dataesr/scientific-paragraphs-categorization huggingface dataset:
We chose all genes that have a differential expression using a log-threshold of 1. 5 for at least 24 hours, resulting in 78 genes. We clustered these 78 genes over 154 time windows between 0. 5 and 21 days, with all time window lengths between 1 and 8 days, i. e. clustering at intervals [0,1], [0,2], [0,3],. , [0,8];  [1, 2] ,  [1, 3] , etc. We chose all clusters from all intervals that have a mean fit > 0. 8 inside the cluster according to the OVL kernel, resulting in 89 clusters across various time windows. Furthermore, some clusters represented exact subwindows of other clusters, which we pruned. This resulted in 43 final clusters, which contain 49 of the 78 differential genes (see S1 Table for names, descriptions and accession numbers of these 49 genes). Hence, 29 genes were not clustered and remain singletons, representing a 37% outlier ratio.
We extracted all putative transcription motifs related to the original 301 genes from the MotifMap system (motifmap. ics. uci. edu) [39, 40] , with a BBLS score of at least 1 and an FDR level of 0. 1. These motifs are given in S2 Table. We furthermore filtered from these motifs only those that applied to the 83 differential genes. These are displayed in S3   [46]. In the end, this resulted in 47 distinct associated TFs retrieved from this database (see S4 Table for names, descriptions and accession numbers). Table  1  summarizes the characteristics of the 43 clusters and predicted TFs found with the MotifMap system (see also S5 Table for a complete list of the clusters, motifs and TF names from MotifMap and Pathway Studio). We additionally plotted an eye diagram  [41]  where we matched 1) the 49 clustered genes to 2) the 43 time window clusters to 3) the 47 associated TFs found through MotifMap (Fig  5 ).
Interestingly, there are more TF occurrences in the early and late times than expected when considering the number of differentially expressed genes or the number of clusters (compare the green curve to the blue and red curves in Fig  3C ). This suggests that endothelial cells may quickly activate the transcriptional machinery by modulating a few genes, which are potentially controlled by many TFs. Considering the TFs found in early times (ARNT, ETS1, HNF4A and TP53) (cluster 1), we established that they were interconnected and related to radiation through TP53 using the text mining algorithm of the software Pathway Studio (PS) by querying direct interactions (Fig 8A ). PS identified 201 references that link radiation to TP53, 13 references that link TP53 to ETS1, 6 references that link TP53 to ARNT and 13 references that link TP53 to HNF4A.
The Cluster visualization. The 43 cluster means of differential genes were determined using a ratio threshold of 1. 5 and a minimum cluster kernel similarity of 0. 75. Clusters are displayed as colored curves and clustered genes as black curves over the 3-week study period. Gene expression profiles between the control and the irradiated samples are plotted as continuous gray curves in periods when the genes were differentially expressed and as a gray dotted curve when the genes were not differential. involvement of TP53 in the response to DNA damage induced by ionizing radiation has been extensively documented  [53]. In our previous investigation of the same dataset  [7] , we showed by bioinformatics tools (PANTHER pathway classification) that the TP53 pathway was likely activated in the early time points post-irradiation.
We evaluated the proposed novel kernels on a simulated clustering dataset. Using the real experimental data we generated and published earlier  [7] , temporal clustering over time windows, enrichment analyses and molecular pathway analysis indicate the temporal activation of biological entities and TFs by expression profile clusters. Overall, our results highlight that a dose of 2 Gy, which corresponds to a conventional RT dose fraction, could be sufficient to activate a basic molecular program, such as cell survival, activation or cell death, and on the other hand the process of cell adhesion, which is the first step of tissue infiltration. Furthermore, based on the cluster analysis, this new method allowed us to propose putative transcription factors involved in the regulation of gene expression following radiation, and five key genes as drivers of the response to ionizing radiation in endothelial cells.
Table. These motifs finally corresponded to 52 TFs (S3 Table) that were searched with the software Pathway Studio Web Mammal version 11. 2 by querying the Mammal (Che-mEffect; DiseaseFx; CellEffect) version 11. 2. 5. 6 (Updated May 28, 2016) database from Elsevier (www. elsevier. com/pathway-studio).
PATIENTS AND METHODS • PROMPT is an international European Phase III/IV study that commenced in 2015 (ClinicalTrial. gov registry: NCT02297945). • PROMPT is the first prospective study to document the safety and efficacy of metyrapone using modern assay techniques, in endogenous Cushing's syndrome over 36 weeks. • In Belgium, four patients with CD (three women and one man) were treated with metyrapone for the duration of the 9-month PROMPT study. • One patient had received prior medical treatment with ketoconazole and cabergoline and three patients had received prior pituitary surgery. • Metyrapone was individually titrated during the first 3 months of PROMPT based on urinary free cortisol (UFC) and/or serum cortisol performed at each visit (Figure  2 ). • UFC measurements during the study was performed centrally by liquid chromatography tandem-mass spectrometry (LC-MS/MS) method.
Stimuli and procedure All participants performed two successive experiments, a Scene experiment and a Face experiment (Fig  2 ). Stimuli were elaborated using the MATLAB image processing toolbox (Mathworks Inc. , Sherborn, MA, USA). Stimuli in the Scene Experiment were selected from previous research  [17, 18]. These were gray-scale photographs of 20 scenes (192 × 144 pixels) belonging to two semantic categories: indoors (kitchens, living-rooms) and outdoors (streets, buildings, houses). Both categories were equivalent in terms of visual cluttering and all scenes had a similar energy distribution in both spatial frequency and dominant orientations (similar mean amplitude spectrum). Thus, categorization based on physical properties of the images were avoided. Stimuli in the Face experiment, selected from previous research  [19] , were gray-scale photographs of 10 human faces, five animals, and five vehicles (192 × 192 pixels).
Stimuli were displayed using E-prime software (E-prime Psychology Software Tools Inc. , Pittsburgh, USA) on a 30' monitor, with a resolution of 2560 × 1600 pixels, at a refreshing rate of 60 Hz and with a viewing distance of 38 cm. Optical correction was used for participants requiring visual correction at this distance. Stimuli covered the central 6˚of the visual angle width at this viewing distance. Hence, the region covered by the four central points of the automatized visual field evaluation used to determine whether POAG patients presented a central defect was matched. To maintain the distance and central position, participants' heads were supported by a chinrest. We measured the gamma function of our monitor (i. e. the luminance of the display for different values of uniform gray level stimuli generated with Matlab) using a calibration tool (Spyder5Elite).
3% (Switzerland) to 6. 2% (Estonia). 22 [25]  In the Japanese population, the point prevalence of adult AD was estimated to be 2. 9%, with 1-year and lifetime prevalence rates of 3. 0% and 3. 3%, respectively. 26 e above epidemiologic data indicate distinct gaps in our knowledge of the prevalence of AD in adults. Challenges that have contributed to these gaps include inconsistency in applying diagnostic criteria to epidemiologic evaluation, lack of a universally accepted measure for assessment of severity, and less than adequate representativeness of the samples that would enable generalizability to the broader population (eg, the US sample in the ECRHS study was represented by only a single city). 22 e primary objective of this study was to fill these gaps by providing global data on the prevalence of AD in representative samples of adults from different countries using standardized diagnostic criteria and consistently applying established methods from previous studies.
A secondary objective was to enable comparative and robust estimation of AD prevalence by disease severity in each country using validated patient-reported outcomes. Now, all countries except Japan; AIP, Japan; Netquest, Spain; Instantly, US; and Asking Canadians, Canada). Recruitment was by broad-reach portals, special interest sites, and direct emailing campaigns, and panel members who completed the questionnaire received points redeemable for items in a prize catalogue. For inclusion, members of an online respondent panel were required to be 18-65 years old, inclusive, and able to read and write the native country language. 27  reduce selection bias, panelists were blinded to the research topic when invited. To ensure robustness of the data collected, the personal information provided was matched to a third-party database to confirm validity; inactive e-mails and inactive members are regularly removed from the panels.
The Internet Protocol address of the respondent was verified against a known list of fraudulent servers to identify fraud at the time of registration. Individuals who completed the survey in an unreasonably short time (<2 minutes for those without self-reported AD; <4 minutes for those with selfreported AD) were excluded. Individuals were excluded if their responses on severity scales lacked consistency (ie, very low scores on 1 scale and very high scores on another). Interim quality checks were conducted on the data after 1000, 2000, and 3000 respondents were recruited; in particular, consistency in the distribution of AD severity was confirmed in panels within and across countries.
Data Collection In-depth interviews were conducted either face-to-face or over the telephone over a 12-week period. A topic guide was informed through relevant literature relating to self-management in people with type 2 diabetes and their use of technology-based systems. Topics in the interview guide broadly included the following: knowledge and understanding, controlling and managing symptoms using technology, self-monitoring, tailored goals, dealing with complications, use of services, and feeling supported. Prompts were used to gain a deeper understanding of participant responses on important topics. Participants were also encouraged to discuss any other topics they deemed appropriate. Interviews lasted, on average, 48 min and were recorded and transcribed. Transcription of the interviews was outsourced and accuracy checked on their return by LK.
Understanding Individual Health and Personal Data Participants sought to understand the impact of diet and, in some cases, exercise on their blood glucose levels. All participants took regular blood glucose readings. In most cases, readings were tracked and monitored throughout the day, with 2 participants wearing a continuous blood glucose (CBG) monitor that offered readings (and alerts) in real-time. Regular monitoring enabled participants to learn about daily patterns in their blood glucose levels and gave a greater understanding of its relationship with diet and exercise. Having a greater understanding of how their body behaved contributed to an overall sense control when managing diabetes: I don't measure my bloods all day every day, but I do it as a testing thing to basically learn from it.
Acknowledgments The authors would like to thank  Diabetes UK and Diabetes Support Forum UK  who helped with recruitment by circulating the study recruitment link. The authors would also like to thank the participants who volunteered their time to take part in the study. This study was funded internally within the  Health Service Research Unit, Nuffield Department of Population Health, University of Oxford. LK receives funding from the  National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care Oxford at Oxford Health NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the  Department of Health and Social Care.
In Fig 4D  we  show a representative time trace of the log-ratio DL (Eq (1)) in a simulated teleportation session. Alternate intervals of positive and negative DL signal the presence of map instability, as in  [27] , following the light switch (red vertical lines) and the path-integrator realignment (green vertical lines). Applying to the simulated data the same positional-error analysis as for the recorded data (Fig 3A&3B ), we observe the same qualitative picture, see Fig 3C&3D. In particular, position is coherently encoded in the recalled cognitive map at all times. In order to test these two predictions on CA3 recordings, we introduce a method to disentangle the flickering dynamics of the cognitive map and the realignment of the PI, the latter bringing an end to the former. We first infer the most likely PI-realignment time for each light-switch event, given the sequence of identified flickers (Methods).
Data used in this work are the CA3 microarray recordings published in the https://www. nature. com/articles/ nature10439 study. Interested readers may obtain the data by contacting the authors of the original paper, referring to Nature's availability policy for data & materials (https://www. nature. com/authors/ policies/availability. html). The source code for the inverse-Ising inference is available at https://github. com/johnbarton/ACE. Supporting code for CANN simulations is available at https://github. com/ lposani/CANN.
These data are then compared to the hard-copy shift charts that the EDRAs use to ensure that all potentially eligible patients are being screened during enrollment hours. The shift chart allows the EDRA to demonstrate that work is being done, even on patients who are not eligible, who refuse to participate, or who are missed for other reasons. Formal performance assessments are done quarterly throughout the program, as well as at the end of the EDRA's time as a student or at the request of the individual. These assessments are based on feedback from clinical and research staff, and focus on five core competencies identified in successful EDRAs: communication skills, community interaction skills, critical thinking, personal presentation, and policy adherence. EDRAs are also assessed on their enrollment performance and any quality assurance and improvement concerns that have arisen.
Global diversity of B. dendrobatidis To resolve these inconsistencies, we isolated B. dendrobatidis from all the candidate source continents and sequenced the genomes of 177 isolates to high depth then combined our data with published genomes from three prior studies  (11, 12, 18)  to generate a globally representative panel of 234 isolates (Fig. 1A ). This dataset covers all continents from which B. dendrobatidis has been detected to date, and spans infections of all three extant orders of Amphibia (Fig. S1  and Table  S1 ). Mapped against the B. dendrobatidis reference genome JEL423, our sequencing recovered 586,005 segregating single nucleotide polymorphisms (SNPs). Phylogenetic analysis recovered all previously detected divergent lineages (Fig. 1B  and Fig. S2 ). The previously accepted lineages BdGPL (global), BdCAPE (African), BdCH (European) and BdBRAZIL (Brazilian), were all detected  (19) , but our discovery of a new hyperdiverse lineage in amphibians native to the Korean peninsula (BdASIA-1) redefined these lineages and their relationships.
We then investigated whether our dataset contained sufficient signal to perform tip-dating inferences by building phylogenetic trees using PhyML (25) (Fig. 2A  and 2C ) then fitting root-to-tip distances to collection dates both at the whole-tree and within-lineage scales. We observed a positive and significant correlation within BdGPL only, for both the mitochondrial and nuclear genomes, demonstrating sufficient temporal signal to perform thorough tip-dating inferences at this evolutionary scale (Fig. 2B  and 2D ). Tip-dating in BEAST was used to co-estimate ancestral divergence times and the rate at which mutations accumulate within the BdGPL lineage. The mean mitochondrial substitution rate was 1. 01 x 10 -6 substitutions/site/year (95% highest posterior density (HPD) 4. 29 x 10 -7 -1. 62 x 10 -6 ).
2G ). We further tested for differences in virulence among lineages by using our global dataset to examine whether chytridiomycosis was non-randomly associated with B. dendrobatidis lineage. We detected a significant difference (p < 0. 001) in the proportion of isolates associated with chytridiomycosis among the three parental lineages (BdASIA-1 and BdASIA-2/BdBRAZIL were grouped due to low sample sizes), and post hoc tests indicated significant excess in virulence in both BdGPL and BdCAPE lineages relative to the combined BdASIA-1 and BdASIA-2/BdBRAZIL (all p < 0. 05). However, we did not detect a significant difference between BdGPL and BdCAPE (Fig. S16  and Table  S5 ). These data suggest that although BdGPL is highly virulent, population-level outcomes are also context dependent  (32) ; under some conditions other lineages can also be responsible for lethal amphibian disease and population declines  (33).
Acknowledgments: DNA sequencing was carried out in the  NBAF GenePool genomics facility  at the  University of Edinburgh , and we thank the  GenePool  staff for their assistance. This work used the computing resources of the UK MEDical BIOinformatics partnership -aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the  Medical Research Council  [grant number  MR/L01632X/1 ]. We thank  Dr Johanna Rhodes  for the provision of flow cells and reagents for MinION sequencing. We thank the staff at  Oxford Nanopore Technologies  for admission to the  MinION Early Access Programme. We thank the three anonymous reviewers for constructive comments and suggestions during the peer review process. Azure for Research Sponsorship  (subscription ID: ab7cd695-49cf-4a83-910a-ef71603e708b).
TWJG, AL, AAC, DSS, EC, CM, JB, DA, FC and MCF were also supported by the  EU BiodivERsA scheme (R. A. C. E. , funded through  NERC  directed grant  NE/ G002193/1  and  ANR-08-Biodiversa-002-03 ) and  NERC  ( standard grant   NE/K012509/1 ). MCF, EC and CM acknowledge the Nouragues Travel Grant Program 2014. RAF was supported by an  MIT / Wellcome Trust  Fellowship. TWJG was supported by the  People's Trust for Endangered Species , the  Morris Animal Foundation  ( D12ZO-002 ). JS and MCF were supported by the  Leverhulme Trust  ( RPG-2014-273 ) and the  Morris Animal Foundation  ( D16ZO-022 ). FB was supported by the  ERC  (grant  ERC   260801 -Big_Idea ). DMA was funded by  Wellcome Trust  Grant  099202. JV was supported by the  Hungarian Scientific Research Fund  ( OTKA K77841 ) and  Bolyai János Research Scholarship, Hungarian Academy of Sciences  ( BO/00579/14/8 ).
DG was supported by the  Conservation Leadership Programme  (grant  0134010 ) with additional assistance from  F. Gebresenbet ,  R. Kassahun  and  S. P. Loader. CS-A was supported by  Fondecyt Nº11140902  and  1181758. TD-B was supported by the  Royal Geographical Society  and the  Royal Zoological Society of Scotland  with assistance from  Mareike Hirschfeld  and the  Budongo Conservation Field Station. BW was supported by the  National Research Foundation of Korea  ( 2015R1D1A1A01057282 ). LFT was supported by  FAPESP  (# 2016/25358-3 ) and  CNPq  (# 300896/2016-6 ). LB (Berger), LFS and RJW were supported by the  Australian Research Council  ( FT100100375 ,  DP120100811 ). AAC was supported by a  Royal Society Wolfson Research Merit  award. JH, AL and SM were funded by the  Swedish Research Council Formas  (grant no.
Data availability: Sequences have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). All sequences are available from NCBI BioProject accession PRJNA413876 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA413876). The supplementary materials contain additional data. Phylogenetic trees are available from TreeBASE, project accession url: http://purl. org/phylo/treebase/phylows/study/TB2:S22286. A browsable version of the phylogeny and metadata in Fig. 1B is accessible at: https://microreact. org/project/GlobalBd.
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 670325 ) to CTB, as well as from  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL*. r e f e r e n c e s.
Separation of the metabolites was achieved by reversed-phase high performance liquid chromatography (RP-HPLC) (Waters, ACQUITY UPLC BEH C18 1. 7 µm, 50 mm × 2. 1mm) using a stepwise elution of H 2 O/CH 3 CN/formic acid from 85:15:0. 1 (v/v/v) to 0:100:0. 1 in 8 min, at a flow rate of 0. 5 mL/min. The first minute of the LC run was diverted to waste to prevent salts from reaching the mass spectrometer. MS data were acquired in the positive mode at the 300 to 2000 m/z range, using a data-dependent top 5 method dynamically choosing the most abundant precursor ions from the survey scan for HCD fragmentation using a stepped normalized collision energy of 35, 40, 45 eV. Survey scans were acquired at a resolution of 70,000 at m/z 200 on the Q Exactive. The spectrometer analyzer parameters were set as follows: dry gas, N 2 (2 L/min); capillary temperature, 300 • C; capillary voltage, 3.
Acknowledgments: This work was funded by the  National Oceanic and Atmospheric Administration (NOAA) Prevention, Control and Mitigation of HABs (PCM HAB) program  (Grant  NA15NOS4780176 , to KJC,  M. Warner ,  J. Cohen , and  T. Targett ). This work was also supported by the  Delaware Sea Grant College Program  (Grant #  HCE-25  to KJC). All spectral analyses were performed at the  Mass Spectrometry Facility of the department of Chemistry and Biogeochemistry of Delaware University , benefiting from the  Delaware COBRE program , supported by a grant from the  National Institute of General Medical Sciences  ( NIGMS ,  P30 GM110758-02 ).
The definitive approach to distinguishing somatic mutations from germline variants requires sequencing the tumor alongside a patient matched normal, and subsequently performing a comparison: variants detected in tumor tissue but not present in the normal control are advanced as mutation candidates  [7] [8] [9] [10]. However, while it is possible to establish protocols for paired collection in the academic cancer center setting, sequencing a patient matched normal specimen is not part of broad oncology practice, and known cancer drivers targetable by approved or investigational therapies can usually be discerned from tumor sequencing alone from well-established databases such as COSMIC  [11]. It is therefore likely that as clinical cancer sequencing becomes routine and wide-spread, matched normal data will not be available for the majority of cases, foreclosing a significant opportunity for novel discovery and potential future therapeutic benefit unless this limitation is overcome.