dataesr/scientific-paragraphs-categorization huggingface dataset:
Training Dataset: ADNI The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (http://adni. loni. usc. edu/) contains T1w MRI scans for AD and CN subjects at several time points  [Jack et al. , 2008]. In the proposed study, we used all baseline scans of ADNI1 at 1. 5T of 225 CN subjects and 192 AD patients as training data to estimate hippocampal grading of the testing dataset. The clinical AD diagnosis was made according to the NINCDS-ADRDA Alzheimer's Criteria  [McKhann et al. , 1984]. We did not find any statistically significant differences for age (P 5 0. 52) or gender (P 5 0. 61) between the AD and CN groups using a general linear model (GLM). Demographic details for the training dataset are provided in Table  I. The ADNI MRI scans were acquired on a 1. 5 T GE, Philips, and Siemens scanners using a 3D MPRAGE T1-w sequence, as recommended by the MRI Core of the ADNI consortium  [Jack et al.
Test Dataset: Bdx-3C In this study, we used subjects from the Bordeaux site of the Three-City (Bdx-3C) dataset, a prospective population-based cohort designed to assess the risk of dementia and cognitive decline due to vascular risk factors  [Group, 2003]. In this longitudinal dataset, neuropsychological tests were performed by trained psychologists at baseline and at 2, 4, 7, 10, and 12 years. MRI scans were acquired at baseline and at 4 and 9 years. The neuropsychological evaluation consisted of several tests performed at each followup, always including the Mini Mental State Evaluation (MMSE)  [Folstein et al. , 1975]  to evaluate global cognitive function. During followup, subjects suspected of dementia were seen at home by a neurologist or a geriatrician who confirmed the diagnosis and specified the etiology of dementia.
Therefore, Bdx-3C dataset was divided into two groups of 309 sCN and 37 cCN. Demographic details of both Bdx-3C groups are provided in Table  I. We found statistical difference between sCN and cCN groups for age (P 5 0. 0001) but not for gender (P 5 0. 17). At baseline, no statistical difference was observed between groups for a global cognitive scale (i. e. , MMSE) using generalized linear model. This indicates that cCN subjects are in a silent phase. We estimated the mean time before AD diagnosis for the cCN group at 103. 05 months (SD: 24. 26) using the baseline MRI and the followup exams where AD diagnosis was established. However, low frequency of followup exams might introduce a bias by overestimating this duration. Therefore, we also computed an estimated average time before conversion to compensate for this bias.
Image Preprocessing All the images (ADNI and Bdx-3C) were preprocessed through the same fully automatic pipeline  [Aubert-Broche et al. , 2013]. The preprocessing included the following steps: estimation of the standard deviation of noise  [Coupe et al. , 2010] ; denoizing based on an optimized nonlocal means filter  [Coupe et al. , 2008] ; correction of inhomogene-ities using N3  [Sled et al. , 1998] ; registration to stereotaxic space (stx) based on a linear transform to the ICBM152 template (1 3 1 3 1 mm 3 voxel size)  [Collins et al. , 1994]  using a population-specific template derived from the ADNI database  [Fonov et al. , 2011] ; linear intensity normalization of each subject on template intensity; brain extraction using BEaST  [Eskildsen et al. , 2011] ; image cropping around the structures of interest; and crossnormalization of the MRI intensity between the subjects within the estimated brain mask  [Nyul and Udupa, 2000]  (Fig.
Hippocampal Grading Inspired by our work based on a nonlocal patch-based framework for MRI denoizing  [Coupe et al. , 2008]  and for MRI segmentation  [Coupe et al. , 2011] , we recently proposed a new method to estimate structure grading called SNIPE  [Coupe et al. , 2012a,b]. The grading or scoring of the structure under consideration is achieved by estimating the nonlocal similarity of the subject under study to different training populations. With the nonlocal framework, SNIPE is able to handle intersubject variability by enabling a one-to-many mapping between the subject's anatomy and those of the training templates. Moreover, by employing the patch-based comparison principle, SNIPE can detect subtle anatomical changes caused by the disease. In short, the technique uses a library of manually labeled MRI datasets from healthy aging subjects and patients with AD.
For each voxel in a new subject to be analyzed, the method defines a 7 3 7 3 7 voxel patch centered on the voxel in question (Fig. 2 ). The procedure then searches the template library for similar patches. Template structure labels are weighted by the patch similarity, and the structure label with the maximum weight is then associated with the voxel. At the same time, the template group (11. 0 for normal controls and 21. 0 for AD subjects) is also weighted by the patch similarity. The resulting average weight is used as a grading value to indicate how similar this voxel is to the CN group or the AD group  [Coupe et al. , 2012a]  for details). In this paper, we also propose to adapt this grading framework to image-based estimation of clinical scores as explained in the following. After the preprocessing pipeline, we estimated hippocampal grading on the Bdx-3C dataset using the following steps: selection of the training subjects, structure grading and feature extraction.
Training subjects selection AD and CN populations of ADNI were used as training library to achieve hippocampal grading of the cCN and sCN groups of the Bdx-3C dataset. During our experiment, the selection of the 50 closest subjects from each training population (i. e. , AD and CN) was achieved using the sum of the squared difference (SSD) over an initialization mask. r Preclinical AD Detection Using Hippocampal Grading r r 4761 r In a previous study  [Coupe et al. , 2012a] , we showed that 50 subjects provides a good tradeoff between accuracy and computational burden.
Validation Framework To study the AD prognostic accuracy of the imaging biomarkers and the considered global cognitive test evaluated at baseline (i. e. MMSE), we performed classification experiments on sCN vs. cCN subject of the Bdx-3C dataset. In the proposed experiments, we used a Leave-One-Out Cross Validation (LOOCV) procedure and the classification step was based on linear discriminant analysis (LDA) as done in  [Coupe et al. , 2012; Wolz et al. , 2011]. In this type of crossvalidation, the classifier is trained on n21 samples and then the trained classifier is used to classify the remaining sample. The biomarker values were corrected for age difference. All the classification results were analyzed in terms of Correct Classification Rate (CCR), specificity (SPE) and sensitivity (SEN).
DISCUSSION In this study, we showed that using hippocampal grading for early prognosis exhibited higher classification accuracy than hippocampal volume with a gain of 14. 4pp. This result on the Bdx-3C population is in line with our previous results obtained on the ADNI dataset with subjects who converted to AD in 18. 24 months on average  [Coupe et al. 2012b] , where we obtained 71% correct classification using hippocampal grade (p<0. 0001) and 62% correct classification using hippocampal volume (P 5 0. 0007). Recently, we showed that combination of hippocampal grading with cortical thickness could improve classification accuracy    Eskildsen et al. , 2015]. We also showed that performing gray matter grading over the entire brain could even improve this rate to 75. 6% at the expense of a more complex framework requiring automatic ROI selection  [Komlagan et al.
, 2014]. A very recent independent study on the ADNI dataset confirmed these previous results and showed that hippocampal grading provides higher AD prognosis accuracy than current state-of-the-art MRI-based methods  [Tong et al. , 2014]. Here, our results support the hypothesis that, although hippocampal volume has been found to be an efficient imaging biomarker on subjects close to AD diagnosis  [Ewers et al. , 2012; Wolz et al. , 2011] , hippocampal volume loss is not sufficiently sensitive to enable automatic classification at a very early preclinical stage of the disease. The use of more sophisticated measures of the neurodegenerative process impacting hippocampal structure, such as the pathological pattern recognition method used here, appears to be crucial for the identification of an early presymptomatic phase of AD 7 years before conversion to AD.
Such subtle changes are likely to be picked up by the grading principle, thus increasing the sensitivity of hippocampal grading compared to conventional MRI based anatomical biomarkers. In our study, imaging biomarkers exhibited higher classification accuracy than global cognitive status, reflected by MMSE scores. In accordance, hippocampal volume has been shown to be a more efficient predictor of AD conversion than a large number of cognitive tests in the ADNI dataset for subjects close to AD diagnosis  [Ewers et al. , 2012]. However, when using subjects with longer followup  [Devanand et al. , 2007]  or amnesic subjects  [Fleisher et al. , 2008] , cognitive tests seem to provide better predictive accuracy. Here, on the Bdx-3C dataset, hippocampal biomarkers were better than MMSE 7 years before conversion to AD.
Further works should investigate longitudinal aspect of clinical score prediction as done in  [Zhang et al. , 2012]. In the present work, we used an external dataset as training library (i. e. the ADNI dataset) to estimate hippocampal grade on the Bdx-3C dataset. The superior results obtained using hippocampal grade demonstrate the robustness of the proposed preprocessing pipeline to multi-site and multi-sequence datasets. Moreover, the possibility of using a large freely available dataset to train our pathological pattern recognition method highlights the clinical potential of such an approach. Finally, our study gives support to the hypothesis that North American and European populations share similar atrophic patterns caused by AD as discussed in  [Westman et al. , 2011]. To the best of our knowledge, this is the first study presenting an automatic MRI-based prognosis method that enables AD prediction 7 years before conversion to AD.
First, we proposed a new computer-aided prognosis method based on latest advances in image processing. Then, we demonstrated the highest prognosis accuracy provided by hippocampal grading compared with the hippocampal volume. At present, hippocampal volume is considered as state-of-the-art MRI-based biomarker for diagnostic criteria and clinical trial  [Jack et al. , 2011]. To ensure that subjects were in presymptomatic phase, we used a dataset with a followup period of 12 years. Compared to previous studies investigating new biomarkers for AD prediction on shorter periods  [Mapstone et al. , 2014; Hansson et al. , 2006] , the very long followup used here enables to really evaluate the.
Coupuet al. r r 4768 r clinical potential of the proposed imaging biomarkers. The high accuracy obtained with the proposed hippocampal imaging-biomarker paves the way to the automatic identification of elderly subjects with high risk to develop AD. The Three-City study is conducted under a partnership agreement between the Institut National de la Sant e et de la Recherche M edicale (INSERM), the University Bordeaux 2 Victor Segalen, and Sanofi-Aventis. The Three-City study is also supported by the Caisse Nationale Maladie des Travailleurs Salari es, Direction G en erale de la Sant e, MGEN, Institut de la Long evit e, Conseils R egionaux d'Aquitaine et Bourgogne, Fondation de France, Ministry of Research-INSERM Programme "Cohortes et collections de donn ees biologiques". Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI).
fnih. org). The study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU, TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "D efi ImagIn". We also acknowledge funding from the Fonds de Recherche Qu ebec -Sant e (FRQS-Pfizer). This work has been also supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovaci on.
r Coupuet al. r r 4768 r clinical potential of the proposed imaging biomarkers. The high accuracy obtained with the proposed hippocampal imaging-biomarker paves the way to the automatic identification of elderly subjects with high risk to develop AD. The Three-City study is conducted under a partnership agreement between the Institut National de la Sant e et de la Recherche M edicale (INSERM), the University Bordeaux 2 Victor Segalen, and Sanofi-Aventis. The Three-City study is also supported by the Caisse Nationale Maladie des Travailleurs Salari es, Direction G en erale de la Sant e, MGEN, Institut de la Long evit e, Conseils R egionaux d'Aquitaine et Bourgogne, Fondation de France, Ministry of Research-INSERM Programme "Cohortes et collections de donn ees biologiques". Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI).
MD Simulation Shows Decreased Water Dynamics Around the Fibrillated Hexapeptide 306 VQIVYK 311. The structure of tau fibers is heterogeneous in the sense that about 30% of the monomer is included in a cross-β structure (referred as the fiber core), whereas the rest remains disordered (fuzzy coat; see Fig. 1 ). Because NS provides information on the average dynamics of all hydrogen atoms in the sample, we used MD simulations to assist the interpretation of our experimental results (see Discussion). We carried out all-atom MD simulations on the amyloid-prone hexapeptide 306 VQIVYK 311 , which belongs to the tau fiber core  (8)  and was therefore used as a model of the fiber core  (32, 33). The simulation was performed on the monomeric peptide in solution (Fig. S6A ) and the peptide fiber based on the crystal structure  (34)  [Protein Data Bank (PDB) entry 2ON9; Fig.
S6A ). The fiber model was based on the work of Zhao et al. (35). The fiber was made of the antiparallel two-sheet crystal structure published by Sawaya et al. (34)  (PDB entry 2ON9). Five strands were stacked on top of each other with a distance of 4. 7 Å (Fig. S6B ) and placed in a box of 9,258 water molecules. After 18 ns of equilibration, the dynamical parameters shown in Fig. 4  were computed over 2 ns for water molecules within 3 Å of the protein surface (see Fig. S6 ). Details on the simulation protocol and the calculations of the dynamical parameters are given in SI Materials and Methods.
ACKNOWLEDGMENTS. We are grateful to  Daphna Fenel  [EM facility within the  Grenoble Partnership for Structural Biology (PSB) ] for having carried out EM experiments and to  Denis Le Bihan  for discussion on a potential implication of the present work in diffusion MRI. The authors acknowledge  Matthias Heyden  for providing MD simulation analysis programs. This work used the platforms of the  Grenoble Instruct Center [Unité Mixte de Service 3518 Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA)-Université Joseph Fourier (UJF)-European Molecular Biology Laboratory (EMBL) ] with support from  French Infrastructure for Integrated Structural Biology (FRISBI)  ( ANR-10-INSB-05-02 ) and  Grenoble Alliance for Integrated Structural Cell Biology (GRAL)  ( ANR-10-LABX-49-01 ) within  PSB.
Financial support by  CEA ,  CNRS , and  UJF  is acknowledged, as well as a grant from the  Agence Nationale de la Recherche  (Project  ANR-11-BSV5-027 ) to M. W. This work has benefited from the activities of the  Deuteration Laboratory (DLAB) consortium  funded by the  European Union  under Contracts  HPRI-2001-50065  and  RII3-CT-2003-505925 , and from  UK Engineering and Physical Sciences Research Council (EPSRC) -funded activity within the  Institut Laue Langevin EMBL DLAB  under Grants  GR/R99393/01  and  EP/C015452/1. Support by the  European Commission  under the 7th  Framework Programme  through the  Key Action: Strengthening.
(C and E) In the Brazilian Amazon (C) and Atlantic Forests (E), the proportion of forest area at each distance to forest edge for both the current and estimated historic extent of forest. (D and F) In the Brazilian Amazon (D) and Atlantic Forests (F), the number of fragments and the total area of fragments of that size. The total number of fragments in the smallest bin (1 to 10 ha) is an underestimate in both the Atlantic Forest and Amazon data sets because not all of the very smallest fragments are mapped. Robust knowledge of how habitat fragmentation affects biodiversity and ecosystem processes is needed if we are to comprehend adequately the implications of this global environmental change.
Acknowledgments: For comments, we thank  R. Dunn ,  P. Ehrlich ,  L. Frishkoff ,  F. Gould ,  A. Leidner ,  C. Mendenhall ,  A. Tal ,  J. Watling , and the  Fukami and Peay labs at Stanford. For symbols in figures, we thank the  Integration and Application Network, University of Maryland Center for Environmental Science. Funding: R. M. E. is supported by  ERC (European Research Council ) Project number  281986. A. G. is supported by the  Natural Sciences and Engineering Research Council , the  Canada Research Chair program , and the  Quebec Centre for Biodiversity Science. C. N. J. was supported by the  Brazilian agency CAPES  through the  Ciência Sem Fronteiras program. R. D. H. thanks the  University of Florida Foundation. J. C. is supported by the labex  TULIP  and the  ANR Infrastructure grant AnaEE-France.
The commonly held belief that the emergence and establishment of farming communities in the Levant was a smooth socio-economic continuum during the Pre-Pottery Neolithic (ca. 12,000-9,000 cal BP) with only rare minor disruptions is challenged by recently obtained evidence from this region. Using a database of archaeological radiocarbon dates and diagnostic material culture records from a series of key sites in the northern Levant we show that the hitherto apparent long-term continuity interpreted as the origins and consolidation of agricultural systems was not linear and uninterrupted. A major cultural discontinuity is observed in the archaeological record around 10,000 cal BP in synchrony with a Holocene Rapid Climate Change (RCC), a short period of climatic instability recorded in the Northern Hemisphere.
Acknowledgments Funding for this research was provided by the  Spanish Ministry of Science and Innovation  (Grant  HAR2012-31036  and Project  CSD2010-00034  "Social and environmental transitions: Simulating the past to understand human behavior", see: http://www. simulpast. es) to  J. A. Barceló. F. Borrell  was supported by a  Bettencourt-Schueller Foundation  postdoctoral grant. We would like to express our gratitude to  T. Yartah  for kindly providing access to two unpublished 14 C dates from 'Abr 3; to  M. Molist  and  K. Schmidt  for kindly giving permission to reproduce illustrations from their published work on Halula and Göbekli respectively; and to  B. Weninger  for sharing the chart with palaeoclimate proxies. We also acknowledge the comments and constructive criticism made by  N.
Background Defined as an acceleration-deceleration mechanism in the neck, whiplash is the most common injury in road accidents, particularly for motorists: over the 2007-2010 period (French road safety database), 35 % of injured car occupants were concerned  [1]. The annual incidence rate of casualties suffering from whiplash seems to be of the same order of magnitude in most countries (annual incidence between 0. 04 and 3. 2/1,000 inhabitants  [2, 3] ). A study of drivers in collisions involving two cars found similar results in French  (1997) (1998) (1999) (2000) (2001) (2002) (2003)  and Spanish databases  (2002) (2003) (2004)   [4] : 12. 2 % were diagnosed with whiplash in France and 12. 0 % in Spain. Although considered a minor injury, whiplash is reported to generate both short-and long-term consequences, such as neck pain, headache, dizziness, sensory disorder and reduced neck mobility.
All subjects have provided written consent. Each individual lesion is coded using the Abbreviated Injury Scale (AIS)  [25] : from 1 (minor injury) to 6 (fatal injury). The Maximum-AIS (M. AIS) is the injury's highest AIS score and defines overall initial injury severity. The inclusion period for road accidents was initially from October 1 st , 2004 to December 31 st , 2005, later extended to July 31 st , 2006 for the most seriously injured (M. AIS ≥ 3). The protocol has been described in detail in a previous publication  [26]. The cohort comprises 1,168 adults, aged ≥16 years at the time of the accident. At inclusion, the injured were asked to answer a questionnaire specifically drawn up for the ESPARR study, administered in a face-to-face interview. The questionnaire gathered data on the accident and previous familial, occupational and health status.
Exploratory variables At inclusion, the questionnaire collected sociodemographic and accident-related data, plus some medical information and psychological history. Specifically, sociodemographic data comprised age, gender, family situation, educational level and socio-occupational category. Accident-related information comprised type of road-user with position inside the vehicle and impact direction, antagonist, reason for travel at time of accident, subjective responsibility for accident (admitting being at fault in the accident or not), friend or family member also injured in the same accident, and intention to lodge a complaint 1. Financial problems (job loss, financial difficulties, failure) in the year before the accident and psychological history (sleep disorder, consumption of antidepressants/anxiolytics, regular appointments with a psychologist) in the year before the accident were also considered in this analysis.
Post-traumatic stress disorder (PTSD) at 6 months and/or 1 year after the accident was also taken into account and was assessed using the Post-traumatic Check-List Scale (PCLS)  [29]. The PCLS includes 17 items relating to the 3 dimensions of the disorder (re-experiencing, avoidance and hyper-arousal), and has been shown to have good specificity for the diagnosis of PTSD; validation studies of the PCLS  [29] [30] [31] [32]  have shown good psychometric properties. The validation of the French version  [31]  showed that PCLS score ≥ 44 indicates presence of PTSD and the existence of disturbances which will necessarily affect lifestyle. The presence of pain and sequelae of accident-related injuries was collected in the 5-year questionnaire. A free text about pain location allowed data to be classified according to body region (head, neck, face, spine, thorax, abdomen, upper limbs, and lower limbs); a dichotomous variable was created for each region (pain vs.
Statistical analysis The representativeness of the study population (whiplash and non-whiplash subjects) was assessed by comparing sociodemographic and accident data between respondents and non-respondents at 5 years' follow-up. Descriptive statistics were used to describe the distribution of variables. Chi 2 test (significance level, 5 %) or Fisher's exact test (small samples) were used for categoric variables; Student's test (normal data distribution) or Kruskal-Wallis test (non-normal data distribution) were used for continuous variables. QoL data (the two generic questions and mean scores in the 4 domains) collected at 1 and 5 years after the accident were compared for subjects responding at the both follow-up steps. Thus, it was possible to detect a significant improvement or deterioration in QoL for each group, using McNemar's test (significance level, 5 %) or Student's test for matched data.