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OBJECTIVE. To assess whether HS severity is mirrored at the level of large-scale networks. METHODS. We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 TLE patients with histopathological diagnosis of HS (n=25; TLE-HS) and isolated gliosis (n=19; TLE-G), and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm which simulates functional dynamics from structural network data. RESULTS. Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from grey matter atrophy. CONCLUSIONS. Severe HS is associated with marked remodeling of connectome topology and structurally-governed functional dynamics in TLE, as opposed to isolated gliosis which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales. Data_phen_conn_dryadPhenotypic information and mean connectome feature data for Bernhardt et al. (2019) Temporal lobe epilepsy: hippocampal pathology modulates white matter connectome topology and controllability. Neurology

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

Objective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions. Prev of MS in the US-E-Appendix-Feb-19-2018

Microglia respond to CNS injuries and diseases with complex reactions, often called “activation.” A pro-inflammatory phenotype (also called classical or M1 activation) lies at one extreme of the reactivity spectrum. There were several motivations for this study. First, bacterial endotoxin (lipopolysaccharide, LPS) is the most commonly used pro-inflammatory stimulus for microglia, both in vitro and in vivo; however, pro-inflammatory cytokines (e.g., IFNγ, TNFα) rather than LPS will be encountered with sterile CNS damage and disease. We lack direct comparisons of responses between LPS and such cytokines. Second, while transcriptional profiling is providing substantial data on microglial responses to LPS, these studies mainly use mouse cells and models, and there is increasing evidence that responses of rat microglia can differ. Third, the cytokine milieu is dynamic after acute CNS damage, and an important question in microglial biology is: How malleable are their responses? There are very few studies of effects of resolving cytokines, particularly for rat microglia, and much of the work has focused on pro-inflammatory outcomes. Here, we first exposed primary rat microglia to LPS or to IFNγ+TNFα (I+T) and compared hallmark functional (nitric oxide production, migration) and molecular responses (almost 100 genes), including surface receptors that can be considered part of the sensome. Protein changes for exemplary molecules were also quantified: ARG1, CD206/MRC1, COX-2, iNOS, and PYK2. Despite some similarities, there were notable differences in responses to LPS and I+T. For instance, LPS often evoked higher pro-inflammatory gene expression and also increased several anti-inflammatory genes. Second, we compared the ability of two anti-inflammatory, resolving cytokines (IL-4, IL-10), to counteract responses to LPS and I+T. IL-4 was more effective after I+T than after LPS, and IL-10 was surprisingly ineffective after either stimulus. These results should prove useful in modeling microglial reactivity in vitro; and comparing transcriptional responses to sterile CNS inflammation in vivo. Lively & Schlichter_6h_24h NanoString count dataNormalized mRNA counts

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)

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 gc: percent gc of the bin map: average mappability of the bin 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.

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.

Supplementary Figure e-1. Test day and exercise protocolThe participant arrived at the laboratory at ~13:00 following a 4 hr fast, having abstained from smoking, alcohol, and caffeine for the preceding 16 hrs. Routine bowel management was completed >24 hrs prior to each trial. Upon arrival, the participant emptied his bladder and confirmed compliance with these pre-exercise requirements. The participant was fitted with surface electrodes to record EMG (Bagnoli; Delsys Inc) signals across multiple muscles, a 12 lead ECG (Cardio-card; Nasiff Associates Inc) and heart rate monitor (T1; Polar) prior to testing (A). We took three resting brachial blood pressure (BP) measurements (Dinamap Pro 300V2; General Electric) before an un-blinded clinician adjusted the neurostimulator (B, C), as per the trial allocation, while investigators left the room. We attached a facemask to an online metabolic cart (TrueOne 2400®; ParvoMedics) to capture breath-by-breath variables. Following a 5 min rest, whereby BP measurements were recorded every minute (D), the participant performed a two-minute warm-up with no resistance (0W) on an arm-crank ergometer (Angio; Lode). We increased power output by 10W/minute, until the participant reached volitional exhaustion (E). The participant cycled at 50 revolutions per minute (rpm) throughout. During every minute of the test we recorded the participants global rating of perceived exertion (RPE) using a (Borg 6 -20) RPE scale. We defined peak oxygen uptake (V̇O2peak) and ventilation as the highest 15-breath rolling averages recorded during exercise. A schematic of the trial day is shown in panel (F).Supplementary Figure e-1.pdf N/A