dataesr/scientific-paragraphs-categorization huggingface dataset:
In many HMMs, it is assumed that state transitions can only occur at the time points of the measurement  (Das et al. , 2009). However, this approximation introduces a bias toward higher fractions of fast diffusing molecules. Instead, we assume state transitions to occur at the middle of steps (see Materials and methods, Parameter fitting. ). Additionally, ExTrack can consider sub-steps to further reduce bias at high transition rates. ExTrack is available both as a Python library  (Simon, 2022 ) and as a TrackMate module  (Ershov et al. , 2022)  on Fiji. Performance and comparison to alternative methods First, we tested the performance of the ExTrack fitting module by applying it to computationally simulated noisy tracks of molecules (10,000 tracks of 10 positions each, if not stated otherwise) that transition between an immobile state (state 0) and a slowly diffusive state (state 1).
ExTrack computes state probabilities at every time point and refines positions Next, we tested the performance of the single-molecule probabilistic annotation module of ExTrack, which is based on global model parameters and annotates state probabilities for every time point  (Bernstein and Fricks, 2016). Fig. 3 , a and b, shows tracks from the simulation of a twostate model with an immobile and a slowly diffusive state (d* = 2). Despite the small value of d*, motion states are reliably estimated in this example. Then, we estimated the predictive power of the probabilistic annotation module by applying it to large sets of tracks with different values of d* (0. 5, 1, or 2). As expected, higher d* values result in higher confidence in predicting states (Fig. 3 c ). To demonstrate the accuracy of the probabilistic annotation, we confirmed that among all molecules predicted to reside in the diffusive state with probability p 1 , the fraction of molecules actually diffusive also equals p 1 (Fig.
S4 a ). Previous methods often classify molecules categorically into the most likely state  (Forney, 1973; Persson et al. , 2013; Lindén et al. , 2017). We used this approach to measure the performance of ExTrack and compare it to previous methods: First, we estimated the accuracy of categorical state annotations depending on the diffusion length and the transition rates (Fig. S4 b ). Increasing transition rates led to worse estimates because states are easier to estimate at time points that are distant from transitions (Fig. S4 c ). Second, we found ExTrack annotations to be robust with respect to wrongly chosen global parameters (Fig. S4 d ). Finally, we found that the ExTrack state annotation module performs better than vbSPT (Fig. S4 e ). Next, we tested the capacity of ExTrack to refine positions by calculating the most likely physical position for each time point.
ExTrack computes distributions of state durations to characterize transition kinetics beyond the Markov transitions assumption ExTrack provides a histogram module that generates probability distributions of state durations. Instead of considering only the most likely set of states, ExTrack considers a large number of potential state vectors and weighs them with their corresponding probabilities. To test the histogram module, we first simulated a two-state model with Markovian transitions. The predicted diffusive and immobile state durations are distributed exponentially, as expected, and in agreement with the simulated data (Fig. 3 f ). Therefore, any deviation from exponential decay can reveal more complex transition behavior: As an example, we simulated molecules that transition between two immobile states and one diffusive state (Fig.
Such behavior would be expected if RodZ could bind anywhere along extended MreB filaments, since filaments constrain diffusion in two dimensions (Fig. 4  g). To test whether proximal rebinding occurs more often than randomly, we compared tracks that were initially bound, then diffusive for four steps, and then either rebound or remained diffusive (Fig. 4 g ). Short distances were indeed over-represented among rebinding molecules compared to molecules that remained diffusive (Fig. 4 h ). This behavior contrasts with PBP1b, which appears to bind to random sites (Fig. S5 e ). The annotation module of ExTrack thus allows us to identify spatial patterns of molecule binding that can be responsible for non-Markovian binding  (Mahmutovic et al. , 2012).
We found that ExTrack using the approximate relationship performs better in the case of two-state and three-state models for a large range of transition rates. However, ExTrack (Python version) also allows using the generator-matrix-based relationship if the user desires. f C,B C, B | θ ( ). When transition rates are high (when k Δt > 0. 4 for the twostate model), our method allows to sub-divide time steps into a number of u sub-steps (where u = 2 corresponds to dividing each step into two). This allows ExTrack to account for multiple transitions and transition times that are different from the midpoints of time steps Δt. To take states at sub-steps into account, we introduce a new state vector B = (b 1,1 , b 1,2 , … b 1,u , … , b n-1,u , b n,1 ) and new physical positions, that require integration according to Eq.
Annotation module The annotation module allows to compute the probabilities to be in any state at any time point of all tracks. According to conditional probabilities and results from ExTrack fitting module, we can compute the probability of the sequences of states given the parameters θ for each track C: P B | C, θ ( ) f C,B C, B | θ ( ) f C C | θ ( ). For a given track C, at each time point i, the probability of the current state b i to be in state s 2 {0, 1} can then be computed by summing over all B with b i = s: P b i s | C, θ ( ) X B P b i s | B, θ ( ) f C,B C, B | θ ( ) f C C | θ ( ). The annotation module can also take advantage of the window approximation described in Using a window. to reduce computation time and make the computation tractable in case of long tracks. Since the annotation module does not require parameter fitting and thus many iterations, the window length can be chosen larger than for the fitting module.
Position-refinement module ExTrack can improve the estimation of molecule positions based on a track, in particular if molecules move slowly. Positions can be estimated by computing the probability density function of each real positions r i  (Lindén et al. , 2017). To do so, we compute f C (C | θ) without integrating at position r i. This results in a probability density function f (r i |C, θ) (Fig. 3, d  and e ), which is a sum of Gaussian functions for each sequence of states. While this probability density can be obtained explicitly, it is much faster to obtain the expected value and SD of the density function. Those values are computed by averaging the parameters of the k 43 = 0. 01, k 45 = 0. 05, k 50 = 0. 15, k 51 = 0. 05, k 52 = 0. 01, k 53 = 0. 02, k 54 = 0. 05. The 10,000 tracks of 10 time points were then fitted to a three-state model, window length = 6.
Computational analysis of molecule rebinding To assess the propensity of RodZ molecules to rebind in close vicinity of their initial binding site, we first annotated tracks using parameters obtained from the ExTrack fitting module (three biological replicates). We considered the 16 first time points of tracks of at least 16 time points (pooling the tracks of the three replicates). Among tracks labeled as initially immobile for at least three time points (p immobile > 0. 5) then diffusive for four time points (with at least three time points of probability p diffusive > 0. 7), we grouped tracks into two sub-groups, the ones rebinding right after and the ones which continue to diffuse for at least one more time point. The histograms represent the distributions of distances between initially bound position and the position at the fourth time point after unbinding.
Images were acquired using an Andor iXon Ultra EMCCD camera with an effective pixel size of 130 nm. Image acquisition was supervised with MicroManager. Tracks were built from movies using TrackMate with LoG peak detection (estimated blob diameter of 0. 5 µm, quality threshold of 15, with subpixel localization). Then, peaks were linked into tracks using the "Simple LAP Tracker" (max distances of 0. 4 µm, and "gap" of 2). Data analysis with ExTrack was restricted to tracks with at least three positions. For the longest tracks, only the first 50 positions were analyzed. Online supplemental material Fig. S1  shows ExTrack parameter fitting. Fig. S2  shows error on two-state model parameters for different methods. Fig. S3  shows robustness of ExTrack to biases due to wrong model assumptions. Fig.
S4  shows capacity of the annotation module. Fig. S5  shows complementary results for GFP-PBP1b and GFP-RodZ tracks. Video 1 shows msfGFP-PBP1b single-particle tracking. Video 2 shows GFP-RodZ single-particle tracking. Table  S1  shows using ExTrack to fit parameters of a three-state model to different simulations of three-state data with qualitatively different types of transitions. Table  S2  shows two-state and threestate fits of tracks from simulated particles either in immobile state (state 0) or in one of five diffusive states (states 1-5).
Acknowledgments We thank the former lab members  Andrey Aristov  for setting up the TIRF microscope,  Antoine Vigouroux  for the GFP-PBP1b strains, and  Gizem Özbaykal  for her guidance for experiments. We also thank  Felipe Bendezú  ( Umeå University, Sweden ) and  Piet De Boer  ( Case Western Reserve University, Cleveland, OH, USA ) for the  GFP-RodZ fusion. This work was supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 679980 ) to S. van Teeffelen, the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (ANR-10-LABX-62-IBEID, S. van Teeffelen ),  France BioImaging  ( ANR-10-INBS-04 ,  J. -Y. Tinevez ), the  Mairie de Paris "Emergence(s)" program  to  S.
In a second part, using an aggregated dataset from published and unpublished studies conducted in our lab, we aimed at providing additional evidence of the contribution of first order performance to metacognitive sensitivity by matching participants according to memory performance using either recall or recognition. Knowing the limits of standard measures of FOK sensitivity, we also intended to evaluate eFOK sensitivity in OA and YA using classical gamma correlations as well as measures less contaminated by metacognitive bias (e. g. , type-2 d'). In both parts, we expected a reduced difference in eFOK sensitivity between OA and YA when first order performance (recall or recognition) was controlled for than when it was not considered.
Non-preregistered analyses As the two meta-analyses splitting participants according to recall performance led to unclear results due to the small overlap between confidence intervals, we conducted a complementary analysis. The global meta-analysis revealed a moderator effect of recognition performance (and a trend for recall) going toward the idea that memory function is involved in eFOK sensitivity. This moderation by recognition however cannot disentangle between a proper memory-metamemory interaction, a spurious relationship due to gamma, or more likely both factors (because of the trend effect of recall). To see whether recognition performance better explains eFOK than recall also in our aggregated dataset, we performed the same split-analysis using recognition performance. The two datasets were therefore created according to the median split.
Discussion We conducted a systematic review and a meta-analysis of eFOK and sFOK in aging. We confirmed our main hypothesis of a preserved sFOK sensitivity and a moderately impaired eFOK sensitivity in aging (g = 0. 53). The large heterogeneity observed in the qualitative and the quantitative analyses is the result of a variety of methodologies in the different studies that were mostly used to control for memory differences between OA and YA. Such variability is not present in sFOK studies and OA have the same (or sometimes even better) performance in semantic memory tasks as YA. We also found that the aggregated dataset meta-analysis slightly inflated the estimation of the eFOK deficit in aging (from g = 0. 53 to g = 0. 78), probably due to the fact there was no attempt to control for first order performance (also confirmed by the non-significant heterogeneity for this meta-analytic model).
We investigated whether episodic memory performance explains the eFOK deficit observed in OA. Moderator analyses revealed that recall (as a trend) and especially recognition reduced the overall effect size. The analysis of the model's intercept suggests that when there is no difference between OA and YA in terms of recognition performance, the eFOK difference vanishes. Using aggregated dataset, reducing or increasing group difference in recall memory by selecting best/worst 50% of OA and worst/best 50% YA respectively only slightly reduced (from g = 0. 78 to g = 0. 52; see Figure  4C ) or enlarged (from g = 0. 78 to g = 1. 06 ; see Figure  4D ) the ageeffects and the two models did not differ significantly. These analyses included only half of the sample and reduced statistical power could explain the overlap in confidence intervals of the effect sizes.
First, FOK sensitivity is calculated from recognition performance which explains that the larger the metacognitive sensitivity measure is biased by first order performance, the larger the estimated deficit. This is in line with our exploratory analysis showing a larger estimated effect using type-II d' than gamma correlation. Theoretically, type-II d' is less influenced by bias, as signal detection theory is proposed to distinguish between bias and sensitivity (but see  46  ). However, first-order performance is more likely to influence this measure as type-II d' assumes that the distributions for "correct" and "incorrect" are Gaussian with equal variance which is rarely the case at the type-II level  47. Metacognitive sensitivity measures have also been shown to be influenced by guessing during the recognition task in modeling on hypothetical data  48.
Selection and inclusion The systematic review and meta-analysis were conducted following the PRISMA guidelines and recommendations  60. Summary of the selection steps are described in Figure  2. Identification. Records published as of October 15th 2020 were identified from PubMed and Web of Sciences online databases. Two searches were carried out each using the keyword "feeling-of-knowing" associated first with "older adults" and then with "aging". Additional reports were identified by checking references in selected papers. No time limit was set regarding the year of publication. After records were identified, duplicates were removed. Screening. Articles were first selected on the basis of their title and abstract. Records not dealing with aging or not using the FOK procedure were excluded.
Statistical analyses Analyses were performed on the measure of metacognitive sensitivity using four models (M1 to M4). The FOK scales were different across studies (see Table  1 ), however all studies used gamma correlations as a measure of sensitivity that is not biased by the type of judgements scale  63. Hedges's G was calculated to measure the effect size of age on metacognitive sensitivity. All analyses were performed using R software and multilevel meta-analysis models were carried out with the metafor package. Our first meta-analytic model (M1) estimated the overall effect-size of a difference in metacognitive sensitivity between YA and OA taking also variability into account at three levels: the study level, the experiment level, and the effect level. We ran two versions of this model: one for eFOK (eM1) and one for sFOK (sM1).
Dataset description Data was taken from 5 published (  28, 31  ; two studies from  19, 27  and 1 unpublished studies conducted in our lab (note that the 5 published studies were also included in the global meta-analysis). The unpublished data included 20 OA and 18 YA in a list recognition task. In total, data included recall performance and counts of recognized and unrecognized items given a yes or no FOK, producing a 2x2 table. This structure allowed us to calculate gamma correlation (for our preregistered confirmatory hypotheses) but also Hamman coefficient and type-II d' (according to our preregistered exploratory hypotheses). Because several participants had no data in at least one of the boxes of 2x2 of the table, we calculated the corrected gamma, Hamman, and type-II d'. Note that we found some discrepancies between calculated means and means reported in corresponding articles as a probable consequence of disparities in methods used for correction of scores or rounding methods.
This aggregated dataset of individual data allows us to have more sensitive tests of our hypothesis. Therefore, we aimed to (1) reproduce findings of the global metaanalysis, (2) compare gamma across 50% of the OA with the best recall performance and 50% of the YA with the worst recall performance, (3) compare gamma for OA and YA with recall performance as a covariable, and (4) explore the effect of type of metacognitive sensitivity measure. For our confirmatory hypothesis (points 2 and 3), we expect to find a lower eFOK difference between YA and OA.
Statistical analyses Analyses were also performed using the R software with the metafor package. Hedges's G was calculated to measure the effect size of age on metacognitive sensitivity for each study. Several meta-analytic models were performed. The first (M5) estimated the overall effect-size of a difference in eFOK metacognitive sensitivity between YA and OA as a reproduction of the global meta-analysis. Model M6 estimated the overall effectsize of a difference in recall performance between YA and OA. Then, the following models were run in order to test the influence of memory performance in metacognitive sensitivity. Models M7a and M7b were performed on half of the sample size. For each study both YA and OA were split into 2 groups according to their recall performance (median split). M7a compared eFOK for OA with the highest recall performance and YA with the lowest recall performance.
Confocal microscopy and data analysis Sections were imaged using a confocal microscopy (Leica SP5) with constant gain and laser power across compared samples. Care was taken that no signal clipping was present. For quantification of GAD67 and VAChT puncta, images were taken with a 40× objective (2048 × 2048 resolution). Background fluorescence was subtracted with ImageJ. VAChT and GAD67 puncta were quantified using the 'SynQuant' ImageJ plugin  (Wang et al. , 2019) , creating ROIs corresponding to synaptic puncta. For each image, we calculated the average size of identified puncta, the number of puncta per unit of area, and the percentage of image area identified as part of punctum.
Using a linear silicon microelectrode (A1x16-5mm-25-177-A16, 16 channels spaced 50 μm apart, Neuronexus), extracellular recordings from V1 and dLGN were performed separately. Visual stimuli were projected by a gamma-corrected projector (PLUS U2-X1130 DLP) on a back-projection screen (Macada Innovision, covering a 60 × 42 cm area) positioned 17. 5 cm in front of the mouse. The visual stimuli were programmed using the MATLAB (MathWorks) scripts package Psychophysics Toolbox 3  (Brainard, 1997). V1 was first recorded at a depth of approximately 800 μm from the cortical surface. Receptive field position was checked by showing white squares (5°) at random positions on a black background. If the receptive field was not within 30 o from the center, we relocated the electrode and checked again. ODI was measured by presenting alternating white and gray full-screen stimuli to each eye in turn.
The extracellular signals were amplified and bandpass filtered at 500 Hz to 10 kHz and digitized at 24 kHz using a Tucker-Davis Technologies RX5 Pentusa base station. The spike detection was done by a voltage thresholder at 3× s. d. online per recording or offline using the open-source sorting package KiloSort  (Pachitariu et al. , 2016). Spikes were sorted and clustered by either principal component analysis-based custom-written MATLAB scripts or integrated template matching-based KiloSort scripts.
Analysis of electrophysiological data Data analysis was done using custom-made MATLAB scripts (https://github. com/heimel/inVivoTools, copy archived at  Heimel, 2023). Data and code are shared on OSF (https://osf. io/dbhgw/). For each 3 s stimulus-related activity, we treated the last 500 ms of the previous trail as baseline. Therefore, we defined the visual responses as the difference between the first 500 ms of the stimulus and the mean of the last 500 ms activities of the previous stimulus. The peak visual responses of stimuli were considered as the maximum firing rates in the first 300 ms of visual-related responses. The visual responses were calculated as average responses of 300 ms. ODI was calculated as ( R contra -R ipsi ) / ( R contra + R ipsi ) , where the Rcontra is the average firing rate of the unit when contralateral eye was open and ipsilateral eye was covered; R ipsi is the opposite.
For receptive field mapping, we computed the spike-triggered average of the random sparse squares stimulus. The peak rate threshold was set to 5 Hz when the patch was within the receptive field. The actual position and size of visual field were computed and corrected for the actual distance between stimuli and animal. For the assessment of the ODI, only included recordings from channels with receptive fields corresponding to the central 30 o of the visual field were included. To assess the monocular and binocular categorical responsiveness, we performed ZETA test (https://github. com/JorritMontijn/ZETA;  Montijn et al. , 2021; Montijn, 2023)  over all trials on all units that we recorded. ZETA (Zenith of Event-based Time-locked Anomalies)-test is a more powerful and sensitive tool than t-test or ANOVA to detect whether a cell is responsive to stimulation in a statistically robust way and avoids binning and parameter selection together.
Acknowledgements The authors thank  Emma Ruimschotel  for technical assistance,  Robin Haak  and  Huub Terra  for help with spike sorting,  Huub Terra  for providing feedback on the manuscript, and the  Mechatronics department and Animal facilities  of the NIN for their services. Figures 1A&B,2A, 3A and 4A were created with BioRender. com (2022). This project has received funding from the  NeuroTime Erasmus+ program  of the  European Commission  and the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreements no. 785907  ( HBP SGA2 ) and  945539  ( HBP SGA3 ).
RABV sequence and metadata acquisition and composition All 25,787 available sequences for the five genes of the RABV genome were downloaded from the NCBI Virus database  5. After quality control, the RABV data set was reduced to 14,752 sequences that spanned 121 countries and were extracted from 192 different host species from 1972 to 2020. The multiple sequence alignment (MSA) comprised a concatenation of the five genes of the RABV genome (Fig. 1a  and Supplementary Table  2 ). The maximum-likelihood (ML) tree from this MSA (Supplementary Fig. 3 ) revealed three major phylogenetic groups with bootstrap values greater than 0. 95 corresponding to bat-, skunk-/ raccoon-, and canine-related RABV, consistent with previous global RABV analyses  26, 29. The tree topology is spatially structured with clades, while gene fragments had a very low impact on sequence clustering (Supplementary Fig 3: color tips for gene fragments and simplified clade), indicating that sequences are not clustering by genetic region.