dataesr/scientific-paragraphs-categorization huggingface dataset:
Z)  33  , acetylation of histone H3 at lysine 9 (H3K9ac)  5  , nucleosome depletion as determined by FAIRE-seq  32  (FAIRE), and RNA-seq coverage (RNA) at the genomic locus  6. c, Circos plot of gPfMT-A70 dCas9 ChIP-seq (normalized to gControl) across all 14 nuclear chromosomes (exterior grey bars) shows no substantial enrichment of dCas9-binding other than at the PfMT-A70 promoter (blue arrowhead), demonstrating the high specificity of the method. Chromosome name and length (in megabases) are indicated at the exterior of the plot. The y axis indicates ChIP enrichment on a scale from 0 to 150 RPKM in 1,000 nt windows. dCas ChIP-seq data (b,c) are representative of two independent biological experiments. d, Comparison of PfMT-A70 transcription measured by RT-qPCR (top) and m 6 A/A measured by LC-MS/MS (bottom) between gControl (grey) and gPfMT-A70 (blue) parasites at three different time points over the IDC (indicated at the top).
1% formic acid in acetonitrile was as follows: 0-12 min, held at 0%; 12-15. 3 min, 0-1%; 15. 3-18. 7 min, 1-6%; 18. 7-20 min, held at 6%; 20-24 min, 6-100%; 24-27. 3 min, held at 100%; 27. 3-28 min, 100-0%; 28-41 min, 0%. The HPLC column was directly connected to an Agilent 6490 triple quadrupole mass spectrometer or Agilent 6530 q-ToF with ESI Jetstream ionization operated in positive ion mode. The voltages and source gas parameters were as follows: gas temperature, 50 °C; gas flow, 11 l min -1 ; nebulizer, 20 psi; sheath gas temperature, 300 °C; sheath gas flow, 12 l min -1 ; capillary voltage, 1,800 V; and nozzle voltage, 2,000 V. The molecular transition ions were quantified in multiplereaction monitoring (MRM) mode (Supplementary Table  1 ). For the collisioninduced dissociation using the q-ToF, data was acquired from 200-500 m/z with an acquisition rate of 5 spectra s -1 in MS mode and from 50 to 500 m/z with an acquisition rate of 2 spectra s -1 in MS/MS mode.
The mass spectrometer was operated in a data-dependent mode. The parameters for the full scan MS were: resolution of 70,000 across 350-2000 m/z, AGC 3e  6  , and maximum IT 50 ms. The full MS scan was followed by MS/MS for the top 15 precursor ions in each cycle with a NCE of 28 and dynamic exclusion of 30 s. Raw mass spectral data files (. raw) were NATuRe MiCRoBiology | www. nature. com/naturemicrobiology Articles NATURe MICRObIOlOgY searched using Proteome Discoverer (Thermo Fisher) and Mascot (version 2. 4. 1)  56. Mascot search parameters were: 10 ppm mass tolerance for precursor ions; 15 milli mass units for fragment ion mass tolerance; two missed cleavages of trypsin; fixed modification was carbamidomethylation of cysteine; variable modifications were methionine oxidation and serine, threonine and tyrosine phosphorylation.
Articles NATURe MICRObIOlOgY known members of the m 6 A writer complex from other eukaryotes against the P. falciparum proteome using blastp  59. Protein alignments were generated using mafft  60  and visualized in Geneious  61. For the phylogenetic analysis, gaps in the protein alignments were removed with trimal  62  and the best phylogenetic model was calculated using protTest3  63. Phylogenetic trees were constructed using MEGA (v7)  64  with 1,000 bootstrap replicates, and the bootstrap consensus trees were visualized in FigTree (v1. 4. 3, http://tree. bio. ed. ac. uk/software/figtree/). Low-complexity disorder regions were annotated using D2P2  65  Identification and quantification of mRNA modifications by LC-MS/MS. LC/ MS data was extracted using the MassHunter Qualitative and Quantitative Analysis Software (version B06.
05) m 6 A peaks for each replicate (that is, 2× gControl and 2× gPfMT-A70) using macs2  71  with default settings but without prior peak modelling (option 'nomodel'), the fragment size set to 150 bp (option '-extsize 150') and the genome size (option -g) set to 11 × 10 6. To generate a single robust set of m 6 A peaks for each time point, we identified overlapping m 6 A peaks using bedtools 'multiinter'  72  and only retained those that were present in ≥two of the four samples at each time point. Overlaps of m 6 A peaks and genic regions were identified using bedtools 'intersect'  72. m 6 A motif identification. For the m 6 A motif analysis, sequences ±100 bp of the m 6 A peak summit were extracted from the P. falciparum genome using bedtools 'getfasta'  72  , taking the strandedness of the underlying transcript into account (option '-s').
A random set of control sequences was constructed as following. For each m  6  A peak in a transcript, a random position in the same transcript was calculated using bash 'shuffle' implemented in a custom script. Sequences ±100 bp surrounding the random site were extracted using bedtools 'getfasta' as described above. Significantly enriched motifs in the m 6 A peak region were subsequently identified with homer2 'findMotif. pl'  73. 5-mers in the m  6  A peak region and controls sequence set were counted using jellyfish  74. The deduced sequence motif was generated using all 5-mers with an AC context that were enriched ≥two-fold in the m  6  A peak region compared to the random control using weblogo  75  , taking their frequency in the m  6  A peak region into account. The central enrichment of the deduced sequence motif was calculated using homer2 'annotatePeaks.
RPKM (reads per kilobase per exon per one million mapped reads) values were calculated in R using the command rpkm() from the package edgeR  80. mRNA stability and translational efficiency analysis. For the comparative analysis of m  6  A methylation and mRNA stability or translation efficiency, we used m 6 A-methylated transcripts that were expressed in the top 50% of all transcripts at a given time point as calculated by RNA-seq of the m 6 A-input samples and with a peak density of ≥ 0. 2 m 6 A peaks per kilobase of exon. Corresponding mRNA stability (measured as mRNA half-life) and translational efficiency values of the same parasite developmental stages were extracted from refs. 13, 15 , respectively. Independent measurements of mRNA stability data were retrieved from ref. 10.
Statistical analysis. All statistical analysis were performed in R 81. To test for a normal distribution of the data, the Shapiro-Wilk normality test was used. To test for significance between two groups, a two-sided independent-samples t-test or two-sided Mann-Whitney U-test were performed as indicated. Gene Ontology enrichments were calculated using the build-in tool at plasmoDB. org. Correlations between replicates were calculated in R using function cor() with default settings (calculation of Pearson correlation coefficient r). All heatmaps were visualized using the function heatmap2() in the R gplots package with row z-score normalization. Z-scores were calculated over all samples ('rows') following the formula z = (x-u)/s, with x = given value of a specific sample; u = average over all samples and s = standard deviation over all samples.
Acknowledgements We thank  A. Claës ,  C. Scheidig-Benatar  and  P. Chen  for help with parasite culture. Protein mass-spectrometry was performed at the  Biopolymers and Proteomics core of The Koch Institute Swanson Biotechnology Center. This work was supported by a  European Research Council   Advanced Grant  ( PlasmoSilencing 670301 ) and the  French Parasitology consortium ParaFrap  ( ANR-11-LABX0024 ) to A. Scherf. Work in the labs of P. R. P. and P. C. D. was funded by the  National Research Foundation Singapore  under its Singapore- MIT Alliance for Research and Technology (SMART) Centre, Infectious Disease and Antimicrobial Resistance IRGs. S. B. and J. M. B. were supported by an  EMBO  fellowship (S. B. : ALTF 1444-2016; J. M. B. :  ALTF 180-2015 ). A. Si. acknowledges financial support from the  Singapore-MIT Alliance (SMA) Graduate Fellowships.
Data availability All sequencing data are accessible on the Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo) under study accession number GSE123839. Raw sequence data are accessible on the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA473770. Proteomics and mRNA modification LC-MS/MS data are deposited at the Chorus database with accession number 1579.
The collection of digital images is growing at ever-increasing rate which rises the interest of mining the embedded information. The appropriate representation of an image is inconceivable by a single feature. Thus, the research addresses that point for content based image retrieval (CBIR) by fusing parametric color and shape features with nonparametric texture feature. The color moments, and moment invariants which are parametric methods and applied to describe color distribution and shapes of an image. The nonparametric ranklet transformation is performed to narrate the texture features. Experimentally these parametric and nonparametric features are integrated to propose a robust and effective algorithm. The proposed work is compared with seven existing techniques by determining statistical metrics across five image databases.
The outcomes depend upon the human labelling which leads to irrelevant results and wastage of time  [5]  whereas, CBIR technique relies on low-level image features and reduces human labour drastically. However, these features are failed to represent a required image. CBIR is an image searching method from large image database depending upon extracted features by the visual descriptors. Here, user asks for an image and CBIR system searches for relevant images based on extracted features from stored images of the databases. This system structure of CBIR is described in Figure  1  where, user makes query, then visual content descriptor or feature extractor extracts low level features (e. g. , color, texture, and shape) from the query image. Thus, CBIR is also known as feature based image retrieval (FBIR)  [6, 7].
Scope & contributions Though, the aforementioned CBIR literature is quite strong still it needs improved computation due to the recent advancement. This includes availability of vast image database, fast computation for quick and relevant image retrieval. Thus, the proposed CBIR study is motivated by the fact of appropriate image retrieval for user search. Existing research is focused on: parametric features, nonparametric features, fusion of different parametric features, and fusion of different nonparametric features. The parametric approaches derive features by making assumption of pixel distribution where, nonparametric approach doesn't make assumption of pixel distribution and determines ranking from available numerical values  [32]. Hence, a CBIR system which, employs only parametric color features for image retrieval faces problem to characterize an image containing more significant texture and shape information.
The main focus of this research is the ranklet transformation which is nonparametric statistical method for texture analysis based on nonlinear rank based filtering technique, provides alternative series of measurements that requires very limited assumptions to be made about the data points. This is invariant in nature towards transformations (brightness, contrast changes and gamma correction). Also, moment invariants are executed for shape analysis which, is weighted average (moment) of the image pixel's intensities of object in an image. Subsequently, these three parametric and nonparametric fea-tures are concatenated for making a better feature vector of an image which is able to represent all low-level characteristics present there. II. After, enhancing the feature extraction process, different similarity measurements are evaluated to find out suitable similarity method and enlarge the number of relevant images for a search.
Four distinct distance algorithms: Chi-squared test or X 2 statistics, Manhattan, Euclidean, and Canberra distance are investigated and found Euclidean distance outperforms among them with better precision and recall. The Euclidean measure is executed with the enhanced features to retrieve images from five different image databases by applying three different frame sizes  (10, 12, 15). III. The proposed CRM model is also compared with seven different existing CBIR techniques: color difference histogram (CDH)  [33] , edge histogram descriptor (EHD)  [34] , multi-texton histogram (MTH)  [19] , color auto correlogram (CAC)  [10] , distribution of color ton (DCTon)  [27] , golomb-rice (GR) coding based indexed histogram  [13] , and local binary pattern (LBP) based method with the combination of discrete wavelet transformation (DWT) and legendre moments (LM) which is denoted as (LDM)  [30].
Proposed Method Proposed work is divided into two phases: online and offline along with four sub-stages such as, feature extraction, similarity measure, performance evaluation, and hypothesis test. Figure  2  shows the flow of this work and connection between each parts. Initially, the color, texture, and shape features are extracted from all images of a database to create the database of feature vectors using CRM method and stored in offline phase. In the online phase, when user makes a query image to retrieve relevant images then, CRM algorithm is performed to derive features to form feature vector of the query image. Then, the feature vector of the query image is compared with the stored feature vectors by the help of similarity tolerance to fetch the relevant images for the user. Thereafter, the statistical metrics are determined over the retrieved images to check the ability of the proposed CRM method for searching relevant images.
Color Moments Color is extremely used feature for image retrieval techniques  [35]. Before selecting an appropriate color description, selection of color space is important and needs to choose a color model for color feature extraction process. Generally databases consider images in RGB format. However, this format contains highly redundant and correlated intensity values which degrade the efficiency of an algorithm. Thus, the proposed algorithm adapted CIE Lab color space for pulling out the color feature because of these pros and cons of RGB color model. It is a 3-axis color system with dimension L for lightness, a and, b for the color dimensions. The Lab color space includes all the colors of spectrum, as well as the colors of outside human perception. This is the most exact means of representing color and this is device independent color space.
Performance Metrics Precision and recall are well-known performance metrics in information retrieval. Precision or positive predictive value (PPV) specifies the retrieved outcomes which are relevant, whereas recall or sensitivity specifies relevant outcomes which are retrieved. Hence, high precision value means a system returns more relevant images than irrelevant ones. Conversely, high recall indicates the employed algorithm returns most of the relevant images. Here, precision (P r ) and recall (R e ) are defined as (  30 ) and (31), P r = N umber of relevant images retrieved T otal number of imgaes retrieved R e = N umber of relevant images retrieved T otal number of relevant imgaes in the database (31).
Experiment and Result Analysis In this study, three features (color,texture, and shape) are combined to propose CRM method. The generation of feature vector is explained in previous sections, where, 9, 231, and 7 number of features are extracted by color moments, ranklet transformation, and moment invariant respectively for representing an image (both the database and query images). The ultimate feature vector, F , is formed by sequentially concatenating (9+231+7=247) all these features as represented in  (34). F = {f color (1),. , f color (9), f texture (1),. , f texture (231), f shape (1),. , f shape (7)}  (34)  Therefor, the performance of the proposed methodology is evaluated based on the results produced for each query image and explained in the following section.
Retrieval Performance Different distance measurement are investigated first and analyzed the outcomes for choosing a suitable similarity measure for CRM. There are four similarity measures are applied in CRM to acquire relevant images and the performance metrics are determined and listed in Table  1. From Table  1  it is shown that the Chi-squared distance resulted the lowest precision compared to the other distance metrics investigated. This may be due to the fact that the samples used in the experiment are unpaired or independent data from large sample images  [42] , means the set of images are taken from separate individuals. The Manhattan distance function determines a grid like path distance between two data points. Hence, distance between two integers is the sum of the differences in their corresponding components.
Investigation using Manhattan distance achieved improved precision values (minimum precision in Caltech dataset is 0. 3766, and maximum precision in MSR dataset is 0. 3990) than Chi-squared test. The outcomes of Canberra distance is slightly better than Manhattan distance, yet the result is not satisfactory, because it could produce better result when data are scattered around origin. Here, the distinction of the absolute difference between the variables of the two images are divided by the sum of the absolute variable values prior to summing. Euclidean distance obtained best results among the compared distance metrics. It is obtained the minimum distance between two sample images on a plane. Euclidean distance measures feature wise difference of query and database images by squaring and adding the difference which, effectively increases the divergence between them.
Huang et al. proposed color correlograms for image indexing and retrieval by a joint probabilistic style. Color correlogram is modified as color auto correlograms (CAC) to manage the high dimensionality of feature vector. Huang's used the concept of color quantization in RGB plane to present CAC  [10]  which generates a 256-dimensional feature vector and apprehends spatial correlation of color intensities. It provides maximum precision 0. 4977 and recall 0. 0597. The obtained precision and recall are mapped in Figure  6  for comparing the performance of implemented algorithms visually. The plot also includes the outcomes for different image database. MTH illustrates spatial correlation of color intensities and edge orientation by employing texton analysis  [19]. MTH considers four types of texton which may not depict the complete content of texton elements, because it fails to consider the perceptual uniform color difference.
It is a strong feature descriptor for images which, represents color and texture information. This method produces high recall value, which signifies that, it retrieves more relevant image instances. The highest precision and recall obtained by this method are 0. 5514 (in Simplicity database) and 0. 0689 (in Corel-10K database) respectively. This method is limited by high computational complexity and scattering property of light. In  [13] , an indexed and encoded representation of color information is used to retrieve images from large databases. In first stage, the insignificant colors are discarded and important color information are kept to make an indexed color histogram. Then, the GR encoding is used to represent the indexed color histogram. This indexed histogram with GR encoding based CBIR method achieves good precision 0.
6631 in Simplicity database. In case of Corel-5K and Corel-10K, it retrieved low amount of relevant images which results low precisions of 0. 6319 and 0. 6202 respectively. Also, it delivers low recall (0. 0422 and 0. 0410) in Corel-10K and MSR databases, which indicates the number of relevant images over total images is low. This GR encoding based method only considers the color information of images but in practice the images contain highly complex color, shape, texture information, and semantic content. Also, the histogram ignores shape and texture which, causes the problem to distinguish different objects having same color, e. g. black dog and black elephant. The indexing method of the histogram uses limited number of simultaneous colors per image which, is unable to describe the complex color as well as other semantic information.
Though, the second phase uses a lossless GR encoding, still it again employs predicted pixel value for optimizing the representation which may obscure actual meaning of an image. Srivastava and Khare  [30]   The multi level decomposition of images causes intensive computational complexity, but the method shows a good set of average precision and recall. In some cases it cannot discriminate among the images. The MSR dataset contains images with complex color. However, this method uses gray scale which is a bottleneck for describing the color distribution. It may be a reason for not to produce good result in MSR. The result shows that, the method is retrieved few number of relevant images over the frames as well as from each database which, causes a low precision and recall fraction. Experiment also indicates that, this method unable to discriminate among different views of an object.
Result Validation The precision and recall measurements illustrate the accuracy of the proposed method based on relevant and irrelevant images are being returned for a query. The precision and recall data are systematically examined with the purpose of highlighting useful information and the results of this quantitative experimental work communicated via tables and graphs. The hypothesis test is conducted to determine the impact and quality of the work presented here. The CRM method used continuous interval variables throughout the experiment, therefore, a quantitative inferential analysis is possible based on the probability distribution of the precision and recall samples. A sample of probability distribution of precision and recall values for 30% query image from MSR and Caltech-101 image database are included in Figure  9 , and the data distribution curves are of bell shape or normal distribution.
Therefore, a parametric statistical test is performed based on the shape of the curve. According to theoretical assumptions, normally distributed data could be tested via t-test or t-statistic. In the proposed work, images are independent in nature, so the one sample t-test is performed on determined precision and recall samples. The test is used to prove that, the means of precisions and recalls are determined from the same image databases. The t-values of precision and recall are calculated from five image databases using  (33). The precision and recall mean are obtained from CRM are considered as sample mean. Sample and hypothetical means are compared to determined t and p values. The p value less than significance level (α) and t value is conventionally considered as significant which, also decides the acceptance and rejection of null hypothesis (described in Section 2.
Thus, the selection of different feature extraction algorithms is vital step towards the CBIR process. Color moment, ranklet transformation, and moment invariant are selected to make an efficient CBIR tool by using their individual capabilities. Also, the other aspects of CBIR such as, similarity measure and performance analysis over different frame size  (10, 12, and 15)  are explored to tune the performance properly where, Euclidean measure delivers optimal performance for all the relevant image search. The proposed method is executed over five different image dataset and compared with recent CBIR techniques. This comparison assure the performance of this method is prevailing among other method across all the five database images. Eventually, the one sample t-test is executed to validate the work theoretically and practically.