dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenotypes associated with TS The study included 330,841 unrelated individuals from the UKB dataset that were selected after filtering out individuals with non-European ancestry and other quality control measures. We tested the association of the scores with 2,242 phenotypes grouped into five major categories. The overall results of our TS PRS-PheWAS are shown in Fig. 2  and additional file 1. The analysis identified 57 outcomes significantly associated with TS PRS after Bonferroni correction for multiple testing (p < 2. 23 × 10 -5 ) (Fig. 3  and Supplementary Table  3a -e). The variable "Seen doctor for nerves, anxiety or depression" had the strongest association with TS PRS (beta: 0. 031, p-value: 9. 2 × 10 -17 ). Twenty other cognition and mental health phenotypes were also observed to be significantly associated with TS PRS, with all of them having a positive association at the significant thresholds.
DISCUSSION We conducted a PRS-based PheWAS analysis to understand and identify associations between genetic liability for Tourette syndrome and 2242 phenotypes available in the UK Biobank dataset. Significant associations were observed with multiple general health, mental health, and sociodemographic traits. The strongest associations were found between TS PRS and mental health factors. Several associations with complex medical diseases were also observed, including associations with metabolic and respiratory disorders. Cross-disorder comparisons indicated that there was considerable overlap of phenotypes associated with the genetic risk of TS, ADHD, and ASD. Interestingly, OCD PRS had associations with a number of these same phenotypes albeit with an opposite direction of effect. Finally, sex-specific PheWAS analysis highlighted difference in associations of complex disorders with TS PRS in males and females.
BM Biochemical Measures. Although we identified strong associations with multiple traits using a PheWAS approach, there are certain limitations of this study. We used a large dataset of over 300,000 samples but there were only about 30 individuals with tics and stuttering and no individual with TS diagnosis (defined by ICD-10 diagnoses) even though the expected number would have been around 1800 (0. 6%) based on the prevalence of the condition. This could be because individuals with psychiatric conditions are less likely to participate in such studies and more likely to drop out during an ongoing study thus leading to a selection bias in the dataset  [40]. Given the lack of TS cases, we could not calculate the exact predictive power of the TS PRS scores in the UKB dataset, which could have validated how well the PRS captured the differences between TS patients and controls.
Additionally, due to reduced overlap among the base and target datasets, the number of SNPs used for PRS calculation was lower for TS compared to the other neurodevelopmental disorders we studied here. This could possibly explain the low levels of correlation between TS PRS and the PRS of the other three disorders. We observe a higher number of associations for ADHD and ASD compared to TS and OCD, which could be attributed to the differences in the sample size of the discovery GWAS used for PRS calculation of these disorders  [11, [25] [26] [27]. Finally, our observed associations do not provide information about causality but should be followed up with additional study designs like mendelian randomization in appropriate datasets, or twin studies. We observed correlations with various social outcomes such as education and employment; but again, our results do not infer causality.
FUNDING This work was funded by  NIH  grants  R01NS105746 ,  R01MH126213 ,  NSF  grants  1715202 , and  2006929 , EMTICS ( FP7-HEALTH , Grant agreement ID:  278367 ), and  TS-EUROTRAIN  ( FP7-PEOPLE , Grant agreement ID:  316978 ) to P. Paschou. Z. Tumer was supported by  Lundbeck Foundation  ( R100-A9332 ). C. Depienne was funded by the  Deutsche Forschungsgemeinschaft. D. I. Boomsma  was supported by  KNAW   Academy Professor Award  ( PAH/6635 ). P. Janik  and  C. Zekanowski  were funded by the National Science Center, Poland:  UMO-2016/23/B/NZ2/03030. D. Grice was supported by  NIH  grant  R01MH124679. B. Hengerer  is an employee of  Boehringer Ingelheim Pharma. A. Schrag  received support from the  NIHR UCL/H Biomedical Research Centre. C. Mathews was supported by  NIH  grants ( R01NS105746 ;  R01NS102371 ).
The instrument operated at 40 kV and 40 mA with a 0. 013 2θ step between 10 • and 80 • and a scan step time of 45 s. The XRD diffractograms were analyzed using X'Pert HighScore software. The Scherrer equation was used to estimate the crystallite size (nm) based on the broadening of the (002) XRD peak according to the following Equation (1): L = Kλ βcos(θ) (1) where L = crystallite size, K = 0. 9 is the Scherrer constant, λ = 1. 5406 Å is the X-ray beam wavelength, β is the 002 peak width at half the maximum intensity (radians), and θ is the Bragg angle. Finally, the amount of Sr ion released from Sr-nanoHA_50 and Sr-nanoHA_100 upon their dissolution was analyzed through the inductively coupled plasma atomic emission spectrometry technique (ICP-AES) (ICP-MS, Thermo Scientific, ICAP Q) following the protocol optimized in  [43].
Adhesion and Morphology of Pre-Osteoblastic Cells in Direct Contact with the Hydroxyapatite Materials The cell viability of nanoHA was further evaluated using a live/dead assay (Biotium, Fremont, CA, USA), according to the manufacturer's instructions. Briefly, after 3, 7, and 14 days of culture, the cells were washed with PBS and then incubated with a working solution of 2 mM calcein-AM (494 nm excitation/517 nm emission) and 4 mM ethidium homodimer III (EthDIII) (532 nm excitation/625 nm emission) at room temperature for 45 min. The cells were washed with PBS and observed under an inverted fluorescence microscope. Visualization of the fluorescently labelled cells was performed using the ImageJ software (ImageJ software, sLOCI, University of Wisconsin, Madison, WI, USA).
The Primer-Blast software (http://www. ncbi. nlm. nih. gov/BLAST (accessed on 10 November 2020)) was used for the primer design, as shown in Table  2. The amplification profiles for PCR were optimized for primer sets. For osteocalcin, osteonectin, and osteopontin, the real-time PCR reaction was run at 95 • C for 3 min followed by 40 amplification cycles at 95 • C for 3 s and 58 • C for 30 s. For ALP, the reaction was run at 95 • C for 3 min followed by 40 amplification cycles at 95 • C for 3 s and 60 • C for 30 s. The run was completed with the dissociation curve beginning at 65 • C for 5 s and increasing to 95 • C with 0. 5 • C increments. Data were analyzed with the Bio-Rad CFX manager software version 3. 0. The relative expression of the target genes was calculated using the ∆∆Cq (where Cq is the threshold cycle) method after normalization to two housekeeping genes, hypoxanthine phosphoribosyltransferase-1 (Hprt) and actin, evaluated by BestKeeper.
Statistical Analysis All experimental results are presented as mean ± standard deviation (SD). The statistical analysis was performed using the one-way ANOVA Dunnett's multicomparison test in GraphPad Prism version 8 software (GraphPad Software, San Diego, CA, USA) comparing each nanoHA material with the TCPS control, as well as comparing the three nanoHA compounds at each experimental time point. p-values indicate statistically significant differences (* p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001, ***** p < 0. 00001).
Characterisation of the nanoHA Particles The different nanoHA samples, with and without Sr substitution, showed particles with a rod-like shape (Figure  1 ). The length/width ratio appeared to increase for the fully Sr-substituted samples, whose grains were more elongated than those of pure nanoHA (Figure  2 ). A calculation of the presented particles (n = 4) in the images of Figure  2  show a length/width ratio of 2. 7 nm and 3. 7 nm for nanoHA and Sr-nanoHA_100, respectively. EDS analysis confirmed the incorporation of Sr. It has to be noted that a small amount of Ca was still measured in the Sr-nanoHA_100 (Figure  1 ). The (Ca + Sr)/P molar ratio was 1. 83 for nanoHA, 1. 65 for Sr-nanoHA_50, and 1. 73 for Sr_nanoHA_100 powders. The XRD showed that the three nanoHA-based materials presented reflection peaks ascribable to the HA crystalline phase, based on the ICDD (International Centre for Diffraction Data) database.
5 °C increments. Data were analyzed with the Bio-Rad CFX manager software version 3. 0. The relative expression of the target genes was calculated using the ΔΔCq (where Cq is the threshold cycle) method after normalization to two housekeeping genes, hypoxanthine phosphoribosyltransferase-1 (Hprt) and actin, evaluated by BestKeeper. Triplicates from each time point were analyzed.
Statistical Analysis All experimental results are presented as mean ± standard deviation (SD). The statistical analysis was performed using the one-way ANOVA Dunnett's multicomparison test in GraphPad Prism version 8 software (GraphPad Software, San Diego, CA, USA) comparing each nanoHA material with the TCPS control, as well as comparing the three na-noHA compounds at each experimental time point. P-values indicate statistically significant differences (* p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001, ***** p < 0. 00001).
The cumulative release kinetics of Sr 2+ showed that nearly 20% of Sr was released from the Sr-nanoHA_50 after 24 h and from the Sr nanoHA_100 after 3 days. At the end  The XRD showed that the three nanoHA-based materials presented reflection peaks ascribable to the HA crystalline phase, based on the ICDD (International Centre for Diffraction Data) database. In detail, Figure  3  highlights that the materials presented XRD reflections characterized by different heights and widths. In particular, the pure nanoHA presented three separate peaks of diffraction at 211, 300, and 202; Sr-nanoHA_50 and Sr-nanoHA_100 presented only one and two merged peaks, respectively. Additionally, according to the Scherrer equation, the crystallite length for the na-noHA, the Sr-nanoHA_50, and the Sr-nanoHA_100 were 27.
DNA metabarcoding can be a promising alternative to microscopy for analysing phytoplankton, a key ecological indicator for freshwater ecosystems. The aim of this study was to evaluate the performance of different barcodes and associated primer pairs to assess microalgal diversity with DNA metabarcoding using a single barcode targeting all microalgae. We investigated barcodes in 16S and 23S rRNA genes, encoding for prokaryotic ribosomal sub-units, that are present in Cyanobacteria as well as in chloroplasts. In silico PCR tests were carried out on eight 16S and five 23S primer pairs using the Phytool reference library. Two and three pairs were selected for 16S and 23S, respectively, to perform an in vitro metabarcoding test based on a mock community made of DNA extracts of 10 microalgae strains.
g. 100 clusters of 10 sequences for 1000 initial sequences). (3) For each cluster, a multiple alignment was carried out with the msa package  (Bodenhofer et al. , 2015)  using Muscle  (Edgar, 2004 ) with default settings. (4) Conserved regions were checked and the most redundant were selected with BioPython  (Cock et al. , 2009). From these multiple alignments the variability of the different regions within the genes were evaluated with Shannon index based on nucleotide diversity calculated at each base position. Regions which appear to be the most redundant through the different clusters were kept as candidate regions for primers. Additionally, a special attention was given to the followings: primers length ranging from 18 to 23 base pairs; GC percentage between 40 and 60% to ensure efficient polymerisation; presence of GC in the 5 first and last nucleotides to ensure good fixation; low number of ambiguous bases; resulting amplicons size suited for MiSeq sequencing.
The amplification efficiency was assessed in silico on sequences of microalgal species, allowing a maximum of 2 mismatches between the amplicon and its target sequence, following indications by  Nossa et al. (2010)  and  Klindworth et al. , (2013). This was done using pcr. seqs command from Mothur  (Schloss et al. , 2009). This enabled to assess the number of sequences that matched perfectly with the primer pair (no mismatches) and the number of sequences that are likely to be amplified by a standard PCR (maximum 2 mismatches allowed) out of the total number of reference sequences used from the corresponding reference library. In complement, an estimation of the resolution of each barcode was assessed by the proportion of the number of species with strictly different barcodes among the total number of species amplified in silico (maximum 2 mismatches allowed).
Moreover, in anticipation of later routine implementation, an automated DNA extraction method was compared to GenElute DNA extraction used for Mock 1  (Kermarrec et al. , 2013). This automated DNA extraction was performed using the Magnet-aPure 32 automated system (Dutscher) and the NucleoMag Microbiome extraction kit (Macherey-Nagel) following the manufacturer 0 s instructions. DNA extract from each culture was then amplified by PCR, 2 mL of the amplification product was controlled on agarose gel (1%), and the remaining volume of amplicon (23 mL) was purified using Illustra GFX PCR DNA kit (Cytiva). DNA concentrations of the purified amplicons were measured with Qubit 4 Fluorometer (Invitrogen) in order to build three types of mock communities with controlled amplicon concentrations (Supplementary data 2).
Bioinformatic analyses Primers were checked and removed from demultiplexed FASTQ reads using Cutadapt v3. 5  (Martin, 2011). Only reads with no error in primer sequences were kept (max-n 0). Next steps for the read quality filtering, merging and determination of Amplicon Sequence Variants (ASVs) were done using the DADA2 package v1. 20  (Callahan et al. , 2016)  on R v4. 1 (R Core Team, 2018). Briefly, reads were filtered using the function filterAndTrim with parameters set as default (i. e. maxEE = c(2,2), truncQ = 2, maxN = 0) and by only keeping the first 220 and 200 bases for the forward and reverse sequences respectively, due to their expected lower quality at the end of the read  (Schirmer et al. , 2015). Reads were then merged with default parameters of mergePairs function, and chimera were detected de novo and removed using the function removeBimeraDenovo with the consensus method.
Sequences were then size-filtered by keeping sequences with a size comprised between 245 and 270 bp, to avoid aspecific ASVs. Taxonomic assignment of each ASV was done with the RDP (Ribosomal Database Project) naive Bayesian classifier  (Wang et al. , 2007)  implemented in DADA2, using Phytool v2 database  (Canino et al. , 2021)  as a reference. In parallel, a blast search of each ASV sequence was done on the same database as well as against all NCBI nucleotide database using blast 2. 13  (Altschul et al. , 2009). The combination of these three taxonomic assignations permitted an efficient recovery of almost all species that compose the Mock 2 community. Bioinformatic pipelines are available at https://github. com/ Github-Carrtel/2023_Canino_MBMG.
Thus, sampling lakes where gap-taxa are known to be present in high relative abundance would be a way to both ascertain the 23S efficiency, and complete its reference library. Finally, a fully operational and standardised reference database should follow the recommendations by  Rimet et al. (2021)  when building barcode reference libraries for aquatic life. Providing tools for lake biomonitoring requires them to be well-fitted to environmental stakeholder needs in terms of production rate and ease of use  (Blancher et al. , 2022). Here we tested an automated DNA extraction protocol to start assessing the potential for higher throughput sample processing. Although promising, these first results show a need to improve the protocol. To be operational for professionals, the approach tested here will also require going toward standardisation.
Public sequencing databases contain vast amounts of biological information, yet they are largely underutilized as one cannot efficiently search them for any sequence of interest. We present kmindex, an innovative approach that can index thousands of highly complex metagenomes and perform sequence searches in a fraction of a second. The construction of the index is an order of magnitude faster than previous methods, while search times are two orders of magnitude faster. With negligible false positive rates below 0. 01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. We demonstrate the scalability of kmindex by successfully indexing 1,393 complex marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server "Ocean Read Atlas" (ORA) at https://ocean-read-atlas.
mio. osupytheas. fr/, which enables real-time queries on the entire Tara Oceans dataset. The open-source kmindex software is available at https://github. com/tlemane/kmindex. Public genomic datasets are growing at an exponential rate  [7]. They contain treasures of genomic information that enable groundbreaking discoveries in many biological domains such as agronomy, ecology, and health  [8, 17]. Unfortunately, despite their public availability in repositories such as the Sequence Read Archive  [12] , these resources are rarely ever reused globally because their total size, measured in petabytes, makes them inaccessible. Recent years have seen many methodological developments in search engines that can perform queries on terabyte or petabyte-sized sequencing datasets  [11]  (see [15] and [5], for a review).
Current tools for indexing sequencing data all adopt a similar framework. Given a database of sequencing data experiments (referred to as samples), they create an index able to associate each k-mer (word of fixed length k) that occurs in at least one of the input samples to all the samples in which it occurs. At query time, search hits are reported by looking for k-mers shared between a queried sequence q and each of the indexed samples -more shared k-mers mean a more significant search hit. The apparent simplicity of this approach hides a major difficulty: the volume of k-mers to be indexed, which can be in the order of thousands of billions, across thousands of samples or more. The computational challenge is immense, as it consists in first extracting all k-mers to be indexed from sequencing samples and then building a large associative data structure.
The first step is based on k-mer counting and has received much attention in the last few years, resulting in very efficient tools (e. g. KMC3 [13]). The second step can either use exact or approximate data structures. Exact data structures are well-suited to low-or medium-diversity samples, such as collections of human transcriptomes or gut microbiomes (e. g. MetaGraph [11], BiFrost [10], and ggcat [6]). However, these exact methods do not scale to more complex datasets such as environmental metagenomics sequencing reads, as we show next in our tests on seawater samples. Approximate data structures have the potential to index large and complex datasets. They are called AMQs for "Approximate Membership Queries", among them, the Bloom filter [4] stands out as one of the most widely used. However, they typically yield false positive results, meaning that certain k-mers are wrongly reported as present without existing in the indexed data.
Bloom filters can index billions of kmers using only a few dozen of gigabytes of space (e. g. COBS [3], SBT [19], later improved by HowDeSBT [9], and more recently by MetaProFi [20]). However, when applied to large and complex datasets, these tools have significant limitations such as prohibitive disk usage, memory usage, computation time (either during indexing and/or query), and high false positive rate. Existing methods suffer from a combination of two or more of the aforementioned limitations. Overcoming simultaneously all these limitations makes the design of an efficient data indexing strategy particularly challenging. In this work, we present kmindex, a new tool that performs indexing in significantly less resources than previous approaches. kmindex can index thousands of highly complex sequencing datasets, including environmental metagenomes such as seawater, which contain thousands of billions of distinct k-mers.
Terabytes of complex metagenomic raw data can be indexed with kmindex in a matter of hours. No other method could index the largest dataset tested in this work, with the exception of MetaProFi which requires 10x more time for indexing, 100x more time for queries, and 50x more disk space for achieving the same result quality as kmindex. The methodological novelty of kmtricks is a data model which has been carefully designed to maximize query throughput by minimizing both cache misses and I/O operations. kmindex leverages the highly efficient k-mer counter and k-mer filtration processes of our previous tool kmtricks  [14]. It then indexes k-mers using Bloom filters. Capitalizing on recent results for lowering the false positive rate of approximate data structures (findere  [18] ), kmindex provides results with negligible false positive (FP) rates, approximately three orders of magnitude smaller than those obtained by coexisting tools with no impact on disk or computation time.
The index does not need to be loaded in RAM to answer queries. When it is stored on local SSD hard drives, query execution is instant and millions of sequencing reads can be queried in a minute. Also, kmindex provides a way to dynamically append new datasets to an existing index. This is critical for the usability of the index, even though most other indexing algorithms do not support it. We developed an API, a command line interface, and a web server that can all be invoked to perform queries. Thus kmindex can be used to build a local private genomic search engine preserving data confidentiality. It can also be used as a public server, easily interacting with visualization tools as shown.
in this manuscript. To showcase the features of kmindex on a dataset of high biological interest, we introduce a web server named "Ocean Read Atlas" (ORA) available at this URL: https://ocean-read-atlas. mio. osupytheas. fr/. ORA allows query one or several sequences across all of the Tara Oceans metagenomic raw datasets  [21]. Query results can be downloaded and an interface enables the visualization of the results on a geographic map. Furthermore, because each indexed sample is linked to a variety of environmental variables collected during the circumnavigation campaign (56 distinct measures, including pH, temperature, salinity, and so on), the interface allows users to see how the queries are related to those variables using bubble plots. In addition to demonstrating the capabilities of kmindex, ORA is the first web server capable of performing instant searches on such a large and complex dataset.
Design of kmindex We first describe the design of kmindex from a high-level perspective. For each input dataset, k-mers are counted and filtered based on their abundance and on their co-occurrences in the other datasets. A Bloom filter is built for each input dataset, storing the presence/absence of indexed k-mers. As explained in the Methods section, in practice Bloom filters are inverted, avoiding cache misses at query time. At query time, k-mers from queried sequences are grouped into batches, and Bloom filters are queried to determine the presence or absence of each k-mer in each input dataset. A single command line enables the indexing of a collection of raw reads. When several distinct collections are indexed using kmindex, the resulting indexes can be registered into a single meta-index allowing users to easily query multiple indexes.