dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic location of TE insertions To characterize the genomic environment of TE insertion loci, we split the reference genome into non-overlapping windows of 10 kb using the function splitter from EMBOSS. TEs were located in the reference genome using RepeatMasker providing consensus sequences from RepeatModeler (http://www. repeatmasker. org/). To analyze coding sequence, we retrieved the gene annotation for the reference genome  (Grandaubert et al. , 2015). We estimated the percentage covered by genes or TEs per window using the function intersect in bedtools. Additionally, we calculated the GC content using the tool get_gc_content (https:// github. com/ spundhir/ RNA-Seq/ blob/ master/ get_ gc_ content. pl;  Sachin, 2021). We extracted the number of TEs present in 1 kb windows around each annotated core gene in the reference genome IPO323, using the function window in bedtools.
We calculated the relative distances between each gene and the closest TE with the function bedtools closest. For the TEs inserted into genes, we used the function intersect in bedtools to distinguish intron and exon insertions with the parameters -wo and -v, respectively. TEs that overlap more than one exon were only counted once. For each 100 bp segment in the 1 kb windows as well as for introns and exons, we calculated the mean number of observed TE insertions per base pair. We calculated the mean number of TEs per window and calculated the log2 of the observed number of TE insertions divided by the expected value. We extracted information about recombination hotspots from  Croll et al. , 2015. This dataset is based on two experimental crosses initiated from isolates included in our analyses (1A5 × 1E4, 3D1 × 3D7).
Core genome size estimation Accessory chromosomes show the presence/absence variation within the species and length polymorphism  (Goodwin et al. , 2011; Croll et al. , 2013)  and thus impact genome size. We controlled for this effect by first mapping sequencing reads to the reference genome IPO323 using bowtie2 with --very-sensitive-local settings and retained only reads mapping to any of the 13 core chromosomes using seqtk subseq v1. 3-r106 (https:// github. com/ lh3/ seqtk/;  Heng, 2021). Furthermore, we found that different sequencing runs showed minor variation in the distribution of the per read GC content. In particular, reads of a GC content lower than 30 % were under-represented in the Australian (mean reads <30 % of the total readset: 0. 05%), North American 1990 (0. 07%) and Middle East (0.
1%) populations, and higher in the Europe 1999 (1. 3%), North American 2015 (3. 0%), and Europe 2016 (4. 02%) populations (Figure  1 -figure supplement 3). Library preparation protocols and Illumina sequencer generations are known factors influencing the recovery of reads of varying GC content  (Benjamini and Speed, 2012). To control a potential bias stemming from this, we subsampled reads based on GC content to create homogeneous datasets. For this, we first retrieved the mean GC content for each read pair using geecee in EMBOSS and binned reads according to GC content. For the bins with a GC content <30%, we calculated the mean proportion of reads from the genome over all samples. We then used seqtk subseq to subsample reads of <30% to adjust the mean GC content among readsets. We generated de novo genome assemblies using the SPAdes assembler version with the parameters --careful and a kmer range of  '21, 29, 37, 45, 53, 61, 79, 87'.
Fungicide resistance assay To quantify susceptibility toward propiconazole, we used a previously published microtiter plate assay dataset with three replicates performed for each isolate and concentration. Optical density was used to estimate growth rates under different fungicide concentrations (0, 0. 00006, 0. 00017, 0. 0051, 0. 0086, 0. 015, 0. 025, 0. 042, 0. 072, 0. 20, 0. 55, 1. 5 mg L -1 )  (Hartmann et al. , 2020). We calculated dose-response curves and estimated the half-maximal lethal concentration EC 50 with a four-parameter logistics curve in the R package drc  (Ritz and Streibig, 2005).
Acknowledgements We thank  Andrea Sánchez Vallet ,  Anne C Roulin ,  Luzia Stalder ,  Adam Taranto ,  Emilie Chanclud , and  Alice Feurtey  for helpful discussions and comments on previous versions of the manuscript. We also thank the three reviewers for very helpful suggestions. We thank  C Sarai Reyes-Avila  for advice on statistical analyses. DC is supported by the  Swiss National Science  (grants  31 , 003A_173265 ) and the  Fondation Pierre Mercier pour la Science.
The ATP Real-Time rate kit is the only assay that quantifies the ATP production from glycolysis and mitochondria simultaneously. The Agilent Seahorse Mito Fuel Flex Test kit measures the basal state of mitochondrial fuel oxidation in live cells by providing information on the relative contributions of glucose, glutamine and long-chain fatty acid oxidation to basal respiration. All analyses were performed using the software Wave 2. 6. 1 (Agilent, Les Ulis, France). mtDNA Quantification Total DNA from cardiac fragments and AC-16 cells was extracted using the DNA extraction kit FlexiGene DNA kit (QIAgen) according to the manufacturer's instructions. PCR reaction was conducted using MAXIMA SYBR Green/ROX qPCR Master Mix (2x) (Thermo Scientific), 1 ng of template DNA and primers for mitochondrial encoded genes NADH: ubiquinone oxidoreductase subunit 1 (MT-ND1, 5 pmol/μl), cytochrome b (MT-CYTB, 5 pmol/μl), cytochrome c oxidase subunit 3 (MT-COXIII, 5 pmol/μl).
RNA Extraction and Sequencing AC-16 cells were crushed with ceramic beads (VK05, diameter 1. 4 mm) in 350 μl of RLT lysis buffer supplemented with 3. 5 μl of b-mercapto-ethanol. Total RNA was extracted by using the RNeasy Mini Kit adapted with Trizol. RNA quantity was measured with a 2100 Bioanalyzer. Total RNA Sequencing was provided by Genewiz. Ribosomal RNAs have been depleted, and samples were prepared for sequencing according to the Illumina TruSeq RNA Preparation Kit and subjected to pairwise sequencing (2x150 bp) with the Illumina Novaseq sequencer. After sequencing, the raw data files (fastq) were generated.
Differential Expression Analysis Raw data quality has been verified with FastQC (v0. 11. 5). Lowquality reads, Illumina adapters and reads smaller than 20 nucleotides were removed with Trimmomatic (v0. 39) (26), using default values for other options. Reads were aligned on GRCh37 (hg19) human reference genome from Ensembl using STAR (v2. 5. 4b)  (27) , specifying that the reads are 2x150 nucleotides (paired-end). Alignment quality has been checked by BAMQC (qualimap v2. 2. 1)  (28). Gene quantification was done with featureCounts (v2. 0. 0) (29), using the exons as features and the genes as attributes. The DESeq2 package (v1. 26. 0) has been used to normalize data and to perform the differential expression test  (30). To improve log2 fold change accuracy, DESeq2 shrinkage function has been applied to the test results, and p-value was corrected with Benjamini-Hochberg method.
Genes having an adjusted p-value ≤ 0. 05 and a |log2 (FC)| ≥ 1. 5 were considered to be differentially expressed genes (DEG). Then, results were analyzed by graphical methods with EnhancedVolcano (v1. 4. 0) and ggplot2 packages. Moreover, hierarchical clustering (HCA) based on spearman correlation was computed using gplots (v3. 0. 4) package, and principal component analysis (PCA) with factoextra (v1. 0. 7) and FactoMineR (v2. 3) packages. We compared the AC-16 gene expression profile with the CCC gene expression profile (GSE84796)  (31).
Statistical Analysis Cell data are reported as the ratio to non-treated cells. Cell viability was calculated as the ratio of the number of live cells (NucGreen-negative) and total cells (NucGreen-negative plus NucGreen-positive cells) x100. The results were expressed as mean ± standard deviation (SD) or standard error of the mean (SEM) when noted. Statistical analysis was performed using the GraphPad Prism 9. 2. 0 software (GraphPad Software, Inc. , CA). Data were tested for normality using the Shapiro-Wilk test before statistical tests. A nonparametric Mann-Whitney test was applied if data were not normally distributed. In all other cases, one-way ANOVA followed by Dunn's post hoc test was applied for multiple comparisons. A p-value < 0. 05 was considered statistically significant.
Twelve out of the 19 were mitochondrial or related to energy metabolism, and 8 of them were similarly modulated in both proteome datasets. Among matching and differentially expressed mitochondrial proteins, we observed reduced expression of proteins including HADH and ACAT1 (fatty acid beta-oxidation), NDUFV2 (OXPHOS), and FH (TCA), with increased expression of PKM (glycolysis) and LAP3 (apoptosis).
Participants Forty-three adult volunteers (mean age = 21 years old, range 19-29, including 12 males) from the University of Plymouth participated in the study in exchange for money or course credit. All participants reported being right-handed and having normal vision. Due to the use of a VR headset, participants wearing correction glasses were not accepted. EEG data from six participants were removed due to excessive numbers of artifacts. The experimental procedure and written consent form for this study were approved by the ethics committee of the University of Plymouth and conform with the 2008 Helsinki Declaration.
Experimental setup and procedure The experiment used Unity software (Unity technologies, version 7. 1. 0f3) to create the virtual environment and the HTC Vive (HTC Corp. ) headset and controllers. Participants were wearing both the EEG and VR headsets and were seated in a chair next to a desk. A button box was placed on the desk situated on the right side of the participants and connected to the computer to detect movement onsets. The virtual environment was composed of a small wooden textured box, a white and a red dashed area situated on the table, a big box situated in front of the participant and, a small black cube on their left (Fig. 1A ). The size and height of the room, virtual table, and the button box were fitted to the dimensions of the physical environment. For a comfortable position of the hand on the button box, the distance between the chair and the desk was adjusted for each participant.
EEG data were collected from 61 actively amplified Ag/AgCl electrodes (easyCAP, Brain Products, Gilching, Germany) mounted on an elastic cap and following the standard International 10-20 montage. Electrode impedances were kept below 20 kΩ. The signals were amplified using a BrainAmp MR Plus amplifier (Brain Products) and continuously sampled at 500 Hz. The virtual environment and the EEG recording were run on separate computers.
Data processing The training paradigm implemented in this experiment was chosen to estimate the Training Effect (TE) of a given object, reflecting the specific consequences of learning the function and usage of the objects on reaction times and EEG activities. This TE was calculated with the following formula: TE = object post-training -object pre-training. This TE was calculated separately for the trained and non-trained objects in both conditions (learning usage or learning function). Hence, the comparison of the TE for the trained and non-trained objects allowed to isolate the effect of the training. Therefore, it is hypothesized that the TE values concerning the non-trained object would be minimal whereas the TE values about the trained object would be maximal. Only successful trials during the pre-and post-training phases were used for the behavioral and EEG analyses.
Statistical analysis RStudio (v. 0. 99. 489) and the rstatix (v. 0. 6. 0) package were used to perform analysis of variances (ANOVAs) and planned comparisons analysis with Tukey's HSD tests. Concerning the behavioral data, repeated-measures mixed-design ANOVAs were performed, with the Stimulus (trained vs non-trained object) as a within-subjects independent variable and the Training (usage vs function conditions) as a between-subjects independent variable. The TE on Movement, Grasping and Action Times were entered as dependent variables. Concerning the EEG data, the activation of the visual system has been evaluated through the analysis of the alpha-band (8-12 Hz) activity recorded over the midline occipital electrode Oz and the activation of the motor system through the analysis of the mu-band (8-12 Hz) activity recorded over the midline centro-parietal electrode CPz (as in  Proverbio, 2012; Wamain, Gabrielli, & Coello, 2016 , 2018).
Indeed, participants reported using a different hand grip to perform the tool use during the training phase. However, the analysis of the behavioral TE effect did not reveal such lags. The most likely reason is that, in our delayed-response paradigm, the pre-tone periods were long enough to plan robust motor decisions. Considering only our behavioral data, the study would support the literature proposing that motor knowledge about objects is selectively activated upon task requirements  (Daprati & Sirigu, 2006 ; Lindemann, Stenneken, van Schie,   & Bekkering, 2006). However, our EEG data surely challenge this claim. In both learning conditions, the trained objects were associated with the perceptual outcome of the box opening. Thus, the theta-band modulations induced by learning the object usage might rely primarily on the manipulative information rather than the visual information associated with the novel object.
5% of the phenotypic variance of smoking status (current/ever/never) and alcohol intake (units per week), respectively  [23]. Imputation of unmeasured environmental exposures could therefore help stratify patients across diagnostic boundaries, which may provide stimuli for additional analyses and clinical followup. Using a similar concept to genome-wide association studies, methylome-wide association studies (MWAS) methods have started to emerge in order to address the effect of differentially methylated positions (DMPs) on complex diseases. Recently, the OmicSdata-based Complex trait Analysis (OSCA) software has been developed  [24]. OSCA implements two reference-free mixed-linear model approaches that model different genome-wide architecture of DNA methylation: MOA and MOMENT (see the "Methods" section).
The MCI patients were not included in analyses, due to lack of power. We also had available two schizophrenia (SCZ1 and SCZ2) and one rheumatoid arthritis cohorts, used to check specificity of results to neurodegenerative disorders. In total, 5551 cases and 4343 controls were available for analyses, after quality control (QC). (  2 ) QC and normalization of DNA methylation data were conducted using the R package meffil  [27] , which applies an automated estimation of functional normalization parameters that reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. (3) To discover differentially methylated positions (DMPs), we applied mixed-linear model-based association studies of DNA methylation for each of the eight available cohorts, using two different methods: MOA and MOMENT  [24].
Significant correlation of CTP profile scores with blood protein markers of inflammation in a healthy aging cohort To determine if CTP-scores and MPS were capturing immune-related signals, we calculated correlations between these scores and blood inflammatory markers in the Lothian Birth Cohort 1936 (LBC1936). The LBC1936 is a healthy aging elderly cohort for whom we had access to whole blood DNA methylation data measured from 450k Illumina arrays (N = 980), 92 blood inflammatory protein markers measured with the Olink® inflammation panel (pg/mL) (N = 1048), and real cell-counts (10 9 /L), including lymphocytes, monocytes, and granulocytes measured at wave 1 (N = 909). After quality control (see the "Methods" section), 823 individuals had blood DNA methylation, measured cell counts and inflammatory markers measures available, and were included in the correlation analyses.
We then compared the results obtained when utilizing disease-associated scores derived from CTP, low-grade chronic inflammation-related CRP-scores, or with measured cell counts. As expected, we found measured cell counts, CTP-scores, and low-grade chronic inflammation-related CRP-scores to be highly positively correlated with blood protein inflammatory markers, whereas MOMENT-MPS (derived from genome-wide significant results of a meta-analysis of ALS, AD and PD) showed no statistically significant correlations (Additional file 1: Figure  S19 ), suggesting these MPS are not strongly confounded by CTP. Our study has several limitations. Firstly, although growing in number, the current scarcity of homogeneously collected DNA methylation datasets, largely from small cohorts, makes it difficult to perform replication analyses, particularly in a mixed-linear model setting due to lack of power, as we previously performed for ALS and PD  [18, 20].
DNA methylation data for the NL sample were measured using Illumina Infinium HumanMethylation450 BeadChip and generated under similar protocols to the AUS cohort. King's College London ALS cohorts The King's College London (KCL) ALS cohort was collected under Project MinE  [68]. The participants of this study consisted of a subset of 1433 individuals of UK nationality from the UK National DNA Bank for MND Research who were put forward for DNA methylation profiling. Cases were diagnosed with ALS in one of 20 UK hospitals by neurologists specialized in motor neuron diseases; patients had no family history for ALS and were of self-reported European descent. All cases and controls gave written informed consent. The national Integrated Research Approval System (IRAS) approved the study, reference: 08/H0405/60.
DNA was extracted by use of standard methods at three centers within 1 week of the blood being drawn (usually on the same day) and was stored centrally at the UK DNA banking network in Manchester. We used a barcode-based sample tracking system to minimize the risk of clerical error. DNA methylation status of the participants was extracted from whole blood samples using Illumina Infinium HumanMethylation450 BeadChip array following manufacturer's protocol. These samples were run in two separate batches at two different time points (batch1 N = 666; batch2 N = 767). Both batches followed the same quality control pipeline and DNA methylation data were normalized together. Since we did not observe major batch effects, we analyze them as a unique cohort.
Parkinson's disease (PD) cohorts System Genomics of Parkinson's Disease (SGPD) cohort The SGPD case-control cohort comprises genotype, phenotype and DNA methylation data for a total of 2333 participants (1292 PD cases, 1041 controls) recruited from three different studies across Australia and New Zealand: (1) the Queensland Parkinson's Project (QPP), (2) the New Zealand Brain Research Institute PD case-control cohort (NZBRI), and (3) the Sydney PD case-control cohort (SYD). The study design, diagnostic criteria, and DNA methylation assays have been described elsewhere  [18] , but briefly, the QPP cohort includes 1791 participants (867 PD cases, 924 controls), mostly of European ancestry (individuals with genetically confirmed non-European ancestry were excluded prior to analysis). The NZBRI cohort comprises 210 participants (151 PD cases, 59 matched-controls) recruited by the NZBRI.
Exclusion criteria for PD patients were prior history of learning disability, severe head injury, stroke, or other neurological impairment and major psychiatric complications at the point of study entry. Whole-blood samples were collected at the same time as phenotypic measurements, which included demographic, medical, and environmental exposure information for all participants. In the SYD cohort, 332 participants (274 PD cases, 57 matched-controls) were recruited from the Parkinson's Disease Research Clinic, Brain and Mind Research Centre at the University of Sydney. The DNA methylation data were measured using Illumina Infinium HumanMethylation450 BeadChip. Parkinson's disease, environment, and genes (PEG) cohort The PEG study is a large population-based study of PD of mostly rural and township residents of California's central valley  [70].
The PEG study comprises of 508 European (289 PD cases, 219 controls) and 63 Hispanic individuals (45 PD cases, 18 controls) for a total of 334 PD cases and 237 controls. Study design, diagnostic criteria, and DNA methylation assays have been described elsewhere  [70, 71]. The DNA methylation data measured using Illumina Infinium HumanMethylation450 BeadChip.
Alzheimer's disease (AD) cohorts The Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort The AIBL cohort is a prospective longitudinal study of aging aimed to recruit 1000 individuals aged over 60 to assist with prospective research into AD. Data was collected by the AIBL study group. AIBL study methodology has been reported previously  [72]. Participants with AD (N = 211) had neuropsychological profiles which were consistent with AD and were more impaired than participants with mild cognitive impairment (MCI) (N = 133) or healthy controls (N = 768), who performed within expected norms for age on neuropsychological testing. A subset of the AIBL cohort (N = 162 AD cases, N = 94 MCI cases, and N = 485 controls) were subjected to a similar DNA methylation assay protocol as the AUS ALS cohort  [20] , but bisulfite DNA samples were hybridized to the 8 sample, HumanMethylationEPIC BeadChip Array.
Participants were excluded if they had depression or any other neurological syndrome. A subset of 301 samples were selected from the cohort for DNA methylation profiling, including 96 controls, 111 individuals with MCI, and 94 AD cases. DNA extraction methods have previously been described elsewhere  [15, 78]. DNA methylation was measured using Illumina HumanMethylation450 BeadChip arrays and the Illumina HiScan System. Samples were randomized by sex, diagnostic status, and recruitment center. Clinical data for this cohort can be accessed according to the data terms of use for AddNeuroMed https://www. synapse. org/#!Synapse:syn2790911/wiki/235389.
Schizophrenia cohorts University College London schizophrenia cohort (SCZ1) The University College London schizophrenia case-control sample study design, diagnostic criteria, DNA methylation collection details, and assay characteristics have been thoroughly described elsewhere have been described elsewhere  [21, 79] , but briefly comprises of unrelated ancestrally matched cases (N = 353) and controls (N = 322) from the UK. Case participants were recruited from UK National Health Service (NHS) mental health services with a clinical International Classification of Diseases 10th edition (ICD-10) diagnosis of schizophrenia. The DNA methylation data were measured using Illumina Infinium HumanMethylation450 BeadChip. Aberdeen schizophrenia cohort (SCZ2) The Aberdeen schizophrenia case-control sample study design, diagnostic criteria, DNA methylation collection details and assay characteristics have been thoroughly described elsewhere have been described elsewhere  [21, 80] , but briefly contains patients with schizophrenia (N = 414) and controls (N = 433) who have self-identified as born in the British Isles (95% in Scotland).
Rheumatoid arthritis cohort The raw methylation data from Illumina Infinium HumanMethylation450 BeadChip arrays and phenotypic data for rheumatoid arthritis cases and controls were obtained from the publicly available Gene Expression Omnibus submitted dataset GSE42861, which was part of the Epidemiological Investigation of Rheumatoid Arthritis (EIRA) study  [81, 82]. Only incident rheumatoid arthritis cases were invited for the study within the years 1996-2009 from middle Sweden. The controls matched by sex, age, smoking status, and residence area were selected from the same population. The cohort and DNA methylation collection details and assay characteristics have been thoroughly described elsewhere  [81].