dataesr/scientific-paragraphs-categorization huggingface dataset:
While other datasets also have decreasing enrichments with increasing altitude beyond some point, this decrease is usually much weaker and occurs at a different altitude. This feature is unlikely to be a retrieval artefact, and is probably inherent in the MIPAS data; it may be caused by photolytic effects, as suggested by theoretical models. The large standard deviations of the retrievals in the Southern Hemisphere, with a magnitude several times larger than the estimated accuracy, indicate variability in the profiles within a latitude band. The enrichment of the asymmetric isotopomer is typically higher (δ ∼ 8 -10 %) than that of the symmetric isotopomer (δ ∼ 2 -3 %). In conclusion, isotope information can be obtained with acceptable precision from MIPAS measurements. Although systematic errors, in particular from spectroscopic uncertainties, are hard to quantify, such systematic effects are expected to remain constant in time, and likely also in space.
Acknowledgements. The authors want to thank  Vanessa Haverd ,  Bill Irion ,  David Johnson , and  Tomohiro Sato  for providing their measurements of heavy ozone enrichment. This work was funded by the  Netherlands Organisation for Scientific Research  as project  ALW-GO/12/05 , Isotope measurements from space on stratospheric ozone (ISOZONE). The authors acknowledge provision of MIPAS level-1b data by the  ESA. Edited by:  J. Worden Reviewed  by: two anonymous referees www. atmos-meas-tech. net/9/6069/2016/Atmos. Meas. Tech. , 9, 6069-6079, 2016.
Mountain areas are particularly sensitive to climate change. Species distribution models predict important extinctions in these areas whose magnitude will depend on a number of different factors. Here we examine the possible impact of climate change on the Rhododendron ferrugineum (alpenrose) niche in Andorra (Pyrenees). This species currently occupies 14. 6 km 2 of this country and relies on the protection afforded by snow cover in winter. We used high-resolution climatic data, potential snow accumulation and a combined forecasting method to obtain the realized niche model of this species. Subsequently, we used data from the high-resolution Scampei project climate change projection for the A2, A1B and B1 scenarios to model its future realized niche model. The modelization performed well when predicting the species's distribution, which improved when we considered the potential snow accumulation, the most important variable influencing its distribution.
955, very close to the value for perfect agreement (χ = 1). Finally, to determine the realized niche model of R. ferrugineum in Andorra we used a dataset with 2000 randomly distributed presence/absence points (262 presence points, 1738 absence points, at least 200 m apart). Due to Andorra's small size, and the wide distribution of R. ferrugineum within the country, 80% of the absence points are located less than 2. 5 km from a R. ferrugineum presence point (1400 points), and almost all absence points less than 5 km from a presence point, which gives a more reliable representation of the species habitat suitability map, as suggested by  [40].
Climatic and environmental data selection In addition to the three available climatic variables (Tmin, Tmax and Pann) and the potential snow accumulation, four topographic variables were also considered in the modelization: elevation, slope, topographic aspect (cosine transformed) and solar radiation (obtained from the Andorra 10-m spatial resolution DEM). Multicolinearity is frequent between environmental variables and can cause adverse effects, especially explication or estimation, which can lead to erroneous estimations of model predictions  [48, 49]. The variables explaining over 60% of colinearity (r pairwise 0. 6) were removed from the analyses to give a more parsimonious model without collinear predictor variables  [50]. Next, the multicolinearity between all the remaining explicative variables was tested with the variance inflation factor (VIF),  [51, 48]  in which the explicative variables with a value over 3 are removed from the analyses  [52].
Spatial autocorrelation Spatial autocorrelation due to the spatial structure of ecosystems, which cause special relationships between the elements that compose them  [53] , remains an important issue in the spatial prediction of species distribution  [54]  and can negatively affect the significance of correlation or regression coefficients between explicative and response variables  [55, 56]. Our sampling methods did not overcome this problem  [57]  and by using Moran's test  [58]  to check for spatial autocorrelation in our dataset we detected significant spatial autocorrelation (Moran's I observed: 0. 2449; Moran's I expected: -0. 0005; P < 0. 001). In addition, we applied Spatial Eigenvector Mapping (SEVM) to add a new variable to the analysis. SEVM is one of the best methods for resolving the problem of spatial autocorrelation in species distribution models  [59].
It is based on the extraction of eigenvectors from a connectivity matrix among spatial patterns, where the eigenvectors capture the spatial arrangement of the data points. Given that, each eigenvector represents a particular spatial patterning, the overall eigenvectors describe well the variation in space of the spatial autocorrelation. Eigenvectors permit the spatial arrangement of the data points to be translated into explanatory variables and to be used as predictors of the response variables. They also enable the spatial autocorrelation to be reduced notably  [60, 59]. We obtained 13 eigenvectors from the SEVM computation, which were incorporated into the modelling process.
Phenotyping pipeline and mutant detection. We developed and implemented a high-throughput phenotyping pipeline to examine the G 3 mice generated in the ageing screen (Table  1  and summarized in Supplementary Fig. 1b ). This incorporated many of the established standard operating procedures that have been utilized in the International Mouse Phenotyping Consortium (IMPC, www. impc. org) phenotyping pipeline  13. The IMPC has implemented IMPReSS (www. mousephenotype. org/impress), a database of standardized phenotyping pipelines that encompasses a large number of disease systems and ensures data reproducibility and integrity. The design of the pipeline considered carefully the timing and order of the phenotyping tests to ensure that we minimized the impact of individual tests on other phenotype outcomes.
We defined a mutant line as one where multiple affected G 3 mice (Z3) in a pedigree display very similar phenotypes. All raw data from the phenotyping platforms was initially captured into local laboratory information management systems according to the data standards defined in the IMPReSS database. Following quality control procedures, a step-wise strategy was devised to detect outliers from temporal data in ENU pedigrees. Briefly, we used a reference range approach that established percentile values for the entire data set that provides high and low critical values, approximately equating to ± 2 s. d. Parameter values outside of these critical values for three or more animals in a pedigree led to a phenodeviancy call. Subsequently, phenodeviant mice were annotated with the appropriate phenotype (MP term) as specified by IMPReSS.
All raw data and automatically annotated phenotype data are available via the publically accessible data portal MouseBook (http://www. mousebook. org/). To assess the false positive rate (FPR) of the reference range method, we calculated the FPR of wild-type C57BL/6NTac animal data from the IMPC project as a test data set. IMPC uses similar phenotype procedures to the ageing project. We then modelled the ageing pedigrees by sampling random IMPC baseline animals to produce a simulated pedigree and employed the reference range method on each procedure. Wild-type animals should not be called as outliers therefore any positive calls from the method are classed as false positive calls. Using simulated pedigrees, we detected between 0. 58 and 1. 22% phenodeviance, suggesting a low average FPR of 0.
71% across the procedures examined (Supplementary Table  1 ). Categorical data, such as X-ray abnormalities, were scored as potential mutants when observed in multiple affected G 3 mice (Z3) in a pedigree. We also, on occasion, considered mice as phenodeviant when outliers were o2 s. d. from the mean but where deviations were sustained over multiple time points or, alternatively, additional phenotyping data confirmed a phenotype was present. Each putative mutant was confirmed by one or all of the following; appropriate statistical analyses of primary phenotyping data, comparison of data from other relevant phenotypic tests, inheritance testing and mapping data. Mutant lines. In total, 105 distinct mutant lines were identified in 72 out of 157 pedigrees completed or currently undergoing analysis (Supplementary Table  2 ).
1c ). Mapping and mutation detection. Individual mutations were mapped utilizing the Illumina Medium Density SNP mapping panel, which is informative at over 900 single-nucleotide polymorphisms (SNPs) for the C3H. Pde6b þ and C57BL/6J strains. The genotypes of affected animals were compared with those of unaffected individuals from the same pedigree, generally resulting in a map location of around B20 Mb. Following mapping, we carried out whole-genome sequencing (WGS) of the G 1 founder male, or, on occasion, an affected G 3. A local database was developed to manage the WGS data from G 1 mice. All WGS data was analysed through a standard next-generation sequencing (NGS) pipeline. The MouseBook portal holds the complete range of ENU mutations identified in G 1 mice (noncoding and coding mutations) and within noncoding regulatory regions (such as lincRNAs, miRNAs and promoter regions) annotated from WGS analysis pipeline.
The remaining SNVs, termed high-confidence mutations, were then compared with the precompiled SNPs found in 17 inbred strains from the Mouse Genome Project  35  and an in-house library of SNVs. The overlapping sites were removed resulting in a final list of unique ENU SNVs for each G 1. These SNVs were then functionally annotated (for example, missense, intronic and so on) using NGS-SNP  36. All high-confidence mutations are available on MouseBook. For individual mutant lines SNVs were confirmed by Sanger sequencing of affected mice. Data import. Phenotype data was initially captured in different formats including an in-house LIM system (Anonymus), Excel spreadsheets, structured files (for example, CSVs) or plain text. For automatic phenotype detection, customised code was generated for each data type, which imported the data into a common data format.
This data was transferred to a single import engine that validated and then added the data to the Ageing database as appropriate. Raw data validation was carried out using the standard operating procedure in IMPRESS and Anonymus. The former was used to validate phenotype parameter features such as unit type and the minimum, maximum value whilst the latter was used to validate mouse features, for example, mouse id, sex, date of birth. Outlier detection and visualization. To automatically detect potential phenodeviants we developed approaches to identify animals with abnormal data values from a genetically mixed data set. Where there is no baseline phenotype data to identify potential outliers inferential statistical tests are not applicable. We therefore first constructed a reference range from the whole Ageing pipeline data set, as commonly used for clinical chemistry data  37.
These pedigree sizes have a mean size of 92 animals and a standard deviation of 46 (from 157 total pedigrees within the ageing data set). Outliers were then identified for each random sample, using the previously calculated reference ranges for each parameter. The number of simulated pedigrees with Z3 outliers (either high or low) was then recorded. This simulation was then repeated 100,000 Â to provide an estimation of the FPR for each parameter (number of outlier calls/total number of samples), and the total number of calls for each phenotype procedure. Auditory-evoked brainstem response. Mice were anaesthetized with ketamine (10%v/v) and xylasine (5% v/v) administered at the rate of 0. 1 ml/10 g body mass. Animals were placed on a heated mat inside a sound attenuated chamber. Electrodes (Grass Telefactor F-E2-12) were placed subdermally over the vertex (active), right mastoid (reference), and left mastoid (ground).
ABR responses were collected, amplified and averaged using TDT System III (Tucker Davies Technology, Alachua, FL, USA) in conjunction with SigCal and SigGen (Biosig) software. Click stimuli consisted of a 0. 1 ms broadband click of alternating polarity. Tone-burst stimuli totalled 7-ms duration, including 1 ms rise/fall time; frequencies used were 8, 16 and 32 kHz. Recordings were performed beginning at 95 dB SPL and decreased in 5 dB increments. ABR thresholds were defined as the lowest dB SPL level at which an ABR trace pattern could be clearly distinguished. Animals were recovered using anaesthetic reversal agent Antipamezole (Antisedan, 0. 1 ml 1% v/v). For the analysis of the trombone model the number of mice tested for each genotype at 2-, 6-, 9-and 12 months of age were: Slc4a10 þ / þ n ¼ 18, 13, 6, 7; Slc4a10 þ /trmb n ¼ 54, 21, 25, 13; and Slc4a10 trmb/trmb n ¼ 44, 10, 26, 17, respectively.
Acknowledgements This work was funded by the  Medical Research Council, UK , (primarily by reference  MC U142684172  and partly by MC  U142661184 ,  MC U142684173 ,  MC U142684175  and  MC_PC_U127561112 ). The work was also partly funded by  Arthritis Research UK Centre for Osteoarthritis Pathogenesis  (A. B. , T. V. ), grant reference  20205 , the  BBSRC  (S. N. P. ), the  EC-TREATRUSH  ( Health-F2-2010-242013 ),  ERC  advanced grant ' Hair bundle'  ( ERC-2011-AdG 294570 ), and a  Wellcome Trust   Strategic Award  ( 098461/Z/12/Z ) to the Sleep and  Circadian Neuroscience Institute (SCNi)  (R. G. F. and S. N. P. ). We thank the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  (funded by  Wellcome Trust , grant reference  090532/Z/09/Z  and  MRC Hub  grant  G0900747 91070 ) for the generation of the sequencing data.
Data availability. All raw data and automatically annotated phenotype data and SNVs are available via the publically accessible data portal MouseBook (http:// www. mousebook. org/), and metadata can be found at http://www. mousephenotype. org/harwellageingscreen. The Whole Genome Sequencing data has been deposited to the NCBI Short Read Archive database (https://www. ncbi. nlm. nih. gov/Traces/sra/ sra. cgi?) under accession number PRJNA322401. Individual SRA biosample IDs are as follows; G1-C57BL/6J-C3H. pde6 þ mice; MPC-81, SAMN05172945; MPC-91,.
Participating studies Principal investigators from 14 studies, including 12 cohort studies, responded to the survey and consented to participation. As a result, the network currently has enrolled 14 studies operating in 21 different countries in Asia, Europe, Australia and North America (Fig. 2 ). Some of these cohort studies are still enrolling; hence the exact number of study participants continues to grow. Apart from the 12 cohort studies, this network, as mentioned above, also includes large registry studies from Uruguay and Australia, where patients are entered in a database by healthcare providers who participate in the National Renal Healthcare Program (NRHP-Uruguay) and the Queensland Health Renal services, respectively. As of September 30, 2015, this network included nearly 32,000 patients from cohort studies and 20,000, from registries.
Kidney measures, baseline covariates, and follow-up intervals All participating studies have either biennial or annual follow-up, which includes serum creatinine and urine albumin-to-creatinine ratios or urine protein-to-creatinine ratios. Despite lack of standardization of measurements of common laboratory parameters, calibration may be achieved through exchange of samples and detailed descriptions of the collection process. All studies have data on potential confounders of associations between kidney function and outcomes. Among these are: age, race/ethnicity, smoking, history of cardiovascular disease, diabetes mellitus, hypertension status, and body mass index. Furthermore, all studies collect data on medications and all, except one, on hospitalization. Although major laboratory parameters, such as serum creatinine and albumin, are being measured annually in most studies, there is some heterogeneity in the data collection with regard to imaging studies (Table  3 ).
Specific features of iNET-CKD as compared with other CKD consortia The creation of this iNET-CKD will provide a unique opportunity for a scientific exchange among CKD investigators around the globe, as well as to enhance training opportunities for young researchers. The diversity represented within the network will facilitate the identification of yet-unknown factors (genetic, health-related behaviors, healthcare delivery) associated with the development and the course of CKD. Such findings may not only have implications for clinical care of patients with CKD but also for health policy makers. The prospective design of the participating studies is associated with a high quality of study data and the potential to combine study samples across the network will enhance the ability to examine multiple health outcomes related to CKD.
Complementary to the CKD-PC, iNET-CKD primarily focuses on CKD cohort studies, involving patients with well-phenotyped CKD using extensive patient-level data,and biosamples. Rather than implementing meta-analyses, we seek opportunities to combine primary data from different groups to implement joint analyses. Moreover, iNET-CKD focuses on diverse CKD-related outcomes well beyond progression of CKD and mortality. The granularity of the collected data is very high, and biomaterials are available in almost all participating cohorts, which present the opportunity for coordinated analysis of biomarkers, and serve as an important resource for comparative studies and validation of findings. Projects in the near future involve observations on international variations in dietary patterns related to dietary constituents such as phosphate and sodium as well as variations in clinical strategies for the management of CKD and their respective effects on variations in CKD outcomes.
■ ACKNOWLEDGMENTS The authors acknowledge financial support from  EPSRC  ( EP/ K039466/1 ,  EP/L0505067/1 , and  EP/P505739/1 ), the  Royal Society  (L. J. B. ) for a  Dorothy Hodgkin Fellowship ,  Region Normandie , and the  European Regional Development Fund (ERDF)  for cofinancing the  AI-Chem  project through the  INTERREG IV A France (Channel) -England cross-border cooperation Programme.
At least 300 patients will be enrolled and followed for at least 10 years. Data will be collected in conjunction with usual care visits and analyzed annually. LOWER includes a cardiovascular imaging substudy; an independent pregnancy exposure registry is also open. RESULTS: Events of special interest include hepatic abnormalities, gastrointestinal events, certain gastrointestinal tumors, major adverse cardiovascular events, and events associated with coagulopathy. Data will be collected on demographics, diagnosis, patient history, lomitapide dosing, concomitant treatment, lipid profile, and other laboratory results.
Epidemiology of homozygous familial hypercholesterolemia Historically, the prevalence of HoFH has been reported as 1 case per million. 2, 4 However, emerging studies suggest that the prevalence of HeFH, and consequently HoFH, may be higher than previously thought. Recent literature suggests an estimated prevalence of HeFH of w1 case in 200  2  and of deleterious LDLR mutations in 0. 45% of a control population and 1. 9% of individuals with earlyonset myocardial infarction or coronary artery disease. 11 xtrapolating from these data, the prevalence of HoFH is estimated to be w6 cases per million. Analysis of a Dutch database of molecularly defined HoFH suggested a prevalence of w1 case per 160,000-300,000. 3 Prevalence data are, however, continuing to evolve, and these calculations could be underestimates or overestimates of the prevalence in the general patient population.
Lomitapide registry: LOWER Establishment of a registry of lomitapide-treated patients is a regulatory requirement from the US FDA, the EMA, Health Canada, and the Taiwan FDA. Aegerion Pharmaceuticals has shown its commitment to meeting this requirement by establishing LOWER (ClinicalTrials. gov Identifier: NCT02135705). Specifically in Europe, lomitapide was approved under exceptional circumstances and, in accordance with Article 14(8) of Regulation No 726/ 2004, Aegerion Pharmaceuticals is required to conduct the following measures: To set up a long-term prospective observational study to systematically collect information on the safety and effectiveness outcomes of patients treated with lomitapide To evaluate the occurrence and outcomes of pregnancy in women of reproductive potential treated with lomitapide LOWER will provide data on the long-term safety and effectiveness of lomitapide in clinical practice and yield the opportunity to learn more about HoFH in patients taking lomitapide, including patient demographic and risk characteristics, treatment approaches, and outcomes in accordance to the agreed requirements of the risk management plan.
Finally, using dynamic contrastenhanced MRI can provide information on increased plaque microvasculature and permeability; also hallmarks of high-risk or vulnerable lesions. The CAPTURE (Effects of Lomitapide on Carotid and Aortic Atherosclerosis in Patients Treated with Lomitapide in Usual Care) study, a substudy of LOWER, will investigate the effects of lomitapide on the regression and/ or stabilization of atheroma in the carotid artery and aorta. CAPTURE will examine the hypothesis that LDL-C reduction with lomitapide results in regression and/or stabilization of atheroma in the carotid artery and aorta. Such an effect, if demonstrated, would provide meaningful clinical data based on an ''intermediate outcome'' and establish a biologically plausible link between LDL-C reduction and expectation of a reduction in the incidence of major cardiovascular events (MACE) in this patient population.
There are no protocol-mandated procedures or tests, and all patients receive usual care. Patient registration will be accomplished by the treating physicians with the consent of the lomitapidetreated patients. All health care professionals treating adult patients with lomitapide in countries where LOWER is being conducted are eligible to participate and to include patients in the registry. Regions include, but are not limited to, North America, Europe, Latin America, and Asia. The criteria for patient inclusion are broad so that the HoFH population included in LOWER should be representative of that seen in clinical practice. All adult patients ($18 years) who initiate lomitapide treatment at the time of registry enrollment or within the previous 15 months are eligible. Data collection will reflect the information generated as part of the usual care of patients with HoFH.
Clinical events The events of special interest to be evaluated in LOWER have been defined based on data from the clinical program or events considered to be of particular interest in the HoFH population and are defined in Table  2. Events of special interest will be considered as serious and reported to the FDA and relevant EU Competent Authorities, as appropriate. Other effectiveness endpoints include the reduction in LDL-C from baseline, absolute and percentage change from baseline in other lipoprotein parameters, and changes in concomitant medications or apheresis treatments. Data will also be collected for demographic and diagnostic information, family and medical history, medications, dietary supplements and apheresis schedule, weight and body mass index, lomitapide dosing, hepatic, GI and cardiovascular procedures and diagnostic tests, lipid profile, hepatic safety monitoring according to country-specific labeling, pregnancy testing, creatine kinase, and international normalized ratio.