dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). Libraries were sequenced on an Illumina MiSeq. 150 PE platform (300 bp pairedend sequencing). A negative and positive control (ZymoBIOMICS Microbial Community Standard D6300) were included to check for artifacts in library preparation and sequencing. Results for the mock community can be found in Table  A1. After verifying the quality of reads with FastQC  (Andrews, 2014) , we used the DADA2 pipeline  (Callahan et al. , 2016)  to remove primers and truncate reads (to a final length of 260 bp for forward reads and 240 bp for reverse reads resulting in ca. 36 bp overlap) and to fit the parametric error model for the identification and filtering of sequencing errors (2 × 10 6 reads used for model fitting). Filtering was based on maxEE = 1 in DADA2 (the complete DADA2 pipeline is available on GitHub and in Zenodo at https://doi.
org/10. 5281/ zenodo. 6810766). For error estimation, the first two million sequences were used in the error model construction. Before pairing forward and reverse reads and filtering out chimeras, unique amplicon sequence variants (ASVs; 100% unique sequence identity) were extracted using the Bayesian classifier method of DADA2. The Silva v. 132 database was used for the taxonomic classification of ASVs (percentage of identity = 97% similarity, p-minconsensus = 0. 51). Chloroplast and mitochondrial sequences were removed using the R package decontam  (Davis et al. , 2018). Core (stable associates) bacterial genera and ASVs were identified using the abundance-ubiquity method with a 50% minimal ubiquity threshold. This statistic evaluates whether a bacterial taxon is not more abundant than expected for its ubiquity.
| Microbiome diversity and predictive functional profiling Three α diversity metrics were calculated: the Abundance coverage estimator (ACE) to assess ASV richness, the Inverse Simpson index (ISI) to assess ASV evenness, and Faith's phylogenetic diversity (FPD) to investigate phylogenetic richness. To evaluate differences in microbial α diversity between larvae raised on different host plants and/or between sites, we used two-way ANOVAs  (Underwood, 1997)  which for F 1 larvae included Site (BP and M) as a random factor and Host Plant (C. grandis, C. sativus, and S. melongena) as a fixed orthogonal factor and for F 0 adults included Site (BP and M) as a random factor and Parental Group (groups 1, 2, and 3) as a random factor nested in Site. ANOVA was implemented using the GAD package  (Sandrini-Neto & Camargo, 2015).
Count data from which diversity metrics were calculated were not normalized as all rarefaction plots reached a plateau (Figure  A1 ). To ensure homoscedasticity, a log transformation was applied to the ISI and a fourth root transformation was applied to the ACE and FPD. Cochran's C tests were used to test for homogeneity of variances with the GAD package  (Fox, 2006). Pairwise comparisons were done by using an F test with Holm correction for multiple comparisons implemented in the phia package  (De Rosario-Martinez, 2013). Before calculating β diversities, we first removed all ASVs that occurred in only one sample  (Chakrabarti et al. , 2016; L. J. Clarke et al. , 2019)  and we normalized counts by transforming them into proportions to represent community structure  (McKnight et al. , 2019; McMurdie & Holmes, 2014).
Generalized UniFrac distances using the d5 matrix and unweighted UniFrac distances were calculated as β diversity metrics  (Chen et al. , 2012). As UniFrac distances take into account the phylogenetic relationships, we constructed a midpoint-rooted maximum likelihood tree of the bacterial relationships using a general time-reversible substitution model in the program Fasttree  (Price et al. , 2009). Bacterial 16S sequences were aligned with the DECIPHER algorithm  (Wright, 2015). Differences in microbiome β diversity between larvae raised on different host plants and/or between sites were tested using a two-way PERMANOVA  (Anderson, 2017)  with Site (BP and M) as a random factor and host plant (C. grandis, C. sativus, and S. melongena) as a fixed orthogonal factor. The false discovery rate (FDR) correction  (Benjamini & Hochberg, 1995)  with experiment-wise p < 0.
05 was used to correct for multiple testing. PERMANOVA was also used to estimate the components of variance explained by each factor. Differences between larvae raised on different host plants were visualized with a principal coordinate analysis (PCoA) and 95% confidence ellipses were drawn using the ggplot2 package  (Wickham & Chang, 2016). To test for a differential abundance of microbial genera (i. e. , genera with relatively more sequences assigned to them) among larvae raised on different host plants and from different sites, we used ALDEx2 HENDRYCKS ET AL. | 3 of 16  (Fernandes et al. , 2013). ASVs that could not be classified were assigned to distinct, unidentified genera. Genera that showed differential abundance between two treatments with an effect size difference between 1 and -1 were filtered out to reduce the false positive rate  (Gloor, 2015).
Significance was assessed by both the Welch t test and the Wilcoxon rank-sum test followed by FDR correction with experimentwise p < 0. 05 as FDR is better suited for exploratory analyses  (Lee & Lee, 2018). We selected 23 genera that showed the greatest differences in ALDEx2 to visualize patterns in heatmaps using the pheatmap package  (Kolde, 2015). Clustering of sites and diet sources was done using Euclidean distances. For eight of these genera, we constructed boxplots with ggplot and arranged them in a single figure using the ggarrange function in the ggpubr package (Kassambra & Kassambra, 2020).
ACKNOWLEDGMENTS We thank the  Joint Experimental Molecular Unit of RMCA  and  RBINS  for technical and conceptual feedback and stimulating discussions. We would also like to thank  Kwinten Hendrycks  for helping with the graphical design of the figures. This study was supported by funds from a  "Bijzonder Onderzoeksfonds" grant  from the  University of Antwerp , a "Fonds voor wetenschappelijk onderzoek" Ph. D. fellowship of the  FWO  ( 11G9221N ), and was supported by the  International Atomic Energy Agency (IAEA, Vienna)  through the technical contract n. 20876 " Comparative Microbiomics of African Fruit Flies " (CMAFF).
DATA AVAILABILITY STATEMENT All raw sequence read data are available from the European Nucleotide Archive under the accession number PRJEB49793: https://www. ebi. ac. uk/ena/browser/view/PRJEB49793. Sample data set, sample metadata, and codes for analysis are available on GitHub: https://github. com/ wouterhendrycks/tephritid_microbiome_host_switch_project and in Zenodo: https://doi. org/10. 5281/zenodo. 6810766. Supporting Information table (Differential abundance analysis of bacterial genera between larval microbiomes from larvae between different host plants and between different sites using ALDEx2) is available in the Zenodo repository at https://doi. org/10. 5281/zenodo. 6811204). ETHICS STATEMENT None required. ORCID Wouter Hendrycks http://orcid. org/0000-0002-0441-2139.
( Figures 2 and 3 and Supporting Information table at https://doi. Larvae raised on Cu showed a higher abundance of genera belonging to the bacterial families Clostridia and Negativicutes from the phylum Firmicutes and of the genus Leucobacter. org/10. 5281/zenodo. 6811204). Ketogulonicigenium, Devosia, Although our observations are largely consistent with other tephritid studies, some of our results are not in line with patterns reported in earlier studies (Andongma et al. , 2015; Augustinos et al. , 2019; Choudhary et al. , 2021; De Cock et al. , 2020; Morrow et al. , 2015), but this is not surprising considering the high variability already described in the microbiome diversity of the closely related genera Bactrocera and Zeugodacus (De Cock et al. , 2020). Part of the variability reported for the microbiome patterns of Bactrocera/ Zeugodacus and more in general for frugivorous tephritids can An additional three genera of the Clostridia were more abundant in certainly be related to the different lab rearing and experimental Cu than in Co larvae.
Source of materials Reagents were obtained from the following sources: PhosSTOP (4906845001), cOmplete EDTA-free Protease Inhibitor Cocktail (11836170001), MISSION ® shRNA: TRCN0000225722 (FUS-KD-1), TRCN0000225724 (FUS-KD-2), TRCN0000010598 (hFUS-KD-1), TRCN0000119421 (NCDN-KD-1), TRCN0000119417 (NCDN-KD-2) and SHC002 (pLKO. 1-puro Non-Mammalian shRNA Control Plasmid) are from Sigma-Aldrich; Cycloheximide (CHX) (AC357420010) from Thermo Fisher Scientific; PDL-and Laminin-PDL coated coverslips from Neuvitro Corporation (GG-12-PDL and GG-12-LAMININ). The following antibodies were used for immunofluorescence experiments: antibodies to FUS/TLS (HPA008784) and MAP2 (MAB3418) from Sigma-Aldrich; goat anti-rabbit Alexa Fluor ® 488 (A-11034) and goat anti-Mouse Alexa Fluor ® 546 (A-11030) from Thermo Fisher Scientific.
Finally, the 2% SDS insoluble pellet was extracted in 70% formic acid (Ac. Form) and evaporated in a SpeedVac system. The dried pellet was resuspended in 3X Laemmli buffer containing 100 mM DTT. For immunoblot analysis, equivalent amounts of each fraction were resolved by 10% sodium dodecyl sulphate-polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes (Bio-Rad Laboratories, Hercules, CA, USA). Membranes were blocked with phosphate-buffered saline containing 0. 05% Tween and 5% non-fat dried milk and probed with anti-FUS antibody (1/5000; Bethyl Laboratories, Inc. Montgomery, TX, USA). Gel loading was normalized by Stain-Free detection of total proteins using a Geldoc ™ EZ Imager (BioRad laboratories). The Stain-Free signal obtained in each lane was quantified with the ImageLab ™ software (Bio-Rad Laboratories).
Primary antibody was detected with horseradish peroxidase-conjugated antirabbit (Jackson ImmunoResearch, West Grove, PA, USA). Signals were detected with chemiluminescence reagents (ECL Clarity, Bio-Rad Laboratories) and acquired with a GBOX (Syngene, Cambridge, UK), monitored by the Gene Snap software (Syngene). The signal intensity was quantified using the Genetools software (Syngene).
Replication analysis We gathered exome sequencing data or DNA samples from multiple international cases with FTLD-FET (Additional file 1: table 1)  [40]. All patients or legal guardians provided written consent for genetic analyses. Overall, we performed WES (i) from DNA isolated from blood of two patients and from brain of 4 patients from France and (ii) from DNA isolated from brain of 5 patients from Spain. One WES was sequenced using the same procedures as the patient carrying the NCDN nonsense variant. The other patients were also sequenced using the Illumina technology following capture using an Agilent Human all exons capture kit, V5UTR, at the CNRGH (CEA, Evry, France) with an average depth of coverage of 130x. Bioinformatics pipelines applied to all WES were the same as described above with the exception of sequencing of parents, whose DNA was not available.
In addition, we performed Sanger sequencing of the NCDN gene in three patients from Spain, two previously reported Japanese patients  [40]  and in four patients from France, then we analyzed the NCDN gene sequencing data out of WES data previously generated in 5 Dutch patients. The last 5 patients underwent exome sequencing using a SeqCap V2 capture kit and Illumina sequencing with an average depth of coverage of 60x.
Confocal microscopy image acquisition Neurons were imaged using a ZEISS LSM700 inverted confocal microscope using constant acquisition settings within image groups from each type of immunofluorescence measurement. Images were acquired using a 63 × oil immersion objective (1. 4 NA, Zeiss), in z-stack mode with an interval of 0. 45 μm and analysed by ZEN imaging software (Zeiss) to generate maximal intensity projections (MIP) or 3D images. Confocal images showing the cytosolic localization of FUS were captured using settings where the nuclear FUS signal was saturated (Fig. 5 ). Whereas the confocal images showing the depletion of FUS by shRNA-FUS lentiviruses were captured using settings that did not contain saturated pixels for FUS (Fig. 7 ).
Outliers greater than 2 standard deviations from the mean of each condition were removed. To estimate the median distribution with a 95% confidence interval (CI) we resampled the data points with replacement 10 000 times. Quantification of FUS granules fluorescence intensity: For the segmentation of FUS granules in the confocal z-stacks, we used the 3D spot filter of the Allen Cell Structure Segmenter (Chen et al. , 2020 https:// doi. org/ 10. 1101/ 491035). We used auto contrast pre-processing and 3D gaussian smoothing (σ = 1). The segmentation (range: 0. 75-1,0. 015-0. 045). Thresholds were applied to remove detections of non-specific staining or measurement artifacts from the segmented clusters (area under 5 pixels or over 300 pixels, Intensity above 30,000 photon counts). The maximum intensity of each cluster was computed to generate the intensity distribution histogram of the FUS granules.
The histogram was fitted using a skew normal function and the peak of the fitted distribution was calculated. For each experimental week, the peak position of the fitted distribution of all the images of the CTL condition were used for normalization. Outliers greater than 2 standard deviations from the mean of each condition were removed. To estimate distribution with a 95% confidence interval (CI) we bootstrapped the data points  [5]. For the bootstrap analysis the data points were resampled with replacement 10,000 times and their bootstrapped differences were plotted. Quantification of FUS granules volume, number and area: Confocal images of primary neurons were used for 3D analysis using Imaris software. A region of interest (ROI) was defined for all cell bodies of each data set. For each ROI, FUS signal was detected using 488 nm channel using the Imaris spot detection module.
All protein extracts were prepared in 1X Laemmli buffer and boiled (5 min, 95 °C) for western blot analysis. Samples were resolved on SDSpolyacrylamide gels and transferred to nitrocellulose membranes followed by standard western blotting procedure as described in  [69]. Membranes were imaged using the LI-COR Odyssey imaging system. Analysis of signal intensity was done using Image Studio Lite Software Version 5. 2. Quantification of FUS expression in the subcellular fractions was normalized to GAPDH for TCL and S1 and ponceau red for P1.
She had no family history of any neurodegenerative or psychiatric disease, except late onset dementia in the maternal grandfather who died at age 96. Both parents were unrelated and were healthy at age 80 and 75, respectively for the father and the mother, at the time of death of their daughter. Post-mortem examination of the brain revealed severe atrophy in the frontal and temporal lobes (Fig. 1a ). The substantia nigra and the locus coeruleus were moderately depigmented. On coronal sections of the right hemisphere, the cerebral white matter was irregularly discoloured. The caudate nucleus, putamen and pallidum were atrophic (Fig. 1b ), as well as the hippocampus (Fig. 1c ). Ventricular dilatation was also noted, more pronounced in the anterior horns. The cerebellum and brainstem displayed no macroscopical lesions (data not shown).
It is absent from the genome aggregation database (gno-mAD, v2. 1), gathering exomes and whole genomes of 141,456 individuals of diverse ethnicities  [38]. More importantly, the NCDN gene appears to be very intolerant to loss-of-function variants, with an observed/ expected ratio of 0. 04 (upper boundary of the 95% confidence interval: 0. 17) and a maximum loss of function intolerance score of 1 in gnomAD  [38]. This indicates that the general population is highly depleted in truncating variants (nonsense, frameshift), as also further confirmed by the absence of any deletion in copy number variant databases including the Database of Genomic Variants (DGV,  [50] ) and gnomAD-SV  [9]. When looking further into gnomAD, we could identify two frameshift variants seen 4 times each and a splicing variant observed once.
Both frameshift variants mapped to the penultimate exon (exon 6/7) within the last 50 base pairs and were hence not predicted to trigger NMD. The splice site variant was observed once and mapped to the 5' canonical splicing site of intron 6; it was also not predicted to trigger NMD if altered splicing would introduce a premature stop codon. Overall, this suggests that there is no human being reported in a control database so far with a truncating variant predicted to trigger NMD as is the case for the c. 1206 > A, p. (Trp402*) variant. We hypothesized that this de novo variant would result in haploinsufficiency. The NCDN gene encodes the 75 kDa Neurochondrin protein, also known as Norbin. NCDN is highly expressed in the brain and even appears to be brain-specific according to the Genotype-Tissue Expression (GTEX) database  [10].
Under conditions where the mTOR pathway is inhibited, FUS is shown to localize to the cytoplasm where it promotes translation repression  [69]. To determine the impact of NCDN haploinsufficiency on FUS pathology, we depleted primary rat cortical neurons of NCDN using lentiviruses containing shR-NAs targeted against NCDN (NCDN-KD) and examined the effects on FUS localization and expression. We found that depleting neurons of NCDN resulted in changes in FUS-positive cytoplasmic granules (Fig. 5a  Fig. 5  NCDN depletion in neurons affects FUS cytoplasmic granule dynamics. Lentivirus containing shRNAs targeting NCDN (NCDN-KD1 or -KD2) or non-targeted scramble (CTL-KD) were used to deplete primary rat cortical neurons (RCN) of NCDN. a Confocal images of RCN (DIV16) stained with antibodies against FUS (HPA008784, green), MAP2 (red) and DAPI (blue).
A number of de novo mutations in sporadic ALS patients have been identified in ALS trios studies, although none affected the NCDN gene, to our knowledge  [8, 73, 80]. In the ALS variant server, which gathers genetic data of 1277 ALS cases, three missense NCDN ultra rare variants are reported (NM_014284. 3:c. 1417G > A, p. (Val473Ile) in a sporadic ALS patient, c. 509G > A, p. (Arg170Gln) in a familial ALS patient and c. 1507C > G, p. (Pro503Ala) in 3 familial ALS patients), but their inheritance is not mentioned and they are mainly predicted as benign by bioinformatics tools. No truncating variant is reported on this server (ALS Variant Server, Worcester, MA (URL: http:// als. umass med. edu/) [nov, 2021 accessed]. Until very recently, NCDN was not associated with any Mendelian disorder. Missense variants were however reported in patients with a neurodevelopmental phenotype with epilepsy.
One family exhibited a missense homozygous variant, with unaffected heterozygous parents, while 3 unrelated patients showed missense de novo variants with a more severe phenotype  [18]. Functional analyses suggested a loss-of-function effect, the bi-allelic variant being most likely hypomorphic, and de novo variants affected NCDN function more severely. The authors suggested that two of the variants, located in the mGluR5 binding domain, specifically altered this pathway. It remains unclear whether haploinsufficiency is the most accurate model to mimic these missense variants' effects, although it is clear that they are pathogenic through loss of NCDN function. In addition, although there are no truncating NCDN variant predicted to trigger NMD in variant databases or deletions in controls, a few patients with a developmental disorder were reported with deletions encompassing NCDN and multiple other genes.
However, it is unclear (i) whether NCDN plays a role in the developmental phenotype of the latter patients and (ii) whether the same patients as well as recently described patients with missense variants will eventually develop FTLD-FET. Here, the nonsense mutation was detected as a mosaic. We hypothesize that such an NMDtriggering variant, if carried in the germ line, might be lethal during in-utero development, which could explain absence from control databases and absence of association with developmental disorders. In a non-Mendelian manner, NCDN has been implicated in several neurological disorders including epilepsy, depression, schizophrenia and spinal muscular atrophy (SMA)  [1, 12, 53, 59, 76, [83] [84] [85] 87]. Previous studies in mice have shown that forebrain-specific knockout of NCDN causes impaired mGluR5 dependent long-term potentiation (LTP) and long-term depression (LTD), as well as schizophrenia-like phenotypic behaviors  [84].
Acknowledgements Collaboration  CEA-DRF-Jacob-CNRGH-CHU de Rouen. This work did benefit from support of the  France Génomique National  infrastructure, funded as part of the  « Investissements d' Avenir » program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We thank the neurological tissue bank of the  Biobanc-Hospital Clinic-IDIBAPS, Barcelona, Spain  for sample and data procurement.
Funding This work was supported by  Fonds de recherche du Québec Santé (FRQS) ,  Alzheimer Society of   Canada Young Investigator Research Grant  ( 15-29 ), Alzheimer's  Association New Investigator Research Grant  ( NIRG-14-321584 ),  Natural Sciences and Engineering Research Council of Canada (NSERC ,  RGPIN-2020-06376  and  DGECR-2020-00060 ),  Brain Canada   Future Leaders Grant  to C. F. S. ;  CERVO foundation and Natural Sciences and Engineering Research Council of Canada  discovery grant ( RGPIN-2019-06704 ) to F. L. C, who is a Canada Research Chair Tier II;  Fonds de recherche du Québec Santé (FRQS)  to P. A. D; and CNRMAJ to G. N. and D. W.
Observational data for the derivation and validation of the metabolite scores: the EPIC-Norfolk study We developed and validated the metabolite scores for 3 types of meat consumption (red meat, processed meat, and poultry) using baseline data from the EPIC-Norfolk study, which originally recruited 25,639 males and females aged 40-79 y between 1993 and 1998 in the United Kingdom. Details of the recruitment procedures and data collection have been described previously  (14). Briefly, baseline characteristics for all participants were collected, including sociodemographic factors (age, sex, and education level), health behaviors (smoking status, alcohol drinking, and physical activity), and dietary measures. Blood samples were collected at baseline and stored in liquid nitrogen at -175 • C. The EPIC-Norfolk study was approved by the Norwich Local Ethics Committee (REC Ref: 98CN01); all participants gave their informed written consent before entering the study.
We developed metabolite scores for different types of meat consumption in an exploratory set which included 11,432 participants who had both untargeted metabolomics and dietary data. We excluded from this exploratory data set individuals who were part of a nested case-cohort study for incident T2D; those with extreme energy intake measures (<500 and >3500 kcal/d for females, <800 and >4200 kcal/d for males); or those with prevalent diabetes at baseline. Participants from the subcohort of an independent nested T2D case-cohort study  (15)  were used as a validation set, which included 853 participants after exclusions.
Metabolomics measurement and data processing in the EPIC-Norfolk study We measured untargeted metabolomics using ultraperformance LC-tandem MS on the Metabolon DiscoveryHD4® platform from plasma samples collected at baseline. The measurement of metabolites was performed in 3 subsets in March 2015, January 2016, and March 2017 successively. The data quality control and processing methods have been described previously  (16)  and are summarized in the Supplemental Methods. After data quality control and data management, the 3 subsets included 1503, 5992, and 5980 individuals, in which 944, 1168, and 1219 metabolites were measured, respectively, and 781 metabolites were identical across all subsets. Before analysis, we undertook the following steps for each metabolite within each subset: log-transformation, replacement of outliers with 5 SDs from the mean (Winsorization), and standardization to a mean of 0 and SD of 1.