dataesr/scientific-paragraphs-categorization huggingface dataset:
Approximately 40% of the predicted proteins of A. spinosum could not be matched to the PFAM database. This is in line with previous findings in dinoflagellates and might be an indication for novel pathways expressed in dinoflagellates  [5, [52] [53] [54] [55]. On average a quarter of the genes found in the dinoflagellate species studied here had no recognizable homologues in other dinoflagellates genera. The estimated number of these 'orphan' genes totaled 27% of all genes for the dinoflagellates we studied; this is within the range for other well-studied phyla (e. g. bacteria  [56] , and nematodes  [57] ), for which 2-50% of identified genes are orphans. Different evolutionary mechanisms can lead to the formation of orphan genes. For example, duplication can free gene copies from evolutionary constraints -while the original function is retained in the original gene, the duplicated gene copy can possess new protein functions  [58].
Function and evolution of PKS All polyketide biosynthetic enzyme functions needed to synthesize the azaspiracid carbon backbone are present in A. spinosum (Table  3 ). This is to our knowledge the first dinoflagellate dataset where all genes needed for polyketide synthesis were identified. Whether these PKS-related sequences are coding, are involved in fatty acid synthesis or toxin production, and/or have another function remains unclear. However, this set of genes opens new avenues to study toxin synthesis in A. spinosum and maybe other dinoflagellates also up to now it remains unknown in which cellular compartment toxin synthesis takes place. PKS enzymes have been shown to be expressed in both chloroplast and cytoplasm, but we could not identify signal peptide sequences which may hint towards a specific cellular compartment.
In any case, the complete AZA structure could be synthesized with the total number of KS, KR, AT and the other domains identified in our dataset. Analyses of A. spinosum full-length PKS transcripts (Figure  3 ) and of the corresponding Western blots (Additional file 3: Figure  S1 ) confirms the mono-modular functional Type I PKS structure, which might be a common feature in dinoflagellates  [29, 30]. It has been proposed that this structure likely diverged from multimodular PKS around the time of origin of the dinoflagellates  [30, 43]. The other protistan groups studied here (haptophytes, chlorophytes and apicomplexa) exhibit the multimodular Type I form. The protist KS groups form well-supported monophyletic clades in the current analysis, consistent with earlier findings  [26, 30]. The flanking regions of KS proteins identified in A.
spinosum contain less conserved motives than in other organisms, but this sequence feature was also found in other dinoflagellates  [30]. In the C-terminal region, in particular, no conserved motif was identified in the alignment, supporting the assumption that this is a relict of the linker sequences of the multi-modular PKS Type I. With this linker structure, the single domains could have been separated to allow structural function of the complex enzyme. Adding A. spinosum sequences to the existing C-terminal dataset did not significantly alter the overall topology present in the N-terminal dataset, despite weaker phylogenetic support. In contrast, within the N-terminal sequences, a high degree of conservation was found in the alignment, arguing that the N-terminal has another function rather than acts as a linker region.
Conclusions We characterized the transcriptome of the toxic dinoflagellate A. spinosum, revealing that it possesses a low number of orphan genes compared to other tested dinoflagellate species. With respect to toxin synthesis, we detected transcripts of all genes essential for polyketide synthesis in A. spinosum. As the number of transcriptomic datasets for different dinoflagellate species increases, deeper insights into the functional differences between toxic and non-toxic variants, even within a species, as well as into mechanisms of HAB formation, will be gained.
Sequencing post-run processing The associated 454-sequencing software (Roche GS Junior Version 2. 8) was used for quality control and contig assembly. All sequencing reads featuring inaccurate key sequences, chimeric sequences, biased nucleotides, or unidentified nucleotides were regarded as low quality reads and were discarded. A total of 103,860 high quality reads, yielding 37,739,423 bp of genomic information, were retained for analysis.
Sequence analysis of genomic DNA reads Low overall genomic coverage and a very large genome size meant that assembly of the retained reads was unrealistic. Reads were thus analysed with the program RepeatMasker ver. 4. 0. 5  [34]  to establish the repeat structure of the genome. To determine the potential proportion of protein-coding sequence, annotation of the high quality reads was also performed. The Blast2go algorithm (www. blast2go. org) was applied against the Swissprot database, and only reads with an e-value below 10 -7 were considered in the analysis.
Homology analysis We used the NCBI blast suite (version 2. 2. 28+) to compare the set of 69,956 predicted protein fragments with gene predictions and EST data from the following species: dinoflagellates: Alexandrium andersonii, Alexandrium ostenfeldii, Amphidinium massartii, Ceratium fusus, Crypthecodinium cohnii, Dinophysis acuminate, Glenodinium foliaceum, Gymnodinium catenatum, Heterocapsa triquetra, Karenia brevis, Karlodinium micrum, Oxyrrhis marina, Peridinium aciculiferum, Polarella glacialis, Prorocentrum minimum, Protoceratium reticulatem, Scrippsiella trochoidea, Symbiodinium minutum; diatoms: Fragilariopsis cylindrus, Phaeodactylum tricornutum, Thalassiosira pseudonana; haptophyte: Emiliania huxleyi; cryptomonad: Guillardia theta; and the chlorophyte Chlamydomonas reinhardtii. We computed all pairwise BLAST comparisons (BLASTp, tBLASTx, BLASTx, and tBLASTn, depending on the type of database) at a threshold of e-value < 0.
Phylogenetic analyses Additional amino acid sequences were obtained from the NCBI GenBank and from  [30]. A representative dataset of 65 Type I and Type II PKS sequences covering the major clades of prokaryota, fungi, animals, apicomplexa, haptophytes and chlorophytes was used for maximum likelihood phylogenetic analyses. A multi-sequence alignment was created in MEGA 5. 2  [65]  with the implemented MUSCLE algorithm  [66]. A maximum likelihood tree was computed in PhyML 3. 0  [67]  under assumption of the La-Gascuel amino acid replacement matrix  [68] ; likelihood ratio tests  [69]  and 1,000 bootstrap analyses were performed as a measure of the validity of each branch.
Figure1The 20 most abundant protein domains in the Azadinium spinosum transcriptome. Black bars denote the number of proteins with a certain domain. Black and grey bars together indicate the total number of proteins for the first n domains. The subset and order of domains was iteratively chosen to maximize the total number of proteins (black plus grey bars) for the first n domains. The 20 most abundant domains make up 28% of the total proteome (28, 357)  that could be matched to the PFAM database (e-value 0. 001).
Acknowledgements The authors are grateful to  Dr. Frances Van Dolah  ( NOAA Center for Coastal and Environmental Health and Biomolecular Research, Charleston, South Carolina, USA ) for the custom peptide polyclonal antibodies specific for Karenia brevis KS domain ( KB2006 ; GenBank accession no. EF410007 ). This research was conducted within the  PACES research program  of the  Alfred-Wegener-Institute Helmholtz-Zentrum für Polar-und Meeresforschung  as part of the Coastal theme ( WP2 ).
We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city's hotspots, defined by the presence of points of interests.
More precisely, we analyse a high-resolution dataset of mobile phone records collected in Milan, which has the most populous metropolitan area in Italy, that reports the international code of all calls and text messages sent and received by a user over a two-month period. Based on such call records, collected at a very high spatial and temporal resolution, we define an entropy function that measures the level of heterogeneity in the number of distinct countries -identified by their international phone code -that are present in the activity record of a city's neighborhood. By analysing the spatial and temporal features of such entropy function, we aim to identify locations that attract the most visitors and characterize how international communities occupy and make use of the urban spaces. In particular, here we show and discuss how an entropy measure of the phone activity can be used to: (i) recognize the most attractive locations for tourists and visitors in the city, as indicated by the presence of point of interests; (ii) characterize the spatial distribution of diverse communities, recognizing -in conjunction with information obtained from topological data analysis -the different use of the metropolitan area made by visitors and residents.
Additionally, we discuss how our proposed method can be advantageous with respect to only using the volume of mobile phone activity for similar analyses. The paper is structured as follows: in Section  we describe the main features of the dataset under study; in Section  we link the diversity arising from mobile phones activity aggregates to the population diversity of Milan; in Section  we introduce an entropy measure to quantify the diversity of mobile phones activity and present the results obtained by analysing its properties; in Section  we describe how a topological data analysis based on persistent homology is able to discriminate between resident and visiting foreigners in the city, once a spatial entropy-activity distribution has been properly defined. Finally, we conclude and discuss our results.
Dataset description The dataset has been provided by Telecom Italia in the context of the 'Telecom Big Data Challenge' [] and contains the mobile phone activity records -including both SMS and calls made or received -of all users in the Milan area (Italy) during a time span of two months, from November  to December , . Data is aggregated over a  minutes interval and is geolocalized on a grid of  ×  squares covering the city metropolitan area with a resolution of about  meters (map shown in Figure  A ). Traffic volume data has been provided with the same arbitrary units for text messages and calls. In this way, the provider assured both quantities to be directly comparable. In our study, we focused on a specific information provided with the dataset, that is, the list of international country phone codes that are present in the activity record of each cell, with their corresponding traffic volume, for every -minutes time slice.
As shown in Table   ,  out of the top  countries ranked by phone traffic volume are also present in the top nationalities according to the census data. The Kendall tau correlation between the ranked vectors of nationalities equals .  (p < . ), showing a strong and significant correlation between the two rankings. To further investigate the correlation between mobile phones and census, we also compared the rankings of the different nationalities emerging from the two datasets at a finer spatial resolution. Aggregating the cells at the NIL level (namely, Nuclei di identità locale -a subdivision of the urban area comprising  neighborhoods) and summing the whole traffic over the entire period of observation, we built for every NIL a vector of country-specific activities. Neighborhoods for which no census data was available (mostly parks and peripheral neighborhoods) were dropped, leaving a total of  NILs.
Entropy outliers Given the definition of entropy provided by Equation (), for every cell i and for every time step t (in the following, a daily resolution is chosen) we are able to measure a certain entropy value and starting from the original dataset we recover , entropy time series. By calculating a modified z-score where mean and standard deviation are replaced by the median and the median absolute deviation values [], we are able to systematically detect the days when the entropy profile shows a significantly different behavior than usual. An entropy outlier may be triggered by an unexpected increase or decrease in the mobile phone activity inwards/towards foreign locations. The rationale behind the automatic detection of outliers is to use the entropy level as a fingerprint to characterize an urban area, where the outlying points indicate some exceptional events occurring in that location.
In the rest of the paper we used a threshold value z = -, corresponding to an entropy that is  standard deviations smaller than the average entropy. We performed sensitivity testing by varying z ∈ [-, -. ] and we found that the patterns are robust under changes in z. Figure B shows that imposing these two constraints peculiar country-specific patterns emerge. Direct observation points to two main types of configurations: • (single or multiple) spatially coherent clusters; • spatially dispersed and annular clusters, with possible branching structures. It is tempting to try to characterize quantitatively the similarity between configurations of different countries and possibly use them to cluster countries according to them. However, the intrinsic noise in the dataset and the variability in the configurations' shapes require robust shape descriptors that allow for stretching, rotations, thinning and translations.
Persistent homology [] -a parametrised version of homology for data spaces -provides the main tool to detect and quantify these differences between point clouds. For a given data space, persistent homology yields a description of its homological structure in terms of the properties of the homology generators along a series of successive approximations of the real dataset. For our purposes however, it will be sufficient to focus on two simple derived quantities, r  and r . The former, r  , measures the spatial coherency of the point cloud, in terms of the difference between the point cloud's diameter and the radius of the point neighborhoods necessary to obtain a single connected component. We normalize r  by the diameter in order to obtain a value in (, ). The latter, r  , similarly conveys the large scale circularity of the point cloud by considering the ratio of the two largest circles obtained by gluing neighborhoods together.
Since (A) Scatterplot of the (r 0 , r 1 ) pairs for all the countries appearing in the dataset. Two main groups can obtained from a simple k-means algorithm. (B-C) The two groups of countries display significantly different distributions for average GDP per capita and average remittance per capita (p < 0. 05), suggesting that the different detected spatial features might be rooted in distinct socio-economic conditions in the various national communities. the formal definition these quantities requires some mathematical technicalities, we refer the interested reader to Section  in the Additional file . However, it is easy to see that the (r  , r  ) pairs calculated for the three examples are clearly distinct (Figure  B ) and correspond to intuitive description: the bullseye and the eye examples have larger spatial decoherence and circularity than the dots example, with the eye having the largest circularity, due to the bullseye's central cluster marring its symmetry.
Discussion In the past few years, the availability of detailed user-generated data has unveiled a tremendous potential for the development of computational social sciences  []. A fundamental contribution to this research field has derived from the analysis of mobile phones data, which has been proved to be an important source of information to study how people move [, ] and interact [, ], especially in a urban environment [, -]. In this work, we have studied a highly resolved dataset containing the international country codes of millions of anonymized and geo-referenced records of calls that have been made or received during a two-month period in the city of Milan, Italy. We focused our attention on the heterogeneity of the international codes appearing in the dataset in order to extract meaningful information about the spatial distribution and the land use related to the presence of migrant communities or tourist hubs in the city.
Similar biases arise from the fact that mobile phones include individuals who are not permanent residents. A solution to overcome the biases related to such issues could be mixing the analysis of large-scale mobile phone datasets with the collection of small-sample surveys  []. The use of an entropy measure to quantify the diversity of behavioral patterns and social interactions and the analysis of its relations with socio-demographic attributes has been the subject of previous studies [, ]. Here, we used it as a complementary measure to characterize the metropolitan spaces based on the heterogeneity of the country codes that appear in the calling records. Previous works showed that a larger diversity of social interactions was linked to high socio-economic levels  []. Here, we found that both high entropy and high calling activity identify the most attractive locations of a city.
In this context, our results confirm previous works where mobile phones have been used to infer the crowd density during mass gatherings []. In our case, we demonstrated how mobile phone data can provide very specific spatial and temporal trajectories of visitors from a given country (the Netherlands, in our case) during a mass gathering event. Similar situations are typical of large sport, artistic and religious events, whose management requires large coordinated efforts. The analysis of the football match also hinted at the possible use of entropy outliers to automatically detect unusual events. This is, however, only an hypothesis that needs further testing on a larger dataset to be verified. The proposed characterization of different urban areas by means of an entropy function provides a helpful description of the locations where a large diversity of people tend to gather together.
Further research is needed to fully explore the potential applications of the proposed method and validate it. First of all, our work is limited by the fact that the dataset is restricted to one city. Thus, an extension of our analysis to different cities, both in developed and developing countries, is needed. Even if strong regularities are observed across cities [], each city has its own peculiar structure, such as being monocentric or polycentric [], and we may expect such features to affect the spatial distribution of the entropy function. Also, the attractiveness for foreigners may significantly vary across cities in the same country, leading to a more or less important contribution of foreign country codes to the mobile phone activity. In general, we expect our results to hold for large and diverse metropolitan areas, as in the case of Milan.
Acknowledgements The authors acknowledge  Telecom Italia  for providing the original dataset. The work has been partially supported by the  EC FET-Proactive  Project  MULTIPLEX  (Grant No. 317532 ) to MT and the  EC FET-Proactive  Project  TOPDRIM  (Grant No. 318121 ) to GP. We gratefully acknowledge  Marco Quaggiotto  for help with GIS mapping and  Joanna Cordero  for helpful comments. Received: 2 October 2014 Accepted: 13 April 2015.
Description and processing of the PCC/PGL COMETE cohort. A collection of 202 PCC/PGL samples from the French 'Cortico et Medullosurrenale: les Tumeurs Endocrines' (COMETE) Network was profiled on a set of genomic platforms. Tumour samples were collected from 190 patients, including 116 women and 74 men with a mean age at diagnosis of 42. 4 years (range: 7-82 years; Supplementary Data 1). Overall, 168 (83. 2%) tumours were PCCs, 25 (12. 4%) were abdominal PGLs, 3 (1. 5%) were thoracic PGLs and 6 (3%) were metastases. Twenty-seven (13. 4%) tumours were from patients with a metastatic form of the disease. Importantly, all cases were previously genotyped for the presence or the absence of germline and somatic mutations in well-known susceptibility genes. For this study, we also screened the whole cohort for hotspot mutations of HRAS and found 10 of 202 (4.
2a ), corresponding to a relatively low mutation rate of 0. 3 mutations per megabase. The mutation rate did not differ between molecular subgroups or regarding the malignant status. The spectrum of mutations was enriched in C4T transitions (Fig. 2b ), which are typically found in the majority of cancers analysed so far  25. This result is compatible with spontaneous deamination rather than with a carcinogen-induced process. These mutations were also more frequent, yet not significant, in the transcribed strand. We then combined damaging mutations (indels, nonsense or missense mutations predicted to be damaging by PolyPhen-2 software) and homozygous deletions obtained by SNP array (Supplementary Data 4), to identify putative driver genes in PCCs/PGLs. Seven genes presented at least two damaging events   (Fig.
2c ). As expected, we confirmed the FH and NF1 mutations in corresponding tumours. Two other common cancer genes, TP53 and CDKN2A, were altered in three (10%) and two (7%) cases, respectively, whereas the genes CLPTM1L, SYNE1, CAPN2 and RFPL4A have not been yet implicated in cancer. Other genes of interest carrying damaging variants and listed in the Cancer Gene Census included MET (activating mutation p. E355K), CDH1, ARHGEF12, SLC45A3, SMO, CARD11, MLL2, PCM1, ATRX, GNAS, FLT3 and FHIT. These genes were altered in a single tumour (Fig. 2c ). Of note, the sample with the highest number of mutations (162) carries one somatic mutation in POLE and one in POLD1. All of these mutations were confirmed by Sanger sequencing (Supplementary Data 2). As expected, given the low mutation load of PCCs/PGLs, none of these variants was significantly mutated when MutSigCV was applied.
To validate the exome-sequencing results, we screened the whole PCC/PGL cohort for mutations in TP53, CDKN2A and MET genes. In total, five (2. 5%) tumours harbour hotspot mutations in MET, whereas no additional damaging variants were identified in TP53 or CDKN2A genes (Supplementary Data 3). DNA methylation profiling. We previously identified three homogeneous DNA methylation-based subgroups of PCCs/PGLs: the M1 group corresponding to SDHx-and FH-mutated cases and displaying a hypermethylator phenotype, the M2 group comprising exclusively VHL-mutated samples and the M3 group comprising mostly tumours of the C2 expression cluster. We have already extensively described the DNA methylation changes occurring in the M1 cluster  13. Here we extended our analyses to DNA methylation changes present in the two other groups.