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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"]}

OV2295 Tables ov2295_breakpoint_counts.csv.gz: Table of breakpoint counts per cell prediction_id: identifier for the breakpoint cell_id: identifier for the cell read_count: number of reads library_id: identifier for the DNA library sample_id: identifier for the sequenced sample chromosome_1: chromosome of breakend 1 strand_1: orientation of break end 1 position_1: position of break end 1 chromosome_2: chromosome of breakend 2 strand_2: orientation of break end 2 position_2: position of break end 2 ov2295_cell_cn.csv.gz: Table of cell specific copy number cell_id: identifier for the cell sample_id: identifier for the sequenced sample library_id: identifier for the DNA library chr: chromosome of bin start: start of bin end: end of bin reads: number of reads copy: raw normalized copy number state: copy number state ov2295_cell_metrics.csv.gz: Table of cell metrics cell_id: identifier of the cell unpaired_mapped_reads: number of unpaired mapped reads paired_mapped_reads: number of mapped reads that were properly paired unpaired_duplicate_reads: number of unpaired duplicated reads paired_duplicate_reads: number of paired reads that were also marked as duplicate unmapped_reads: number of unmapped reads percent_duplicate_reads: percentage of duplicate reads estimated_library_size: scaled total number of mapped reads total_reads: total number of reads, regardless of mapping status total_mapped_reads: total number of mapped reads total_duplicate_reads: number of duplicate reads total_properly_paired: number of properly paired reads coverage_breadth: percentage of genome covered by some read coverage_depth: average reads per nucleotide position in the genome median_insert_size: median insert size between paired reads mean_insert_size: mean insert size between paired reads standard_deviation_insert_size: standard deviation of the insert size between paired reads index_sequence: index sequence of the adaptor sequence column: column of the cell on the nanowell chip img_col: column of the cell from the perspective of the microscope index_i5: id of the i5 index adapter sequence sample_type: type of the sample primer_i7: id of the i5 index primer sequence experimental_condition: experimental treatment of the cell, includes controls index_i7: id of the i7 index adapter sequence cell_call: living/dead classification of the cell based on staining usually, C1 == living, C2 == dead sample_id: name of the sample primer_i5: id of the i5 index primer sequence row: row of the cell on the nanowell chip library_id: identifier for the DNA library index: ignored multiplier: during parameter searching, the set [1..6] that was chosen MSRSI_non_integerness: median of segment residuals from segment integer copy number states MBRSI_dispersion_non_integerness: median of bin residuals from segment integer copy number states MBRSM_dispersion: median of bin residuals from segment median copy number values autocorrelation_hmmcopy: hmmcopy copy autocorrelation cv_hmmcopy: ignored empty_bins_hmmcopy: number of empty bins in hmmcopy mad_hmmcopy: median absolute deviation of hmmcopy copy mean_hmmcopy_reads_per_bin: mean reads per hmmcopy bin median_hmmcopy_reads_per_bin: median reads per hmmcopy bin std_hmmcopy_reads_per_bin: standard deviation value of reads in hmmcopy bins total_halfiness: summed halfiness penality score of the cell total_mapped_reads_hmmcopy: total mapped reads in all hmmcopy bins scaled_halfiness: summed scaled halfiness penalty score of the cell mean_state_mads: mean value for all median absolute deviation scores for each state mean_state_vars: variance value for all median absolute deviation scores for each state mad_neutral_state: median absolute deviation score of the neutral 2 copy state breakpoints: number of breakpoints, as indicated by state changes not at the ends of chromosomes mean_copy: mean hmmcopy copy value state_mode: the most commonly occuring state log_likelihood: hmmcopy log likelihood for the cell true_multiplier: the exact decimal value used to scale the copy number for segmentation order: order of the cell in the hierarchical clustering tree quality: random forest classifier proability score that cell is good ov2295_clone_alleles.csv.gz: Table of clone specific allele data chr: chromosome of bin start: start of bin end: end of bin hap_label: haplotype block identifier clone_id: clone identifier allele_1_sum: number of reads for allele 1 of the haplotype block allele_2_sum: number of reads for allele 2 of the haplotype block total_counts_sum: total reads for the haplotype block ov2295_clone_breakpoints.csv.gz: Table of breakpoints per clone for OV2295 samples. Columns: prediction_id: identifier for the breakpoint chromosome_1: chromosome of breakend 1 strand_1: orientation of break end 1 position_1: position of break end 1 chromosome_2: chromosome of breakend 2 strand_2: orientation of break end 2 position_2: position of break end 2 clone_id: clone identifier read_count: number of reads is_present: presence=1, absent=0 ov2295_clone_clusters.csv.gz: Table of cell clusters as putative clones cell_id: identifier for the cell clone_id: clone identifier ov2295_clone_cn.csv.gz: Table of allele specific copy number per clone for OV2295 samples. Columns: chr: chromosome of bin start: start of bin end: end of bin total_cn: HMMCopy predicted total copy number minor_cn: HMM predicted minor copy number major_cn: HMM predicted major copy number clone_id: clone identifier ov2295_clone_snvs.csv.gz: Table of SNVs per clone for OV2295 samples. Columns: chrom: chromosome coord: genome position ref: reference nucleotide alt: alternate nucleotide clone_id: clone identifier ref_counts: number of reads at this position matching the reference nucleotide alt_counts: number of reads at this position matching the alternate nucleotide total_counts: total number of reads at this position is_present: presence=0, absent=1 is_het: is heterozygous is_hom: is homozygous for the alternate ov2295_nodes.csv.gz: Table of phylogenetic information for SNV evolution variant_id: identifier for the SNV as chrom:coord:ref:alt node: node in the phylogenetic tree loss: probability the SNV was lost at this node origin: probability the SNV originated at this node presence: probability the SNV is present at this node ml_origin: binary indicator the SNV originated at this node ml_presence: binary indicator the SNV is present at this node ml_loss: binary indicator the SNV was lost at this node ov2295_snv_counts.csv.gz: Table of SNV counts chrom: chromosome coord: genome position ref: reference nucleotide alt: alternate nucleotide ref_counts: number of reads at this position matching the reference nucleotide alt_counts: number of reads at this position matching the alternate nucleotide cell_id: identifier for the cell total_counts: total number of reads at this position sample_id: identifier for the sequenced sample ov2295_tree.pickle: Phylogenetic tree in python pickle format. Requires installation of the stochastic dollo code at: https://bitbucket.org/dranew/dollo, version 0.4.2. Note the following sample mapping: ‘SA922’: ‘OV2295(R2)’, ‘SA921’: ‘TOV2295(R)’, ‘SA1090’: ‘OV2295’, Plots ov_supp_clone_allele_cn.png: Clone allele ratios for each OV2295 sample. ov_supp_clone_total_cn.png: Clone copy number for each OV2295 sample. ov_supp_sample_total_cn.png: Bulk copy number for each OV2295 sample. ov_supp_sample_allele_cn.png: Bulk allele ratios for each OV2295 sample.

This is a local CSV file of YMDB 2.0 (http://www.ymdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/). Data was extracted to CSV from the SDF, with column headers for compulsory fields adjusted to fit the MetFrag format. One entry with no SMILES was filled in using the InChI in OpenBabel; entries with no monoisotopic mass or formula were filled in using functions in RChemMass (https://github.com/schymane/RChemMass/), finally one generic formula (row 746) was replaced with the formula from the InChI. This file is for users wanting to integrate the latest YMDB into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one. Anyone using this resource should also cite the original publications from the Wishart Lab: YMDB 2.0: A Significantly Expanded Version of the Yeast Metabolome Database. Ramirez-Guana M, Marcu A, Pon A, Guo AC, Sajed T, Wishart NA, Karu N, Djoumbou Y, Arndt D and Wishart DS. Nucleic Acids Res. 2017 Jan 4;45(D1):D440-D445. PubMed: 27899612 YMDB: The Yeast Metabolome Database. Jewison T, Neveu V, Lee J, Knox C, Liu P, Mandal R, Murthy RK, Sinelnikov I, Guo AC, Wilson M, Djoumbou Y and Wishart DS. Nucleic Acids Res. 2012 Jan;40(Database ussue):D815-20. PubMed: 22064855

This is a local CSV file of HMDB4.0 (http://www.hmdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/). Data was extracted from the XML, metals and entries with no monoisotopic mass were removed, one naming error for http://www.hmdb.ca/metabolites/HMDB0037436 was fixed and the XML fields adjusted to headers for MetFrag import. This file is for users wanting to integrate the latest HMDB into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one. The two versions are identical, the two names fit various formatting conventions used behind the scenes in MetFragWeb.

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

Background: Cannabinoids induce biphasic effects on memory depending on stress levels. We previously demonstrated that different stress intensities, experienced soon after encoding, impaired rat short-term recognition memory in a time-of-day-dependent manner, and that boosting endocannabinoid anandamide (AEA) levels restored memory performance. Here, we examined if two different stress intensities and time-of-day alter hippocampal endocannabinoid tone, and whether these changes modulate short-term memory. Methods: Male Sprague-Dawley rats were subjected to an object recognition task and exposed, at two different times of the day (i.e., morning or afternoon), to low or high stress conditions, immediately after encoding. Memory retention was assessed 1 hr later. Hippocampal AEA and 2-arachidonoyl glycerol (2-AG) content and the activity of their primary degrading enzymes, fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), were measured soon after testing. Results: Consistent with our previous findings, low stress impaired 1-hr memory performance only in the morning, whereas exposure to high stress impaired memory independently of testing time. Stress exposure decreased AEA levels independently of memory alterations. Interestingly, exposure to high stress decreased 2-AG content and, accordingly, increased MAGL activity, selectively in the afternoon. Thus, to further evaluate 2-AG's role in the modulation of short-term recognition memory, rats were given bilateral intra-hippocampal injections of the 2-AG hydrolysis inhibitor KML29 immediately after training, then subjected to low or high stress conditions and tested 1 hr later. Conclusions: KML29 abolished the time-of-day-dependent impairing effects of stress on short-term memory, ameliorating short-term recognition memory performance.