dataesr/scientific-paragraphs-categorization huggingface dataset:
Rarefaction curves were performed for the 83 molecular inventories (81 sediment samples + 2 PCR replicates) using the function "rarecurve" from the vegan R package  [49]. To make the analyses more robust, we also removed the OTUs represented by less than 4 DNA reads in the entire dataset. Additionally, 9 of the sediment samples gave molecular inventories with a low number of DNA reads (<23,500 reads) and were removed. The 74 resulting molecular inventories were rarefied at 23,500 reads. To evaluate the similarity between analytical replicates, we performed a Bray-Curtis dissimilarity-based hierarchical clustering analysis on the molecular inventories from the 16 sediment samples and two PCR replicates from ZF10 using "vegdist" and "hclust. order" functions from the vegan R package  [49]. Our outputs showed that the replicates clustered together and had lower Bray-Curtis dissimilarity values (0.
26 and 0. 18 for the two PCR replicate samples from the sediment samples with IDs 18 and 20, respectively) as compared to the average between-sample difference for the other sediment samples (Supplementary Figure  S2a ). The PCR replicates were not used for any further analyses of the data. In addition, we removed the sample at the base of the sediment record from Lake ZF10, because this sample, consisting only of glacial clay, likely corresponds to a time prior to lake formation (Supplementary Figure  S3 ). The final dataset thus includes molecular inventories for 71 sediment samples from the four lakes.
Data Analysis We used the rarefied OTU abundance table to calculate the OTU richness, Shannon diversity index and Simpson dominance index. Using the vegan R package  [49] , we calculated the Bray-Curtis dissimilarity indices and performed nonmetric multidimensional scaling (nMDS) and hierarchical clustering analyses ("metaMDS" and "hclust" functions, vegan R package)  [49]. To evaluate the differences in community structures between the four lakes, ANOSIM analysis was performed using PAST  [50]. Venn-diagrams showing the number of OTUs specific to single lakes and those shared between lakes were drawn using the software Venny 2. 1  [51]. In order to investigate the relationships between the structure of the microbial eukaryotic community, LW-TOC and sediment chlorophyll concentrations in a certain sample, we employed the function "env.
The exception might be Apicomplexa, where some studies have suggested that it is only found in very low abundance in lake water  [57]. However, others have described members of Apicomplexa as freshwater parasites  [75, 76] , and their relatively high abundance in all four sediment profiles suggests that their origin is the water column. It is noticeable that the molecular signal from certain microbial eukaryotic groups with an aquatic origin, such as Cryptophytes and Haptophytes, might not be well-preserved in sediments  [68]. Several abundant OTUs could not be robustly taxonomically identified using the SILVA database, most likely because they have never been cultivated or characterized by sequencing. The most abundant OTU of the dataset (OTU1407) was, for instance, assigned as an unclassified alveolate, as were three others of the most abundant OTUs (OTU2226, OTU3337 and OTU1035) (Supplementary Table  S3 ).
Acknowledgments: The authors acknowledge support from the  National Genomics Infrastructure in Stockholm  funded by the  Science for Life Laboratory , the  Knut and Alice Wallenberg Foundation  and the  Swedish Research Council  and the  SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science  for assistance with massively parallel sequencing and access to the  UPPMAX computational infrastructure. We are grateful to  Anders Dahlén ,  Jämtland County Board  for excellent field assistance,  Doreen Huang  for contribution to the lab work and the ancient DNA lab facilities from the  Department of Ecology and Environmental Science at Umeå University. We are grateful for the useful comments and suggestions from the two anonymous reviewers.
Growing experimental evidence points at relationships between the phase of a cortical or bodily oscillation and behavior, using various circular statistical tests. Here, we systematically compare the performance (sensitivity, False Positive rate) of four circular statistical tests (some commonly used, i. e. Phase Opposition Sum, Circular Logistic Regression, others less common, i. e. , Watson test, Modulation Index). We created semi-artificial datasets mimicking real two-alternative forced choice experiments with 30 participants, where we imposed a link between a simulated binary behavioral outcome with the phase of a physiological oscillation. We systematically varied the strength of phase-outcome coupling, the coupling mode (1:1 to 4:1), the overall number of trials and the relative number of trials in the two outcome conditions.
Besides, the reasons why a certain method is favored in a given experimental situation are usually not provided, and only few systematic investigations of the properties of statistical tests relating phase to behavior exist  ( VanRullen, 2016 ; Zoefel et al. , 2019 ). Here, we systematically compare the performance of four statistical circular tests (Phase Opposition Sum, Circular Logistic Regression, Watson's test and Modulation Index) to quantify relationships between oscillatory phase and a binary outcome with opposite preferred phases. We created semi-artificial datasets based on real data acquired from 30 participants at rest, from which we extracted the phase of a neural rhythm (alpha rhythm, 8-12 Hz) and the phase of a bodily rhythm (gastric rhythm, ~0. 05 Hz -  Wolpert et al. , 2020 ).
two-alternative forced choice experiment, where participants have to choose between two mutually exclusive options at each trial -stimulus seen vs. not in an experiment probing vision at threshold, or dog vs. cat in a categorization experiment with morphed images. Behavior was generated as transient events belonging to two categories ( "hits " and "misses ") with a flat distribution over time. We then imposed a statistical link between phase and behavior ( Fig. 1  ), with hits more likely (resp. misses less likely) in one phase range, hence creating two behavioral outcomes with opposite preferred phases. The use of a semi-artificial dataset has the advantage of retaining all the complexity of real data, that can be difficult to model, such as the inter-and intra-individual variability in power law exponent  ( Podvalny et al.
Experimental data We used real data to extract physiological phase time series, on which we simulated behavioral output. Data were obtained from 30 healthy participants (16 male, mean age 24, range 19-30) in restingstate with eyes open, 21 corresponding to already analyzed and pub-lished data  ( Richter et al. , 2017 ; Wolpert et al. , 2020 )  and the rest to an unpublished pilot study. All participants signed a written informed consent and were paid for participation. The procedures were approved by the Ethics Committee CPP Île de France III and were in accordance with the Helsinki declaration. Recordings were of 12-15 min length. Brain spontaneous activity was measured with an Elekta Neuromag® TRIUX magnetoencephalography (MEG) system with a sampling frequency of 1000 Hz. Signal Space Separation (tSSS) was performed using MaxFilter (Elekta Neuromag) to remove external noise.
Subsequent analysis was conducted on magnetometer signals. The cardiac artifact was corrected using Independent Component Analysis (ICA), as implemented in the FieldTrip toolbox  ( Oostenveld et al. , 2011 ). Briefly, MEG data were highpass-filtered at 0. 5 Hz (zero phase shift 4th order butterworth filter) and epoched from 200 ms before to 200 ms after each R-peak. The number of independent components to be identified was the rank of the time × trial matrix. Continuous magnetometer data were then decomposed according to identified ICA components. The pairwise phase-consistency (PPC,  Vinck et al. , 2010 )  was computed between the ICA-decomposed signals and the ECG signal to isolate those components most reflective of ECG activity. Components with PPC values larger than 3 standard deviations than the mean were rejected iteratively from the continuous MEG data from each block until either no component exceeded 3 standard deviations or 3 components were rejected.
In practice, this resulted in 3 components being rejected in each subject. Blink artifacts were defined by the EyeLink eyetracker system, padded by ± 100 ms. Muscle and movement artifacts were identified automatically based on a z-value threshold on the MEG data filtered into a band of 110-140 Hz and 4-30 Hz respectively. Concomitant to MEG, electrogastrogram (EGG) data were recorded by means of seven active electrodes placed on the abdominal skin (for details on EGG acquisition and preprocessing see  Wolpert et al. , 2020 ). Since we wanted to compare results using phase time series of two oscillations with very different frequencies, we extracted both the phase of MEG alpha oscillations (8-12 Hz) from the magnetometer channel with the largest alpha power, as well as the phase of the gastric slow rhythm (~0.
05 Hz) from the abdominal electrode showing the largest EGG signal. To obtain the alpha phase time series, we first applied an 8-12 Hz bandpass 6th order Butterworth zero-phase shift filter using the Fieldtrip toolbox  ( Oostenveld et al. , 2011 ). The EEG time series were filtered around gastric peak frequency (mean 0. 049 ± 0. 005 Hz) with a third-order frequency sampling designed finite impulse response filter (MATLAB: FIR2), with a bandwidth of ± 0. 015 Hz around gastric peak frequency. We then retrieved instantaneous phase applying the Hilbert transform to the filtered data.
Data and code availability statement The custom code as well as a set of phase time series from real participants for performing the simulations for this article can be accessed online at the following address: https:// github. com/niwolpert/Simulations _ phase _ statistics. Our scripts make use of Matlab's Circular Statistics Toolbox  ( Berens, 2009  , available at: https://fr. mathworks. com/matlabcentral/fileexchange/10676circular-statistics-toolbox-directional-statistics ). In addition, we made use of Rufin van Rullen's code on Phase Opposition ( VanRullen, 2016 , available at: www. cerco. ups-tlse. fr/~rufin/PhaseOppositionCode/ ).
Sensitivity of statistical tests across different coupling modes We investigated the sensitivity of four statistical circular tests (circular logistic regression, Phase Opposition Sum (POS), Watson's test, the Raleigh test and Modulation Index (MI)) to coupling between oscillatory phase and behavioral outcome (i. e. , hits vs. misses). We extracted physiological oscillations from real data of 30 participants, and created artificial series of behavioral outcomes where we controlled the statistical relationship between phase and outcome, systematically varying the percentage of events where the outcome probability depended on phase, which we call the phase-outcome coupling strength. We also varied coupling mode from 1:1 to 4:1 ( Fig. 1 b ). For 30 virtual participants, we set the number of trials to 250 and distributed hits and misses with respect to phase, following a given coupling mode and strength of phaseoutcome coupling, and applied the four statistical tests.
Acknowledgments This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement No. 670325  , Advanced grant BRAVIUS) and by a senior fellowship of the  Canadian Institute for Advanced Research (CIFAR) program in Brain, Mind and Consciousness  to C. T. -B. , as well as from  ANR-17-EURE-0017. The authors thank  Rufin Van Rullen  for providing code for  Phase Opposition Sum , and  Adriano Tort  for providing Modulation Index code.
Added value of this study In this context, the LILAC trial NCT02659306 was performed to monitor the effects of metformin, a widely used drug for the treatment of diabetes and metabolic disorders, on multiple immunological and virological parameters in a cohort of nondiabetic ART-treated PLWH, with CD4/CD8 ratios below 0. 8. The originality of our study is represented by state-of-the-art immunological and virological investigations performed not only in the blood but also in sigmoid colon biopsies before and after metformin treatment. Metformin is an indirect negative modulator of the mechanistic target of rapamycin (mTOR), a metabolic checkpoint documented to facilitate HIV transcription. The idea to target mTOR with metformin is justified by our previous studies demonstrating that mTOR activation preferentially occurs in Th17-polarized CCR6 + T-cells, a subset of CD4 + T-cells highly permissive to HIV infection and enriched in HIV reservoirs in the blood and colon of ART-treated PLWH.
Statistics Statistical analyses were performed using the GraphPad Prism 8 Software (GraphPad Software, Inc. ). The non-parametric Wilcoxon test was used to determine statistical significance for differences between matched groups. The use of non-parametric tests is justified by the fact that data sets did not pass the normal distribution test Kolmogorov-Smirnov (data not shown), calculated using GrapPad Prism Software. Based on Martin Bland's recommendations relative to p-value interpretation (https://www-users. york. ac. uk/~mb55/ msc/applbio/week4/signif_compact. pdf) p-values are indicated on the Figures and interpreted, as follows: little/no evidence (p>0. 1), weak evidence (p=0. 1-0. 05), evidence (0. 01-0. 05), strong evidence (<0. 01), and very strong evidence (<0. 001). Finally, pairwise correlations between measurements and corresponding "CA HIV RNA/DNA" values were performed using Spearman's method, as implemented in R stats package (version 4.
Discussion Here, we report results from the Lilac pilot clinical trial (NCT02659306), in which metformin was administered for 12 weeks to n=22 non-diabetic ART-treated PLWH with CD4/CD8 ratios below 0. 8. Our investigations revealed metformin-mediated functional changes in the sigmoid colon, including i) decreased infiltration of memory CD4 + T-cells; ii) reduced mTOR phosphorylation/activation in CCR6 + CD4 + T-cells, and iii) decreased residual HIV transcription in memory CD4 + T-cells 61. 5% study participants. At Baseline, the HIV-RNA/HIV-DNA ratios, a surrogate marker for residual HIV transcription, were positively correlated with the frequency of CCR6 + CD4 + T-cells expressing CCR5 and phosphorylated mTOR. At Week 12 vs. Baseline, changes in residual HIV transcription in the colon positively correlated with a decreased infiltration of memory CD4 + T-cells in the colon.
This study was approved by the Research Ethics Boards of the Research Institute of the McGill University Health Centre (MUHC) number MP-37-2016-2456, and by the Health Canada Therapeutic Products Directorate. The study was also approved by the Internal Review Board (IRB) of the Ottawa Hospital Research Institute, ON, Canada (IRB No. 20160433-01H) and the IRB of the CHUM Research Centre, Montr eal, QC, Canada (IRB No. 17. 074). This study was conducted in accordance with the Declaration of Helsinki of 1975. Each participant provided written informed consent before any study procedure. The CIHR/CTN protocol CTNPT027 Trial registration is NCT02659306.
Acknowledgements This study was funded by grants from the  Canadian Institutes of Health Research  (CIHR; # IBC-154053  to PA; # HOP-103230 ,  PTJ-166049  to JPR); the  Vaccines & Immunotherapies Core of the CIHR Canadian HIV Trials Network  (grant  CTN PT247  to JPR); the  Fonds de Recherche du Qu ebec-Sant e (FRQ-S)/AIDS and Infectious Diseases Network  (to PA and JPR); the  FRQÀS Cell Therapy Network  (to JPR); the  Canadian HIV Cure Enterprise Team Grant  ( CanCURE 1. 0 ) funded by the  CIHR  in partnership with  CANFAR  and  IAS  (CanCURE 1. 0; #  HIG-133050  to PA, EAC, JBA, and JPR); and The  Canadian HIV Cure Enterprise Team Grant  ( CanCURE 2. 0 ) funded by the  CIHR  (# HB2À164064  to PA, EAC, JBA, NC, and JPR). Core facilities and HIV-infected patients' cohorts were supported by the  Fondation du CHUM  and the  FRQ-S.
Procedure Data collection was carried out via online using the LimeSurvey software. For the first measurement, this study was disseminated via social networks since three days before the Argentinean quarantine started and throughout the first 103 days of this quarantine  (March 17-June 30, 2020). No personal identification data were asked of participants during the survey except for an email to contact participants a month later for the follow-up survey. All participants gave their informed consent prior to participation. Safety procedures included a feedback email to each subject after participation, which contained the scores obtained in each instrument along with a brief description on what these scores mean, and contact information on mental health services available free of charge. These emails also had the function of raising mental health status self-awareness.
Data analysis We performed all data analysis with RStudio version 4. 0. 3 (R Core Team, 2020   S2 ), and multicollinearity (Table  S3 ) were assessed to be in the range of acceptable values for parametric tests  (Brown, 2006). We calculate descriptive measures and paired Student's t test for both suicidal risk and impulsivity-related traits. To address the main aim of this research, we ran mixed effects modeling by means of the multilevel approach for suicidal risk as the outcome variable. We analyzed models including random (intercepts) and fixed effects in a three-level hierarchical data structure. In the random part of the model, we included two-repeated measures (level 1) nested within participants (level 2) and, in turn, nested within suicidal behavior history (level 3). In the fixed part of the model, we tested the following predictors as additive effects: time (or first and second measurement), sex, age, quarantine duration, mental disorder history, loneliness, and the five impulsivity-related traits as measured in the first time.
For the predictor having more than two conditions, we have set non-orthogonal contrasts, which compared the baseline versus each one of the remaining conditions. The first model only contained the intercept. Models were built by adding one predictor at a time to test the overall main effect of each predictor. Finally, to test the stability of the main effects of impulsivity-related traits on suicidal risk, this entire process was repeated, but considering the measurements of impulsivity-related traits during the follow-up. We used the nlme package  (Pinheiro et al. , 2020)  with the maximum likelihood (ML) method. Comparisons on the fit of the models were based on the Akaike Information Criterion (AIC). For calculating effect sizes (ES), we used the DSUR. noof package  (Aufheimer, 2021). We adopted the Cohen's ES conventions: 0.
e. , impulsivity-related traits). Besides, we included baseline data prior to quarantine. However, no data prior to the COVID-19 pandemic were available in our country, which should be accounted as a limitation beyond our study. Suicide risk is widely affecting college students during lengthy quarantines due to the COVID-19 pandemic. Studies tracking suicide risk changes in this group are needed during college closures and after they recommence. Education on managing negative emotions may help decrease suicide risk in college students during the COVID-19 pandemic.
ON/OFF responses in AC reflect transiently amplified population dynamics We analyzed the population responses of 2343 cells from the AC of three awake mice recorded using calcium imaging techniques (data from  Deneux et al. , 2016). The neurons were recorded while the mice passively listened to randomized presentations of different auditory stimuli. In this study we consider a total of 16 stimuli, consisting of two groups of intensity modulated UP-or DOWN-ramping sounds. In each group, there were stimuli with different frequency content (either 8 kHz pure tones or white noise [WN] sounds), different durations (1 or 2 s) and different intensity slopes (either 50-85 dB or 60-85 dB and reversed, see Table  1  and Materials and methods, Section 'The data set'). We first illustrate the responses of single cells to the presentation of different auditory stimuli, focusing on the periods following the onset and offset of the stimulus.
The data set Neural recordings Neural data was recorded and described in previous work  (Deneux et al. , 2016). We analyzed the activity of 2343 neurons in mouse AC recorded using two-photon calcium imaging while mice were passively listening to different sounds. Each sound was presented 20 times. Data included recordings from three mice across 13 different sessions. Neural recordings in the three mice comprised respectively 1251, 636, and 456 neurons. Recordings in different sessions were performed at different depths, typically with a 50 mm difference (never less than 20 mm). Since the soma diameter is ~15 mm, this ensured that different cells were recorded in different sessions. We analyzed the trialaveraged activity of a pseudo-population of neurons built by pooling neural activity across all recording sessions and all animals.
For the first control shuffled data are obtained by shuffling the labels 'initial state' and 'peak state' across trials. Significance levels were evaluated as outlined above for the first control (Figure  5 -figure supplement 3A). To test for the second hypothesis, we computed correlations between activity vectors defined at the same time point, but averaged over two different sets of 10 trials each. For each trial permutations we computed these correlations for all time points belonging to the OFF response (35 time points) and average over time points. Significance level was then assessed as outlined above for the second control (Figure  5 -figure  supplement 3B ).
Since ridge regression involves computationally expensive matrix inversion, we first reduced the dimensionality of the original data set by using PCA. Unless otherwise stated, we kept a number of PC components equal to K = 100 for the fit of individual stimuli (Figure  3G ,H, Figure  5A-C , Figure  8 , Figure  7 ) and for the fit of all stimuli together (Figure  3I , Figure  4 , Figure  7C , Figure  3.
Software Numerical simulations and data analyses were done using Python 3. 6 (using NumPy  [Harris et al. , 2020] , SciPy  [Virtanen et al. , 2020] , and the scikit-learn package  [Pedregosa et al. , 2011] ). Code is available at https://github. com/gbondanelli/OffResponses; Bondanelli, 2021; copy archived at swh: 1:rev:2438e688ad719eb9870af8c032803a7367fe1140. Surrogate data sets for model validation (see Materials and methods, Section 'Control data sets', Figure  3 , Figure 3-figure supplement 2, Figure 3-figure supplement 3) were generated using the Matlab code provided by Elsayed and Cunningham, 2017 available at https://github. com/gamaleldin/TME.
Related Work In the recent years, a lot of publicly available data sources emerged to provide a universal access to multilingual information. A lot of them took benefit from Wikipedia, the largest knowledge base in the history of human kind. Through the years, many ontologies projects aimed at extracting the semantic knowledge of Wikipedia articles. Back in 2007, the DBPedia project  [15]  was the first knowledge graph (KG) to gather data from Wikipedia articles, infoboxes and lists. Released a few years after, YAGO2  [11]  inherits the same characteristics. It is built from Wikipedia and supplemented with WordNet  [18]  and GeoNames information. YAGO2 is linked to DBPedia entities. A year after, the Wikimedia Foundation unveiled Wikidata  [30] , a community maintained knowledge graph. This one is used as a reference graph to harmonize content across versions of Wikipedia.