dataesr/scientific-paragraphs-categorization huggingface dataset:
Zumbühl, 1980; Masiokas and others, 2009; Nussbaumer and others, 2011; Purdie and others, 2014). Glacier mass-balance time series are derived from both glaciological and geodetic surveys. The glaciological dataset provides glacier-wide results of 260 glaciers with >4150 annual observations over the past seven decades, often including seasonal balances, mass-balance distribution with elevation, ELA, AAR and the corresponding point measurements. Thickness and volume change data are available for 444 glaciers with 1100 observations. These geodetic data come with decadal resolution and extend back into the mid-19th century. Quantitative information on glacier fluctuations is available from glaciers covering about one-quarter of the current total glacier area (cf. Table  1  and Arendt  and others, 2012 ).
Changes in glacier mass and volume The development in global glacier mass balance since the mid-19th century is depicted in Figure  3  which shows the annual average balances for the glaciological and the geodetic datasets together with the corresponding sample sizes. The difference in survey periods between the glaciological and the geodetic data becomes manifest in the variability of the two graphs: a smooth line with step changes towards more negative balances for the geodetic sample and a strong variability with a negative trend for the glaciological observations. For the glaciological balance, the global mean annual value of the early 21st-century observations (2001-10) is the most negative of all decades, with -0. 54 m w. e. a -1. The series shows strong annual variability (standard deviation 1951-2010: 0.
81 m w. e. a -1. Overall, the geodetic results are more negative than the glaciological ones (cf. discussion in Section 4. 2). The annual variability (standard deviation 1951-2010: 0. 12 m w. e. a -1 ) is much smaller than the glaciological one, and the increase in the last two decades comes with a stepwise drop in sample size. Regional glacier mass balances derived from both the glaciological and the geodetic methods, including corresponding sample sizes, are shown in Figure  4 , and decadal results are summarized in Table  2. Analysing the available observations in the 19 regions, the first decade of the 21st century exhibits the most negative mass balances in the majority of regions with available data, followed by the final decade of the 20th century. For the glaciological sample, the observation period 2001-10 is the most negative decade in nine regions (ASC, ASN, CEU, GRL, ISL, SCA, WNA; in ACN and SJM when ignoring the first decade with limited data coverage), the second most negative after the 1990s in four regions (ALA, ASW, CAU, SAN), and equally negative as the two preceding periods in the Low Latitudes (TRP).
Five regions have no (ACS, ANT, ASE, RUA) or too-limited (NZL) observations for such a comparison. A tendency towards increasing mass loss over the past few decades is apparent in most regions (CAN, ALA, ASN, CAU, CEU, GRL, ISL, WNA), while in some there are negative mass loss rates but no clear trend (ASC, SAN, SJM, TRP). A common feature of most regions with long-term data coverage is the reduced mass loss between the 1960s and the 1980s. This feature is even more pronounced in Scandinavia where coastal glaciers were able to gain mass from the 1970s to the 1990s while the glaciers further inland continued to lose mass. In the geodetic sample, the early 21st century is the most negative decade in eight regions (ALA, ANT, ASE, ASW, CAU, CEU, SAN, TRP) and second most negative in one region (WNA).
In Scandinavia and Svalbard as well as in Asia North, the geodetic results show few variations but slightly higher mass losses in the early to mid-20th century. The remaining regions have only one dataset (GRL) or no data (ACN, ACS, ASC, ISL, NZL, RUA) reported for the last decade. In Figure  5 , anomalies of glaciological annual and seasonal balances are plotted to provide insight into the components of the annual mass changes. In the majority of regions, the annual balances are highly correlated with summer balances (ALA, CAN, WNA, SJM, ISL, CEU, ASC, CAU). In two regions (SAN, SCA), the correlation with winter balances is even higher. An exception is Asia North, where the correlations between annual and both seasonal balances are low (maybe because the seasonal balances in fact are net ablation and net accumulation; cf.
Global centennial glacier retreat and mass loss The retreat of glaciers from their LIA (and Holocene) moraines and trimlines can be observed in the field as well as on aerial and satellite images for tens of thousands of glaciers around the world (e. g. Grove, 2004; Svoboda and Paul, 2009; Davies and Glasser, 2012; Kargel and others, 2014). Large collections of historical and modern photographs  (NSIDC, 2009 (NSIDC, , updated 2015) )  document this change in a qualitative manner. The dataset presented here allows these changes to be quantified at samples ranging from a few hundred to a few thousand glaciers with observation series. There is a global trend to centennial glacier retreat from LIA maximum positions, with typical cumulative values of several hundred to a few thousand metres. In various mountain ranges, glaciers with decadal response times have shown intermittent readvances which, however, were short and thus much less extensive when compared to the overall frontal retreat.
5 m w. e. a -1 in the first decade of the 21st century. Looking at individual fluctuation series, a high variability and sometimes opposite behaviour of neighbouring glaciers are found which can be explained by differences in glacier hypsometry and aspect and thus accumulation conditions  (Kuhn and others, 1985) , or debris cover  (Nakawo and others, 2000; Scherler and others, 2011) , or differences in resulting response time  (Jóhannesson and others, 1989; Pelto and Hedlund, 2001). In some cases local differences are also due to ice dynamics rather than climate forcing (e. g. for glaciers dominated by calving (cf. Benn and others, 2007)  or surging (cf. Lingle and Fatland, 2003; Yde and Paasche, 2010; Nuth and others, 2013)  processes). The present observational dataset thus confirms the findings from earlier scientific studies (e.
Source: WGMS (2012, and earlier issues). 1999;  Cox and March, 2004)  or a bias in the glacier sample (e. g. Kaser and others, 2006). Earlier studies showed that the glaciological dataset is subject to issues related to moving sample size. However, the temporal variability and the absolute cumulative values of the global glaciological sample agree well with corresponding values of the subset of 'reference' glaciers with >30 years of continued observation (cf. Zemp and others, 2009). Also, looking at differences in observation methods in individual regions, the overall trends from the glaciological and geodetic methods agree well (ASC, ASE, ASN, ASW, ISL, SCA, SJM, WNA). The larger biases seem to stem from differing glacier samples at a regional level as discussed below. In Alaska, the results from the large geodetic sample follow the general trend of the glaciological sample but are clearly more negative.
For extending this picture globally and back in time, we can include the length change dataset but have to consider that the frontal variation of a glacier is an indirect, delayed and filtered response to climatic changes of the past (Jóhannesson and others, 1989). As a consequence, a direct and quantitative comparison of length change rates is not straightforward but requires analytical or numerical models that consider climate sensitivity as well as reaction and response times of each individual glacier. Such reconstructed decadal change rates are available from studies by  Hoelzle and others (2003; between -0. 1 and -0. 3 m w. e. a -1 since the mid-19th century),  Haeberli and Holzhauser (2003; -0. 4 m w. e. a -1 for the 20th century and between +0. 5 and -0. 5 m w. e. a -1 for the past 2000 years) and  Leclercq and others (2011; -0.
5 ) and readvances. The balanced mass budgets exhibited in the 1970s were followed by accelerated mass losses in many regions, becoming more homogeneous at the global scale in the past few decades. During the first decade of the 21st century, glaciers lost almost 0. 7 m w. e. a -1 of ice, when averaging the results of glaciological and geodetic observations. By simply weighting the regional averages with corresponding regional glacier areas, this results in an annual global contribution of almost 500 Gt a -1 to runoff, or of 1. 37 mm a -1 to mean sea-level rise. Corresponding mean annual values for the 1970s/80s/90s are 150/160/390 Gt a -1 or 0. 42/0. 43/1. 08 mm a -1 , giving a cumulated contribution of 33 mm to mean sea-level rise over the past four decades. The latter values are slightly higher than earlier studies using similar glaciological and geodetic datasets but different ways of averaging (e.
g. Kaser and others, 2006; Cogley, 2009). The corresponding annual contributions to sea-level rise for the 6 year period 2004-09 were 360 Gt a -1 (260 Gt a -1 when excluding GRL and ANT) or 0. 98 mm a -1. This is 37% (23% when excluding GRL and ANT) higher than estimates based mainly on satellite gravimetry and altimetry by  Gardner and others (2013). Hence, further research is needed in order to assess the influence of different data samples and observation techniques on regional and global estimates of glacier mass budgets. The above estimates can be extended by the committed mass loss due to strong imbalance, especially of large glaciers, which are the primary contributors to sea-level change. For this purpose, mass balance and AAR are used to calculate the committed loss in glacier area as (1 -AAR)/ AAR 0 (cf.
CONCLUSIONS AND OUTLOOK More than a century of internationally coordinated glacier monitoring efforts have resulted in a comprehensive collection of data on worldwide glacier fluctuations. This dataset is not perfect but nevertheless constitutes a unique treasure for the scientific analysis of glacier changes. Direct glaciological measurements are available only for a few hundred glaciers but they provide rich insights into the annual variability and seasonal components of glacier mass changes. The volume changes from the geodetic method come at lower temporal resolution but allow the glaciological sample to be extended in both space and time. A large number of datasets from recent studies using DEM differencing is expected to be provided soon to the database. Observations of front variations provide indirect and more qualitative information on glacier changes.
Braithwaite, 2009)  for further process understanding and model calibration, (3) intensify the compilation of geodetic data in order to assess glacier volume changes over entire mountain ranges, (4) extend the dataset of glacier front variations from observations and reconstructions both in space and back in time making use of existing remote-sensing data, (5) better understand and openly discuss the uncertainties of in situ, air-and spaceborne methods as well as their representativeness for an individual glacier and the entire glacier coverage, and last but not least (6) make all data freely available through the designated world data centers and services.
ACKNOWLEDGEMENTS We thank the thousands of observers and their sponsoring agencies (as listed in our data reports, i. e. WGMS (2012WGMS ( , 2013)), and earlier issues) from around the globe for long-term collaboration and willingness to share glacier observations. All data were compiled and made freely available by the  World Glacier Monitoring Service  (and its predecessor organizations). We thank two anonymous reviewers for constructive comments, and  Susan Braun-Clarke  for carefully polishing the English. M. Zemp,  H. Frey ,  I. Gärtner-Roer  and  S. U. Nussbaumer  acknowledge financial support by the  Swiss GCOS Office at the Federal Office of Meteorology  and  Climatology MeteoSwiss , and  F. Paul  by the  European Space Agency project Glaciers_cci  ( 4000109873/14/I-NB ). This is NRCan/ESS Contribution No.
Using the API 50CH strip (bioMérieux), no positive reaction was observed, including for glycerol, D-arabinose, D- xylose, L-rhamnose, amygdalin, D-cellobiose, D-fucose, potassium 5-ketogluconate, L-arabitol, starch, D-maltose and Dmannose. B. ndiopicus was susceptible in vitro to penicillin, amoxicillin, amoxicillin-clavulanic acid, ceftriaxone, imipenem, gentamicin, ciprofloxacin, erythromycin, doxycycline, rifampicin and vancomycin, but resistant to nitrofurantoin and metronidazole. When compared with representative species from the genus Bacillus, B. ndiopicus strain FF3 T exhibited several phenotypic differences, which are summarized in Table  2. MALDI-TOF protein analysis was performed using a Microflex LT (Bruker Daltonics, Leipzig, Germany), as previously reported  [14]. The scores previously established by Bruker allowing validating (or not) the identification of species compared to the database of the instrument were applied.
Briefly, a score of 2. 000 with a species with a validly published name provided allows the identification at the species level; a score of 1. 700 and <2. 000 allows the identification at the genus level; and a score of <1. 700 does not allow any identification. We performed 12 distinct deposits from 12 isolated colonies of strain FF3 T. Two microliters of matrix solution (saturated solution of alpha-cyano-4-hydroxycinnamic acid) in 50% acetonitrile and 2. 5% trifluoroacetic acid were distributed on each smear and submitted at air drying for 5 minutes. Then the spectra from the 12 different colonies were imported into MALDI Biotyper 2. 0 software (Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 6252 bacterial spectra including 199 spectra from 104 Bacillus species.
NMNI differences with other members of the genus Bacillus, which support that Bacillus ndiopicus strain FF3 T likely represents a new bacterial species. This strain is part of a study aiming to characterize the skin flora of healthy Senegalese people. Currently there are more of 270 sequenced genomes of Bacillus species  [8]. Strain FF3 T is the first genome of B. ndiopicus sp. nov. , and its GenBank accession number is CCAP000000000. The genome consists of 23 large contigs. Table  3  shows the project information and its association with minimum information about a genome sequence (MIGS) The genomic DNA was diluted to 1 ng/μL to prepare the paired-end library. The tagmentation step fragmented and tagged the DNA with an optimal size distribution at 0. 95 kb. Then limited-cycle PCR amplification (12 cycles) completed the tag adapters and introduced dual-index barcodes.
NMNI Orthologous Groups (COGs) databases using BLASTP. tRNAs, rRNAs, signal peptides and transmembrane helices were identified using tRNAscan-SE 1. 21  [18] , RNAmmer  [19] , SignalP  [20]  and TMHMM  [21] , respectively. Artemis  [22]  was used for data management, and DNA Plotter  [23]  was used for visualization of genomic features. In-house Perl and bash scripts were used to automate these routine tasks. ORFans were sequences which have no homology in a given database-that is, nonredundant (nr) or identified if their BLASTP E value was lower than 1e-03 for alignment lengths greater than 80 aa. PHAST was used to identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids  [24]. To estimate the nucleotide sequence similarity at the genome level between B.
ndiopicus and other members of Bacillaceae family, orthologous proteins were detected by Proteinortho software  [25]  (with the following parameters: E value 1e-5, 30% percentage of identity, 50% coverage and algebraic connectivity of 50%) and genomes compared two by two. After fetching the corresponding nucleotide sequences of orthologous proteins for each pair of genomes, we determined the mean percentage of nucleotide sequence identity using the Needleman-Wunsch global alignment algorithm. The script created to calculate AGIOS (average genomic identity of orthologous gene sequences) values was named MAGi (Marseille Average genomic identity) and is written in Perl and Bioperl modules.
Genome properties The genome of B. ndiopicus strain FF3 T is 4 068 720 bp long (one chromosome, no plasmid) with a 37. 03% G+C content (Fig. 6 ). Of note, we acknowledge the fact that because the genome of Bacillus ndiopicus is a draft sequence, its exact size might be slightly different from that of our sequence, but given the fold coverage (52×), we are confident that the missing fragments are probably small and do not significantly influence the genome size. Of the 3982 predicted genes, 3915 were protein-coding genes and 67 were RNAs. A total of 1697 genes (43. 34%) were assigned a putative function. The properties of the genome are presented in Table  4. Using PHAST software,.
Genomic comparative Today there are more than 277 sequenced genomes of Bacillus species (finished and draft) available in Genomes Online Database  [3]. Here we compared B. ndiopicus genome sequence against other members of genus Bacillus, including Bacillus coagulans strain 2-6, B. coagulans strain 36D1, Lysinibacillus sphaericus strain C3-41, Bacillus bataviensis stain LMG 21833, and Bacillus isronensis strain B3W22. Bacillus ndiopicus strain FF3 T has a G+C content (37. 03%) lower than those of all the compared species such as B. coagulans strain 2-6 (47. 3%), B. coagulans strain 36D1 (46. 5%), B. bataviensis strain LMG 21833 (39. 6%), B. isronensis strain  As it has been suggested in the literature that the G+C content deviation is at most 1% within species, these data are an additional argument for the creation of a new taxon  [26].
NMNI The G+C content of the genome is 37. 03%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers HG315675 and CCAP000000000, respectively. The type strain FF3 T (= CSUR P3025 = DSM 27837) was isolated from the skin of a healthy 39-year-old Senegalese man living in Ndiop, Senegal.
91 Nucleotide transport and metabolism H 89 2. 27 Coenzyme transport and metabolism I 70 1. 78 Lipid transport and metabolism P 155 3. 95 Inorganic ion transport and metabolism Q 24 0. 61 Secondary metabolites biosynthesis, transport and metabolism R 348 8. 88 General function prediction only S 303 7. 73 Function unknown - 195 4. 98 Not in COGs COGs, Clusters of Orthologous Groups database.
We assessed the significance of the relationship between interaction outcome (IO) of target species and ISAR values at local neighborhood (d = 20 cm) with a linear model. The local neighborhood of individuals of a target species could be as diverse as expected by the null model even when such target species interacts significantly with a few species or plant types. To assess this effect, we classified plant species into four types based on life form: annual, perennial grass, shrub, and small shrub. Then, we computed ISAR  (d)  for each combination of target species and plant type. Thus, at each study site, we performed ISAR analyses by considering the entire diversity (excluding j when j = t) and by considering different plant types separately. ISAR analyses and comparisons with null models were performed with MATLAB R2010b.
Compositional changes of species associated with target species In order to assess compositional changes of species spatially associated with target species, we employed the Chao-Jaccard similarity index  (Chao et al. 2005). For each target species, we calculated this index for all possible pairs of transects of the same study site, using data of co-occurrence between pair of species (number of times that target species and other species appeared at the same point in a transect). Chao-Jaccard index is based on the probability that two randomly chosen individuals that co-occur with the target species (one from each of two transects) both belong to any of the species shared by the two transects  (Chao et al. 2005 ) (i. e. , species that co-occur with the target species in both transects). This index considers the similarity of the species list that co-occurs with a given target species, and the similarity of the relative abundances of co-occurring species.
Thus, a high similarity among transects indicates that nearly the same species co-occur with a target species and approximately the same frequencies. The Chao-Jaccard similarity index was calculated using the vegan package in R  (Oksanen et al. 2013). We compared Chao-Jaccard similarity index among target species at each study site by use of one-way ANOVA. Tukey 0 s post hoc HSD test was employed to detect differences among pairs of target species. These statistical analyses were performed with R (R Core Team 2013).
Study Population Data are drawn from the Whitehall II cohort study, established in 1985e1988 on 10,308 individuals (67% men), aged 35e55 years. 24 ndividuals gave written consent to participate in the study, and the University College London ethics committee approved the study. Since inception, sociodemographic, behavioral, anthropometric, and health-related factors have been assessed approximately every 5 years (1985/1988, 1991/1994, 1997/1999, 2002/2004, 2007/2009,  and 2012/2013). Accelerometer measurement was added to the study at the 2012/2013 wave of data collection for participants seen at the central London clinic and for those living in the South-Eastern regions of England who were screened at home. Whitehall II data, protocols, and other metadata are available to the scientific community.
Accelerometer-Assessed Physical Activity Participants with no contraindications (that is, allergies to plastic or metal, traveling abroad the following week) were asked to wear a triaxial accelerometer (GeneActiv; Activinsights Ltd, Kimbolton, Cambs, UK, http://www. geneactiv. org/) on their nondominant wrist for 9 consecutive (24-hour) days. The accelerometer was sampled at 85. 7 Hz and as in previous studies using new generation accelerometers, 28e30 acceleration was expressed relative to gravity (g units; 1 g ¼ 9. 81 m. s À2 ) to reflect the fact that sensors are calibrated relative to gravity. Calibration error was estimated based on static periods in the data and corrected if necessary. 31 The Euclidean norm (magnitude) of the 3 raw signals minus 1 g, with negative numbers rounded to zero, was used to quantify the acceleration related to the movement registered and expressed in milligravity (mg, 1 mg ¼ 0.
00981 m. s À2 ). 32 ccelerometer data were processed in R (cran. r-project. org/) using the GGIR package and executed on MOVEeCloud (http://movelab. org/ research/moveecloud/), a computing facility for physical activity research. 33 Data extracted between the first and last midnight were retained for the analysis leading to a maximum of 24-hour measurements for 8 days. Participants were included in the analysis if they had valid data (!16 hour/day) for at least 2 weekdays and 2 weekend days. Nonwear time was estimated on the basis of the standard deviation (SD) and value range of each accelerometer axis, calculated for moving windows of 60 minutes with 15-minute increments. 32 For each 15-minute period of time detected as nonwear time over the valid days, missing data were replaced by the mean value calculated from measurement on other days at the same time of day.
Adiposity Markers Adiposity markers were assessed during the clinical examination by a trained nurse. BMI was calculated as weight (kg) divided by height (m) squared. Weight was measured in light clothing, using an electronic Soehnle scale with digital readout (Leifheit AS, Nassau, Germany). Height was measured using a stadiometer with the participant standing completely erect with their head in the Frankfort plane. Waist circumference was taken as the smallest circumference at or below the costal margin. FMI was calculated as the ratio of fat mass (kg) by height (m) squared. 35, 36 Fat mass was estimated by bioimpedance using the Tanita TBF-300 body composition analyzer (Tanita, Arlington Heights, Ill, www. tanita. com/en/). Bioimpedance data were available on participants seen at the central London clinic (N ¼ 4524) but not at home.