dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we investigated the spatio-temporal distribution of small pelagic fish in the Gulf of Lions using a unique dataset of 10 years of acoustic surveys. Because small pelagic fish are key species of the pelagic ecosystem due to their central place in the food web, transferring energy from the lowest trophic levels (plankton) towards top-predators  [16] , and because their life-history traits (short lifespan, large fecundity) make them strongly dependent on the abiotic environment  [17] , changes in their distribution and abundance have been a major source of concern for scientists and managers worldwide (  [18] ; Global Ocean Ecosystem Dynamics program -GLOBEC;). Over the past years, important changes in the two main target species (the European sardine, Sardina pilchardus and the European anchovy, Engraulis encrasicolus) biomass  [19] , along with a shift in the size-distribution of these species towards smaller individuals  [20]  have been observed in the Northwestern Mediterranean Sea (Gulf of Lions), resulting in important economic losses for fisheries.
Ethic statement The study was conducted in the Gulf of Lions (Longitude in  [3. 05u; 5. 20u ] and Latitude in  [42. 44u; 43. 44u] ), a public sea area. No sampling was operated from private land and field studies did not involve endangered or protected species. All data used in this study came from PELMED acoustic surveys, which comply with the MEDIAS (Mediterranean Acoustic Survey) protocol. The sampling has been performed under repeated international standardized surveys where the research vessel had full permission to sample from all relevant national public authorities (governments). Acoustic data were collected at a distance, which does not require any particular ethic approval. Further data used in this study came from scientific trawls conducted during these same PELMED surveys. Again, no approval by an ethic committee was required as the targeted species are exploited species and trawling methods done according to international standard trawl surveying.
h 21. All 4 frequencies were visualized during sampling to help deciding when to trawl for species identification. However, only energies from the 38 kHz (typical frequency used for fish) channel were used to estimate fish density. Acoustic data analyses, such as bottom correction, were later performed using Movies+  [24]  and FishView IFREMER softwares. Species discrimination and echo-partitioning were performed by the combination of echo trace classification and trawl outputs  [25]. Species biomass and abundance were finally estimated from species energy using specific target strength (TS = 20 log(L) -71. 2, where L is the length of the fish for all 3 species, see  [26]  for more details on acoustic surveys and analyses). Main survey features are summarised in Table  1.
Analyses We defined and used different spatial indicators and geostatistical methods described in details below. An overview of these indicators, including their calculation formula, representative scale and biological meaning are summarised in Table  2. A key concept in statistics in general and in spatial statistics in particular is the support of the information, i. e. the geographical area over which measures are recorded  [27] [28]. In this study, the support size was the size of the Elementary Sampling Distance Unit, that is 1 nm. All statistics derived from these data are thus associated to the 1 nm sampling support and, for all of them, their values would have been different if computed at another support. Even though some metrics are labelled like spatial statistics (e. g. space selectivity index, local index of collocation), they are not sensitive to the location of the points in space (i.
e. any exchange between two data does not change the result). They are sensitive to the spatial patterns that exist at scales smaller than the support size and that are integrated in the observations. For some of them (e. g. Empirical Orthogonal Functions), it remains possible to represent them as geographical distributions making the confusion even worth. Aggregation patterns and spatial structure. Presence Area (PA) was calculated as the percentage of sampled points at which the species was found, independently of its abundance. A space selectivity index was defined on the basis of annual concentration curves  [29] [30]. Like Lorenz curves (as used in  [31] ), concentration curves represent the maximum proportion of biomass as a function of the proportion of samples  [30]. For instance, a proportion of biomass of 0.
EOF analysis is a decomposition of a spatio-temporal dataset in terms of orthogonal basis functions. It is thus very similar to a Principal Component Analysis, except that it is applied to spatio-temporal data, using time as a descriptor and space (i. e. sampling locations) as objects  [36]. EOF was performed on standardized data to compare annual spatial distributions independently of annual total levels of biomass. The first axis (eigen vector) of the EOF is the linear combination of years which maximizes the percentage of the interannual variance of spatial distributions. When all yearly contributions to this first axis share the same sign, the greater the percentage of variance explained by the first EOF, the more persistent the spatial distributions  [37;12]. In order to have a local estimation of temporal variability/stability and see whether some areas were more stable than others, we also calculated the Coefficient of Variation (CV) of the 10 annual values in each sampling point.
To evaluate the deviation of these indices from random expectation, we used a randomization test by non-parametric bootstrap of 1000 values. For species of strong local overlap (i. e. anchovies and sardines), we also calculated a dominance index based on the relative difference in biomass of the two species in each point (Table  2 ). This index varies between -1, where only the species j is present and 1 where only the presence i is present, 0 corresponding to a situation where species i and j are equally abundant  [40]. This index enabled us to better represent which species dominate where. Softwares. Statistics were conducted using R v. 2. 15. 0  [41]. Geostatistical analyses were performed with the package RGeoS  [42]. Spatial data are given in WGS84 coordinate system. Analyses were conducted on both raw and kriged data.
Acknowledgments We are very grateful to the crew of the Ifremer RV L'Europe and everyone who participated to the PELMED surveys between 2003 and 2012. PELMED surveys are cofunded by  IFREMER  and the  European Union  through the Data Collection Framework (DCF). This study is part of the program EcoPelGol ( Study of the Pelagic ecosystem in the Gulf of Lions ), funded by  France Filiere Peche (FFP). We also wish to thank  Vincent A. Viblanc  for his helpful suggestions and his English-proofing of the manuscript. Finally, we thank 2 anonymous reviewers for their helpful comments.
, 2013). This high conservation of the domain organization of the protein in the vast majority of eukaryotes, including unicellular organisms such as yeasts, is also indicative of its conserved functional features. Functional studies of ATAD2 family members carried out in mammals were mostly in cancer settings and have recently been reviewed  (Boussouar et al. , 2013). However, in addition to these studies, there is a relatively rich literature on Yta7 (Yeast Tat-binding Analog 7), the unique ATAD2 homologue expressed in the budding yeast Saccharomyces cerevisiae. This review considers all the molecular data available on Yta7 in the light of our knowledge on ATAD2. The data reported highlight the implication of Yta7 not only in gene expression, as expected, but also in genome organization, as a possible histone chaperone acting at boundary sites and regulating transcription.
ATAD2-LIKE PROTEINS ARE CONSERVED FROM YEAST TO HUMAN A phylogenetic tree of ATAD2-like proteins, shown in Fig. 1A  (left), illustrates the strong conservation of this protein family within the eukaryotic kingdom, including yeast. These proteins all share a two-partite AAA+ ATPase domain, which is found Nterminal to a bromodomain. This tree also shows that in several organisms the genome encodes two ATAD2 paralogs. These include Chordata and the fission yeast Schizosaccharomyces pombe that possess two ATAD2-like proteins known as ATAD2A and B and Abo1 and 2, respectively. It is of note that the vast majority of the published functional studies concerns the ATAD2A (also termed ATAD2) member in both human and mouse. The high sequence similarity between these paralogs argues in favor of a possible functional redundancy across all species.
FROM YTA7 TO ATAD2A: FUNCTIONAL ORTHOLOGS? The high similarity between Yta7 and ATAD2 domains organization (Fig. 1B ) supports the hypothesis that at least part of their function is shared. An implication of both proteins in transcriptional regulation is clear. However, although data on Yta7 suggest a role for this factor in the organization of the genome and chromatin dynamics, such indications are scarce for ATAD2. Indeed, only one experiment that measured histone H2A mobility in a lung cancer cell line showed that a decrease in ATAD2 content modifies H2A turnover  (Caron et al. , 2010). Even in this case there is no indication on the mechanism that links ATAD2 and histone mobility. Considering the importance of Yta7 in regulating nucleosome density, it is tempting to propose that ATAD2 can also act as a histone chaperone, evicting histones from chromatin to avoid potentially deleterious effects associated to an increase in histone density.
, 2010) , where one of the most extensive chromatin remodeling takes place  (Goudarzi et al. , 2014). However, neither in human cells nor in yeast is such N-terminally shortened protein found expressed, suggesting that, if the above hypothesis is right, in the cases of human or yeast cells, a regulatory mechanism should inactivate the transcriptional role of the N-terminal domain to transform the protein into a pure chromatin regulator. This regulation could involve changes, such as Yta7 phosphorylation at its N-terminal part, which would lead to the release of the protein from histone genes  (Kurat et al. , 2011). The data discussed above therefore support the idea that ATAD2 and Yta7 follow similar regulatory rules, since the underlying activity remains conserved (even if their mechanisms of action could be regulated differently due to specific requirements and evolutionary adaptations).
ACKNOWLEDGMENTS ATAD2 project in SK laboratory is supported by  INCa. YM is post-doctoral fellow supported by foundation  ARC  and MJ is recipient of a PhD fellowship from  INCa. Research done in AV laboratory is supported by funds from the  French Institut National de la Santé Et de la Recherche Médicale (INSERM) , the  Université Joseph Fourier (UJF) , the  Agence National de la Recherche (ANR)  and the  Association pour la Recherche contre le Cancer (ARC).
Background Stem/precursor cells are able to differentiate into almost every cell type and are therefore promising tools for the development of new therapeutic approaches in regenerative medicine  [1]. Much attention has focused on understanding stem cell biology and the regulation of differentiation to help realize these clinical aspirations  [2, 3]. However, the key biochemical networks controlling the stem cell biology remain poorly described or undefined, contributing to a lack of knowledge of the principle events regulating differentiation processes. Quantitative proteomics allows to gather unbiased wide range evidences instrumental to gain deeper insight into the regulatory networks controlling cellular biology  [4, 5]. This is now possible by integrating proteomic data sets with literature databases  [6].
Bioinformatic analysis of the differentially expressed membrane proteins In order to unveil the molecular functions underlying RLSC differentiation, identified proteins were functionally grouped according to the Ingenuity Pathways Analysis literature database. All the differentially expressed proteins were therefore uploaded to the IPA server. Firstly, we analyzed our proteomic data set in the frame of molecular and cellular functions. Cell-to-cell signaling/interaction, energy production, lipid metabolism, small molecule biochemistry, cellular movement, and cell death and survival were the most significant listed functions, as determined by the p value (Figure  3A ). Interestingly, proteins involved in the cell-to-cell signaling/interaction processes were listed as a top-listed function (with a significant p value <0.
Data analysis Differentially expressed proteins were analyzed using Ingenuity Pathway Analysis (IPA, Ingenuity Systems; see www. ingenuity. com). The over-represented biological processes, molecular functions, and canonical pathways were generated based on information contained in the Ingenuity Pathways Knowledge Base. Right-tailed Fisher's exact test was used to calculate a p-value determining the probability that each biological function and/or disease involved in that proteome profile alteration is due to chance alone.
Financial support This work was supported by grants from  MIUR Ministero dell'Università e Ricerca Scientifica  ( FIRB 2012 , codice progetto  RBFR12NSCF ),  Associazione Italiana per la Ricerca sul Cancro (AIRC) and Ministero della Salute  ( Ricerca Corrente ). Progetto  Regione Lazio -Legge Regionale  26 del 28 Dicembre 2007 Art. 33comma 4 lettera b, title: " sviluppo di terapie cellulari alternative al trapianto di fegato mediante cellule staminali epatiche adulte ".
In B. subtilis, both m 5 U747 and m 5 U1939 are catalyzed by a single AdoMet enzyme, RlmCD  (9) , while m 5 U54 in tRNA is added by the COG1206 flavoprotein TrmFO, which methylates using N 5 ,N 10 -methylenetetrahydrofolate in conjunction with reduced flavin adenine dinucleotide, FADH 2  (10). Database searches suggest that other bacteria possess orthologous folate-dependent methyltransferases  (11)  and the few characterized examples also methylate tRNAs  (10, (12) (13) (14). All previously characterized rRNA methyltransferases, irrespective of their nucleotide targets, are dependent on AdoMet as a cofactor  (3, 5). We have investigated whether the present picture of RNA modification mechanisms is also applicable to bacteria such as mycoplasmas with minimal genomes. The stable RNAs of mycoplasmas possess many characteristic bacterial modifications, including m 5 U, and certain mycoplasmas have more than one copy of trmFO-like genes  (15).
The exposed regions within the RNAs were digested away with mung bean nuclease and RNase A, and the sequences protected by hybridization were separated on gels  (34, 35). The rRNA fragments of ∼48 and 55 nucleotides were extracted and digested with either RNase A or RNase T1 in aqueous solution containing 3-hydroxypicolinic acid and analyzed by MALDI-MS (Ultraflextreme, Bruker Daltronics) recording in reflector  and positive ion mode  (36). Spectra were processed using the program Flexanalysis (BrukerDaltonics).
ACKNOWLEDGMENTS Benoit Desmolaize ,  Damien Bregeon ,  Céline Fabret  and  Christina S. Sørensen  are thanked for their help during the early stages of rRNA analysis. Our thanks also go to  John Glass  for providing the yeast strain containing the Mcap-CAH genome, and to  Pascal Sirand-Pugnet ,  Laure Beven  and  Nelson Enrique Arenas Suarez  for helpful discussions and to  Antoine Boullet  for help with complementation experiments. We are indebted to  Valerie de Crécy-Lagard  and  Stephane Skouloubris  for first pointing out the duplication of trmFO orthologs in Mcap.
FUNDING Danish Research Agency [FNU-rammebevilling  10-084554  to S. D. ];  Conseil Régional d'Aquitaine  and the  French National Funding Research Agency  [ ANR-13-JSV5-0004-01  'SYNBIOMOLL' to C. L. , A. L. and A. B];  National Institute of Health  [ R01 GM70641  to  Valérie de Crécy-Lagard  and B. E. Y. ]. Funding for open access charge:  Danish Research Agency [FNU -rammebevilling  10-084554  to S. D. ].
Focus groups with women In February 2004, five focus groups were conducted with women who attended various maternity wards in the Health District of Bogodogo (four urban facilities and one rural facility). These focus groups collected data to assess the perceptions of CS by women in the community. Home interviews were embedded in the action research and served as an exploratory phase to prepare for user-provider meetings in the second part of the project. Each group consisted of about 15 women, which allowed us to look into the subject in greater depth. The composition of the groups was determined during antenatal consultations in the four urban facilities and one rural facility. The activity was proposed to the women, and if they accepted, details, days, and hours of the discussion were given.
Individual home visit interviews and discussions in the user focus groups were conducted in Moore (the national language), audio-recorded, and transcribed into the French language using Microsoft Word. A content analysis was undertaken manually according to the analysis methodology of qualitative data. 24, 25 No software was used. The authors first read all the interviews to identify the different themes, and each interview was then analyzed according to the themes identified during the first round of reading. The results were triangulated by the two social anthropologists (FO and SZ). Permission to perform the AQUASOU action research and its different nested studies was obtained from the Ministry of Health of Burkina Faso (MS/SG/DGSP/DSF/27. 02. 2002) and the Regional Directorate of the Health Center Region.
Acknowledgments This work is based on data gathered as part of the "  Amélioration de la Qualité et de l'Accès aux Soins Obstétricaux d'Urgence" (AQUASOU)  project funded by the  French Ministry of Foreign Affairs  (Projet  FSP 2001-149 ). We would like to thank the Bogodogo District management team in Ouagadougou, the maternity and operating theater staff for their commitment to and involvement in the home visits, the women and their families for their trust, and the team at the  Health and Family Directory of the Ministry of Health  for its constant support.
Quantification with stereology test grid RV were obtained by marking Ki67-positive and negative tumour cell profiles, using a stereological method for 2D object enumeration  [14, 15]  implemented by the Stereology module (ADCIS, Caen, France) with a test grid of systematically sampled frames (frame size -125 pixels, spacing of frames -250 pixels) overlaid on a spot image in ImageScope (Aperio Technologies, USA), Figure  1. The percentage of Ki67 positive tumour cell profiles established by the test grid estimation (Ki67-Count) was calculated as 100*Ki67-positive nuclear profiles/(Ki67-positive nuclear profiles + Ki67-negative nuclear profiles). To test the degree of uncertainty of the RV, inter-observer variation was estimated based on Ki67-Count values produced by three observers (Ki67-Count-1, 2 and 3) independently in a subset (n = 30) of the TMA images.
Characteristics and measurement uncertainty of the reference value dataset Summary statistics of the RV (n = 30) obtained by three independent observers' marking of the tumour cell profiles in the test grid are presented in Table  2 , along with the results of other measurements in this dataset for reference. No significant variance between the three Ki67-Counts was revealed by one-way ANOVA (F = 0. 08, P = 0. 9217), while strong pairwise correlation among the values was found: r = 0. 98, r = 0. 98, r = 0. 97 (P <0. 0001). Similarly, the total number of nuclear profiles marked did not differ significantly, although Observer 1 tended to mark less; the total number of nuclear profiles of Observer 1 correlated with that of observers 2 and 3 at r = 0. 94, while the latter two correlated at r = 0.
98 (P <0. 0001). Uncertainty introduced by variance among the three counts to produce Ki67-Count for each individual spot was low: for the 30 spots, mean standard deviation and mean standard error were 2. 6% and 1. 5%, respectively. Of note, the five visual estimates (Ki67-VE), summarized for the same individual 30 spots, revealed much higher uncertainty -mean standard deviation and mean standard error were 10. 9% and 4. 9%, respectively. Interestingly, a scatter plot of the five VE' standard deviations against their means (n = 164, Figure  3 ) uncovered nonlinear relationship reflecting higher variation in the middle of the means' scale. Meanwhile, the positive linear relationship between the standard deviations and the means for the three observers of Ki67-Count was found in the dataset available (n = 30, not shown).
Uncertainty caused by subsampling the tissue by the test grid of frames was estimated by computation of CE providing confidence intervals for each individual Ki67-Count value. Overlap of the Ki67-Count confidence intervals for all three and each pair of the three observers was considered as agreement between the generated Ki67 LI values (Figure  4 ). The agreement within the same confidence interval among all three measurements was 69%; whereas the pairwise agreement varied from 83 to 86%. The uncertainty of the RV generated was therefore considered satisfactory. The RV for the whole image dataset (n = 164) were based on a single observer  count per spot (Ki67-Count). Yet, the subsampling uncertainty was further taken into account in the accuracy estimates.
The repeatability of the DIA in strictly the same conditions is expected to be perfect, the reproducibility of the DIA involving all phases of the Ki67 LI test as well as its robustness to the IHC staining variation was beyond the scope of this study. Third, we did not validate our DIA prediction accuracy on an independent dataset, since it requires another set of criterion standard data that is planned as an output of our next experiment. Fourth, the DIA validation tests in the present study are based on summarized data per image (Ki67 LI), while more rigid individual cell-based comparisons would provide even more granular information on the performance of the DIA tools. Our approach uncovered a non-linear bias in the opposite directions depending on the magnitude of the measurement of the Ki67-DIA-2, which could hardly be documented by only the subjective assessment of the DIA accuracy of selected images on a computer monitor.
Theories of biodiversity rest on several macroecological patterns describing the relationship between species abundance and diversity. A central problem is that all theories make similar predictions for these patterns despite disparate assumptions. A troubling implication is that these patterns may not reflect anything unique about organizational principles of biology or the functioning of ecological systems. To test this, we analyze five datasets from ecological, economic, and geological systems that describe the distribution of objects across categories in the United States. At the level of functional form ('first-order effects'), these patterns are not unique to ecological systems, indicating they may reveal little about biological process. However, we show that mechanism can be better revealed in the scale-dependency of first-order patterns ('second-order effects').