dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). QUAST aligns contigs to a reference genome, allowing to compute contiguity and completeness statistics that are corrected for misassemblies (NGA50 and Genome fraction metrics respectively in Table  3 ). Assemblies were all run using 8 threads on a Xeon 2. 60 GHz CPU. For rust-mdbg assemblies, contigs shorter than 50 Kbp were filtered out similar to as shown in  Nurk et al. (2020). We did not report the running time of the base-space conversion step and graph simplifications, as they are under 15% of the running CPU time and run on a single thread, taking no more memory than the final assembly size, which is also less memory than the mdBG. Table  1  (leftmost) shows assembly statistics for D. melanogaster HiFi reads. Our software rust-mdbg uses ~ 33× less wall-clock time and 8× less RAM than all other assemblers.
Minimizer-space POA enables correction of reads with higher sequencing error rates We introduce minimizer-space partial order alignment (POA) to tackle sequencing errors. To determine the efficacy of minimizer-space POA and the limits of minimizer-space de Bruijn graph assembly with higher read error rates, we performed experiments on a smaller dataset. In a nutshell, we simulated reads for a single Drosophila chromosome at various error rates and performed mdBG assembly with and without POA (see STAR Methods for more details). Figure  2A (left)  shows that the original implementation without POA is only able to reconstruct the complete chromosome into a single contig up to error rates of 1%, after which the chromosome is assembled into ≥2 contigs. With POA, an accurate reconstruction as a single contig is obtained with error rates up to 4%.
Highly efficient assembly of real HiFi metagenomes using mdBG We performed an assembly of two real HiFi metagenome datasets (mock communities Zymo D6331 and ATCC MSA-1003, accessions GenBank: SRX9569057 and GenBank: SRX8173258). Rust-mdbg was run with the same parameters as in the human genome assembly for the ATCC dataset, with slightly tuned parameters for the Zymo dataset (see "genome assembly tools, versions, and parameters"). Table  2  shows the results of rust-mdbg assemblies in comparison with hifiasm-meta, a metagenome-specific flavor of hifiasm. In a nutshell, rust-mdbg achieves roughly two orders of magnitude faster and more memory-efficient assemblies, while retaining similar completeness of the assembled genomes. Although rust-mdbg metagenome assemblies are consistently more fragmented than hifiasm-meta assemblies, the ability of rust-mdbg to very quickly assemble a metagenome enables instant quality control and preliminary exploration of gene content of microbiomes at a fraction of the computing costs of current tools.
DISCUSSION Three areas we hope to tackle in our assembly implementation are: (1) its reliance on setting adequate assembly parameters, (2) lack of base-level polishing, and (3) haplotype separation. Regarding (1), we are experimenting with automatic selection of parameters ℓ, k, and δ. A heuristic formula is presented along with its implementation and results in the GitHub repository of rust-mdbg; however, it leads to lower-quality results (e. g. , 1 Mbp N50 for the HG002 assembly versus 14 Mbp in Table  3 ). We also provide a preliminary multi-k assembly script inspired by IDBA  (Peng et al. , 2010). While automatically setting mdBG parameters is fundamentally a more complex task than just determining a single parameter (k) in classical de Bruijn graphs, we anticipate that similar techniques to KmerGenie  (Chikhi and Medvedev, 2014 ) could be applicable, where optimal values of (ℓ, k, δ) would be found as a function of the k-min-mer abundance histogram.
, 2013; Loman et al. , 2015). We will demonstrate that our minimizer-space representation is applicable to error-free sequencing reads and PacBio HiFi reads, which boast error rates lower than %1; however, in order to work with long reads with a higher error rate such as PacBio CLR and ONT, we present a resource-frugal error correction step that uses partial order alignment (POA)  (Lee et al. , 2002) , a graph representation of a multiple sequence alignment (MSA), in order to rapidly correct sequencing errors that occur in the minimizer-space representation of reads. Stand-alone error correction modules such as racon  (Vaser et al. , 2017)  and Nanopolish  (Loman et al. , 2015)  have also relied on POA for error correction of long reads; however, these methods work in base-space, and as such, are still resource-intensive.
We present an error correction module that uses POA in minimizer-space that can correct errors in minimizer-space, requiring only the minimizer-space representation of reads as input. An overview of the minimizer-space POA procedure is shown in Figure  1C , and the detailed processes for the stages of the error-correction procedure are shown in Section "Minimizer space partial order alignment". The input for the procedure is the collection of ordered lists of minimizers obtained from all reads in the dataset (one ordered list per read). As seen earlier, the ordered list of minimizers obtained from a read containing sequencing errors will likely differ from that of an error-free read. However, provided the dataset has enough coverage, the content of other ordered lists of minimizers in the same genomic region can be used to correct errors in the query read in minimizer-space.
In turn, k-min-mers are identified with their reverse; no complementation is performed in minimizer-space, as the complement of a canonical ℓ-mer is itself. Similarly to base-space assembly, any k-min-mer appearing only once in the multiset V is removed from V due to the likelihood that it is artefactual. Assembly graph simplifications are performed using gfatools (https://github. com/lh3/gfatools), with alternating rounds of tip clipping and bubble removal (see "gfatools command line" Section), except for simulated perfect reads, which were only compacted into base-space unitigs. In order to reduce memory usage, we write k-min-mers and the base-space sequences spanned by k-min-mers on disk, and retrieve them once the contigs are generated in minimizer-space. rust-mdbg includes a binary program (to_basespace) that transforms a simplified minimizer-space assembly into a base-space assembly.
Consensus generation on POA graph Minimizer-space POA evaluation set-up-We extracted chromosome 4 (~ 1. 2 Mbp) of the D. melanogaster reference genome, and simulated reads using the command randomreads. sh pacbio=t of BBMap  (Bushnell, 2014). We generated one dataset per error rate value from 0% to 10%, keeping other parameters identical (24 Kbp mean read length and 70X coverage). Reads were then assembled using our implementation with and without POA, using parameters ℓ = 10, k = 7, and δ = 0. 0008 experimentally determined to yield a perfect assembly with error-free reads. We evaluated the average read identity in minimizer space using semi-global Smith-Waterman alignment between the sequence of minimizers of a read and the sequence of minimizers of the reference, taking BLAST-like identity (number of minimize matches divided by the number of alignment columns).
For δ≥0. 0025, an increasingly smaller portion of the reads are discarded, consistently yielding k-min-mer recovery rates of >90%. We further observe that for fixed values of δ = 0. 003 and ℓ = 12, k-min-mer recovery rate is consistently above 95% for k-min-mer lengths of 10 to 35. Since δ = 0. 003, a sufficient portion of the reads are transformed into k-min-mers at this k-min-mer length, and higher values of k will result in a larger portion of the reads to be discarded. gfatools command lines-The following (relatively aggressive) GFA assembly graph simplifications rounds were performed for all mdBG assemblies, using https:// github. com/lh3/gfatools/. Rounds are of two types: -t x,y removes tips having at most x segments and of maximal length y bp, and -b z removes bubbles of maximal radius z bp.
In addition, gfa_break_loops. py is a custom script (available in the rust-mdbg GitHub repository) that removes self-loops in the assembly graph, as well as an arbitrary edge in x y cycles. Genome assembly tools, versions, and parameters-HiCanu (v2. 1) was run with default parameters, hifiasm (commit 8cb131d) with parameters -l0 -f0, and Peregrine (commit 008082a) with command line: 8 8 8 8 8 8 8 8 8 -with-consensus -shimmer-r 3 -best_n_ovlp 8. rust-mdbg was run with parameters k = 35, ℓ = 12, and δ = 0. 002 for D. melanogaster, and k = 21, ℓ = 14, δ = 0. 003 for HG002. For metagenomes, rust-mdbg was run with parameters k = 21, ℓ = 14, δ = 0. 003 for the ATCC MSA-1003 dataset (same parameters as the human dataset), and k = 40, ℓ = 12, δ = 0. 004 for the Zymo D6331 dataset. Hifiasm-meta (commit cda13b8) was run with parameters -S -lowq-10 50 for ATCC MSA-1003 and default for Zymo.
Consequently, cloud computing for genome assembly and analysis will likely underpin future large-scale genomics collaborations and efforts to re-analyze archived data. Our method, mdBG, significantly reduces the computational resources required for performing whole-genome assembly, making such analyses possible on desktop computers. We specifically demonstrate its use through three examples: human genome assembly, metagenome assembly, and the construction of large pangenome graphs. For microbiome and pangenome analyses, our approach offers the possibility of constructing graphical pangenomes at the scale of the largest existing collections quickly and accurately, enabling us to simultaneously analyze the myriad of genomes available in databases. Given the rise of next-generation sequencing technologies and faster and less expensive genome assembly, we expect our advances to be essential to the convergence among next-generation sequencing (NGS), cloud computing, and precision and personalized medicine, and beneficial in creating the infrastructure necessary to formulate and test disease mechanisms and develop new treatments at scale.
The mdBG is then converted back into base space by concatenating the base-space sequences spanned by the minimizers in the mdBG, and a set of contigs is reported. (C) Overview of the minimizer-space partial order alignment (POA) procedure with a toy dataset of 4 reads. (1) Error-prone reads and their ordered lists of minimizers (ℓ = 2) are shown, with sequencing errors and the minimizers that are created as a result of errors denoted in colors (insertion as red, deletion as orange, substitution in blue, no errors in green). (  2 ) Before minimizer-space error-correction, the ordered lists of minimizers are bucketed using their n-tuples (n = 1). (  3 ) For a query ordered list (the first read in the read set in the figure), all ordered lists that share an n-tuple with the query are obtained, and the final list of query neighbors are obtained by applying a heuristically determined distance filter d j (Jaccard distance threshold of φ = 0.
5). (4) A POA graph in minimizer space is constructed by initializing the graph with the query and aligning each ordered list that passed the filter to the graph iteratively (weights of poorlya supported edges are shown in red). (  5 ) By taking a consensus path of the graph, the error in the query is corrected. Top panel: a complete δ = 0. 001 pangenome mdBG is constructed for the whole 661,405 bacterial collection and the first five connected components are displayed here (using Gephi software). Each node is a k-min-mer, and edges are exact overlaps of k -1 minimizers between k-min-mers. Middle panel: a collection of anti-microbial resistance gene targets was converted into minimizer space, then each k-min-mer is queried in a 661,405 bacterial pangenome graph (δ = 0. 01) yielding a bimodal distribution of gene retrieval: genes with high identity (99%+) to those in the pangenome are found, while those with lower identity are not found.
The Abundance column shows the relative abundance of the species in the sample. The two rightmost columns show the species completeness of the assemblies as reported by metaQUAST. Table  3. Comparison of assembly statistics between original universe minimizers and universe minimizers with LCP Assembly statistics using both universe minimizers (denoted by "Universe," same datasets as in Table  1 ) and universe minimizers with LCP (denoted by "Universe + LCP") of D. melanogaster real HiFi reads (left), simulated perfect reads (center), and Human real HiFi reads (right), evaluated using the same metrics in Table  1. Parameters for both schemes were.
ACKNOWLEDGMENTS B. E. was partially funded by grant  NIH   R01HG010959  (to B. B. ) and B. B. by  NIH   R35GM141861. R. C. was funded by grants  ANR Inception  ( ANR-16-CONV-0005 ),  PRAIRIE  ( ANR-19-P3IA-0001 ), and  PANGAIA  ( H2020 MSCA RISE 872539 ). The authors are grateful to  A. Limasset ,  P. Peterlongo ,  B. Hie , and  R. Singh  for remarks on the manuscript and to  Simon Barnett  for inspiration for the Graphical Abstract.
Only two prospective clinical trials are reported for pediatric r/r NHL, both recruiting r/r B-NHL. The first trial evaluated a response and therapy-related toxicities of rituximab plus ifosfamide, carboplatin, and etoposide (ICE)  [32]. The trial was stopped early for insufficient accrual after enrollment of 20 patients. The second trial evaluates ibrutinib in combination with chemo-immunotherapy (NCT02703272)  [33]. Part one was recently published with 21 patients showing that ibrutinib can be combined with RICE  [32]  (rituximab plus ICE) or RVICI  [20]  (rituximab, vincristine, ifosfamide, carboplatin, idarubicin, and dexamethasone). In both trials, the type and details of HSCT were not part of the clinical assessment. Due to the lack of consistent data on r/r NHL in children and adolescents, the current study aims to analyze international strategies on re-induction treatment and HSCT in r/r NHL.
Materials and Methods The study was performed within the network of the International Berlin-Frankfurt-Muenster group (I-BFM) and the European Inter-Group for Childhood and Adolescent Non-Hodgkin Lymphoma (EICNHL). Each national group ensured that the transfer of data was covered by the respective ethics committee and data protection rules. Data on eligible cases were collected on a study-specific case report form. Further information is provided in the Supplementary Materials. For statistical analyses, overall survival (OS) after relapse was calculated from the date of relapse to date of death from any cause or last follow-up. Survival probabilities were estimated using the Kaplan-Meier method, with differences compared using the log-rank test. Associations of the types of HSCT with patients' characteristics were analyzed using the χ 2 test.
Conclusions The current data will form the basis of treatment selection for patients and planning future clinical trials in r/r NHL. Further intensification of re-induction treatment (with either increased dose or addition of other agents) with cytostatic agents cannot solve the problem of disease resistance at relapse. To overcome this resistance, molecular profiling of the disease and drugs with new mechanisms of action is required. For B-cell malignancies, several new drugs fulfilling this need are available  [35]. In the database of ClinicalTrials. gov (accessed on 4 March 2021), roughly 70 interventional trials are open for recruitment of refractory or relapsed NHL (Table  S9 ). The vast majority of the trials are not limited to NHL but open for various diagnoses. The impact of these (basket) trials on the outcome for r/r NHL will be limited as the trial designs usually do not address the specific medical needs of patients with highly aggressive and fast proliferating NHL.
35 n the initial sample, all percentages were weighted to take differences in the recruitment periods into account for infants born at 24-26 weeks and 27-31 weeks. Comparisons between groups for treatment exposure through hospital discharge used the Rao-Scott F-adjusted c 2 test. Differences in outcomes between groups were expressed as risk differences calculated with a generalized estimating equation regression analysis to take the center effect into account. Survival in both groups was estimated by Kaplan-Meier survival curves and compared with Cox models in the IPTW sample. All tests were 2-sided and P values of <05 were considered significant. Missing data were handled with multiple imputations using chained equations with the R package 'mice. ' Imputation model variables included exposure to continuous opioids and/or midazolam, propensity score variables, and outcomes.
Categorical variables were imputed by using logistic or multinomial regression and continuous variables by a linear regression model. We generated 50 independent imputed datasets with 30 iterations each, pooled according to the Rubin rule. 36 ll analyses were performed with R (version 3. 6. 1; The R Foundation) and SAS (version 9. 4; SAS Institute) software packages. ] [39] [40] [41] [42]  We performed exploratory analyses similar to the main analysis in these subgroups defined a priori: (1) infants born before 29 weeks of gestation, (2) infants treated only with opioids in the treated group, (3) infants treated with midazolam in the treated group, and (4) only infants who never received continuous opioids and/or midazolam at any time in the naive group. A new propensity score was calculated in each of these specific populations.
55 he strengths of this study include its population-based cohort design and the prospective enrollment of very preterm infants in France in 2011. The data collected reflect daily bedside practices and enabled the inclusion of a broad nonselected sample, as opposed to a randomized trial requiring perinatal consent and perhaps with a risk of selection bias affecting outcomes regardless of the allocated arm. 56 This study also has a much larger population than any previous analgesia/sedation study with a 2-year follow-up. Standardized definitions of outcomes following international recommendations and systematic and prospective data collection increase its external validity and comparability with previous studies. Because French practices in this area do not differ greatly from those in many other European countries, these results may be applicable elsewhere.
Exploratory subgroup analyses among infants exclusively treated with opioids in the treated groups: Primary and secondary outcomes in the initial and IPTW samples after multiple imputation Percentages and risk difference are weighted to take the differences in recruitment periods between gestational age groups into account. †Data are presented as percentages only unless otherwise indicated since numerators are not relevant for the IPTW sample. ‡Risk differences and mean difference were calculated with a generalized estimation equation regression analysis to take a potential center effect into account. §Severe neuromotor or sensory disabilities included any of Gross Motor Function Classification System level 3-5 cerebral palsy or severe visual or auditory impairment; moderate disability included Gross Motor Function Classification System level 2 cerebral palsy and/or moderate visual or auditory impairment.
Exploratory subgroup analyses among infants who never received continuous opioids or midazolam in the naive group: primary and secondary outcomes in the initial and IPTW samples after multiple imputation Data are presented as number (percentages) of patients unless otherwise indicated. Percentages and risk difference are weighted to take the differences in recruitment periods between gestational age groups into account. †Data are presented as percentages only unless otherwise indicated since numerators are not relevant for the IPTW sample. ‡Risk differences and mean difference were calculated with a generalized estimation equation regression analysis to take a potential center effect into account. §Severe neuromotor or sensory disabilities included any of Gross Motor Function Classification System level 3-5 cerebral palsy or severe visual or auditory impairment; moderate disability included Gross Motor Function Classification System level 2 cerebral palsy and/or moderate visual or auditory impairment.
1  and S1 ToF, S2 neonatal aortic hypoplasia, S3 VSD: 3D PDF). These four CHDs are among the most common and are included in the French medical program. We used Mimics and 3-Matic (Materialise HQ, Leuven, Belgium) software for segmentation and to generate the final real scale 3D virtual model exported as an STL file. STL files were finally printed with a Stream 20 pro printer (Volumic, France) and biodegradable polylactic acid (PLA) filament. For the ASD model from echocardiography data, image was acquired by 3D transesophageal echocardiography in 3D zoom mode using the EPIQ system (version 7C, Philips Medical Systems, Andover, MA) and an X8-2t phased array transducer. All models were printed in duplicate for a total of 26 models. The mean period time needed to segmentate models was around 15 min for ASD and CoA, 30 min for VSD and 60 min for ToF.
A C T Heart failure (HF) is among the leading causes of death. Its prevalence is increasing dramatically causing considerable healthcare costs as well. Remote patient monitoring (RPM) is one of the solutions to enhance patient well-being. Taking advantage of new advances in artificial intelligence and RPM digital platforms, we propose HeartPredict: a novel machine learning algorithm for early detection of HF episodes and associated unplanned hospitalizations. The algorithm relies on a telemonitoring dataset from the largest European clinical study on HF. It uses balanced random forests on significant features extracted from patient weight time series, symptoms and socio-demographics. We benchmark HeartPredict with rules from medical guidelines and state of the art machine learning models. HeartPredict has better performance in terms of sensitivity (72% vs.
Predictive models are key elements to anticipate patient risks, enabling a care continuum from hospital to home and vice-versa. However, there are many challenges that make the success of machine learning (ML) based RPMs deployment difficult. Access to data and sufficient medico-economic evidence are issues that hamper the implementation and the adoption of ML-based RPM tools. Recently, Air Liquide Healthcare launched an RPM solution for the remote monitoring of patients suffering from heart failure. The workflow includes connected medical devices and a monitoring platform. Remote monitoring relies on three stages: (1) The patient's vital signs are recorded and transmitted using a connected weight-scale to a remote server. (2)  The measurements are processed by an algorithm that generates alarms in case of abnormal variations.
(3)  The alarms are categorized in the monitoring platform where appropriate medical actions are taken. Typical actions include recommendations from a nurse, treatments by a doctor, or hospitalizations. In this paper, we introduce HeartPredict: a machine learning algorithm to improve the performance of Air Liquide Healthcare RPM solution. Our goal is to improve early detection of HF episodes, while reducing the number of false alarms. The proposed algorithm uses balanced random forest on patient time series representing weight, symptoms and socio-demographics. We demonstrate the effectiveness of HeartPredict through an intensive evaluation over the OSICAT dataset  [6, 7] , a large study on monitoring for patients with heart failure by tele-cardiology. Before describing the HeartPredict algorithm and the results, preliminary studies of HF telemonitoring will first be presented.