dataesr/scientific-paragraphs-categorization huggingface dataset:
a Representation of the most abundant microbial eukaryotic groups classified by 'Class' division using PR2 V4 SSU rRNA database assignation (including all groups with relative abundance >5%) and b representation of the MAST and labyrinthulomycetes groups detected. Reads from Runs '1' and '2' were pooled by sediment core and normalised. All groups that appear in the colour coded key were detected in the study but some of them had a low relative quantity (<0. 06%) so are not visible in the bar chart and so their colour code circle is removed. Asterisk symbol indicates OTUs affiliating with the wider taxonomic group but of uncertain Class designation. Taxon groups appended with _X (etc. ) represent uncharacterised phylogenetic groups that branch with the assigned taxon in the PR2 database classification.
Acknowledgements We are grateful to the captain, crew and ROV pilots of the R/V Western Flyer as well as  Jim Barry and Barry lab members  who provided the cruise berth and dive time for collection samples studied herein. We thank  J. del Campo  for providing alignment data from his previously published work. This project was supported by a  Gordon and Betty Moore foundation grant  ( GBMF3307 ) to TAR, AES, AZW and PJK, and a  Philip Leverhulme Award  ( PLP-2014-147 ) to TAR. Field sampling and injector sampler design and construction was supported by the  David and Lucile Packard Foundation  and  GBMF3788  to AZW. TAR was supported by a  Royal Society University Research Fellowship. RRM was supported by a  Gordon and Betty Moore foundation grant  ( GBMF3307 ) and by  CONICYT   FON-DECYT 11170748.
| Parasite transmission For V. culicis we considered two routes of horizontal transmission. We defined the potential for transmission among larvae as the percentage of individuals that died as juveniles that harboured spores and defined the potential of female-to-larva transmission as the percentage of mosquitoes that survived to become adult females and harboured spores. For E. aedis, we defined the potential for horizontal transmission among larvae as the proportion of individuals that died as juveniles and harboured uninucleate spores. For vertical transmission we analysed the proportion of mosquitoes that emerged as females and harboured binucleate spores. We ran a separate model for each parasite and for each transmission route. We therefore ignored the data with single infections by E.
Each analysis included the focal parasite, the time of infection by the focal parasite, the presence or absence of a co-infection, the amount of food and all interactions as nominal factors. Since co-infected mosquitoes were assigned randomly to each focal parasite, we ensured that our results were not due to a sampling artefact by repeating these steps 100 times. We evaluated the strength of each factor with the proportion of the 100 repeats that the factor was significant (at the 0. 05 level) and with the mean p-value. Note that the plots do not include 'focal parasite', but simply show the data of single infections and of co-infections with V. culicis before E. aedis (Vav First in Figures  3 4 5 ) or E. aedis before V. culicis (Ed First in Figures  3 4 5 ).
Gene expression RNAseq data shown here originate from a previously published dataset  [29]  and were processes anew by concentrating on the different P supply (Low P and High P) only; differences in gene expression in response to Low P supply were assessed. In the C6 accession 178 genes were differentially regulated in Low P supply in comparison to High P supply while in the F103 accession 320 Fig. 3  Log2 values of fold change (Low phosphorus (P) / High P) in relative concentrations of metabolites in root exudates detected in two Brassica oleracea accessions (C6 and F103) grown hydroponically for two weeks in solutions containing 0. 025 mM P (Low P) and 0. 25 mM P (High P). SA, sugar alcohols; T, tannins DEGs were detected (Fig. 6a ). These DEGs (Additional file 2: Table  S4 ) are referred to as P-responsive.
Discussion As expected, when grown for 2 weeks in hydroponics the two selected B. oleracea accessions exhibited Fig. 4  Photographs of root system architecture and average main and lateral root length and number of lateral roots in two Brassica oleracea accessions (C6 and F103) grown in a high throughput root phenotyping system for two weeks and supplied with solutions containing 0. 025 mM P (Low P) and 0. 25 mM P (High P). Length of lateral roots was determined in each quartile of the main root length, hence the three horizontal columns (in the fourth quartile there were no lateral roots). Numbers to the left of the columns indicate the average number of lateral roots per quartile. Data are means and standard errors. Scale bars indicate 1 cm measurable ionomic (Fig. 1 , Additional file 1: Figs.
Gene expression study through RNAseq analysis Procedures for RNA extraction from roots (n = 3), library preparation, sequencing and bioinformatics analysis have been described in detail previously  [29] ; this previously published RNAseq dataset was processed anew by concentrating solely on the two P treatments: the Low P and the High P treatments. Differentially expressed genes (DEGs) were calculated as a ratio of the gene expression in the Low P treatment in comparison to the gene expression in the High P treatment. A filter of fold change > 2 and a diverge probability greater than or equal to 80% was applied to define the P-responsive DEGs. Significantly enriched gene ontology (GO) terms were also sought based on "GO Term Finder" (http:// www. yeastgenome. org/help/analyze/go-term-finder).
The B. oleracea orthologues of A. thaliana genes were sought using the BioMart tool of EnsemblPlants release 46  [87]. The A. thaliana orthologues for the P-responsive DEGs in B. oleracea accessions C6 and F103 were identified using Ensembl. Of all 447 P-responsive DEGs in B. oleracea, 354 orthologues were identified in A. thaliana and the expression data for these was loaded into MapMan Version 3. 5. 0 BETA (https://mapman. gabipd. org/home) to analyse pathways affected by a reduced P supply. Line graphs were drawn in R, ggplot2 package  [88] , using MapMan outputs.
On the second half of each of the root exudates collected, polar compounds were extracted and derivatised as described previously  [90]  and metabolite profiles were acquired using a gas chromatography-mass spectrometer (GC-MS; DSQII, Thermo-Finnigan, Hemel Hempstead, UK) system using a DB5-MSTM column (15 m × 0. 25 mm × 0. 25 μm; J&W, Folsom, CA, USA) as described by  [91]. Compounds were identified by their mass spectrum and co-elution with authentic standards as determined using an in-house database. Data is presented as a relative value based upon the peak area of the specific ion used for quantification.
High throughput root phenotyping (HTRP) experiment Root system architecture was studied in a system similar to the one described by  [85]. Seeds were placed on germination paper moistened with the same nutrient solutions used in the hydroponic experiments. Experiments were performed in 25 × 25 cm Petri dishes, which were arranged vertically in a growth chamber (maintained at, on average, 16 °C during 8 h night and 18 °C during the 16 h day). After 2 weeks of growth, roots were photographed and the length of the main root, and the number and length of lateral roots in each quartile of the main root length was determined using Fiji image processing software  [93]. Three independent experiments were conducted, each having 8-11 replicates of each accession and treatment.
Phosphorus distribution in roots Plants from the HTRP experiments were used for the determination of P distribution in roots using microproton induced X-ray emission (micro-PIXE). Samples were prepared as described previously  [94]. Pieces of roots below the first lateral root were placed into stainless-steel needles, cryo-fixed, cryo-sectioned to 50 μm thickness, freeze dried and sandwiched between two Pioloform foils (approx. 400 nm thickness; SPI supplies, West Chester, PA, USA) stretched over an aluminium frame. Micro-PIXE analysis was performed as described previously  [95]. The micro-PIXE spectra were analysed and distribution maps were generated in the GeoPIXE II software package  [96]. Because the distribution maps are quantitative each pixel provides information on the concentration of an element.
Statistical analysis One-way analysis of variance (ANOVA) with accession as the independent variable and two-way ANOVA with accession and treatment as independent variables followed by a Holm-Sidak post-hoc test at p < 0. 05 were employed when data were normally distributed. A nonparametric test (ANOVA on ranks, followed by Dunn's test at p < 0. 05) was employed in the case of nonparametric (lateral root length) data. The Student t-test (at p < 0. 05) was used for comparisons of two groups. All statistical analyses were performed in SigmaPlot version 13. 0 (Systat Software, San Jose, CA). The processing of ATR-FTIR spectra and the principal component analysis (PCA) for these spectra were performed using Orange 3. 18 software  [97, 98]  and the workflow is shown in Additional file 1: Fig. S5.
Funding This work was supported by the  Rural and Environment Science and Analytical Services Division of the Scottish Government  and an  EU Marie Curie Intra-European Fellowship (REA grant  agreement  n°623305 ) to  Paula Pongrac , who also acknowledges financial support from the  Slovenian Research Agency  (Research programmes  P1-0212  and  P1-0112 , and projects  N7-0077  and  J7-9398 ). Juan Reyes-Herrera acknowledges the  Mexican National Council for Science and Technology (CONACYT)  for the funding of his post-doctoral position ( CONACYT CVU# 177448 ).
Data We chose four datasets of increasing complexity to evaluate the efficacy of subclass recovery in explanation space. Firstly, simple synthetic data for a proof-of-concept. Secondly, Fashion-MNIST as a machine learning benchmark. Thirdly, age prediction from brain imaging data as a simple biomedical example. Lastly, cancer subtype detection as a challenging real-world biomedical problem. Brain imaging data was provided by the UK Biobank, cancer transcriptome data from the Cancer Genome Atlas. The two are among the world's largest biomedical data collections and represent the two most likely fields of applications: precision psychiatry and oncology, with big data in both p (dimensionality) and n (sample size). The original Madelon dataset from the 2003 NIPS feature selection challenge  13  is a synthetic binary classification problem, with data points placed in clusters on the vertices of a hypercube in a subspace of the feature dimension.
The scikit-learn implementation  14  of the generative algorithm was used to create a variation with 16 classes, 50 features, and data points distributed in one cluster per class on the vertices of a fourdimensional hypercube. Zalando provides Fashion-MNIST  15  as a more challenging drop-in replacement for MNIST, with the same dimensionality (782) and sample size (70,000). Instead of the 10 classes of digits (0-9) of the original MNIST dataset, Fashion-MNIST consists of grayscale images of ten classes of clothing (e. g. T-shirts, pants, dresses). We chose Fashion-MNIST, because the original MNIST has been argued to be too easy a problem for modern methods. Indeed, most classes in the dataset can already be separated in the first two principal components of the feature space, making it ill-suited for the present analysis.
We chose the cancer tissue's immune model based subtype (6 classes, i. e. Wound Healing, IFN-gamma Dominant, Inflammatory, Lymphocyte Depleted, Immunologically Quiet, TGF-beta Dominant) as a complex, clinically relevant target variable. Our synthetic and Fashion-MNIST datasets have balanced classes by design. The UK Biobank brain imaging data is cut into 4 age-quartiles resulting in balanced classes. The TCGA dataset is imbalanced. For classification it was sampled inversely proportional to class frequencies.
Methods For each dataset, classes were split into two supersets, one containing the first three (The first and fourth class for UKBB. ) classes, the other containing the remainder (The decision to split into three-vs-rest was due to ease of visualization, and simplicity of choice. ). This yielded a new binary classification problem. This setup allows for evaluating approaches to subtype discovery. The binary classification data is intended to represent observable characteristics (e. g. the healthy vs. diseased distinction in medicine) while the original classes represent the hidden subtypes. A Random Forest classifier  18  was trained on the binary classification problem and SHapley Additive Explanations (SHAP)  10  were used to generate instance-wise explanations for the predictions of the classifier.
Investigating the applicability of other explanation methods and classifiers to specific domains would be a natural direction for future work. Each explanation is represented as a vector of the same dimensionality as the original feature space, indicating how strongly and in what direction each feature contributed to the prediction result of a given observation. The space spanned by the model explanations was then compared to the original feature space, with respect to the distinguishability of ground-truth subtypes (the original classes of the dataset).
Results To compare the correspondence of clustering in the respective spaces to the underlying distribution of class labels we used three qualitatively distinct approaches. Firstly, we visually inspected the projection into the first two principal components. Distinct clusters in the data would constitute major directions of variance and should be easily visible in the PCA projection, allowing for a first sanity check (Fig. 1 ). Secondly, we evaluated standard clustering quality indices to quantify structural differences between representations. We chose the Davies-Bouldin index, defined as the average similarity between each cluster C i and its most similar other cluster C j 19 , the Silhouette Coefficient, which balances the mean distance between a sample and all other points in its cluster with the mean distance between that sample and all the points in the next nearest cluster  20  , and the Calinski-Harabaz Index, which is given as the ratio of the between-clusters dispersion mean and the within-cluster dispersion  21.
Lastly, to ensure that transformation from feature space into explanation space really does lead to improved subclass recovery, we applied Agglomerative Clustering and report the Adjusted Mutual Information  22  between reconstructed subclasses with ground-truth labels. To compare improvements derived from transformation into explanation space with those which can be gained by dimensionality reduction alone, we apply (All run with scikit-learn default parameters. ) PCA  23  , Isomap  24  , and T-SNE  25  to both feature space and explanation space, reducing their dimensionality down to two Quantitative results are shown in Table  1. Clustering quality indices and post-clustering Mutual Information consistently improved (average AMI gain of 0. 45) when moving from feature space to explanation space in all four datasets, both before and after dimensionality reduction.
Figure1. Projection into first two principal components after PCA of feature space (top row) and explanation space (bottom row) of Synthetic, Fashion-MNIST, UK Biobank Brain-Age, and Cancer Genome Atlas datasets. Shown are the (sub-)classes of the three-class superset used for binary classification. As can be seen, clusters are well defined in explanation space; for biomedical datasets, these sub classes correspond to real subtypes. For example in TCGA (rightmost plot), the visible clusters correspond to Wound Healing (black), IFN-gamma Dominant (yellow), and Inflammatory (pink) cancer tissue immune model types.
1975)  for approximately 200 days. Prior to each transfer, cell density after 24 h of culture was measured using a Beckman Coulter (San Diego, CA) and the number of cell divisions between passages was calculated as approximately five cell divisions per day. During the experiment, we also calculated the change in fitness per day (defined as the growth rate) using the formula  (Kishimoto, et al. 2010) :. Growth rate data were pooled ln ( cell density_24h cell density_0h ) 24 into 100-generation intervals (generation 100 = 0-100, generation 200 = 100-200, etc. ) and the mean growth rate within each interval (about 20 data points) was calculated for each population. In addition, cells from each evolving population were cryopreserved in liquid nitrogen (Cassidy-Hanley 2012) every 100 generations.
Data processing and de novo base-substitution mutation detection Raw sequencing reads were adapter and quality trimmed using Trim Galore V0. 4. 1 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore) with default parameters. Trimmed reads were then mapped to the MAC reference genome  (Eisen, et al. 2006 ) using novoalign V3. 09. 00 (http://www. novocraft. com/products/novoalign). To reduce false positive sequencing artifacts, library amplification errors and read mismapping, only uniquely mapped reads with a mapping/sequencing quality score at least 30 were used. PCR duplicate reads were marked using Picard V1. 114 (http://broadinstitute. github. io/picard) and removed using Samtools V1. 4  (Li, et al. 2009 ). After processing, the mean sequencing depth was ~60× (Figure  S1 ).
Validation of putative mutations All putative de novo mutations were tested by both Sanger sequencing and the MassARRAY system (Agena Bioscience, San Diego, CA). For assays with the MassARRAY system, PCR amplification and single base extension primers for each mutation were designed using Assay Design Suite software (Agena Bioscience), and PCR amplification and single base extension reactions were performed using reagents supplied in the iPLEX Gold reagent kit (Agena Bioscience). To evaluate background noise, PCR reactions containing no template DNA were http://mc. manuscriptcentral. com/gbe also performed. MassARRAY data were analyzed using a Typer Analyzer (Agena Bioscience), which facilitates visualization of the raw spectra and determines the degree of confidence for wild-type and mutant peak signals.
Acknowledgments We thank  Dr. Zhixian Qiao  at The Analysis and Testing Center of  Institute of Hydrobiology, Chinese Academy of Sciences  for her help with the MassARRAY system. This work was supported by the  Bureau of Frontier Sciences and Education, Chinese Academy of Sciences  [grant number  ZDBS-LY-SM026 ]; the  National Natural Science Foundation of China  [grant numbers  31525021 ,  91631303 ,  31900316 ]; and the  Wuhan Branch, Supercomputing Center, Chinese Academy of Science, China.
Database This comparative effectiveness study was performed as part of the Comparative Effectiveness of Innovative Treatments in Cancer (CEIT-Cancer) project. Full details regarding the database and the processes used for data identification, selection, extraction, and handling have been described elsewhere. 16, 17 This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline when applicable. 18 We used only published information and aggregated clinical trial-level data. The University of Basel and University Hospital Basel, Switzerland, do not require institutional review board approval for this type of research as the data used were anonymized and not subject to the health regulations of Switzerland (as confirmed by the regulatory team at the Department of Clinical Research, University of Basel).
In brief, we identified all novel drugs and therapeutic biologic therapies (ie, new molecular entities or novel biologic drugs approved for the first indication) that received FDA approval as treatment for any malignant disease for the first time between January 2000 and December 2016. In this study, we did not consider any additional indications for drugs that received later approval for those indications. We excluded supportive care or imaging drugs that did not produce anticancer activity. We obtained the corresponding FDA approval documents from drugs@FDA,  19  a publicly available database containing information on drug and biologic products approved for human use in the US, and we searched the documents for randomized clinical trials (RCTs) in which the novel drug was compared with some type of active control, placebo, or standard of care and for single-arm clinical trials that may have provided data on treatment benefits.
Limitations This study has several limitations. First, our analysis was restricted to data presented to the FDA and reported in approval packages. There may be other studies that have evaluated the drugs for these indications. We assumed that a manufacturer would present the most favorable supporting evidence to the FDA. Moreover, our sample not only included clinical trials that were explicitly labeled as pivotal, it included any RCT of the same target population. Thus, because it is more likely that positive results were submitted for approval, there is a low risk that the results from the included clinical trials underestimated the actual benefits of the approved drugs. Second, our analysis was restricted to drugs that received FDA approval and entered the market. This sample is highly selective and, thus is prone to substantial regression to the mean,  34  which would lead to inflated benefits.
Third, we examined indications for drugs that were approved for the first time. Data regarding further indications for which a drug was subsequently approved were not considered. For some drugs, we are aware of more substantial benefits for OS or PFS that were found in later-approved indications (eg, nivolumab as a first-line treatment for metastatic melanoma was associated with an increase in 5-year survival from 26% to 52%  30, 35  ),but we are also aware of drugs without such benefits for other indications (eg, ramucirumab  [36] [37] [38] [39] [40]  ). Because we only considered data that were available at the time of approval, additional insights from longer follow-up periods were usually not available, and we did not assess, for example, 5-year survival outcomes. Fourth, we focused on drug indications, clinical and regulatory details, clinical trial characteristics, and reported treatment outcomes but did not conduct an appraisal of the evidence with a thorough risk-of-bias assessment.
Mass spectra were then analyzed using BioPharma Finder™ software (version 2. 0. 66. 12, Thermo-Fisher scientific, Waltham, MA, USA). The Uniprot database (http://www. uniprot. org/) was used to identify the camel and bovine whey proteins (Camelus dromdarus, Taxon identifier: 9838 and Bos taurus, Taxon identifier 9913). The quantification of camel and bovine whey protein fractions was estimated based on the integrated peak areas of HPLC chromatographs (see Supplementary data, Fig. S1 ) and protein content of unprocessed whey and their corresponding reconstituted powders. The denaturation extent was evaluated as follow: = × Denaturation extent 100 Concentration before drying -Concentration after drying Concentration before drying.
Dynamic vapor sorption Dynamic vapor sorption was used to assess the water sorption isotherms at 25 °C. About 20 mg of each produced powder were loaded into a clean and a dry aluminum pan. The filled pans were subjected to water adsorption cycle using surface measurement DVS advantage (Surface Measurement Systems Ltd. , London, UK) equipped with a Cahn microbalance. The DVS program was setup as described by  Carpin et al. (2017). The lactose crystallization characteristics [water activity and necessary water for lactose crystallization (X c )] were identified. The experimental data (up to 0. 40 of a w ) were fitted to the GAB (Guggenheim, Andersen and de Boer) model using the OriginPro 8 software (OriginLab Corporation, Northampton, USA). The GAB model constants X m , K, and C were, then, calculated and used to interpret the obtained experimental data following the equation below: = - - + X X C k a k a k a C k a m.