dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics and plotting All statistics and plotting were performed with R v. 3. 6. 1. The permutation tests were done by randomly permuting the source categories and cluster labels (either species or SC depending on the dataset) 10,000 times and then deriving a P value by dividing the number of permutations that were at least as extreme as the observed data by 10,000. The test was two-tailed; therefore, a significance level of 0. 025 was used at each tail. The P values were corrected using the Benjamini-Hochberg method from the p. adjust function in R. Other tests of association were done using the chi-squared test. All the plotting was done in ggplot2 v. 3. 3. 5 and the multi-panel plots were produced using cowplot v. 1. 1. 0. https://doi. org/10. 1038/s41564-022-01263-0 Extended Data Fig.
Acknowledgements This work was funded by the  SpARK  project, awarded to E. J. F. , 'The rates and routes of transmission of multidrug-resistant Klebsiella clones and genes into the clinic from environmental sources', which has received funding under the  2016 Joint Programming Initiative on Antimicrobial Resistance call 'Transmission dynamics'  (medical research council (MRC) reference no. MR/R00241X/1 ) and by the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (no. ANR-10-LABX-62-IBEID ). J. C. and H. A. T. were funded by the  European Research Council  grant no. 742158. J. C. and T. K. were funded by the  Norwegian Research Council  grant no. 271162. The use of the  MRC Cloud Infrastructure for Microbial Bioinformatics 49  was critical for the computational aspects of this work.
Data availability All raw Illumina sequence data are available from the European Nucleotide Archive under accession no. PRJEB27342. The individual accession numbers for all sequenced isolates are in Supplementary Table  9. The metadata and phylogenetic analysis are available for download from the Microreact project https://microreact. org/project/KLEB-PAVIA; additional metadata is provided in Supplementary Tables  1  and 2. The PlasmidFinder and ResFinder databases were downloaded on 21 May 2021 from https://bitbucket. org/genomicepidemiology/ plasmidfinder_db and https://bitbucket. org/genomicepidemiology/ resfinder_db, respectively. Map tiles by Stamen Design, under CC BY 3. 0. Data by OpenStreetMap, under ODbL.
Data Policy information about availability of data All manuscripts must include a data availability statement. should provide the following information, where applicable: -Accession codes, identifiers, or web links for publicly available -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy availability: Short-read data available under accession numbers ERR3412430 to ERR3412448;ERR3440341 to to ERR3449598;ERR3469775 to ERR3469909;ERR3479903 to ERR3480717;ERR3844616 to ERR3844777;ERR3904469 ERR3904709;ERR3931787 to ERR3932313;ERR3967745 to ERR3967936;ERR4022833 to ERR4023150;ERR4139181 to ERR4139191;ERR4374646 to ERR4374837. The metadata and tree are avilable to download form the Microreact project at https://microreact.
org/project/KLEBPAVIA. PlasmidFinder and Resfinder databases downloaded on 21/05/2021 from https://bitbucket. org/genomicepidemiology/plasmidfinder_db and https://bitbucket. org/genomicepidemiology/resfinder_db, respectively. Map tiles by Stamen Design, under CC BY 3. 0. Data by OpenStreetMap, under ODbL.
Several pieces of research have spotlighted the importance of count data modelling and its applications in real-world phenomena. In light of this, a novel two-parameter compound-Poisson distribution is developed in this paper. Its mathematical functionalities are investigated. The two unknown parameters are estimated using both maximum likelihood and Bayesian approaches. We also offer a parametric regression model for the count datasets based on the proposed distribution. Furthermore, the first-order integer-valued autoregressive process, or INAR(1) process, is also used to demonstrate the utility of the suggested distribution in time series analysis. The unknown parameters of the proposed INAR(1) model are estimated using the conditional maximum likelihood, conditional least squares, and Yule-Walker techniques.
Introduction Many studies have underlined the importance of modelling count data as well as the time series of counts, which has sparked considerable interest in a variety of sectors, including medical science, earth science, physics, finance, and insurance. For modelling count datasets, the Poisson distribution is the most frequently used, but it has the drawback of not being able to model over-dispersed datasets, while the negative binomial distribution is used for modelling over-dispersed data. When investigating counting data, the existence of at least one overdispersion warrants extra consideration when selecting a count model (for details, see  [1, 2] ). As a consequence, the conventional Poisson regression model is rarely used in these situations. Furthermore, one of the most widely utilized methodologies for analyzing timedependent data are time series analysis.
The corresponding time series is known as a time series of counts or, equivalently, an integer-valued time series when the data are generated by a random counting procedure. The demand for modelling count time series is seen in diverse real-life situations-for example, the monthly number of earthquakes in a specific place, the monthly number of automobile sales, the monthly number of deaths due to a specific disease, the monthly number of traffic accidents, and the number of living cells (see  [3] ). Refs. [4] [5] [6]  proposed the class of first-order non-negative integer-valued autoregressive, shortly INAR(1), processes with Poisson innovation based on the binomial thinning operator to model time series of counts. INAR models are frequently built using the binomial thinning operator. Moreover, the INAR(1) model can be altered by changing the innovation distributions according to various real-life situations.
According to  [7] , the INAR(1) model with respect to negative binomial innovations (NB-INAR  (1) ) is effective for generating overdispersion. Since the common occurrence in overdispersion is that the incidence of zero counts is higher than expected from the Poisson distribution, Ref. [8]  proposed an INAR(1) process with geometric distribution as the innovation model. Ref. [9]  proposed the PL-INAR(1) model, which combines the INAR(1) model with Poisson-Lindley innovations. Then, Ref. [10]  introduced the INAR(1) process with Poisson-Bilal innovations. These are a few of the most significant studies on the overdispersed INAR(1) process. Even while these methods give fine solutions for over-dispersed time series count datasets, they still have significant drawbacks that can pose computing challenges in real-world applications.
Thus, discovering more INAR(1) models will provide more possibilities for superiorly fitting the real datasets by choosing those models according to the situations. In this paper, we construct a novel discrete two-parameter distribution by mixing the Poisson and Mirra distributions, which can be used to model overdispersed count data sets. Hereafter, we call this new distribution the Poisson-Mirra distribution (PMiD). The two-parameter Mirra distribution by  [11]  is considered to be the generalization of the Xgamma distribution with one parameter, which is proposed by  [12]. In addition, Ref. [13]  considers the Poisson-Xgamma distribution to be an alternative or rival to the well-known one-parameter Poisson-Lindley distribution proposed by  [14]. Interestingly, we discovered that the proposed PMiD is the generalization of the Poisson-Xgamma distribution.
As a result, more research into the PMiD is unavoidable, both theoretically and in terms of applied aspects, which is the primary motivation for this work. The suggested distribution has the advantages of having a simple probability mass function (pmf ) and cumulative distribution function (cdf ), as well as explicit probability and moment generating functions, and can monitor over-dispersed count datasets, which are common in real-world data modelling. By using the PMiD as an innovation process, we also emphasize the significance of the PMiD in the INAR(1) process. The following is how the rest of the article is sorted. The detailed description of the Mirra distribution is covered in Section 2. The definition of the new distribution, its factorial moments, moments about the origin, skewness, kurtosis, mode, generating functions, entropy measures, and other details are presented in Section 3.
Now, by solving ∂ log L n ∂α = 0, and ∂ log L n ∂θ = 0, we obtain the associated nonlinear log-likelihood equations. They are respectively given by n ∑ i=1 (x i + 1)(x i + 2) 2(1 + θ) 2 + α(x i + 1)(x i + 2) n θ 2 + α = 0, and 3n θ - 2nθ θ 2 + α - n ∑ i=1 x i + 1 1 + θ 1 - 2α(x i + 2) 2(1 + θ) 2 + α(x i + 1)(x i + 2) = 0. The solutions of these two equations give the MLEs. We obtained the MLEs numerically using the fitdistrplus package of the R software (see  [15] ). For more details on the fitdistrplus package, one should go through the lin k https://CRAN. R-project. org/ package=fitdistrplus accessed on 14 February 2021. For the detailed R-code for finding the MLEs of the PMiD, see Appendix A of this article.
COVID-19 Data: Armenia First, we utilize the dataset of the daily new deaths in Armenia due to the COVID-19 disease. The data are available at https://www. worldometers. info/coronavirus/country/ armenia/ accessed on the 10 January 2021 and are also studied by  [19]. They contain the daily new COVID cases between 15 February 2020 and 4 October 2020. To demonstrate the PMiD's potential benefit, the distributions given in Table  4  are considered for comparison. We compare the competitive distributions to the suggested distribution using the statistical techniques provided, namely the negative log-likelihood (-log L), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), χ 2 statistic, and p-value. Tables  5  and 6  display the corresponding MLEs (with standard errors (SEs) and CIs) and goodness-of-fit (GOF) results, respectively.
The PMiD's GOF statistics values are less than the other examined distributions, as shown in these tables. As a result, the suggested model is the most appropriate for modelling the given COVID-19 data. Now, the empirical mean, variance, and DI of this Armenia dataset are 4. 1931, 18. 7944, and 4. 4822, respectively, and the theoretical values for the mean, variance, and DI measures of the PMiD are 4. 1931, 19. 6659, and 4. 6900. It is observed that the empirical and the theoretical means are exactly the same, and the empirical and the theoretical values of variances, and DIs are the closest to each other. The empirical cdf, pmf, and P-P plots for the Armenia dataset are respectively given in Figures  4 5 6. It again gives some better-shaped curves for those fitted in the PMiD. The next goal is to use the Bayesian technique to estimate the parameters of the PMiD using the above-mentioned COVID-19 dataset.
Length of Hospital Stay By applying the PMiD regression model to an actual dataset, we can confirm its prominence. The dataset is about the 1991 Arizona cardiovascular patients (AZPRO data), which is available in COUNT package of the R software. The goal of this study is to investigate the combined relationship between the length of hospital stay (y i ) with the covariates x i1 -the cardiovascular procedures (CABG = 1, PTCA = 0), x i2 -the sex of the patients (male = 1, f emale = 0), x i3 -the type of the admission (urgent = 1, elective = 0), and x i4 -the age of the patients ((age > 75) = 1, (age ≤ 75) = 0). The fitted nonlinear regression model is given by µ i = exp(τ 0 + τ 1 x i1 + τ 2 x i2 + τ 3 x i3 + τ 4 x i4 ). In Table  8 , we compare the performance of the PMiD regression model with the Poisson regression model, the NPWE regression model, and the PXGD regression model and also display the summaries due to the real dataset, which include the SEs, p-value, negative log-likelihood, AIC, and BIC values.
We use data from the ETAS package of R software to calculate annual counts of earthquakes with a magnitude of 4. 5 or higher that occurred in Japan between the years 1926 and 2007. The data comprise 82 observations. For more details on this package, one can go through  [23]. In addition, Figure  7  depicts the Japan earthquake catalog. The spatial distribution of earthquakes in the study area is depicted in the top-left picture. The three figures in the right half of Figure  7  depict variations in the latitude, longitude, and magnitude of the earthquakes over time. The two figures in the bottom-left corner of Figure  7  depict the earthquake catalog's completeness and time stationarity. Here, the number of earthquakes having a magnitude higher than or equal to m is represented by N m. If the plot of log(N m ) versus m exhibits a linear trend, it reflects the completeness of the earthquake catalog.
Furthermore, the time stationarity of the catalog can be determined by looking at the plot of N t versus t, where N t is the total number of occurrences in the catalog up to time t. The earthquake time series is stationary if the plotted points of N t versus t have a functional form in such a way that N t = λ 0 t, where λ > 0. We calculated the CML estimates of parameters, as well as the negative log-likelihood (-log L), AIC, and BIC of the PMiD-INAR  (1)   4. Now, Table  9  displays the results corresponding to the earthquake data. We see that thelog L, AIC, and BIC values of the PMiD-INAR(1) model is smaller than those of the other compared INAR(1) models, and we conclude that the PMiD-INAR(1) is the most suitable model for the earthquake data compared to that of the other models. The proposed distribution, we believe, is ideal for researching data from COVID-19 related areas, biology, and earthquake-related fields, and we hope the PMiD applies to other fields of study also.
Context Discovering new count models is inevitable in the scenario of overdispersion, which will provide more possibilities for superiorly fitting the real datasets by choosing the right models according to the situations. In that sense, we proposed a new overdispersed count model, discussed its regression aspects, and constructed an INAR(1) process based on them. The main motivation behind the construction of this model is also discussed. In all the aspects, we found that the proposed model outperforms the compared models.
This Work The Poisson-Mirra distribution (PMiD), a novel two-parameter discrete distribution, is introduced and thoroughly investigated. We delivered specific expressions for the factorial moments, mean, variance, dispersion index, skewness, kurtosis, mode, probability generating function, moment generating function, characteristic function, and the entropy measures. The distribution parameters were estimated by using the classical maximum likelihood and Bayesian estimation methods and were also studied in their real-data analysis. A simulation study on MLE was also conducted. A new regression model for count data based on the PMiD is proposed and compared with its competitive regression models based on a real dataset. More importantly, we introduced the integer-valued autoregressive model based on the PMiD, known as the PMiD-INAR(1) process.
French Guiana Parasites Show Increasing Relatedness over a Period of Declining Malaria Prevalence. To empirically assess these model predictions, we assembled a longitudinal genomic dataset from French Guiana that spanned from 1998 to 2015. Over these years, French Guiana saw P. falciparum malaria cases decline by an order of magnitude, driven by a large antimalaria campaign that included the introduction of rapid diagnostic tests (RDTs), distribution of insecticide-treated bed nets, and a switch to Artemether-Lumefantrine as the official frontline antimalarial treatment in 2008 (Fig. 3A )  (17). Our dataset includes 148 whole-genome sequenced monoclonal samples derived from a combination of direct patient samples and newly culture-adapted clones (<80 d). These mixed sources yielded highly variable amounts of DNA and therefore highly variable genome coverage following Illumina sequencing (Fig.
Transcription Factors Involved in Sexual Commitment and Gametocyte Development Contain Variants with Large Frequency Changes. Our outlier approach successfully captured a previously documented instance of positive selection on a variant implicated in drug resistance (PfCRT C350R)  (21). In our dataset, this variant experienced an allele frequency shift of 63%, which places it in the top 11% of all changing variants. While this shows that we are detecting true selection signals, a proportion of outlier loci will have risen in frequency due to drift rather than selection. We therefore focused on gene set enrichment rather than the identity of single variants. We hypothesized that a change in conversion phenotype could be driven by genetic changes in transcription factors as studies in multiple systems have highlighted transcriptional changes as a major mode of local adaptation.
7 (Fig. 4C  and Dataset S2). Additional large frequency shifts are seen in PF3D7 1222400. While not as extensively studied, PF3D7 1222400 is expressed immediately following ap2-g in sexually committed parasites. It is known to evolve loss-of-function mutations in culture, suggesting that it is nonessential in asexual parasites and may play an early role in gametocytogenesis  (34). ap2-g and PF3D7 1222400 are within 9 kb of each other on chromosome 12, and both are centrally positioned within a peak of high IBD among post-2011 parasites (SI Appendix, Fig. S12 ). This could suggest either genetic draft or selection on a haplotype carrying multiple beneficial mutations. We included three additional genes (ap2-g2, ap2-g5, and ap2-g3) in this high-confidence set based on functional studies. Both AP2-G2 and AP2-G5 are required for gametocyte maturation and act as transcriptional repressors in asexual parasites  (35, 36) , potentially impacting ap2-g expression  (36, 37).
47, P < 0. 00001), we additionally compared each of these AP2s to other genes of comparable length and found that ap2-g, ap2-g2, ap2-g5, PF3D7 1222400, and PF3D7 0613800 were in the 91st, 97th, 82nd, 99th, and 85th percentiles, respectively (SI Appendix, Fig. S13 ). An alternative hypothesis is that these commitment-related transcription factors experienced relaxed constraint rather than directional selection as population size decreased. In total, we identified 25 nonsynonymous or indel variants within ap2-g, ap2-g2, ap2-g5, PF3D7 1222400, and PF3D7 0613800 (Dataset S2). Of these, 22 (88%) are present in a large set of global variants sampled from Africa and Asia, with 19 (76%) having a frequency of at least 2% in one other country. The global distribution of these variants suggests that they were segregating in French Guiana prior to the start of our study and that they are unlikely to be strongly deleterious as purifying selection would be more efficacious in these other larger populations.
falciparum (ap2-o, PF3D7 1107800, and PF3D7 1305200)  (53) , and two of these (ap2-o and PF3D7 1107800) contained alleles with frequency shifts of 0. 76 and 0. 69, respectively (SI Appendix, Fig. S15  and Dataset S2). Continued theoretical and empirical explorations are warranted on these topics. On the empirical side, our understanding of the specific factors at play in the French Guiana population is currently incomplete. While we know that transmission declined after 1998, we lack reliable estimates of complexity of infection (COI) decline due to uneven sequencing coverage. The exact phenotypes conferred by these French Guiana AP2 variants also remain unknown as we were unable to successfully generate parasite lines with allelic replacement. Further work is therefore warranted in this and other regions with declining infection rates to explore how specific sexual development phenotypes impact both clinical disease and transmission.
05 and 0. 2 for low-and high-transmission-investing genotypes (S low and S high ), respectively. These conversion rates fall within the range [0. 01 -0. 7] explored in McKenzie and Bossert  (12). The model's parameters were fitted by McKenzie and Bossert to the malaria therapy dataset  (12) , which is a collated dataset of patient records for which malaria was used as a therapy to treat patients with tertiary neurosyphilis  (56). Treatment is implemented as an additional killing rate of the asexual parasites, , clears all asexual parasites almost instantly, and is activated the day the asexual parasite density reaches 10 3. 5 parasites per microliter. This is assumed to represent the pyrogenic threshold parasitemia, which falls within the range observed in the malaria therapy dataset  (15, 16).
4130, Sigma Aldrich) with 25 mM Hepes, 5 mM L-glutamine, 22 mM glucose, 25 mM NaHCO35%, 20 mM gentamycin, 0. 37 mM hypoxanthine, and 1. 6 mM orotic acid. We extracted DNA from whole blood collected in an Ethylenediaminetetraacetic Acid (EDTA) tube using the QIAmp DNA mini kit (Qiagen) and following the manufacturer's protocol. Genomic Sequencing and Variant Calling. We created Illumina sequencing libraries using a Nextera XT low-input library kit. We sequenced the libraries on an Illumina HiSeq 2500 instrument using a 200-cycle run mode, generating 100-bp paired-end reads, and indexing 92 libraries per lane. Raw sequence data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive as BioProject PRJNA336113  (59). Reads were aligned with BWA-mem to the PlasmoDB P.
Dirichlet distribution of swimming strategy in MWM Dirichlet distribution is a multivariate generalization of beta distributions, where in the recent paper it has been used a stand-alone test to determine whether the animals spend the same time in the four imaginary quadrants of MWM tank  (Maugard et al. 2019 ). Four quadrants were conceptualized as one target quadrant (TQ), one opposite quadrant (OQ), and two alternative quadrants (AQ). Null hypothesis was adapted only when a group's fraction of time spent in TQ=AQ=OQ. Further, for understanding the outcome authors has proposed a new way to plot the data from the four quadrants as the aforementioned conceptions, where graph will show mean values and interindividual variability at the same time, on an easily interpretable chart. For the analysis we used code available at https://github.
With a delay of fifteen minutes of 'sample phase' animals were allowed to explore the same arena for 5min, except one of the sample object is being changed with a novel one. This phase is called 'test phase'. We have chosen a time gap of 15 min between two phases to target short term memory. In both phases, exploration of the animals was tracked with CCD camera (Panasonic, Japan) using Ethovision XT (ver. 07, Noldus Co. , Netherlands) and data acquired and analysed for recognition and discrimination index, using the formulae: ¨.
Behavioral scoring with kinoscope for EPM Apart from the regular analysis of the quantitative variables for elevated plus maze test from the video tracking software (Ethovison XT; Noldus), we also performed post-hoc behavioral scoring from the video tracked by a separate researcher blind to the experiment (intra rater reliability ≥0. 9). This is being performed using an available algorithm program 'kinoscope'  (Kokras et al. 2017)  where manually behavioral execution was coded using specific key tagging for different ethograms (viz. exploration to five zones, unprotected head dip), and visual maps were graphically depicted corresponding to individual animals to get an idea about the intra-group variability. These maps of events from start to the end of paradigm (0 to 300 th seconds) were coded with specific color.