dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis 2. 4. 1. General model We studied the impact of various method characteristics (such as input feature and algorithm) on the performance of the classification task, separating sMCI from pMCI individuals. Several experiments were reported for each article, so we had to account for the dependency between experiments coming from the same article. In order to do so, we used linear mixed-effects models with a random intercept on the article. For the model to have enough power, we grouped the characteristics in a hierarchical manner, creating broad categories that can be expanded into finer ones several times. The categories were created as such : • linear models: linear regression, orthogonal partial least square (OPLS), linear discriminant analysis (LDA), manual threshold • generalized linear models: linear support vector machine(SVM), logistic regression, survival analysis • non-linear models: random forest, multi-kernel learning, non-linear SVM, bayesian methods, neural networks, others • other features • longitudinal approach • use of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set • number of subjects A first model was created with the broadest categories, and we used a two-sided ttest on the regression coefficients to identify the categories of characteristics which had a significant impact on performance.
A recent trend Figure  1a  shows that the number of articles published each year on the prediction of the progression of MCI to AD dementia has been steadily increasing since 2010. Figure  1a  also shows that the number of individuals used for the experiments is increasing over time (p= 10 -5 , slope of 12. 15 subjects per year, R 2 = 0. 10). 84. 6% of articles used data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Starting in 2004, this multicenter longitudinal study provides multiple modalities for the early detection of AD. As the recruitment of this largely used cohort is still ongoing, it is not surprising to see the number of included individuals increasing over the years. Studies often select individuals with a minimal follow-up time, of 3 years for example, and over the years more and more MCI individuals from the ADNI cohort fulfill these criteria, so more individuals can be included.
Varoquaux et al. (2017)  also studied the impact of the crossvalidation strategy on a range of classification tasks performed on fMRI and MEG data sets, and showed that differences in performance tend to be smaller than the variance of the estimated performance using a cross-validation method, mitigating the importance of the choice of cross-validation strategy. This study still warns against the use of leave-one-out validation, leading to less stable estimates. Reporting variance or confidence intervals is an important best practice to compare results from different studies and experiments. We did not collect this information, and further work regarding the adoption of this practice could complete this study. We also run the performance analysis using only the experiments performed on the ADNI cohort.
Medical imaging and biomarkers Imaging modalities are not as widely available as cognitive features, but they can represent a good opportunity to better understand the disease process by showing the changes that appear before the individuals progress to AD dementia. Among the used imaging modalities, we showed that using FDG PET leads to a better performance. Using T1-ROI features also leads to a better performance on the ADNI experiments, but this effect is not significant when considering all experiments. All the experiments using T1 MRI are performed on the ADNI database, so one can assume the effect on performance is small and is diluted when considering all experiments instead of the experiments performed on ADNI. Even considering the ADNI experiments, the effect of using T1-ROI features is 2.
Other methodological characteristics One could expect the performance to increase when the data set size increases, however we find that the effect of the number of subjects is not significant (coefficient=-0. 39, p=0. 76). The impact of data set size is further investigated in 5. 1. 2. The impact of using the ADNI data set is not significant (coefficient=0. 011, p=0. 995). This finding is mitigated by the fact that our results slightly vary when using all experiments or only the ADNI experiment. As only 14 included experiments do not use the ADNI database it is difficult to estimate the impact of its usage independently from the other characteristics. Although we used a hierarchical grouping of the variables in order to have more statistical power, few p-values and fewer corrected p-values are significant.
Methods focusing on AD should therefore target individuals who have already been identified as at risk of developing AD, by providing insight on the date at which this conversion is likely to happen. Such methods could be trained on MCI subjects that are at risk to develop Alzheimer's disease, defined for instance as the ones who have a MMSE of 27 or smaller and are amyloid positive. In addition to being closer to what can be expected in clinical practice, such data sets of at risk subjects should include a larger proportion of pMCI, leading to a better performance compared to the constant prediction. For example in ADNI, 71. 6% of MCI subjects stay stable 2 years after inclusion, whereas this proportion drops to 53. 7% for MCI subjects who are amyloid positive and have a MMSE of 27 or lower.
, , 2020)) , or even better in a prospective clinical trial  (Bruun et al. , 2019). This review focused on the prediction of progression to dementia, as this problem has, by far, attracted most attention from the scientific community. Nevertheless, predicting the future values of the biomarkers or the images may be of greater interest for such clinical decision support systems to be adopted in practice  (Marinescu et al. , 2020; Koval et al. , 2020; Ansart, 2019). and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List. pdf gitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer's disease: a prospective cohort study.
Acknowledgements Federica Cacciamani ,  Baptiste Couvy-Duchesne ,  Pascal Lu  and  Wen Wei  participated in reading articles to conduct this review. We thank the reviewers for their insightful comments that helped us to improve the manuscript, including Gaël Varoquaux who purposely disclosed his name. The research leading to these results has received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ) from the  European Union   H2020 program  (project  EuroPOND , grant number  666992 , project  HBP SGA1  grant number  720270 ), from the  ICM Big Brain Theory Program  (project  DYNAMO , project  PredictICD ), from the  Inria Project Lab Program  (project  Neuromarkers ), from the  European Research Council  (to  Dr Durrleman  project  LEASP , grant number  678304 ), from the  Abeona Foundation  (project  Brain@Scale ).
Magellan galaxy spectroscopy The Magellan galaxy survey design consists of two components: (1) a deep and narrow component with Magellan/LDSS-3C that targets all galaxies with  > 0. 1  star and  1 (and fainter at lower redshifts) within 1 in angular radius from the QSO sightline ( < 325 -500 pkpc for 0. 4 <  < 1. 0) and (2) a shallow and wide component https://github. com/spacetelescope/calcos/ with Magellan/IMACS  (Dressler et al. 2011 ) that targets  star galaxies at 10 ( < 3 -5 pMpc) from the QSO sightline. For the deep and narrow LDSS-3C survey, we aim to establish a statistically representative sample of galaxies at  < 1 for a comprehensive study of the CGM on individual halo scales ( < 300 pkpc) across a broad range in both stellar mass and star formation rate. To increase the efficiency of our ground-based survey, we devised a color-based prioritization scheme using the Ultra-VISTA survey  (Muzzin et al.
The observations were divided among four masks, with ∼ 300 galaxies observed for  exp = 5400 -6300 s per mask. Multislit spectral reductions were performed using the CPDMap routine in the CarPy Python distribution, which implements techniques for rectification and sky subtraction as outlined in  Kelson et al. (2000)  and  Kelson (2003). Redshifts were measured using custom software that constructs a template spectrum at regular redshift intervals by performing a  2 minimization of a linear combination of the galaxy eigenspectra from  Bolton et al. (2012). The redshift with the overall minimum  2 was then selected as the galaxy redshift, with manual verification and exclusion of poor spectral regions.
RESULTS We detect absorption from the Lyman and Werner bands of molecular hydrogen (H 2 ) at a redshift of  = 0. 576 in the COS spectrum of J0111 -0316. The COS and MIKE data reveal absorption from accompanying atomic hydrogen and a suite of neutral and ionized metals. In this Section, we describe the column densities and kinematics of the atomic ( §4. 1) and H 2 -bearing gas, including the excitation state of the molecular hydrogen ( §4. 2). We also examine the chemical abundance pattern ( §4. 3) and galactic environment ( §4. 4) of the absorber.
19 ). Pairing such observations with comprehensive galaxy survey data thus remains a critical avenue for gaining further clarity about the diverse range of galactic evolutionary states and environments that host dense and potentially star-forming gas. log[N J (H 2 )/cm 2 /g J ] B 3 J(J + 1) (K) J tract NAS 5-26555. This research has made use of NASA's Astrophysics Data System and the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Software: CarPy  (Kelson 2003)  voirs that persist in some systems despite the quiescence of their host galaxies.
19 ). Pairing such observations with comprehensive galaxy survey data thus remains a critical avenue for gaining further clarity about the diverse range of galactic evolutionary states and environments that host dense and potentially star-forming gas. log[N J (H 2 )/cm 2 /g J ] B 3 J(J + 1) (K) J Software: CarPy  (Kelson 2003)  voirs that persist in some systems despite the quiescence of their host galaxies.
Martin Bureau, Jay Gallagher, and Evan Kirby for useful discussions. HWC, EB, and MCC acknowledge partial support from HST-GO-15163. 001A and NSF AST-1715692 grants. TC and GCR acknowledge support from HST-GO-15163. 015A. SL was funded by project FONDECYT 1191232. This research was supported by the Munich Institute for Astro-and Particle Physics (MIAPP) which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC-2094 -390783311. This work is based on observations made with ESO Telescopes at the Paranal Observatory under programme ID 0104. A-0147(A), observations made with the 6. 5m Magellan Telescopes located at Las Campanas Observatory, and spectroscopic data gathered under the HST-GO-15163. 01A program using the NASA/ESA Hubble Space Telescope operated by the Space Telescope Science Institute and the Association of Universities for Research in Astronomy, Inc.
, under NASA contract NAS 5-26555. This research has made use of NASA's Astrophysics Data System and the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Type Ia SNe. We discuss possible origins for the H 2 -bearing gas in the outskirts of this galaxy, including interactions with companion galaxies and a past epoch of AGN activity. Although we cannot rule out the presence of an undetected, intervening galaxy lost in the glare of the QSO light, the lack of [O III] 5007 emission in the quasar spectrum at the absorber redshift places a 3 upper limit on the star formation rate of SFR < 0. 1 M yr 1.
ACKNOWLEDGMENTS We thank the referee for helpful comments, and  Sergei Balashev ,  Martin Bureau ,  Jay Gallagher , and  Evan Kirby  for useful discussions. We are grateful to  Dan Kelson  for lending his expertise on reducing the galaxy survey data from the  Magellan Telescopes. EB, HWC , and  MCC  acknowledge partial support from  HST-GO-15163. 001A  and  NSF   AST-1715692  grants. TC and GCR acknowledge support from  HST-GO-15163. 015A. SDJ acknowledges support from a  NASA Hubble Fellowship  ( HST-HF2-51375. 001-A ). SC gratefully acknowledges support from  Swiss National Science Foundation  grants  PP00P2_163824  and  PP00P2_190092. KLC acknowledges partial support from  NSF   AST-1615296. CAFG was supported by  NSF  through grants  AST-1517491 ,  AST-1715216 , and  CAREER  award  AST-1652522 , by  NASA  through grant  17-ATP17-0067 , by  STScI  through grants  HST-GO-14681.
011 , HST-GO-14268. 022-A ,and  HST-AR-14293. 001-A ,and by a  Cottrell Scholar Award  from the  Research Corporation for Science Advancement. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311. This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163. 01Aprogram using the  NASA/ESA Hubble Space Telescope  operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc.
, under  NASA  contract  NAS 5-26555. This research has made use of  NASA 's Astrophysics Data System and the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Martin Bureau ,  Jay Gallagher , and  Evan Kirby  for useful discussions. HWC, EB, and  MCC  acknowledge partial support from  HST-GO-15163. 001A  and  NSF   AST-1715692  grants. TC and GCR acknowledge support from  HST-GO-15163. 015A. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311.
This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163. 01Aprogram using the  NASA/ESA Hubble Space Telescope  operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc. , under NASA con-ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Martin Bureau ,  Jay Gallagher , and  Evan Kirby  for useful discussions. HWC, EB, and  MCC  acknowledge partial support from  HST-GO-15163. 001A  and  NSF   AST-1715692  grants. TC and GCR acknowledge support from  HST-GO-15163. 015A. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311.
This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163. 01Aprogram using the  NASA/ESA Hubble Space Telescope  operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. This research has made use of  NASA 's Astrophysics Data System and the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Martin Bureau ,  Jay Gallagher , and  Evan Kirby  for useful discussions. HWC, EB, and  MCC  acknowledge partial support from  HST-GO-15163. 001A  and  NSF   AST-1715692  grants. TC and GCR acknowledge support from  HST-GO-15163. 015A. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311. This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163.
CAFG was supported by  NSF  through grants  AST-1517491 ,  AST-1715216 , and  CAREER  award  AST-1652522 , by  NASA  through grant  17-ATP17-0067 , by  STScI  through grants  HST-GO-14681. 011 , HST-GO-14268. 022-A ,and  HST-AR-14293. 001-A ,and by a  Cottrell Scholar Award  from the  Research Corporation for Science Advancement. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311. This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163.
01Aprogram using the  NASA/ESA Hubble Space Telescope  operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. This research has made use of  NASA 's Astrophysics Data System and the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Martin Bureau ,  Jay Gallagher , and  Evan Kirby  for useful discussions, and  Dan Kelson  for lending his expertise on reducing the galaxy survey data from the  Magellan Telescopes. EB, HWC , and  MCC  acknowledge partial support from  HST-GO-15163. 001A  and  NSF   AST-1715692  grants.
TC and GCR acknowledge support from  HST-GO-15163. 015A. SDJ acknowledges support from a  NASA Hubble Fellowship  ( HST-HF2-51375. 001-A ). SC gratefully acknowledges support from  Swiss National Science Foundation  grants  PP00P2_163824  and  PP00P2_190092. KLC acknowledges partial support from  NSF   AST-1615296. CAFG was supported by  NSF  through grants  AST-1517491 ,  AST-1715216 , and  CAREER  award  AST-1652522 , by  NASA  through grant  17-ATP17-0067 , by  STScI  through grants  HST-GO-14681. 011 , HST-GO-14268. 022-A ,and  HST-AR-14293. 001-A ,and by a  Cottrell Scholar Award  from the  Research Corporation for Science Advancement. SL was funded by project  FONDECYT 1191232. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311.
This work is based on observations made with  ESO Telescopes at the Paranal Observatory  under programme  ID 0104. A-0147 (A), observations made with the 6. 5m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163. 01Aprogram using the  NASA/ESA Hubble Space Telescope  operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. This research has made use of  NASA 's Astrophysics Data System and the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Martin Bureau ,  Jay Ga  and  Evan Kirby  for useful discussions, and  Dan  for lending his expertise on reducing the galaxy data from the  Magellan Telescopes.
EB, HWC , and acknowledge partial support from  HST-GO-1516  and  NSF   AST-1715692  grants. TC and GCR ac edge support from  HST-GO-15163. 015A. SDJ ac edges support from a  NASA Hubble Fellowship  ( HS 51375. 001-A ). SC gratefully acknowledges suppor  Swiss National Science Foundation  grants  PP00P2_  and  PP00P2_190092. KLC acknowledges partial s from  NSF   AST-1615296. CAFG was supported b through grants  AST-1517491 ,  AST-1715216 , and  CA  award  AST-1652522 , by  NASA  through grant  17-A 0067 , by  STScI  through grants  HST-GO-14681. 011GO-14268. 022-A ,and  HST-AR-14293. 001-A ,and by  trell Scholar Award  from the  Research Corporation  f ence Advancement. SL was funded by project  FOND 1191232. This research was supported by the  Munic tute for Astro-and Particle Physics (MIAPP)  which is by the  Deutsche Forschungsgemeinschaft (DFG, Germ search Foundation)  under  Germany's Excellence Str   EXC-2094 -390783311.
This work is based on o tions made with  ESO Telescopes at the Paranal Obse  under programme  ID 0104. A-0147 (A), observation with the 6. 5m  Magellan Telescopes  located at  Las Cam Observatory , and spectroscopic data gathered under th GO-15163. 01Aprogram using the  NASA/ESA Hubbl Telescope  operated by the  Space Telescope Science I and the Association of Universities for Research in A omy, Inc. , under  NASA  contract  NAS 5-26555. This r has made use of  NASA 's Astrophysics Data System NASA/IPAC Extragalactic Database (NED) which i ated by the  Jet Propulsion Laboratory, California Inst Technology , under contract with the  National Aeronau Space Administration. ACKNOWLEDGM We thank  Sergei Balashev ,  Evan K Jay Gallagher  for useful discussions acknowledge partial support from H  NSF   AST-1715692  grants.
TC and port from  HST-GO-15163. 015A. T ported by the  Munich Institute for As (MIAPP)  which is funded by the  D meinschaft (DFG, German Research many 's Excellence Strategy -EXC-2 work is based on observations mad at the  Paranal Observatory  under  0147 (A), observations made with t scopes located at Las Campanas O scopic data gathered under the H gram using the  NASA/ESA Hubble  ated by the  Space Telescope Science ation of Universities for Research in   NASA  contract  NAS 5-26555. Thi of NASA's Astrophysics Data Syste Extragalactic Database (NED) whic Propulsion Laboratory, California  under contract with the National Ae ministration. Software: CarPy (Kelson 2003) ACKNOWLEDGMENTS We thank  Sergei Balashev ,  Evan Kirby ,  Martin Bureau , and  Jay Gallagher  for useful discussions. HWC, EB, and  MCC  acknowledge partial support from  HST-GO-15163.