dataesr/scientific-paragraphs-categorization huggingface dataset:
In other words, the apparently endless solutions discovered by species to simultaneously satisfy multiple requirements and deal with multiple stresses might actually be much more limited and structured than we anticipated. Yet we do not know what the full "entangled bank" of species looks like or if there are structural patterns at the community level that reflect common solutions in the way species manage being involved in different interaction types. Indeed, the analysis tools from network science are only recently addressing the "multiplex" nature of most natural networks, i. e. , the fact that they include different interaction types between a given set of species (e. g. ,  [8] [9] [10] [11] ). As the first datasets including several interaction types between a given set of species are now emerging in ecology  [5, [12] [13] [14] [15] [16] , we have a unique opportunity to disentangle the bank of species interactions.
Interactions between the multiplex clusters. Nodes are sized by the number of species in the cluster. Numbers correspond to the cluster ID used in the text. Link widths are proportional to the interaction probability between clusters. Only edges whose probability is superior to 0. 5 are plotted, and cluster 3 (benthic diatoms) is not shown. Cluster 4 is absent because it is not involved in any interaction type with a probability >50%. Clusters of the same color have similar 3D connectivity but differ in the identity of interacting species. These colors reflect the "multiplex functional groups" defined later on. The networks were plotted with VibrantData (http://vibrantdata. io). Underlying data can be found in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21]. doi:10. 1371/journal.
e. , they belong to the same "multiplex functional group" defined later on. Please see http://pbil. univ-lyon1. fr/software/multiplex for an interactive version of the figure. Underlying data can be found in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21]. doi:10. 1371/journal. pbio. 1002527. g002 involved (or not involved) in similar combinations of interactions, i. e. , "multiplex functional groups" (Figs 4A and S11). The Chilean web thereby further collapses into a set of only five multiplex functional groups (Figs 4A and S11). Those multiplex functional groups can broadly be characterized as groups dominated by consumers  (1, 4, 7, 9, 14) , one composed mostly of competitors  (3, 11, 12) , another dominated by facilitators/competitors  (6, 10, 13) , a more heterogeneous group composed of consumers/competitors (2, 8), and, finally, one overall hub of species interacting with many other species in many different ways  (5).
Shore height could thereby mediate the frequency of facilitation of mobile by sessile species in this dataset. In sum, the five multiplex functional groups gather species that engage in roughly similar ecological interactions (Fig  4 ): (1) A group of mobile consumers (clusters 1, 4, 7, 9, 14), mostly carnivores, composed of crabs, sea snails, chitons, starfishes, and birds, most of which consume prey species and often find themselves in competition with others. (2) A small group of sessile, inedible consumers (anemones; clusters 2 and 8) that eat dead or detached animals or their fragments are the source and target of many competitive links with other sessile species and are key players in the resilience of the community. Their classification into a separate group likely reflects their peculiar life habits (sessile scavengers).
Conversely, some groups may be unique to marine benthic systems, such as the group of sessile, inedible scavengers formed here by anemones. It is noteworthy that a number of key groups of species are absent from the current version of the dataset (Materials and Methods). In particular, parasites are not included in the web. Studies have shown that food webs that also take parasites into account have increased connectivity and longer food chains, and the parasite-host links dominate numerically over predator-prey links  [12]. Detritus (and thereby decomposers) are known to play an important role for the dynamics and structure of many communities and may also affect their stability  [49, 51, 52]. It is unclear, however, what the significance is of local nutrient recycling in benthic marine and stream communities.
For instance, Lurgi et al. [41]  showed, using a spatially explicit individual-based model, that an increasing proportion of mutualistic links in a food web positively affected the dynamic stability of model communities. How the topological structure of multiplex ecological networks modulates the multi-species dynamics and the resilience of ecosystems to perturbations, such as climate change, must be further investigated through other datasets  [15] , further dynamical modelling  [1, 5, 20, 41, 45] , and other approaches incorporating link weighting  [3]. Until then, our results will help us guide future empirical studies and move toward a more general theory of how to leverage the full diversity of species interactions for understanding and predicting the dynamics and resilience of complex ecological systems.
Materials and Methods The Dataset: The Chilean Web The dataset  [14]  includes all the species that were found to co-occur during community structure surveys carried out at several rocky intertidal sites with similar wave exposure spread along 700 km of the central Chilean coast (  [27] , see  [54, 55]  for sampling details and species list). Construction of the network was based on expert knowledge  [14]. An interaction was included in the network if one species plausibly had a direct measurable effect on the growth, survival, or feeding rates of another species over an ecologically relevant time period (1-2 y)  [14]. The dataset does not include parasites, endo-symbionts, or decomposers, because such data was unavailable for that community. The network was split into three separate matrices for trophic, positive non-trophic, and negative non-trophic interactions (in each matrix, interactions are coded as 0 or 1)  [14].
e. , benefit of doubt) the interaction to competition for space than when dealing with pairs of mobile species at higher trophic levels. This would create a bias in favor of negative non-trophic interactions at lower trophic levels. However, the sheer number of species at bottom versus high trophic levels would make it difficult to alter the general pattern"  [14]. Data deposited in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21].
Taxonomy and functional groups. We also explored whether taxonomic proximity between species explained functional group membership. We compiled the taxonomic information for 100 species from the WoRMS database (www. marinespecies. org), AlgaeBase (www. algaebase. org), and Macroalgal Herbarium Portal (http://macroalgae. org); we also manually added recovered taxonomic knowledge for six species. From this information, we built the cladogram and computed the patristic distance between all the species with the SeaView program (doua. prabi. fr/software/seaview). We calculated the statistical significance of the association between functional groups and taxonomy with a permutation test (10 5 cluster membership permutations).
Cluster 4 is absent because it is not involved in any interaction type with a probability >50%. Clusters of the same color have similar 3D connectivity but differ in the identity of interacting species. These colors reflect the "multiplex functional groups" defined later on. The networks were plotted with VibrantData (http://vibrantdata. io). Underlying data can be found in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21].
e. , the number of secondary extinctions. The difference between the two distributions (red and grey) is visible but not statistically significant (chi-square, p-value = 0. 0879). Underlying data can be found in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21]. doi:10. 1371/journal. pbio. 1002527. g003.
Acknowledgments We thank  Manolo Gouy  and  Franck Picard  for their advice. This work was performed using the computing facilities of the  CC LBBE/PRABI. We thank  Stéphane Dray  for initiating the collaboration between SK and VM. The authors thank the  Center for Marine Conservation (Nucleo Milenio Initiative ,  P10-033F ) for making this international collaboration possible.
5 are plotted, and cluster 3 (benthic diatoms) is not shown. Cluster 4 is absent because it is not involved in any interaction type with a probability >50%. Clusters of the same color have similar 3D connectivity but differ in the identity of interacting species. These colors reflect the "multiplex functional groups" defined later on. The networks were plotted with VibrantData (http://vibrantdata. io). Underlying data can be found in the Dryad repository: http://dx. doi. org/10. 5061/dryad. b4vg0 [21].
Introduction The role of benchmark data sets As much as biosequence databases are an invaluable resource in molecular biology research, bioinformatics benchmarks are a crucial aid in the continued development and evaluation of bioinformatics tools and algorithms (see  [1]  for multiple sequences alignment). They allow us to compare old and new approaches with the same problem with respect to their use of computational resources, as well as with respect to their qualitative performance. When tool developers can make use of established benchmarks, reproducibility of results benefits greatly. Good benchmark data sets must satisfy a number of criteria. They should contain the best, curated data sets available at their time, should reflect the diversity in their problem domain, and should not be biased towards problem subdomains of a specific nature.
Naturally, these criteria are not easy to fulfil, especially when the problem domain itself is a new or difficult research topic, and available data at a given time may only partially reflect its diversity and complexity. For practical use, a benchmark should be subdivided, such that users can extract subsets with characteristic properties, such as sequence length, degree of conservation or phylogenetic classification. Note that benchmarks do not need to encompass the complete data available in their domain, contrasting in that respect with databases used in applications. However, if domain knowledge grows, benchmark data sets need to be updated once in a while to remain representative of their problem domain.
Our best test data: tRNAs Next to ribosomal RNA, the tRNA may be the most well-studied class of functional RNA molecules. As structure conservation extends over the whole molecule, and as tRNAs are part of the universally conserved translational machinery, we have an enormous amount of well-curated structural alignments, where structure conservation is high even with low sequence identity. As tRNAs are so short that all tools can handle them in reasonable time, they constitute a favourite test case for algorithm developers. This is a strong argument to include a large number of tRNA alignments in a benchmarking data set. In fact, tRNA is the most highly represented family in BRaliBase, contributing 2039 data points. Can too much of the good stuff be hurting in the (bitter) end?.
Both show a decline with decreasing similarity, but the decline for non-tRNAs (yellow) is much steeper. Where non-tRNA data become sparse (around 40% identity), the tRNA curve (red) almost entirely determines the shape of the overall curve (blue). Additionally, the non-5S_rRNAs curve (green) shows a behaviour similar to the overall curve, indicating that the second most abundant family does not influence the presence of the dent. Therefore, we can summarize that the dent in the 2006 version of BRaliBase is caused by the interaction of two data set-specific factors: the exceptionally good performance of tools over tRNAs, as well as the lack of non-tRNA alignments, which would probably yield lower performances, in the identity region below 40%. To eradicate the imbalance, we tested a sampling approach in which no single RNA family is allowed a bigger representation than 20% within each sequence identity region.
This evaluation confirms the observations of the SPS-based analysis. Here, the scores of the tRNA reference alignments (red) are close to the ones of the optimal alignments for the whole range of sequence identities. The score differences of the optimal alignments and the non-tRNA reference alignments (yellow) increase with decreasing sequence identities. Together, the score differences of all RNA alignments show the same properties as the SPS-based analysis, with the biggest difference being around 45% identity. This further supports our conclusion that the BRaliBase dent is in fact not an artefact of the SPS or the curve smoothing factor, but rather arises from the biased composition of the overall data set.
Conclusion The BRaliBase dent, which for a decade has puzzled the authors of this article as well as many colleagues in the field of comparative RNA structure prediction, can be explained by a bias in the benchmark data set, which holds 'too much of the good' data in some conservation regions. The lesson learned from this experience is simple: • When different data subsets in the benchmark show deviating overall characteristics, one should refrain from merging them into an overall performance diagram. • When a benchmark holds data with diverging characteristics and a potential representation bias, it should provide means for sampling unbiased subsets. In the case of BRaliBase, the bias was caused by too much of the best data dominating the performance analysis in low similarity regions, leading to a perceived decay of tools performance, the dent, in the consecutive 40-60% identity region.
Quite understandably, this observation has attracted our attention to this region, and this feature of the benchmark data ended up being misdiagnosed as a localized weakness in our algorithms. This conclusion was seemingly confirmed by the apparent demonstration, in recent publications, that the dent could be overcome by innovative approaches. However, as explained above, this evidence turned out to be deceptive. As a word of consolation: a good amount of creative and beautiful algorithmics in the field of comparative structure prediction has been initiated by the misinterpretation of the BRaliBase dent as a challenge to algorithm designers. A new version of the BRaliBase benchmark, which draws from the increased knowledge about RNA families collected in the past decade, is clearly desirable.
oBjective: We investigated whether placental mtDNA content is a possible mediator of the association between prenatal nitrogen dioxide (NO 2 ) exposure and birth weight. Methods: We used data from two independent European cohorts: INMA (n = 376; Spain) and ENVIRONAGE (n = 550; Belgium). Relative placental mtDNA content was determined as the ratio of two mitochondrial genes (MT-ND1 and MTF3212/R3319) to two control genes (RPLP0 and ACTB). Effect estimates for individual cohorts and the pooled data set were calculated using multiple linear regression and mixed models. We also performed a mediation analysis. results: Pooled estimates indicated that a 10-μg/m 3 increment in average NO 2 exposure during pregnancy was associated with a 4. 9% decrease in placental mtDNA content (95% CI: -9. 3, -0.
qBase software (Biogazelle, Zwijnaarde, Belgium) was used to normalize data and correct for run-to-run differences  (Hellemans et al. 2007). Ambient air pollution assessment. In INMA, ambient concentrations of NO 2 were measured with the aid of passive samplers (Radiello, Fundazione Salvatore Maugeri, Padua, Italy) distributed outside across the study areas according to geographic criteria, taking into account the expected pollution gradients and the distribution of the residences of the participating women. The samplers remained exposed during various 7-day sampling periods through pregnancy. The methodology has been described in detail elsewhere  (Aguilera et al. 2008; Iñiguez et al. 2009 ), and further sampling information is given in Supplemental Material, Table  S1. Temporally adjusted land use regression (LUR) models were used to predict NO 2 levels at women's residential addresses and taking into account residential changes if women lived at least 2 months of pregnancy in the new residence.
To calculate individual exposure during pregnancy, annual average NO 2 estimates from the LUR were temporally adjusted using serial records from the network of monitoring stations covering each of the four INMA study areas. The validation statistics gave a spatial explained variance (R 2 ) for annual mean NO 2 from 0. 52 to 0. 75 in the four INMA subcohorts (see Supplemental Material, Table  S1 ). In ENVIRONAGE, regional background levels of NO 2 for each woman's home address were calculated using a kriging interpolation method  (Jacobs et al. 2010; Janssen et al. 2008 ) that uses land cover data obtained from satellite images combined with a dispersion model  (Lefebvre et al. 2011). This model chain provided NO 2 values, combining data from the Belgian telemetric air quality network, point sources and line sources, which were then interpolated to a high-resolution receptor grid.
This method was used to obtain NO 2 levels at women's residential addresses, taking into account any residential change during pregnancy. The validation statistics gave a temporal explained variance (R 2 ) for hourly averages > 0. 80 and a spatial explained variance (R 2 ) for annual mean NO 2 of 0. 82. To explore potentially critical exposures during pregnancy, we calculated individual NO 2 concentrations for the three trimesters of pregnancy using the same procedure used for the entire pregnancy: 1-13 weeks starting from date of conception (trimester 1), 14-28 weeks (trimester 2), and 29 weeks to delivery (trimester 3). More details on exposure measurements for INMA and ENVIRONAGE can be found in Supplemental Material, Table  S1. Statistical analysis. Continuous data were checked for normality using the Shapiro-Wilk test statistic.
Placental mtDNA content data were right skewed and therefore logarithmically transformed (log 10 ). Generalized additive models (GAMs) were used to assess the linearity of the associations between a) prenatal NO 2 exposure and mtDNA content, b) mtDNA content and birth weight, and c) prenatal NO 2 exposure and birth weight (see Supplemental Material, Figure  S1 ). Multiple linear regression models were used in ENVIRONAGE. Multiple linear mixed models with a random cohort effect were used in INMA and in the pooled data set (four INMA cohorts and ENVIRONAGE). Covariates used in the model were gestational age (linear and quadratic term), newborn's sex, maternal age, ethnicity, parity, smoking status, education, season of birth (January-March, April-June, July-September, October-December), and prepregnancy maternal BMI.
For the present analysis, we excluded 14 mother-newborn pairs from INMA and 6 mother-newborn pairs from ENVIRONAGE with missing values in the outcome, exposure, and confounders. After these exclusions, the final study population consisted of 376 subjects for INMA and 550 subjects for ENVIRONAGE. To determine whether placental mtDNA content is a potential mediator of the association between prenatal NO 2 exposure and birth weight, we performed a mediation analysis. The direct effect (DE), indirect effect (IE), and total effect (TE) were estimated using the SAS macro developed by  Valeri and VanderWeele (2013). When assumptions of the mediation analysis hold, the DE represents the effect of prenatal NO 2 exposure on birth weight after controlling for mtDNA content, and the IE is the estimated effect of NO 2 exposure during pregnancy operating through mtDNA content  (Valeri and VanderWeele 2013).
2011) , we calculated the time spent at home from self-reported information (questionnaire at week 32) and restricted in INMA our analysis to women who spent ≥ 15 hr/day at home  (Estarlich et al. 2011 ). This information was not available for the ENVIRONAGE cohort. All statistical analysis were conducted using SAS software (version 9. 3; SAS Institute Inc. , Cary, NC, USA).
Patients Metastatic breast cancer patients who underwent a biopsy in the context of the SAFIR01  [6]  (NCT01414933), SAFIR02 (NCT02299999), SHIVA  [7]  (NCT01771458), and MOSCATO (NCT01566019) prospective trials were potentially eligible for this study. These French multicenter trials used high-throughput genome analysis on fresh frozen tumor biopsies as a therapeutic decision tool for metastatic cancer patients, with solid cancers (SHIVA and MOS-CATO) or specifically with breast cancer (SAFIR01 and SAFIR02). SAFIR01 included patients with metastatic breast cancers resistant to therapy, and SHIVA and MOSCATO included patients with metastatic cancers eligible for phase I trial, while SAFIR02 included patients with metastatic breast cancers who were starting first-or second-line chemotherapy.
We defined the final list of somatic mutations with the following filters: frequency of the reads with the altered base in the tumor > 10%; number of reads with the altered base in the tumor sample ! 5; frequency of the reads with the altered base in the normal sample < 2%; number of reads with the altered base in the normal sample 3; and total coverage in normal and tumor samples ! 10. The resulting somatic mutations were annotated with the snpEff and snpSift algorithms  [13] , and we selected somatic mutations occurring in coding regions only. We removed variants that were also detected in at least one normal sample in our cohort or annotated as known polymorphisms (reported by 1000 Genomes or the ESP databases) unless the variant was also reported in Catalogue of Somatic Mutations in Cancer (COSMIC)  [14]  or ClinVar (http://www.
Copy Number Analysis For deriving somatic copy number variations from whole-exome sequence data, we used the following strategy: we first computed the normalized ratio of reads between each tumor and corresponding normal sample using the package ExomeCNV in R and created the segmented profiles with the DNAcopy package. For defining amplifications and deletions, we used the Gistic2 algorithm  [20]  with the following thresholds for the log2 ratios: amp > 0. 3 and del < -0. 3. Gistic2 was run including all the samples and specifically for the HR+/HER2-samples and for the HR-/HER2-samples in order to control for disease subtypes. Focal peaks are listed in S4 Table.
Mutational Processes The Cancer Genome Atlas (TCGA) Data Somatic mutations for breast cancer TCGA cohort were extracted from the genome. wustl. edu_BRCA. IlluminaGA_DNASeq. Level_2. 5. 3. 0. somatic. maf file available for download on the TCGA data matrix website, with somatic mutations available for primary tumors of 772 patients. We extracted ER, PR, and HER2 status from the clinical file downloaded from the TCGA data matrix, retrieving 419 HR+/HER2-, 100 HR-/HER2-, and 145 HER2+. In order to fairly compare mutational loads between TCGA and the metastatic cohort, we downloaded raw data for 33 randomly selected TCGA patients and processed the BAM files with the same pipeline described in this manuscript. We found that the number of mutations identified by our pipeline and by the TCGA pipeline was very similar (S1 Fig, linear regression R 2 = 0.
We analyzed outcome data for comparing the OS of patients with metastatic tumors carrying at least one of the mutations enriched in the metastatic setting (n = 76) to the rest of the population (n = 140). Results of the univariate and multivariate analyses are reported in S7 and S8 Tables. In a multivariate analysis, mBC with at least one mutation in the 8 genes enriched in the metastatic setting presented a 2-fold increase in the hazard of death (hazard ratio = 1. 97, 95% CI: 1. 34-2. 89, p = 0. 001). Survival curves are reported in Fig  3. As this analysis might be biased by the difference in distribution of HR and HER2 subtypes between eBC and mBC, we also focused the analysis on the HR+/HER2-subtype (n = 143), in which 278 genes were more frequently mutated in mBC as compared to eBC (FDR < 0.