dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of polyQ polymorphism by sizing the PolyQ repeat of the AR gene In order to validate the results on AR obtained by LASSO logistic regression, we sized the number of triplets in the male subset (351 subjects) using the gold standard technique that uses a fluorescent PCR reaction followed by the use of GeneScan Analysis software Ò (Applied Biosystems)  [20]. We identified a 98% concordance between the results of the two techniques in measuring the polyQ repeats. Based on the AR polyQ length, male patients were subdivided into two categories, those having a number of PolyQ repeats less than or equal to 22 repeats, and those having a number of PolyQ repeats greater than or equal to 23 repeats, being 23 repeats the reference sequence on genome browsers and the reported cut-off value  [23] [24].
2% in short repeats), two powerful sings of low androgenicity, and severe erectile dysfunction (22% in long repeats vs. 9% in short repeats) were more frequently reported in subjects with longer repeats, but not osteopenia/osteoporosis (6% in long repeats vs. 7% in short repeats) (Supplementary Table  S4 ). These results indicate a trend toward clinical hypogonadism for those with longer repeats. Conversely, in the entire male dataset, 6 cases of prostate cancer were found annotated in the past-medical history, all in the 22 glutamines group, suggesting an increased prostate sensitivity to androgens in this group. No difference was found in the prevalence of BPH or 5-alpha-reductase inhibitors use. As the reduced signal transduction of AR might be partially compensated by higher T levels, we tested whether the decreased AR negative feedback was sufficient to overcome larger polyQ repeats size (Fig.
Acknowledgements This study is part of the  GEN-COVID Multicenter Study , https://sites. google. com/dbm. unisi. it/gen-covid, the Italian multicenter study aimed at identifying the COVID-19 host genetic bases. Specimens were provided by the  COVID-19 Biobank of Siena , which is part of the  Genetic Biobank of Siena , member of  BBMRI-IT, of Telethon Network of Genetic Biobanks  (project no. GTB18001 ), of EuroBioBank, and of RD-Connect. We thank the  CINECA consortium  for providing computational resources and the Network for Italian Genomes (NIG) http://www. nig. cineca. it for its support. We thank private donors for the support provided to A. R. ( Department of Medical Biotechnologies, University of Siena ) for the COVID-19 host genetics research project (D. L n. 18 of March 17, 2020).
Data availability and data sharing statement The samples referenced here are housed in the GEN-COVID Patient Registry and the GEN-COVID Biobank and are available for sharing. The sequencing data are deposited in http://www. nig. cineca. it/, specifically, http://nigdb. cineca. it) and available for consultation. For further information, you may contact the corresponding author, Prof. Alessandra Renieri (e-mail: alessandra. renieri@unisi. it).
MRI pre-and post-processing at Berlin reading centre Pre-processing included alignment to Montreal Neurological Institute (MNI) -152 standard space (using fslreorient2std), white and grey matter brain masking (using Computational Anatomy Toolbox 12 Toolbox for MATLAB), 28 N4-bias field correction (Advanced Normalization Tools, http://stnava. github. io/ANTs/) and linear, rigid body registration of T2weighted (FLAIR) images to T1-weighted (MPRAGE, BRAVO, and FSPGR) images (FSL FLIRT). 29, 30   1-weighted and FLAIR follow-up scans were co-registered to the individual first session using the transformation matrices saved from the first session transformation from native space images to MNI-152 standard space using FSL FLIRT. Post-contrast agent T1-weighted images were also co-registered to MNI-152 standard space and longitudinally when available.
Disconnectome maps Disconnectome maps were calculated using BCBtoolkit. 34 This approach uses diffusionweighted imaging data from 10 healthy controls  35  derived from the 7T tractography made available in de Schotten et al. , Nat Commun, 2020  8  to track fibers passing through each lesion. 36 Patients' lesions in the MNI152 space were registered to each control native space using affine and diffeomorphic deformations  37, 38  and subsequently used as seed for the tractography in Trackvis. 39 The resulting tractograms were transformed to visitation maps,  36  binarised, and brought to MNI152 space using the inverse of model deformations. Finally, a percentage overlap map was computed by summing at each voxel in MNI space the normalized visitation map of each healthy subject. A white matter mask was made by including all non-zero values across the individual disconnectome maps and subsequently used in analysis of voxel-wise and global effects of disconnectome maps.
Acknowledgements We thank  Ingvild Sørum Leikfoss ,  Ingrid Mo  and  Fernanda Kropf  for their help with laboratory work at  Oslo University Hospital , MRI technicians  Susan Pikol  and  Cynthia Kraut  for their lesion segmentations and  Priscilla Bäcker-Koduah  for her laboratory work at  Charité-Universitätsmedizin Berlin , and all the participating multiple sclerosis patients and healthy individuals. H. F. Harbo, MD ,  PhD University of Oslo and Oslo University Hospital Author Design  and conceptualized study; contributed to the design of the study and revised the manuscript for intellectual content L. T. Westlye ,  PhD University of Oslo and Oslo University Hospital Author Design  and conceptualized study; interpreted the data; drafting the article; revised the manuscript for intellectual content E.
We have estimated global air-sea CO 2 fluxes (fgCO 2 ) from the open ocean to coastal seas. Fluxes and associated uncertainty are computed from an ensemble-based reconstruction of CO 2 sea surface partial pressure (pCO 2 ) maps trained with gridded data from the Surface Ocean CO 2 Atlas v2020 database. The ensemble mean (which is the best estimate provided by the approach) fits independent data well, and a broad agreement between the spatial distribution of model-data differences and the ensemble standard deviation (which is our model uncertainty estimate) is seen. Ensemble-based uncertainty estimates are denoted by ±1σ. The space-time-varying uncertainty fields identify oceanic regions where improvements in data reconstruction and extensions of the observational network are needed. Poor reconstructions of pCO 2 are primarily found over the coasts and/or in regions with sparse observations, while fgCO 2 estimates with the largest uncertainty are observed over the open Southern Ocean (44 • S southward), the subpolar regions, the Indian Ocean gyre, and upwelling systems.
Laruelle et al. (2017)  adapted the two-step neural network approach described in  Landschützer et al. (2016)  to the coastal-ocean pCO 2. The coastal and open-ocean products were combined into a single reconstruction to yield a global monthly climatology of pCO 2 presented in  Landschützer et al. (2020). Notwithstanding these advances, a global reconstruction and its uncertainty assessment of monthly varying coastal surface ocean pCO 2 and air-sea fluxes are still missing. In this work, we propose a new inference strategy for reconstructing the monthly pCO 2 fields and the contemporary air-sea fluxes over the period 1985-2019 with a spatial resolution of 1 • × 1 •. It is based on a Monte Carlo approach, an ensemble of 100 neural network models mapping sub-samples drawn from the monthly gridded SOCATv2020 data and available data of predictors.
2. 2 An ensemble-based approach for the reconstruction of sea surface pCO 2 and air-sea CO 2 fluxes The sea surface partial pressure of CO 2 in Eq. (  1 ) is estimated monthly over each point of the global ocean by analysing sparse in situ measurements of CO 2 fugacity, gathered and gridded at a monthly and 1 • resolution in the 2020 release of the Surface Ocean CO 2 Atlas (SOCATv2020, https://www. socat. info/, last access: 16 June 2020). SO-CATv2020 covers the period 1985-2019. First, monthly gridded pCO 2 data were converted from SOCATv2020 CO 2 fugacity  (Körtzinger, 1999). We then regressed these pCO 2 values against a set of predictors with non-linear functions, i. e. feed-forward neural network (FFNN) models. As illustrated in Fig. 1 , our predictors are biological, chemical, and physical variables commonly associated with the variations in pCO 2 (e.
Exceptionally, predictors for SSH before 1993 were climatologies plus a linear trend in order to retain the overall response to the global warming. The MLD before 1992 was taken as the average MLD between 1992 and 1997. An ensemble of 100 FFNN models was used to reconstruct monthly pCO 2 fields with a 1 • × 1 • resolution over the global surface ocean during the years 1985-2019. This ensemble approach was developed at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE) as both an extension of and an improvement on the first version  (LSCE-FFNN-v1; Denvil-Sommer et al. , 2019). Our model outputs are part of the Copernicus Marine Environment Monitoring Service (CMEMS). Throughout the paper, it is hence referred to as CMEMS-LSCE-FFNN. To reconstruct the pCO 2 fields over the global ocean for each target month over the 1985-2019 period, all the available SOCAT data and the co-located predictors have been collected for the month before and the month after the target month.
We randomly extracted two-thirds of each one of these datasets to make training datasets for the FFNN models, leaving the remaining third to be corresponding test datasets. The FFNN models were then trained for each target month. Moreover, the exclusion of the reconstructed month itself in the training and test datasets follows a leave-p-out cross-validation approach, where p is the number of gridded SOCAT data in the target month. This approach allows us to reduce model over-fitting, as well as to assess the quality of the reconstruction against SOCAT data that are fully independent from the training phase. The random extraction and the FFNN training were repeated 100 times so that 100 versions of the monthly FFNN models have been obtained. Note that our ensemble approach belongs to the classes of bootstrapping and Monte Carlo methods in statistics.
2 and 3 and their values in Fig. S3c  and g ). The reconstructed pCO 2 field matches SOCAT data well: both are normally distributed with the same mean of 361. 3 µatm (Fig. 3a ), and a high agreement for all percentiles (Fig. 3b ) is seen. The slight under-or overestimation at high and low percentiles implies that the model is slightly biased towards the mean value, as is expected when predictor variables do not fully explain predictand variables in the training dataset. This reduced variability is also reflected in the difference between the data standard deviation based on SOCAT pCO 2 (41. 79 µatm) and the one based on CMEMS-LSCE-FFNN (36. 30 µatm). Displayed in Fig. 3c , both misfit standard deviation (σ misfit ) and model uncertainty (σ ensemble ) empirically follow the exponential distribution.
σ misfit is much higher than σ ensemble as the CDF and frequency histogram of the former (blue) show heavier tails than those of the latter (orange), which brings the P-P curve below the bisector in Fig. 3d. When dividing the misfit standard deviation values shown in Fig. S3c  by 2, σ misfit (green) shares a similar distribution to σ ensemble (orange). A natural explanation for this 2-fold tuning factor would point to a simple lack of spread of the ensemble, either because the FFNN ensemble would be too small or because the uncertainty in the predictors (not accounted for here in the ensemble) would be significant. The SOCAT CO 2 fugacity data are sampled at an uneven spacetime resolution (e. g. the sampling frequency varies between one read per minute to one per hour). Gridded data correspond to the average of measurements collected within a 1 • × 1 • box and in a month over the entire cell area.
Atlantic The North Atlantic stands out as a region with high data coverage (Fig. S1a ) and a rapidly increasing number of data since 2000 (Fig. S4 ). A sustained sampling effort adds between 2000 and 4000 data each year to the database over the Subtropical Atlantic (StA) and Subpolar Atlantic (SpA) regions (including between 10 %-40 % of coastal data). The data density over the North Atlantic stands in strong contrast to the often fewer than 1000 gridded data per year collected over the Equatorial Atlantic (EA) and South Atlantic (SA) and their strong year-to-year variability. The comparison between the reconstructed open-ocean pCO 2 and evaluation data over the four sub-regions of the open Atlantic (Fig. 4c  and Table  S2 ) reveals small mean model-data differences, which together with the two other scores, identify the Atlantic as the basin with the highest reconstruction skill.
The ensemble mean µ ensemble and ensemble spread σ ensemble (Eq. 2) are computed from the CMEMS-LSCE-FFNN ensemble of model outputs at the four nearest model grid boxes of each location. Results confirm a reasonably good reconstruction of the proposed approach. The model best estimates (thick coloured lines) characterize pCO 2 trends and variations in in situ data well, and the model ensembles almost catch the observation-based data in their 99 % confidence interval (light shaded envelope). For over 90 % of the time series stations, the model estimation obtains a moderate to high coefficient of determination r 2 with a linear model-data correlation r larger than 0. 5 (e. g. BTM, 0. 98; CRESCENTREEF, 0. 92; HOGREEF, 0. 84; SOFS, 0. 79; TAO110W, 0. 75; WHOTS, 0. 73). Mean bias µ misfit (Eq. 3) and the RMSD (Eq.
5d  (Fig. 5e  and Fig. 5f ), the magnitude of the uncertainty estimates (model errors) of air-sea CO 2 flux estimates appears to be much less corre-lated to measurement density (Fig. S1 ) than the pCO 2 field (see also Fig. 7a  and b ). The model uncertainty and errors in fgCO 2 estimates are highest over the open SO (> 44 • S), the subpolar regions, the Indian Ocean gyre, and upwelling systems. In this study, the uncertainty quantified for the reconstruction of pCO 2 and ultimately fgCO 2 is a result of randomly sampling training and validation datasets from predictors and SOCAT observation-based data for 100 FFNN model runs (see Sect. 2. 2). This sub-sampling approach permits taking into account an assumption about uncertainties in predictors and SOCAT data; i. e. random errors exist through changes in the range between their sub-samples.
Acknowledgements. The authors would like to thank the associate editor  Jean-Pierre Gattuso , all the three referees, and  Nicolas Metzl  for their constructive comments, suggestions, and support to improve our manuscript. Many thanks go to everyone involved in data collection and processing of surface ocean CO 2 fugacity. The  Surface Ocean CO 2 Atlas  (SOCAT, https://www. socat. info,last access:  16 June 2020 ) is an international effort, endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research (IMBER)  programme, to deliver a uniformly quality controlled surface ocean CO 2 database. Financial support. This research has been supported by the  European Copernicus Marine Environment Monitoring Service (CMEMS)  for the MOB TAC project in 2018-2021 (83-CMEMS-TAC-MOB contract, https://marine.
Data availability. The dataset of sea surface pCO 2 and air-sea CO 2 fluxes analysed in this study is under the quality control of the European Copernicus Marine Environment Monitoring Service (CMEMS) and has been available for use since 2019. Our dataset can be downloaded through the CMEMS portal (https://doi. org/10. 48670/moi-00047, Chau et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-1087-2022-supplement.
Literature search, study selection, and data collection We performed a search of PubMed, Medline (via Ovid), Cochrane Central Register of Controlled Trials (via Ovid), Embase (via Ovid), and ISI Web of Science (Science Citation Index). The publication database search was executed by an academic librarian (LS). The literature search was supplemented by a manual search of bibliographies of published articles. See Supplemental Appendix S1 for details of search strategy. The search strategy focused on studies of human subjects with AF who underwent TEE after at least 3 weeks of anticoagulation on DOAC and reported the prevalence of LAAT. The search was conducted from the date of inception for each database until September 23, 2019. There were no language restrictions during the search; however, articles were screened for English language studies only.
No exclusions were set. If a conference abstract was subsequently published in a full manuscript format, the latter was used for data extraction. Articles were independently screened by 2 authors (W. A. and E. G. ) using a standard extraction form. Disagreements were resolved by consensus or adjudication (M. M. S. ). We extracted all relevant details including indication for TEE, type of AF, comorbidities, specific type of anticoagulation used, and TEE strategy (whether TEE was performed routinely or for selected patients). The primary endpoint of the analysis was the prevalence of LAAT. Prespecified subgroup analyses based on indication of TEE (AF ablation vs cardioversion) and TEE strategy (whether TEE was performed routinely or selectively) were undertaken.
Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.
Dataset Data used in this work were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset  1. ADNI is a North American campaign launched in 2003 with aims to provide MRI, positron emission tomography scans, clinical neurological measures and other biomarkers. We use baseline T1-weighted (T1w) MRI of the ADNI1 phase that has been proposed in  [63]. This dataset includes AD patients, subjects with mild cognitive impairment (MCI) and cognitive normal (CN) subjects. MCI is a presymptomatic phase of AD composed of subjects who have abnormal memory dysfunctions. In our experiments we consider two groups of MCI. The first group is composed of patients who have stable MCI (sMCI) and the second one is composed of patients having MCI symptoms at the baseline and then converted to AD in the following 36 months.
Preprocessing The data are preprocessed using the following steps: (1) denoising using a spatially adaptive nonlocal means filter  [49] , (2) inhomogeneity correction using N4 method  [66] , (3) low-dimensional non-linear registration to MNI152 space using ANTS software  [6] , (4) intensity standardization. All the following experiments have been carried out with images in the MNI space.
Segmentation First, to enable analysis of the alterations that occur over different brain structures, segmentation using a non-local label fusion  [24]  and a systematic error correction  [67]  at two different anatomical scales are performed, the hippocampal subfields and the whole brain structures. Segmentation of hippocampal subfields was performed with HIPS, which is a method based on a combination of non-linear registration and patch-based label fusion  [55]. This method uses a training library based on a dataset composed of high resolution T1w images manually labeled according to the protocol proposed in  [71]. To perform the segmentation, the ADNI images were up-sampled with a local adaptive super-resolution method to fit in the training image resolution  [18]. The method provides automatic segmentation of hippocampal subfields grouped into 5 labels: Subiculum, CA1SP, CA1SR-L-M, CA2-3 and CA4/DG.
Afterwards, the segmentation maps obtained on the up-sampled T1w images were down-sampled to fit in the previous MNI space resolution. Whole brain structures have been labeled with a patch-based multi-template segmentation  [48]. This method has been performed using 35 images manually labeled by Neuromorphometrics, Inc. 2  following the brain-COLOR labeling protocol composed of 133 structures. Finally, visual quality control was conducted to remove all incorrect segmentations from the dataset. Moreover, to prevent any bias in the dataset, the pathological status of each subject was hidden during the entire quality control process.
Patch-based grading Once the images are segmented, a patch-based grading of the entire brain is performed using the method described in  [30]. Such a method was first proposed to detect hippocampus structural alterations with a new scale of analysis  [15; 16]. The patch-based grading approach provides the probability that the disease has impacted the underlying structure at each voxel. This probability is estimated via an inter-subject similarity measurement derived from a non-local approach. The method begins with building a training library T from two datasets of images: one with images from CN subjects and the other one from AD patients. Then, for each voxel x i of the region of interest in the considered subject x, the PBG method produces a weak classifier denoted g xi. This weak classifier provides a surrogate for the pathological grading at the considered position i.
Details of implementation The most similar patches were extracted with a patch-match method  [24]. We used the grading method proposed in  [30] , with the same parameters for the size of the patches and K xi. The effect of age has been corrected using a linear regression estimated on CN population  [39]. The elastic net feature selection has been computed with the SLEP package  [45]. The two parameters λ and ρ have been set up with a grid search method resulting in λ = 0. 08 and ρ = 0. 04 being the best parameters for the experiment. The classifications were obtained using a random forest (RF) as implemented in  3. In our experiments, we used the Gini index as impurity criterion. RF has also two parameters, the numbers of three N and the number of randomly selected features T. These two parameters was set as follows, N = 500 and T = 4.
Comparison with state-of-the-art methods Comparison with state-of-the-art methods is divided into two sub-comparisons. First, a comparison of MRI-based methods using similar ADNI datasets is provided in Table  5. Second, a comparison of multimodal methods was conducted. Besides cognitive assessments, the presented methods involved cerebral spinal fluid biomarkers (CSF), positron emission tomography (PET), and fluorodeoxyglucose PET (FDG-PET). The results of this comparison are provided in Table  6. The first comparison of M GG results with state-of-the-art methods is provided in Table  5. M GG is compared with state-of-the-art methods using a similar ADNI1 dataset. Our graph-based method is compared with the original PBG method  [16] , a graph-based grading method  [64] , an ensemble grading method  [46] , a sparse-based grading method  [63] , a VBM method  [50]  and advanced approaches based on deep ensemble learning technique  [60; 44; 7; 68].
This comparison includes a method combining structural MRI and cognitive scores that obtains 80. 7% of accuracy  [63] , a method combining MRI, PET scans and CSF that Table  5 : Comparison with state-of-the-arts methods for Alzheimer's disease classification using similar ADNI1 dataset. In addition to sMCI versus pMCI, we provided results of CN versus AD classification. All results are expressed in percentage of accuracy (ACC) and balanced accuracy (BACC). Best balanced accuracy for each comparison is presented in bold font.