dataesr/scientific-paragraphs-categorization huggingface dataset:
A very low effect due to the different rearing sites was observed on the PCA performed on all genes (data not shown). After analysis of the microarray findings on TMeV 4. 6. 0 software  [39, 40] , one-way ANOVA results of the 4 groups uninfected wild, uninfected offshore, low and high infected (respectively CRIC, CAB, BL and i) having a p value < 0. 01 with Bonferroni's correction found that 249 of 31,918 analyzed ESTs seemed to be differentially expressed (Figure  2A ). A t-test (p<0. 01 and Bonferroni's correction) performed between the sanctuary and offshore sites do not show any significant expressed genes. A hierarchical clustering performed using a Pearson's correlation defined two groups: uninfected and infected animals. A subsequent K-means clustering of genes allowed definition of 5 distinct clusters (Figure  2B ).
Gene function analysis The custom microarray design and the raw and normalized data are available from Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE 46249; GSE46249). Only 51% of the 249 differentially expressed genes on the microarray were annotated. From the recent genome sequencing of the Pacific oyster  [41] , the non-annotated transcripts were blasted in genome data http://oysterdb. cn/blast. html, resulting in an additional 128 genes: 62 and 66 of genes up regulated in infected and uninfected animals, respectively (see Additional files 1 and 2). A classification of the 128 genes has been made to group different genes into their major function (Figure  3 ). Only 13 of the 128 genes were listed as not annotated or having an unknown function. They correspond to hypothetical proteins or non-coding region of the genome.
Discussion In order to understand the molecular events that occurred during the massive oyster mortalities, and the role played by OsHV-1 in them, animals from a same origin were reared in different areas: an oyster grow out site; a sanctuary area far from oyster growing sites and in tank isolated from natural areas. At the end of this experiment, high mortalities were observed only in the oyster grow out site and coincided with the period of repeated massive (See figure on previous page. ) Figure  2  Clustering of 249 ESTs obtained after ANOVA analysis with four groups (CAB, CRIC, BL and isee legend to Figure  1 ) with adjusted Bonferroni and p values< 0,01 made on TMeV 4. 6. 0 software  (Saeed et al. , 2003 and 2006). CRIC and i correspond, respectively, to uninfected spat from sanctuary area or from tank.
Overexpression of genes related to apoptosis such as Bcl-2 (GenBank accession EU678310) have been reported in C. gigas haemocytes after OsHV-1 challenge  [19]. Three of these genes (cbx1, drg2, groupl1) have been validated by qPCR (Figure  3 ) suggesting that cellular proliferation may be reduced in infected spat. Antiapoptosis phenomena appear to be important in infected spat. It may exist a balance between pro apoptosis and anti-apoptosis factors suggest by Jenner and Young in 2005 (for review  [76] ).
Immune system Seven genes are linked to immunity function. Two of them, which were overexpressed in infected spat, are associated with the response to fungal infection, such as hlp2 and a putative fungi-static metabolite, CHFF_05463. Leng8 is involved in regulation of the inflammation process and these receptor present homologues in herpes saimiri  [88]. CD109 represents an evolutionary conserved class of α2-macroglobulin/ complement gene family  [38]  and is involved in regulation of the TGFbeta pathway  [88]. The α2-macroglobulin molecules are protease inhibitors  [89]. In C. gigas, ESTs from hemocytes show high identity with α2-macroglobulin and its receptor  [90]. These genes are overexpressed in infected spat and suggest a role in defense against the pathogen. Moreover, in SSH studies overexpression of a gene presenting homologies with alpha macroglobuline genes (GenBank accession EU678312) has been reported in C.
Acknowledgments This research was supported by  Conseil Régional de Basse-Normandie  ( CRBN, France ) and from  Conseil Général du Calvados  and  Conseil Général de la Manche. The authors are grateful to  Susan Ford  ( Rutgers University ) for language corrections. The authors want to thank  Michel Le Guillois  and his students from  Centre de Formation Professionnelle et de Promotion Agricole of Coutances  and  Isabelle Roblin  from  Lycée Maritime Aquacole of Cherbourg  for spat care.
Mitochondrial DNA (mtDNA) and Y chromosome (NRY) genetic markers have been often contrasted to investigate sex-specific dynamics. Traditionally, isolation by distance, intrapopulation genetic diversity and population differentiation are estimated from both markers and compared. Two possible sources of bias are often neglected. First, kilometric distances are frequently used as predictor of the connectivity between groups, hiding the role played by environmental features at a microgeographic scale. Second, the comparison of intrapopulation diversity and population differentiation between mtDNA and NRY is hampered by their different mutational mechanisms and rates. Here, we show how to account for these biases by analyzing from a different perspective a published dataset of eight West New Guinea (WNG) populations for which mtDNA control region sequences and seven linked NRY microsatellites had been typed.
To this end, we analyzed in a new light a published dataset of mtDNA and NRY microsatellites variation in eight West New Guinea (WNG) groups  (Tommaseo-Ponzetta et al. 2002; Kayser et al. 2003). The long human presence in the island as well as the environmental and temporal heterogeneity makes WNG an interesting region to investigate population dynamics and contrast male and female histories. New Guinea was colonized in the last part of the Pleistocene when the island, together with Australia and Tasmania, was part of one paleocontinent called "Sahul". Although the earliest human presence in the highlands of New Guinea is documented 49,000 years ago  (Summerhayes et al. 2010) , by analogy with Australian prehistoric evidence this occupation may have been even earlier  (Roberts et al. 1990; Fullagar et al.
Population samples We included 200 mtDNA  (Tommaseo-Ponzetta et al. 2002)  and 163 NRY  (Kayser et al. 2003)  samples belonging to five populations of hunter/fisher gatherers (Asmat, Awyu, Citak, Mappi, Muyu) and three populations of agriculturalists (Dani, Ketengban, Una). The hunter/fisher gatherers groups (HG) inhabit the southern lowlands of WNG whereas agriculturalists (AG) inhabit the central and western highlands (Fig. 1 ). More details on these populations are available elsewhere  (Tommaseo-Ponzetta et al. 2002). We analyzed 350 bp of the hypervariable segment 1 of the mitochondrial control region  (Tommaseo-Ponzetta et al. 2002 ; data available from GenBank) and seven NRY microsatellites, namely DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, and DYS393  (Kayser et al. 2003 , data available upon request).
Isolation by distance was further tested at both genetic markers by means of the Mantel correlogram  (Legendre and Fortin 1989)  using GEO or DAY as a geographic distance matrix. The number of classes was determined using Surge's rule and significance within each class was assessed after 1000 permutations. All these analyses were performed using the libraries ecodist and gdistance implemented in the R statistical package (R Development Core Team 2011).
2003; Wilkins 2004; Stadler et al. 2009). To determine the shape of the genealogy, we used a Bayesian-based coalescent approach and compared different demographic models by means of Bayes Factors (BF). We used BEAST  (Drummond et al. 2012)  to contrast a constant size model versus the extended Bayesian skyline plot (Heled and Drummond 2008) for mtDNA, and Batwing  (Wilson et al. 2003)  to contrast a constant size model versus a constant population starting an expansion T generations before present for NRY. Default priors were used in BEAST, whereas priors as in  Mona et al. (2007)  were set in Batwing. We ran all datasets with both software for 100,000,000 iterations with a 10% burn-in and a thinning of 1000. Convergence was checked by running each dataset twice and by reaching an effective sample size higher than 200 for all parameters in each analysis.
Marginal likelihood was evaluated using the harmonic mean estimator  (Kass and Raftery 1995). BF were computed as twice the difference of the natural logarithm of the marginal likelihoods and interpreted using the Jeffrey scale as reported in  Kass and Raftery (1995). The harmonic mean is a simple estimator of the marginal likelihood and some concerns have recently emerged on its performance  (Baele et al. 2012). The advantage of using the harmonic mean is its computational efficiency, which is important because we run a large number of model comparison analyses. The analyses were performed in: (i) each of the eight groups; (ii) the total pooled sample; (iii) 100 datasets of (a) 16 lineages obtained by resampling two lineages per village (group 2L); (b) 24 lineages obtained by resampling three lineages per village (group 3L); (c) 32 lineages obtained by resampling four lineages per village (group 4L).
Spatial explicit simulations We performed a set of spatial explicit simulations of the demographic history of males and females in WNG using the software SPLATCHE  (Currat et al. 2004). SPLATCHE allows the simulation of a range expansion of haploid individuals over a two-dimensional array of demes arranged on a lattice and exchanging migrants with their four nearest neighbors. Simulations are done in two consecutive steps, namely the forward (demographic) and the backward (coalescent) steps. The forward simulation starts from an ancestral deme, which sends migrants to its neighboring demes. Migrations to empty demes represent new colonization events. Each deme has an intrinsic growth rate g and its density is logistically regulated by its carrying capacity (N)  (Ray et al. 2003). SPLATCHE uses a map of N values which can be also changed at userdefined time points.
In this way, it is possible to model both spatial and temporal heterogeneity, by varying habitat quality (the N values) in space and time. After the regulation step, migrants are sent to the four neighboring demes at rate m. The process is repeated for successive generations for each nonempty deme, resulting in a wave of advance of the whole population. At equilibrium, each deme will send Nm migrants per generation to its surrounding demes. At each generation, the demographic and migration histories of every deme are stored in a database, which is then used in the backward coalescent step. The second phase of the algorithm then starts at the present generation, proceeding backward in time. The effective number of individuals present in a deme is used to compute the probability of a coalescent event, and the migration rates determine the probability of each sampled gene to emigrate, backwards in time, to the surrounding demes.
Results Despite being highly and significantly correlated (Table  2 ), DAY and GEO matrices behave rather differently when compared with both Rst M and Fst F pairwise distances. DAY is a good predictor of Rst M matrix (0. 693, P < 0. 05), but not of Fst F (Table  2 ). Moreover, the Mantel correlogram shows also a different pattern between the two markers, even though we note that the apparent clinal distribution of the NRY variation is mainly driven by the outlier Dani population (Fig. 2 ). Indeed, the correlation between DAY and Rst M drop to 0. 38 when excluding the Dani (P % 0. 20). Conversely, GEO is not correlated with any of the two genetic distance matrices (Table  2 ) and shows no clear trend when used in the Mantel correlogram analysis (Fig. 2 ). We computed the BF contrasting a constant size model versus the extended Bayesian skyline plot in the mtDNA dataset.
2010). We computed BF contrasting a constant size model versus an expansion model in the NRY dataset. According to the scale of Jeffrey, we found very strong evidence in favor of a constant population size model in all populations and particularly in the pooled sample (Table  3 ). We also reported the median and 95% credible interval of the Ne for all populations for both markers (Table  3 ). AG groups have lower NRY Ne compared with  HG groups, while there is no trend in the mtDNA data (Table  3 ). We computed the BF in the resampled dataset and present the results in Table  4. At all sampling intensities, there are approximately the same number of dataset supporting a constant or Bayesian skyline model in mtDNA. Accordingly, the mean BF shows no correlation with the number of lineages sampled per population and in any case supports one of the demographic models tested (Table  4 ).
We also plot the average skyline for all sampling intensities (Fig. 3 ). In accord with the pooled sample, all skylines support an expansion around 50 Kya, with a stronger signal for higher sampling intensities (Fig. 3 ). A completely different picture emerged for the NRY. In the 16 lineages case (group 2L) there are nine datasets supporting an expansion, but only one in the 32 lineages case (group 4L). All the resampled datasets support strongly the constant size model in the 48 lineages case (group 6L) (Table  4 ). Moreover, there is a positive correlation between the mean BF and the sampling intensities. This result indicates that by increasing the number of lineages per population, the shape of the gene genealogy resembles more what would be expected under constant demography. This means that there will  (C) Una.
In (D), we plot the average skyline computed on 100 resampled datasets obtained by pooling 2, 3, 4, 6, or 9 lineages per population. Lines used are: solid, dashed, dotted, dot dash, long dash, or two dashes for the 2, 3, 4, 6, or 9 cases, respectively. Ne and coalescent times were scaled assuming the mutation rate reported in  Soares et al. (2009). be more coalescence during the scattering phase, which is what we expect for small Nm value. The difference in the shape of the gene genealogy between mtDNA and NRY is therefore consistent with an Nm M much smaller than Nm F. We computed an Rst M of 0. 488 (P < 0. 001) and an Fst F of 0. 116 (P < 0. 001) among the sampled populations. The Rst M /Fst F ratio is 4. 2 which correspond approximately to an Nm F /Nm M ratio of 7. 26 assuming the  Wright (1931)  island model.
8  and S6 ). In Scenario 2, we tested several combinations of Nm M and Nm F of AG for fixed Nm in HG. Under this hypothesis, we never found an Rst M /Fst F ratio higher than 2 (Fig. 5 , Nm HG of 1), independently on the Nm in HG In the last row, we reported the mean BF over 100 resampled datasets. (Fig. S3 , Nm HG of 20). This result suggests that different behavior between males and females in West New Guinea are not (only) related to the emergence of agriculture as they must be present in HG as well in order to obtain such values of Rst M and Fst F. Scenario 3 and 5 assume that differences in the same magnitude between Nm M and Nm F are present both in HG and AG. In Scenario 5, such differences were established after the emergence of agriculture in all groups, whereas in Scenario 3 they have been always present.
This value is based on equilibrium model (Wright island model) which also overlooks the molecular properties of the two markers and their effect on the estimation of the fixation indices. To compare Nm between the chosen markers without worrying about their different molecular properties, we investigated the shape of the gene genealogy in all demes and in datasets created by resampling lineages from each of them. Range expansion is still visible in mtDNA, where resampled datasets support a growth around 50,000 years b. p. (Fig. 3 ), consistent with archaeological records  (Summerhayes et al. 2010). Contrary to what we expected from theory  (Stadler et al. 2009) , we found a stronger expansion for higher sampling intensities. However, the power to detect an expansion using coalescent theory is correlated with the sample size for small datasets (Ramos-Onsins and Rozas 2002).
Therefore, the inferred skyline depends both on the true shape of the gene genealogy and the power to correctly recover it. The balance between these two factors also explains why there is no trend in the BF distribution. We found no signature of expansion in the single populations, which suggests that Nm F must be lower than 20, as simulation studies showed expansions can be detected for Nm > 20  (Ray et al. 2003). The picture for the NRY is drastically different, as only a few resampled datasets at the lowest intensity show a gene genealogy consistent with an expansion (Table  4 ). The signature of the range expansion into WNG has therefore been lost at the male level, which suggests a reduced Nm M. Even though, the resampling strategy we adopted does not provide a quantitative estimate of the Nm F /Nm M ratio, it is an exploratory tool which has several important advantages over other approaches.
Many studies have suggested that HG groups are more flexible in terms of postmarital residence pattern, whereas AG groups are more predominantly patrilocal  (Wilkins and Marlowe 2006). Indeed, in our small dataset with only three AG and five HG populations we found an Nm F /Nm M ratio of 14. 6 and 1. 5 for AG and HG, respectively, under the equilibrium island model. However, considering our more complex simulation framework, we could show that in WNG the differential behavior between males and females in HG is also necessary to generate the observed pattern of differentiation (see Figs. 5  and S3 ). Moreover, the Rst M /Fst F ratio may have originated even assuming no sex-specific behavior in AG groups, provided that either the differences between males and females emerged early in HG groups (Fig.
Long-term soil organic carbon (SOC) and soil organic nitrogen (SON) following cultivation of grassland soils (100/120-year tillage (T) + 20/30-year no tillage (NT)) of the Rolling Pampa were studied calibrating the simple AMG model coupled with the natural 13 C abundance measurements issued from long-term experiments and validating it on a data set obtained by a farmer survey and by long-term NT experiments. The multisite survey and NT trials permitted coverage of the history of the 140 years with agriculture. The decrease in SOC and SON storage that occurred during the first twenty years by a loss through biological activity was 27% for SOC and 32% for SON. The calibrated model described the SOC storage evolution very well and permitted an accurate simultaneous estimation of their three parameters.
3%). The texture of the A horizon is silty loam with 25% and 64% clay and silt, respectively. The mean annual temperature is 16. 7 ∘ C, and the mean annual rainfall for the 1910-2012 period was 965 mm (agroclimatological network database, INTA). To validate the model during the T period, we used a survey, which consisted in selecting different sites in which time of continuous cultivation since natural grassland and crop successions were known. We opened profiles to start Bt2 horizon depth in the highest site of the landscape of each field. We considered mainly the pedological profiles of Argiudolls. Only two fields classified as Argialbolls were included because their A2 horizon was very thin. Hence, we excluded the surface horizons with a clay content lower than 19%, the clay soils truncated by erosion and without A horizon, and flooding soils.
Calibration Procedure. As stated previously, the data set obtained from two long-term experiments (sites A and B) was used to calibrate the AMG model. The second and third terms of (2) were determined separately by using the 13 C natural abundance technique which allows obtaining a unique evaluation of the parameters ,  1 , and C s in longterm experiments  [43, 58]. Since we needed measurements of natural  13 C abundance to establish the origin of the SOM in each site, we used the technique developed by Cerri et al. [59]  and Balesdent et al. [60]. The proportion of soil C derived from C3 crops () was calculated as 𝛼 (%) = (𝛿 𝑚 -𝛿 1 ) (𝛿 2 -𝛿 1 ) ⋅ 100, (6) where   is the isotopic abundance of 13 C in the soil at time  (‰),  1 is the isotopic abundance of 13 C in the soil at time  = 0 (‰) taken at the reference, and  2 is the isotopic abundance of 13 C of the crop in the crop sequence (‰).
SON stocks were also calculated at 2500 Mg soil ha -1. We tried to fit the two C models (young and old) at the same time, that is, by minimizing the sums of the square of deviations from the model for the variables C DC and C DP. While optimizing, we took account of the variance of the data by weighting the sums of squares of deviations. The following quantity was minimized as follows: SSQ = SSQ DC 𝑆 2 DC + SSQ DP 𝑆 2 DC , (9) where SSQ DC and SSQ DP are the sums of squares of deviations (observed, simulated) of the variables C DC and C DP , respectively;  2 DC and  2 DP are the mean experimental variances obtained for the variables C DC and C DP. Fitting was performed by fixing C 0 and  and optimizing three parameters: C s ,  1 , and. In the case of N, ,  1 , and N s were treated as follow.
At equilibrium, this fraction would represent between 20 and 25% of the overall SOM, corresponding to very high and low soybean frequency in crop sequences, respectively. In 2010, we sampled fourteen new sites of Pergamino soils with a long time of continuous agriculture and at least five years under NT to obtain the SOC and NOC stocks at time zero of the future independent validation sites. The SOC and NOC values were 42. 8 ± 3. 3 and 3. 6 ± 0. 2 (data not shown). The AMG model previsions for the next 30 years are 15 and 20% in SOM loss in the sites where organic reserves are highest and annual C additions are 2. 8 and 4. 8 Mg ha -1 year -1 , respectively; meanwhile, SOM will remain steady and will tend to go up slightly for the same inputs in the sites where organic reserves are smallest.
Acknowledgments The authors thank  Jérôme Guérif  for contributing to put all the available means to carry out this work and  Denis Angers  for the assistance in compiling the paper,  Leticia García ,  Liliana Darder , and  Olivier Delfosse  for laboratory assistance, and  Diego Colombini ,  Fabio Villalba , and  Alberto Rondán  for field assistance. This study was supported by  National INTA  and PICT 2004 21078 Projects.
INTRODUCTION In the recent past, 3D urban reconstruction and Visualisation of cities have become a hot topic of research in the scientific community. Several geographical navigators like Google Streetmap Viewer, Microsoft Visual Earth or Geoportail provide real-like representation of the terrain and ground based models, created by mobile terrestrial image acquisition techniques. However, in urban environments the quality of data acquired by these hybrid terrestrial vehicles is widely hampered by the presence of temporary stationary and dynamic objects (pedestrians, cars, etc) in the scene. As a result there is a problem of occlusion of regions. Moving objects or certain temporary stationed objects (parked cars, traffic, pedestrian etc) present in the area hide certain zones of the urban landscape  (buildings, road sides etc.
CONCLUSIONS In this work, we present a new method for 3D urban cartography that successfully addresses the difficult problem concerning the automatic update and completion of occluded regions in the urban environment by combining both multiple views and multiple passages. The proposed method ensures that the resulting 3D point cloud of the cartography is most accurate that it contains only the exact and actual permanent features/regions. The evaluated results demonstrate the technical prowess of the proposed method which can be easily integrated in different commercial and non-commercial applications pertaining to urban landscape modeling and cartography that need to update their database frequently.