dataesr/scientific-paragraphs-categorization huggingface dataset:
Signal Acquisition and Cell Tracking To monitor US-and microbubble-mediated cell uptake, the fluorescence signal time curve for each individual cell was obtained at a frame rate of 8. 5 Hz, i. e. the lowest allowed by the FCFM hardware, as described in Derieppe et al. [19]. Image analysis was conducted using MATLAB ® 2013 (MathWorks, USA). Briefly, a first series of 6 minutes (min) was acquired during which cells were sonicated for 5 s, ten seconds after the acquisition was started (figure  1a ). In these images, detection of the cell nuclei was performed using a non-local means approach followed by an intensity threshold for nucleus delineation. The nuclei were then labelled in each frame and automatically tracked over the time series using the iterative closest point method  [20]  applied between successive images, which is justified since the acquisition frame rate is high compared to the cell motion.
Sensitivity of the Three-compartment Model and Influence of the Acquisition Time In the range of uptake rates and PB rates obtained experimentally using the 3CM ref (Eq. 3), the sensitivity of the three-compartment model was evaluated for 3 time windows. The normalized errors of were small, and showed no clear dependence on the true values of k up and k pb. The maximum normalized error found was 0. 6 %, 0. 2 % and 0. 2 % for acquisitions of 3 minutes, 6 minutes and 9 minutes, respectively, indicating that for uptake rates in our experiments it is beneficial to acquire data for at least 6 min, but that the normalized error is not lowered when the acquisition time is further to 9 min (figure  4a, c, e ). The precision of was better than 8. 2 %, 3. 3 % and 2. 7 %, for all simulated k up and k pb (figure  4b, d, f ), for acquisition windows of 3 minutes, 6 minutes and 9 minutes, respectively.
Acknowledgments This study was funded by the  ERC  project  268906  " Sound Pharma"  ( Prof. C. Moonen ). The authors thank  Dr. Sébastien Benzekry  (IMB,  UMR 5251 ,  CNRS ,  Université de Bordeaux, Bordeaux, France)  for the fruitful discussion on the theoretical robustness of the 3-compartment model.
Study Selection Informed consent was obtained from all participants or their assigned surrogate decision makers, and the institutional review boards for human research of the participating centers approved all studies. The MEDLINE and Web of Science databases were searched from January 2004 (when the first human amyloid PET study was published with carbon 11labeled Pittsburgh Compound B [{  11  C}PIB] 6 ) to April 7, 2015, on amyloid PET studies in patients with dementia. The search terms used were PET and amyloid or abeta or PET tracer (ie, PIB, Pittsburgh, florbetapir, AV-45, florbetaben, or flutemetamol). Due to its affinity to both amyloid and tau pathology, 2-(1-{6-[(2-fluorine 18-labeled fluoroethyl)methylamino]-2-napthyl}ethylidene) malononitrile ([ 18 F]FDDNP) was not included. 17 The search resulted in 3250 studies.
Titles and abstracts were reviewed and 227 relevant full-text articles were retrieved to assess their eligibility. Studies were excluded if they presented case reports, included duplicate participants, or involved neurological or psychiatric diseases other than dementia. The search identified 40 unique cohorts. We asked 37 study contact persons to provide participant-level data on amyloid status, age, sex, education, APOE ε4 status,  18  Mini-Mental State Examination(MMSE)score, and Clinical Dementia Rating (CDR) scale score (3 cohorts published their studies after our inclusion stop in April 2014). Eight contact persons declined or did not respond, leaving participantlevel data from 29 cohorts for analysis (Figure  1 ). Seven cohorts provided additional unpublished participant-level data, acquired using peer-reviewed clinical and PET procedures (eTable 1A in the Supplement).
Only 1 cohort provided data that were not yet published in a peer-reviewed journal (n = 37, participants with dementia only). Following the same procedure, we selected 1849 healthy control participants from 23 cohorts (eFigure 1 in the Supplement), defined as participants who performed cognitive testing within normal limits and without any major neurological or psychiatric disorder. 19 The quality of primary reports from each cohort was systematically assessed by examining the setting, generalizability, selection, measurements, reference, bias, participant flow, descriptives, outcome, and dichotomization using combined STROBE  20  and QUADAS  21  guidelines (eTable 2A and eTable 2B in the Supplement). All cohorts reported their studies following the STROBE and QUADAS guidelines, although bias could not be assessed in 17 of 29 dementia cohorts and 13 of 23 control cohorts.
Data Collection and Operationalization Information on study procedures, extracted from the publications or provided by the study contact person, was used to create a common set of variables. Participants-Participants met diagnostic criteria for AD (including the atypical variants posterior cortical atrophy and logopenic-variant primary progressive aphasia), frontotemporal dementia (including behavioral, semantic, and progressive nonfluent variants), dementia with Lewy bodies (DLB), vascular dementia, and corticobasal syndrome. All diagnoses were made clinically without using amyloid PET or cerebrospinal fluid biomarker information. Detailed characteristics for each study are in eTable 1 in the Supplement. For an indirect comparison between in vivo and postmortem prevalence of amyloid positivity, the National Alzheimer's Coordinating Center (NACC) database  22  provided autopsy data of participants who were clinically diagnosed with probable AD dementia at their last visit.
Statistical Analysis We conducted a meta-analysis with individual participant data. Baseline characteristics were compared using analysis of variance and Fisher exact tests where appropriate. Generalized estimating equations (GEE, using SPSS software [IBM], version 21. 0) were used to estimate probabilities for amyloid positivity on PET and odds ratios. Generalized estimating equations was the method of choice for the study as it allows analysis of binary-correlated data, such that participant-level data from all cohorts can be modeled while simultaneously accounting for participants within studies. A logit link function for binary outcome with an exchangeable correlation structure was assumed to account for within-study correlation. Analyses were conducted using the total study population, unless specified otherwise.
Results The study included 1897 participants with a clinical diagnosis of dementia (AD, 1359 participants; frontotemporal dementia, 288 participants; DLB, 51 participants; vascular dementia, 138 participants; corticobasal syndrome, 61 participants) and 1849 healthy control participants with PET data (Table  1 ). From the NACC database, 1369 participants with AD dementia and autopsy data were included (eTable 2 in the Supplement). Amyloid positivity refers to positive (abnormal) amyloid PET scans or presence of moderate-to-frequent plaques on neuropathological examination.
Acknowledgments receiving grant funding from the  International Stichting Alzheimer Onderzoek, Center for Translational Molecular Medicine, Philips , and  Avid  and personal fees from  Philips. Dr Barkhof  reports receiving consulting fees for Bayer Schering Pharma, sanofi-aventis,  Biogen ,  TEVA ,  Merck Serono ,  Novartis ,  Roche ,  Synthon BV ,  Jansen Research ,  Genzyme , grants from the  Dutch Multiple Sclerosis Society , and speaker fees from  Serono Symposia  and  Medscape. Dr Jagust  reports receiving personal fees from  Banner Alzheimer Institute/ Genentech ,  Synarc/Bioclinica , and  Novartis. Dr Miller  reports receiving grant support from the  National Institutes of Health/National Institute on Aging  ( NIH/NIA)  and the  Centers for Medicare & Medicaid Services (CMS)  as grants for the Memory and Aging Center; serving as medical director for the  John Douglas French Foundation , scientific director for the  Tau Consortium , director/ medical advisory board of the  Larry L.
Alzheimer's  Disease Neuroimaging Initiative  (ADNI);  Australian Imaging ,  Biomarkers & Lifestyle  (AIBL) study; Avid Pharmaceuticals multicenter study for the AV45-A17 Study Group; Florbetaben (FBB) Phase 2 multicenter study;  Leiden Alzheimer-Research Nederland  (LeARN) project; Multicenter study by  UK Hospitals and University Hospital of Turku. Part of the data used in preparation of this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative (ADNI) database  (http://adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this article. A complete listing of ADNI investigators can be found at http://adni. loni. usc. design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Funding/Support:  The National Alzheimer's Coordinating Center (NACC ) database is funded by grant  U01 AG016976  from the  National Institute on Aging. The Alzheimer's  Disease Neuroimaging Initiative  (ADNI;  National Institutes of Health , grant  U01 AG024904 ) is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from  Abbott ;  Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Amorfix Life Sciences ;  AstraZeneca ;  Bayer HealthCare; BioClinica; Biogen Idec; Bristol-Myers Squibb; Eisai; Elan Pharmaceuticals; Eli Lilly; F. Hoffmann-La Roche  and its affiliated company  Genentech ;  GE Healthcare; Innogenetics , NV; IXICO;  Janssen Alzheimer Immunotherapy Research and Development; Johnson & Johnson Pharmaceutical Research and Development; Medpace ;  Merck and Company ;  Meso Scale Diagnostics ;  Novartis Pharmaceuticals ;  Pfizer ; Servier; Synarc; and  Takeda Pharmaceutical.
Techniques and methods Numerical simulations of wavefront shaping by the 3D SZP structures were conducted using a commercially available FDTD software package (FDTD Solutions, Lumerical Solutions, Inc. ). The spiral patterns were generated by calculating the binary transmittance function of a SZP  [13] : t(r 2 , ϕ) = ⎧ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎩ 0 (2k -1)π ≤ ϕ -πr 2 λ f ≤ 2kπ 1 2kπ ≤ ϕ -πr 2 λ f ≤ (2k + 1)π (1) where the polar coordinates in the SZP plane are denoted as (r,ϕ), λ is the wavelength of monochromatic light, f is the corresponding focal length and index k = 0, -1, -2. defines the spiral zone number. The overall diameter of the SZP was chosen to be R = 30 μm with a corresponding minimum k = -10. The spiral pattern was subsequently incorporated onto a geometry that closely approximates the subsequently manufactured gold coated photopolymerized structures on a glass substrate.
Acknowledgements This work was partially supported by the  Australian Research Council Discovery  grant  DP130101205  and the Linkage grant LP120100161 with Raith-Asia. DH and EB acknowledge support by the  French State  in the frame of the 'Investments for the future'  Programme IdEx Bordeaux  (reference  ANR-10-IDEX-03-02 ). AZ and MM acknowledge funding by the  Research Council of Lithuania  under the Lithuanian- French Programme "Gilibert " (No. TAP LZ-4/2015 ). SJ acknowledges the startup funding of the Nanotechnology facility by strategic funding from  Swinburne University. FDTD simulation work was performed on the swinSTAR supercomputer at  Swinburne University of Technology.
p. m. , 20 min at room temperature. Mononuclear cells were harvested and washed with PBS/FCS/PS followed by centrifugation at 1,000 r. p. m. , 10 min at 4 °C. Viable cells were counted by Trypan Blue exclusion (Sigma, 0. 4%). FL and adult BM LSK/SLAM cell stainings included LinPE cocktail (anti-CD3/ 1:400, anti-B220/1:2000, anti-Ter119/1:400, anti-Ly6c/1:4000 antibodies), anti-Sca1PE-Cy7(1:800), anti-cKitAPC-Alexa Fluor780(1:600) or BV421(1:500), anti-CD48PE(1:500) and anti-CD150APC(1:500) antibodies. Anti-CD11b-PE antibody (1:2,000) was added in the Lin staining for adult BM. Cells were analysed on a FACSAria SORP or FACSAria III (BD) with FloJo software. Dead cell exclusion was with Hoechst 33258 (1:10,000, Molecular Probes). Lineage output was tested on primary recipient peripheral blood at 4 months post transplantation using myeloid lineage (GM) markers, anti-CD11b PE and anti-Ly6c PE antibodies, lymphoid lineage (B þ T) markers, anti-CD3APC(1:400) and anti-B220APC(1:2,000) antibodies and the donor marker with anti-CD45.
2 FITC(1:500) antibody. All antibodies used were from BD Pharmingen except the anti-CD150 antibody (eBiolegend). Haematopoietic lineage output in peripheral blood of clonal transplant recipients (4 months post transplantation) was considered balanced when the B þ T value /GM value / was 4-16, myeloid-biased when the ratio was 0-4 and lymphoid-biased when the ratio was 416. Transplantation assay. Single cell suspensions were injected intravenously into female ((129SV Â C57BL/6)F1 or Ly5. 1) mice (3-6 months old) irradiated with 9 Gy (split dose, 137 Cs source). A total of 2 Â 10 5 recipient background spleen cells were co-injected. BM cells from primary recipients were injected into irradiated (9 Gy, split dose) secondary recipients. For (129SV Â C57BL/6) F1 recipients, peripheral blood peripheral blood chimerism was quantified by DNA PCR for gfp and calculated by DNA normalization (myoD) and comparison with gfp contribution controls with Image Quant software.
Acknowledgements We thank our lab colleagues and  Derk ten Berge  for critical discussions of our data and  Danny Huylebroeck  for comments on the manuscript. This work was supported by  EMBO LongTerm Fellowship  ( ALTF 260-2009 ),  ZonMW Dutch Medical Research Council  ( VENI 916-12-088 ,  VICI 911-09-036 ),  NIRM  ( Dutch Innovation Grant   FES0908 ),  NIH  ( RO37 DK54077 ),  Erasmus   MC Fellowship  ( 103. 494 ) and  Landsteiner Society for Blood Research  ( 1109 ).
S. Kulawik et al. : Evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0. 9, 0. 9, 1. 7, and 2. 1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1. 9 and 0. 9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error 2 = a 2 + b 2 /n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.
3 ppm, with biases larger than the TCCONpredicted bias uncertainty of 0. 4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 • N, MACC overpredicts between 26 and 37 • N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIA-MACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.
g. ,  Stephens et al. , 2007) , and the localization of sources and sinks on regional scales  (Canadell et al. , 2011; Baker et al. , 2006). The un-certainties in top-down source and sink estimates are a consequence of uncertainties in model transport and dynamics (e. g. ,  Prather et al. , 2008; Stephens et al. , 2007)  and sparseness of available surface-based CO 2 observations  (Hungershoefer et al. , 2010; Chevallier et al. , 2010). Satellites offer a much denser and spatially contiguous data set for top-down estimates, but are much more susceptible to biases as compared to ground-based measurements (e. g. , see summary in Sect. 3. 3. 2 of  Ciais et al. , 2014). This paper tests different characteristics of model and satellite CO 2 (e. g. , seasonal cycle amplitude and phase, regional and seasonal biases, effects of averaging, and diurnal variations) through a series of specialized comparisons to the Total Carbon Column Observing Network (TCCON).
The findings from this work can be propagated into assimilation systems to determine the influence of various findings on topdown flux estimates (e. g. , see  Miller et al. , 2007; Deng et al. , 2014; Chevallier and O'Dell, 2013; Chevallier et al. , 2014). For example, this paper characterizes biases by latitude and season; these biases can be assimilated to determine their effects on flux estimates (e. g. ,  Kulawik et al. , 2013). This paper also shows a set of comparisons and tests that may be useful for evaluating bottom-up flux estimates or transport schemes in models. The remainder of the paper is organized as follows: Sect. 2 describes the satellite X CO 2 data and models used. The various satellite and model X CO 2 data are compared against TC-CON observations in Sect. 3. Temporal characteristics of the different X CO 2 data and models, including the seasonal cycle amplitude and phase, are compared in Sect.
, 2010; Messerschmidt et al. , 2011). The precision and accuracy of the TCCON X CO 2 product is ∼ 0. 8 ppm (2σ ) after calibration  (Wunch et al. , 2010). The TCCON data used in this paper are from the GGG2012 and GGG2014 releases, available from http://tccon. ornl. gov/. In this paper we use 90 min average TCCON values which have 1σ precision of 0. 4 ppm (see Appendix B). We use 20 TCCON stations, distributed globally (see Fig. 1 ), and these data have been used extensively for satellite validation and bias correction (e. g. ,  Butz et al. , 2011; Morino et al. , 2011; Wunch et al. , 2011b; Reuter et al. , 2011; Schneising et al. , 2012; Oshchepkov et al. ; 2012) , in flux inversions  (Chevallier et al. , 2011) , and in model comparisons  (Basu et al. , 2011; Saito et al. , 2012). We use the GGG2014 data when available, and the GGG2012 data from sites Four Corners and Tsukuba_120HR.
Stations which have special circumstances regarding validation and are considered locally influenced are Garmisch which is in the midst of complicated terrain, for which local atmospheric transport is difficult to model and to measure from space; Four Corners, which is located in the vicinity of two power plants with large CO 2 emissions  (Lindenmaier et al. , 2014). The meteorology is such that Four Corners regularly samples large localized plumes with column CO 2 increases of several ppm that last hours in the late morning. Therefore, the low bias in models and satellite data relative to the Four Corners TCCON is attributed to the smaller scale enhancements from the power plants measured in TCCON which are significantly diluted in the model and satellite results; Bremen is also affected by local urban sources, and satellites and models would be expected to be biased low, though it is similar to adjacent stations; and JPL, Pasadena, and Edwards are in or adjacent to a megacity with complex adjacent terrain.
It also produces a number of other retrieval variables, such as surface pressure and aerosols, which are used in both filtering and bias correction. Postretrieval filter is employed based on a number of variables associated with the retrieval. In addition to filtering, a revised bias correction scheme has been developed for the v3. 5 retrievals. This scheme is similar to the approach described in  Wunch et al. (2011b) , which characterized the errors in earlier versions of the ACOS retrieval using a simple spatial uniformity assumption of X CO 2 in the Southern Hemisphere     MACC 13. 1 1979 MACC 13. 1 -2013 MACC 13. 1 (used 2007 MACC 13. 1 -2013) )  3 h 1. 9 × 3. 75 • (sometimes referred to as the Southern Hemisphere Approximation) to assess errors and biases in the retrievals. V3. 5 has corrections of GOSAT high (H ) and medium (M) gain data over land, as well as glint-mode data over the ocean, by using not only the Southern Hemisphere Approximation, but also TCCON observations, and comparisons to an ensemble mean of multiple transport model output.
7 ppm for CT2013b, and 0. 2-0. 3 ppm for the other three sets. The overall bias is less of a concern than the bias variability in satellite data which indicates regional errors that will translate to regional errors in flux estimates. The bias variability is 0. 4, 0. 4, 0. 5, and 0. 3 ppm for CT2013b, MACC, SCIAMACHY non-polar, and GOSAT non-polar respectively. Note SCIAMACHY data are corrected to have an average zero bias with respect to TCCON GGG2012 which is 0. 3 ppm higher than GGG2014 (https://tccon-wiki. caltech. edu/Network_Policy/Data_Use_Policy/Data_Description# CORRECTIONS_AND_CALIBRATIONS). The overall standard deviations are 0. 9 ppm for CT2013b, 0. 9 ppm for MACC, 2. 1 ppm for SCIAMACHY (all latitudes), and 1. 7 ppm for GOSAT (all latitudes). These values represent the overall performance of CT2013b, MACC, and single soundings from SCIAMACHY and GOSAT.
The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0. 2 and 0. 8 in the NH, with models showing 10 to 50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e. g. , the SH for models and 45-67 • N for GOSAT and CT2013b. We focus on validating aspects of model and satellite data which may be important for accurate flux estimates and CO 2 assimilation, including accurate error estimates, overall biases, biases by season and latitude, impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability.
Acknowledgements. Funded by  NASA  Roses  ESDR-ERR 10/10-ESDRERR10-0031 , "Estimation of biases and errors of CO2 satellite observations from AIRS, GOSAT, SCIAMACHY,  TES , and  OCO-2 ". Maximilian Reuter  and  Michael Buchwitz  received funding from  ESA  ( GHG-CCI project of ESA's Climate Change Initiative ) and from the  University and state of Bremen. Information about all TCCON sites and their sources of funding can be found on the TCCON website (https://tccon-wiki. caltech. edu/). Manvendra K. Dubey  is grateful for the funding for monitoring at Four Corners by  LANL-LDRD ,  20110081DR. Frédéric Chevallier received funding from the  EU H2020 Programme  (grant agreement no. 630080 , MACC III). NCEP Reanalysis data used in dynamic coincidence criteria were provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their website at http://www.
Bioinformatics and statistical analyses Raw data were trimmed and analysed using the software CLOTU  [10]. Reads with undetermined nucleotide, with any mismatch against tags and primers, and shorter than 150 bp were removed from the data set. Sequence reads from each sample were clustered into similaritybased OTUs (operational taxonomic units) using BLASTClust (single linkage clustering) at 97% similarity and 75% overlap in sequence length between reads in the pairwise alignments  [11] [12] [13] [14]. One representative sequence from each OTU was submitted to BLASTn  [15]  for comparison against the GenBank non-redundant (NCBI-nr) database. OTUs were only assigned to species level if i) the query sequence matched database sequences from fungal isolates (including at least one vouchered specimen) with E-values e-100 and percentage sequence identity 97% and ii) at the lowest E-values, when there were no contradictions among different species within the same genus  [16].