dataesr/scientific-paragraphs-categorization huggingface dataset:
The approach to fuse the triplicate is simply an approximation, producing more than 3 segmentations of the same datasets would be necessary to perfectly evaluate intra-rater reproducibility. However, the 5 datasets used for this study represent sufficiently similar tasks to consider our approximation adequate for this work. Preliminary analysis showed low correlation values, between a participant "score" for intra-rater reproducibility and inter-rater reproducibility. Correlation was between 0. 2 and 0. 4 for all measures, this indicates that there is no clear link between the reproducibility of a participant's own segmentations and the agreement with other participants.
Evaluation of protocols This work illustrates that intra-rater and inter-rater agreement is far from perfect even when following a strict and "simple" segmentation protocol. The intra-rater and inter-rater agreement must be taken into account when researchers compare bundles obtained from manual segmentations. When human expertise is required for a project, it is crucial that people involved in the manual segmentation process evaluate their own reproducibility, even if they have sufficient neuroanatomy knowledge and extensive experience in manual segmentation. This measure of error will likely increase the threshold for statistical significance. In such case, either more datasets will be needed, or a better protocol for segmentation needs to be designed  [Gwet, 2012; Boccardi et al. , 2015].
Most measures used are simplified to have easily interpretable results. The previous example shows the importance of using more than one type of measurements to obtain a complete picture of the reproducibility. Reproducibility "scores" are likely to vary with the project and the bundle of interest. This needs to be addressed as a community. The discrepancy between protocol quality, reproducibility and conclusion put forward in the literature can be problematic. For binary measures (accuracy and specificity), scores were both above 95% as it is easy to discard true negatives, and consequently did not provide much insight. Similarly to the curse of dimensionality in machine learning  [Verleysen and François, 2005; Ceotto et al. , 2011] , our datasets typically contain millions of voxels (or streamlines), of which only a few thousands true positives are considered during segmentation.
Thus, the vast majority of true negatives are rapidly discarded resulting in both accuracy and specificity almost reaching 100%. Sensitivity provides more information, as true positives are more difficult to get, since they are rarer in the tractograms (few thousands out of millions)  [Maier-Hein et al. , 2017]. This needs to be taken into account using precision, as in some cases, strict segmentation is encouraged because false positives are more problematic than false negatives. Streamline-wise measures show lower agreement, meaning that reproducible results are likely more difficult to achieve with the streamlines representation. More complex measures need to be designed to fully capture the complexity of tractography datasets and compare them, even across datasets or for longitudinal studies.
Other tractography algorithms, such as global tractography  [Kreher et al. , 2008; Mangin et al. , 2013; Christiaens et al. , 2015; Neher et al. , 2012] , are likely to have different reproducibility "scores", even with the same segmentation protocol. Any change to the preprocessing could lead to unexpected change in the reproducibility "scores". Using the same datasets and tractography algorithm, but increasing or decreasing the number of streamlines could also change the reproducibility "scores". Investigations of other bundles of interest would likely lead to different reproducibility "scores", using another anatomical definition of the PyT or even having the anatomical definition taught to participants would have the same effect. However, the general conclusion remains that reproducibility needs to be quantified for specific projects and protocols.
Impact on analysis If variability needs to be minimized further than the defined protocol, a simple recommendation is to have a single rater perform each task multiple times or multiple raters perform each task multiple times (or a subset of tasks). This way, it is guaranteed that each dataset is segmented more than once, decreasing the error risk. Regardless of the decision made, it is of great importance to quantify the reproducibility of manual segmentation of raters involved in the project before doing any statistics or group comparisons. This could drastically change the statistical significance of results. As of now, to the best of our knowledge, human variability and errors are rarely considered. Sources of variability needs to be accounted to truly enable synthesis of work across multiple centers.
Even when automatic or semi-automatic methods are used, they first need to be evaluated with agreed upon measures and reach or surpass human standards. The extension to other bundles of interest or other segmentation plans is not trivial and the only conclusion that stands is that agreement is never 100% and that a unique measure is not enough to represent the whole picture for tractography segmentation. The desire to simplify measures or have only one value to describe quality or reproducibility of segmentations needs to be discouraged. The nature of our datasets makes this task much more complex to interpret than 2D or 3D images, and it is imperative that the field comes to understand and agree on measures to report. This is more relevant than ever as the field grows and now that open science is becoming more popular and reproducibility studies are encouraged.
Similarly to other neuroimaging fields, such as hippocampi segmentation, standardized protocols need to be developed and designed to be used across multiple centers without active collaboration or micromanagement. 20 4. 5. Future work Future work includes the creation of a database containing bundle segmentations and metadata from participants that will be available online so further analysis can be done. As for now, a preliminary upload of the participants segmentation is available on Zenodo (https://doi. org/10. 5281/zenodo. 2547025), which will be updated. In this work, metadata was not used to evaluate duration as a variable influencing reproducibility. Investigating the relationship between variability and duration of a task or looking for bias (inter-hemispheric or software influence).
The stock of capital refers to the historical value of fixed assets and their corresponding accumulated revaluation value. We use the US capital and labor shares as a benchmark because their economy is considered to be comparatively undistorted. To obtain the US labor share, 5  we use the NBER-CES Manufacturing Industry database presented by the National Bureau of Economic Research (NBER) and the Center for Economic Studies (CES). Labor share is used as an annual average. For the years 2012-2015 where data are not available, we calculate the average growth 6  of labor, share is calculated and we project the values. Once the productivity (both physical and revenue) of the firms has been calculated, we trim the 1% tails of plant productivity and distortions to make the results robust to outliers.
If R has been incorrectly established, it would only affect the average capital distortions (  16 ) Y efficient Y -1 but not the liberalization experiment. The elasticity of substitution is established at σ = 3 , according to the model proposed by  Hsieh and Klenow (2009). 7  In the NBER and CES databases, labor compensations omit the additional benefits and social security contributions made by the employer. According to the Income and National Products Accounts, the labor share presented in the database is 2/3 of the total labor share, which includes other forms of compensation apart from the employees' salary. So, we multiply the labor share of the NBER and CES databases by 3/2 to obtain the elasticity of the labor that will be used for the calculations. The capital share is established as 1 minus the US labor share.
The Creative Commons Public Domain Dedication waiver (http:// creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated. In animal production, antimicrobials are widely used as a growth promoter and in the treatment of infectious diseases. The use of antimicrobials in poultry production industries for the promotion of growth largely contributes to the high resistance to antimicrobial agents in normal flora of poultry and pathogenic microorganism  [5]. Due to its ubiquity, E. coli has become one of the bacterial species that are commonly resistant to antibiotics and can transmit antibiotic-resistance genes from other Enterobacteriaceae species in the environment  [6, 7]. To date, little information has been available on characteristics of avian E.
PFGE was conducted in a CHEF-DRIII apparatus (Bio-Rad). Gels (1% agarose) were run at 14°C for 24 h in TBE buffer (4 mM Tris, 4 mM borate, 1 mM EDTA, pH 8. 3) at 6 V/cm. PFGE was conducted in a CHEF-DRIII apparatus (Bio-Rad). The gels (1% agarose) were run at 14°C for 24 h in TBE buffer (Tris, 4 mM; borate, 4 mM; EDTA, 1 mM; pH 8. 3) at 6 V/cm. The pulse times were increased from 10 to 30 s. As size markers, XbaI restriction fragments of Salmonella enterica serovar Braenderup H9812 were used. Cluster analysis using dice similarity indices was done in BioNumerics 6. 6 software (at 0. 5% tolerance and 0. 5% optimization) (Applied Maths, Ghent, Belgium) to determine similarities and differences and to find or characterize the relationships among isolates.
To do so, we first purified microglia from brains of adult Grn À/À mice by fluorescence-associated cell sorting (FACS) using microglia-specific anti-FCRLS and anti-CD11b antibodies. We then investigated the expression pattern of gene characteristic for MGnD and HM using NanoString gene expression profiling (MG534;  Butovsky et al, 2014; Krasemann et al, 2017; Mazaheri et al, 2017; Dataset EV1). Gene expression levels in each sample were normalized against the geometric mean of five housekeeping genes including Cltc, Gapdh, Gusb, Hprt1, and Tubb5. Out of 529 genes analyzed, 58 genes were significantly upregulated and 58 genes were downregulated. Strikingly, genes most strongly upregulated in Grn À/À microglia are those previously described for MGnD  (Fig 1A; Butovsky et al, 2015; Holtman et al, 2015; Keren-Shaul et al, 2017; Krasemann et al, 2017).
These include ApoE, as the most upregulated gene, Ly9, Clec7a, Dnajb4, Cccl4, and many others suggesting that PGRN-deficient microglia adopt the MGnD state. We then compared the Grn À/À microglial signature to the previously analyzed molecular signature of Trem2 À/À microglia  (Fig 1B and C; Mazaheri et al, 2017). Both NanoString gene expression panels overlapped in 418 genes. Out these, 359 mRNAs could be detected in both screens (Dataset EV1). In the Grn À/À microglia, 40 mRNAs were upregulated and 55 mRNAs were downregulated, whereas in the Trem2 À/À microglia, 87 mRNAs were increased and 27 mRNAs were decreased (Fig  1C ; left two panels). Interestingly, while only six mRNAs were equally up/downregulated in both phenotypes, 32 mRNAs showed an opposite regulation  (Fig 1C; right panel).
Soluble amyloid precursor protein (APPs) and calnexin were used as a loading control. The asterisk indicates an unspecific band. H Quantification of immunoblots normalized to BV2 Grn wt levels (n = 3-5). Data represent the mean AE SD. I ELISA-mediated quantification of sTREM2 in conditioned media of BV2 Grn wt and Grn mut cells. Data represent the mean AE SD (n = 4). J Quantification of relative mRNA levels of Apoe, Clec7a, P2ry12, and Trem2 in BV2 Grn wt and Grn mut cells. Data represent the mean AE SD (n = 3). Data information: For statistical analysis, unpaired two-tailed Student's t-test was performed between Grn mut or Grn À/À in comparison with Grn wt or Grn +/+ : n. s. , not significant; *P < 0. 05; **P < 0. 01; ***P < 0. 001; and ****P < 0. 0001. For (F), the one-way ANOVA with Tukey post hoc test was used.
The RNA counts for each gene and sample were normalized to the mean value of wt followed by a log2 transformation (n ≥ 5 per genotype). *labeled genes were not analyzed or below detection limit in Trem2 À/À microglial mRNA expression dataset  (Mazaheri et al, 2017). C Changes in gene expression of 359 detected genes in Grn À/À microglia and Trem2 À/À microglia. Note that from the 38 genes significantly altered in both genotypes, 32 genes are regulated in opposite direction. D Expression levels of significantly altered homeostatic and MGnD genes of Grn À/À and Trem2 À/À microglia from the data set in (B). Gene expression is normalized to the mean of the wt cohort in comparisons with the published normalized dataset of Trem2 À/À microglia  (Mazaheri et al, 2017). Data represent the mean +/À SD.
Data information: For statistical analysis, unpaired two-tailed Student's t-test was performed between Grn À/À microglia in comparison with Grn +/+ microglia or Trem2 À/À microglia in comparison with Trem2 +/+ microglia: not significant P > 0. 05; *P < 0. 05; **P < 0. 01; and ***P < 0. 001. A Flow cytometric analysis of phagocytic capacity in BV2 wt and BV2 mut cells using pHrodo green E. coli as target particles. Phagocytosis was terminated after 30 and 60 min (min) of incubation. Data are presented as mean percentage of cells positive for pHrodo uptake AE SD (n = 3). B Enhanced migration of CD68-positive, PGRN-deficient cells in an ex vivo model. Immunofluorescence analysis of cultured Grn +/+ and Grn À/À organotypic brain at 7 DIV immunostained using microglial marker CD68. Nuclei were counterstained using DAPI.
Data normalization and analysis NanoString data were normalized and analyzed using nSolver TM software. RNA ncounts were normalized using the geometric mean of five housekeeping genes including Cltc, Gapdh, Gusb, Hprt1, and Tubb5 using nSolver TM Analysis Software, version 3. 0 (NanoString Technologies, Inc. ). A cutoff was introduced at the value two-fold of the highest negative control present on the chip. Fold changes were calculated using the average of each group. For each experiment, the fold changes were calculated comparing the experimental group to their appropriate controls. To compare the normalized gene expression levels in Grn À/À and wt mice, unpaired two-tailed Student's t-test was performed (*P < 0. 05, **P < 0. 01, ***P < 0. 001). The volcano blot was generated employing GraphPad Prism 7 software.
Protein concentrations of supernatants were determined using the BCA protein assay (Pierce, Thermo Scientific), and equal amount of protein was analyzed for TREM2. sTREM2 levels in mouse serum or mouse brain extracts were quantified using the Mesoscale platform essentially as described previously (Kleinberger et al, 2017) using 0. 25 lg/ml biotinylated polyclonal goat anti-mouse TREM2 (R&D Systems; BAF1729) as capture antibody and 1 lg/ml rat monoclonal anti-TREM2 antibody (R&D Systems, MAB17291) as detection antibody. Calculation of the concentration of sTREM2 was performed with the MSD Discovery Workbench v4 software (MSD).
In-house antibodies were as follows: PGRN (Rat 8H10 1/50; Gotzl et al, 2014) and TREM2 (Rat 5F4 1/50;  (Xiang et al, 2016) ). Commercial antibodies were as follows: CLEC7A (R&D Systems, AF1756, 1/2,000), P2RY12 (Abcam, [EPR18611] (ab184411), 1/1,000) and ApoE (Merck, AB947, 1/2,000), and APPs (Merck, MAB348 clone 22C11, 1/5,000). For immunoblotting in primary microglia: ApoE (HJ6. 3, 1/700), kindly provided by David E. Holtzman  (Kim et al, 2012) , CD68 (Abcam, ab125212, 1/1,000), and IBA1 (GeneTex, GTX100042, 1/1,000) were used. HRP-conjugated secondary antibodies and ECL Plus (Pierce TM , Thermo Fisher Scientific) were used for detection. For the quantitatively analysis, images were taken by a Luminescent Image Analyzer Vilber/Peqlab Fusion SL and evaluated with the Multi Gauge V3. 0 software (Fujifilm Life Science, Tokyo, Japan).
Phagocytosis assay Assays to determine the phagocytic capacity were carried out as described before  (Kleinberger et al, 2014 (Kleinberger et al, , 2017)). Briefly, BV2 cells were seeded at a density of 100,000 cells per well of a 12-well dish and 1 day after seeding, incubated with pHrodo green E. coli for 30 and 60 min at 37°C after (pHrodo â Green E. coli BioParticles â Thermo Fisher). To analyze E. coli uptake, cells were resuspended in FACS buffer (PBS, 2 mM EDTA and 1% FBS) and subjected to flow cytometry. Data were acquired using the MACSQuant â. Cytochalasin D was used as an uptake inhibitor at a final concentration of 10 lM. Phagocytosis assays with primary microglia were performed similar to BV2 cells with the following modifications: Cells were isolated from adult mouse brain (2 months of age, n = 3 per genotype, male) as described above.
Brains were subsequently cryoprotected in 30% sucrose for 48 h. After freezing, 50-lm-thick microtome sections were sequentially collected in phosphate buffer saline with 15% glycerol and kept at À80°C until further use. Free-floating sections were blocked in 5% goat serum for 1 h at room temperature and then incubated in primary antibodies P2RY12 (Biolegend, 1/100) and IBA1 (Invitrogen, Thermo Fisher Scientific, 1/300) for two nights at 4°C. Sections were washed and incubated in secondary antibodies (goat anti-rat Alexa Fluor 555, 1/500, goat anti-rabbit Alexa Fluor 488, 1/500) for 3 h at room temperature. Lastly, slides were washed and stained with 4 0 ,6-diamidino-2-phenylindole (DAPI, 5 lg/ml) before mounting coverslips with ProlongTM Gold Antifade reagent (Thermo Fisher Scientific). Images were acquired using a LSM 710 confocal microscope (Zeiss) and the ZEN 2011 software package (black edition, Zeiss).
Laser and detector settings were maintained constant for the acquisition of each immunostaining. For analyses, three images were taken per slide using 20× objective (Plan-Apochromat 20×/0. 8 M27) at 2,048 × 2,048 pixel resolution, with Z-step size of 1 lm at 16 lm thickness. Approximately 150 plaques per genotype were counted using FIJI software (ImageJ) to calculate the number of IBA1-positive microglia around plaques in the cortex. Microglia coverage was quantified by importing acquired images to FIJI, and data channels were separated (Image\Color\Split Channels). Gaussian filtering was used to remove noise, and intensity distribution for each image was equalized using rolling ball algorithm, which is implemented as background subtraction plug-in in FIJI. For the feasibility of the quantification, all layers from a single image stack were projected on a single slice (Stack\Z projection).
Genetic testing was performed because of a positive family history (triplet brother with early-onset dementia). Exome sequencing from blood DNA and data analysis was performed as described before  (Kremer et al, 2017). In brief, exonic regions were enriched using the SureSelect Human All Exon Kit V6 from Agilent followed by sequencing as 100 bp paired-end runs on an Illumina HiSeq4000 (Illumina, San Diego, CA, USA) to an average read depth of 144x. Reads were aligned to the UCSC human reference assembly (hg19) with Burrows-Wheeler algorithm (BWA v. 0. 5. 9). More than 98% of the target sequences were covered at least 20× in all samples. Single nucleotide variants (SNVs) as well as small insertions and deletions were detected with SAM tools v. 0. 1. 19. In-house custom Perl scripts were used for variant annotation.
PGRN serum levels measured by ELISA confirmed the GRN mutation carrier. PGRN levels were measured and quantified using the Mesoscale platform essentially as described previously  (Capell et al, 2011)  using a biotinylated polyclonal goat anti-human PGRN antibody at 0. 2 lg/ml (R&D Systems; BAF2420) as capture antibody and a monoclonal mouse anti-human PGRN antibody at 0. 25 lg/ml for detection (R&D Systems, MAB2420). Calculation of the concentration of PGRN was performed with the MSD Discovery Workbench v4 software (MSD). Prior to PET in the patient, the affinity of the TSPO receptor for binding of PET radioligands was assessed by determining the genotype of the SNP rs6971 as previously described  (Mizrahi et al, 2012; Owen et al, 2012). The patient was scanned at the Department of Nuclear Medicine, University hospital of Munich, by an established TSPO-PET protocol  (Albert et al, 2017).
In brief, PET was acquired with a PET/CT scanner (Biograph 64, Siemens) 60-80 min after intravenous injection of 187 MBq of 18 F-GE180. A low-dose CT scan preceded the PET acquisition and served for attenuation correction. An SUVR image was generated by a global mean scaling and compared visually with TSPO-PET data deriving from a 56-year-old female patient with bvFTD, indicating no mutation in GRN and the same binding affinity status. This patient was scanned secondary to informed consent following the same TSPO-PET scanning procedure. In this patient, mutations in GRN, TREM2, and MAPT were excluded by panel sequencing as well as the C9orf72 hexanucleotide expansion by repeat-primed PCR.
Statistical analysis All data were analyzed using GraphPad Prism 7. Data are presented as mean AE SD or SEM as indicated. Statistical significance was calculated by unpaired two-tailed Student's t-test  C, D, H, I and J, and 3A, C, D and EV1A and B) , one-way ANOVA with Tukey's post hoc test  (Figs 2F, 4A and 5)  for multiple comparison, and the nonparametric two-tailed Mann-Whitney U-test  (Figs 3F and EV1C). Statistical evaluations were displayed as follows: n. s. not significant for P > 0. 05; statistical significance was assumed at *P < 0. 05; **P < 0. 01; ***P < 0. 001; and ****P < 0. 0001. For the lPET experiments, animal numbers were calculated for a significance of 0. 05 and a power of 80%. Animal numbers were not changed during the experiments, and no outliers were excluded from datasets.
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft (DFG)  within the framework of the  Munich Cluster for Systems Neurology (EXC 1010 SyNergy ), the  Koselleck  Project  HA1737/16-1 , the Cure Alzheimer's fund and the  NOMIS Foundation  (to C. H. ), by a dedicated  PET imaging grant  to  M. B. & A. Ro. ( BR4580/1-1 & RO5194/1-1 ) and the  German Federal Ministry of Education and Research  (project: FTLDc  01GI1007A ; to J. D. -S. ), and a PhD stipend of the  Hans and Ilse Breuer Foundation  to  A. Re. We would like to thank  Dr. M. Nishihara  ( Department of Veterinary Physiology, The University of Tokyo ) for providing the founder of the Grn À/À mouse strain,  Dr. David M. Holtzman  ( Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA ) for providing the ApoE antibody,  Dr.
Data availability The datasets produced in this study are available in the following databases: • Pathogenic variant in GRN: ClinVar SCV000897721. 1 (https:// www. ncbi. nlm. nih. gov/clinvar/?term=SCV000897721. 1). • NanoString gene sequence, raw, and processed data: Gene Expres- sion Omnibus GSE129709 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE129709). Expanded View for this article is available online.
Spectra were searched against Swiss-Prot database (downloaded in 10/2017, 5201 entries) containing all the reviewed protein sequences available for S. pombe, and three human keratin sequences (P04264, P35908, P13645). The Paragon algorithm embedded in ProteinPilot 5. 0 software (AB Sciex, Framingham, MA USA) was used to perform the database search using phosphorylation emphasis and gel-based ID as special factors, and biological modifications as ID focus. An independent false discovery rate (FDR) analysis was carried out using the target-decoy approach provided with Protein Pilot software, and positive identifications were achieved using a global FDR threshold below 1%.