dataesr/scientific-paragraphs-categorization huggingface dataset:
WES Genomic DNA was extracted from whole blood according to standard methods, and WES and data analysis were performed as previously described  (Martin et al. , 2014; Izawa et al. , 2017; Winter et al. , 2018). Genomic DNA regions flanking PIK3CD and TNFRSF9 mutations were amplified using the forward primer 59-CTTGACCATGCCATTTGC-39 and reverse primer 59-CTTGGA CTTCAGCCAGTTG-39 (for PIK3CD) or the forward primer 59-CGTGTACCACTATTGTCTGCC-39 and the reverse primer 59-GAACTCATACCTTTACACTGCC-39 (for TNFRSF9) with Platinum Taq DNA Polymerase (Invitrogen) according to manufacturer's recommendations, gel purified with the High Pure PCR Product Purification Kit (Roche), sequenced with the BigDye terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems), and analyzed on a 3500xL Genetic Analyzer (Applied Biosystems).
5, PE-Cy7, peridinin-chlorophyll, peridinin-chlorophyll-Cy5. 5, allophycocyanin, allophycocyanin-Cy7, allophycocyanin-Vio7, Brilliant Violet 421 (BV421), BV510, BV605, BV650, BV711, or BV785. For phosflow staining, 10 6 cells were incubated in 1 ml PBS with 1 µg/ml anti-CD3, pelleted, resuspended in 100 µl hot PBS containing 2 µg/ml rabbit anti-mouse cross-linker, incubated at 37°C for 5 min, fixed with 1 ml preheated phosflow fix buffer 1 (BD Biosciences), and then permeabilized with phosflow perm buffer 3 (BD Biosciences) according to manufacturer's instructions. Cells were then incubated with Alexa Fluor 647conjugated anti-AKT phosphorylated on S473 antibody (D9E; Cell Signaling) or Alexa Fluor 647-conjugated anti-AKT phosphorylated on T308 antibody (C31E5E; Cell Signaling). All data were collected on an LSRFortessa X-20 cytometer (BD Biosciences).
, 2002; Izawa et al. , 2017). Briefly, irradiated P815 expressing or not CD137L or irradiated EBV B cell lines (LCLs) expressing CD70 were preincubated with soluble 0. 25 µg ml -1 anti-CD3 antibody, washed, and cocultured with PBMCs or T cell blasts labeled with CellTrace Violet dye. The proliferation index (corresponding to the total number of divisions divided by the number of cells that went into division) was calculated using FlowJo software (TreeStar).
Figure4. Identification in the patient of a homozygous mutation in PIK3CD affecting the catalytic site. (A) Schematic representation of PIK3CD coding the p110δ. Intron-exon organization (coding regions in dark gray) and corresponding protein structure. ABD, adaptor-binding domain; RBD, Ras-binding domain. The mutation is indicated in red, and the previously GOF mutation E1021K in gray. (B) Family pedigree with DNA electropherograms and the PIK3CD c. 2462 G>A genotype of each individual (also see Fig. 1 A). (C) Ribbon representation of the p110δ-p85α complex (PDB: 5T8F), highlighting possible bonds between the p110δ kinase domain and p85α, in addition to contacts made by C2 and ABD. (D) Comparison of the experimental three-dimensional structures of PIK3CD (PI3KD; PDB: 5T8F; Castanedo et al.
, 2017 ) and PIK3CA (PI3KA; PDB: 2RD0; Huang et al. , 2007)  in complex with p85α. The catalytic residues K779 (PI3KD) and K802 (PI3KA) are shown and specific contacts with p85α (in purple) are highlighted. (E) Alignment of isoforms of p110 within the kinase domain. The observed secondary structures of p110δ/PIK3CD (PDB: 5T8F) and p110α/PIK3CA (PDB: 2RD0) are reported above and below the alignment, respectively (UniProtKB accession numbers PIK3CD_HUMAN: O00329, PK3CB_HUMAN: P42338, PK3CG_HUMAN: P48736, and PK3CA_HUMAN: P42336).
Acknowledgments We acknowledge the patient, his family, and the healthy donors for cooperation and blood gifts. S. Latour is a senior scientist at the  Centre National de la Recherche Scientifique (France). R. Rodriguez  was supported by the  Ministère de la Recherche (France)  and  Ligue Contre le Cancer (France) ,  D. Boutboul  by the  Agence Nationale de la Recherche (France) ,  S. Winter  and  D. Jorge Cordeiro  by the  Imagine Institut PhD program  funded by the  Fondation Bettencourt Schueller , and  B. Fournier  by the  Fondation pour la Recherche Médicale  ( FDM20170638301 ). This work was supported by grants from the  Ligue Contre le Cancer Equipe Labelisée (France ;  S. Latour ),  Institut National de la Santé et de la Recherche Médicale (France) ,  Rare Diseases Fondation (France; S.
Latour) , the  Agence Nationale de la Recherche  ( ANR-14-CE14-0028-01  and  ANR-18-CE15-0025-01  [ S. Latour ] and  ANR-10-IAHU-01  [Imagine Institut]), and the  European Research Council  ( ERC-2009-AdG_20090506 n°FP7-249816 ;  A. Fischer ). The authors declare no competing financial interests. Author contributions:  R. Rodriguez ,  D. Jorge Cordeiro ,  S. Winter ,  S. Latour , and  B. Fournier  designed the research and performed experiments and analyzed the data. C. Picard ,  D. Boutboul ,  K. Izawa ,  C. Lenoir ,  E. Martin ,  J. Bruneau ,  S. Fraitag , and I. Callebaut performed experiments and analyzed the data. B. Neven  identified the patients and provided clinical data. B. Neven ,  S. Latour , and  A. Fischer  analyzed clinical data. S. Kracker  and  T. H. Watts  provide key reagents.
Logger settings and deployments Field work was conducted at the "La Grande Manchotière" colony (20,000 breeding pairs) on Possession Island, Crozet Archipelago, during 2013/2014 and 2014/ 2015. Tracking data were collected from juveniles (N = 17) and non-breeding adults (N = 6). Almost all juveniles left the colony between the end of November and the end of December (the beginning of the austral summer) whereas, non-breeders left the colony between mid-February and mid-March, after completion of their annual moult. All birds were equipped with SPLASH tags (SPLASH10-283; Wildlife Computers, Redmond, WA, USA) just before their departure to sea. Tags (109x32x26 mm; LxWxH; mass: 62 g, ~0. 3% of a bird's body mass) were fixed to the feathers of the middle lower back using Loctite glue and cable ties.
The tags had a flexible antenna which was 8 cm long, 1. 6 mm in diameter and faced backwards at an angle of ~45°. SPLASH tags are composed of a logger module, which collects and processes pressure data and a transmitter module (Argos PTT) which relays data summaries via Argos satellites and enables the computation of locations by the Argos system. Due to battery limitations, tags were programmed to record and transmit dive data every 3rd day (i. e. 1 day ON, 2 days OFF), while locations were computed on average every two hours. During a recording day, pressure was sampled continuously at a rate of 1 Hz and was processed by an on-board algorithm to create a dive-profile log. During processing, depth data were divided into 'dive' and 'surface' events, from which dive profiles were reconstructed which included information on maximum dive depths, dive durations, and time spent at the surface between dives.
Environmental variables Environmental variables were either extracted directly from online data portals or estimated from indirect oceanographic data products (e. g. oceanic fronts). Sea Surface Temperature (SST, in °C), Mixed Layer Depth (MLD, in meter), Sea Ice Concentration (SIC, in percentage), Chlorophyll a concentration (CHLA, in mg/ m 3 ) and Total Oceanic Current speed (in m. s -1 ) and direction were downloaded from the Copernicus Marine Environment Monitoring Service of the European Union (http://marine. copernicus. eu/; Global Ocean Physics Analysis and Forecast), with a spatial resolution of 1/12°a t a daily temporal resolution. The ocean circulation data provided by this service are based on the NEMO model (Nucleus of European Modelling of the Ocean  [32] ) and includes all current components.
As CHLA satellite measurements are sparse for the Southern Ocean, especially during winter, when extended cloud coverage prevents measurements  [33] , the model outputs extracted from the Global Ocean Biogeochemistry Analysis and Forecast database with a spatial resolution of 1/2°at a weekly temporal resolution were therefore used. Wind speed (in m. s -1 ) and direction data were downloaded from the Global Forecast System of the United States National Weather Service with a spatial resolution of 1/2°degrees at a temporal resolution of 3 h (https://www. ncdc. noaa. gov/). The location of oceanic fronts (Fig. 1 ) was estimated using Sea Surface Height (SSH) values following  [34]. SSH data were downloaded from Aviso (http://www. aviso. altimetry. fr) with a spatial resolution of 1/4°at a daily temporal resolution.
Argos locations Erroneous Argos locations were filtered using the speed filter within the R package argosfilter  [36] , applied to the travel speed. The maximum travel speed was fixed to 14 km h -1  [37]. The speed filter procedure removed 15% (n = 4283) of all received Argos locations (n = 28,449). The average speed of the Antarctic Circumpolar Current (ACC) was low (0. 11 ± 0. 11 m. s -1 ) in comparison to the travel speed of both juveniles and non-breeding adults (~1. 1 ± 0. 2 m. s -1 ). The effects of the currents on the trajectories of the individuals were thus negligible, and the current corrected movements were therefore not implemented in further analyses  [38].
Movement patterns To investigate whether juveniles and non-breeding adults occupy a specific wintering area, Net Squared Displacement (NSD) analysis and a latent state model, within the lsmnsd package  [39] , was first used. NSD analysis measures the squared distance (great circle distance calculated with the argosfilter package) between each average daily track location of an individual and the first recorded location of its trip. The latent state model then distinguishes between three states from the NSD distribution: 'state 1' (in summer) and 'state 2' (in winter) correspond to different geographical areas where movements were concentrated (equivalent to two encamped movement modes), while 'state 3' corresponds to movements outside and between these areas (equivalent to a transiting mode).
Inference of behavioral modes (traveling versus foraging dives) To distinguish between traveling and foraging dives within the movement trajectories of birds, a cluster analysis was used (following  [40] ) based on both traveling speed and dive parameters (dive duration, maximum depth, and surface interval duration). Travel speed was calculated by dividing the distance between two consecutive dive locations (using the great circle distance with the argosfilter package) by the time difference between the initiation of the respective dives (i. e. including dive duration). We then computed hourly averages of the traveling speed and dive parameters. A distance matrix was then calculated to determine the similarity between these hourly averaged dive parameters and traveling speeds. A 'Manhattan' measures was used to estimate the distance matrix and created a hierarchical cluster tree, using unweighting average distance with the HCPC function in the FactomineR package  [41].
Orientation preferences The penguins' orientation preferences related to the oceanic current and wind were investigated by calculating the angular difference (circular package  [49] ) between animal heading and current and wind directions, respectively. Since there was a mismatch between the number of transmitted Argos locations per day and the available temporal resolution for current and wind datasets, Argos locations were re-interpolated at 12 h intervals (i. e. 2 locations per day). The zoo package with the na. approx function  [50]  was used as this function was specifically designed for irregular time series. The angular difference between the observed animal track ('heading' = 0/360°) and the current or wind directions (separately) was calculated. Angular difference values between 315°and 45°indicate a similar direction for both bird trajectory and current or wind (downstream orientation).
By contrast, values between 135°and 225°indicate an opposing direction for bird trajectory and current or wind (upstream orientation, see  [37, 38] ). All remaining values indicate a perpendicular direction. Differences in the proportion of downstream and upstream orientation of the birds were assessed with Generalized Linear Mixed Models (GLMMs, lme4,  [51] ), with the proportion of downstream versus upstream orientation used as the response variable (for current and wind separately). A binomial error distribution was used and individual identification was included as a random effect within models. Post-hoc pairwise comparisons between stage and season were made with the lsmeans package  [52] , using the Bonferroni procedure  [53]. Additionally, the effect of ocean productivity on bird orientation with respect to prevailing current and wind directions (upstream/downstream) was investigated.
Interestingly, adult non-breeders also moved south-west in autumn and foraged in the same general area as juveniles (APZ, Fig. 1 , Additional file 1: Figure  S1 ). However, they arrived in the area after the juveniles. Hence, juvenile king penguins managed to find these areas without  As non-breeding adults left long after the juveniles, sufficient summer data for them is lacking. Differences between stage (juveniles/non-breeding adults), direction and season were assessed with 2 different GLMMs per medium (wind or current) with 'proportion' as response variable, using a binomial family. Cells that share the same letter per type of medium (wind or current) are not significantly different from each other. The Bonferroni procedure was used for multiple comparisons following adult birds from the Crozet islands (breeders do not frequent these areas and non-breeders arrived after the juveniles).
Acknowledgments We thank  Philippine Chambault ,  Joffrey Jouma 'a,  Julien Collet ,  Cédric Cotté ,  Yves Lebras ,  Ryan Reisinger  for their help with the analyses and  Yves Cherel ,  Simon Benhamou  and  Gabrielle Nevitt  for their helpful comments on the manuscript. We are grateful to the many fieldworkers that helped with the deployment and retrieval of tags. We are also grateful to  Tegan Carpenter-Kling  for English editing and comments on the manuscript. Lastly, we thank two reviewers for their valuable comments.
Funding This study was funded by an  Advanced Grant  from the  European Research Council  as part of the  EARLYLIFE program  under the  European Commission's Seventh Framework Programme  (grant agreement  FP7/2007-2013/ERC-2012-ADG_20120314 ; PI  Dr. Henri Weimerskirch ) and was supported by  IPEV  ( Institut Paul Emile Victor, program 109 and program   394 ).
Study design and participants Data were used from the ANRS-VESPA2 survey (ANRS is the French National Agency of research on AIDS and viral hepatitis; VESPA2 is the acronym for "VIH: Enquête sur les Personnes Atteintes"), which is representative of PLWH followed up in 2011 in 73 French care units with caseloads of more than 100 HIV+ patients. Eligibility criteria were > 18 years old, diagnosed as HIV+ and living in France for at least 6 months. The 3022 patients included in the sample provided information about their living conditions including socio-demographic, economic, psychosocial and behavioral aspects, in a face-to-face interview. A self-administered questionnaire collecting other patient information was used to assess QoL, self-perception of health status, and quality of HIV care received.
Discussion Our results suggest that PQoL is primarily driven by diagnosis-specific cohort effects, which may be attributable to exposure to different clinical contexts over time. In contrast, the evolution of MQoL appears to be an individual process which changes with age. HIV status disclosure was driven by the combined effect of age at the time of diagnosis and the year of diagnosis. Our semi-parametric generalized additive model on data from the ANRS-VESPA2 study confirmed the importance and value of disentangling diagnosis-specific cohort effects from age effects in order to understand the interplay between contextual and developmental influences on the evolution of psychosocial and  behavioral outcomes among PLWH. The particular evolution of the need for moral and material support shows that these outcomes are individual processes that differ from one another only in terms of age.
Acknowledgements We thank all the PLWH who agreed to participate in the  ANRS VESPA2  survey, all the members of the different medical staff in participating hospitals, and the community-based organizations AIDES and  Act-Up Paris  who supported the survey. Finally, we thank  Jude Sweeney  for the English revision and editing of our manuscript. The authors declare no conflict of interest. The  ANRS VESPA2 Study Group: France Lert and Bruno Spire (scientific coordinators ),  Maria Patrizia Carrieri ,.
Data will be analyzed using SAS software version 9. 4 (SAS Institute Inc). Continuous variables will be reported as means and standard deviations, and categorical variables will be reported as frequencies and percentages. The Shapiro-Wilk test will be used to assess the normality of the distribution Results: Clinical investigation has been performed. Results are expected to be published at the beginning of 2019. Conclusions:This phase II clinical trial aims to evaluate the non-inferior efficacy and superior tolerability of N-PDT compared to that of C-PDT. If N-PDT is both efficacious and tolerable, N-PDT could become the treatment of choice for AK, due to its ease of implementation in hospitals.
G. Allocation/Randomization Patients who met all of the eligibility criteria were included in the study by central randomization. The randomization schedule was generated by a statistician using the PROC PLAN procedure in SAS statistical software (SAS Institute Inc) with a 1:1 allocation ratio and a block size of 6. The allocation was concealed using sequentially numbered, opaque, sealed envelopes that were opened sequentially by the investigator at the beginning of the treatment.
J. Variables/Data Collection Collected data consisted of demographic data, medical history reviews, previous radiotherapy histories, history of surgery and treatment for AK, definition of AK lesions (localization, number, grade, and photographs), and assessments of the subjects' skin aspects. For women of childbearing age, a urine pregnancy test was performed at screening or before the beginning of the treatment. Several scales (pain, aesthetic aspect, and treatment tolerance) and questionnaires (DLQI and satisfaction) were used.
K. Data Management All medical observations were maintained in the patient's file; the data to be analyzed in the study were reported on an electronic case report form according to Good Clinical Practices and the sponsor's standard operating procedures. The data collection procedure was exhaustive and verified regularly by a clinical research associate according to the protocol. Any deviation from the protocol was noted, and the reason for the deviation was documented. Discrepancies in the data were brought to the attention of the clinical team and investigational site personnel in the form of a query. Resolutions to these issues are reflected in the database.
Analysis of the secondary objectives The percentage of patients in each group with a reduction in the lesion number greater than 75% will be calculated and compared using a generalized linear mixed model. The aforementioned method will be used for comparisons of the other qualitative variables between the two groups (N-PDT or C-PDT). For continuous variables, we will use the linear mixed model. The pain levels reported at the end of each treatment will be compared using a linear mixed model, with patients as the random effects (the significance level will be set to. 05). All statistical analyses will be performed using SAS software version 9. 4 (SAS Institute Inc).
RESULTS Figure  7  shows the evolution of the number of subjects included, followed, and considered in the statistical analysis. Enrollment is closed. A total of 27 patients were recruited and followed instead of the planned 42 subjects due to the early termination of the Flexitheralight study, resulting from the launch of the competing Phos-Istos European study. Of the 27 patients, 23 completed all visits of the study, The clinical investigation was performed by July 2018. Data analysis was performed at the end of 2018, and results are expected to be published in early 2019.
ACKNOWLEDGEMENTS The authors wish to thank all research and medical personnel involved in the design and execution of this study. This study is supported by the  French National Research Agency  ( Projet-ANR-12-EMMA-0018 , www. flexitheralight. com)and sponsored by the  Centre Hospitalier Universitaire de Lille. Galderma International graciously provided Metvixia  (the form of MAL used in the intervention),.
MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe clustered MiRNA Master Regulator Analysis (ClustMMRA), a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of protumorigenic signalling pathways.
As the MMRA pipeline, ClustMMRA is a multi-step workflow that requires in input miRNA/mRNA expression profiles from matched tumor samples classified in different subtypes according to subtype-specific gene signatures. The final output of ClustMMRA provides key miRNA clusters contributing to the regulation of particular subtypes of the disease. We tested this novel pipeline to search for oncogenic / tumour suppressor polycistronic miRNAs driving breast cancer subtypes. ClustMMRA was applied to two independent breast cancer datasets whose samples were previously classified into four subtypes (luminal A, luminal B, HER2+ and triple negative). We obtained statistically concordant results between the two analysis, identifying five clusters of miRNAs with aberrant expression in a specific subtype of both datasets.
Clusters having at least two miRNAs with subtype-specific expression change in the same direction (both up-regulated or down-regulated) are selected for step (ii). In step (ii), we extract miRNA clusters having their predicted targets enriched for the gene signature of the corresponding subtype. Only miRNAs of the cluster classified as differentially expressed in step (i) are considered in step (ii). The targets of individual miRNAs have been predicted using four different databases (miRTarBase 2. 5, doRiNA-PicTar 2012, microRNA. org 2010, PITA 2007 and TargetScan 7. 1), requiring the prediction by at least two of them. The set of targets of a cluster has been defined as the union of the targets of individual miRNAs. The objective of step (i) and (ii) is to identify co-clustered and co-expressed miRNAs potentially regulating a gene expression signature in a joint manner, without necessarily having a high overlap in terms of target genes  23.
Finally, in step (iii) a miRNA-mRNA interaction network is constructed for each selected cluster using the ARACNE algorithm  33, 34. In this step, we identify modules of co-clustered miRNAs and interacting genes, including indirect interactions, that are believed to participate in the phenotype of a given cancer subtype (we call these modules regulons). Unlike the results of the MMRA pipeline, in which regulons can include only one miRNA, the ones identified by the ClustMMRA pipeline contain multiple miRNAs of the genomic cluster. Interference of indirect interactions may introduce links between miRNAs and spurious genes in the regulon. A Fisher's exact test has been performed to evaluate the statistical significance of the overlap between the genes included in each regulon and the gene signature of the associated subtype.