dataesr/scientific-paragraphs-categorization huggingface dataset:
CLUMPP  (Jakobsson and Rosenberg 2007)  was used to obtain proportion averages across the multiple runs. Those with high a proportion of one population only are not very admixed while those with proportions closer to 0. 5 show a mixture of ancestry. To establish if there is a geographic pattern to individual genome proportions, and thus to what extent the introductions are introgressing, pie charts of individual genome proportions were mapped using ArcGIS (ESRI). This was done using genome proportions from STRUC-TURE under the admixture model with allele frequencies uncorrelated and MIGRPRIOR set to 0. 4 to allow for significant introgression. To determine whether individual genome proportions differed for amyloid individuals alongside any geographic pattern, we plotted west-to-east (the orientation chosen to bisect the observed cline) the logarithmic proportion of each individual's genome estimated by the microsatellite data to be of English origin against geographic longitude.
This measure accounts for genotyping errors and inbreeding, has a reduced sampling variance, produces results that are biologically constrained, and unlike many other maximum likelihood methods, does not overestimate relatedness  (Wang 2007 ). In addition, by calculating Pearson correlation coefficients, we confirmed that this measure of inbreeding correlated with other individual measures of inbreeding, including methods-of-moments estimators (r = 0. 375), regression estimators (r = 0. 843), internal relatedness (r = 0. 898), standardized heterozygosity (r = À0. 821), and heterozygosity-homozygosity by loci (r = 0. 848)  (Ritland 1996; Coltman et al. 1999; Lynch and Ritland 1999; Amos et al. 2001; Aparicio et al. 2006). To obtain individual TrioML values, we used the program COANCESTRY  (Wang 2011).
A Kruskal-Wallis ( K-W) rank sum test was conducted to establish if there were differences between the populations and cause of death in the Jersey population and Mann-Whitney U (M-W-U) tests were used to compare between diseased and nondiseased groups of individuals on Jersey. Kruskal-Wallis rank sum tests and Mann-Whitney U tests were carried out within the R statistical computing software (R Development Core Team 2009). To examine the possibility that admixture could be influencing the presence of amyloidosis, a generalized linear model (GLM) incorporating the logarithmic proportion of an individual's genome, its longitude and an interaction between the two factors were carried out. As an additional assessment of the data to test for an association between genotype and amyloidosis, locus-by-locus tests were carried out using a chi-squared test for each allele at each marker to establish if any of the markers genotyped are associated with amyloidosis.
DNA sequence data Only two mitochondrial control region haplotypes were detected on Jersey with eight variable sites between them (Genbank accession numbers JX853758-JX853759), indicating that they have separate evolutionary origins. The two Jersey haplotypes are located in two distinct clades, one is predominantly British (JE162), the other predominantly European (JE120) (Fig. 1 ). The phylogeny also illustrates that there are many haplotypes, which appear to be geographically out of place. Highlighted are the French haplotypes and the Isle of Wight haplotypes (IOW6, 29, 3, and 1) (Genbank accession numbers JX645360-JX645469 and JX853760-JX853763) to illustrate this point: both have their haplotypes located throughout the phylogeny, providing evidence of substantial movement of individuals by translocation and introduction throughout Europe.
4, 1. 6, 1. 8 and 2. 0, which approximately reflect low active (sedentary), moderately active, active and very active lifestyles. For estimating REE in adults, body heights measured in representative national surveys in 13 EU Member States and body masses calculated from heights assuming a body mass index of 22 kg/m 2 were used. For children, median body masses and heights from the WHO Growth Standards or from harmonised growth curves of children in the EU were used. Energy expenditure for growth was accounted for by a 1 % increase of PAL values for each age group. For infants (7-11 months), the AR was derived from TEE estimated by regression equation based on doubly labelled water (DLW) data, plus the energy needs for growth. For pregnant and lactating women, the additional energy for the deposition of newly formed tissue, and for milk output, was derived from data obtained by the DLW method and from factorial estimates, respectively.
TEE expended over 24 hours is the sum of basal energy expenditure, the energy expenditure of physical activity and the thermic effect of food. In this Opinion, resting energy expenditure (REE) was used as a proxy for the slightly lower basal energy expenditure, as most studies measured REE. TEE is best measured with the doubly labelled water (DLW) method, which provides energy expenditure data over biologically meaningful periods of time and under normal living conditions. One approach to determine the AR for energy is to use regression equations which describe how TEE measured with the DLW method varies as a function of anthropometric variables (such as body mass and height) for defined population groups and for an activity constant that accounts for the level of physical activity. However, this approach has been criticised because of the inability of such TEE prediction models to account for the variations in energy expenditure of physical activity in a transparent way.
In addition, limited TEE data generated with the DLW method are available, and they may not be representative for the European population; moreover, some age groups are underrepresented. Another approach for estimating TEE is by the factorial method in which the energy spent in various activities is added to measured or predicted REE. This is achieved by using the physical activity level (PAL), which is defined as the ratio of TEE to REE per 24 hours and reflects the part of TEE that is due to physical activity. Accordingly, TEE is predicted as PAL x REE. During growth, pregnancy and lactation, additional energy is needed for the synthesis and deposition of new tissues, and for milk production. In this Opinion, TEE of children and adults was estimated factorially to account for the diversity in body size, body composition and habitual physical activity among children and adult populations with different geographic, cultural and economic backgrounds.
(1985)  and  Henry (2005)  derived from large datasets and covering wide age groups were considered to be the most suitable. PAL can be estimated either from time-allocated lists of daily activities expressed as physical activity ratio values or from the ratio of TEE (measured by the DLW method) to REE (either measured or estimated). However, the same limitations apply to the derivation of PAL values from DLW data as to the estimates of TEE with this method. Within the general population, PALs associated with sustainable lifestyles have been observed to range between 1. 35 and 2. 5, and to decrease only marginally with age. When assigning PAL values to descriptions of activities/lifestyles (such as light, moderate or heavy activity), the range of PAL values in each lifestyle category is large.
Thus, the allocation of lifestyles to defined PAL values can only be considered a rough indication of PAL, but may be useful for decisions about which PAL values to apply in various circumstances and applications. In the absence of arguments for the selection of one predictive equation best fitted to adults in the European Union (EU), REE was calculated with five widely applied predictive equations using individual data of measured body heights of adults obtained in 13 representative national surveys in EU Member States, with corresponding body masses calculated for a body mass index (BMI) of 22 kg/m 2 , i. e. the midpoint of the range of healthy BMI of adults as defined by the WHO. This yielded a range of ARs calculated for PAL values from 1. 4 to 2. 4 in steps of 0. 2, and demonstrated the magnitude of uncertainty inherent in these values.
However, for practical reasons, only one AR is proposed for a defined age and sex group with a healthy BMI of 22, and for PAL values selected to approximate corresponding lifestyles. The predictive equations of  Henry (2005)  were used to estimate REE because, at present, the underlying database is the most comprehensive as regards number of subjects, their nationalities and age groups. To derive TEE as REE x PAL, PAL values of  1. 4, 1. 6, 1. 8 and 2. 0 were chosen to approximately reflect low active (sedentary), moderately active, active and very active lifestyles. Because of a lack of anthropometric data from EU countries for age groups from 80 years onwards, average requirements were not calculated for adults ≥80 years. For infants from birth to six months of age, energy requirements were considered to be equal to the energy supply from human milk, and no DRV is proposed.
Thus, the predictive value of equations can vary substantially according to sex, BMI (which reflects body composition), age and ethnicity of the subjects  (Hasson et al. , 2011). In 1985, the two most frequently used equations were the Harris-Benedict and the Schofield equations, but they are suspected to overestimate REE. The Harris-Benedict database included a relatively small number of subjects, with no children or adolescents below the age of 15 years, and a significant number of measurements were obtained by the use of closed-circuit indirect calorimetry, whilst the Schofield database included a large number (~40 %) of physically very active (Italian) subjects. The main studies that reassessed the historical equations and generated new equations are as follows:  Owen et al. (1986; 1987)   Despite the development of numerous new equations intended to improve the predictive power of the Harris-Benedict and Schofield equations, the FAO/WHO/UNU consultation in 2001 (FAO/WHO/UNU, 2004), after re-analysis of the data and attempts to define new equations  (Cole, 2002; Henry, 2001) , decided to keep the equations proposed by Schofield and colleagues in 1985 that formed the basis for the equations used by FAO/WHO/UNU in 1985.
, 1993; Rodriguez et al. , 2002). Both the Schofield and the Henry equations were derived from large datasets covering the age groups from 0 to 18 years, whereas the equations of  Maffeis et al. (1993) ,  Molnar et al. (1995)  and  Müller et al. (2004)  were developed from smaller samples not including all age groups (see Table  1 ). The Panel concludes that the equations of Schofield and Henry are equally valid for predicting REE in children with a wide age range. Table  1 : Number of male (m) and female (f) children in the datasets from which the prediction equations for children and adolescents were derived.
The calculated REE values were then multiplied with PAL values ranging between 1. 4 and 2. 2011 ) derived PAL values from two studies in middle-aged US adults in which TEE was measured with the DLW method and REE was either measured  (Moshfegh et al. , 2008)  or calculated  (Tooze et al. , 2007). The resulting distribution of PAL values was assumed to also represent PAL values of the UK population, and the median PAL value was used to derive average energy requirements according to age and sex. IoM  (2005)  collected DLW data on adults separately for those with normal body mass and for overweight/obese subjects. The normal body mass database comprised 169 men and 238 women with a BMI between 18. 5 and 25 kg/m.
Infants and children It is generally accepted that TEE is different for breast-fed and formula-fed infants. Some authorities derived values for formula-fed infants only  (AFSSA, 2001; D-A-CH, 2012; SCF, 1993) , whereas others estimated energy requirements according to feeding mode (FAO/WHO/UNU, 2004; SACN, 2011) (Appendices 6, 7). Older estimates of energy requirements of infants were based on measurements of energy intake (FAO/WHO/  UNU, 1985; SCF, 1993). More recent estimated energy requirements have been based on measurements of energy expenditure using DLW data from healthy, well-nourished, full-term infants available from 1987 onwards. The DLW database has subsequently been extended to comprise also older infants and young children up to 24 months of age. The energy expended for growth was estimated from changes in body mass and body composition, i.
e. gains in protein and fat mass during growth, and was added to the estimated TEE. The DLW database used by FAO/WHO/UNU (2004) comprised 13 studies with DLW measurements performed on a total of 417 healthy, well-nourished, non-stunted infants aged 0-12 months and growing along the trajectory of the WHO reference standard  (1983). Most (11/13) studies were done in the UK, the US, and the Netherlands. Linear regression analysis using body mass as the predictor for TEE was applied. An allowance for energy deposition in tissues during growth was added, which was calculated by gains in protein and fat, and corresponding energy deposition, assuming that the energy equivalents of protein and fat deposition are 23. 6 and 38. 7 kJ/g (5. 64 and 9. 25 kcal/g), respectively  (Butte et al. , 2000b). Since formula-fed infants had higher TEE during the first year of life, separate predictive equations for breast-fed and formula-fed infants were proposed.
For infants and children up to two years of age, the DLW database of IoM (  2005 ) comprised children within the 3 rd and 97 th percentile for US body mass-for-length values. A single equation involving only body mass was found suitable to predict TEE in all individuals irrespective of sex. Because of the small sample size of the data used and the limited range of estimated physical activity, PAL was not included in the TEE equation. The IoM calculated the estimated energy requirements (EERs) for infants and very young children as TEE plus energy deposition for growth. The energy requirement for growth was computed from rates of protein and fat deposition in a longitudinal study of infants (0. 5-24 months of age)  (Butte et al. , 2000b) , and applied to the 50 th percentile of gain in body mass for boys and girls of similar ages  (Guo et al.
Examples of activities performed with less physically active than average lifestyles as well as for those performed with vigorous lifestyles were given. For US children aged 3-18 years, EERs were calculated for boys and girls separately, because of variations in growth rate and physical activity (IoM, 2005) (Appendix 7). DLW data from the normative database with US children within the 5 th and 85 th percentile of BMI were used to develop equations to predict TEE based on a child"s sex, age, height, body mass and PAL category. Energy deposition was computed based on published rates of gain in body mass  (Baumgartner et al. , 1986)  and estimated rates of protein and fat deposition in children  (Fomon et al. , 1982)  and adolescents  (Haschke, 1989 ). An average of 84 kJ/day (20 kcal/day) for energy deposition for children aged 3-8 years, and of 105 kJ/day (25 kcal/day) for children and adolescents aged 9-18 years  (IoM, 2005) , was therefore added to the calculated TEE.
The median change in TEE per gestational week was calculated based on a dataset of pregnant women with normal pre-pregnancy BMIs (18. 5-25 kg/m 2 ) and longitudinal DLW measurements of TEE throughout pregnancy. It was found that the energy expenditure of pregnancy was not equally distributed over pregnancy. No increase in energy intake was recommended for the first trimester, as TEE was considered to change little and gain in body mass was considered to be minor. For pregnant women aged 19-50 years the EERs were calculated as follows: EER pregnant =EER non-pregnant + additional energy expenditure during pregnancy + energy deposition For pregnant adolescent women aged 14 to 18 years the same equations were applied, taking into account the adolescent EER non-pregnant instead of the adult EER non-pregnant.
The second approach is to use TEE, as measured by the DLW method, directly to derive regression equations which describe how TEE varies as a function of anthropometric variables (such as body mass and height) for defined population groups. This approach has been applied by FAO/WHO/UNU (FAO/WHO/UNU, 2004) for children and by  IoM (2005)  for the US Dietary Reference Intake (DRI) values for energy for all population groups except lactating women. For children and non-pregnant adults  (IoM, 2005) , the level of physical activity was accommodated within the regression by designating an activity constant for each individual calculated from TEE and REE values in the datasets. One of four physical activity constants representing a predefined PAL range was used (sedentary, low active, active, very active).
In this way, sex-specific regression equations for the prediction of TEE were identified based on age, body mass, height and physical activity categories. Although the DLW dataset assembled for the US DRI report (IoM, 2005) includes most of the UK studies published up until the writing of that report, SACN (2011) considered this dataset as not being suitable for their approach because study subjects were not recruited explicitly as a representative sample of the UK or any other adult population; furthermore, several of these DLW studies involved investigations of physical activity measurement devices (e. g. accelerometers), and specifically recruited subjects with relatively high activity lifestyles. Instead, for adults, SACN (2011) considered two studies in which energy expenditure was measured using the DLW method, i.
e. the OPEN study (n=451, 40-69 years)  (Subar et al. , 2003; Tooze et al. , 2007)  and the Beltsville study (n=476; 30-70 years)  (Moshfegh et al. , 2008). Both studies were comprised of an urban population with subjects recruited from the Washington DC metropolitan area who were considered as comparable to the current UK population as regards distribution of BMI values and ethnic mixture. However, no objective measures of physical activity were made in either study. Therefore, regression modelling as an approach to derive AR for energy was abandoned by SACN because of the inability of TEE prediction models to account for variation in EEPA. The Panel notes that in addition to these objections, the normative database from which the regression equations were derived by IoM (2005) includes only a small number of individuals who were not randomly selected (adults aged 19 to 96 years: 238 women, 169 men; children aged 3-18 years: 358 girls, 167 boys).
Calculation of resting energy expenditure (REE) The Panel calculated REE for men and women aged 18-79 years based on individual body heights measured in nationally representative surveys in 13 EU countries, and corresponding individual body masses calculated to yield a BMI of 22 kg/m² (see Table  4 ). As is illustrated in Figure  1 , the discrepancy in the results for REE calculated with the various prediction equations becomes larger with increasing age (from 5 % at age 18-29 years to 11-13 % at age 70-79 years). The predictive validity of these equations with regard to older adults has been tested in a sample of free-living older persons. A dataset of measurements of REE by indirect calorimetry in 551 elderly subjects (385 women and 165 men, age range 60-96 years) participating in the GISELA study  (Lührmann et al.
5 km/h daily resulted in an increase in PAL of 0. 2 while 60 minutes of jogging at 9 km/h daily increased PAL value by 0. 4. SACN derived PAL values from the combined dataset of the OPEN  (Subar et al. , 2003 , Tooze et al. , 2007)  and the Beltsville study  (Moshfegh et al. , 2008)  by dividing TEE by REE measurements (SACN, 2011). There was no evidence of any significant variation of PAL with either body mass or sex. Regression analysis did show that PAL values decrease slightly with age. However, age explained < 1 % of the variance (i. e. PAL = 1. 69 at 30 years and 1. 63 at 70 years). It was concluded that energy reference values can be defined independently of age at least up to the age of 70 years. A meta-analysis of studies that involved a total of 319 men and women aged 18-64 years showed a modal value for PAL of 1.
60 (range 1. 55 to 1. 65) for both men and women  (Black et al. , 1996). For the most part, subjects were from affluent societies in developed countries. Typical sub-populations included students, housewives, white-collar or professional workers, and unemployed or retired individuals; only three people were specifically identified as manual workers. Hence, the authors of the meta-analysis defined the study participants as people living a "predominantly sedentary Western lifestyle". SACN (2011) used the distribution of PALs observed in the combined OPEN  (Subar et al. , 2003; Tooze et al. , 2007)  and Beltsville  (Moshfegh et al. , 2008)  datasets and defined the median PAL (1. 63) as the assumed population activity level, and the 25 th (1. 49) and 75 th percentile (1. 78) boundary PAL values as reference values for population groups of men and women thought to be less or more active than average.
Although this approach of deriving PAL values took advantage of the measurement of TEE in free-living conditions by the DLW method, the Panel decided not to adopt it for defining reference PAL values for the population of the EU because of the limitations outlined under Section 5. 2. Furthermore, within the dataset of DLW studies in healthy adults assembled for the SACN report, all of the studies which report PAL values were examined for descriptions of the activities/lifestyles of the subjects. PAL values were assigned to three categories of light, moderate or heavy activity. The values show that the range of PAL values is considerable within subjects classified as exhibiting similar lifestyles, and demonstrate only a weak relationship between lifestyle or self-reported physical activity and PAL.
These predictions of energy deposited during growth derive from a relatively small study by  Butte et al. (2000b)  which was validated against other datasets  (Butte, 2005). The Panel notes that the evolution of body mass and composition studied by  Butte (2005) , especially regarding gains in FM and FFM during the first year of life, differs from other studies  (de Bruin et al. , 1998; Fields et al. , 2011; Fomon et al. , 1982). Since the impact on energy requirements was only marginal, the Panel decided to use the values proposed by Butte in line with FAO/WHO/UNU (2004) and  SACN (2011). It is assumed that these values for the energy deposited in new tissue are appropriate for infants growing according to the WHO body mass velocity values, even though in the original study  (Butte et al.
Calculation of resting energy expenditure (REE) From the available prediction equations for REE in children, the equations from  Schofield et al. (1985)  and  Henry (2005)  (Appendix 13) were derived from a large number of subjects covering the age range from 0 to 18 years, and therefore are both used to calculate REE. For the ages 1-2 years, median body masses and heights from the  WHO Growth Standards (WHO Multicentre Growth Reference Study Group, 2006)  were used in the equations to calculate the REE, whereas for children aged 3-17 years, the 50 th percentiles of recently calculated reference body masses and heights for EU children  (van Buuren et al. , 2012)  were used (for details of the database and computation of reference body heights and body masses see Appendix 11). Because the equations of  Schofield et al.