dataesr/scientific-paragraphs-categorization huggingface dataset:
Height changes due to melt or snow deposition were measured with a sonic height ranger. Due to high melt rates ( 6 cm day -1 ), the height of the sensors changed with time. Instruments were manually lowered every 10 to 15 days. A picture of the instruments installed on the glacier is shown in Fig. 1. We also used data from an automatic weather station located outside the glacier (AWS-M), on a nearby moraine north of the site (1. 5 km), measuring the same variables as the AWS-G. This station is operated in the framework of the GLACIOCLIM program (les GLACIers, un Observatoire du Climat) that undergoes a follow-up of glaciers mass balance and meteorology in order to understand glacier fluctuations in terms of climatic variations. Table  1  summarises all the instruments used and their characteristics.
Data processing Data have been processed in a similar way to  Litt et al. (2015b). All data were split into 1 h runs, since the AWS-M provides only 1 h means. High-frequency data from the EC system were checked for quality  (Vickers and Mahrt, 1997). Problems were mainly related to precipitation events or frost on the sensor heads. Bad-quality runs were discarded (30 %). Remaining good-quality runs (GQRs) were despiked  (Vickers and Mahrt, 1997). A planar-fit rotation  (Wilckzak et al. , 2001)  was applied on each 10-to 15-day period between field visits when they were manually lowered. From this rotation we derived the horizontal (u and v) and vertical (w) wind-speed components. The sonic temperature was corrected for water vapour influences  (Schotanus et al. , 1983). The changes in instrument heights were taken into account using measurements from the sonic height ranger and regular field visits and controls.
Surface energy balance and melt The SEB was calculated as SEB = SW inc -SW out + LW inc -LW out + H + LE. (9) All fluxes are expressed in W m -2. The symbols SW and LW stand for hourly mean shortwave and longwave radiation respectively. The subscripts inc and out stand for incoming and outgoing radiation respectively. Subsurface conductive heat flux was neglected because the surface remained close to melting day and night (T s > -3 • C). A simulation of the conduction flux below the snow or ice surface, using the surface temperature as the only input data, showed that this flux was negligible, even when surface temperature fell below zero during short periods of a few hours. The rare events of strong surface cooling, with surface temperature reaching ∼ -11 • C during the night, were associated with precipitation events or freezing for which meteorological data quality was low due to frost or snow deposition on the sensors.
01 m, which results in a 40 W m -2 error on the mean hourly melt energy. The error on the turbulent fluxes was translated into melt errors. Uncertainties on the net shortwave radiation were estimated by calculating the potential solar radiation following  Pellicciotti et al. (2011). We first assumed no inclination and then a maximum inclination of the sensor of 10 • in any direction. The maximum relative difference between the two values was taken as the relative net solar radiation error. The SEB was converted to melt, assuming the ice density was 900 kg m -3. At the beginning of the campaigns, old snow was present at the surface, with measured density of 480 kg m -3. The precipitated water equivalent during the occasional snowfalls on the glacier were obtained from SAFRAN reanalysis data  (Durand et al.
Conclusions During the summers of 2006 and 2009, field measurement campaigns were undertaken in the ablation zone of Saint-Sorlin Glacier, in the French Alps. We analysed EC data from the 2006 campaign and temperature and wind-speed vertical profiles from the 2009 campaign. We characterized the wind regimes and associated surface-layer turbulent flows in relation to the large-scale forcing as characterized from the weather pattern decomposition of  Garavaglia et al. (2010). We evaluated the turbulent fluxes with the profile method using observed and melt-calibrated (effective) roughness parameters and the sonic (EC) method. We also calculated the surface energy balance and studied the impact of the choice of flux calculation methods on the modelled melt, in relation with weather patterns. Errors resulting from random measurement errors, uncertainties in surface parameters (roughnesses or emissivity) and radiation measurements were assessed.
Acknowledgements. This work was funded by the  French SO/SOERE GLACIOCLIM  (http://www-lgge. ujf-grenoble. fr/ServiceObs/index. htm) and the  ANR program  TAG  05-JCJC-0135. We kindly thank  EDF  for providing the daily time series of WPs. We thank  Romain Biron  and  Jean-Philippe Chazarin  for the technical and field work,  Adrien Gilbert  and  Marion Reveillet  for stimulating discussions and an unknown reviewer for his meticulous and helpful review. Edited by: V. Radic Reviewed by: two anonymous referees.
K stands for T/G. (Supplementary Fig. S4 ). Of the remaining twenty-five small annotated ORFs, only six have been functionally characterized, at least to some extent (ecnB, yqaE, chaB, osmB, yahO and yciG, Supplementary Table  S1  and references therein). Nineteen ORFs were putative or of unknown function (Table  1 ). Examination of their sequence features, and alignment of their predicted amino acid sequences with that of identified homologs in other bacterial genomes, prompted us to re-annotate the start codon of STM14_2239, STM14_2409, and STM14_5481 (Table  1 , Supplementary Fig. S5 , Supplementary Dataset S1). The uncharacterized ORFs ymdF and STM14_1829 are paralogous to yciG (Table  1 , Supplementary Fig. S6 ). YciG  33, 34  belongs to the group of "hydrophilins", proteins defined by high glycine content and hydrophilicity index  35.
Interestingly, while some of the uncharacterized small ORFs appear to be restricted to bacteria of the Enterobacteriaceae family, others are more widely distributed (Table  1  and Supplementary Dataset S1) and might be involved in conserved biological processes. In the following part of the work, a LC-MS-based proteomics approach was used to validate production and σ S regulation of these small proteins, and to assess, to our knowledge for the first time, global effects of σ S at the protein level in Salmonella.
Global effects of σ S on protein abundance in Salmonella. A comprehensive quantitative proteomic analysis was performed using the wild type and ΔrpoS strains of Salmonella ATCC14028, grown in   1. Uncharacterized ORFs of less than 300 nucleotides tightly controlled by σ S. a Information about protein families (DUF), motifs (Interpro IPR), 3D structures (PDB code), predicted α-helical trans-membrane (TM) fragments, signal peptide (SP) and lipoprotein (Lipo) are indicated (Supplementary Methods). The start codon was re-annotated for three ORFs (Supplementary Fig. S5 ). In most cases, the protein was identified by nLC-MS (Supplementary Table  S1  and Datasets S2-S3). b Eight ORFs had annotated homologs in E. coli K12 and homologs were found for five additional ORFs (Supplementary Fig. S5 ). c Possible homologs were identified as described in Methods.
For each protein, a complete list of homologous sequences is given in Supplementary Dataset S1. d RpoS dependency for expression of E. coli K12 orthologs in transcriptomic analyses. e STM14_1275 and STM14_1829 are paralogous to STM14_2091 (Supplementary Fig. S6  and Table  S1 ). nutrient-rich LB medium to late stationary phase, i. e. the growth conditions previously used to characterize the σ S -transcriptome  16. Three biological replicates of wild type and ΔrpoS strains were grown, and subjected to proteome-wide label-free quantification. After harvesting, cells were lysed, proteins were digested with rLys-C and trypsin, and the resulting peptides were analysed by nLC-MS/MS. A statistical analysis of relative changes in protein abundance between the wild type strain and the ΔrpoS mutant was performed using Perseus, the companion software of MaxQuant (see Supplementary Methods for details).
A protein was considered as being "present" in a strain, if observed in at least two of the three replicates. Proteins detected in less than two replicates of one strain and in at least two replicates of the other strain, were designated as "exclusive" to that latter strain. Two-sided T-tests of the base-2 logarithm (log 2 ) transformed intensity values were employed, using three False Discovery Rates (FDR of 5%, 1% and 0. 1%) to identify differentially abundant proteins with different degrees of statistical significance. A complete list of all proteins, along with their relative abundance pattern and sequence coverage is provided in Supplementary Dataset S2. The (log 2 ) fold change of the level of each protein in the ΔrpoS mutant, with respect to that in wild type strain, is shown. Positive values indicate higher abundance in the mutant than the wild type.
Negative values represent lower levels in the mutant than the wild type. A total of 2444 Salmonella proteins were identified (Fig. 2a , Supplementary Dataset S2), which corresponds to about 46% coverage of the ATCC14028 proteome. The remaining proteins might not be expressed under the growth condition employed in this study, or they might be expressed at levels too low to be detected. Also, hydrophobic integral membrane proteins are difficult to identify by MS-based proteomics  36. Expression of σ S was associated with the significant increase in the level of 401 proteins and a decrease in abundance of 538 proteins (Fig. 2a , Supplementary Datasets S2-S3). For these two groups, the change in the level will be referred to as "up-and down-regulation" regardless of the specific mechanism (or combination of mechanisms) leading to such change.
The log 2 ratios of fold changes were at least 0. 68 (representing a minimum fold change value of 1. 5). Of note, the log 2 fold changes in protein abundance were the highest when the FDR was low, and were higher for up-regulated than for down-regulated proteins (Supplementary Dataset S3). The set of 401 up-regulated proteins contained 54 proteins exclusively found in the wild-type strain (Fig. 2b ). The set of 538 down-regulated proteins contains 79 proteins found exclusively in the ΔrpoS mutant (Fig. 2c ). σ S affected directly or indirectly the abundance of proteins involved in a variety of processes including metabolism, membrane trafficking, regulation, and stress resistance, and of many uncharacterized proteins (Supplementary Dataset S3). These data confirmed at the proteome level the large impact of σ S in bacterial adaptation to a non-growing lifestyle.
1c ). STM14_5479 does not seem to have a canonical ribosome-binding site (Supplementary Fig. S5 ), suggesting that the putative ORF might not be translated. We cannot exclude, however, that its translation requires a specific condition. In contrast to STM14_5469, STM14-5479 is conserved in only a few genomes of Salmonella (Table  1  and Supplementary Dataset S1). With the exception of STM14_5292/ytfK, a good correlation was found for changes in expression of the uncharacterized σ S -dependent small ORFs at the RNA and protein levels (Fig. 1b  and Supplementary Table  S1 ). This finding is consistent with transcription activation of these genes by σ S. However, many genes transcriptionally activated by σ S can also be transcribed by σ 70 during exponential growth, either from a unique promoter recognized by the two sigmas, or from different promoters  3, 8.
Comparison of the global proteomic and transcriptomic σ S profiles. One of the main issues addressed in this study was to determine to which extent changes induced by the ΔrpoS mutation at the transcriptome and proteome levels are correlated. Our proteomics approach identified a protein for about half of genes showing detectable expression by RNA-seq  16  (Supplementary Dataset S3). The other genes might be poorly transcribed and/or translated, or their products might be unstable or hardly detectable by MS. For proteins up-regulated by σ S , a high correlation was observed for changes in gene expression at the transcript and protein levels, especially for proteins selected with a FDR of 0. 1% and 1% (Fig. 2b , Supplementary Dataset S3). Half of the up-regulated proteins selected with a 5% FDR were encoded by genes showing no significant (p-value > 0.
05) regulation by σ S at the transcriptional level. This apparent discrepancy could result from some proteins being encoded by genes activated by σ S post-transcriptionally, or genes with a low level of transcription that excluded them from the comparative transcriptomic analysis. It is also possible that some proteins are damaged and degraded in the ΔrpoS mutant as a result of increased endogenous stress (i. e. endogenous oxidative stress may target proteins that are very sensitive to carbonylation and favour their degradation). Strikingly, a different picture emerged for proteins down-regulated by σ S (Fig. 2c , Supplementary Dataset S3). Most of these proteins were encoded by genes showing no significant regulation by σ S at the RNA level (p > 0. 05). Furthermore, about 5% of down-regulated proteins were encoded by genes activated by σ S at the RNA level, suggesting an inverse correlation between regulation by σ S at the protein and RNA levels.
Most interestingly, Figure  2. The σ S -dependent proteome of S. Typhimurium ATCC14028 and comparison with transcriptomic data  16. Salmonella wild type and ΔrpoS strains were grown in LB medium to stationary phase in three biological replicates. Total protein was digested and the peptides were analysed by nLC-MS/MS. The pie charts show (a) total proteins up-and down-regulated by σ S (p-value < 0. 05) and (b,c,d) comparison with transcriptomic data  16  (see also text and Methods for details). Among the 2444 proteins identified, 939 proteins showed significant differences in abundance between the wild type and ΔrpoS strains, including 54 proteins found exclusively in the wild type strain and 79 proteins found exclusively in the ΔrpoS mutant (p-value < 0. 05, Supplementary Dataset S3). A high correlation was found between the proteome and transcriptome data for proteins upregulated by σ S (b): 97 to 50% (according to the FDR) of the 401 up-regulated proteins were encoded by genes activated by σ S at the RNA level.
In sharp contrast, more than 70% of the 538 proteins down-regulated by σ S (c) were encoded by genes showing no significant σ S regulation at the transcript levels (p-value > 0. 05, Supplementary Dataset S3). For this category of proteins, the degree of correlation between the proteome and transcriptome data sets was not dependent on the FDR. Among proteins showing no σ S regulation, 79% were encoded by genes showing no regulation by σ S at the transcript level (d). these unexpected regulatory patterns were observed independently of the FDR value. These findings suggest that the observed changes at the protein level were the result of post-transcriptional regulatory effects by σ S. It is also possible that some genes were selected in the proteomics approach, and not by RNA-seq, due to differences in the sensitivity of the two methods.
Overall, the proteomics data confirmed the positive effect of σ S on transcription of genes involved in central energy metabolism (glycolysis and the pentose phosphate pathway, mixed acid fermentation), glycogen and trehalose metabolism, arginine degradation, putrescine synthesis and degradation, and antioxidant pathways (catalases, superoxide dismutase, glutaredoxins, ferritins and Fe-S repair proteins) (see details in ref. 16 and Supplementary Dataset S3). Genes down-regulated by σ S at both the protein and RNA levels encode porins, TCA cycle enzymes and proteins of unknown functions  16, 17  (Supplementary Dataset S3). Regulatory effects of σ S at the RNA level, not transferred to the protein level. The abundance of 1504 proteins was found unchanged in the ΔrpoS mutant, with respect to the wild type strain (Fig.
2a , Supplementary Dataset S3). Surprisingly, a fraction of the corresponding genes (309 genes, Fig. 2d ) was differentially expressed in RNAseq experiments, among which 135 showed highly significant regulation  16  (p < 0. 001, Supplementary Dataset S3). Thus, for these genes, σ S regulation at the RNA level was not transferred to the protein level. This regulatory profile was similar to that of STM14_5292 (ytfK), one of the small genes strongly activated by σ S at the RNA level  16  (Supplementary Table  S1 ) but not at the steady state level of the protein (Fig. 1b ). Consistent with these data, the ΔrpoS mutation strongly impaired expression of a transcriptional STM14_5292-lacZ fusion, but had a minor (and positive) impact on the expression of the translational STM14_5292-lacZ fusion (Fig.
05, are indicated (Supplementary Dataset S3). Proteins in red and green were down-and up-regulated by σ S , respectively. DHAP: Dihydroacetone phosphate. Novel physiological effects of σ S revealed by proteomic analyses. Among genes negatively controlled by σ S at the protein level, we noticed several products of the iol, pdu, eut and fuc genes, involved in the catabolism of myo-inositol, propanediol, ethanolamine and L-fucose, respectively (Fig. 4  and Supplementary Dataset S3). Western blot analysis of relative levels, in the ΔrpoS and wild type strains, of products of the fuc, iol, pdu and eut genes validated the proteomics data (Fig. 5a ). Also consistent with these results, the ΔrpoS mutant grew better than the wild type strain at the expense of myo-inositol, fucose, propanediol and ethanolamine as sole carbon sources (Fig.
5b ) and the ΔrpoS mutation was complemented by a plasmid carrying rpoS (Supplementary Fig. S9 ). Production of the MgsA protein that converts dihydroacetone phosphate (DHAP), a product of myo-inositol and fucose degradation, into methylglyoxylate was also down-regulated by σ S (Figs 4 and 5a and Supplementary Dataset S3). To our knowledge, this is the first report of positive effects of a ΔrpoS mutation on catabolism of myo-inositol, L-fucose, ethanolamine and propanediol. Transcript levels of the corresponding genes were not significantly affected by the ΔrpoS mutation  16  (Supplementary Dataset S3), suggesting that σ S down-regulates production of the gene products post-transcriptionally. The underlying molecular mechanisms are unknown and will be the subject of more detailed studies. Interestingly, the Salmonella prp and pdu genes are down-regulated, at the protein level only, by the RNA-binding protein Hfq  43  (Fig.
The bacterial growth of the wild-type strain and ΔrpoS mutant was also compared in liquid medium containing L-fucose. Stationary phase cultures in LB were washed, resuspended in PBS  54  to an OD 600 of 1. 0, and bacteria were inoculated to minimal medium containing L-fucose at an initial OD 600 of 0. 01. The growth phase was determined by measuring culture turbidity at an optical density of 600 nm. As mentioned above, transcript levels of the pdu, iol, eut, fuc, and mgsA genes were not significantly increased in the ΔrpoS strain, compared to the wild type strain  16. However, their low transcript abundances (Supplementary Dataset S3) might have excluded them from the list of σ S -activated genes during the comparative transcriptomic analysis  16. To address this issue, regulation of some of these genes was assessed in strains into which lacZ was inserted downstream of the flag-tagged genes to create transcriptional fusions (Fig.
It is possible that expression of the Salmonella prp, iol and fuc genes is activated in vivo by σ S and provides fitness advantages to the pathogen. However, as hypothesized above for the prp genes, in the growth conditions used in the present study, the amount of the fuc, pdu, iol, and eut products may be adjusted according to the activity of the TCA cycle (Fig. 4 ). In addition, catabolism of myo-inositol and L-fucose produces DHAP that can be converted by the MgsA protein into methylglyoxal, a cytotoxic and mutagenic product affecting DNA and proteins  49. DHAP can also be produced from glyceralhedyde-3 phosphate via excessive carbon flux through glycolysis. Since σ S activates expression of genes in the glycolytic pathway (Supplementary Dataset S3 and ref. 16), an increased carbon flux in the first half of glycolysis, and myo-inositol and L-fucose degradation, might give rise to excess DHAP and its conversion to methylglyoxal by MgsA, a process detrimental for the cell.
Therefore, down-regulation of mgsA by σ S might confer a fitness advantage to the cell. From the few examples discussed here, it is tempting to speculate that a number of negative effects of σ S on protein production, in the growth conditions used, aim at buffering toxic effects that metabolic rewiring by σ S might generate in stationary phase cells. Since several genes appear to be down-regulated by σ S only or mainly at the protein level in Salmonella (Supplementary Dataset S3), their study might pinpoint to yet uncovered σ S post-transcriptional mechanisms maintaining the delicate balance between cellular preservation during quiescence and re-growth potential, under various conditions.
LC-MS/MS analysis. Online chromatography was performed with a Thermo EASY-nLC 1000 UHPLC system (Thermo Fisher Scientific, Bremen, Germany) coupled online to the Q Exactive HF instrument with a nano-electrospray ion source (Thermo Fisher Scientific). For each samples, 1 μg of peptides was injected onto a 50 cm column (EASY-Spray column, 50 cm × 75 µm ID, PepMap C18, 2 µm particles, 100 A pore size -ES803 -Thermo Fisher Scientific) and separated with a multi-step gradient from 2 to 23% acetonitrile in 135 min and 23 to 45% acetonitrile in 20 min, at a flow rate of 250 nL/min over 190 min. Column temperature was set to 50 °C. MS data were acquired using Xcalibur software, using a data-dependent top10 method with a survey scans (300-1700 m/z) at a resolution of 60,000, and a MS/MS scans (fixed first mass 100 m/z) at a resolution of 15,000.
The AGC target and maximum injection time for the survey scans and the MS/MS scans were set to 3 E 6 , 100 ms and 1 E 5 , 45 ms, respectively. The isolation window was set to 1. 6 m/z and normalized collision energy fixed to 28 for HCD fragmentation. We used an underfill ratio of 2. 0% for an intensity threshold of 4. 4 E 4. Unassigned precursor ion charge states as well as 1, 8 and >8 charged states were rejected and peptide match was disable. Exclude isotopes was enabled and selected ions were dynamically excluded for 45 seconds. Data analysis. Raw data were analysed using MaxQuant software version 1. 5. 3. 8  56  , using the Andromeda search engine  57. The MS/MS spectra were searched against the Salmonella Typhimurium strain 14028 s UniProt database containing 5,369 proteins, and against the contaminant file included in MaxQuant.
Quantification was performed using the XIC-based LFQ algorithm, with the Fast LFQ mode as described in ref. 58. Unique and razor peptides, included modified peptides, with at least 2 ratio counts were accepted for quantification. Statistical analysis. Output protein group file was integrated into Perseus  59  , the companion software of MaxQuant, to perform data filtering and statistical tests. First, contaminants, reverse identifications, and proteins only identified by site were excluded from further data analysis and a categorical annotation was applied to create two sample groups according to the two types of bacterial strain in triplicate. Second, LFQ intensities were log 2 transformed. A protein filtering was set for the validation process, such as a protein was integrated in the final list only if the protein was identified in at least two replicates of one sample group.