectronics  for providing equipment and support as well as  Åbo Akademi University, Finland  for use of their irradiation facility. Imran Aziz is thankful to  MUST, Pakistan  for their financial assistance and  Sebastian Dittmeier  acknowledges support by the  International Max Planck Research School for Precision Tests of Fundamental Symmetries.
The groups were matched on age, sex, handedness, pubertal development, intelligence quotient (IQ; verbal comprehension and perceptual reasoning index scores), socioeconomic status (SES), total GMV, alcohol use, and nicotine use across group. All participants denied any other illicit substance use, and none reported using the fictional control substance, relevin, supporting the integrity of the self-report metrics. Table  1  summarizes the demographic information. We also ensured that similar numbers of cannabis users and controls were selected from each site (Mann-Whitney U tests; Table  1 ) and confirmed that the proportion of cannabis users and controls did not differ by site using a Kruskal-Wallis test ( 2 (6) ϭ 5. 919, p ϭ 0. 432). For a subset of the 14-year-old cannabis-using participants, data were available at 2 year follow-up for substance use, cognitive ability, and psychopathology at age 16 to allow us to assess the implications of cannabis-related GMV differences for future functioning in these domains.
Voxel-based morphometry T1-weighted images were processed using the Statistical Parametric Mapping v8 (SPM8; http://www. fil. ion. ucl. ac. uk/spm/software/spm8/) VBM toolbox (http://dbm. neuro. uni-jena. de/vbm/) with default parameters incorporating the DARTEL toolbox implemented in MATLAB 7. 0 (MathWorks). Image processing comprised iterative tissue segmentation and spatial normalization using both linear (12-parameter affine) and nonlinear transformations  (Ashburner and Friston, 2000; Ashburner, 2007)  without skull stripping. SPM8 default settings were used to be consistent with other VBM publications from the IMAGEN Consortium. To preserve information about absolute volume, the gray matter concentration images were modulated by multiplying by the linear and nonlinear components of the Jacobian determinants generated during spatial normalization.
We explored whether individual differences in GMV in those regions that differed between cannabis users and controls were associated with substance use factors (lifetime alcohol or nicotine consumption, recent cannabis use, or age of onset of cannabis use) in those participants reporting cannabis use at baseline. We also assessed whether GMV in regions that differed between cannabis users and controls were associated with individual differences in specific cognitive and psychopathological domains previously related to cannabis misuse in those participants reporting cannabis use. Spatial working memory, risk-taking, delay discounting, psychomotor speed, depression, generalized anxiety, and ADHD were assessed at baseline. For a subset of those participants reporting cannabis use at baseline, psychopathology (n ϭ 33), delay discounting (n ϭ 31), and substance misuse data (n ϭ 31) were also available at follow-up 2 years later.
The map of the GMV comparison between cannabis users and controls was downsampled to the resolution of the PET map (3 ϫ 3 ϫ 3 mm 3 voxels) and Spearman correlations were conducted between the t statistic at each voxel and the average CB 1 receptor availability at the same site using the AFNI program 1dCorrelate. First, we tested all voxels within a gray matter mask; we then tested only those voxels within regions showing significant GMV differences between cannabis users and controls. Gene expression. Associations between the spatial distribution of group differences in GMV and expression of the gene that encodes the CB 1 R were tested with reference to the Allen Human Brain Atlas  (Hawrylycz et al. , 2012). Using the alleninf toolbox  (Gorgolewski et al. , 2014)  we ex-tracted normalized gene expression values for CNR1 (averaged within spherical ROIs with radii of 3 mm) from within a gray matter mask and then used random-label permutation to test for an association between CNR1 expression and the t statistic of GMV effects.
Further support was provided by Grants from:  ANR  (project  AF12-NEUR0008-01-WM2NA , and  ANR-12-SAMA-0004 ), the  Fondation de France , the  Fondation pour la Recherche Medicale , the  Mission Interministerielle de Lutte-contre-les-Drogues-etles-Conduites-Addictives , the  Assistance-Publique-Hopitaux-de-Paris  and  INSERM (interface Grant ),  Paris Sud University  IDEX 2012; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ), (Axon,  Testosterone , and  Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH  Consortium Grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centers of Excellence. R. R. is supported by  Dana Foundation   David Mahoney program  and  CTSA  Grant Number  UL1 TR001863  from the  National Center for Advancing Translational Science , components of the  National Institutes of Health (NIH) , and  NIH roadmap for Medical Research, and C.
The ever-increasing use of next-generation sequencing technologies to explore the genome has generated large quantities of data in recent years. Numerous publications have described several thousand sequences of microRNAs, all species included. A new database (RumimiR) has been created from the literature to provide a detailed description of microRNAs for three ruminant species: cattle, goats and sheep. To date, 2887, 2733 and 5095 unique microRNAs from bovine, caprine and ovine species, respectively, are included. In addition to the most recent reference genomic position and sequence of each microRNA, this database contains details about the animals, tissue origins and experimental conditions mentioned in the publications. Identity to human or mouse microRNA is also indicated. The RumimiR database allows data filtering by selecting microRNAs on the basis of defined criteria such as animal status or tissue origin.
Introduction MicroRNAs are small, highly conserved, non-coding RNAs ∼22 nt in length  (1)  that participate in the post-transcriptional regulation of genes through their impact on targeted messenger RNAs (mRNAs). This phenomenon can lead to translation repression or degradation of the targeted mRNAs and depends on the base-pair binding of the microRNAs to their target via a recognition site, the 'seed' sequence  (2, 3). Many studies currently use next-generation sequencing (NGS) technology to explore the transcriptome, notably in a context of microRNA discovery. A significant amount of data is thus being generated, which can attain more than 1000 microRNA sequences in a single publication  (4, 5). Regularly updated tools for data exploration are therefore required and essential. Animal, plant and virus microRNAs are already listed in several databases, such as miRNEST 2.
0 (covering more than 400 different species)  (6)  or miROrtho, which contains 46 animal genomes  (7). Some databases are restricted to human microRNAs in a disease context, such as EpimiRBase (microRNAs associated with epilepsy)  (8) , miRCancer (microRNAs and cancer)  (9)  or the MicroRNA SNP Disease Database (MSDD; genetic variants affecting microRNAs in a disease context)  (10). The recent miRCarta database  (11) , which lists microRNAs for 148 species, is solely based on the prediction of novel microRNAs. A database portal, miRTools-Gallery, which contains more than 1000 tools for studying, identifying or predicting the targets of microRNAs, has recently been set up  (12). The database most widely used at present is miRBase, which was created in 2006 by Griffiths-Jones and collaborators at the University of Manchester  (13)  and lists microRNAs for 271 species  (14).
Studies in cattle, goats and sheep have often focused on production traits such as dairy and meat products  (15) (16) (17) , health [mastitis resistance  (18, 19) ] or reproduction [fertility and fecundity  (20) (21) (22) ]. Due to genome conservation between the three species, the miRNomes (all microRNAs expressed in a tissue or cell type) are relatively similar. However, there are some differences, as well as those between breeds  (23). This explains the importance of generating a database that includes all three species, together with specific information on the breed and physiological status of the animals, as this is frequently lacking in the most commonly employed databases. RumimiR provides a single access portal to an integrated database containing all the information currently published on ruminant microRNAs.
RumimiR is freely available online at the following URL: http://rumimir. sigenae. org/. This database offers an exhaustive list of bovine, caprine and ovine microRNAs, collected from the literature. Pertinent information, notably in the context of dairy production, has also been added to the description of each mature microRNA, and a filtering option for all these data is included. The entire database (and data filtered by applying one or more filters) can be downloaded in different formats. Moreover, past database versions are traceable and can be downloaded.
Data collection and processing RumimiR contains data collected from all publications describing ruminant microRNAs, which corresponds to the 78 publications cited in PubMed (https://www. ncbi. nlm. nih. gov/pubmed). The references for all these publications are listed in Supplementary Data (Supplementary Figure  1 ). Titles and authors, with a hyperlink to the publication, are provided. RumimiR includes all known microRNA sequences, as well as those described as 'novel' in the publications, which are not always found in the miRBase database. All microRNA sequences were obtained from publications (usually from the text, figures or supplementary data). The microRNA sequences included in RumimiR database are processed and filtered by the authors of each publication. The RumimiR data were standardized by aligning the sequences to a unique reference genome for each species.