dataesr/scientific-paragraphs-categorization huggingface dataset:
Following correct trials, the mouse is transferred from the home cage to a holding cage for about 10 s while the new bowls were set up (ITI). After making an error on a trial, a mouse was transferred to the holding cage for about 2 min (ITI). For Extended Data Fig. 4p -z, the ITI following errors was 30 s in the holding cage. All animals performed the initial association in a similar number of trials (average: 10-15 trials). Experiments were performed blind to the virus injected. Videos were manually scored with a temporal resolution of 1 s. For analyses (described below), the onset of digging was chosen as the time of a decision for two reasons. First, as noted above, once a mouse began to dig in the incorrect bowl, the other (correct) bowl was removed. Second, only upon the commencement of digging could a mouse determine whether reward was present in the chosen bowl and obtain feedback about whether or not it had made a correct choice.
Extended Data Fig. 2  shows experiments designed to control for potential behavioural effects of scattered light from one hemisphere activating eNpHR in PV neuron cell bodies in the contralateral hemisphere. These experiments used a final light power of 0. 1 mW when connected to the 532 nm green laser (OEM Laser Systems) or 638 nm red laser (Doric Lenses). To determine the appropriate light power for these experiments, a mouse was implanted with a dual fibre-optic cannula (Doric Lenses; DFC_200/240-0. 22_2. 3mm_GS0. 7_FLT) without virus injection in order to measure light scattering from one mPFC to the contralateral hemisphere. Using a dual fibre-optic patch cord (Doric Lenses; DFP_200/240/900-0. 22_2m_GS0. 7-2FC), light was delivered to the mPFC on one side, and the light coming through the other side was measured using a light meter (ThorLabs, PM100D).
In vivo dual-site voltage indicator imaging Analogue signals were digitized by a multichannel real-time signal processor (Tucker-Davis Technologies; RX-8). The commercial software Synapse (Tucker-Davis Technologies) running on a PC was used to control the signal processor, write data streams to disk, and to record synchronized video from a generic infrared USB webcam (Ailipu Technology, ELP-USB100W05MT-DL36). Lock-in amplifier outputs were digitized at 3 kHz.
Combined dual-site voltage indicator imaging and optogenetics analysis Analysis of voltage indicator data was described previously  8  and was facilitated using the signal processing toolbox and MATLAB (Mathworks), using the following functions: fir1, filtfilt, and regress. All four signals during the entire time series of the experiment (left mNeon, left tdTomato, right mNeon, right tdTomato) were first filtered around a frequency of interest. To quantify zero-phase lag cross-hemispheric synchronization between left and right mNeon signals, a linear regression analysis was performed to predict the right mNeon signal using the following inputs: left mNeon signal, left tdTomato signal, and right tdTomato signal. The goodness of fit is compared to how well the regression works if the left mNeon signal is shuffled, i.
e. , if a randomly chosen segment of the original left mNeon signal is used, instead of the segment recorded at the same time as the right mNeon signal. R 2 values are calculated as a function of time using one second segments and compared to the 99th percentile of the distribution of R 2 values obtained from 100 fits to randomly shuffled data. The fraction of time points at which the R 2 obtained from actual data exceeds the 99th percentile of the R 2 values obtained from shuffled data was used to measure zero-phase lag synchronization between the left and right mNeon signals. This analysis was performed at the time of the decision (for example, immediately following the beginning of digging in one bowl, until the end of digging), and smoothed measurements over a 5-min time window following the time point of interest.
Shorter-timeframe resolution dual-site voltage indicator imaging and optogenetics analysis For high temporal resolution quantification of synchrony between signals from dual-site voltage indicator recordings, we first bandpass-filtered Ace-mNeon and tdTomato signals as described above. Then, we 'corrected' the filtered Ace-mNeon signal (to minimize artefacts and noise) by fitting the ipsilateral filtered tdTomato signal via robust linear regression using the robustfit function in Matlab and a time windows of 250 ms. Then, we subtracted off this fit of the tdTomato signal from the filtered Ace-mNeon signal to obtain a corrected Ace-mNeon signal. We then calculated zero-lag cross-correlation between the corrected Ace-mNeon signals from the left and right mPFC across the whole session using 1-s windows.
Combined calcium imaging and optogenetics Imaging data were collected using a miniaturized one-photon microscope (nVoke2; Inscopix). GCaMP7f signals (calcium activity) were detected using 435-460 nm excitation LED (0. 1-0. 2 mW), and optogenetic stimulation of eNpHR-expressing axons was performed using a second 590-650 nm excitation LED (1-2 mW light power). nVoke2 software (Inscopix) was used to control the microscope and collect imaging data. Images were acquired at 20 frames per second, spatially downsampled (4×), and were stored for offline data processing. An input TTL from a separate ANY-maze computer (Stoelting Europe) to the nVoke2 acquisition software were used to synchronize calcium imaging and mouse behaviour movies.
Combined calcium-imaging and optogenetics analysis Calcium-imaging movies were preprocessed using Inscopix Data Processing Software (IDPS; Inscopix). The video frames were spatially filtered (bandpass) with cut-offs set to 0. 008 pixel -1 (low) and 0. 3 pixel -1 (high) followed by frame-by-frame motion correction for removing movement artefacts associated with respiration and head-unrestrained behaviour. The mean image over the imaging session was computed, and the dF/F was computed using this mean image. The resultant preprocessed movies were then exported into MATLAB, and cell segmentation was performed using an open-source calcium-imaging software (CIAPKG)  30. Specifically, a principal component analysis/independent component analysis (PCA/ICA) approach was used to detect and extract regions of interest (presumed neurons) per field of view  31.
For each movie, the extracted output neurons were then manually sorted to remove overlapping neurons, neurons with low signal-to-noise ratio, and neurons with aberrant shapes. Accepted neurons and their calcium activity traces were exported to MATLAB for further analysis using custom scripts as previously described  32. In brief, the s. d. (σ) of the calcium movie was calculated and this was used to perform threshold-based event detection on the traces by first detecting increases in dF/F exceeding 2 (over 1 s). Subsequently, events were detected that exceeded 10 for over 2 s and had a total area under the curve higher than 150σ. The peak of the event was estimated as the local maximum of the entire event. For an extracted output neuron, active frames were marked as the period from the beginning of an event until the calcium signal decreased 30% from the peak of the event (up to a maximum of 2 s).
Data analyses and statistics Statistical analyses were performed using Prism 8 (GraphPad) and detailed in the corresponding figure legends. Quantitative data are expressed as the mean and error bars represent the s. e. m. Group comparisons were made using two-way ANOVA followed by Bonferroni post hoc tests to control for multiple comparisons unless otherwise noted. Paired and unpaired two-tailed Student's t-tests were used to make single-variable comparisons. Similarity of variance between groups was confirmed by the F test. Measurements were taken from distinct samples and from samples that were measured repeatedly. *P < 0. 05, **P < 0. 01, ***P < 0. 001, ****P < 0. 0001. Comparisons with no asterisk or 'NS' had P > 0. 05 and were considered not significant. No statistical methods were used to pre-determine sample sizes but our sample size choice was based on previous studies  6, 8  and are consistent with those generally employed in the field.
793, P = 0. 1693; different location two-way ANOVA (trial type × virus); interaction: F (1,13) = 8. 948, P = 0. 0104; different and correct post hoc t (26) = 2. 991, P = 0. 0120; different and incorrect post hoc t (26) = 2. 991, P = 0. 0120). j-k, The overall speed (meters per second) of mice during the first 5 IA and RS trials across days in the cohort of mice used for microendoscopic Ca 2+ imaging (n = 6 eNpHR-negative controls; n = 6 DIO-eNpHR mice; two-way ANOVA (IA vs. RS × task day) for control mice: interaction: F (1,10) = 0. 00271, P = 0. 9595; two way-ANOVA (IA vs. RS × task day) for DIO-eNpHR mice: interaction: F (1,10) = 1. 378, P = 0. 2677). l-m, There is no difference in the amount of time (seconds) mice spent exploring bowls before making a decision during the first 5 IA and RS trials across days in the microendoscope experimental dataset (n = 6 eNpHR-negative controls; n = 6 DIO-eNpHR mice; two-way ANOVA (IA vs.
RS × task day) for control mice: interaction: F (1,10) = 0. 5053, P = 0. 4934; two way-ANOVA (IA vs. RS × task day) for DIO-eNpHR mice: interaction: F (1,10) = 0. 1147, P = 0. 7419). n-o, The first move of the mouse toward the correct bowl (percent) during the first 5 IA and RS trials across days in the Ca 2+ imaging experimental dataset (n = 6 eNpHR-negative controls; n = 6 DIO-eNpHR mice; two-way ANOVA (IA vs. RS × task day) for control mice: interaction: F (1,10) = 3. 347, P = 0. 0973; two way-ANOVA (IA vs. RS × task day) for DIO-eNpHR mice: interaction: F (1,10) = 0. 1316, P = 0. 7244). p-z, Effects of inhibiting callosal PV+ projections during a version of the RS task using a shorter (30 second) intertrial interval (ITI). p, Experimental design: Day 1, no light delivery; Day 2, continuous light during the RS for optogenetic inhibition of callosal PV terminals ; Day 3, no light was delivered.
039), but not on Day 2 when mice receive optogenetic inhibition of callosal PV+ terminals (post hoc t (12) = 1. 041, P = 0. 9545) nor on Day 3 (post hoc t (12) = 1. 153, P = 0. 8136). f, We performed a shorter timeframe re-analysis of TEMPO data collected from PV-Cre Ai14 injected in one mPFC with AAV-DIO-eNpHR. This was originally collected for Fig. 3. Gamma synchrony was calculated as the zero-phase lag cross-correlation ('x-corr') between 'corrected' mNeon signals that had been filtered in the 30-50 Hz as described in the Methods. Synchrony was averaged over the 5 s following correct choices or errors during the rule shift (RS). This measure of interhemispheric gamma synchrony is higher after RS errors than RS correct choices on Day 1, but was not different after errors vs. correct choices on Days 2 and 3 (n = 5 mice; two-way ANOVA; main effect of type of decision: F (1,8) = 5.
349, P = 0. 0495; Day 1: post hoc t (24) = 2. 619, P = 0. 0451; Day 2: post hoc t (24) = 0. 04492, P > 0. 9999; Day 3: post hoc t (24) = 1. 763, P = 0. 2717). g-i, We also re-analyzed the TEMPO data collected from PV-Cre Ai14 mice injected in one mPFC with DIO-eNpHR to identify specific times when optogenetic inhibition of callosal PV+ projections disrupts gamma synchrony. We measured the change in gamma synchrony (calculated in 1 s windows for various time points relative to behavioral events) between Day 1 (control) and Day 2 (optogenetic inhibition) for both errors (filled circles) and correct choices (open circles) during the first 5 RS trials in PV-Cre mice expressing DIO-eNpHR. g, This change in gamma synchrony (change = Day 2 -Day 1) is not significantly different for error vs. correct trials around the time of trial start (n = 5 mice; two-way ANOVA (time point × error vs.
Acknowledgements V. S. S. is supported by grants from the  US National Institute of Health  ( R01MH121342  and  R01NS116594 ), the  McKnight Endowment Fund for Neuroscience (Memory and Cognitive Disorders award) , and the  Brain Research Foundation (Scientific Innovations  Award  BRF-SIA-2019-02 ). K. K. A. C. is supported by the  Institut National de la Santé et de la Recherche Médicale (Inserm)  and the  Marie Skłodowska-Curie Individual Fellowship (MSCA-IF ). The authors thank  O. Lavielle  for providing PV-cre heterozygous mice;  L. Frank  and  M. Brainard  for feedback on earlier versions of this manuscript; members of the  Alberto Bacci laboratory  for discussions; and the  ICM  technical staff of the facilities PHENO-ICMice and Histomics. This work was also supported by ' Investissements d'avenir'   ANR-10-IAIHU-06.
Data and co-variables Data were obtained from the electronical medical records of each patient: Sex, age (>65 years old), smoking status (smoker/no smoker) comorbidities (chronic lung disease, cardiovascular disease, chronic kidney disease, diabetes) and obesity (defined by BMI >30 kg/m 2 ). The clinical severity at admission in the emergency unit was evaluated by the NEWS 2 (National Early Warning Score 2) score  [15]. This score, ranging from 0 to 20 points, was determined from respiration rate, oxygen saturation, need of supplemental oxygen, systolic blood pressure, pulse rate, level of consciousness and temperature. According to the total score, we identified 4 degrees of clinical severity at admission: low (score from 0 to 4), moderate (score from 5 to 6); high (score from 7 to 8) and severe (score !9).
Statistical analysis Data are presented as means (±SD) for continuous variables, and categorical data are represented as frequencies and percentages. Chi-squared tests were used for categorical variables and Studentt-test or ANOVA for continuous variables to study differences between low and normal SMI groups. In order to identify risk factors, we performed univariate logistic regression analyses using the different outcomes (death, LOS >23 days, acquired infection and ICU admission) as the dependent variables and low SMI and other co-variables as independent variables. Then, only variables with a p < 0. 05 on the univariate regression were included in a multivariate model. Statistical analysis was performed with SPSS version 20. 0.
Low SMI and acquired infection We recorded a total of 116 confirmed infectious complications (66 patients had one infection, 10 patients had 2 different sites of infection, 6 patients had 3, and 3 patients had 4). Data regarding the type of infection are shown in Fig. 2. The most frequent site of infections were lower respiratory tract (59 cases, 50. 9%), followed by urinary tract (31 cases, 26. 7%), blood stream (20 cases, 17. 2%), and gastrointestinal tract infection (6 cases, 5. 2%). During their stay, 85 patients (34. 8%) developed at least one confirmed infectious complication. Infectious complications occurred significantly more frequently in patients with low SMI (45. 1% vs 27. 5%, p < 0. 001). On univariable model, low SMI is associated with infection (OR ¼ 2. 17  [1. 27e3. 71] ). This association remained significant after adjustment in the multivariate model (ORa ¼ 1.
7) in ICU patients vs 61 years (±18. 7), p ¼ 0. 44). Schiaffino et al. reported inversely that low thoracic SMI predicted ICU admission  [12]. However, in this study, data have been collected from multiple centers with potentially different ICU admission criteria for patients with COVID-19. Giraudo et al. also found that reduced thoracic muscle mass was a predictor of ICU hospitalization in COVID-19 patients  [22]. Authors did not measure thoracic SMI but the mean Hounsfield Unit (Hu) value of the right paravertebral muscle at the T12 level. On the other hand, visceral obesity, defined as high visceral fat area, has been associated with in-hospital mortality in COVID-19 patients  [10]. In our study, we observed that COVID-19 patients with low SMI were significantly less obese. It would be interesting to assess their fat distribution.
Peptides were then fractionated by chromatography using a strong cation exchange (SCX) column to discriminate acetylated and unacetylated internal peptides. Individual fractions 2-11 were analyzed by LC-MS using 55 min methods on the LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA), and three pools of fractions (2-5, 6-8, and 9-11) were analyzed using 100 min methods on the TIMS-ToF (Bruker, Billerica, MA). Data were searched against the Mus musculus SwissProt database through Mascot Distiller then parsed using the in-house eNcounter script to validate the detected N-termini and obtain the Nt-acetylation yield  30.
Label-free proteome quantitation In parallel with the N-terminal acetylation assay, the same MEF samples were used for full proteomic analysis. 30 µg of protein extract were loaded on an SDS-PAGE gel and digested following an in-gel digestion protocol  31. The resulting peptides were extracted, dried, and then analyzed on the TIMS-ToF mass spectrometer using the same 100 min method as before. Data were processed using MaxQuant to identify and quantify.
Statistical analysis Statistical analyses were performed using Prism 8 (GraphPad Software, La Jolla, CA). Normality of groups was assessed with the Shapiro-Wilk test. The unpaired t-test was used to compare parametric data the Mann-Whitney test for non-parametric data. Two-way ANOVA was used to analyze proliferation data. The significance of enriched NatB substrates in different MS quantifications was calculated using the built-in two-tailed t-test function for comparison of two samples with equivalent variances in Microsoft Excel for the N-terminome analysis, while a two sample Student t-test, with a threshold p-value of 0. 05, was used in Perseus for the label-free proteome analysis.
NatB substrates We next performed N-terminomics analysis of WT and Naa20 -/-MEFs using the 'Stable Isotope Labeling Protein N-terminal Acetylation Quantification' method to identify and quantify Ntermini (SILProNAQ 29 ). Analysis of WT and Naa20 -/-MEF cells identified 25,048 protein Ntermini corresponding to 1988 non-redundant proteoforms (Supplementary Dataset 1 and Supplementary Table  1 ). The acetylation yields of 1191 of them (1073 in the WT and 945 in the NatB mutant background) could be quantified. The analysis of these unique proteoforms unravelled that 769 underwent removal of the initial Methionine (iMet) in agreement with known N-terminal methionine excision (NME) rules in eukaryotes  32  , whereas 258 proteoforms retained their iMet (Supplementary Table  1 ). The remaining 164 underwent larger cleavages in keeping with leader peptide removal.
The quasi-totality of the iMet starting N-termini featured at position two an amino acid with a large lateral chain (Supplementary Dataset 1). preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. In Naa20 -/-MEFs, there was an overall reduction in Nt-acetylation, primarily in proteins retaining their iMet (Fig. 2A, B ), with a relative decrease in the number of fully acetylated Ntermini to only partially acetylated N-termini (Supplementary Dataset 1 and Supplementary Table  1 ). By contrast, acetylation of N-termini devoid of iMet or N-termini processed downstream were unaffected (Fig. 2C, D ). We compared our N-terminomics data to those previously reported for human cells in which NatB was downregulated  10. Common identified and quantified N-termini of the two datasets revealed mostly decreases of the acetylation yields of NatB substrates (Supplementary Dataset 2).
This effect was significantly stronger in Naa20 -/-MEFs (Supplementary Dataset 2). N-termini with the highest decreased Ntacetylation in Naa20 -/-MEFs (26, Table  1  and Fig. 2E, F ) corresponded to NatB substrates (23), of which 18 displayed Asp and Glu at position two and, to a minor extent (5), Asn and Gln (Table  1  and Supplementary Dataset 1). In Naa20 -/-MEFs, only three non-NatB substrates also showed strongly decreased Nt-acetylation (Table  1 ), but for which no information is available for the human counterparts (Supplementary Dataset 2). We observed that the functions of many retrieved proteins with reduced Nt-acetylation in Naa20 -/-MEFs were apoptosis-related (Supplementary Dataset 1 and Table  1 ). Among them, we found TMEM258, a central mediator of ER quality control via apoptosis  33  ; importin 7, which is involved via the Hippo pathway in the expression of genes important in apoptosis  34  ; and IFM3, a member of the Interferon-induced transmembrane proteins (IFITMs) family  35.
This intriguing observation led us to note that procaspases-3, -8, -9, BAX, APAF1, and BID, which are major contributors to extrinsic and intrinsic apoptosis (Supplementary Fig. 1 ), systematically displayed Asp or Glu at position 2. These proteins are also inferred as NatB substrates based on sequence similarity between mouse and human (100% conservation of the first amino acids) and a combination of experimental and computational evidence  10, 36, 37,  To further explore the relationship between NatB-dependent Nt-acetylation and proteostasis, we next examined the NatB-dependent proteome, particularly that of caspases. Label-free quantification (LFQ protein ratio) analysis showed that only 36 of 1235 statistically relevant proteins, among which 189 are NatB-substrates, were drastically deregulated in Naa20 -/-MEFs (Supplementary Dataset 1, Table  2 , and Fig.
2G ). Almost all upregulated proteins were NatA substrates and, where Nt-acetylation could be measured, no variation was observed (Table  2 ). By contrast, the amino terminal sequence of 10 of 21 strongly downregulated proteins indicated that they were predicted as NatB substrates, including ANP32B (Q9EST5), a multifunction protein directly cleaved by caspase-3 and a negative regulator of caspase-3-dependent apoptosis (Supplementary Dataset 1 and Table  2 ). Although, Nt-acetylation of procaspase-3, -8, and -9 was not quantifiable, caspase-3 and the UBR4 E3-ligase were also found to be reduced in Naa20 -/-MEFs to some extent (Supplementary Dataset 1 and Table  1 ). We obtained information of the Nt-acetylation yield of 12 out of the 36 most drastically deregulated proteins. Of 9 downregulated proteins, Ntacetylation yields were unmodified for five of them (% Nt-acetylation difference <1.
Acknowledgements This work was supported by the  KatNat  ( ERA-NET ,  ANR-17-CAPS-0001-01 ) and  CanMore (France-Germany PRCI ,  ANR-20 CE92-0040 ) grants funded by the  French National Research Agency (ANR)  to C. G. to support JB. B, by  Foundation ARC  ( ARCPJA32020060002137 ) grants to T. M. , from the facilities and expertise of the I2BC proteomic platform (Proteomic-Gif, SICaPS) supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Saclay University , and from  ProteoCure COST (European Cooperation in Science and Technology)  action  CA20113. The proteomic experiments were partially supported by  Agence Nationale de la Recherche  under projects  ProFI (Proteomics French Infrastructure ,  ANR-10-INBS-08 ) and  GRAL , a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ).
Retrieval of protein sequences We identified some highly conserved and experimentally verified potential antigenic penicillin-binding protein 2A (PBP2A) from the genomes of Methicillin Resistant Staphylococcus aureus (MRSA), consisting of ALJ10988. 1, ORN56903. 1, AFJ06714. 1, AEO00772. 1, WP_000721309. 1, WP_057521704. 1, and WP_063851348. 1. The National Center for Biotechnology Information  (NCBI, Retrieved February 23, 2023)  was utilized to retrieve the protein sequences. After running a query with their accession numbers, the protein sequences were obtained as a FASTA format.