dataesr/scientific-paragraphs-categorization huggingface dataset:
For the empirical data, variants with a minor allele frequency greater than 1% and at sites with greater than 1000X coverage were retained. These variants were then annotated, removing variants in primer  binding sites and the two Myotinae repeats in the control region. An illustration of the bioinformatic workflow for a single sample from quality processing through to annotated variants is depicted in Supp. Fig. 6. GNU Parallel (v20170122)  79  was used to parallelise the mapping and processing of SAM and BAM files, markedly decreasing computational time. The number of samples to be run in parallel can be defined by the user. Variant Simulation. In order to gauge the sensitivity, accuracy and false positive rate of our bioinformatic pipeline, known variants and Illumina sequence data were generated in-silico using the GemSIM package (v 1.
5)  46. Sequence reads in FASTQ format from a reference individual (MMY104, used to generate the reference mitogenome for this species) were mapped to the previously published M. myotis mitogenome 69 using the BWA-MEM algorithm, producing a SAM file which was used to produce a general error model using the GemErr. py module. The reference mitogenome was used to generate haplotype information for variant simulation with the GemHap. py module. In total 500 known variants were simulated, in sets of 100 at frequencies of 1%, 4%, 7%, 10% and 15%. 7 sites were found in two datasets and were excluded from the analysis, for a total of 486 heteroplasmic sites out of 17,211. The reference mitogenome, error profile and haplotype information were used to generate 250 bp paired end Illumina reads. Datasets were simulated at 50X, 100X, 250X, 500X, 1000X, 2500X, 5000X, 10,000X and 25,000X coverage.
Predicted variant positions were checked against the known set. Power (proportion of true sites called), false positive rate (proportion of erroneous sites called) and accuracy (proportion of true sites with predicted frequency within ±0. 01 of the true frequency) were calculated on each dataset for each caller, FreeBayes, LoFreq and VarScan2 (see Heteroplasmy Detection Pipeline). Finally a score between 0 and 1 was assigned to each caller defined as (Power*Accuracy)*(1-False Positive Rate). All variants called from empirical samples used the best performing caller. Characterising heteroplasmy in M. myotis. All filtered and annotated heteroplasmies were collected and manually inspected. Heteroplasmies were binned into several overlapping classes: protein-coding, synonymous, nonsynonymous, non-protein-coding, tRNA, rRNA, non-coding, and by each mitogenome feature.
Nonsynonymous mutations were further binned for possible effect as "Deleterious" or "Neutral" based on the PROVEAN algorithm as implemented on the dedicated web server. Default cut-offs were used such that a mutation was binned as "Deleterious" if the computed PROVEAN Score was greater than 2. 5 or less than -2. 5  80, 81. Frequency distributions for each bin were compared using a two tailed non-parametric Kolmogorov-Smirnov Test or Mann-Whitney U-test. The number of unique heteroplasmic sites in a bin was tested for enrichment against the remainder of mitogenome sites analysed (length of mitogenome -primer regions -number of sites covered by bin), using Pearson's Chi-Square test. Association between heteroplasmy and age. A primary dataset of n = 167 was constructed from unique individuals of known age and an oldest cohort, divided into cohorts from 0 to 6+, Tables  1  and 2 , taking the most recent sample of an individual had the bat been sampled multiple times.
The number of heteroplasmies for samples was compared between sequencing batches using Kruskal-Wallis test. The primary dataset was used to test for a significant association between number of heteroplasmic sites and age by fitting a generalised liner mixed model (GLMM) in R (v3. 3. 3) using the "lme4" package  82  with age as the only fixed effect and incorporating sequencing batch as a random effect, and number of heteroplasmic sites as the response variable modelled under a negative binomial distribution. A negative binomial distribution was used due the presence of overdispersion in the data making a Poisson distribution unsuitable. Subsequently a generalised linear model (GLM) was fit using the "MASS" package  83  with age as the sole explanatory variable. The "boot" package  84, 85  in R (v3.
3. 3) was used to diagnose samples which may be unduly influential points using the Cook Statistic 86. Influential points were removed and the model was fitted again. 1000 bootstrap replicates were generated for each fitted model by randomly sampling with replacement from the datasets using the "boot" package in R. The standard error and bias for the Intercept and Age estimates were recorded. To investigate any bias that may be introduced by manually selecting a sample from recaptured individuals, we performed 1000 samplings in which the sample from recaptured individuals of usable age was chosen at random, to produce 1000 datasets (of n = 165) and the model fit again. Longitudinal Analysis. M. myotis show natal philopatry, returning to the same roost or broad area to give birth each year.
Acknowledgements The authors would like to thank the  European Research Council  for funding. For assistance in the field we would like to thank: members of  Bretagne Vivante ,  Dr. Serena Dool , students from  UCD  and all owners/local authorities for allowing access to the sites. We would like to thank the members of the  Teeling lab  and  Dr. John A. Finarelli  for helpful discussion of the analyses. This project was funded by a  European Research Council   Research Grant   ERC-2012-StG311000  awarded to E. C. T. The French field study is supported by a  Contrat Nature  grant awarded to  Bretagne Vivante.
Fatigue criteria As seen in Figure  10 , using equivalent stress (i. e. load divided by specimen section) does not yield satisfactory results. Therefore, a local strain energy density approach is used. The elastic strain energy density ∆  is defined by: ∆𝑊𝑊 𝑒𝑒 = 1 2 𝝈𝝈: 𝜺𝜺 𝒆𝒆 Where  is the stress tensor and   the elastic strain tensor. The fatigue life  can then be estimated by: 𝑁𝑁 = 𝐴𝐴 × ∆𝑾𝑾 𝑒𝑒 𝑏𝑏 Where  and  are material parameters. Even though the parameter  can be modified to take into account the stress ratio, it is not detailed in this study, since all tests are done using identical stress ratios. Using HBM Prenscia's nCode DesignLife® software, the elastic strain energy quantity was calculated for the 0, 45 and 90° specimens. Two approaches were then used to calculate the maximum value: 1.
Background: Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system. Methods: A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence.
The quality of data reporting through a country's routine health information system, however, can vary, potentially impacting the reliability of the nationally aggregated health statistics as malariometric indicators, especially in a resource-limited setting such as Madagascar, which has a fragile health and communications infrastructure  [12]. At   [4]  every step of the data reporting chain, cases are lost from the system  [13] , for example from patients not interacting with public health facilities during malaria episodes  [14] , rapid diagnostic test stock-outs resulting in unconfirmed infections, or accessibility difficulties for health workers submitting monthly activity reports to the centralised database etc. [4]. Reported numbers can, therefore, be strongly influenced by factors external to the true underlying epidemiology.
MIS datasets provide a complementary perspective on recent trends in malaria endemicity, free from the intrinsic limitations of reported case data. Their strength lies in their standardised methodology applied repeatedly over multiple years across nationally representative sites. This present study uses the MIS data to map the prevalence of infection by Plasmodium falciparum in those under 5 years old (6 to 59 months in age: PfPR 6-59mo ) to obtain a deeper insight into epidemiological trends than that available from the published MIS overview analyses, which reported aggregated national prevalence rates of 6. 2% for 2011 [9], 9. 1% for 2013  [10]  and 7. 0% for 2016  [11]. This study aims to generate spatially continuous prevalence maps that account for disparities in sample collection dates between the different MIS years.
Study data Parasite prevalence data for this study came from MIS activities conducted across Madagascar in 2011, 2013 and 2016. Alongside many other indicators, an MIS includes measurements of the prevalence of P. falciparum infection among children 6 to 59 months old (PfPR 6-59mo ), based on a standardised protocol with a stratified sampling strategy across the island that is designed to capture a nationally representative assessment of prevalence. Data are freely available from the Demographic and Health Surveys (DHS) online repository. Across the three MIS years, a total of 898 spatially unique geo-positioned datapoints were available, which reported prevalence of infection among 19,986 children. The initial survey in 2011 included 266 sites (29. 6% overall; 6827 individuals), screened from March to May.
Three products of temperature data were derived from land surface temperature (LST), including (i) daytime LST, (ii) night-time LST and (iii) the diurnal difference of daytime to night-time LST  [30]. MODIS reflectance data were used for measurements of vegetation and moisture: enhanced vegetation index (EVI)  [31] , tasselled cap brightness (TCB) and tasselled cap wetness (TCW)  [32]. The values of the covariate data at the MIS screening locations were extracted for all points. Temporally dynamic covariates were matched to the same month as the survey, as well as 1, 2 and 3 months prior to the survey (lags).
The model framework The Bayesian spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of P. falciparum malaria infection among children aged 6 to 59 months in age (PfPR 6-59mo ) across Madagascar for 2011, 2013 and 2016. Validation of the final model gave Pearson correlation coefficients of 0. 96, 0. 94 and 0. 83 for each prediction year, respectively, between month-specific predicted and observed prevalence (Additional file 2: Figure  S2 ), justifying a good fit of the Bayesian spatio-temporal model to the observed prevalence data. To assess the predictive power of the specified model, prevalence data were split into two sets: a training set (D t ) and a validation set (D v )  [37]. After being trained on D t , the model accuracy was determined by its ability to predict D v as well as the entire prevalence dataset.
A percentage of D v was chosen to be a random sample of 10%, 20%, 30% and 40% of the overall dataset. Each validation was repeated 100 times and the model accuracy was averaged across the 100 fitted models. The box plots of cross-validated correlations appeared satisfactory (Additional file 3: Figure  S3a ). The cross-validated R 2 had a mean of 0. 78 and a range of (0. 70, 0. 83) for 10% D v ; a mean of 0. 72 and a range of (0. 62, 0. 79) for 20% D v ; a mean of 0. 65 and a range of (0. 55, 0. 74) for 30% D v ; and a mean of 0. 58 and a range of (0. 45, 0. 68) for 40% D v (Additional file 3: Figure  S3b ). The model was deemed accurate in its ability to predict a wide range of validation sets (Additional file 4: Figure  S4 ).
For example, EVI with a 3month lag indicates that the malaria prevalence prediction for January 2011 was informed by the EVI level in October 2010. LST_delta, or the diurnal variation in land surface temperature, was influential across the whole time window up to 3 months prior to the prediction months. Predictions for January 2011, for example, were influenced by the difference in daytime and nighttime temperatures (LST_delta) in October, November and December 2010, as well as January 2011. Seasonal variation in the different dynamic predictors differed in amplitude, with the most extreme being the CHIRPS dataset (a precipitation indicator), which dropped to very low between May and September. In contrast, the tasselled cap wetness (a remotely sensed transformed index of land surface water availability) remained relatively constant over time, with no significant monthly variation when summarised to the national level.
Discussion Three MIS studies have taken place in Madagascar since 2011, providing a valuable source of information about the status of malaria from a standardised data collection protocol applied across a nationally representative set of locations. A broad range of indicator metrics are included in the MIS reports. This present study looks at the parasitology data in particular to assess how the prevalence of malaria infection in children 6 to 59 months old has changed in recent years. Given the limitations of the routine health data reporting chain discussed previously  [4] , this study provides a complementary perspective into the recent status of malaria endemicity in Madagascar, identifying important increasing trends since 2011 despite a relatively small fall in prevalence between 2013 and 2016.
This is reflected in the raw MIS results, which suggest a 6. 2% national PfPR 6-59mo in 2011 and 7% in 2016, a 13% increase over that period. In contrast, the spatiotemporal model presented here identifies a 127% increase, resulting in an estimated national mean 9. 3% annual PfPR 6-59mo in 2016. The MIS specifically excludes three highland districts considered to be malaria-free (the cities of Antananarivo-Renivohitra, Antsirabe I and Fianarantsoa I) as well as communes above 1500 m in altitude. Despite not being represented in the mapping input dataset, model predictions are derived for these areas, which then inform the ecozone-and national-level summaries. Without prior exclusion of these zones, the model-based approach risked over-inflating the estimates of endemicity. Environmental covariates, however, appear to have discerned the unsuitability of the highland urban habitat, and the malaria-free districts are predicted to have <0.
Comparison with previous prevalence maps While geostatistical methods have been previously applied to infection prevalence datasets to predict spatially continuous prevalence maps of Madagascar, this has been within the context of continental or global-level mapping  [37, 39]. The country-specific modelling approach applied here allows more freedom to the environmental covariates to adapt to Madagascar's specific ecological context, with the model selecting those covariates most pertinent to the island's environmental diversity. The continentlevel spatio-temporal prediction cube developed by  Bhatt et al. ([39] , reproduced for Madagascar  [4] ), indicates a much coarser granularity than the present predictions, with little variation between years. Nevertheless, the continental maps do allow malaria endemicity in Madagascar to be viewed in its broader context, showing the relatively low prevalence of infection in Madagascar relative to many countries in sub-Saharan Africa (incidence rate ranked 13th lowest out of 43 countries in 2015  [39] ).
Comparison with health metrics information system data In this study, we map the parasite reservoir, as detectable by microscopy. In parallel, clinical case numbers are collated by the routine health metrics information system  [4, 40, 41]. While the two metrics report different characteristics of malaria, spatio-temporal trends from both are similar, with a comparable geographic distribution of the burden of disease and an important increase in burden particularly along the west coast. A major increase in clinical cases was reported between 2014 and 2015, which subsequently reduced in 2016 (the third MIS year) following a mass distribution of bed nets treated with insecticide at the end of 2015  [41]. The longitudinal nature of the data from the health metrics information system allows extreme events to be identified, such as epidemics, which may drastically affect the annual case totals, as in 2015, but which may not be distinguishable as exceptional, or even captured by cross-sectional surveys that assess prevalence at isolated time points.
Limitations to the approach In this study, we have used data on the prevalence of infection to assess the current spatio-temporal trends of malaria infection in Madagascar. This metric is independent of clinical symptoms, and instead quantifies the extent of the parasite reservoir across the population. The value of such a metric lies in its simplicity and standardised collection methods, and, in the context of the MIS, repeated national representation. A recent review by Cohen and colleagues  [43] , however, has argued for a multi-component mapping process that will adequately identify the underlying drivers of transmission, to enable NMCP to target control measures optimally. Elimination requires both the reduction of the parasite reservoir and the prevention of transmission. Prevalence data alone cannot fully characterise the local epidemiology without a parallel understanding of a site's historical context, the significance of imported cases, the impact of recent control efforts, entomological and host behavioural/genetic factors, and so on  [43].
The highland areas into which malaria is mainly imported are not covered by routine ITN distribution. The limited temporal window considered in this present study does not allow for the protective effect of high ITN coverage to be learnt by the model, and instead the coarse learnt association is that ITN coverage increases as prevalence increases. Treatment seeking was excluded for reasons of sample sizes. MIS data on treatment seeking from individual cluster locations are limited to mothers with infants who suffered from fever in the 2 weeks preceding the MIS interviews. Sample sizes at the cluster level are, therefore, very small, producing spurious results when analysed at the high resolution of the present analysis. Despite these barriers to including intervention covariates in the model, the suite of environmental and socio-demographic variables that were used allowed robust predictions of malaria prevalence, so this was not considered a major limitation to the mapping model presented here.
Madagascar is noted for its mosaic landscape of ecological habitats, with land cover varying across short distances  [44]. The critical importance of the environmental covariates in the modelling process is evident from the methods described here, with a strong predictive role associated with vegetation cover that explains differences in malaria prevalence between different areas (Table  2 ). The covariates associated with each MIS site describe the local conditions associated with the observed malaria prevalence at the time and location of sampling. However, MIS datasets are geopositioned with a deliberate degree of spatial uncertainty (displacement) to promote anonymity of up to 2 km in urban areas, 5 km in rural areas and 10 km for 1% of rural points  [45]. This spatial displacement, therefore, introduces uncertainty into the associations between reported PfPR and their attributed covariate values, which could impact the model's predictions.
For this study, we assumed that while the island is ecologically heterogeneous, the impact of this spatial uncertainty will be acceptably low (with ecological conditions similar for most points even at a distance of 5 km or 10 km), and similar enough to allow a prevalence signal to be identified. The model validation statistics corroborate this assumption. A further limitation of the MIS datasets that informed the current mapping analysis stems from their sampling design and sample sizes. The broad range of indicators included in the MIS activities present conflicting demands on sample sizes, which are further constrained by financial and logistical considerations. Sample sizes cannot, therefore, be optimised for all indicators, but instead are focussed on a limited number of these. A recent retrospective model-based analysis of the 2011 and 2013 Madagascar MIS datasets estimated that sample sizes were under-powered by 17% and 36%, respectively, to reach effective sizes for infection prevalence rates  [46].
This was based on rapid diagnostic test results and not microscopy (as considered in this present study), meaning that it may be a slight overestimate. The approach followed here, namely to consider samples from the three MIS datasets within a common Bayesian hierarchical modelling framework and to draw on a broad range of associated covariate surfaces, provides a solution to sample size limitations. Furthermore, at very low transmission levels, such as in the central highlands and parts of the central fringe regions of Madagascar, microscopy-based cross-sectional surveys of parasite prevalence risk are underpowered to detect rare and low parasitaemia infections adequately  [47]. In these areas, higher sensitivity diagnostics (such as nucleic-acid amplification-based approaches) and alternative indicators (such as serological markers) are required to monitor malaria epidemiological trends more effectively  [47] [48] [49] [50].
Mucopolysaccharidosis VI (MPS VI) is a progressive lysosomal storage disorder with multiorgan and multisystemic pathology. Currently, galsulfase enzyme replacement therapy (ERT) is the only approved treatment for MPS VI. A crosssectional survey study of 121 patients with MPS VI conducted in 2001 to 2002 and a 10-year follow-up study of the same patients (resurvey study; ClinicalTrials. gov NCT01387854) found that those receiving galsulfase at any time showed physical improvements and a lower mortality rate (16. 5%) versus treatment-naive patients (50%). After *15 years, galsulfasetreated patients (n ¼ 104) continue to have a survival advantage over treatment-naive patients (n ¼ 14), as demonstrated by a 24% versus 57% mortality rate. This survival advantage is further supported by data from the commercial use of galsulfase (2005-2016), which show a 5-year mortality rate for galsulfase-treated patients of 12.
Patient Population The current study is a 15-year continuation of the resurvey study (ASB-00-03; ClinicalTrials. gov NCT01387854), which was a multicenter, multinational study to obtain repeat 10-year survival data on patients who took part in the survey study (ASB-00-02) in 2001 to 2002. 9 Only patients who had previously participated in the survey study ASB-00-02 and had met the criteria of an MPS VI diagnosis were eligible. Of the 121 patients who participated in the survey study, survival status was available for 117 participants as of October 24, 2013, for a total survival follow-up of up to 12 years. The remaining 4 patients were considered lost to follow-up, since no information was available after 3 attempts to reach them via study sites. By 2016, survival data were available for 111 of 121 of these patients, with 10 patients lost to follow-up between 2013 and 2016, 3 of whom were also lost to follow-up at the time of the resurvey study.