dataesr/scientific-paragraphs-categorization huggingface dataset:
LIFE sample Participants took part in the LIFE-Adult-Study (life. uni-leipzig. de, Loeffler et al. , 2015)  of the Leipzig Re-2 search Centre for Civilization Diseases (LIFE) and were randomly selected, community-dwelling volunteers. The study was approved by the institutional ethics board of the Medical Faculty of the University of Leipzig. Participants signed an informed consent form and were paid for their participation. Of the 10000 volunteers participating in the LIFE-study, approximately 2600 also underwent MRI assessment and neuropsychological testing. In the present study, data from 2354 individuals were included. Exclusion criteria included neuroradiological findings, missing MRI data or excessive head motion in the functional scans (mean FD > 0. 6 mm,  Power et al. , 2012). Subjects were between 19 and 82 years (M = 58.
MR data Brain imaging was performed using a 3T Siemens Trio scanner equipped with a 32 channel head coil. Resting-state functional images were acquired using an T2*weighted echo-planar imaging sequence with an in-plane voxel size of 3×3 mm, slice thickness of 4 mm, slice gap of 0. 8 mm, 30 slices, echo time (TE) of 30 ms, repetition time (TR) of 2000 ms and a flip-angle of 90 •. This sequence lasted 10 min (300 volumes), during which participants were instructed to keep their eyes open and not to fall asleep. A gradient-echo fieldmap with the same geometry was recorded for distortion correction (TR = 488 ms, TE 1 = 5. 19 ms, TE 2 = 7. 65 ms). High resolution T1-weighted structural images were acquired using an MP-RAGE sequence with 1 mm isotropic voxels, 176 slices, TR = 2300 ms, TE = 2. 98 ms, and inversion time (TI) = 900 ms.
MR data preprocessing MRI data processing was implemented in a python pipeline via Nipype (v0. 10. 0, Gorgolewski et al. , 2011) , which included routines from FSL (v5. 0. 9,  Jenkinson et al. , 2012 ), FreeSurfer (v5. 3, Fischl, 2012) ,  ANTS (v2. 1. 0, Avants et al. , 2011), CPAC (v0. 3. 9. 1, fcp-indi. github. io), and Nilearn (v0. 2. 3, nilearn. github. io, Abraham et al. , 2014). The pipeline is available at github. com/fliem/LIFE_RS_preprocessing. Functional MRI. After removal of the first five volumes (to allow the magnetization to reach a steady state) and motion correction (FSL mcflirt), rigid body coregistration of the functional scan to the anatomical image (FreeSurfer bbregister), as well as EPI distortion corrections (FSL fugue) were calculated and jointly applied in a subsequent step to each volume of the functional scan.
Denoising included removal of (i) 24 motion parameters (CPAC,  Friston et al. , 1996) , (ii) motion and signal intensity spikes (Nipype rapidart), (iii) six components explaining the highest variance from a singular value decomposition of white matter and cerebrospinal fluid time series  (CompCor, Behzadi et al. , 2007 , signals extracted from individual masks created with FSL fast, decomposition executed with CPAC), and (iv) linear and quadratic signal trends. Subsequently, functional data were morphed to MNI space via transformation fields estimated from the structural data (ANTS). Functional data were then band-pass filtered between 0. 01 and 0. 1 Hz (Nilearn). Structural MRI. The FreeSurfer software package was used to create models of the cortical surface for cortical thickness and cortical surface area measurements.
subcortical volumes After extracting feature vectors for each subject and modality (see Figure  1. 1), vectors were stacked to obtain the input data matrices for the age prediction analysis (see Figure  1. 2). Brain function. Functional connectomes were derived from preprocessed functional MRI data using the Nilearn package. Mean time-series were extracted from cortical and subcortical regions of the functionally defined BASC parcellation atlas  (Bellec et al. , 2010, obtained via  the Nilearn data fetcher fetch atlas basc multiscale 2015). Functional connectivity between all pairs of regions was quantified via Pearson correlation, resulting in a symmetric connectivity matrix. Since measures derived from connectomes vary with parcellation resolution  (Fornito et al. , 2010)  and there is no 'right' number of parcels, we investigated two different levels of spatial granularity.
Based on  Thirion et al. (2014) , who recommend parcellations consisting of around 200 to 500 regions, we reconstructed connectivity matrices from 197 and 444 regions. Connectivity matrices underwent Fisher's r-to-z transformation and a feature vector was extracted from the lower triangle (N f eatures (connectivity matrix 197) = 19306, N f eatures (connectivity matrix 444) = 98346). The shape of the input matrix was N sub jects × N f eatures (with N sub jects varying between analyzes; see section 3. 2). Brain anatomy. Native surface models for cortical thickness and surface area were transformed into the fsav-erage4 standard space. The data for the two hemispheres was concatenated (N f eatures (cortical thickness) = N f eatures (cortical sur f ace area) = 5124). Volumetric data for subcortical regions and measures of global volume were extracted from the aseg.
Predictive analysis Predictive models were implemented in a two-level approach (see Figure  1. 4). On the first level, linear support vector regression models (SVR,  Drucker et al. , 1996)  were used to predict age from neuroimaging data (single-source models). On the second level, predictions from the single-source models were stacked with random forest (RF,  Breiman, 2001)  regression models. Using RF models for stacking multiple neuroimaging modalities has previously been shown to produce better predictions with smaller variability in prediction errors as compared to other stacking methods  (Rahim et al. , 2016). All predictive analyses have been performed using the python-based Scikitlearn package  (Pedregosa et al. , 2011). The code is available at github. com/fliem/LeiCA_LIFE. In detail, this procedure entailed (see Figure  1 ): 1.
2. Training of single-source models (see Figure  1. 4). Two parallel approaches have been used to train single-source models. First, using the neuroimaging data of the entire training set, single-source SVR models were fitted, resulting in one trained model per source. Second, in parallel, using the neuroimaging data of the training set, single-source SVR models were trained in a 5-fold crossvalidation (CV) approach (see Figure  1. 4. 1). This was done to obtain unbiased CV-predictions (to be used in the following step). 3. Training of multi-source models. To aggregate information from multiple sources into one prediction, the previous step's CV-predictions were stacked (concatenated). A feature vector based on the single-source predictions was constructed. Based on this feature vector, the multi-source models were fitted, to obtain trained multi-source models (see Figure  1.
5. 1). Second, these predictions are stacked and fed into the trained model from step 3 (see Figure  1. 5. 2), to receive single-and multi-source test set predictions. 5. Evaluate generalization performance. Finally, the models' generalization performance can be assessed via the test set's absolute error (AE) of the age predictions (obtained in step 4) from chronological age. Additionally, the coefficient of determination (R2) is also reported. Statistical tests. To compare models, non-parametric statistical tests were run on the absolute prediction errors, using the SciPy package (v0. 17. 0, scipy. org). Correction for multiple comparisons was done using the false discovery rate (FDR) procedure described by  Benjamini & Hochberg (1995). Results were plotted with the Seaborn package (v0. 7.
2. 1). Generalization to new site. In this step we investigated how the models generalize to data from a new site (different country, scanner, acquisition protocol, subjects). Age predictions were performed on data from the NKI set using models trained on LIFE data (one sample training; section 3. 2. 1). In a subsequent analysis, models were re-trained on a training set that combined the original LIFE training sample with a small number of subjects from the NKI set, to increase the generalizability of the predictive models (two sample training). Finally, to increase the training sample size, the one and two sample training was repeated, including the majority of the LIFE data into the training sample, not only the original training set (99 % of the entire LIFE sample; 1% was retained for a rough check of the models on LIFE data).
Results Our results demonstrate that i) incorporating multiple brain imaging modalities increases age prediction performance (Figure  2  and 3); ii) subjects with objective cognitive impairment show advanced brain aging compared to subjects without objective cognitive impairment (Figure  4 ); iii) our prediction models are robust against confounds (Figure  5 ), i. e. , not driven by head motion and generalize to new datasets. For the comparison of modalities, data for models of all modalities will be presented. After showing that the multimodal approach outperforms the others, the remainder of the results, for the sake of brevity, will focus on this model. The full results can be found in Appendix B.
Multimodal data increases prediction performance Based on the entire LIFE sample, age prediction models were trained on the training set (N = 1177) and evaluated on the test set (N = 1177). Figure  2  shows prediction performance on the test sample (for full statistics see Table  B. 1). All models show good prediction performance (mean absolute error between 4. 29 and 7. 29 years, R2 between 0. 62 and 0. 87). The stacked models show better performance than single source models, with the stacked-multimodal model outperforming all other models. Additionally, this model also shows the least prediction variability. By going from the second best model, stacked-anatomy, to the best, stacked-multimodal, approximately half a year in prediction accuracy is gained. Table B. 2 shows feature importances for the multi-source models.
N LIFE = 2377; N NKI = 46). These trained models were then evaluated with the (remaining) NKI data. This further increases generalizability and reduced the prediction error. For the stacked-multimodal (two sample) analysis it decreases to 6. 56 years (Figure  5. 4), which is a slight reduction compared to the original two sample result of 6. 93 years (for full data see Table  B. 12 and Figure B. 12, for a scatter plot of test predictions Figure B. 13).
Discussion The aim of the current study was to establish a novel multimodal brain-based age prediction framework that makes use of information from anatomy and functional connectivity. We found that (i) including multimodal information increases prediction accuracy, (ii) objective cognitive impairment is associated with increased brain aging, and (iii) our framework is robust against confounds, most importantly, against head motion, and generalizes to new datasets, especially if the training set is composed of a large and heterogeneous dataset. Age prediction was best achieved using the multimodal approach (stacked-multimodal), which showed a mean absolute age prediction error of 4. 29 years. This is approximately a half-year more accurate than when only taking anatomical information into account (stacked-anatomy).
However, to obtain stable models large datasets with brain and behavioral data is required. Assessment of brain-age offers an alternative and complementary measure that is already available through several publicly available large-scale brainimaging datasets. Such data can be used to train models, which are then complemented by a smaller, but richer dataset that includes information about cognitive performance, in order to test specific hypotheses. Confounding effects of head motion in brain-imaging studies have received increased interest in the recent years (e. g. , see  Power et al. , 2012; Reuter et al. , 2015). The present study demonstrated that head motion does not drive brain-based age prediction and that regressing out motion might also affect meaningful age-related variance. While the estimation of head motion in functional MRI scans is well established, this is much more challenging for structural MRI scans due to their longer acquisition times.
These effects have not yet been studied systematically and deserve attention in future studies. Age prediction models perform significantly better when trained and tested on data from the same site, as compared to data from different sites. Training models on a larger and more heterogeneous dataset modestly improves the prediction accuracy. However, since even in this case within-site prediction outperforms between-site prediction this topic deserves more attention in future work. Several factors may have contributed to the better generalizability observed in the NKI dataset using anatomical rather than functional information. First, the anatomical sequences used in both studies are quite similar, while the functional sequences differ with regards to temporal and spatial parameters. Second, anatomical information analyzed with surface-based morphometry shows higher reliability  (Liem et al.
Conclusions In the present study, we demonstrated that including information from multiple MR modalities, i. e. , anatomy and functional connectivity, increased accuracy of brain-based age prediction. Brain-age measured with this multimodal framework was accelerated in subjects with cognitive impairment. Importantly, head motion does not drive brain-based age prediction and predictive models generalize to new datasets, especially if those are trained on large and heterogeneous datasets. Given these findings, measuring brain aging using machine learning methods holds promise for establishing brain-based biomarkers that could aid diagnosis of neurocognitive disorders and be relevant for clinical practice.
We would like to thank the  Enhanced Nathan Kline Institute-Rockland Sample initiative  for sharing their data. Franziskus Liem is supported by the  Swiss National Science Foundation (SNSF) , grant number  P2ZHP1 155200. Gaël Varoquaux  and  Mehdi Rahim  are supported by the  NiConnect  project ( ANR-11-BINF-0004  NiConnect). Jana Kynast is supported by the  Max-Planck International Research Network on Aging (MaxNetAging). This work is supported by the  European Union , the  European Regional Development Fund , and the  Free State of Saxony  within the framework of the excellence initiative, and  LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig  (project numbers  713-241202 ,  713-241202 ,  14505/2470 ,  14575/2470 ), and by the  German Research Foundation  ( CRC1052   Obesity mechanisms  Project  A01 ).
The dominant irreducible background is the Z/γ * → µµ (Drell-Yan) process. A simultaneous fit to distributions of the dimuon invariant mass m µµ in all the categories is performed in the range 110 to 160 GeV to extract the overall H → µµ signal strength and determine the background normalizations and shapes. The fitting range is chosen to avoid the Z boson mass peak and have enough data events to constrain the background. The ATLAS detector  [17]  at the LHC covers nearly the entire solid angle around the collision point  [18]. It consists of an inner tracking detector (ID) surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer (MS) incorporating three large superconducting toroidal magnets. Events used in this analysis were recorded using a combination of single-muon triggers, with the transverse momentum (p T ) threshold being 26 GeV for isolated muons or 50 GeV for muons without any isolation requirement imposed.
; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; SRNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.
Background: Dengue surveillance data in India are limited and probably substantially underestimate the burden of disease. A community-based study was undertaken to assess the prevalence of denguespecific immunoglobulin G (IgG) antibodies in children across India and to examine historical dengue exposure rates. Potential associations between socio-economic factors and dengue seroprevalence were also assessed (registered at ctri. nic. in: CTRI/2011/12/002243). Methods: A convenience sample of 2609 healthy children aged 5-10 years was enrolled; these children were registered at or were living in the vicinity of eight centres located at six geographically distinct sites across India. Blood samples were drawn to test for the presence of dengue IgG antibodies using ELISA. Serotype-specific neutralizing antibody titres were measured in dengue IgG-positive children using dengue plaque reduction neutralization tests.
Introduction Dengue is caused by a mosquito-borne flavivirus that is endemic in tropical and subtropical countries, including India. 1  Sporadic outbreaks have been reported in India for over 200 years. The scale and severity of two major epidemics in the 1990s prompted the implementation of a number of strategies to aid the control and surveillance of dengue. In particular, a passive surveillance programme and publication of guidelines for dengue prevention and control was launched as an initiative of the National Vector Borne Disease Control Programme, 2 in collaboration with the existing Integrated Disease Surveillance Programme. Due to non-specific and often mild symptoms, dengue is significantly under-reported in nearly all countries. This is exacerbated in India, where dengue surveillance data are collected from only approximately 500 sentinel hospitals.
Data and sample collection Socio-demographic data (participant's demographic characteristics, household occupancy, water supply/storage, self-reported history of dengue or Japanese encephalitis (JE) virus infection, and education levels attained by the parents/guardians) were collected using a questionnaire. The questionnaire was administered by the health worker through interviews with the participant's parents or legal guardians during the first visit. Participants were asked to report to the affiliated centre for blood sample collection (5 ml) by a trained laboratory technician. The participant's height and weight were recorded using standard methods. Significant medical history, current or previous medical conditions, concomitant medication, recent vaccinations, and reasons for refusal of blood sampling, where relevant, were recorded.
Assays Samples were sent to the Microbiology Department of the Maulana Azad Medical College (New Delhi) for analysis. Dengue IgG antibody levels were assessed using commercially available ELISA kits. The EL1500G kit (Focus Diagnostics, California, USA) was used for samples from the first two sites (New Delhi); however, due to supply issues, the E-DEN 10G kit (Panbio Diagnostics, Brisbane, Australia) was used for the other sites. A sensitivity analysis of the two dengue IgG-specific ELISA kits performed on 30 samples confirmed 100% concordance; data from all centres were thus pooled. JE IgG antibody testing by indirect ELISA was also performed using commercially available kits (InBios, Washington, USA). Dengue IgG-positive samples were sent to the Centre for Vaccine Development (Mahidol University, Thailand) for measurement of dengue serotype-specific neutralizing antibody titres using PRNT based on 50% or greater reduction in plaque counts (PRNT 50 ).
Statistical analysis Descriptive statistics reported baseline characteristics and immunogenicity results. Associations between all demographicsocio-economic factors and dengue serostatus were assessed by univariate analysis using the Chi-square test or t-test (for age) and multiple logistic regression with backward selection (significant if the p-value is 0. 05). JE serostatus was not included as a covariate due to possible cross-reaction between flavivirus antibodies. Statistical significance was considered at and below a p-value of 0. 05. Statistical analyses were performed using SAS software version 9. 1 (SAS Institute, Cary, NC, USA) and Stata v. 12. 1 (StataCorp LP, College Station, TX, USA).
Acknowledgements This study was sponsored by  Sanofi Pasteur. The authors wish to thank the following investigators for their contributions to the conduct of the study:  Suman Kumar ,  Gandhari Basu ,  Chitra Chatterjee ,  Ganesh Oruganti ,  Sathish Chandra MR , and  Malatesh Undi. The authors also thank  Shelke Pallavi  who was involved in the conduct of the study and  Claire Dakowski  for statistical advice ( Sanofi Pasteur) , and  Jo-Ann West  for editorial assistance and manuscript coordination on behalf of  Sanofi Pasteur. Editorial assistance with the preparation of the manuscript was provided by professional medical writers,  Simon Lancaster  and  Juliette Gray  of inScience Communications,  Springer Healthcare , funded by  Sanofi Pasteur. The wider DNG10 authorship group included  Suman Kumar ,  Gandhari Basu ,  Chitra Chatterjee ,  Ganesh Oruganti , and  Shelke Pallavi.
The Siple Dome ice core δ 18 O record exhibits a similar isotope history to the RICE δD record. Siple Dome isotope data reveal an abrupt warming or isotopic enrichment at 605 CE, some 25 years later than in RICE but within the cumulative age uncertainty of the two records. After 605 CE, Siple Dome isotope temperatures remain stable, although recording somewhat warmer isotope temperatures from about 1875 CE. Late-Holocene elevation changes (thinning) at Siple Dome have been reported to be negligible  (Price et al. , 2007)  and are unlikely to have caused the observed abrupt isotopic shift at 605 CE. In contrast to records from the western Ross Sea  (Bertler et al. , 2011; Rhodes et al. , 2012; Stenni et al. , 2002)  and West Antarctica  (Orsi et al. , 2012) , RICE and Siple Dome do not show an isotopic warming or cooling associated with the Medieval Warm Period (MWP) or the Little Ice Age (LIA), respectively.
Regional snow accumulation variability Investigating long-term trends in snow accumulation records (Fig. 6b ), the decadally smoothed RICE shows a discernible positive trend from about 600 CE of about 0. 2 ± 0. 1 cm w. e. per century  (Winstrup et al. , 2017)  for the eastern Ross Sea, while WDC  (Fudge et al. , 2016)  displays a decreasing trend for central West Antarctica. The RICE snow accumulation data reach a maximum in the 13th century, with a trend towards lower values from the late 17th century onwards of about -0. 9 ± 0. 6 cm w. e. per century  (Winstrup et al. , 2017). Since 1950 CE, this trend has steepened to -6. 6 cm w. e. per century  (Winstrup et al. , 2017). Based on the negative correlation between RICE snow accumulation and SIC in recent decades, we interpret the long-term increase in snow accumulation as representing a long-term reduction in SIC in the Ross Sea, consistent with a long-term increase in RICE isotope temperature.