dataesr/scientific-paragraphs-categorization huggingface dataset:
Tissue biopsy punches on frozen brain sections: The scalable Allen Mouse Brain volumetric atlas 2012  86  was used to determine the location of each brain region of interest, and their distance from bregma. The atlas' Nissl-stained images were used as a reference for estimating each section's distance from bregma, which determined which brain slices would be used for punch biopsy collection of each region of interest (Extended Data Fig. 3b ). Brain slices were punched using 1. 00mm diameter Robbins True-Cut Disposable Biopsy. Two bilateral punches were collected for each brain region over dry ice. For brain regions in the midline, punches were taken from two consecutive slices. All tissue punch locations were approximated by using landmarks on the slice and by comparing to the atlas. It is important to note that the 1mm puncher was larger than the actual brain region in some instances, and so parts of neighboring brain regions may have been included in the punches.
Confocal imaging The immunofluorescent staining was imaged on a Leica SP8 confocal microscope equipped with Lightning super resolution software. The regions of interest were located using a Leica 20x 0. 75 NA objective to determine areas of suitable staining surrounding the purkinje cell layer in the cerebellum. High-resolution images of the area were taken with a Leica 63x 1. 40 NA oil objective. For highest resolution, the images were formatted at 2496 x 2496 pixels, with a zoom of 2. 50x, line average of 8, pinhole size of 0. 50 AU. The final pixel size was 29. 58nm x 29. 58nm and a z-stack with a step size 0. 15μm was taken through the stained area. A 405nm laser was used for exciting the DAPI channel with a power of 8. 0% and gain of 60%. A 561nm laser was used for the AlexaFluor 546 channel with a power of 4.
SDHA staining analysis Final images were deconvolved in the Lightning software using the default settings. Images were analyzed in ImageJ version 1. 52p. Fifty consecutive slices from each stack were selected for a section thickness of 7. 5μm, which roughly covered the depth of penetration of the antibodies. The 16bit images were first thresholded using the Triangle algorithm within the integrated Auto-thresholding plugin to create a binary image. The particle analyzer was then used to quantify the percent of the area stained for each slice. The percent area stained from each slice was summated to calculate the percent of volume stained. For compartmentalized data, the molecular and granular layers were separated using a rectangular selection of approximately 50px width through each stack.
The correlation is a measure of the average co-expression between the two voxels. The co-expression values were obtained for all possible pair of brain regions (17x17 matrix), yielding a gene co-expression correlation matrix to which the structure of our mitobased networks could be compared. The structural connectome data were obtained from The Allen Mouse Brain Connectivity Atlas  63  , which is a mesoscale connectome of the adult mouse brain. We utilized the normalized projection strength values for the all brain regions of interest. Because this atlas does not distinguish between dorsal and ventral dentate gyrus, data was obtained for 16 brain regions. For three brain regions where the Atlas provides connectivity values for multiple subregions (OFC, VN, Cerebellum), the connectivity values were averaged across the subregions to yield a global measure for each region, thus matching the dimensionality of our mitochondrial dataset.
Correlations between tissues' mitochondrial activities were measured as Pearson's r, and hierarchical clustering was performed on the data using Euclidian distance with Ward's clustering algorithm. T-tests were used to compare groups' correlations. Permutation testing was used to analyze mitochondrial features between versus within brain regions and to analyze the mitochondrial networks against gene co-expression and structural connectome data. To assess the group (CORT vs CSDS) differences in the regional response to stressors are statistically robust, a phase randomized (PR) null approach (Theiler et al. 1992) was used, where we generated 1000 iterations of null data separately for CORT and CSDS groups and ran our TDA-based Mapper approach on each null dataset. PR null preserved the covariance across mice but shuffled any relation between regional responses.
(b) Average effect sizes of 6 mitochondrial features of non-recovered CSDS mice and recovered mice compared to naïve mice in three selected brain regions from A (red boxes). (c) Frequency distribution of the effect size of CORT, CSDS, and recovery as compared to naïve mice on all 6 mitochondrial measures in all 17 brain regions, gaussian-fitted curve, indicating that recovered mice have lower MHI than naïve mice and marginally higher MHI than CSDS (****p<0. 0001, one-sample t test (two tailed)). (d) TDA analysis using Mapper of mitochondrial measures in naïve mice, CORT, CSDS, and CSDS-recovered mice, revealing the recovered group to be more similar to the non-recovered CSDS group than to naïve mice. Extended Data Fig. 9. Behavioral test results (a) To account for both the % time and % distance traveled in the center, the two measures were z-scored and averaged, creating a single Open Field Test (OFT) score.
) and resilient (res. ), and recovered (rec). Social avoidance was measured as the average of 4 scores; z scored social interaction ratio, z scored time spent in interaction zone, z scored time spent in corner, and z scored corners ratio. Social interaction ratio and time spent in SI zone were inverted so that higher values for all four measures indicate higher avoidance. p<0. 05, p<0. 01, Tukey's multiple comparison ordinary one-way ANOVA. Extended Data Fig. 10. Frequency distribution of all correlations of mitochondrialbehavior pairs by behavioral test. Gaussian fits, p values from one-sample t test (two tailed) againt the null hypothesis (r=0). Extended Data Fig. 11. To examine whether the functional organization of the brain revealed using mitochondrial features is also evident cross-modally, we utilized non-parameteric permutation stastics with 10,000 permutations to compare the topology of our mito-derived networks with the expected topology based on the transcriptomic and structural connectomes in the Allen Brain Atlas (see Online Methods for details).
Specifically, we examined i) whether the tightly knit networks (or communities) observed in the mitochondrial features are more densely connected than expected by chance (a-b); and ii) whether the networks derived from mitochondrial features were also more densely connected than expected by chance in other modalities, including gene-coexpression data (c-d) and EYFP-based structural connectivity (ef). To measure the degree of within-network connectedness we used two established metrics: modularity index (Q_mod; Newman 2006) and strength fraction (S. F. ; Richardi et al. 2015). The histograms depict distribution of within-network connectedness generated using 10,000 permutations (or data shuffling). The real value of within-network connectedness is shown using red dots across all plots. For each modality, and across the two metrics, the networks derived from mitochondrial features were more tightly knit than expected by chance.
The reproducibility of scientific studies grounded on language corpora requires approaching each step carefully, from data selection and preprocessing to significance testing. In this paper, we report on our reproduction of a recent study based on a well-known conversational corpus (Switchboard). The reproduced study  [9]  focuses on speech rate convergence between speakers in conversation. While our reproduction confirms the main result of the original study, it also shows interesting variations in the details. In addition, we tested the original study for the robustness of its data selection and pre-processing, as well as the underlying model of speech rate, the variable observed. Our analysis shows that another approach is needed to take into account the complex aspects of speech rate in conversations.
Introduction Throughout the course of a conversation, each conversational partner, the -'speaker'-and the -'interlocutor'-, changes a number of parameters of speech production. Convergence phenomena refer to the tendency of conversational partners to co-adjust their speaking styles. Convergence between conversational partners has been shown to occur at various levels, including syntactic and lexical levels  [36] ,  [4] ,  [18] ,  [6]  and acoustic levels (intensity  [32] ,  [29] ; fundamental frequency  [17] ,  [13] ; speech rate  [46] ). Most of these studies use carefully controlled datasets in which all parameters except the scrutinized variable have been neutralized. This study sought to reproduce and expand the research  [9]  grounded on an existing corpus rather than experimentally controlled material.
Cohen Priva et al. showed evidence of convergence in speech rate production using the Switchboard corpus  [14]. The goal of our study was firstly to show that it was possible to reproduce the results of Cohen Priva et al. following the same procedures and using the same statistical tools and then to check the robustness of their findings. Replicability and reproduction have become a major focus as can be judged by the proliferation of special issues and conferences on these subjects in various fields, including psychology  [34] ,  [8]  economics, and  [42]  medicine. The difference between replicability and reproducibility had been explored in  [16, 37]  and more specifically in language issues in  [5]. Reproducibility is the calculation of quantitative scientific results by independent scientists using the original datasets, while replication is the practice of independently implementing scientific experiments to validate specific findings.
In the study reproduced here, Cohen Priva et al. compared the speech rate of both participants with the average value of their speech rates, or baseline, taken from other conversations. In the second part of their study, the conversants' baselines, along with their gender and age, were investigated. It was shown that a speaker may increase their usual speech rate, or baseline, in response to a fastspeaking interlocutor, or vice versa. Computing the baseline speech rate using more than one conversation makes it possible to compare speech rate robustly. Another benefit of this approach is to smooth out other external factors that could affect speech rate, such as the topic of the conversation. Cohen Priva et al. 's study is well suited for reproducibility studies due to its precise baseline model and the general availability of the dataset, the Switchboard corpus  [14].
Statistical Models The statistical model used in the original study was a linear mixed regression model with speech rate as the predicted value. The slope of the linear regression gives information about the effect of the fixed effect scrutinized. In Study 1 (Table  1 ), the model captures the differences between male and female populations, also illustrated in Figure  2. In this example, the negative slope indicates that the female population has a faster speech rate compared to the male population. Fig. 2  Gender effect on speech rate; in this corpus the female population has a higher speech rate value compared to that of the male population. The lme4 library in R, version 3. 4. 3 [1]  was used to fit the models and provide t-values. The lmerTest package  [27] , which encapsulates lme4, was used to estimate degrees of freedom (Satterthwaite approximation) and calculate p-values.
Taking a More Conservative Stance on Baseline Estimates As seen above, external factors like the topic of a conversation can affect speech rate. A speaker might vary their speech rate depending on how immersed they are in the discussion or according to how important they consider the topic to be. We mitigated this effect by applying the same model to subsets of the Switchboard corpus which only included speakers who were involved in at least N = 2, 3, 4, 5, or 6 conversations. We preferred to use a greater number of conversations per speaker to compute S B and I B , even if this meant that the analysis was then based on a smaller number of total speakers. In this way, we obtained five different datasets with 483, 442, 406, 385, and 357 different speakers, respectively, and 4788, 4630, 4418, 4264, and 4018 conversation sides.
The decision to use these datasets was also due to other factors. For example, emotion can affect a speaker's manner of speaking and subsequently their speech rate. Previous studies such as  [47]  compared the effect of emotions by recognizing them through speech analysis using several databases, while  [43]  demonstrated that people who feel sad may speak more slowly and softly. Using a greater number of conversations per speaker made it possible to smooth out these effects when computing the baseline. As for Study 2, we only took into account the predictors which were significant in the previous study. Table  3  the magnitude of the estimates (for Study 1) for each subset. The magnitude of the effect of gender on speech rate increased with the number of conversations, while the effect of age decreased.
Moreover, both variables preserved significance with an adjusted p-value which in the worst case (corresponding to the dataset with six conversations per speaker) was p = 0. 009 for speaker age and p ∼ 10 -8 for speaker gender. The meaning of the estimates was still significant, even when a smaller amount of data was used. These results demonstrate the model's robustness. In our extension of Study 2, we only took into account significant predictors. The results in Table  4  show that the magnitude of the speaker baseline, the interlocutor baseline and the interlocutor age all increased, but age lost significance as the minimum number of conversations increased. The speech rate results were mainly affected by the speaker baseline and interlocutor baseline. Moreover, the fact that interlocutor age did not seem to affect speech rate convergence implies that the results would not be reproduced if we reduced the size of the dataset.
Variation on Expected Duration Computation The definition of speech rate at the utterance level is taken to be the ratio between utterance duration and expected utterance duration. Speech rate is therefore influenced by the way the expected duration of each word is computed. Assuming that the duration of a word depends on the length of the utterance, the position of the word in the utterance and the median duration of that word in the entire corpus, we fitted the expected duration using an artificial neural network regression with a one-hidden layer of 10 neurons and an adaptive learning method. The model was integrated by the use of the Scikit-Learn package in Python  [35]. In this case, we found that the median of the expected word duration was ∼ 205 ms, just like the median word duration in the corpus.
Validation of the Model on Smaller Datasets Finally, to further validate the model, we applied a cross-validation (k-fold) approach to determine if the results were still significant in smaller datasets. We used k = 5 to obtain each subset from the main corpus. We filtered the data to create a non-independent (the subset could contain overlapping data) with conversation size representing 80% of the total duration of the corpus, used in Section 3. In this way, each dataset contained 3830 conversation sides with the condition that each speaker participated in at least two conversations. We compared the results of Study 2 (Section 3. 4) with the mean and standard deviation of the results computed on the subsets as detailed in Table  4. 3. We found that although interlocutor baseline and interlocutor age (estimates and standard deviation values) were consistent with the values in Section 3 and showed the same direction of effect, they no longer were statistically significant.
Moreover, the estimate for the speaker baseline appeared to be slightly lower compared to the result of the whole dataset but still was significant. The lack of significance cannot be attributed to the smaller number of speakers in the datasets. The minimum number of speakers involved in the subsets was 452, which is about 95% of the total number used in Section 3. The difference in the results could be attributed to the use of fewer conversation sides per speaker in the k-fold subsets (after the filtering processing), which reinforces our proposal to take into account more than two conversations per speaker. These results suggest that speech rate is mainly affected by the speaker baseline when both the number of conversations and the number of speakers decrease.
GitHUb Repository In order to facilitate further reproductions and replications, we created a JuPyteR  [26]  notebook with the code developed to reproduce the study of  [9]  as well as the additional analyses described in this paper in Sections 4, 4. 1, and 4. 2. The notebook contains Python scripts and can be used to perform the following tasks: 1. Pre-Processing the transcripts of the Switchboard corpus 2. Computing the speech rate as described in detail in Section 3. 1 3. Computing the baseline and standardizing the data In addition, we added R scripts to use to perform the statistical analysis described in Sections 3. 2, 3. 3, and 3. 4. The code is accessible at https://github. com/simonefu/Converging_ to_baseline.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement  No713750. Also, the project was carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and with the financial support of the A* MIDEX  (n  ANR-11-IDEX-0001-02 ), funded by the  Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). Our research was also supported by  ANR-16-CONV-0002  ( ILCB ) and  ANR-11-LABX-0036  (BLRI) grants.
Fluorescence activated cell sorting analysis: The number of nucleoid was monitored using BD fortessa cytometer with a 488 nm argon laser and 515-545 nm emission filter at a maximum of 5000 event per second. A minimum of 100 000 cells were analyzed per time point. Calibration was done with the samples of the stationary phase of the WT and parEts at 30°C. For all samples, approximately 10 8 cells were fixed in 70% EtOH, washed, marked with propidium iodide (2-20µg/ml), washed again, and resuspended in sterile 1× PBS pH 7. 2 prior to analysis. FCSalyzer software (https://sourceforge. net/projects/fcsalyzer/) was used for data analysis.
coli analysis pipeline developed by Axel Cournac (https://github. com/axelcournac/EColi_analysis). Briefly, reads recovered from the sequencing were aligned with bowtie2 on the reference E. coli genome (NC_000913), the two ends were merged and the reads were filtered to have a mapping quality strictly > 30. Then each read was assigned to a HpaII restriction fragment (fragment attribution), more than 80% of the reads on average are conserved as informative reads. The contact matrices files are then generated, binned at 2, 5 or 10kb and normalized through the sequential component normalization procedure (SCN  (Cournac et al. , 2012) ).
An average of 600 cells were analyzed. Heat maps of the foci position are automatically generated by MicrobeJ. Two color localization was performed with gusC parS pMT1 (ter) and aidB parS P1 locus (ori) tags. Cells were grown at 25°C in Minimal medium A supplemented with casaminoacids and glucose until culture reached OD = 0. 1 and then cells were shifted to 30°C for 1h. Cells were observed live on agarose pad on a thermo-controlled stage with a Zeiss inverted epifluorescence microscope equipped with led illumination. Localization of foci was recorded with the ObjectJ plugin of ImageJ https://sils. fnwi. uva. nl/bcb/objectj/. An average of 600 cells were analyzed. imaging facility. We thank Jean-Yves Bouet, Pat Higgins, Frédéric Boccard and Ken Marians for strains and plasmids. This research was supported by funding from the European Research Council under the 7th Framework Program (ERC Grant Agreement 771813 to R.
Acknowledgements We thank other members of the  Espeli and Koszul groups ,  Sylvie Rimsky ,  Adrien Camus ,  Martial Marbouty ,  Théo Foutel Rodier  for insightful discussions. Axel Cournac ,  Etienne Jean ,  Lyam Baudry ,  Vittore Scolari ,  Cyril Matthey Dorey ,  Remi Montagne  for Computational analysis tips. We thank  Ivan Matic ,  Arnaud Gutierrez  and  Wei Lin Su  and  Agnès Thierry  for technical advices. We thank the CIRB.
Of 86 molecularly confirmed LHON patients investigated by full mtDNA sequencing and WES in the Research Centre for Medical Genetics in Moscow, 18 (20. 9%) harbored the homozygous p. Tyr51Cys DNAJC30 mutation, while 68 (79. 1%) harbored pathogenic mtDNA mutations. Male prominence was demonstrated in both forms of LHON (arLHON 17:1 affected male to affected female ratio, mtLHON 15:2 affected male to affected female ratio). Additional evidence for incomplete penetrance was provided by screening of the Research Centre for Medical Genetics institutional database of WES data from 1036 patients with inherited disease, where we found 10 heterozygous carriers of the p. Tyr51Cys variant, indicating a minor allele frequency of 0. 48% in the founder population. This predicts 2. 3 per 100,000 homozygous carriers.
If the variant were fully penetrant, this figure would exceed the expected number of LHON cases, thereby strengthening the argument for incomplete penetrance. The marked incomplete penetrance in females is supported by an equal allele frequency in male and female individuals of the p. Tyr51Cys founder variant in the gnomAD database (male 0. 12%, female 0. 13%). the first eye to involvement of the second eye was 1 week (range 0 to 2 years), and 14 patients (46. 7%) demonstrated bilateral involvement at onset. The median time from onset to nadir was 8 weeks (range 0 to 2 years; Table  2 ). We acknowledge that the natural history of the cohort may be confounded to some extent by the concurrent use of idebenone in the majority of the cases. Clinically relevant recovery (CRR) of visual impairment from nadir, defined as improvement in logMAR (logarithm of the minimum angle of resolution) visual acuity (VA) of 0.
2 ± (SD) 7. 8 months in idebenone-treated cases, and 27. 7 ± (SD) 22. 5 months in idebenone-naive cases (5). We also report 1 patient with Leigh syndrome in the absence of optic involvement. This is a female patient, presenting at 2 years of age with spasticity, dysarthria, disturbance of gait, a moderate lactate peak on magnetic resonance spectroscopy (MRS), and bilateral necrosis of the putamen with lesions in the pedunculi cerebelli suggestive of Leigh syndrome on brain MRI (see Supplemental Methods for the case report). DNAJC30 mutations result in impaired exchange of specific subunits of mitochondrial complex I. Due to the consistent complex I defect found in the patient muscle biopsies, we analyzed quantitative proteomic data from patient-derived (n = 3) and control (n = 105) fibroblast cell lines, to determine whether DNAJC30 defects lead to a decrease in complex I abundance.
The CI MOD subunits, accounting for the remainder of the complex I N-module subunits, have a mean turnover of 18. 3% (± 5. 7% SD). The patient-derived fibroblast cell lines (n = 6), spanning all 3 DNAJC30 mutations, demonstrated a significant decrease in the turnover of N-module subunits in assembled complex I (Figure  3B  and Supplemental Tables  10 11 12 13 14 15 ). The mean turnover of the CI HIGH subunits was most strongly reduced to 16. 8% from 33. 6% (P < 0. 0001) followed by the CI MOD subunits to 12. 5% from 18. 3% (P < 0. 001) (Figure  3, B-E , and Supplemental Tables  10 11 12 13 14 15 ). In explanation of this finding, consultation of the BioPlex database of protein-protein interactions confirmed 4 out of the 5 CI HIGH proteins to be the direct interaction partners of DNAJC30, across 5 independent experiments (Figure  3C , complete results in Supplemental  Table 16; and refs.