dataesr/scientific-paragraphs-categorization huggingface dataset:
Our study has several limitations. First, health status, diet, and host parameters were either lacking or self-reported. Second, our study relied primarily on westernized adults and therefore needs to be complemented by underexplored populations. Third, the longitudinal study was not controlled and had varying stool collection periods on a limited number of subjects per partition, and should be reproduced on other longitudinal datasets. In addition, the American Gut project database is not representative of the global population, and the subcohort with multiple samples analyzed may be even less representative. Future more controlled longitudinal studies combining higher gut microbiome resolution and clinical data would allow further exploration of how partitions' composition and functional potential may vary with clinical outcomes.
Studies datasets CuratedMetagenomicData (CMD). We extracted taxonomic and functional tables from the curatedMetagenomicData R package  26  (version 3. 0, release 2021), which aggregates read counts per species using MetaPhlAn3 and metabolic pathways per species using HUMaNN3 from 86 studies. Only stool samples counting more than five million reads and excluding one duplicated study (referred to as "LeChatelierE_2013") were retained. For taxonomic analyses, the samples' read counts were sum-collapsed per genus and per species. The resulting feature table was rarefied to a depth of 1,000,000 counts per sample for alpha diversity analysis. Outliers analysis were performed following the criteria in ref. 44. American Gut Project (AGP). In the AGP initiative, stool samples were collected at home and shipped at room temperature before microbial DNA extraction and 16 S rRNA amplicon sequencing, which were performed as previously described  4.
We used redbiom  45  to fetch data from Qiita  46. 20,454 stool sample identifiers were available in the database on the date 2019 December 5th, within the Deblur-Illumina-16S-V4-100nt-fbc5b2 context. Analyses were performed as previously described  44. In short, bioinformatic analysis was performed with QIIME 2019. 10, bloom sequences were removed as previously described  47  , and taxonomy was assigned using the GreenGenes database (v 13. 5). We retained samples ≥1000 reads. 1579 samples were defined as technical outliers and excluded following the criteria in ref. 44. A genus count matrix with 16,021 samples was analyzed.
Gut microbiome partitioning and branches In the exploration cohort both studies' datasets, CMD and AGP, the 30 genera with the highest read mass were extracted for downstream analyses. Samples were partitioned using Dirichlet's multinomial mixture (DMM) modeling on the microbiota data  29. We trained DMM models using five subsets from the whole dataset to reduce population bias. Each subset was sampled from the whole dataset with stratification by each combination of sex, geographic region of origin (region of birth for the AGP dataset), and age class (n up to 30 by strata). Subsampling was not limited to a single sample per subject. Of note, participants having at least two samples represented less than 5% of the participants. DMM modeling was performed for each subset constituting our training set, and the best model was picked up using BIC and Laplace minima, and majority vote (i.
e. , which optimal value of the k parameter was picked up more often). Partitions homogeneity were assessed using theta index extracted from DMM models. Low values of theta correspond to highly variable partitions. The whole dataset was modeled with DMM using the corresponding genera from the training dataset in the remaining dataset. Genera alpha weights for each DMM component from the CMD and AGP datasets were compared using hierarchical clustering on the Jensen-Shannon distance (Ward's method). We assessed microbiome branch latent structure on the CMD and AGP datasets (genus relative abundance) using the PHATE algorithm (phateR version 1. 0. 7), with the gamma parameter set to zero  27  for visualization purposes and all other parameters set to default. Based on DMM genera alpha weight, we extracted microbiome branches.
Longitudinal analysis To assess stability patterns within partitions and switching between partitions over time, we extracted data from 745 participants of the AGP dataset who had provided at least two samples, resulting in 2998 samples. Partition stability was assessed by comparing the proportion of individuals who remained in their initial assigned partition and the proportion of individuals who remained in their randomly assigned partition. 100 random assignations were performed to compute a confidence interval for each partition. The percentile 95th for stability obtained from randomized assigned partitions was retained as a threshold for significatively. Stability and proportion of change were computed for each partition. A network was built to show how individuals can change partitions as a function of time using igraph R package 1.
Health, demographic, and dietary associations with partitions and branches To assess whether health, demographic, and diet participants' metadata were associated with gut microbiome partitions and branches in AGP, we fitted a multinomial log-linear model via neural networks using the nnet R package (version 7. 3-15). Gut microbiome partitions were used as responses and metadata as predictors. The partition with the highest alpha-diversity assessed with the Shannon index was defined as the reference. Categorical variables, like region of birth, were one-hot encoded. Binary predictors, like disease self-declaration, were coded by 0 to 1 (e. g. , "Diagnosed by a medical professional" encoded to 1, "I do not have this conditions" encoded to 0, other categories were considered as missing values).
Continuous predictors, like Age and BMI, were scaled from 0 to 1. Ordered predictors, like food groups frequency, were coded from 0 (never) to 1 (daily). Antibiotic history was considered as an ordered factor from "I have not taken antibiotics in the past year" to antibiotic intake within the "week" of fecal sample collection. In short, 100 health, demographic and dietary predictors were used to build the model. Missing values were replaced by the average computed for each predictor. Log odds ratios by predictor and gut microbiome partition and their respective p-values were extracted from the resulting model using broom_helpers R package (version 1. 2. 1). False discovery rate was computed by predictors. All statistical analyses were computed using R software version 3. 6  48.
Functional analysis of gut microbiome branches The OXYTOL database v 1. 3 was used to associate each microbial genus to an aerotolerant or obligate anaerobic metabolism  49. A score resulting on dividing aerotolerant aggregated read count by anaerotolerant aggregated read count was performed for each sample. Differentially-abundant metabolic pathways were identified using multinomial regressions via neural networks in songbird version 1. 0. 3  50. This analysis was performed on the curatedMetagenomicData pathways, after collapsing (i. e. , summing per sample) the read counts across taxa, hence resulting in a list of coefficients for each pathway, indicating their strength of association with each model's target. Models of pathway enrichment were run on sample subsets belonging the DMM partitions associated to branch tips and DMM partition identified as central that serve as reference group.
Pseudotime analysis To confirm the ordering of our partitions along enterobranches, we performed a pseudotime analysis using Wishbone, which orders samples along a two-way trajectory branching off from a common root  34. Although Wishbone is suited to avoid short-circuit between trajectories for complex datasets, we performed the analysis on a subset of the samples consisting of the central partition m8 (expected trunk) and the partitions enriched in Prevotella (m4, m12, m13, m14, m16, m17) and Bacteroides (m2, m3, m5, m20, m21) (expected branches). The trajectory was detected using a randomly selected sample from the central partition (m8) as a starting point (EGAR00001421178_9002000001557655LL) as well as the first two components resulting from the eigendecomposition operated on the diffusion matrix (see Wishbone methods).
Data availability All data used in this study are publically available. Cur-atedMetagenomicData are available at https://github. com/ waldronlab/curatedMetagenomicData. The AGP data used in this study are available in Qiita under the study ID 10317, and the associated sequences can be found under EBI accession ERP012803. Source data for all main figures are provided as supported data files. Source data are provided with this paper.
Contents Based on the analysis of comprehensive literature review conducted using the Ovid MEDLINE and PubMed databases, many evaluation tools for IUD exist  [13]. Our choice was directed using a checklist for a two-handed technique of insertion of IUD, currently used in Europe. Among the available checklists, the Jhpiego checklist was chosen because it was concise and complete compared to others  [8]. It was enriched by some items from the IUD Levonorgestrel (Mirena ® ) leaflet to implement missing items for "good practice of IUD insertion" (Bayer HealthCare France, Mirena ® 52 mg (20 micrograms/24 hours), intrauterine device)  [14]. This Mirena ® IUD was chosen because its placement procedure was similar to those of the majority of IUDs. It was more convenient for the simulation model used, i.
Statistics Analysis was performed with Statview version 4. 5 (SAS Institute Inc. , Cary, NC). The number of required participants was determined to analyze internal consistency and interobserver reproducibility in sample A, and to analyze performance in sample B. Based on a previous simulation study with similar design that aimed at developing a performance checklist  [17]. The number of required simulations was at least 22 for sample A and 26 for sample B. Kolmogorov-Smirnov test was used to check normal distribution for assessed measures. Continuous variables were described as mean (SD) or median and 1 st and 3 rd quartile (Q1; Q3). Categorical variables were described as numbers and percentage (%). Internal consistency of the checklist was analyzed by the Cronbach alpha coefficient. Interobserver reproducibility was analyzed by the intra-class coefficient (ICC), comparison of means, and linear regression analysis (Spearman coefficient and R2).
F-test was used to compare variance of scores of observers 1 and 2. Performance of participants was assessed at three different times (S1, S2, and S3). The evolution of performance scores was analyzed, using an ANOVA for repeated measures. In case of statistically significant results, the Scheffe Post hoc test was pre-specified to explore differences between multiple means while controlling the experiment-wise error rate. Performance scores were compared at different times using a Student t-test. Success rate was expressed as percentage. A p value < 0. 05 was considered significant. The data collected were used to plot receiver operating characteristic (ROC) curves for the score values. The area under the curve (AUC) as well as the sensitivity, specificity and likelihood ratios were determined for different score thresholds.
concurrently with chemotherapy). Overall survival was analysed by intention to treat. ExteNET is registered (Clinicaltrials. gov: NCT00878709) and is complete. Results: Between July 9, 2009, and October 24, 2011, 2840 women received neratinib (n Z 1420) or placebo (n Z 1420). After a median follow-up of 8. 1 (IQR, 7. 0e8. 8) years, 127 patients (8. 9%) in the neratinib group and 137 patients (9. 6%) in the placebo group in the intention-to-treat population had died. Eight-year overall survival rates were 90. 1% (95% CI 88. 3e91. 6) with neratinib and 90. 2% (95% CI 88. 4e91. 7) with placebo (stratified hazard ratio 0. 95; 95% CI 0. 75e1. 21; p Z 0. 6914). Conclusions: Overall survival in the extended adjuvant setting was comparable for neratinib and placebo after a median follow-up of 8. 1 years in women with early-stage HER2positive breast cancer.
4 years  [6]. Extending the duration of HER2-directed adjuvant therapy with an additional year of trastuzumab did not improve disease-free survival  [7]. In contrast, significant disease-free survival benefits were observed after receipt of an irreversible pan-HER tyrosine kinase inhibitor, neratinib (Nerlynx â ) for 1 year after trastuzumab-based adjuvant therapy in the phase 3 ExteNET trial  [8]. Invasive disease-free survival rates were 94. 2% with neratinib vs. 91. 9% with placebo after 2 years (hazard ratio 0. 66; 95% confidence intervals [CI] 0. 49e0. 90; p Z 0. 008)  [9] , and 90. 2% vs. 87. 7%, respectively, after 5 years of follow-up (hazard ratio 0. 73; 95% CI 0. 57e0. 92; p Z 0. 008)  [10]. Further data on invasive disease-free survival were not collected after 5 years. Based on the findings from ExteNET, neratinib was approved by the Food and Drug Administration (FDA) as extended adjuvant therapy in the intention-to-treat patient population  [9] , whereas the European Medicines Agency (EMA) approved neratinib in patients with HER2positive, hormone receptor-positive early-stage breast cancer who initiate treatment within 1 year of completing trastuzumab-based therapy  [11].
Procedures Patients were assigned to neratinib 240 mg orally once daily or matching placebo continuously for 1 year or until disease recurrence, new breast cancer, intolerable adverse events or consent withdrawal. No crossover was permitted in the study. Neratinib dose reductions (200, 160 and 120 mg/day) were allowed to manage treatmentemergent toxicity; treatment was stopped if neratinib 120 mg was not tolerated or if treatment was interrupted for more than 3 weeks. Antidiarrhoeal prophylaxis was not mandated; loperamide use was recommended as treatment if diarrhoea occurred. Adjuvant endocrine therapy for women with hormone receptor-positive disease was recommended. During years 1 and 2 post-randomisation, survival information was collected every 6 months. From year 2 onwards, survival data were actively collected or gathered from patient medical records or publicly available death records in accordance with Good Clinical Practice guidelines and privacy laws.
Statistical analysis Overall survival was tested when 248 deaths had occurred at a 2-sided 5% significance level with 80% power to detect a hazard ratio of 0. 70 in the intentionto-treat population. In addition, one interim analysis at 50% information fraction (relative to the target 248 events) was planned with the Lan-DeMets alphaspending function approximating the O'Brien Fleming boundary (data not presented). Overall survival was defined as the time from date of randomisation to date of death, censored at the last date the patient was known to be alive. All deaths, including those identified from the study clinical database (RAVE), long-term safety database (ARGUS), and publicly available death records, were included in the analysis. The cutoff date for this analysis was July 10, 2019.
Acknowledgements We thank the Independent Data Monitoring Committee, study investigators, research staff, clinical research organizations, and other vendors, as well as the patients who participated in the ExteNET trial. We also acknowledge the contributions of  Janine Lu ,  Judith Bebchuk  and  Bethann Hromatka  of  Puma Biotechnology Inc. ;  Richard Bryce ,  Feng Xu ,  Yan Yan  and  Bo Zhang  (former employees of  Puma Biotechnology Inc. ); and  Harriet Lamb  and  Lee Miller  of Miller Medical Communications Ltd.
| DISCUSSION In this light, the relevance of heterozygous pathogenic variants in genes involved in autosomal recessive SRNS needs to be stressed. These patients are highly suspicious of having an additional yet-to-be-identified pathogenic mutation in the same or a different podocyte gene. As such, WGS is strongly advised in these cases. In this patient we identified 2 rare intronic variants (MAF <1%) in NPHS2. These are indeed very rare, as can be observed in the Gno-mAD database where a total of 174 rare intronic variants in NPHS2 have been identified for 31 414 alleles studied. In cases like these (where the gene of interest is known and WGS only identified a limited number of variants), it could be theoretically feasible to screen each of these intronic variants separately. As there are about 50 genes involved in monogenic SRNS, a first in silico analysis may help to F I G U R E 3 Minigene splice assay using the pSPL3 exon trapping vector.
(B) Sanger sequencing (only forward strand shown) confirmed the retention of 36 bp between exon 6 and 7. (C) Sashimi plot of the targeted RNA sequencing of NPHS2 showed that 56% of reads contained the 36 bp pseudo-exon. [Colour figure can be viewed at wileyonlinelibrary. com] after the kidney transplantation. A total of 3 inherited rare variants (MAF <0. 01) were identified in NPHS2 (Table S1), including the previously identified paternally inherited variant c. 413G > A. Of the two maternally inherited variants, the deep intronic variant [ENSG0000 0116218:g. 179553625 A > C, NM_014625. 4:c. 794 + 851 T > G; LRG_ 887:g. 27325 T > G] in intron 6 was the most interesting as it was absent from the Genome Aggregation Database (GnomAD) and in silico analysis.
In this study, two geometric variants of the pump were designed to meet the specifications and they differed in blade thickness. The impellers and volutes were designed using internal 3Dturbo software, V2. 0 a comprehensive solution for the design of single-stage centrifugal pumps. This software also allowed for the reverse engineering of centrifugal pumps and facilitated the calculation of performance in design and off-design conditions using a specific 0D/1D solver, as explained in more detail in  [23]. The impellers and volutes were then finalized in CATIA. Apart from a few key dimensions chosen rationally, there are many degrees of freedom available to the centrifugal pump designer to define the other design parameters of the impeller and volute. Therefore, it is necessary to initially choose some design dimensions arbitrarily or intuitively and later verify, through simulations and/or experimental tests, if they meet the requirements.
Fluid Domain and Meshing The three-dimensional fluid volume was designed based on the geometrical characteristics of both prototypes. Computation was performed using Simcenter StarCCM+ 16. 02. 009, a software developed by Siemens that employs a Finite Volume approach to discretize the partial differential equations governing fluid behavior. The software uses the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm to solve the pressure-velocity coupling inherent in these equations. StarCCM+ can simulate flow in both turbulent and laminar regimes for transient or steady states. Polyhedral non-structured grids were used throughout the computational domains, with finer grids selected in specific areas to obtain more realistic simulation results. Figure  4  shows that, in addition to the polyhedral elements in the core of the domain, six prism layers were added to the walls.
The three-dimensional fluid volume was designed based on the geometrical characteristics of both prototypes. Computation was performed using Simcenter StarCCM+ 16. 02. 009, a software developed by Siemens that employs a Finite Volume approach to discretize the partial differential equations governing fluid behavior. The software uses the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm to solve the pressure-velocity coupling inherent in these equations. StarCCM+ can simulate flow in both turbulent and laminar regimes for transient or steady states. Polyhedral non-structured grids were used throughout the computational domains, with finer grids selected in specific areas to obtain more realistic simulation results. Figure  4  shows that, in addition to the polyhedral elements in the core of the domain, six prism layers were added to the walls.
Experimental Hydraulic Performances The hydraulic performances of the two prototypes are shown in Figure  7  for three rotational speeds: 2500, 3000, and 3500 rpm. All data points were also plotted at a common speed of 3000 rpm to facilitate comparison. Regardless of the flow rate, no significant differences in similarity were observed, demonstrating the accuracy and repeatability of the measurements taken for these flow regimes. The target flow-pressure point of 0. 5 L/min and 80 mm Hg was achieved at 3000 rpm and 2900 rpm for SH0. 5 and SH1, respectively. This slight difference in angular speed for the same flow rate is due to the difference in designs of the rotor's blades. In practice, the value of 2900 rpm was determined by scaling the speed with similarity coefficients, based on the measured pressure-flow values for the SH1 pump at a given reference rotation speed of N = 3000 rpm.
The motor, flow meters, and pressure sensors were connected to an acquisition card that oversaw communication between the electronic components and the control system (Labview software 2020 Q3). Two flow meters were used in this study, the Atrato Series Ultrasonic flow meter for flow rates from 0. 002-0. 500 L/min, and the RS PRO flow meter for flow rates from 0. 05-10 L/min (4). Two pressure sensors, type JUMO MIDAS SI  (5) , were used at the inlet and outlet of the pumps. The NI USB-6008 acquisition card was utilized to connect all the sensors. Valves (6) located between the pump and flow meter were utilized to adjust the flow rate. Each test was repeated three times to verify reproducibility.
Rule shift Trial 1 Trial to quantify gamma synchrony within TEMPO measurements on shorter time frames (Methods), we confirmed that interhemispheric gamma synchrony between mPFC PV neurons is higher specifically after digging on error trials compared with correct trials, and that inhibiting callosal PV + projections specifically disrupts increases in gamma synchrony during this period (Extended Data Fig. 9f-i ). Then, to quantify the divergence of activity patterns during and immediately after this period of increased gamma synchrony, we measured the similarity between activity patterns during the 10 s following a choice on correct versus error trials. We measured these activity patterns using microendoscopic calcium imaging in PV-cre mice injected with AAV-synapsin-GCaMP7f (Syn-GCaMP7f) and implanted with a gradient-index (GRIN) lens in one mPFC, and injected in the other mPFC with either AAV-EF1α-DIO-eNpHR-mCherry (DIO-eNpHR), AAV-synapsin-eNpHR-mCherry (Syn-eNpHR) or a control virus (AAV-DIO-mCherry in PV-cre mice or AAV-synapsin-mCherry in wild-type mice) (Fig.
4i, l ). By contrast, there was no change in rule-shift performance nor in the similarity of activity patterns after correct versus error rule-shift trials across days in eNpHR-negative controls (Fig. 4d, h, k ). Consistent with our earlier findings, nonspecifically inhibiting all callosal projections (using Syn-eNpHR) did not disrupt rule-shift learning (Fig. 4f ). Nonspecifically inhibiting all callosal projections also had no effect on the similarity of activity after rule-shift errors to activity after rule-shift correct trials (Fig. 4j ) or initial association errors (Fig. 4m ). ) on day 2 and day 3. g, Overview of Ca 2+ imaging analysis and example data from one experiment. dF/F traces for a subset of neurons and 10 min. We detected events in dF/F traces to generate a binary activity raster, identify periods immediately following correct choices (c) or errors (e), compute population activity vectors, and calculate the similarity between vectors.