dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. Except for Fig. 1a , tests were computed with the assumption of two-tailed distribution. The nonparametric Mann-Whitney U test was used to compare datasets with n < 10. Multiple comparisons of three nonparasitic groups of nematodes with L3 filariae were done with Kruskal-Wallis rank test with Dunn's correction. Comparison of larger groups was made with the non-paired t test when datasets passed the normality test. Mean velocities were compared using an unpaired t-test or one-sample t-test by comparing means to the maximum speed of filariae or nematode migration within lymphatics calculated in Fig. 2d  or Fig. 5a. The unidirectionality of larvae migration within the microfluidic maze, separately for static and flow conditions, were tested with one-sample t test with null hypothesis assuming random migration of filariae (Rf = 0) using GraphPad "QuickCalcs-OneSampleT1", https://www.
Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.
We have created a novel approach and associated software called GECKO for genetic classification using k-mer optimization that is especially designed for HTS data. GECKO is based on k-mer decomposition coupled with an adaptive genetic algorithm that explores HTS data from two or more input conditions. This algorithm searches for groups of k-mers that, combined together are highly informative; they are able to classify the input categories with high accuracy. Because GECKO uses k-mer counts, it can theoretically be applied to any type of HTS experiment and does not rely on a reference genome or transcriptome. Here, we successfully apply GECKO to a variety of biological problems and sequencing data. These include microRNA (miRNA) sequencing to classify normal blood cells, mRNA sequencing to classify subtypes of breast cancer and to predict response to chemotherapy, and bisulfite sequencing (BS-seq) on normal versus chronic lymphocytic leukemia (CLL) samples.
This algorithm is called adaptive because the mutation and crossing-over rates depend on how well individuals in the population perform. Individuals that perform well have lower rates to prevent them from changing drastically and thus enabling them to converge faster to a solution; individuals that do not perform well will have higher rates to enable wider exploration of solutions. In the analyses presented in this study and by default in the software, GECKO's performance is systematically tested on 1/6th of the data that is randomly selected and set aside before running the algorithm (see the section "Methods"). This test set allows us to evaluate the accuracy and overfitting for each run; it measures whether the algorithm fits too closely to the training set and thus will not correctly predict future input samples.
GECKO is thus run on the remaining 5/6th of the data with cross-validation at each generation of the algorithm. Classifying miRNA sequencing data of blood cells. We first tested GECKO's performance on a miRNA expression data of seven types of blood cells sorted from 43 healthy patients for a total of 413 samples  9. We ran GECKO on this dataset using 20mers (k-mer size of 20; miRNAs generally vary in size from 20 to 23) to find a set of k-mers that could correctly classify the seven blood-cell types. After 6000 generations (15 h on 15 cores; see Supplementary Table  1  for parameters and Supplementary Fig. 1  for runtimes and memory usage) GECKO discovered an individual composed of only three k-mers (ACCCGTAGAACCGACCTTGC, CCCCA GGTGTGATTCTGATA, AGTGCATGACAGAACTTGGG) that could distinguish the groups with 0.
96 accuracy (Fig. 2a, b  and Supplementary Data 1 and 2). In the initial study, the authors described a signature of 136 cell-type-specific miRNAs. These 136 miRNAs could classify the groups with 0. 97 accuracy. Thus, we found a much smaller signature that could classify the seven blood-cell types with similar accuracy without the use of a miRNA-dedicated bioinformatics pipeline. We then aligned the three k-mers discovered by GECKO to a database of known miRNAs  10. Two of these mapped perfectly to miRNAs 152-3p and 99b-5p, which were annotated in the original study as specific to NK cells and T helper cells, respectively. The third mapped to miRNA 361-3p which was not found to be specific to any of the seven cell types and was thus ignored in the initial study. Separately, the first two k-mers could classify one cell-type each and the third would have been overlooked.
Together these three k-mers classify all seven groups with high accuracy because of their contrasting expression between each cell types (Fig. 2c ). Classifying breast cancer subtypes using mRNA sequencing data. Breast cancer is a heterogeneous disease in regards to response to treatment and its transcriptional background. Defining the subtypes luminal A (LumA), luminal B (LumB), HER2-enriched (HER2) and basal-like are crucial for prognosis and predicting outcome of breast cancer. These subtypes were initially defined through unsupervised clustering of gene expression and are currently identified using a standard qPCR assay of 50 genes called the PAM50  11, 12. To assess whether GECKO could identify k-mers that classify breast cancer subtypes, we used a dataset of 1087 mRNA-Seq breast cancer samples from the Cancer Genome Atlas Pan-Gyn cohort  13  (patients per class: Basal 175, Her2 73, LumA 513, LumB 185).
We ran GECKO for 20,000 generations (75 h on 15 cores; see Supplementary Table  1  for parameters and Supplementary Fig. 1  for runtimes and memory usage) and extracted the highest scoring individual at its term (Supplementary Table  2 ). We then tested how well these k-mers classified the four cancer subtypes compared to PAM50 expression values calculated as transcript per million (TPM). Both the kmer counts and PAM50 TPMs were trained using a linear support vector machine (see the section "Methods") with identical training data and evaluated on the same test set. The 10 k-mers had higher accuracy rates compared to the PAM50 on all four classes (Fig. 3  and Table  1 ). We then further inspected the 10 k-mers discovered by GECKO by mapping them to the human genome. We found  that four of the k-mers mapped to genes from the PAM50 list (FOXC1, ESR1, KRT14, KRT17).
We then tested GECKO on a dataset with more heterogeneous cell populations and smaller sample sizes. We used a cohort of triple-negative breast cancer patients, an aggressive, heterogeneous subtype of breast cancer with poor outcomes. This cohort taken from the Breast Cancer Genome Guided Therapy (BEAUTY) study  19, 20  was divided into 19 patients that had a complete response to chemotherapy and 20 patients that did not. In such cases of small sample size and high heterogeneity, we recommend using GECKO's voting mode (Fig. 4a ). This mode compensates for bias that may be introduced when splitting a small number of samples between training and test datasets and may thus accentuate batch effects. The voting mode will run 10 instances of the genetic algorithm for 10,000 generations. At their term, it will select k-mers from the top individuals across the 10 instances and run a final genetic algorithm on this subset of k-mers for another 10,000 generations.
1  for runtimes and memory usage) and found a winning individual that was able to classify normal from CLL samples with an accuracy of 1 using 20 k-mers (Fig. 5a ; Supplementary Table  4 ). In addition to this final classification, GECKO plots the evolution of winning organisms across the 20,000 generations (Fig. 5b ). This graph can be used to identify individual k-mers that are essential for classification and thus worth investigating. Here we found three k-mers that were most frequently used by winning individuals for classification (Supplementary Table  5 ). We verified the methylation status of the loci where these kmer sequences were mapped using the Bismark software  24  and found that all three of them displayed dramatic changes in DNA methylation between normal and CLL samples (Fig.
Discussion HTS data analysis often requires extensive data transformations through tailored bioinformatics pipelines to organize the sequences in a manner that is coherent with our understanding of biology. Mapping to a reference, using ad hoc statistical thresholds and grouping sequences by functional elements, such as transcripts are common steps in most bioinformatics pipelines. We designed GECKO with the aim of creating a classifier that could explore HTS data without a reference genome or transcriptome and without the need of bioinformatics pipelines dedicated to a specific library preparation or technology. The approach we describe here can in theory explore any type of sequencing data. Because GECKO considers groups of k-mers for classification, it can make use of co-dependencies between sequences to find smaller and more accurate classifiers.
5 ) and mostly map to unique locations in the genome or transcriptome (Supplementary Fig. 6 ). Thus, GECKO can make use of unique transcriptional elements across a large spectrum of expression. GECKO's ability to work across multiple types of data without the need of dedicated bioinformatics tools could make it invaluable for cross-platform large-scale analyses but also for individual researchers and clinicians who would be able to compare HTS data between cohorts of patients with no bioinformatics training. It is worth noting that the longest and computationally intensive part of our procedure is obtaining the kmer matrix. This step need be performed only once per dataset however and providing a k-mer matrix for online datasets along with sequencing files could result in widespread use of non-biased approaches such as GECKO.
Methods Data preparation. The k-mer decomposition into a matrix of k-mer counts is performed using Jellyfish 2  31. This step can be preceded by a filtering of sequencing adaptors by Trim Galore (bioinformatics. babraham. ac. uk/projects/trim_galore/) if the user selects this option in GECKO. GECKO will then eliminate k-mers for which the count is below a noise threshold, k-mers that are uninformative for the given study and k-mers that are redundant (i. e. that share the same information as another k-mer). The noise threshold is determined empirically from the input samples and is calculated for each separate run of GECKO. To do this, we count the number of times a k-mer count appears in one sample with null values in all other samples from the same group for the same k-mer. Starting at a k-mer count of 1, we search how many times the value 1 appears for a k-mer in one sample with 0 in every other sample for the same k-mer.
In our analysis, we empirically set the limit of SU at 0. 7, above which two k-mers were considered as redundant. GECKO keeps a record of all k-mers eliminated due to redundancy along with the ID of the k-mer that caused it to be eliminated. Thus, when the genetic algorithm finds a solution, GECKO can provide all the redundant k-mers that would have provided a similar solution. All code for the data preparation was implemented in C++. The adaptive genetic algorithm. The algorithm begins by splitting the input data into a training and test set. The test set is created by randomly selecting a number of samples from each input category. By default the number of samples selected is 1/6th of the category with the smallest amount of samples. The test set is used to establish a final test score that will have no impact on the genetic algorithm's evolution but allows us to estimate how well GECKO performs on a given dataset.
Training: At each generation of the AG, all individuals are scored based on their ability to classify the input samples using a machine learning algorithm. In this study, the algorithm used was a Linear Support Vector Classification (LinSVC). This method combines excellent results on smalls datasets and unbalanced groups with a good generalization potential, for a small computational resource cost. LinSVC is implemented in GECKO via the Scikit-learn package  33. GECKO can also be used with a random forest model or neural networks, however these have higher computational costs and require dedicated hardware to be implemented within reasonable time-frames. To calculate the fitness score of an individual at each generation we randomly split up the training set into two. 2/3 of the training set becomes the inner training set and the remaining 1/3 becomes the inner test set.
We recommend using the last model as it shows better exploration and higher convergence rates for the kind of data used for GECKO. This approach aims to maintain the population's diversity while protecting good individuals from modifications. The mutation and cross-over probabilities are decreased when the individual's fitness is high compared to the average and increased if it is low. Similarly, the probabilities are decreased when the population is heterogeneous and increased when the population is homogeneous to favor exploration of novel solutions. These probabilities are modeled by two linear functions depending on whether the individual is above the average fitness of the population or below it and is given by the formula below. Pm ¼ k 1 f avg Àf ð Þþk2 f Àf min ð Þ f avg Àf min ; f < f avg k 2 f max Àf ð Þ þ k 3 f Àf avg ð Þ f max Àf avg ; f ! f avg 8 > < > : Here f is the individual's fitness, f min is the fitness of the population's worst individual, f avg is the population's average fitness and f max is the fitness of the population's best individual.
Acknowledgements We wish to acknowledge the Genotoul platform (genotoul. fr)for providing us with calculation time on their servers. We thank  Jerome Audoux ,  Jean-Philippe Villemin  and  Giacomo Cavalli  for their advice. We wish to acknowledge the  Agence Nationale de la Recherche (ANRJCJC-WIRED) , the  Labex EpiGenMed  and the  MUSE initiative  for their financial support.
Data availability The data that support the findings of this study are available from NCBI Gene Expression Omnibus under the accession numbers GSE100467 and GSE58889; the Cancer Genome Atlas under the Pan-Gyn cohort name; the database of Genotypes and Phenotypes under the accession numbers phs000435. v2. p1 and phs001050. v1. p1 but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available by submitting a request to these repositories.
The experiments were always performed with the ears out of the water. We placed one microphone (Knowles, FG-23329-P07) inside the cavity of each ear, behind the ear-lid and close to, but without The emitted signal was a logarithmic sine sweep (frequency range ¼ 20 -10 000 Hz; duration ¼ 5 s; intensity level ¼ 80 + 0. 5 dB SPL). The frequency range was chosen to mostly cover the hearing range of crocodilians, which is mainly centred on 1-2 kHz  [8, 9]. For analysis purposes, we used a Matlab code to synchronize in time the source signal with the two recorded microphone signals. Prior to each measurement, we calibrated the broadcast signals in the absence of any animal, with both microphones placed at the virtual centre of the crocodile's head. This calibration was a necessary step to take into account the properties of the sound-producing and recording equipment ('transfer function' due to material gains, frequency responses, etc.
Acknowledgements. The authors are grateful to the  zoo Planete Crocodiles  for providing live animals, to  E. Parizet  ( LVA-INSA ) for providing access to the INSA anechoic room, to  G. Regnault  for help during the acquisitions and to  M. Lavandier  and  T. Lecle  `re for providing part of the Matlab code used in the study.
However, although multistate and/or multievent extensions of the Barker/robust design are possible  (Barker & White, 2004; Kendall et al. , 2013) , such models are not currently implemented in program MARK 9. 0  (White & Burnham, 1999) , the most widely used user-friendly software tailored for capture-recapture analyses. The multievent-robust design model we describe here is a general model that combines multistate open robust design capture data collected during discrete breeding seasons with auxiliary live observations made at other times of the year. We fitted the multievent-robust design model using program E-SURGE 2. 1. 2 (Choquet, Rouan, & Pradel, 2009) , a user-friendly software initially developed to analyze capture-recapture data as hidden Markov models.
, 2017). Weithman et al. (2017)  additionally interpreted the "availability" parameter (aʹ, the probability of being available for capture given that the individual was previously unavailable for capture) as a measure of breeding probability in piping plover (Charadrius melodus), a migratory shorebird. The Barker/ robust design implemented in program MARK currently lacks multistate and/or multievent extensions, but the multistate extension of the Barker model  (Barker, 1997)  allows an alternative, direct means of estimating breeding probability and accommodates uncertain identification of states. This model, however, foregoes the advantage of a robust design structure within primary periods. The multievent-robust design model we used to estimate breeding probability in elephant seals is similar to the Barker/robust design  (Kendall et al.
However, without a robust design structure during the breeding season, the multievent model appeared to assign females seen with an "uncertain" event to the breeder state even in years with apparent "perfect" breeder detection probabilities (Supporting Information Appendix S5). Desprez et al. (2018) used a different multievent design with state uncertainty to study intermittent breeding in elephant seals at Macquarie Island, but their results also indicated maximum-likelihood estimation problems in the absence of robust design. Breeding state assignment probabilities were estimated on the boundary of parameter space (i. e. , when a parameter is estimated as 0 or 1), suggestive of p = 1 during the breeding season, which is suspicious for that population. We also considered analyzing our data using a multistate open robust design (MSORD) model  (Kendall & Bjorkland, 2001 ) (Supporting Information Appendix S6).
That both wild-type and mutant miR-122 can rescue the C3U genome S3/GLuc construct, containing Gaussia luciferase (GLuc) and foot-and-mouth disease virus 2A autoprotease between the p7 and NS2 sequence  [23]. Replication defective mutation (AAG) in the NS5B viral polymerase gene is marked. (B) Effects of miR-122 sequestration using miR-106 LNA, miR-122-LNA or RG1649 anti-miRs. Viral RNA replication was measured by luciferase production at the indicated time points. Data is presented relative to the luciferase expression of negative control miR-106b-LNA treatment. (C) Rate of replication following miR-122 sequestration. Cells were treated as in (B) and 24 hours post RNA transfection, RNA was labeled with 200 μM 5-ethynyl uridine (EU) for twenty-four hours, conjugated to biotin and subsequently isolated with streptavidin beads.
RNA replication rates were determined by qRT-PCR. Data is presented as percent labeling relative to of miR-106b-LNA control treated samples ( �� p<0. 0034). (D-F) HCV RNA replication in ΔmiR-122 Huh7 transfected cells. Cells were pretreated with miR-106b, native miR-122, mut-miR-122 alone or in combination one day before and one day after HCV RNA transfection. Supernatants were collected at the indicated time points and GLuc activity was determined. The data are representative of 3 independent replicates ( �� p<0. 005, ��� p<0. 001 ���� p<0. 0001). (G) HCV and actin RNA abundances, monitored by Northern blot analysis, at 72 hours post RNA transfection. https://doi. org/10. 1371/journal. ppat. 1007467. g001 to some extent, but neither as completely as native miR-122 rescues the wild-type genome, suggests that both wild-type and mutant miR-122 might bind to the C3U genome.
Effects of the C3U substitution on viral RNA stability and nascent viral RNA synthesis in the presence of miR-122 To investigate whether the significant reduction in C3U RNA abundance in the presence of miR-122 is a result of diminished RNA stability, Huh7. 5 cells were transfected with HCV RNA for three days and subsequently treated with the nucleoside analog MK-0608 to block viral RNA synthesis. Total RNA was isolated at the indicated time points following drug treatment and the rate of HCV RNA decay was examined by Northern blot analysis. Data showed that wild-type HCV RNA was degraded at a slightly slower rate than C3U mutant HCV RNA (Fig  4A ). The approximate half-life of wild-type RNA is 3. 2 hours compared to 2. 6 hours for the C3U variant (Fig 4A  and 4B ). Although modest, the reduction in RNA stability was statistically significant (Fig 4C ) and could potentially impact the fitness of the C3U virus.
RNA replication rates were determined by qRT-PCR. Data is presented as percent labeling relative to of miR-106b-LNA control treated samples ( �� p<0. 0034). (D-F) HCV RNA replication in ΔmiR-122 Huh7 transfected cells. Cells were pretreated with miR-106b, native miR-122, mut-miR-122 alone or in combination one day before and one day after HCV RNA transfection. Supernatants were collected at the indicated time points and GLuc activity was determined. The data are representative of 3 independent replicates ( �� p<0. 005, ��� p<0. 001 ���� p<0. 0001). (G) HCV and actin RNA abundances, monitored by Northern blot analysis, at 72 hours post RNA transfection.
Acknowledgments We are grateful to  Karla Kirkegaard  for many helpful comments. The gifts of Huh7. 5 Sec14L2 and Huh7. 5 ΔmiR-122 cells from  Joseph Luna  and  Charles Rice  ( Rockefeller University, NY ) are gratefully acknowledged. We are indebted to  Stan Lemon  ( University of North Carolina, NC ) for receiving plasmids H77S. 3 and  H77S. 3/GLuc genotype 1a.
Echocardiographic data Transoesophageal echocardiography (TOE) was indicated because transthoracic echocardiography (TTE) showed vegetations (n = 5) and/or severe valvular regurgitation (n = 4). For the remaining patients, TOE was performed to investigate the cause of systemic embolization (n = 1), positive Coxiella burnetii serology (n = 1) or bacteraemia (Streptococcus; n = 1). One patient had IE diagnosed during cardiac surgery. Endocarditic lesions were located on the aortic valve for five patients, on the mitral valve for four patients and on both valves for four patients. Most patients had severe valvular lesions (62%, n = 8), which included paravalvular abscess (n = 4), severe valve regurgitation (n = 7) and valvular perforations (n = 2).