dataesr/scientific-paragraphs-categorization huggingface dataset:
The first benefit of a multiple IBD-classes model is to better fit the data and to obtain more accurate estimators of inbreeding both locally and globally. Indeed, our simulations under the inference model with several IBD classes clearly showed that the 1G (and 2G) model underestimated F g as some IBD segments were missed while the power to detect IBD segments was decreased. In addition, in the presence of ancient inbreeding, 1G model will tend to interpret recent (and thus longer) IBD segments as consecutive smaller segments of older origins because the estimated age of the single IBD class would tend to be older. Of course, in the absence of genotyping errors, the entire segment would then be correctly declared IBD and would appear as a long tract. However, at higher genotyping error rates (as with NGS data) such segments would be cut into smaller pieces.
This would not happen when analyzing data with a model with multiple classes since recent IBD segments would then be associated to a class with a smaller age and the penalty in the HMM to leave the IBD-class and start a new IBD segment would be too large. Under a single-IBD class model, the age of the longest ROH further tends to be overestimated which might introduce substantial biases in applications that rely on the age of the IBD tracts to estimate some parameters of interest (e. g. , the mutation rate). With two states HMM  (Leutenegger et al. ,2003) , LD pruning is sometimes used to get rid of background LD and to force the model to concentrate on recent inbreeding (and hence avoiding the aforementioned problem). With multiple IBD-classes model (> 2G models), ancient inbreeding associated with background population LD is automatically assigned to the eldest IBD classes making LD pruning unnecessary for that purpose.
Acknowledgements We thank the  Human Genome Diversity Project , the  LUPA consortium  and the  International Sheep Genomics consortium  for data sharing. John McEwan helped us to obtain the sheep data set and shared his knowledge on history of different sheep populations. The  ZooROH  project and this work were supported by the  Fonds de la Recherche Scientifique -FNRS (F. R. S. -FNRS)  under Grant  J. 0134. 16. Tom Druet is Research Associate from the  F. R. S. -FNRS. We used the supercomputing facilities of the " Consortium d'Equipements en Calcul Intensif en Fédération Wallonie-Bruxelles " (CECI), funded by the F. R. S. -F. N. R. S.
Data Accessibility All data sets used in the present study are publicly available. the Human Genome Diversity Panel (HGDP) data was downloaded from ftp://ftp. cephb. fr/hgdp_supp10/Harvard_HGDP-CEPH, the dog LUPA project from http://dogs. genouest. org/SWEEP. dir/Supplemental. html and the Sheep Diversity panel from the.
The minimum inhibitory concentration (MIC) determination of free CFT and CFT-loaded NP, performed using the microtiter plate assay support the data from the agar diffusion test. The MIC value of the CFT-loaded NP in isotonic solution and free CFT were found to be 1. 56 µg/mL for P. aeruginosa, indicating that antibacterial activity was preserved when the drug was loaded into CS/TPP/HA NP. The MIC value of the NP gel formulation was significantly higher (3. 13 µg/mL), probably for the same reason described above. Nevertheless, the prepared nano-formulations preserved the antibacterial efficiency of CFT and can be potentially used to treat ocular diseases. The cell viability of the ARPE-19 and HEK 293T cell lines, as measured by a metabolic and membrane integrity assay, suggested that the tested formulations were not toxic at concentrations below 200 µg/mL (Figure  6 ).
In Vitro Ceftazidime Release Studies The release of CFT from the NP was studied by a dialysis method. One milliliter of NP and eye drop formulations were introduced inside a tube of semi-permeable dialysis membrane (300 kDa) and then dipped in the receptor compartment containing 15 mL of phosphate buffer release medium (10 mM in pH 7. 4). The receptor compartments were closed to prevent dissolution of the release medium and incubated with continuous horizontal shaking at 37 • C at 300 rpm. At regular time intervals, samples were withdrawn and the same volume was immediately replaced with fresh PBS solution maintained at the same temperature. All studies were conducted in triplicate. The amount of CFT released was evaluated by UV-spectrophotometry in a microplate reader at 256 nm. In order to investigate the mechanism of CFT release from the NP, the in vitro release data obtained were analyzed by fitting to five different kinetic models: zero-order (Equation (  3 )), first-order (Equation (  4 )), Hixson-Crowell (Equation (  5 )), Higuchi (Equation (  6 )) and Korsmeyer-Peppas (Equation (  7 )): Q t = Q 0 + K 0 t (3) log Q t = log Q 0 - K 1 t 2.
Statistical Data Analysis The data was expressed as mean and standard deviation (mean ± SD) of experiments (n = 6). Statistical evaluation of data was performed using one-way analysis of variance (ANOVA). Tukey-Kramer multiple comparison test (GraphPad PRISM 5 software, La Jolla, CA, USA) was used to compare the significance of the difference between the groups, and a p < 0. 05 was accepted as significant.
16S rRNA gene sequencing analysis All 16S rRNA gene sequences were processed and analyzed within the QIIME software package v. 1. 9. 1  [27]. Reads were first demultiplexed with the removal of barcodes and primer sequences. Next, sequences were trimmed and quality filtered with the removal of sequences that were: > 550 bp, < 250, had a Phred score of < 30, or had homopolymer runs of > 6 bp. Chimeric sequences were identified and removed using the UCHIME algorithm  [28]  implemented in USEARCH v. 6. 1544  [29]. The chimera-free sequences were then clustered into OTUs (operational taxonomic units) at 97% similarity using the open reference OTU picking protocol in QIIME and the Greengenes 13_5 database  [30]. Sequences that did not match OTUs in the Greengenes database were clustered into OTUs using the de novo approach and USEARCH.
The UCLUST consensus taxonomy assigner  [29]  was used to assign taxonomy to OTUs using Greengenes, with a minimum similarity of 0. 9 and max accepts of 3. Representative sequences for the OTUs were aligned using PyNast  [31]. Singletons, that is those OTUs found only once in the entire dataset, were also removed prior to analysis. To account for uneven sequencing depths among samples, all samples were randomly subsampled to 3,100 sequences. This necessitated the loss of five samples from the analysis: one from the rectum and two each from the distal jejunum and cecum. The diversity within each sample (alpha diversity) was calculated within QIIME using the Chao1 estimator  [32] , observed OTUs, Shannon index  [33] , and PD whole tree  [34]  metrics. Diversity metrics for each intestinal section and shedding status group were analyzed using a two-way ANOVA implemented in PROC MIXED in SAS (version 9.
4; SAS Institute Inc. , Cary, NC, USA) followed by pairwise comparisons using Tukey's HSD (honest significant difference; P < 0. 05). The bacterial community structure (beta diversity) was evaluated using weighted UniFrac distances  [35]  and visualized using PCoA and the program EMPeror  [36]. ANOSIM (analysis of similarities) with 999 permutations was used to compare the weighted UniFrac distances. OTUs that were differentially abundant among the eight different intestinal sections as well as between super-shedders and non-shedders were identified using G-test together with the Benjamini-Hochberg false discovery rate (FDR) correction  [37]  for multiple comparisons (FDR < 0. 05). Linear discriminant analysis effect size (LEfSe) was used to determine which taxa groups differed according to E.
Bovine gastrointestinal tract microbiota Eighty-four 16S rRNA gene libraries, corresponding to the 84 digesta and tissue samples, were sequenced using bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). A total of 755,891 sequences with an average length of 458 bp were clustered into 18,542 OTUs after the removal of singletons and following random subsampling of each sample to 3,100 sequences. There were 21 phyla and 120 genera detected among all intestinal samples. Overall, the ten most abundant OTUs accounted for 27. 9% of sequences (data not shown). Independent of shedding status or intestinal section sampled, Firmicutes and Bacteoidetes comprised greater than 70% of all sequences (Table A in S1 File). There was considerably more variation among genera as only Turcibacter and Prevotella had an average relative abundance of greater than 4% among all samples (Table B in S1 File).
A comparison of only the lower GI tract samples also revealed that super-shedders had significantly greater Chao1 values, OTU richness, and phylogenetic diversity than non-shedders (P < 0. 05; data not shown). The bacterial community structure of samples from super-shedders and non-shedders was compared using weighted UniFrac distances. Samples did not cluster by shedding status when all samples were included in the analysis (Fig 3A; P > 0. 05; Rvalue = 0. 033). When upper GI samples were removed from the analysis, there was a statistically significant difference between shedders and non-shedders (P = 0. 014), but the associated R-value was relatively small (0. 073) indicating that the two groups of samples were not well separated (Fig  3B ). There were also 34 OTUs found in 90% of the lower GI samples from super-shedders (Table F in S1 File) while in the non-shedder lower GI samples, only seven OTUs were shared among 90% of the samples (Table G in S1 File).
As the rectum and rectal tissue are most often believed to be the site of E. coli O157:H7 colonization, super-shedder and non-shedder samples taken from these two sites were compared using weighted UniFrac distances. In addition, due to the fact that spiral colon super-shedder and non-shedder samples differed based on alpha diversity metrics, this location was also isolated and analyzed using the same methods. However, the bacterial community structure at these three locations did not differ significantly based on E. coli O157:H7 shedding status (weighted UniFrac distances; data not shown; P > 0. 05).
Participants' recruitment and clinical protocol description Participants To capitalise on the life course data and to avoid a priori decisions as to who might be at risk of cognitive decline, entry criteria to the sub-study are based only on maximising the life course data available for analysis. A sample of 500 NSHD study members are being selected at random from those who attended a clinic-based assessment age 60-64, had previously intimated they were willing to attend a clinic visit in London and for whom relevant data in childhood and adulthood are available. These relevant data are shown in Table  2. The first 500 study members fulfilling these criteria and agreeing to participate will be included. Excluded are individuals with contraindications to MRI or PET including, but not limited to, claustrophobia, metallic implants such as pacemakers, or research nuclear medicine scans within the last year that would result in an individual exceeding acceptable mandated yearly radiation exposures.
It has therefore been suggested that changes in gait and motor skills in general may reflect and correlate with early cognitive change  [37]. Participants' self-paced gait is assessed over a 20m distance in isolation and while performing a cognitive task (single-letter-cued (phonemic) fluency and dual-letter-cued (phonemic) alternate fluency), and wearing an accelerometer on the lower back (LPMS-B inertial measurement unit (Life performance Research Inc)), with data analysis using a custom program written in LabVIEW2010 (National Instruments, Ireland). Temporal (step time and cadence) as well as spatial (step-, stride-time, walking speed) parameters can be derived. More indepth analysis utilising temporal-spatial parameters and participant metadata can describe parameters indicating motor control  [38].
Non-linear or phase plot analysis can be used to explore subtle gait changes using the whole dataset of a particular participant. Using this approach, it has been demonstrated that changes in gait can be detected in the pre-symptomatic phase of Huntington's disease  [39]. Although parkinsonian features are generally considered to be later clinical manifestations in AD, it has been reported that parkinsonian features may precede the onset of frank dementia  [40, 41]. A standardised neurological examination includes the MDS-Unified Parkinson's Disease Rating Scale (UPDRS) Part III (Motor)  [42] , which quantifies presence of tremor, bradykinesia, rigidity, postural instabilty and gait disorder. Assessments are videotaped for quality control purposes and to enable futher review by a senior neurologist if clinicallysignificant parkinsonian features are identified.
Imaging protocol description Imaging is performed on a Biograph mMR 3 T PET/ MRI scanner (Siemens Healthcare, Erlangen), allowing for simultaneous acquisition of dynamic amyloid PET and MR data whilst minimising scanning time and exposure to radiation (compared with the use of PET-CT). Participants will have one scanning session at each time point. The neuroimaging protocol comprises both structural and functional acquisitions, and is designed to be completed within a 60-min scanning session. Amyloid load is assessed using the 18 F amyloid PET ligand, florbetapir. Amyloid positivity on florbetapir-PET imaging is correlated with post-mortem Aβ burden, neuritic amyloid plaque density, and neuropathological diagnosis of AD  [75]. After intravenous cannulation, 370 MBq florbetapir F18 (Amyvid) is injected.
PET data are acquired continuously during and following injection to allow florbetapir uptake dynamics to be assessed. Final amyloid burden is assessed over a 10-min period, ~50 min after injection, with scope for the previous 10-min period to be used if longer scan periods are not tolerated. PET data, acquired in list-mode, is reconstructed using a 3D ordered-subset expectation-maximisation algorithm with three iterations and 21 subsets, and smoothed with a 4 mm Gaussian kernel. Attenuation maps are computed by default from the ultra-short echo time (UTE) sequences provided by the vendor as well as from the T1-weighted and T2-weighted volumetric scans using a multi-atlas CT synthesis method  [76] , also known as pseudo-CT (pCT). The latter approach significantly improves PET reconstruction accuracy when compared to the UTE-based correction  [77] , as demonstrated in Fig.
Two methods of analyzing the pre-processed data will be used: a seed-based method and independent component analysis (ICA)  [89]. In brief, the remaining residuals are smoothed (Gaussian smoothing with 5 mm FWHM) and mapped into the subsampled group space to create spatial correspondence among individual brains. A seed region is chosen to extract an average time course that is correlated with the time course of every individual voxel. The resulting correlation map per participant is Fisher z-transformed to enable t-test hypothesis testing among participants. For the ICA, time courses of the motionrealigned fMRI scan within a mask of the brain are extracted, centered and variance-normalized, resulting in one voxel-time matrix per participant. All participant matrices are then concatenated in time.
Images failing this quality-control process are removed before running the diffusion analysis. For each participant, if the number of acquisitions that have failed is high enough that it might affect the consistency of the analysis between subjects, then these data are marked as a 'failed acquisition'. Pre-processing of diffusion-weighted images involves first correcting for inter-volume motion registration and eddy currents using FSL's Eddy tool  [96]. This is followed by correction for EPI susceptibility distortion using field maps to the structural T1  [97]  and phaseencode direction constrained non -linear registration to the T1 volume, with modulation based on the Jacobian determinants. The separate diffusionweighted shells (together with their associated b = 0 volumes) are fitted with a diffusion tensor model using NiftyFit  [98].
Arterial spin labelling (ASL) ASL is a non-invasive method for determining regional cerebral blood flow (CBF). In brief, blood flowing through the neck vessels is labelled via spin inversion. After a delay period to allow this labelled blood to perfuse into the brain, images are acquired, and compared with equivalent images acquired without prior spin labelling (unlabelled images). The signal difference between labelled and unlabelled images is proportional to blood flow, thus enabling calculation of a quantitative cerebral blood flow map. ASL data are acquired using a 4-shot segmented 3D GRASE pCASL sequence  [103]  (for more details refer to Table  4 ). The ASL timing parameters were chosen based on the recommendations of the recent ISMRM Perfusion Study Group consensus paper  [104] : labeling duration =1800 ms; post labelling delay =1800 ms.
Ten averages are acquired and saved separately, in order to allow image realignment prior to averaging. Background suppression is used to reduce static tissue signal, and an accompanying set of three saturation recovery data sets (TR = 1 s, 2 s, /2/4 s) are acquired using the same 3D GRASE acquisition scheme for estimation of tissue T1 and M0 (total scan time ~50s). During pre-processing, CBF maps are calculated using the recommended model for ASL images acquired with a single post-labelling delay  [104] , implemented in NiftyFit  [98]  and assuming blood T1 at 3 T of 1650 ms and an overall labelling efficiency of 0. 833 (blood inversion efficiency of 0. 85 and two background suppression pulses with inversion efficiency 0. 99 each). The saturation recovery data are fitted to a monoexponential recovery curve to estimate the underlying tissue magnetisation (M0) and receiver coil sensitivity variation, enabling generation of quantitative CBF maps  [98].
Primary imaging pipelines and analyses All imaging data, derived results, visual quality checks, and radiological reads are stored on a customised web-based server running XNAT 1. 6. 5 (www. xnat. org). As protected health information (PHI) is stored in the proprietary listmode format, but difficult to remove, the decision was made not to enter any PHI on the scanner console of these participants. After data are acquired on the PET/MR scanner they are transferred to a study-specific waypoint. This includes both the DICOM-compliant imaging format used for MRI and PET images that are reconstructed on the scanner and raw listmode data that consists of a customised DICOM file which contains the Interfile header and a corresponding binary data file generated from the full 60 min of PET acquisition.
Twice daily, all recent data are securely synchronised between the study-specific waypoint and the XNAT server, where separate import processes are done for both the DICOM and listmode data. DICOM data are checked for completeness before formally importing the data into the XNAT database. the DICOM data have been imported, listmode data are then added to the PET-MR imaging session in the database and automatic checks are performed to ensure that they contain the full 60-min acquisition, including some small amount of time before the tracer was injected. Next, an in-house workflow management system automatically starts the modality-specific pre-processing steps mentioned in the previous section. Key derived images generated during these pre-processing steps are stored on the XNAT server by attaching them to the original imaging session.
Once pre-processing is complete, visual review for each modality is performed and a customised modality-specific webform of the quality checking is stored on the XNAT server. A consultant neuroradiologist reviews all T1, T2 and FLAIR sequences as outlined in the "Duty of Care" section by downloading the key imaging data from the server and completing a customised radiological read web-form within XNAT. Customised reports are provided to the neuroradiologist which identify the PET-MR imaging sessions where a read needs to be completed. Primary analyses of T1 images include automated segmentation of whole-brain  [105]  and hippocampal regions  [106] , followed by manual checking and editing, semi-automated ventricle segmentation, automated total incracranial volume (TIV) measurement  [107]  and semiautomated cortical thickness calculation  [108].
Acknowledgements We are very grateful to those study members who helped in the design of the study through focus groups, and to the participants both for their contributions to Insight 46 and for their commitments to research over the last seven decades. We are grateful to the radiographers and nuclear medicine physicians ( Professor Ashley Groves ,  Dr. Jamshed Bomanji ,  Dr. Irfan Kayani ) at the  UCL Institute of Nuclear Medicine , and to the staff at the  Leonard Wolfson Experimental Neurology Centre at UCL. We acknowledge the help of the following collaborators for their assistance with specific tests included in the protocol, including  Dr. Patrick Esser ,  Dr. Emily Henderson  and  Professor Helen Dawes  (gait task);  Professor Masud Husain  and  Dr. Yoni Pertzov  ('What was where?' visual short-term memory binding task);  Professor Julie Stout  (visuomotor integration task);  Professor Nilli Lavie  (irrelevant distractor paradigm);  Dr.
The genetic analyses are funded by the  Brain Research Trust  ( UCC14191 ). Florbetapir amyoid tracer is kindly provided by  AVID Radiopharmaceuticals  (a wholly owned subsidiary of  Eli Lilly ) who had no part in the design of the study: we are particularly indebted to the support of the late  Dr. Chris Clarke  of  AVID Radiopharmaceuticals  who championed this study from its outset. The NSHD, DK, MR, NS and AW are funded by the  Medical Research Council  ( MC_UU_12019/1 ,  MC_UU_12019/3 ). Some researchers are supported by the  NIHR Queen Square Dementia BRU (JMS, NCF, SMDH) ,  UCL Hospitals Biomedical Research Centre  (JMS,EDV),  Leonard Wolfson Experimental Neurology Centre (JMS, NCF, MM, DT). TP is supported by a  Wellcome Trust   Clinical Research Fellowship  ( 200109/ Z/15/Z ). DMC is supported by an  Alzheimer's Society   Project Grant  ( AS-PG-205 ).
MM is supported by an  Alzheimer's Society   Project Grant  ( AS-PG-15-025 ). AM is supported by an  MRC grant  ( MR/J01107X/1 ). CS is supported by an  MRC  platform grant ( EP/M020533/1 ). SC is supported by an  Alzheimer's Research UK   Senior Research Fellowship  ( ARUK-SRF2013-8 ). JH receives grant support from an  Anonymous Foundation. SO receives funding from the  EPSRC  ( EP/H046410/ 1 ,  EP/J020990/1 ,  EP/K005278 ), the  MRC  ( MR/J01107X/1 ), the  EU - FP7  project  VPH-DARE@IT  ( FP7-ICT-2011-9-601 , 055 ), and  NIHR BRC UCLH/UCL High Impact Initiative (BW. mn. BRC10269 ). NCF is supported by an  NIHR   Senior Investigator award , and additional funding from the  NIA  and  EPSRC. JMS acknowledges the  EPSRC  ( EP/J020990/1 ) and  European Union 's  Horizon 2020 research and innovation programme  (Grant  666 , 992 ).
Funding Insight 46 is funded by grants from  Alzheimer's Research UK  ( ARUK-PG2014-1946 ,  ARUK-PG2017-1946  PIs Schott, Fox, Richards), the  Medical Research Council Dementias Platform UK  ( CSUB19166 PIs Schott, Fox, Richards ), the  Wolfson Foundation  (PR/ylr/18575 PIs Fox, Schott), the  Medical Research Council  ( MC_UU_12019/ 1 PI Kuh  and  MC_UU_12019/3 PI Richards ), the  Wellcome Trust  (Clinical Research Fellowship 200, 109/Z/15/Z  Parker) and  Brain Research Trust  ( UCC14191 , PI Schott). AVID Radiopharmaceuticals (a wholly owned subsidiary of  Eli Lilly ) provide the PET amyloid tracer (Florbetapir) but had no part in the design of the study.