dataesr/scientific-paragraphs-categorization huggingface dataset:
As an example, the mass measurement errors of all c-and z + 1-type fragment ions identified in the MALDI-ISD FT-ICR MS spectrum of IdeSdigested NIST in the m/z-range 1000-7000 are reported in Figure  S4. Most of the fragment ions were identified in both mFT and aFT spectra and only a minor part were identified exclusively in aFT spectra (data not shown). Phase correction led to additional identifications only when isotopic distributions were not resolved in mFT, for example, in the cases depicted in Figure  2. MALDI-ISD analysis of the same mAb samples has also been performed using TOF MS. It is noted, however, that TOF MS instruments exhibit a lower resolving power, with corresponding limitations, even when Lc and Hc are analyzed separately. Overlapping isotopic distributions can result from fragment ions of different forms of the same chain.
6] [47]  This is exemplified for adalimumab (middle-down LC-MS/MS using electron-transfer dissociation) in Figure  4. 9  In general, the sequence coverage obtained using mFT or aFT data were similar. In fact, the number of fragment ions identified in mFT spectra ranged from 95% to 100% the number of fragment ions in the corresponding aFT spectra. This is rationalized by the fact that the resolving power in mFT was already sufficient to identify most of the fragment ions. Only a minor part of all identified fragment ions were identified solely in aFT spectra. However, the identification in aFT is more reliable since a higher resolution allows detection of possible overlapping species. Glycosylation Analysis. The MALDI FT-ICR MS strategies we report here also provide insight into Nglycosylation of the Fc-part of both NIST mAb and trastuzumab.
Naphtalene (0. 5 g) and small pieces of Na metal (170 mg) were added to the mixture and stirred in a glove box for 6 days at room temperature. Finally, the reduced phase was washed with THF and dried under vacuum. Magnetic properties. The magnetic susceptibility, χ, as a function of temperature was obtained from magnetic moment measurements performed by squid magnetometry, with a MPMS-5T from Quantum Design. The data were collected upon warming from 5K to 300K in a 100 Oe applied magnetic field in zero-field-cooling (zfc) and field-cooling (fc) processes. A diamagnetic correction was applied to χmol. Additional isothermal magnetic moment measurements were made as a function of the applied magnetic field H. To avoid air exposure, the sample was prepared in a glove box. About 0. 1 g of the sample powder was set within a gelatine capsule.
Synchrotron X-ray diffraction Electrodes were loaded inside Kapton capillaries. Data were collected at the 11-ID-B beamline at the Advanced Photon Source at Argonne National Laboratory, using high energy Xrays (λ = 0. 2128 Å) allowing access to high values of momentum transfer Qmax ≈ 22 Å. 21, 22  The diffraction images were integrated within fit2D to obtain the one-dimensional diffraction data. 23 he G(r) function was extracted from the data using PDFgetX2,  24  after correcting for background and Compton scattering. The refinement of the PDF data was carried out using the PDFgui software. 25.
Structural analysis The signature of the first discharge curve for anatase TiO2 and Ti0. 780. 22O1. 12F0. 40(OH)0. 48 pointed out differences in reactivity particularly during the early stage of the sodiation. In our previous study, we showed that the sodiation of TiO2 up to 100 mAh. g -1 and characterized by a rapid drop of the voltage (0. 3 V), did not induce any structural change and was due to electrolyte decomposition. 10 In the case of Ti0. 780. 22O1. 12F0. 40(OH)0. 48 electrode, we observed a smooth decrease of the voltage. To compare the early stage of the sodiation, we discharged an Ti0. 780. 22O1. 12F0. 40(OH)0. 48 electrode up 100 mAh. g -1 and measured high-energy total scattering data from which we obtained the pair distribution function, denoted G(r). We applied a real-space refinement to extract structural information.
The best fit to the PDF data was obtained by using a single phase having a tetragonal symmetry similar to the pristine electrode. We hypothesized that sodium can be inserted in the titanium vacancies and accordingly refined the rate occupancy of Na in the Ti (4a) site. Moreover, we refined the rate occupancy of the interstitial site (4b Wyckoff site). Figure  2  shows the final PDF refinement within the inter-atomic distances, r, from 1 to 30 Å. The structural parameters obtained after the refinement was gathered in Table  1. The refinement of the rate occupancy confirmed that sodiation occurred selectively within the titanium vacancies (up to 0. 17 Na+) rather than in the interstitial site. The calculated capacity based on the number of sodium obtained by PDF refinement yielded 75 mAh. g -  out of the 100 mAh.
Acknowledgements This research used resources of the  Advanced Photon Source , a  U. S. Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. B. J. M. acknowledges support from the  Royal Society  ( UF130329 ). DFT calculations were performed using the  Balena High Performance Computing Service at the University of Bath , and using the ARCHER supercomputer, with access through membership of the UK's  HPC Materials Chemistry Consortium , funded by  EPSRC  grant  EP/L000202.
Data Access Statement. The DFT dataset supporting this study is available from the University of Bath Research Data Archive (doi:10. 15125/BATH-00473),  36 published under the CC-BY-SA-40 license. This dataset contains all input parameters and output files for the VASP DFT calculations, and Python scripts for collating the relevant data used in our analysis. A Jupyter notebook containing code to produce Figure  S4  is also available (Ref 36, doi: 10. 5281/zenodo. 1181872), published under the MIT license.
Follow-up will continue annually through 5 years with telephone contacts at 18 and 30 months. Table  E2  details the information collected at each visit. New pacemaker implantation rate was not a defined end point in the study; however, these data were collected as reported treatments on adverse event (AE) forms when applicable. Patients in whom implantation of the study valve was attempted but not completed were followed for 30 days for safety reporting and then exited from the study.
Discussion Dr W. R. Chitwood (Greenville, NC). Thank you, Dr Sabik, for a wellorganized and clear presentation of the results of the PERIGON Pivotal Trial, which was designed to evaluate the Medtronic Avalus bovine pericardial valve. Bioprosthetic valves are now implanted in more than 80% of aortic valve cases; moreover, the age limit continues to decrease. However, no one has developed a Holy Grail valve that compares favorably to the normal aortic valve. Issues of gradient, PVL, and endocarditis have generally been solved, but long-term durability remains the Gordian knot that is left to be untied scientifically. Dr Sabik, you have shown us 1-year data from this pivotal trial that evaluated this pericardial valve. The comparative pericardial aortic surgical valves to date are the Edwards Magna Ease, with the predicate being the Perimount valve; the St Jude, now Abbott, Trifecta valve; and the Sorin, now LivaNova, Mitroflow valve.
Patients and donors We analyzed several published data sets as well as novel samples from a reference population. These included five patients from a published dataset who suffered from multiply recurrent Clostridium difficile infection (CDI) refractory to standard antibiotic therapies (patients CD1 to CD5) and were treated with fecal microbiota transplantation (FMT)  [9]. Among the five patients (CD1 to CD5) with recurrent CDI who received FMT, four were cured following FMT and one failed. Sixteen healthy subjects who participated as standard stool donors in the University of Minnesota Microbiota Therapeutics Program also participated in this study. Inclusion and exclusion criteria for stool donor qualifications were described previously  [10]. Briefly, in addition to qualifying as blood donors, these individuals took no medications; had no history of recent (< 6 months) antibiotics exposure; had no gastrointestinal, immunologic, neurodevelopmental, or psychiatric problems; had a body mass index of < 25 kg/m2; and had normal metabolic testing.
DNA extraction, PCR, sequencing, and sequence processing and analysis After fecal DNA isolation (MoBio, Carlsbad, CA fecal DNA kit), amplicons spanning the V4 region of bacterial 16S rRNA were generated and sequenced using an Illumina MiSeq platform at the University of Minnesota Genomic Center, Minneapolis, MN, (USA). Amplicons were sequenced in 2 × 250 paired-end mode. The 16S rRNA sequencing data from the Illumina runs were quality controlled, trimmed, and demultiplexed as implemented in Quantitative Insights Into Microbial Ecology (QIIME 1. 8. 1)  [19]  and the Illumina demultiplexing and processing protocol  [20]  with current quality-filtering recommendations  [21]. After quality control and demultiplexing, we picked closed reference OTUs at a 97% similarity cut-off against Greengenes database version 13_8  [22].
Application 1: conformity tests in healthy subjects To assess the ability of our test to identify healthy individuals given a widely varying reference population, we used two large-scale microbiome data sets to populate the multidimensional landscape with healthy microbiomes. We then used hold-out testing to evaluate the type I error rates of the test with repeated subsampling of these reference populations into separate "reference" and "test" groups. First, we analyzed 16S rRNA gene-based data (variable regions V3-V5) from the Human Microbiome Project (HMP), including 239 healthy subjects  [10]. The data are available at https://www. hmpdacc. org/. In this dataset, we used a subset of the gut samples, excluding samples from obese patients, leaving 200 samples from 200 patients. Full metadata and annotation protocols are available on the HMP DACC website (https://www.
hmpdacc. org/HMMCP/). We used the unweighted UniFrac distance matrix of the 200 fecal samples as the ecological distance matrix. Although the CLOUD test is designed for comparing a test subject to an independent reference group, we desired to assess the outlier status of subsets of the reference population with respect to the rest of the reference population. To achieve this result, we subsampled 50 subjects at random as test cases and then subsampled the other 150 subjects down to 100 training cases and repeated the procedure 30 times. In the 30 repeated procedures, using these randomly selected training sets, we applied the CLOUD conformity test with several values of k (number of nearest neighbors), from k = 1 to k = (all test cohort -1) and did not identify any subjects as outliers, except for extreme values of k in several random datasets, demonstrating the robustness of the CLOUD test to neighborhood sizes and the low false-positive rate, as reported in Fig.
4a. We also evaluated the CLOUD test on a previously published cohort of individuals from the Amazonas of Venezuela, rural Malawi and US metropolitan areas  [28]. We only included the stool samples from the subjects older than 15 years old (n = 219). We used the unweighted UniFrac distance matrix of the 219 fecal samples. We subsampled 50 subjects at random as test cases and then subsampled the other 169 subjects down to 100 training cases and repeated the procedure 30 times. In the 30 repeated procedures, using random selected training datasets, we applied our dysbiosis test to several values of k, as described above, and found no outliers in any train/test subsets, as reported in Fig. 4b, c. This demonstrates the robustness of the CLOUD test to different training sets from a given reference population.
Conformity test in two other studies of FMT in recurrent CDI We applied the CLOUD test to a published dataset describing recurrent CDI that explored the fecal microbiota of FMT stool donors and recipients  [32]. This dataset includes 10 samples from donors, 14 pre-FMT samples from recipients, and 16 post-FMT samples. Specifically, 5 post-FMT samples tested positive for concomitant Clostridium difficile and 11 post-FMT samples tested negative. We used the cohort of donors to define the nearest independent healthy neighbors. Using Bray-Curtis distances, we tested all the samples collected from FMT recipients. We showed that with k corresponding to 5 to 40% of the number of healthy donor samples, the post-FMT samples which tested positive for concomitant Clostridium difficile were all considered outliers by CLOUD (outlier percentile < 0.
001). Additionally, the pre-FMT samples were considered outliers (outlier percentile < 0. 001) whereas the post-FMT samples testing negative for concomitant Clostridium difficile (outlier percentile = 0. 4 to 0. 75) were correctly classified as non-outliers. We also applied the CLOUD test to another published fecal dataset that described the relationship between predictive signals from the gut microbiome and the development of recurrent CDI  [33]. This dataset included 10 samples from donors, 11 recipient samples collected from patients who presented a recurrence of CDI, and 21 recipient samples from patients who did not a recurrence as they were considered non-dysbiotic and cured. We used the cohort of donors from the same dataset to define the nearest independent healthy neighbors. Using the Bray-Curtis distances matrix of the fecal samples, we tested all the samples collected in the FMT recipients.
As with the previous dataset, we showed that with a k corresponding to 5 to 40% of the number of healthy donor samples, the samples from the patients who presented a recurrence were all considered outliers by CLOUD (outlier percentile < 0. 001), as were all pre-FMT recipient samples (outlier percentile < 0. 001). In contrast, all samples from the patients who did not experience a recurrence were conformant (not considered outliers, outlier percentile = 0. 6 to 0. 65).
Stability test in the FMT data set To assess the stability of the intestinal microbiome in patients cured of CDI by FMT, we obtained the CLOUD stability metric described above. We observed a high stability of the fecal microbiome in healthy controls over a daily time-course as well as in patients who successfully responded to FMT in the days following the FMT procedure. In contrast, the microbiome of the patient who did not respond to FMT showed dysbiosis and instability across the different sample time collections after the FMT procedure (Additional file 2). Results in Fig. 5c  show high stability between consecutive daily samples of the fecal microbiome in healthy controls and in patients who responded successfully to FMT, whereas the patient who relapsed showed substantially higher instability on average between each two consecutive samples, although there were an insufficient number of independent reference subjects with multiple daily time points to obtain a reliable outlier percentile.
This robust non-parametric test is based on local ecological distances and can be used to identify subjects with microbiomes that are significantly abnormal in terms of conformity or stability over time. Our test further allows unsupervised detection of microbiome outliers. We have validated the dysbiosis test in three clinical data sets to show concordance of the dysbiosis test with clinical outcomes of Clostridium difficile infection recurrence  [17, 32, 33]. We have also demonstrated that local stability analysis within a subject's microbiome cloud over time provides strong separation of patients who underwent successful and unsuccessful FMT procedures, with a failed procedure (defined as presence of later CDI relapse) resulting in a significantly less stable patient microbiome. Such unsupervised identification of conformity and stability outliers in microbiome analyses is especially challenging for three reasons, which we have addressed in our method as follows.
Conclusions As the medical microbiome research field moves closer to translation from epidemiological surveys to clinical applications, clinicians need a robust measure that can determine whether a microbiome is statistically similar to microbiomes in a reference population. This measure must account for the high dimensionality, high inter-individual variability, and high longitudinal variability of the microbiome. The CLOUD test is designed to account for these constraints and is useful for comparing a patient's microbiome to a reference population to determine whether it is significantly abnormal or dysbiotic in terms of conformity or stability  [34]. The test is reliant on having a relevant reference cohort of healthy individuals but is also entirely invariant to the addition or removal of highly discordant samples from the database due to its reliance on local distances.
Availability of data and materials The data can be found under accession number ERP021216 at the European Bioinformatics Institute for our CDI dataset  [9] , https://www. hmpdacc. org/ for the HMP dataset, qiita. microbio. me/ for the Global gut dataset, the NCBI Sequence Read Archive under the associated BioProject ID PRJNA238042 for the first CDI dataset  [28]  and the NCBI Sequence Read Archive under BioProject PRJNA307992 (SRP068473) for the second CDI dataset  [29].
B Figure  5  A label n-ary hierarchy (A) can be represented with a binary tree shaping the Wavelet Tree (B). The label family L 1 has here three descendants, L 1. 1 , L 1. 2 and L 1. 3. Full-size  DOI: 10. 7717/peerj-cs. 148/fig- 5  class of SDSL-Lite, and finally build W A (or W D ) using a shape we added in SDSL-Lite, which depends of the label hierarchy. We evaluated the build time, the index size, as well as the run time of three of the queries detailed in "Queries"-findP(P) behaving similarly in all indexes and countPL(P, L x ) being very similar to findPL(P, L x ). The three indexes were tested on various datasets of labeled texts, each with 100 M characters (Table  2 ). Datasets and code are available at http://www. vidjil. org/data/#2018-peerjcs. Simulated files with random sequences and random labels.
All sequences and labels are random (d ∼ 0. 8n). Simulated files, with random sequences but fixed labels. Here a given label is associated to the same pattern, possibly with some variation, and we alter the proportion of labeled letters (5-100%), the variation in the label's pattern (0-50%, more variations giving random labels), the number of unique labels (10-1,000), the length of the labels (5-100 letters). The dataset has 546 files, two of those files are shown in Table  2. Genomic sequences with immunologic labels. A person's immune system can be described with a set of labeled DNA sequences with V(D)J recombinations. The dataset, detailed below, uses 838 K labels from 355 unique labels, with d ∼ 0. 26n.
A dataset of DNA sequences with immunologic labels The adaptive immunity is the mechanism thanks to which our body defends itself against infections. When B and T-cells, or lymphocytes, are under maturation, their DNA undergo a recombination which allows several billions possibilities from a register of a thousand genes  (Tonegawa, 1983). For example, the V(D)J recombination V4 Ã 02 4/ACGT/0 J1 Ã 12 means that the DNA sequence is composed from the V4 Ã 02 gene without the four last letters, then the ACGT sequence, then the J1 Ã 12 gene. A person's immune system can thus be described with a set of labeled DNA sequences encoding V(D)J recombinations (Fig. 6 ). These sequences can be sampled by nextgeneration sequencing with bioinformatics analysis  (Bystry et al. , 2016 , Duez et al. , 2016).
The tested DNA sequences come from patients 09, 12, 14 and 63 from a public dataset on a study on acute lymphoblastic leukemia  (Salson et al. , 2017). They have 100 M letters Note: Note that we have jZj y occ p. The label(i) and findL(L x ) queries are faster in the HT-index and the TL-index as the HT-index needs a sampling time. However, the countPL(P, L x ) and findPL(P, L x ) are faster in the HT-index. and 838 K labels from 355 unique labels, making a 117 MB file. Each DNA sequence has between 100 and 350 letters and two or three labels, each label having a size between 5 and 200 letters (Fig. 7 ). For a given label, the labeled subsequences may vary up to 15% due to amplification and sequencing errors.
, 2012; Johnson et al. , 2016). At the same time, model evaluation based on available data is necessary for which the primary source of data is ground-based observations of aboveground biomass (AGB) obtained from forest census data  (Lopez-Gonzalez et al. , 2011 , 2014; Brienen et al. , 2014 Brienen et al. , , 2015;; Mitchard et al. , 2014; Johnson et al. , 2016). When conducting model-data comparisons at the plot scale, the spatial resolution of both come into focus. The size of forest plots is typically of the order of 1 ha or less, whereas average DGVM grid-cell resolution is determined by the available gridded climate dataset, which is usually about several thousand square kilometres (> 100 000 ha). Plot observations are affected by observational errors, uneven spatial distribution  (Saatchi et al.
Methods Landscape variability depends on the extent and heterogeneity of the study area  (Turner et al. , 2001). Point measurements within a pixel of larger spatial scale, for example, may reveal small-scale spatial variabilities within the pixel. We derive a "within-pixel variability" that so far has not been accounted for in earlier approaches. We present three steps to calculate three metrics that provide a measure on the best achievable correlation between point and pixel values (see Fig. 1 ). 2. 1 A generic method for point-to-pixel comparisons 2. 1. 1 Calculate the "global variability" across the region of interest Assume that we have a dataset X with a number N of point observations x i at location i (e. g. plot observations from inventory data). In the first step, we calculate the mean x and variance σ 2 x across all plots in a region (e.
Metrics for the comparison of two datasets with different spatial resolutions In a third step, we compare the point data x i with simulated data y i at the pixel scale. Similar to the above procedure, we calculate the mean y and variance σ 2 y for the simulated pixels that contain point observations (hereby we assign each point observation the pixel value in which the point is located). We then compare the simulation results by applying three metrics: 1. Mean bias (MB): the ratio of means y/x across the whole region as a measure of the mean bias in the patterns which is not affected by small-scale variability; 2. Pattern amplitude (PA): the ratio of standard deviations σ y /σ x,corr using the corrected global variability (i. e. removed within-pixel variability) and serves as a measure of differences in pattern amplitude or in the variability in the simulated and observed data; data (see the Supplement).
Accordingly, we can calculate the maximum achievable correlation coefficient r max , which is derived from correlating the observational dataset at the point scale to the same observational dataset at the pixel scale (see Fig. 1a , b and the Supplement). The limited number of point observations and their nonrandom spatial distribution in the Amazon region affects the accuracy of the comparison. We therefore estimate confidence intervals for the comparison metrics MB, PA and SP, respectively, by applying a bootstrapping technique (10 000 repetitions). Because the estimation of σ 2 ε is based on the analysis of the spatial correlation structure of the data, a block-bootstrapping is performed  (Politis and Romano, 1994). For each permutation, the domain of observations is randomly divided into 100 tiles (random orientation and offset, ca.