dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of cognition and diagnosis of dementia Patients in the SDS cohort underwent a comprehensive battery of 17 tests as described previously  [15]. Of these, data from the DRS  [16] , the California Verbal Learning Test  [17] , digit span forward and backward  [18] , the Digit Symbol Substitution Test  [19] , the Wisconsin Card Sorting Test  [20] , semantic fluency for animals  [21, 22] , and the Benton Line Orientation Test  [23]  were included in these analyses. AD diagnoses were made by consensus of 2 independent clinicians according to prevailing clinical consensus criteria  [24]. Date of cognitive assessment was recorded at the time of cognitive assessment. Patients in the CNC cohort underwent routine clinical, neurological, and neuropsychological evaluations, and brain imaging as clinically indicated.
An Illumina HiSeq machine was used to perform sequencing with 101-bp paired-end reads, achieving a coverage of 150M reads for the first 12 samples, which served as a deep coverage reference. The remaining samples were sequenced with coverage of 50M reads. Next, beginning and ending lowquality bases and adapter sequences were trimmed from the reads, and ribosomal RNA reads were removed. The Bowtie 1 software package  [36]  was used to align the trimmed reads to the reference genome. Finally, the RSEM software package was used to estimate, in units of fragments per kilobase per million mapped fragments (FPKM), expression levels for 55,889 individual GENCODE v14 genes, which were then quantile-normalized, correcting for batch effect with Combat  [37]. These data are available through the Synapse AMP-AD Knowledge Portal (Synapse ID syn3388564; 10.
We then did the same for the MARS cohort. Then, we analyzed the ROS, MAP, and MARS cohorts together, controlling for the effect of study cohort as a fixed effect. Although multiple annual cycles of cognitive data are available in some ROS, MAP, and MARS participants, the design of these studies is such that participants are assessed at roughly the same date across cycles (interquartile range -13 to +11 days). Because of the high intra-participant correlation of assessment dates, adding data from multiple cycles per participant would not substantially improve our estimates of seasonal effects nor would it allow for effective estimation of intra-participant seasonal effects. Therefore, in the interests of analytical parsimony, we did not include multiple cycles of data in our primary analyses.
To assess seasonal rhythmicity, we considered the mean expression level of each gene module as a function of date and time of death, adjusted for age at death, sex, level of education, and methodological covariates such as batch, postmortem interval, and RNA quality (RIN score), and accounting for multiple comparisons by permutation, as described previously  [8]. A module was considered seasonally rhythmic if its p-value, adjusted for multiple comparisons, was <0. 05. To assess association with cognition proximate to death, we considered the composite global cognition proximate to death as a linear function of mean module expression, adjusted for age, sex, education, and methodological covariates including postmortem interval, batch, and RNA quality (RIN score), while accounting for multiple comparisons by generation of 10,000 null datasets created by permuting module labels.
Association between season and cognition in adults with AD AD affects circadian rhythmicity and may plausibly affect seasonal rhythmicity. To examine the association between pathologically confirmed AD and seasonal cognitive rhythms, we analyzed the last available assessment in ROS, MAP, and MARS decedents (Table  1 ; n = 1,410). The median (IQR) time between last cognitive assessment and death was 282 days (152-434). Participants with pathological AD had lower mean composite global cognition than those without (estimate = -0. 65 SD, SE = 0. 05, p < 0. 0001); however, the seasonal rhythmicity of cognition did not differ significantly between these groups (p = 0. 81), suggesting preserved seasonality of cognition in pathological AD. To assess generalizability, we replicated our analyses in baseline data from the Canadian SDS cohort, an observational study of cases from a tertiary care memory clinic in Toronto, Canada.
While data on positive and negative affect were collected on some of our participants, they were available on too few participants to test their association with seasonal rhythms of cognition in this study. Fourth, seasonal rhythms in physiological state may potentially drive the association between season and cognition. In our study, adjusting for serum levels of TSH did not substantially attenuate estimates of the association between season and cognition. However, additional metabolic factors that may potentially link season to cognition are vitamin D  [53] , sex hormones like testosterone  [54] , and melatonin  [55]. Unfortunately, data on serum levels of vitamin D, testosterone, and melatonin were not available in our study. Finally, an endogenous seasonal clock may drive rhythms in cognition.
Finally, all cognitive and CSF assessments were performed during a relatively limited window within the 24-hour day-during daylight hours. While this limited the potential for confounding by time of assessment, it also limited the capacity to obtain robust estimates of circadian effects, which would require sampling around the 24-hour clock. This study also had several strengths. Notwithstanding the population and geographic limitations noted above, we replicated our primary analyses in cohorts from 3 different countries, and 2 different racial groups, enhancing generalizability. Second, the temporal convergence of cognitive and molecular data enhanced biological plausibility and allowed us to draw links between molecular and behavioral rhythms. Third, cognitive assessments, lumbar punctures, and dates of death were spread throughout the year, rather than limited to discrete sampling times, maximizing the capacity to detect rhythmicity irrespective of phase.
To avoid weather disturbances, such as rain or wind, that could impair acoustic analyses, five days of recordings with similar stable weather conditions were selected across the study period (i. e. , 20 Assessment of the composition of the acoustic communities. The subset of 1,440 one-minute files was analysed in a random order by aural listening and visual inspection of oscillograms and spectrograms with the audio software Audacity (version 2. 0. 5; spectrogram parameters: Fourier window length: 512 samples; frame overlap: 0%; and window type: Hanning). This analysis focused on the detection of sound events, i. e. , any substantial shift in sound amplitude over background noise showing a singular acoustic structure, expected to be produced by freshwater species or other biotic sources such as gas exchanges due to plant respiration  19.
Composition of acoustic communities and of macroinvertebrate communities. To characterise acoustic and macroinvertebrate communities, we used multivariate analyses revealing differences in composition between the six sites. The hourly presence-absence matrix of sound types, composed of 1,440 rows (number of analysed files) and 128 columns (number of sound types) was reduced to a daily presence-absence matrix per site of 30 rows (5 days × 6 sites) and 128 columns (number of sound types) to compare the composition of the acoustic communities among the six studied sites. To reduce the matrix, the information provided by the two hydrophones within each site was pooled together. This database was then grouped daily, transforming the hourly presence-absence matrix into a daily presence-absence matrix of sound types composed of 30 rows (5 days × 6 sites).
Average site temperature and lateral connectivity were included as fixed effects, and site and date as random effects. Daily deviation in temperature per site was included as a fixed effect only in the models for acoustic community composition as it was not meaningful for macroinvertebrate community composition. To keep type I error at the nominal level of 5%, all required random slopes were also included  23. Site temperature, daily temperature, and lateral connectivity were approximately symmetrically distributed. The environmental variables were z-transformed (mean of zero and a standard deviation of one) to reduce the chance of obtaining a non-converging model. The model was fitted in R  24  using the function lmer of the R-package lme4 25 (version 1. 1. 10). The assumptions of normality and homogeneity of the residuals were checked by visually inspecting a quantile-quantile plot and the residuals against the fitted values, both indicating no deviation from these assumptions.
Model stability was checked by excluding data points one at a time from the data. Variance inflation factors  26  were derived using the function vif of the R-package car 27 (version 2. 1. 0) applied to a standard linear model excluding the random effects and did not indicate collinearity between fixed effects to be an issue. The full model was compared with the null model (e. g. , excluding all the predictors or the predictor tested) to test the model and predictors significance.
Acknowledgements We are indebted to  Claude-Julie Parisot  who unexpectedly initiated this project. We thank  David McCrae  and  Hélène Siméant  for collecting the connectivity and macroinvertebrate community data and helping with field work. We are grateful to  Roger Mundry  ( Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany ) for providing R functions to study GLMM stability, to  Peter Naylor  for reviewing the English, to  Julien Stackowicz  for participating to one field trip session, and to  Juan Sebastian Ulloa  for his helpful insights on early drafts of the manuscript. We thank the two anonymous reviewers for their comments that significantly improved the manuscript. This project was supported by a fieldwork grant from the  LabEx BCDiv , an  ENS  PhD grant (CD), and a Post-doctoral grant from the  Fondation Fyssen  (DL).
Acknowledgements and potential conflict of interest This work was conducted by an expert group of the European branch of the International Life Science Institute (ILSI Europe). The expert group and this publication were coordinated and funded by the Microbiological Food Safety Task Force. Industry members of this task force/expert group are employees of food industry companies, and are listed on the ILSI Europe website at http://ilsi. eu/task-forces/foodsafety/microbiological-food-safety/. All expert group members declare no potential conflicts of interest. The experts were not paid for the time spent on this work; however, the ILSI Europe Microbiological Food Safety Task Force offered to all non-industry members within the expert group support for travel and accommodation cost to attend meetings for discussing the manuscript, and a small compensatory sum (honorarium) with the option to decline.
RNA sequencing and data processing First, mRNA were purified using Oligotex mRNA mini kit (Qiagen). cDNA libraries were then generated following the GS-FLX Titanium cDNA Rapid Library Preparation Method Manual (Roche). Finally, emPCR amplification and 454 sequencing were performed according to the manufacturer's protocol (emPCR Amplification Manual-Lib-L LV and Sequencing Method Manual-GS FLX Titanium Series, Roche). RNA-Seq data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress ) under accession number E-MTAB-5465. Sequence reads were aligned on the human genome (hg19) with GS Reference Mapper software (Roche) and mapped on the human exome using a home-made software named AGSA. Data was then normalized by calculating the 'reads per kilo base per million mapped reads' (RPKM) for each gene.
Methyl-DNA sequencing and data processing First, DNA was fragmented by nebulization during 1min30sec at 2. 1 bar of nitrogen pressure. After DNA purification using Qiaquick PCR purification kit (Qiagen), methylated DNA was captured using MethylCap kit (Diagenode) following the supplier recommendations. Libraries were generated using GS FLX Titanium Rapid Library Preparation Kit (Roche). Finally, emPCR amplification and 454 sequencing were performed according to the manufacturer's protocol (emPCR Amplification Manual-Lib-L LV and Sequencing Method Manual-GS FLX Titanium Series, Roche). Methyl-Seq data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-5468. Sequence reads were aligned on the human genome (hg19) with GS Reference Mapper software (Roche) and mapped on the human proximal promotors using a home-made software named AGSA.
TCGA data analysis Both clinical and RNA sequencing data (Illumina HiSeq RNAseq Version 2 data) of invasive breast cancers were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 449 patients with information on ER, PR and HER2 status were selected to compare the expression profiles of genes in the respective tumors. 71 cases were found to have a negative ER, PR end HER2 phenotype (i. e. , triple-negative), whereas 371 cases were positive for at least one of these receptors.
Data generation We started with a single real 3-s recording of a pupillary response to light, recorded at 1,000 Hz with an EyeLink 1000 (SR Research). This recording did not contain blinks or recording artifacts, but did contain the slight noise that is typical of pupil-size recordings. Pupil size was measured in arbitrary units as recorded by the eye tracker, ranging from roughly 1,600 to 4,200. Based on this single recording, 200 trials were generated. To each trial, a constant value, randomly chosen for each trial, between -1,000 and 2,000 was added to each sample. In Fig. 1b , this is visible as a random shift of each trace up or down the Y-axis. In addition, one simulated eye blink was added to each trial. Eye blinks were modeled as a period of 10 ms during which pupil size linearly decreased to 0, followed by 50-150 ms (randomly varied) during which pupil size randomly fluctuated between 0 and 100, followed by a period of 10 ms during which pupil size linearly increased back to its normal value.
1c, d  the baseline period, baseline pupil size was very small; consequently, baseline-corrected pupil size was very large. These two trials are clearly visible in Fig. 1c  as unrealistic baselinecorrected pupil sizes ranging from 40 to 130 (as a proportion of baseline), whereas in this dataset realistic baselinecorrected pupil sizes tend to range from 0. 3 to 1 (see zoom panel in Fig. 1c ). Pupil sizes on these two trials are so strongly distorted that they even affect the overall results: As shown in Fig. 1d , the overall results suggest that the pupil is largest in the Blue condition, whereas we had simulated an effect in the opposite direction.
Statistical power The results above show that the effects of blinks during the baseline period can be catastrophic, and much more so for divisive than subtractive baseline correction. However, it may be that divisive baseline correction nevertheless leads to the highest statistical power when there are no blinks during the baseline period (even though this is unlikely to happen in real data). To test this, we generated data as described above, while varying the following: & Effect size (Red larger than Blue), from 50 (±2 %) to 500 (±20 %) in steps of 50 & Baseline correction: no correction, divisive, or subtractive & Blinks during baseline in Blue condition: yes (two blinks) or no We generated 10,000 datasets for each combination, giving a total of (10,000 × 10 × 3 × 2 =) 600,000 d a t a s e t s.
The proportion of datasets in which a true effect was detected is shown in Fig. 2a, c ; the proportion on which a spurious effect was detected is shown in Fig. 2b, d. By chance (i. e. , if there was no effect and no systematic distortion of the data) and given our two-sided p <. 05 criterion, we would expect to find a. 025 proportion of detections of true and spurious effects; this is indicated by the horizontal dotted lines. First, consider the data with blinks in the baseline (Fig. 2a, b ). With divisive baseline correction (pink), neither true nor spurious effects are detected (except for a handful of true effects for the highest effect sizes, but these are so few that they are hardly visible in the figure), because the blinks introduce so much variability in the signal that it is nearly impossible for any effect to be detected.
Data The data were collected for a different study, and consisted of 2,520 trials (across 15 participants) in two conditions, here labeled Blue and Red. All participants signed informed consent before participating and received monetary compensation. The experiment was approved by the ethics committee of Utrecht University. For full details, see Exp. 1 in  Mathôt, Van Heusden, Van der Stigchel (2015c). First, consider the uncorrected data (Fig. 3a, b ). The trial starts with a pronounced pupillary constriction, followed by a gradual redilation. Overall (Fig. 3b ), the pupil is slightly larger in the Blue than the Red condition, but this difference is small. (Whether or not the difference between the two conditions is reliable is not relevant in this context. ) The individual traces (Fig. 3a ) show a lot of variability between trials, as well as frequent blinks, which correspond to the vertical spines protruding downward.
Identifying problematic trials Figure  4  shows a histogram of baseline pupil sizes, that is, of median pupil sizes during the first 10 ms of the trial. In this dataset, baseline pupil sizes are more-or-less normally distributed with only a slightly elongated right tail. (But baseline pupil sizes may be distributed differently in other datasets. ) On a few trials, baseline pupil size is unusually small; but these trials are so rare that they are hardly visible in the original histogram (Fig. 4a ). Therefore, we have also plotted a histogram with log-transformed counts on the Y-axis, which accentuates bins with few observations (Fig. 4b ). Looking at this distribution, a reasonable cut-off seems to be 400 (arbitrary units): Baseline pupil sizes below this value are-in this dataset-unrealistic and can catastrophically affect the results as we have described above.
In the notation of the R-package lme4, this corresponds to the following model: The effect of condition corresponds to the time × condition interaction, and here gives:  observations to the analysis, the observations in the analysis are no longer independent. To take this into account, we need to conduct a linear mixed-effects model where Trial is a random effect, and we allow the intercept to randomly vary by Trial (i. e. , random by-Trial intercepts). The outcome of this analysis is t = 2. 668, p = 0. 116 for the time × condition interaction. t = Figure  6b  shows the same analysis, but done after subtractive baseline correction, so that pupil size is set to 0 at time 0 for all trials. The analysis is now reduced to a simple independent samples t-test between the Blue and Red trials. The outcome is t = 2.
In conclusion, we have shown that baseline correction of pupil-size data increases statistical power, but can strongly distort data if there are artifacts (notably eye blinks) during the baseline period. We have made five recommendations for safe and sensible baseline correction, the most important of which are: Use subtractive rather than divisive baseline correction, and check visually whether your baseline-corrected pupil-size data make sense. Materials and availability Data and analysis scripts can be found at https://github. com/smathot/baseline-pupil-size-study. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Materials and Methods The SPP1 genome was fully sequenced using Illumina sequencing of genome libraries at the I2BC NGS platform facility, as described  [34, 35]. Sanger sequencing of PCR fragments covering the SPP1 genome was carried out at GATC Biotech (Germany). Open reading frames (ORFs) identification and visualization of the SPP1 chromosome organization was performed using the SnapGene Viewer software (GSL Biotech, Chicago, IL) and Fgenesb annotator (http://www. softberry. com/berry. phtml?topic=fgenesb&group=programs& subgroup=gfindb)  [36]. A cut-off of ORFs initiated at AUG, GUG or UUG codons that code for putative proteins of at least 45 amino acids-long was used as criterion to identify the ORFs for annotation. Among those, there are 20 ORFs without ribosome binding site (RBS) that are embedded in longer ORFs.
Properties of the SPP1 DNA Molecule The genome of SPP1 was completely sequenced in the context of research projects on phage DNA replication and viral particle assembly, as well as by the necessity for an accurate genome annotation for downstream SPP1 "omics" studies. Very few changes were identified relative to the deposited sequence (Genbank accession code X97918) confirming the high quality of the original Sanger sequencing work  [26]  and its 2006 revision (X97918. 2; [16] ). Changes in the nucleotide sequence are listed in Table  1. The 44,016 bp genome has a GC content of 43. 7% which is similar to the one found in the host B. subtilis genome (43. 5%)  [49]. The distribution of purines and pyrimidines in the two DNA strands is very different. This asymmetry provided the physical basis for their separation after denaturation by density using isopycnic centrifugation  [1].
a structure of gp1 from the SPP1-related phage SF6 (83% amino acid sequence identity with SPP1 gp1); b X-ray crystallography structure PDB access code; c No RBS identified according to the criteria defined in Material and Methods (see Section 2). In case of ORF 42. 2 a G-rich sequence is identified as a potential site for ribosome binding; d the gene 2 beginning and the resulting length of gp2 is based exclusively on the position of the initiation codon assigned during annotation as no RBS was identified (see Section 3. 2 for details) e gp2 is not a membrane protein according to presently available biochemical data; f Structures of the gp2 nuclease domain (residues 232 to 422 of the gp2 amino acid sequence); g cryo-electron microscopy structure EMD access code; h The RBS sequence is compatible with different spacings relative to the ORF initiation codon.
i NMR structure PDB access code; j the gene 16 beginning and the resulting length of gp16 is based on the position of the initiation codon assigned during annotation, as no RBS was identified, and on the amino terminus sequencing of gp16 (see Section 3. 2 for details); k genes 17. 1 and 17. 1* have the same 5 sequence because the product of 17. 1* results from a +1 frameshift at the end of their common reading frame  [91]  (see Section 3. 2 for details); l genes 17. 5 and 17. 5* have the same 5 sequence as the product of 17. 5* results from a putative -1 frameshift within their common reading frame  [91]  (see Section 3. 2 for details); m the essential nature of SPP1 genes coding for the tail chaperones, TMP, Dit and Tal proteins was not demonstrated experimentally but their functional homologs are essential in the phage systems presently characterized; n the SPP1 TMP gp18 features four predicted transmembrane segments but it is an anticipated component of the phage particle occupying the tail tube internal space  [16]  (see Section 3.