dataesr/scientific-paragraphs-categorization huggingface dataset:
The MS analysis was performed on a Q Exactive mass spectrometer (Thermo Fisher Scientific) with a top 10 acquisition method (MS resolution, 70,000; mass range, 400 to 2,000 Da), followed by 1 MS/MS run on each of the 10 most intense peaks at a resolution of 17,500 with a dynamic exclusion for 90 s. Raw data were processed using Proteome Discoverer (v2. 1) software (Thermo Fisher Scientific). The database search was done with the Mascot search engine (Matrix Science Mascot, v2. 2. 04) on a homemade protein data bank containing the putative proteins for endogenous viral elements as well as Aedes proteins (17,756 sequences). The following parameters were used: MS tolerance, 10 ppm; MS/MS tolerance, 0. 02 Da; semitryptic peptides; two miscleavages allowed; partial modifications, carbamidomethylation (on cysteine), oxidation (on methionine), and deamidation (on asparagine and glutamine).
Phylogenetic analysis of AEFE1. DNA was extracted from individual A. aegypti mosquitoes. PCR was performed for the full-length AEFE1 element, and the amplicons were sequenced by the Sanger technique. Forward and reverse sequences were trimmed on the basis of the chromatogram quality and aligned to generate a consensus sequence using the program Geneious (v7)  (52). Sequences from all successfully sequenced individuals, closely related ISFs, and medically important flaviviruses were aligned using the ClustalW program and trimmed to the same length. The program PHYML (53) was used to generate two phylogenetic trees using the PhyML best Akaike information criterion tree and the Fast likelihood-based method. The first tree contains closely related ISFs, medically relevant flaviviruses, and a representative AEFE1 sequence.
The second tree was constructed using only the ISF sequences and the AEFE1 sequences from A. aegypti. The best nucleotide substitution method was general time reverse (GTR) ϩG for both trees. Accession number(s). Sequences are available under NCBI BioProject numbers PRJNA386455 (Aedes aegypti transcriptome DENV1 versus the control at 24 and 96 hpi) and PRJNA386453 (Aedes aegypti transcriptome DENV1 versus DENV3).
ACKNOWLEDGMENTS We thank all members of the M. - C. Saleh  and  L. Lambrechts  labs for fruitful discussions,  Catherine Lallemand  for assistance with mosquito rearing, and  Valerie Dorey  for help with preparation of the DNA libraries. We are grateful to  Christophe Paupy ,  Diego Ayala ,  Davy Jiolle ,  Elisée Nchoutpouen ,  Karima Zouache ,  Alongkot Ponlawat ,  Thanyalak Fansiri , and  Isabelle Dusfour  for the original mosquito colonies. This work was supported by the  European Research Council  ( FP7/2013-2019 ERC CoG 615220  to M. -C. S. ), the  French Government 's  Investissement d'Avenir Program, Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID  to L. L. and M. -C. S. ), the  City of Paris Emergence(s) Program in Biomedical Research  (to L.
■ ACKNOWLEDGMENTS This work was supported by the  UK Engineering and Physical Sciences Research Council  ( EPSRC EP/M008258/1 ). The authors thank the  Department of Chemistry NMR facility, University of Cambridge  for performing some NMR experiments, and the  EPSRC UK National Mass Spectrometry Facility  at  Swansea University  for carrying out high-resolution mass spectrometry. D. Z. acknowledges a grant from the  China Scholarship Council ,  Accueil Doc Bursary from France Rhone-Alpes Region , and  Enveloppe AttractivitéFellowship  from  ENS-Lyon  for Ph. D. studies.
Cell growth and synergy assays Cell lines were treated with various concentrations of MLN8237 or chaetocin, as indicated. Relative cell viability was determined by colorimetric MTS assay (CellTiter 96® AQueous One Solution Cell Proliferation Assay; Promega) according to the manufacturer's suggestions. For drug combinations, cells were treated with 8 independent concentrations at a fixed ratio (1:2) or 5 independent concentrations at fixed ratios (1:3 or 1:5) of chaetocin:MLN8237. The fraction affected (Fa) was calculated based on MTS measurements of treated samples normalized to untreated controls and the maximum biological effect of each cell line. A combination index (CI) was calculated by the median-drug effect analysis method developed by Chou and Talalay  [15]  using the CompuSyn software (ComboSyn, Inc, Paramus, NJ), where CI < 1, 1, or > 1 indicates synergistic, additive and antagonistic effect respectively.
Cell Viability, Apoptosis, and Cell Cycle FACS Analysis Cells were plated at 5. 0E3 cells/well, treated, and cell viability and apoptosis were measured with the ApoTox-Glo™ Triplex Assay (G6321, Promega) on Promega's GloMax at indicated times. For FACS, cells were prepared as previously described  [20]. DNA content was analyzed on a Becton Dickinson LSR II FACS instrument at the Mayo Flow Cytometry Core Facility. 20,000-40,000 events were collected and analyzed with appropriate gating in FlowJo software (FlowJo, LLC, Asland, OR).
RNA-sequencing and real time qPCR RNA was isolated using RNeasy mini kit with optional on column DNA digestion (Qiagen). Libraries were prepared (Illumina TruSeq mRNA v2) and run on the Illumina High Seq-2000 (101 bp paired end reads) in the Mayo Clinic Sequencing Core. RNA-Seq data was analyzed using MAP-RSeq v. 1. 2. 1  [24]. MAP-RSeq consists of alignment with TopHat 2. 0. 6  [25]  against the hg19 genome build and gene counts with the HTSeq software 0. 5. 3p9 (http://www. huber. embl. de/users/anders/HTSeq/doc/overview. html) using gene annotation files obtained from Illumina (http://cufflinks. cbcb. umd. edu/igenomes. html). Principal component analysis and un-supervised hierarchical clustering were performed on the samples using Partek® Genomics Suite® software, version 6. 6©; 2016, Partek Inc.
, St. Louis, MO, USA. Normalization and differential expression analysis were performed using edgeR2. 6. 2  [26]. Data is available through the GEO repository under accession GSE97215. Real time qPCR was performed using RT2 SYBR® Green qPCR Mastermix (Qiagen) and RT2 qPCR Primers (Qiagen) on the Bio-Rad CFX96 system. Fold changes (ΔΔCt) and pvalues were calculated using Bio-Rad CFX manager and SABioscience's RT2 Profiler PCR Array Data Analysis software. Treatment samples were compared to vehicle and normalized to a panel of housekeeping genes.
Chaetocin enhances the inhibitory effects of MLN8237 on PDAC cell growth in culture and in vivo We combined MLN8237 and chaetocin at various concentrations to determine their combination index (CI), using CompuSyn software, which is based on the theory of Chou-Talalay Method to define additive effect (CI = 1), synergism (CI < 1), or antagonism (CI > 1) in drug combinations  [15]. We performed a 8×8 matrix format using MTS assays, which investigated concentration ranges of 60-320nM of MLN8237 and 5-50nM of chaetocin on the same mouse (Pan02) and human (BxPC-3, Capan-2, L3. 6, MIA PaCa-2, and PANC-1) PDAC cells tested with individual drugs. Based on initial concentrations (Figure  1 ), MLN8237 or chaetocin were added to cells in an expanded matrix format to determine IC50 values of each drug alone.
Acknowledgments We want to express our sincere gratitude to  Dr. Raul Urrutia  for critical insight and valuable scientific discussions and  Kristin Mantz  from the  Mayo Clinic Microscopy and Cell Analysis Core facility  for her assistance with the Mikroscan Slide Scanner. Financial Support: This work was supported by funding from the  National Institutes of Health  (grant  R01 CA178627  to GL), the  Mayo Clinic Division of Gastroenterology and Hepatology, Department of Medicine , as well as the  Mayo Clinic Center for Cell Signaling in Gastroenterology  ( P30DK084567 ) and the  Mayo Clinic SPORE in Pancreatic Cancer  ( P50 CA102701 ).
Acknowledgements We are grateful to  Prof Richard Chiverell  for useful discussions and to  Dr Anu Patel ,  Sabena Blackbird ,  Jort Ossebaar ,  Florian Boxberg  and  Janet Rethemeyer  for help in the laboratories. The palynological analyses were facilitated by  André Lotter. The work was funded by the  Leverhulme Trust  (grant  F/00 025/AU ) and the  German Research Foundation  (DFG, grant  WA2109/1-1 ).
Data sets This study was conducted on 115 automatic weather stations located in Switzerland, which are managed by MeteoSwiss (https://gate. meteoswiss. ch/idaweb/). These stations record at least air temperature, precipitation and vapor pressure at a daily time step. SW incident radiations are measured at almost all weather stations (108), while only some of them also include LW radiation measurements (a total of 33 stations). Only one station from this data set measures LW radiations without measuring SW radiations. The 115 stations are well distributed across the country (Fig. 1 ), with 60% of the stations located at less than 1000 m elevation, 25% of stations located between 1000 and 2000 m elevation and 15% above 2000 m elevation. All stations have at least 1 year of measured data. If available, data were collected from 2009 to 2015.
The background topography is from SRTM3 (ref. 14 ) available at https://www2. jpl. nasa. gov/srtm/. The map was generated using GlobalMapper ® GIS software. been located tens of kilometers further inland, sediments were transferred to the coast throughout the structural valleys via the riverine network and permitted a perennial delta accretion. The stratigraphic architecture of glaciofluvial deltas typically shows flat-to gently-sloped topsets deposited by braided streams, upper foresets consisting of stacked graded sand beds deposited by high-density turbidity current, and gently-sloped lower foresets formed by low-density turbidity currents. In spite of high rates of RSL fall forced by the glacio-isostatic rebound  28  , no coeval fluvial entrenchment was identified at least in the early part of this stage.
Evolution of deltaic complexes Stratigraphic relationships between the three above-described morphosedimentary systems arise in a generic architectural framework, which have been investigated from a selection of seven deltaic complexes (Figs  1, 2  and 3 ). This stratigraphic framework has been expanded and adapted to the entire QNS based on aerial and satellite photographs, LiDAR and digital elevation model datasets, as each of the three type of depositional system has a specific morphological expression allowing to confidently identify them on aerial imagery (Fig. 2 ). Thus, thirteen additional deltaic complexes are accounted for (Fig. 4 ), for which the ages of landforms and deposits have been essentially extrapolated from regional RSL curves  13  (Fig. 2  and Table  1 ). The diversity of observed stratigraphic architectures is categorised into four end-member scenarios representative of combined interactions between patterns of ice-margin retreat and inherited topographies (e.
Underfilled dammed valley. In this scenario, an extensive ice-contact depositional system initially develops at the outlet of the valley on a bedrock threshold during a stillstand of the ice margin. Sediment aggradation combined with subsequent RSL fall may ultimately result in development of an initial ice-contact subaqueous fan into an ice-contact delta depending on sediment availability, duration of glacial stillstand and RSL fall rates  34, 39. After further ice-margin retreat from its temporary stable position at the valley mouth, the coarse-grained fanor any bedrock sill on which the fan was anchored-dams a fjord lake at the head of which a lacustrine ice-distal Figure  4. The thirteen other deltaic complexes along the Québec North Shore for which no or limited sedimentological and stratigraphic datasets are available.
Acknowledgements Field campaigns and radiocarbon dating were funded by action  SYSTER  and the  ARTEMIS program  ( LMC14 ) of  INSU-CNRS. This work is a contribution to the ' SeqStrat-Ice '  ANR  project  12-BS06-14. Fieldwork was also supported by an  NSERC   Discovery grant  to  P. L. George Henry  is thanked for his comments and corrections on this manuscript. Authors are grateful to  Editorial Board Member Nick Marriner  and an anonymous reviewer whose comments and suggestions led to the improvement of the quality of the manuscript.
The error of RingAmp compared to RingAmp' as the sum of absolute squares of deviations in each pixel is calculated and will be minimized through the iterative process. The complex amplitude RingAmp' is backpropagated to the SLM plane and SLMphase is retrieved by the Matlab phase function which returns phase angles, in radians, of a complex image. Constraints are applied to the phase distribution: 1. A low-frequency filter prevents the algorithm from optimizing for patterns with high-frequency content to reduce higher-order diffraction. Fig. 2. The double (A) For monochromatic light and a circular symmetric grating of triangular phase on the SLM, we observed such double rings in the front focal plane of L1. A precisely aligned mask in the ring plane could be used to block one of the rings, which is however not applicable when working with a broadband source.
Clinical features A 65-year-old woman was admitted for an acute edematous syndrome associated with asthenia. The patient had a past medical history of hypertension, dyslipidemia, bilateral gonarthrosis and left coxarthrosis. On admission, physical examination revealed arterial hypertension (163/89 mmHg) without fever and a violaceous cutaneous rash of the right leg that had appeared 2 years before. It had started as a small painful round macule of the foot that progressively extended to the whole calf and the gluteal region (Fig. 1 ). Neurological examination revealed painful peripheral neuropathy on the right leg. Electrocardiogram was normal. Laboratory data showed hypoalbuminemia (1,2 g/dl) associated with severe proteinuria (7,03 g/24 h) and a recent decrease of renal function (serum creatinin level increase from 0,84 to 1,3 mg/dl).
Introduction High grade serous ovarian cancers (HGSOC) account for about 75% of all ovarian carcinomas  (1). Standard treatment includes debulking surgery and platinum-based chemotherapy. Although most patients show primary response, they frequently relapse and eventually develop fatal resistant disease. The five year survival rate of HGSOC is between 35-40%  (2). Cancer recurrence and therapy resistance are considered the major causes of death. Recent next generation sequencing (NGS) data on primary ovarian cancer demonstrate that nearly all HGSOCs harbor a mutation in the TP53 gene  (2). Other major molecular alterations are found in DNA damage and repair pathways, in which BRCA1 and BRCA2 genes are frequently mutated  (3) , and in the RB1/CCNE1 pathway (4). In addition, HGSOC is characterized with a high genetic instability and frequent promoter hypermethylation.
For the OCTIPS study cohort, operable recurrent tumor was the inclusion criterion and therefore, cumulative survival probabilities could not be unbiasedly estimated if the date of primary diagnosis served as baseline  (24). Therefore, we compared the mortality hazard after cancer diagnosis between patients with active and silent tumors using a delayed-entry (entry at recurrence) Cox regression model  (25). Time from primary diagnosis to recurrence was compared between patients with active and silent primary tumors using the Mann-Whitney U test. Two-sided p-values <0. 05 or 95% confidence intervals excluding parity were considered as statistically significant.   Analyses of TCGA gene expression data 488 patients with ovarian cancer were included in the TCGA data. We included all HGSOC and excluded cases missing information on residual tumor and on survival, obtaining a total of 421 patients.
 Analyses of RNA sequencing data of Patch et al. (4) In a recent study (4), RNA sequencing data from 80 primary HGSOC using TruSeq RNA Sample Preparation v2 kit and HiSeq2000 instrument (Illumina) were provided. RNAseq data were downloaded from the European Genome-phenome Archive (http://www. ebi. ac. uk/ega/), which is hosted at the EBI, under accession number EGAS00001000397: RNAseq data (exp_seq. OV-AU. tsv). The corresponding clinical data were filed donor. OV-AU. tsv and specimen. OV-AU. tsv. The Access to datasets was approved by the specified Data Access Committee (DAC). We defined the immune activation status of these 80 primary tumors by supervised clustering using the 126 immune relevant genes in the same way as for our primary and recurrent samples. Overall and relapse-free survival were compared between the two groups similarly as described above.
Of the 113 most differentially expressed genes, 90 were specifically expressed in various types of B and T cells, or encoded immunoglobulins and proteins directly related to immune activation. Our data thus showed that HGSOC could be stratified into two subgroups according to the expression of immune specific genes. Hence, we re-clustered the recurrent and primary tumors separately using the 126 immune related genes identified in the recurrent samples. By doing so, primary and recurrent tumors were classified as immune-active (higher expression of the immune genes) and immune-silent (lower expression) (Supplementary Figure  S1A, S1B ). We therefore defined 4 subgroups of matched samples according to the immune status (Supplementary Table  S4 ) and named them active-active (16 pairs), silent-active (21 pairs), active-silent (9 pairs), and silent-silent (20 pairs).
However, this difference could neither be confirmed by our own data nor by the data from Patch et al. , indicating that further studies are needed to verify the results. Studies with a larger cohort of samples and a thorough investigation on the functions of the gene family must be performed in order to design innovative immune therapy strategies. Second, the co-expression of immune genes with that of ECM and adipose tissue remodeling genes is intriguing, because enzymes for both ECM construction and destruction have been found. It could be of important therapeutic consequences, since loosening the structure of the ECM or adipose tissue would not only facilitate contact of immune cells with tumor cells, but might also increase exposure of cancer cells to drugs. Hence, it will be important to fine tune immune therapy strategies in HGSOC.
Finally, some recurrent tumors are quite similar as their primary counterparts while others are not. If recurrent tumors could be analyzed at molecular levels and compared with their primary counterparts, the scheme of treatment could be adjusted according to the primary response of the patients. It is very important to examine if the immune activation status would have an effect on the patients' outcome. Our cohort was rather small for survival analyses, when stratifying the patients by residual tumor status, one of the most important factors affecting both diseasefree and overall survival. In the cohort from a public dataset with 80 primary tumors, the residual tumor status was missing and the results of survival advantage of patients with immune activation were not significant. Studies with larger sample cohort with available information on rest tumor will be needed to clarify the impact of immune activation on clinical outcome of the patients.
The study produced a rather large dataset. Based on the variance in tumor cell contents of the samples, we decided to use stringent conditions to analyze differentially expressed genes in all analytical procedures in order to obtain reliable results. This might lead to the nonobservance of some genes, which had less expression changes or/and lower expression. Alternative analyses using lower stringency could be performed to investigate such genes or their related biological processes. However, results from such analyses must be validated using bigger sample cohorts. In conclusion, we observed a difference of a complex microenvironment network including immune activation and remodeling of tissues around tumors across all tumor samples and defined tumors as immune active and immune silent. By subgrouping paired tumor lesions into active-active, active-silent, silent-active, and silent-silent ones, we found that there was no notable difference between primary and recurrent tumors in the active-active and silentsilent subgroups, suggesting that these recurrent tumors resemble their primary counterparts.
Acknowledgements This work was supported by  European Community 's  Seventh Framework Programme  under grant agreement No. 279113-2  (OCTIPS; www. octips. eu). The documentation of clinical and patient's data was managed with "AlcedisTRIAL the web based documentation system" of  Alcedis GmbH, Winchesterstr. 3, 35394  Giessen, Germany. We thank  Ms. Elisabeth Maritschnegg  and  Ms. Jasmin Karacs  for their technical support. Research. on October 4, 2017. © 2017 American Association for Cancer clincancerres. aacrjournals. orgDownloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 2, 2017; DOI:  10. 1158/1078-0432. CCR-17-1159.
The value of data for answering new questions can grow with sample size, e. g. for replication, increasing population representativeness, and increasing study power. To address this issue, a growing number of electronic databanks including brain imaging are available, either from dedicated cohorts (e. g. Alzheimer's Disease Neuroimaging Initiative, UK Biobank, IMAGEN), or collections of studies (e. g. Brain Imaging in Normal Subjects, Dementia Platform UK, Open Access Series of Imaging Studies): see Table  1. Brain images from 'healthy' subjects are important: The wide variation in brain structure and function both within and between individuals at different ages has long been recognised  (Wardlaw et al. , 2011; Dickie et al. , 2013). Methodologies that use appropriately representative populations are needed to provide normative populations, particularly for healthy subjects (i.
e. those without neurological diseases such as stroke or dementia). They can provide informative reports for users (e. g. 'brain on 5 th percentile for volume at age 70' for a specified population) and simultaneously embrace the spectrum of individual variation  (Dickie et al. , 2015a (Dickie et al. , , 2015b)). Brain imaging is increasingly used in the diagnosis of neurological diseases, and mental health disorders  (Fox and Schott, 2004). Data from existing cohort or population studies (e. g. Marcus et al. , 2009) , can help define boundaries between health and disease, to aid diagnosis and trial inclusion, to provide effect size estimates for planning trials, and, where relevant, controls for case-control studies (e. g. Dickie et al. , 2015a; ADNI: Potvin et al. , 2016). Current status of brain imaging banks: Large repositories of brain imaging data from wellcharacterised subjects in accessible databanks are required to achieve this, while ensuring that data protection concerns are also addressed.
Standards for sharing: The INCF (International Neuroinformatics Coordinating Facility) Standards for Data Sharing Neuroimaging Task Force the Brain Imaging Data Structure (http://bids. neuroimaging. io/) to advance standard organisation and descriptions of data files, and the Neuroimaging Data Model (http://nidm. nidash. org/) for data provenance tracking, but ongoing work is needed around developing community consensus and adoption of standards  (Bjaalie and Grillner, 2007). Issues such as privacy, de-identification, quality control, provenance, avoiding including the same subjects in multiple databases, ethics (historical and future), consent, essential components of 'good guardianship', costs, sustainability, software version control, , definitions of 'normality', and international variations in ethical and legal frameworks, also need further consideration  (Rodríguez González et al.