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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

The genetic component of Alzheimer’s disease (AD) has been mainly assessed using Genome Wide Association Studies (GWAS), which do not capture the risk contributed by rare variants. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals —16,036 AD cases and 16,522 controls— in a two-stage analysis. Next to known genes TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Next to these genes, the rare variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential driver genes in AD-GWAS loci. Rare damaging variants in these genes, and in particular loss-of-function variants, have a large effect on AD-risk, and they are enriched in early onset AD cases. The newly identified AD-associated genes provide additional evidence for a major role for APP-processing, Aβ-aggregation, lipid metabolism and microglial function in AD.

Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. Aim To study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. Methods Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells from 2010 to 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. Results Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and specificity (86%), and detected outbreaks on average 2.5 weeks earlier. Conclusion Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

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.

This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

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.