dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to the ETD online fit results The Exoplanet Transit Database performs a transit fit to the uploaded light curves  (Poddaný et al. 2010 ). The achieved best fit parameters are listed on the webpage and are regularly used in scientific publications (e. g. ,  Southworth et al. 2016; Angerhausen et al. 2017; Lillo-Box et al. 2018). Our reanalyzed timing values of 85 ETD light curves allow for a cross check with these ETD results. We find that on average the absolute timing shows a deviation of only 20% of our 1-σ error bars; that is, there is no systematic offset. However, there is a significant scatter of the individual timing differences with a standard deviation of one when expressed in terms of our derived 1-σ uncertainties. In extreme cases, the deviations between our best-fit values and ETD derived parameters reached 4 σ.
Acknowledgements. E. H. acknowledges support by the  Spanish Ministry of Economy and Competitiveness (MINECO)  and the  Fondo Europeo de Desarrollo Regional (FEDER)  through grant  ESP2016-80435-C2-1-R , as well as the support of the  Generalitat de Catalunya/CERCA program. D. D. acknowledges support provided by  NASA  through  Hubble Fellowship grant   HST-HF2-51372. 001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. E. S. acknowledges support by the  Russian Science Foundation  grant No. 14-50-00043  for conducting international photometric observing campaign. I. S. acknowledges support by the  Russian Foundation for Basic Research  (project No. 17-02-00542 ).
R. B. acknowledges support from  FONDE-CYT Post-doctoral Fellowship  Project No. 3180246. M. L. acknowledges support from the  Austrian Research Promotion Agency (FFG)  under project  859724  " GRAPPA ". S. -H. G. acknowledges the financial support from the  National Natural Science Foundation of China  (No. U1531121 ). L. C. recognizes funding from  DLR -50OR1804. Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Research Foundation  (Grant agreement no. :  DNRF106 ). We thank the staff at the various participating observatories STELLA, CASLEO,  Calar Alto , TJO, and others for their support. Partly based on data obtained with the  STELLA robotic telescopes in Tenerife , an  AIP  facility jointly operated by  AIP  and  IAC , on data acquired at Complejo Astronómico El Leoncito, operated under agreement between the  Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina  and the  National Universities of La Plata ,  Córdoba  and  San Juan , on observations collected at the  Centro Astronómico Hispano Alemán (CAHA) at Calar Alto , operated jointly by the  Max-Planck Institut für Astronomie  and the  Instituto de Astrofísica de Andalucía (CSIC) , and on data obtained with the  Joan Oró Telescope (TJO) of the Montsec Astronomical Observatory (OAdM) , owned by the  Generalitat de Catalunya  and operated by the  Institute for Space Studies of Catalonia (IEEC).
The generation of high-affinity antibodies depends on somatic hypermutation (SHM). SHM is initiated by the activation-induced cytidine deaminase (AID), which generates uracil (U) lesions in the B-cell receptor (BCR) encoding genes. Error-prone processing of U lesions creates a typical spectrum of point mutations during SHM. The aim of this study was to determine the molecular mechanism of SHM in humans; currently available knowledge is limited by the number of mutations analyzed per patient. We collected a unique cohort of 10 well-defined patients with bi-allelic mutations in genes involved in base excision repair (BER) (UNG) or mismatch repair (MMR) (MSH2, MSH6, or PMS2) and are the first to present next-generation sequencing (NGS) data of the BCR, allowing us to study SHM extensively in humans.
From one PMS2 patient (PMS2-01), only DNA from PBMCs was available; therefore, we sequenced the IGH rearrangements on the DNA level precluding subclass assignment and the analysis of SHM levels. Recently, we have shown that patients with CMMRD have no overt clinical immunodeficiency  (4). In this study, we were able to include data from three additional CMMRD patients. In line with the study of Tesch et al. the frequency of SHM was reduced in both IGHG and IGHA rearrangements, except for one of the MSH6-deficient patients (MSH6-01) in which SHM was within the normal range for IGHG rearrangements (Figure  1A ). Despite the fact that the frequency of switch memory B cells was within the normal range in most of the patients (Table  1 ), the distribution of the IGHG and IGHA subclasses was different in the CMMRD-and UNG-deficient patients.
XRR fitting performance To evaluate the prediction accuracy of our NN model, we tested its performance on 20,000 independently simulated curves with the same parameter range as the training data, as well as on each of the five experimental real-time XRR datasets. In the case of the simulated data, the mean average percentage errors of the film thickness, roughness and SLD were 8%, 16% and 6%, respectively. While already quite good, these metrics reveal that for this NN model there is still a significant portion of misclassified curves. Furthermore, within the given q-range, correctly determining the roughness seems to be intrinsically more difficult than the other two parameters. Since the synthetic test data was generated using the same process as the training data, we cannot expect better performance on data that was generated using a different process, i.
e. experimental data. While a reduction of the training and validation loss could be achieved in principle (for example through longer training sessions), we observed that this generally leads to a decrease of the performance on experimental data. This means, that the training loss alone cannot be used to estimate how the neural network will perform on experimental data and the training process is ultimately limited by the fact that the simulated data does not perfectly match the experimental data. For the performance evaluation on experimental data, the film properties predicted by the model were compared to a manual LMS fit using a genetic algorithm (GenX). The studied systems were two DIP films, one CuPc film and one 6T film each grown at 303 K as well a third DIP film grown at 403 K. Three out of five of these datasets have already been analyzed and published before (DIP 303K  (Hinderhofer et al.
, 2010) , DIP 403K  (Kowarik et al. , 2006) , 6T  (Lorch et al. , 2015) ). Panels a-c of Figure  3  show this comparison for a DIP film grown at 303 K (all other datasets are shown in the supporting information). It is immediately apparent that for most of the series, the predicted values are close to the ones obtained via the manual LMS fit. We note that this achievement is already remarkable, since the network has no concept of any temporal correlation between the XRR curves, which is the kind of knowledge a researcher would use when selecting bounds and starting points for an LMS fit. Furthermore, the parameter predictions of each XRR curve were obtained on average within 77 ms when predicting a single curve and 0. 03 ms when predicting 20,000 curves at once. Panel d shows an overlay of experimental reflectivity data with simulated curves using the predicted film parameters at different times during growth.
Acknowledgements We gratefully acknowledge the assistance of  Philip Willmott  and  Oleg Konovalov  at the  Surface Diffraction beamline at the Swiss Light Source  and the ID10 beamline at the  European Synchrotron Radiation Facility , respectively. We also acknowledge the help of  Takuya Hokosai  during data acquisition. Furthermore, we thank  Oliver H. Seeck  ( DESY ) for his support. Lastly, we thank  Phillip Berens  and  Matthias Bethge  from the  Cluster of Excellence in Tübingen  for machine learning in science for our fruitful discussions.
The measurements are statistically comparable considering the associated uncertainties. This demonstrates that the state of the stationary phase does not significantly disturb the separation. However, for a better control of the process, it would be preferable to implant a technical solution allowing detection the level of the eluent flush relative to the level of the resin. Driven by Labview software, an automated bench has been designed to perform the tedious and repetitive tasks in the final steps of 10 Be and 26 Al cosmogenic sample preparation. In addition to freeing the operator, this system allows 9 Be yields as high as those obtained manually (>93 %), it is reproducible and permits processing simultaneously up to eight samples of different volumes in roughly 40 minutes to be compared to the three hours when processed manually.
Aggression assays We tested sucrose-and antibiotic-treated ants from round 1, microbiotaremodelled ants from round 2 and untreated ants against nestmates and nonnestmates from the original colonies (3-5 replicates/combination, total=260 tests in Round 1 and 231 tests in Round 2; Figure  1 , Data set S1). Assays consisted of dyadic encounters in Petri dishes with clean filter paper on the bottom. For two minutes after first contact, an observer (blind with respect to ant treatment and original colony) used the software Etholog 2. 25  (Ottoni, 2000)  to quantify the frequency and duration of biting, mandible opening, antennation and absence of contact (Figure  A3 ). For statistical analyses, we excluded the mandible opening behaviour (usually considered as aggressive) because pooling it with biting produced very similar results (data not shown).
Effects of diet treatments on survival We analysed the effect of diet on survival with Cox proportional hazards models (with censoring), using the R package Survival  (version 3. 1. 1, (Therneau, 2015) ) function 'coxph', following assessment of proportional hazards using 'cox. zph'. Survival in the two experimental rounds was evaluated separately, because the diets, which were used as cofactor affecting the mortality of ants, changed from Round 1 to Round 2. The other cofactors were colony origin and the tetracycline diet for Round 1 and the colony origin and whether the ants had previously been treated with tetracycline in Round 2. Data were plotted using the survival and the GGally packages in R  (Schloerke et al. , 2014; Therneau, 2015).
, 2015) ). In order to monitor how the dietary treatment affected the communities, we screened individual worker guts and associated organs with qPCR (detailed methods in Table  A1  and Supplementary Information) on the five most abundant OTUs (Data set S2, procedures described in  Sapountzis et al. , 2015)  with Cycle threshold (Ct) means of replicated samples as measures of amplicon abundance. The elongation factor 1 alpha (EF-1α) was used as a reference gene  (Andersen, Yek, Nash, & Boomsma, 2015). Each run included two negative controls with no added template for each gene used. Data were ordinated using an unscaled principal coordinate analysis (PCoA) and intersample distances were calculated using Canberra, Hellinger and Bray-Curtis methods. For analysis, we initially used a standard curve with PCR products in tenfold dilution series of known concentration (fold change method) to calculate the PCR efficiency using the REST software  (Pfaffl, Horgan, & Dempfle, 2002).
To add adapters and Index to DNA fragments, we added 2µl of the diluted PCR products to the PCR mix to amplify ca. 458 bp fragments using different combinations of primers presented in Table  A1. PCR was performed for 15 cycles and PCR products were purified using the Agencourt AMPure XP (Beckman Coulter) and quantified using Quant-iT dsDNA High-Sensitivity Assay Kit and Qubit fluorometer (Invitrogen) to allow for dilution and mixing in equal concentrations before sequencing on an Illumina MiSeq. Data [Genbank: SAMN04261407 -SAMN04261536 and SAMN05362797 -SAMN05362832] were analysed using mothur  (Schloss et al. , 2009)  with two adjustments to the MiSeq standard operating procedure (SOP) due to slight differences to the sequencing protocol. First, after assembling the sequences we used the 'make.
contigs' command in the software cutadapt  (Martin, 2011)  to remove the primer sequences, and continued with the mothur pipeline using the output; for the 'screen. seqs' command the start and the end were at 13862 and 23444 (see MiSeq SOP page). After filtering/processing the sequencing data and clustering at 97%, rarefaction tables were constructed using pseudo-replicate OTU datasets containing 1-272000 sequences with 1000 iterations per pseudoreplicate, and the resulting curves were visualized in Microsoft Excel 2013. The final OTU Table was rarefied at 5000 reads after manual inspection of the rarefaction curves, which reduced the number of OTUs to 1500. We used the MiSeq data to calculate Canberra and Bray-Curtis distances, after which we used Non-metric MultiDimensional Scaling (NMDS) to ordinate and visualize the effects.
Our initial integration analysis of GC-MS runs detected 137 peaks, of which we selected 73 that had a relative abundance larger than 0. 1% (Data set S3). Peak areas of cuticular compounds were integrated using R v3. 1. 0 (using package xcms, script available upon request) and normalized using a Z-transformation  (Aitchison, 1986). To compare odour profiles among different rounds, we used linear mixed models (LMM) with the Ztransformed abundance of each compound as the response variable, 'diet treatment' and 'experimental round' as fixed variables, and 'colony' as a random variable. We considered linear hypotheses using the multcomp R package  (Hothorn, Bretz, & Westfall, 2008)  function glht to evaluate differences between diet treatments in the same experimental rounds, and between the same diet treatments across Rounds.
All P-values were corrected for false discovery rate (FDR), given that we had conducted 73 separate tests per contrast. We used the package ade4 to perform coinertia analysis  (Dolédec & Chessel, 1994)  to check for correlations between CHCs (log-transformed peak areas) and qPCR measures (∆∆Ct values) of the six bacterial OTUs considered (see also details below). In short, we generated two independent data matrices (either using the individual profiles or the pairwise differences for each trial), performed PCA analyses and paired them using the coinertia analysis in a Monte-Carlo test with 10000 permutations.
Considering the overall association between cuticular chemical profiles and the abundances of symbiont OTUs as measured with qPCR, we found a significant association between these (Coinertia Analysis, RV=0. 143, P<0. 001; Figure  A4 ). When looking at each OTU separately, the strongest pattern was an association between EntAcro1 and RhiAcro1 and the two before mentioned 4-oxo-octanoic and 4-oxo-decanoic acids. However, even after we analysed the data without these two acids we recovered a significant overall correspondence between chemical profiles and qPCR gut bacterial abundances (RV=0. 141, P<0. 001). On the other hand, we could not find such a significant association between smell and gut microbiota when we used chemical distance and the difference in qPCR counts between individuals in aggression pairs (RV=0.
Methods Four bacteraemia and 27 colonizing S. aureus isolates from January to July 2016 were submitted to the "all in one solution" Episeq® (whole genome sequencing, quality data assessment, MLST, spa type, virulome, resistome and phylogenetic tree construction). More in-depth analyses were performed (wgMLST and wgSNP analysis) with BioNumerics software (Applied Maths, Belgium).
This solution combines a service for WGS (subculture, DNA extraction, library preparation, sequencing run) with secure cloud-based software for data analysis (reads assembly, MLST and spa typing, resistome and virulome characterization, core genome SNPs-based phylogeny). Analysis involves several knowledge bases, including a proprietary and curated collection of well-characterized epidemic S. aureus isolates associating genomic and epidemiological data. All of these data are provided directly to the client via intuitive software. Moreover, WGS data could be used for whole genome MLST (wgMLST) analysis which could also represent an answer to the problem of standardization. The United States Center for Disease Control (CDC) has recently validated the use of wgMLST and the E. coli genotyping plug-in tool employing the commercial software platform BioNumerics (Applied Maths, Sint-Martens-Latem, Belgium).
Whole genome sequencing and bioinformatics analysis The BioMerieux EpiSeq® solution included the different steps listed in the introduction. This full-service costs 300 € per sample, with a guaranteed turnaround time of 7 days starting from isolate reception at the sequencing facility. Isolates should be sent on a Copan swab after a 24 H incubation on blood agar. Then as an element of the EpiSeq workflow, DNA libraries are prepared using the Nextera ® XT DNA library preparation kit (Illumina, San Diego, CA, USA). Sequencing is performed on a MiSeq (Illumina) instrument to generate 200 bp paired end reads. Molecular resistance and virulence determinants, MLST and spa typing are inferred from whole genome sequencing (WGS) data through the EpiSeq® knowledge base and data analysis workflow (https://biomerieux-episeq.
com). Initial phylogenetic analysis is also conducted directly from WGS data. All these results are made available to the customer through a user-friendly web platform. Sequenced genomes of our isolates were also processed for more phylogenetic analysis in this study on the BioNumerics v7. 6 (Applied Maths, Sint-Martens-Latem, Belgium). BioNumerics is a unique software platform for integrated analysis of bioinformatics, which has the ability to combine information from various genomic and phenotypic sources into a global database and to conduct conclusive analysis. A de novo assembly was performed on a cloud-based calculation engine with a SPAdes assembler version 3. 7. 1  [12]. Gene prediction and annotation of the contigs were carry out using BioNumerics. Whole genome MLST (wgMLST) was performed on 3897 loci (core and accessory genes) on the cloud-based calculation engine of BioNumerics v7.
Accurate analysis and interpretation of stained biopsy images is a crucial step in the cancer diagnostic routine which is mainly done manually by expert pathologists. The recent progress of digital pathology gives us a challenging opportunity to automatically process these complex image data in order to retrieve essential information and to study tissue elements and structures. This paper addresses the task of tissue-level segmentation in intermediate resolution of histopathological breast cancer images. Firstly, we present a new medical dataset we developed which is composed of hematoxylin and eosin stained whole-slide images wherein all 7 tissues were labeled by hand and validated by expert pathologist. Then, with this unique dataset, we proposed an automatic end-to-end framework using deep neural network for tissue-level segmentation.
, ). Previous studies do not take into account the non-invasive breast cancer type called "in situ carcinoma" despite its frequency (20 to 25% of newly diagnosed breast cancers). Reporting the presence of both invasive and/or in situ carcinoma is a challenging part of a diagnostic pathology workup since there is a significant difference of treatment options of the disease. There are very few whole slide breast cancer datasets with pixel-level annotations. Regarding breast cancer pathological dataset Spanhol et al introduced The Breast Cancer Histopathological Image Classification (BreakHis) wich is composed of 2,480 benign and 5,429 malignant samples of microscopic images of breast tumor tissue  (Spanhol et al. , ). However, these two categories of tissues are not enough because it does not reflect the complexity of tissue diversity.
To tackle this shortcoming, Grand Challenge on Breast Cancer Histology Images (BACH) had launched an annotated Whole-slide images dataset  (Aresta et al. , 2018). The organization provided 10 pixel-wise annotated regions for the benign, in situ and invasive carcinoma classes present in a entire sampled tissue which represent a partially annotated masks. In recent years, deep learning models, especially convolutional neural networks (CNNs)  (LeCun et al. , )  have emerged as a new and more powerful model for automatic segmentation of pathological images. The power of a CNN based model lies in its deep architecture which allows for learning relevant features at lower levels of abstraction. (Hou et al. , 2016)  proposed a patch-based CNN and to train a decision fusion model as a two-level model: patch-based and image-based model to classify WSIs into tumor subtypes and grades.
Chen et al. proposed an encoderdecoder architecture to gland segmentation in benign and malignant  (Chen et al. , 2016a). Cruz et al. presented a classification approach for detecting presence and extent of invasive breast cancer on WSIs using a ConvNet classifier  (Cruz-Roa et al. , ). The greatest challenge in the medical imaging domain especially in pathology is to deal with small datasets and limited amount of annotated samples, especially when employing supervised convolutional learning algorithms that require large amounts of labeled data for the training process. Previous studies that investigated the problem of breast cancer pathological images analysis, did not provide a proper quantitative and qualitative parameters evaluation for training deep CNN from scratch with few annotated samples only.
Contributions The contribution of this paper is two folds: first since there is no publicly available annotated data for this task we developed a new dataset; second we conducted a set of experiments to evaluated several CNN architectures and settings on that new type of data. More precisely, we: •   (Long et al. , 2015)  and DeepLab  (Chen et al. , 2016b) ), the originality of our work resides in a deep analysis of the parameters of the model. • conducted several experiments to evaluate the settings of each step of the proposed framework in order to get the optimal set of parameters when dealing with this new data for a tissue-level segmentation task. The paper is organized as follows: in Section 2, we present the new data set that we built. Section 3 presents the framework we developed as well as an overview of the experiments and evaluation measures.
NEW ANNOTATED DATASET This work involved anonymized breast cancer slides from the archives of the pathology department of the Toulouse University Cancer Institute. The breast cancer images waere acquired with a Panoramic Digital Slide Scanners 3DHISTECH. This selection was reviewed by an expert pathologist to confirm the presence of at least one of the two cited categories of carcinoma considered in this study. To describe the complexity of the tissue structures present in the image of breast cancer, the pathologist selected seven relevant types of tissue which are identified and analyzed during the biopsy routine of breast cancer pathology (Table. 1). To alleviate the burden of manual annotation and save time and effort for.
Overview of the framework The breast cancer segmentation approach we developed adopts an end-to-end convolutional neural network framework (Figure. 2). In this paper, we have implemented a machine learning workflow for multiclass segmentation applied on new WSI images which can be divided into several steps: 1. Pre-processing: all images are normalized to reduce the color variability within the dataset. 2. Learning: patches are randomly extracted from each image of the training dataset and injected into the network adapted for multi-class semantic segmentation. 3. Prediction and reconstruction: After a close examination of the networks behaviour, we observed that the accuracy at the border area is not precise compared to the central area of the patches. To overcome this problem, we decided that the test image is downsampled by sliding windows with a fixed stride.