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Web Supplementary Files Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences: full_length_reconstruction_input_sequence_fastas.zip Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences. full_length_reconstruction_input_sequence_alns.zip Web Supplementary File 3 - FASTA file of full-length reconstructed sequences: full_length_reconstructions.fa Web Supplementary File 4 - Table of full-length reconstruction statistics: full_length_reconstruction_stats.csv Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences: orf_fastas.zip Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences: ORF_reconstruction_input_sequence_alns.zip Web Supplementary File 7 - Table of ORF reconstruction statistics: ORF_reconstructions.fa Web Supplementary File 8 - Table of ORF reconstruction statistics: ORF_reconstruction_stats.csv Web Supplementary File 9 - Table of Composite Sequences: bestfl_selection_fixed_CS_seqs.csv Web Supplementary File 10 - Database of gold standards: L1_goldstandards.csv Data Underlying Figures RepeatMasker scans of hg38 and ancestral genomes: anc_gen_RM_out_files.zip Figure 4 4A Source alignment of 54 composite sequences: 220121_dropped12+L1ME3A_muscle.nt.afa Tree produced using the alignment and FastTree: 220121_dropped12+L1ME3A.tree 4B Source alignment of 67 Dfam L1 subfamily 3’ end models: 200123_dfam_3ends.fa.muscle.aln Tree produced using the alignment: 200123_dfam_3ends.fa.muscle.aln.tree Figure 5 KZFP-TE enrichment p-values (from Barazandeh et al 2018): TE_KZFP_enrichment_pvals.xlsx KZFP-TE top 500 peak overlap (from Barazandeh et al 2018): top500_peak_overlap.xlsx Figure 6 RepeatMasker .out file for the Composite Sequence custom library queried against hg38: CS_RM_hg38.fa.out.gz Figure S2 RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): hg38+KimDiv_RM.out RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): Progressive_Cactus_Euth+KimDiv_RM.out RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): Ancestors_Euth+KimDiv_RM.out Figure S5 RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: progCactus_RM_outfiles.zip S5A FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: progCactus_reconstruction_bestFL_equivalents.zip S5B FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: progCactus_reconstruction_input_sequence_alns.zip Figure S6 S6A Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: CD_search_results_short_nms.txt S6B-D Character posterior probabilities of “best” full-length reconstructed sequences: best_fl_post_probs.zip Figure S7 S7B-C Results of Conserved Domain scans of translated initial full-length reconstructed sequences: initial_recons_all_3frametrans_CD-search.txt Results of Conserved Domain scans of translated reconstructed ORFs: recons_ORF1-2_all_3frametrans_CD-search.csv Figure S15 S15A Source alignment of 67 composite sequences: bestfl_selection_fixed_CS_seqs_muscle.nt.afa Tree produced using the alignment: bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree S15B-E Source Muscle alignments for phylogenetic trees of reconstructed sequence components: ORF2: ORF2_keep54_muscle.nt.afa 5’ UTR: 5utr_keep54_muscle.nt.afa ORF1: ORF1_keep54_muscle.nt.afa 3’ UTR: 3utr_keep54_muscle.nt.afa Trees produced using above alignments: ORF2: ORF2_keep54_muscle.nt.afa.tree 5’ UTR: 5utr_keep54_muscle.nt.afa.tree ORF1: ORF1_keep54_muscle.nt.afa.tree 3’ UTR: 3utr_keep54_muscle.nt.afa.tree Figure S17 Unfiltered BLAST results of Composite Sequences queried against hg38: CS_hg38_blastn.csv.zip BED file of L1 instances annotated using BLAST pipeline: BLAST_L1_hits.bed

This folder contains supplementary material for the paper An Algorithm Based on a Cable‑Nernst Planck Model Predicting Synaptic Activity throughout the Dendritic Arbor with Micron Specificity: Experimental data: fluorescence data morphometric data A notebook to simulate calcium dynamics in the dentritic arbor using sinaps software, and the algorithm to predict synaptic activity in fluorescence data Simulation results {"references": ["Galtier et al., (2022). Sinaps: A Python library to simulate voltage dynamics and ionic electrodiffusion in neurons. Journal of Open Source Software, 7(73), 4012, https://doi.org/10.21105/joss.04012"]}

Microtubules are cytoskeletal structures involved in stability, transport and organization in the cell. The building blocks, the α- and β-tubulin heterodimers, form protofilaments that associate laterally into the hollow microtubule. Microtubule also exists as highly stable doublet microtubules in the cilia where stability is needed for ciliary beating and function. The doublet microtubule maintains its stability through interactions at its inner and outer junctions where its A- and B-tubules meet. Here, using cryo-electron microscopy, bioinformatics and mass spectrometry of the doublets of Chlamydomonas reinhardtii and Tetrahymena thermophila, we identified two new inner junction proteins, FAP276 and FAP106, and an inner junction-associated protein, FAP126, thus presenting the complete answer to the inner junction identity and localization. Our structural study of the doublets shows that the inner junction serves as an interaction hub that involves tubulin post-translational modifications. These interactions contribute to the stability of the doublet and hence, normal ciliary motility. Cilia were purified from Chlamydomonas, salt-treated to remove outside proteins. The samples were subjected to tandem mass spectrometry according to the protocol described in Dai et al. 2019, bioRxiv. (https://doi.org/10.1101/739383)

1) Sample registration data. Stitch2p is a function that registeres 2p and 1p images of retina. INPUT: PATH: is the string path where the recordings are saved. RANGE is a 1x2 matrix that defines the range of movies to include in the analysis this works because each recording is assigned a number matching their order(movie_n). eg: calling [0 3] will analyze movie_0 to movie_3 in the specified path. CENTER is a path containing the blood vessel pattern acquired during the recording session. OUTPUT: ROIS = struct with all the 2p recording movies and bloodvessels used in the stiching STITCHED: Array with raw stitched image TESTIMAGE: converted 8bit image with color multiplier for visualization to use with provided sample images open matlab and set the path and center variables. For example: path = "C:\Downloads\Remapping process files\sample 2p recordings" center = "C:\Downloads\Remapping process files\sample 2p recordings\movie_10" Stich2p(path,[0 3],center); Should display a stitched image and save it, along with a matfile, to the path defined in 'center' Example images: any folder in the "sample 2p recordings" folder will contain example files of remapped images along with original recording data. Remapped images are based on the image in the "confocal ROIs" folder. File naming definitions: RoiSet: mask containing the cells of interest. bloodvessels: mask containing the bloodvessels for marker intensity calculations stim_x: individual presented stimuli along with relevant information expression_23_06_20_mx: CSV file containing assigned markers remap: the remapped image from which the markers where assigned remap_points: land mark points used to make the Remapped image landmark points are listed as the confocal and 2p image. 2) Visual response data: Matfile (*.mat) containing a single struct called 'compiled'. Struct field definitions: mb: struct containing rawTrace: Response averaged from 2 presentations of a bar moving in 8 different directions. Bar velocity = 1000um/sec. 8-bar sequence was preceded by a brief (.5s) flash. stimTrace: vector showing stimulus timing allignedTrace: Time x bar array with RGC responses corrected for position mbAngleOrder: bar direction vcctor rawTime: time vector for rawTrace allignedTime: time vector for allignedTrace ff: struct containing rawTrace: response averaged from 3 presentations of a full field flash. rawtime: time vector for rawTrace stimTrace: vector showing stimulus timing mbs: struct ordered the same way as mb. Contains data averaged from 2 presentations of a bar moving in 8 different directions. Bar velocity = 200um/sec. 8-bar sequence was preceded by a brief (.5s) flash. ROI: struct containing: mask - binary mask that defines RGC. xy: Roi centroid position. ost, Brn3c, nr2, calb, gfp: 8bit intensity of the indicated marker within RGC ROI defined by mask. size: Roi area. mrk: Marker classification. theta: angular preference computed from moving bar stimulus and set relative to retinal orientation. dsi: direction selective index computed from moving bar stimulus. osi: orientation selective index computed from moving bar stimulus. Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective. Two Datasets are provided. 1) Sample registration data - contains images, code, and ROIs to register two-photon imaged fields of retinae containing GCaMP6f+ RGCs with the same retinae following staining with antibodies to marker genes for Sdk1 RGC types. 2) Visual response data - contains responses of Sdk1 RGCs to a full-field flash and moving bar, grouped according to expression of Ost, Brn3c, Nr2f2, and Calbindin.

The tumor suppressor p53 regulates various stress responses via increasing its cellular levels. The lowest p53 levels occur in unstressed cells; however, the impact of these low levels on cell integrity remains unclear. To address the impact, we used empirical single-cell tracking of p53-expressing and silenced cells, and developed a fate-simulation algorithm. Here we show that p53-silenced cells underwent more frequent cell death and cell fusion, which further induced multipolar cell division to generate aneuploid progeny. Those results suggest that the low levels of p53 in unstressed cells indeed have a role to maintain the integrity of a cell population. Results of a cell-fate simulation that provides a flexible and dynamic virtual culture space confirmed that these aneuploid progeny could propagate. However, p53-silenced cells were unable to propagate in a virtual cell population that was mainly comprised of p53-expressing cells, supporting the notion that p53 acts as a tumor suppressor. In contrast, when DNA damage responses were repeatedly induced in 53-expressed cells, p53-silenced cells became the major cell population, as the growth of p53-expressed cells was more tightly suppressed by the response than that of p53-silenced cells. In this context, the p53-mediated damage response that is supposed to suppress tumor formation has a pro-malignant function. The cellular microenvironment could thus be a major factor to determine the fate of cancer cells and the fate-simulation algorithm can be used to reveal the fate. 

Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.

Funding: This work was supported by CIHR Foundation grant FDN 143312 (DWA), CIHR grant PJT 156167 (LZP), the European Union's Seventh Framework Programme (FP7/2008–2013) under Grant Agreement 627951 (Marie Curie IOF to PM), the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (57212163 to PM), and in part by the Bundesministerium für Bildung und Forschung, Germany (BMBF, grant no. 16GW0191 to PM). JMP is recipient of the Queen Elizabeth II graduate scholarship in science and technology. PM has been supported by the BIH‐Charité Clinical Scientist Program funded by the Charité – Universitätsmedizin Berlin and the Berlin Institute of Health. DWA holds a Tier 1 Canada Research Chair (CRC) in Membrane Biogenesis. LZP holds a Tier 1 CRC in Molecular Oncology. The ZIP file contains the numerical data underlying the figures of the paper, either in MS Excel format or as CSV files. For details see: Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1008630 Phindr3D is available on GitHub: GitHub - DWALab/Phindr3D

The mammalian MRI (MaMI) data set is a comprehensive database that encompasses high-resolution ex vivo diffusion and structural (T1- and T2-weighted) MRI scans of 124 mammalian species, and a total of 225 scans including replicas. This data set was originally used by Yaniv Assaf (Assaf, Y. et al., 2020, Nat. Neurosci.; doi: https://doi.org/10.1038/s41593-020-0641-7). The version of the data set included in this repository only includes the network matrices obtained from the MRI scans. Details on the preprocessing of the data and the reconstruction of the matrices can be found in Suarez, LE. et al., 2022, bioRxiv; doi: https://doi.org/10.1101/2022.03.11.483995.

Recent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states. The filename describes to which figure the data is used for, for example, 'fig_4B_s2.mat' means this data is used for Fig 4B and S2 Fig. All datasets are saved in .mat. For gifti strcuture in '.mat', the vaule can be found in '.cdata' field, and this brain map can be visualized by Connectome Workbench. For more detials about visualization, please visit https://www.humanconnectome.org/software/connectome-workbench If you have any other questions, feel free to contact me (Jianfeng Zhang): jianfeng_zhang@zju.edu.cn The data set was from the Human Connectome Project (HCP). The 3 Tesla dataset was served for main analyses, and 7 Tesla served for replication. All subjects from the HCP 1200-subject data release (Mar 01, 2017) having completed fMRI sessions were included (837 subjects in 3 Tesla and 145 subjects in7 Tesla, respectively).