dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiment 2: The naturalistic environment experiment was carried out to investigate the effect of real-world situations. Therefore, we tested our proposed method for three scenarios: neutral session, motion variance session and skin tone variance session. The experiments were carried out at workstation environment with no control to the illumination of the environment. The facility used for data collection was illuminated by fluorescent lamps that were fixed on the ceiling and the light from the surrounding environment. The non-controlled illumination produced an uneven illumination distribution across the face, causing shadows and specular reflections on the surface of the face, as illustrated in Fig. 11(b). The experiments were conducted on 45 healthy subjects. The subjects were separated into three groups based on the skin tones (i.
e. Fair, Brown and dark). The subject's age varied from 21 to 63 with a mean of 29. 77 years and standard deviation of 7. 88 years. We used the Logitech C920 HD Pro Webcam to record the video. The camera was a 24 bit RGB camera with Bayer mosaic filter that recorded uncompressed data at 30 fps. The camera operated at a resolution of 1080 × 1920 and was mounted at 0. 8 meters from the subject. The ground truth heart rate was captured using a pulse oximeter. The WristOx2 model 3150 wrist-worn pulse oximeter by Nonin Medical.
Experiment 1 The experiment 1 was conducted to evaluate the proposed method along with existing methods on the publicly available database. Here the experiments were carried out for two-time fragments: long-term average using 30s video and short-term average using 10s video. The results of the experiment are tabulated in Table. 1; the BSS approach using RGB spectrum [2] underperformed compared to the recently proposed heart rate measuring methods. The underperformance may have occurred due to the variation of the spectral response of the camera over time. Therefore, affecting the linear mixing coefficient of the R, G, B signals. The underperformance may have also occurred due to the video compression and the possible gamma correction of the videos in the database. The video compression and the gamma correction often suppress the subtle color changes due to blood volume pressure change.
The videos used from the MAHNOB-HCI database were recorded at 61 fps. Therefore, capturing only 610 samples of data during the 10 seconds. The lack of data may have contributed to the underperformance of these data-driven methods. The spatial subspace rotation approach using RGB spectrum proposed by Wang et al. [17]  operated stably by reporting similar error rate during both long-term average and short-term average heart rate estimation sessions. However, the method underperformed for the MAHNOB-HCI database. The video compression and the possible gamma correction of the videos in the database could have also been the cause for the underperformance of the method. Our proposed method using multivariate de-noising approach was developed to operate for both long-term average and short-term average heart rate estimation.
We also evaluated the computational complexity of our PPG heart rate estimation method for experiment 2 using the profiler tool of MATLAB. The overall execution time measurement for the 30s video was 36. 08s; resulting to an overall processing speed of 24. 93 frames per second. Further analysis to the execution time showed that 34. 73s were utilized for ROI detection and tracking component, and 0. 39s for the multivariate de-noising component, and 0. 06s for the periodicity based voting scheme component. Thus, the multivariate de-noising algorithm along with periodicity based voting scheme consuming only about 0. 45s i. e. 1. 25% of the overall execution time measurement.
ACKNOWLEDGMENTS We thank all participants of the Roots of Autism and ADHD  Twin Study Sweden project as well as our colleagues Annelies van 't Westeinde,  Charlotte Willfors ,  Lynnea Myers ,  Elin Vahlgren ,  Eric Zander ,  Kerstin Andersson ,  Steve Berggren ,  Torkel Carlsson ,  Christina Coco ,  Martin Hammar ,  Johanna Ingvarsson ,  Johan Isaksson ,  Elzbieta Kostrzewa ,  Anna Lövgren ,  Anna Råde  and  Kristiina Tammimies  for their contributions. The study was funded by the  Swedish Research Council ,  Vinnova ,  Formas ,  FORTE , the  Swedish Brain foundation (Hjärnfonden) ,  Stockholm Brain Institute, Autism and Asperger Association Stockholm, Queen Silvia Jubilee Fund ,  Solstickan Foundation ,  PRIMA Child and Adult Psychiatry , the  Pediatric Research Foundation at Astrid Lindgren Children's Hospital, Sällskapet Barnavård , the  Swedish Foundation for Strategic Research, Jerring Foundation , the  Swedish Order of Freemasons, Kempe-Carlgrenska Foundation ,  Sunnderdahls Handikappsfond ,  The Jeansson Foundation , the  Innovative Medicines Initiative Joint Undertaking  (grant agreement number  115300 ), which comprises financial contribution from the  European Union 's  Seventh Framework Programme  (FP7 /2007-2013) and in-kind contributions from companies belonging to the  European Federation of Pharmaceutical Industries and Associations (EU-AIMS ).
In addition to plasma concentration profiles of the different compounds, enzyme availability was measured by immunostaining to locate the position of four metabolizing enzymes within the liver lobule. The drug plasma concentration profiles were gathered by administering the drug cocktail to healthy mice. The overall dosage was sufficiently low to exclude the possibility of drug-drug interactions  7, 8. Usually, drug plasma concentration profiles are only determined from samples in the venous blood as such allowing the quantification of total drug clearance at the whole-body level. However, an explicit quantification of liver clearance is difficult if Figure  1. Overall workflow. In a first step, models for healthy mice were established on the basis of literature knowledge and own experimental data including PK measurements and quantification of enzyme expressing area of the liver lobule.
Notably, the pharmacokinetic profiles of the six parent drugs and three of the corresponding metabolites were simultaneously measured at three different sampling sites to quantify liver clearance capacity in healthy mice. The consideration of measurements from different sites of the body allows confining the overall mass balance and thus supports parameter identification. After model establishment, the different PBPK models could describe the experimental PK data of all compounds with excellent accuracy (Fig. 4 ). The advantages as well as the limitations of the different models are given by the mechanistic nature of the PBPK modelling. On the one hand, physiologically-base modelling allows to model and simulate ADME-related processes within the whole body, however, a sufficient degree of prior knowledge is required on the other hand.
Methods PBPK modelling software. PBPK models for the six cocktail drugs and their corresponding metabolites were developed by using the the free PBPK software PK-Sim ® and MoBi v6. 0. 3 (https://github. com/Open- Systems-Pharmacology). The parameters given in Tables  2  and 3  are sufficient to inform each specific PBPK model in this study and to perform the simulations for the healthy reference case. The concepts underlying PBPK modelling with the software PK-Sim, which was also applied in this study, have recently been described in an instructive tutorial  6. Parameter identification. Independent parameters in the PBPK models were identified by comparing the simulation results to the mean experimental concentration values for each time point and each sampling site. As error function, the least squared function between the observed data and simulated data on a logarithmic scale was used.
∑ =             E s t x log ( ) (1) i i i 2 Here, x i is the mean observed value at time t i and s(t i ) is the simulated concentration. For parameter identification the FME-package (Levenberg-Marquardt-algorithm) of the software R (version 2. 5. 13) was used. Since the Levenberg-Marquardt-algorithm is a gradient-based optimization the identification of a global optimum cannot be guaranteed. We hence varied manually the initial estimates of the model parameters in order to obtain good initial guesses for the optimizations. Note, that the model error increases for any alteration in functional damage in Fig. S7  which, together with the correlation coefficients in Tables  S2  and S3 , indicates that an adequate level of optimality has been reached. Animal experiments. 8-10 weeks old male C57BL/6N mice, weighing 20-25 g (Charles River, Sulzfeld, Germany) were used.
Acknowledgements This study was supported by the  BMBF, Germany funded projects Virtual Liver  ( 0315739 ,  0315755 ,  0315769 ,  0315747 ),  LiSyM  ( 031L0052 ,  031L0037 ,  031L0040 ,  031L0039 ); LivSys, LeberSimulator,  EU-ToxRisk and Trans QST.
Medial temporal lobe anatomy presents automatic segmentation procedures with many nontrivial problems, the most important of which is the striking similarity in the intensity distributions of di↵erent anatomical structures such as the amygdala, hippocampus, and entorhinal cortex. As a result, di↵erent brain structures cannot be di↵erentiated based solely on intensity information  [13]. Furthermore, the high anatomical variability, low contrast, small size, and discontinuous boundaries of the hippocampus render its segmentation extremely di cult without the use of prior information  [13]. Semi-automated methods use manual intervention as a first step in defining seed points or bounding boxes  [18]. Yet, even such a simple intervention can pose a major challenge given the size of the databases used in clinical studies (e.
A reliable protocol for manual segmentation is crucial for a valid comparison. To address the issues identified above, this study compares four di↵erent automatic hippocampal segmentation methods, based on a well-defined manual segmentation protocol, using the same dataset. We improve upon the four methods using error correction  [32]  and then compare the resulting eight segmentations. We also evaluate the power of these techniques to be used as an AD biomarker. We characterize some of the factors that lead to more accurate segmentation and further investigate whether this accuracy improves the ability of hippocampal volumetry to distinguish between di↵erent AD clinical groups.
89  [30]. We compared the three multi-template methods  [14, 12, 30]  using a common training and testing dataset, manually segmented using the protocol presented in  [34]. Use of a common dataset avoids the inhomogeneity that might be imposed on comparative studies that evaluate di↵erent datasets. A fourth automatic segmentation method, FreeSurfer (a well-known freely available tool)  [13] , has shown better results in terms of agreement with manual segmentation in comparison with both FSL/FIRST  [35]  and IBASPM  [36]. Therefore, we also compare hippocampal segmentation derived from FreeSurfer with manual segmentation. Finally, since the wrapper-based error correction technique  [32]  can be applied to any host segmentation strategy  [31, 17] , we applied the error correction technique  [32]  to all four compared methods.
Datasets Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). The ADNI was launched in 2003 as a public-private partnership. The project is the result of e↵orts of many co-investigators from a broad range of academic institutions and private corporations led by Michael W. Weiner, MD, as principal investigator. The primary goal of the ADNI has been to test whether serial MR imaging, positron emission tomography (PET), other biomarkers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD (for more information, visit www. adni-info. org).
Experiment 1 To validate the di↵erent approaches to hippocampal automatic segmentation, we selected sixty 1. 5 T images from the baseline ADNI scans. Selection was based on age, sex, handedness, and years of education, so that the groups in di↵erent clinical stages (NC (n=20), MCI (n=20), and AD (n=20)) were comparable in terms of age, sex, and median years of education (see table  1 ). This dataset was used for training for all the selected methods, except for FreeSurfer, both without and with error correction. In FreeSurfer, the segmentation was performed based on its default training dataset. However, the labels are corrected (in FreeSurfer with error correction) based on the dataset of 60 images described above. For the clinical grouping and power analysis study, we selected all the ADNI-1 baseline visit 1.
Preprocessing All selected images went through a preprocessing pipeline, consisting of denoising  [37] , N3 inhomogeneity correction  [38] , linear intensity normalization based on histogram matching between the image and the average template, and a ne registration to ICBM152 template space with 1 ⇥ 1 ⇥ 1 mm 3 resolution  [39]  using a population-specific template from the ADNI-1 database  [40]. Lastly, all the images were coarsely aligned, and image intensities were normalized within each image and among the whole database  [10, 30, 14].
Manual Segmentation The hippocampi were manually segmented on the first experimental dataset (60 subjects from ADNI-1) using the manual segmentation protocol presented in  [34]. The labels were segmented on linearly transformed stereotaxic volumes. We used the same preprocessed dataset as the direct input for all the automated segmentation methods to facilitate the comparison. The tracings were performed by expert raters. Raters were blinded to the hemisphere (images were randomly flipped left-right) and to the group (the filenames did not identify the subject as NC, MCI, or AD).
Experiment 2 As stated above, the entire ADNI-1 1. 5 T dataset was used to investigate the power of the segmented structure's volume to act as an AD biomarker. We used two classification experiments (NC vs AD, and sMCI vs pMCI) to evaluate biomarker performance using a LOO procedure. 2. 6. Metrics 2. 6. 1. Experiment 1 • Dice's kappa metric Volumetric overlap between each automatic segmentation method (i. e. , FreeSurfer, ANIMAL with label fusion, patch-based, nonlinear patchbased, and nonlinear patch-based with error correction) and manual segmentation is measured using Dice's kappa similarity index  [46]. The kappa index for two segmented labels is computed as follows:  = 2 ⇥ V(M ) \ V(A) V(M ) + V(A) , (1) where M and A represent a set of manually and automatically labeled voxels, respectively.
Dataset selection Each method has a di↵erent number of failures; hence, we limited our analysis to the datasets which have successfully passed all the segmentation methods. The largest number of failures belongs to FreeSurfer (see Table  6 ). We further excluded the 60 subjects (20 AD, 8 pMCI, 12 sMCI, 20 NC) of the first dataset. The final dataset consists of 135 patients with Alzheimer's disease, 152 pMCI, 215 sMCI, and 178 normal controls, for which the demographic information can be found in Table  7.
Hippocampal Volume as an AD Biomarker The e↵ect size between the AD and NC groups for each method is given in Table  8. A prognosis study was also completed, where the e↵ect size was measured between the sMCI and pMCI groups (see Table  9 ). E↵ect size was measured as the mean of 200 bootstrapped copies of the dataset. Hippocampal volumes are corrected for age and sex. A paired t-test with Bonferroni correction for multiple comparisons was performed to show significant di↵erences. Based on a conventional operational definition of Cohen's d, small, medium, and large e↵ect sizes were defined as d < 0. 5, 0. 5 < d < 0. 8, and d > 0. 8, respectively. All investigated methods show large e↵ect sizes between the AD and NC groups and medium e↵ect sizes between the sMCI and pMCI groups (see Table  9 ).
Discussion Achieving a fair comparison of the accuracy of di↵erent automatic segmentation methods is not a trivial task. The quality of the MR images segmented, the segmentation protocol itself, inter-and intra-rater variability, and specificity of the participant groups can all a↵ect the reported accuracy  [14]. For example, mean hippocampal volume decreases with age  [49] , which can a↵ect overlap-based statistics like Dice's . In this study, we compared three di↵erent automatic hippocampal segmentation methods using the same testing dataset and manual segmentation protocol  [34]. We then compared the three methods to the hippocampal segmentation of the well-known FreeSurfer tool  [13]. We also applied an error correction technique to each automatic segmentation method using the same library of manual labels for correction  [32].
The FreeSurfer method does not use the population-based specific template. Its probabilistic template is based on a young, healthy population and thus is perhaps not a fair representation of the test database under study here. This distinction partially explains the oversegmentation pattern in the FreeSurfer segmentation: We observed that FreeSurfer oversegments larger hippocampi more than it does smaller ones, whereas AN-IMAL with label fusion undersegments the larger hippocampi while oversegmenting the smaller ones. FreeSurfer's pattern of oversegmentation has been observed in  [50]  as well. As FreeSurfer uses its own atlas to define the hippocampus, it is not surprising that it di↵ers from the segmentation derived from Pruessner's protocol. The error correction method could successfully correct the bias in FreeSurfer results; yet, the variance pattern persists.
Limitations We faced some limitations in conducting this study. First, FreeSurfer was used with its default settings, which may bias the results against it. We applied the error correction method to partially correct for the bias due to di↵erent training datasets. Although we included only the subjects that passed quality control for all processing pipelines, it is important to note that the experiments (AD:NC and sMCI:pMCI) are biased towards FreeSurfer. Indeed, for the 101 (=31+24+45) subjects that failed the FreeSurfer pipeline (see Table  6 ), it was not possible to estimate hippocampal volume. That is, greater than 12% of the test set should receive a random classification (or be considered missing data) in a forced-choice design, which would greatly reduce the advantage when using FreeSurfer hippocampal volumes for both AD:NC and sMCI:pMCI experiments.
LDA) classifier to assign the subjects to di↵erent groups. We used the LDA classifier in two di↵erent classification tasks: to assign subjects to either the AD or NC group and to either the sMCI or pMCI group. For this problem, we used a simple linear classifier fed with mean hippocampal volumes along with age and sex as features. We used scikit-learn [48] , a Python-based implementation of LDA, and all the hyperparameters were set to their default values. The classification task is evaluated based on ROC curves, which are calculated through a LOO cross-validation procedure. Since separate training and test datasets are used in this framework, classification analysis yields a more conservative performance evaluation than Cohen's d e↵ect size.
This work was supported by grants from the Canadian Institutes of Health Research (MOP-111169), les Fonds de Research Santé Québec Pfizer Innovation fund, and an NSERC CREATE grant (4140438 -2012). We would like to acknowledge funding from the Famille Louise & André Charron. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant.
( LDA) classifier to assign the subjects to di↵erent groups. We used the LDA classifier in two di↵erent classification tasks: to assign subjects to either the AD or NC group and to either the sMCI or pMCI group. For this problem, we used a simple linear classifier fed with mean hippocampal volumes along with age and sex as features. We used scikit-learn [48] , a Python-based implementation of LDA, and all the hyperparameters were set to their default values. The classification task is evaluated based on ROC curves, which are calculated through a LOO cross-validation procedure. Since separate training and test datasets are used in this framework, classification analysis yields a more conservative performance evaluation than Cohen's d e↵ect size.
Patient 56007 had a bpVL reaching 5757 copies/mL at D0, and a spontaneous one log 10 drop at D28 (596 copies/mL). Such blips can occur in HIC patients  [6, 8]. This finding emphasizes the importance of measuring HIV-RNA always with the same commercial kit for consistency of the results  [14]. Indeed, the last bpVL measured for patient 56007 in his clinical center, just before he was enrolled in the study, was 100 copies/mL using a different commercial kit from the one used in the present study (data not shown). In our study, the history of blips did not predict the detection of HIV-RNA in semen, which is most probably due to a lack of power. HIV-DNA levels in PBMC were low and stable between the two visits. This is in keeping with previous results in HIC  [8, 15]. HIV DNA in NSC was detected in only one patient with concomitant detectable HIV-RNA in blood plasma and in seminal plasma.
We therefore included, for comparison, the U266 multiple myeloma (MM) cell line, in which CCND1 is activated by the insertion of an enhancer element  15. In these cells, cyclin D1 is overexpressed and present in both in the nucleus and the cytoplasm  9. Cytosol-enriched extracts were purified from both cell lines, subjected to immunoprecipitation with an anti-cyclin D1 antibody (Ab), and the cyclin D1-containing complexes obtained were subjected to mass spectrometry. Proteomic data revealed a large number of putative cyclin D1-interacting factors in each cell line (Tables  S1  and S2 ) in addition to cyclin D1 itself. For validation of our protocol, we compared these two sets of proteins with available datasets for the Granta MCL cell line  4. We identified 66 proteins strictly identical in the three cell lines, and 17 belonging to the same family.
These proteins were classified on the basis of their cellular functions, with the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources v6. 7 (ref. 16 ). In addition to well-known cell cycle regulators (CDKs and CDKNs), cyclin D1 was found to interact with proteins involved in metabolism, transcriptional regulation, DNA repair, replication, protein folding, cell structure and organisation (Table  S3 ). Some previously characterised partners of cyclin D1, such as PCNA, RAD51, HDAC and HSPs  17, 18  , were also detected in our dataset, validating our technical procedure. The set of cytosolic partners of cyclin D1 is enriched in structural proteins. For identification of the most relevant cyclin D1-interacting factors in JeKo1 cells, we performed functional clustering with DAVID tools  16  on the 200 proteins associated with cyclin D1 with the highest peptide coverage (Table  S4 ).
MCL cell lines have different engraftment kinetics in xenograft models. We then investigated the possible association of a cytoplasmic cyclin D1 distribution with greater invasiveness in MCL cells. We engineered three cell lines from JeKo1, Z138 and REC1 cells for expressing the green fluorescence protein (GFP) or mCherry and the luciferase reporter gene (see details in the Supplementary Information). The three cell lines (JeKo1-mCherry-Luc, Z138-GFP-Luc and REC1-GFP-Luc) were injected into the tail vein of either SCID or NOD/SCID/IL2Rγ null (NSG) immunodeficient mice. Tumour growth was monitored weekly by bioluminescence imaging (BLI) after the i. p. injection of D-luciferin. Indeed, areas of luciferase activity are the sign of tumour growth. Based on sequential BLI measurements, tumours grew exponentially overtime, for the whole mouse as well as for individual luminescent foci (data not shown).