dataesr/scientific-paragraphs-categorization huggingface dataset:
2007 ). This change is stronger in lower-mass clusters  (Poggianti et al. 2009 ). The cluster velocity dispersion is mildly dependent on the morphological fraction  (Desai et al. 2007) , not only in low-z but also in high-z clusters, up to z ≈ 1 at least. Fasano et al. (2015)  have recently revisited the morphologydensity paradigm, using the WIde-field Nearby Galaxy-cluster Survey (WINGS) database of galaxies in nearby clusters. They considered 5504 spectroscopically confirmed members of 76 nearby clusters, down to an absolute magnitude limit M V = -19. 5. Fasano et al. (2015)  found that the morphology-radius relation remains valid at all densities, while the morphologydensity relation is valid only in the inner cluster regions, and outside the inner regions only for very regular clusters without substructures.
The data 2. 1. The WINGS database WINGS  (Fasano et al. 2006 ) is an all-sky (|b| > 20) survey of a complete, X-ray selected sample of galaxy clusters in the redshift range 0. 04-0. 07. The goal of the WINGS project is the systematic study of the local cosmic variance of the cluster population and of the properties of cluster galaxies as a function of cluster properties and local environment. The core of the WINGS project is the optical (B, V) imaging survey (called WINGS-OPT;  Varela et al. 2009 ). It provides photometric data for large samples of galaxies (∼550 000) and stars (∼190 000) in the inner field (34 × 34 ) of 76 nearby galaxy clusters, as well as structural and morphological information for a subsample (∼40 000) of relatively bright galaxies  (Pignatelli et al. 2006; Fasano et al.
2012; D'Onofrio et al. 2014). Additional photometric information comes from follow-up imaging surveys in the near-infrared (NIR) J and K bands (WINGS-NIR, 28 clusters;  Valentinuzzi et al. 2009 ) and the U Johnson band (17 clusters;  Omizzolo et al. 2014). Currently, the photometric data set is being expanded by very wide field observations (four times the original WINGS area) taken with the VLT Survey Telescope (VST) OmegaCAM in the usual B and V Johnson bands  (Gullieuszik et al. 2015)  and in the Sloan u band  (D'Onofrio et al. , in prep. ). The WINGS database is made available to the community through the Virtual Observatory  (Moretti et al. 2014). WINGS is currently the only data set providing a homogeneous database of detailed photometric, morphological, and spectroscopic characteristics for several thousand galaxies in galaxy clusters.
(2009, hereafter  Paper I) for a detailed description of the WINGS-SPE spectroscopic survey, including a comparison with literature data. In this work we exploit the full dataset, WINGS and literature data, to perform a detailed dynamical analysis of the whole WINGS-OPT sample, including 76 galaxy clusters (cluster A3562 is excluded because V-band images were obtained under poor seeing conditions). In Paper I we have already presented a detailed data quality analysis and comparison with literature data, detailing the method for including literature data in the WINGS-SPE catalog (see also  Moretti et al. 2014). After updating the catalog with the Sloan Digital Sky Survey (SDSS) DR7 and the latest NASA/IPAC Extragalactic Database (NED) data, our final catalog includes ∼10 000 galaxies with redshift determinations (∼60% from WINGS only), increasing the global spectroscopic completeness level (defined as the number counts of galaxies with redshifts to the number counts of galaxies in the photometric parent sample in magnitude bins, see Paper I) for our sample of galaxy clusters from ∼50% to ∼80% for galaxies below magnitude ∼18, and reaching 50% completeness at a magnitude of ∼19.
In the following we concentrate on the subsample of 73 clusters with ≥10 galaxies with measured spectroscopic redshifts each, for a total of 10 277 galaxies. We complement our sample with the photometric and morphological information from the WINGS database  (Moretti et al. 2014). Galaxy morphologies have been determined using the MORPHOT automatic tool of  Fasano et al. (2012) , which is optimized for the WINGS survey. It applies maximum likelihood and neural network methods to derive the best set of morphological parameters, and the equivalent morphological index, T M (see Table  1  in  Fasano et al. 2012 , for a comparison between MOR-PHOT and visual classification indices). Adopting the MOR-PHOT classification, we define the three subsamples of ellipticals ("E"), with T M ≤ -4, lenticulars ("S0"), with -4 < T M ≤ 0, and spirals + irregulars ("S"), with T M > 0.
For a subset (50 clusters) of the WINGS parent cluster sample, also X-ray temperatures, T X , are available (extracted from the BAX 1  database). We provide alternative estimates of the virial radii using the mass-temperature scaling relation obtained from X-ray observations by  Arnaud et al. (2005). We call r 200,X these virial radius estimates. Yet another possibility is to use a richness-based estimate of the virial radius (named "Num";  Mamon et al. , in prep. ; Old et al. 2014). This is obtained by selecting cluster members in a rectangular area of projected phase space within a projected radius of 1 Mpc and an absolute line-of-sight velocity of ±1333 km s -1 from the cluster center in projected space and velocity. The richness is defined as the total number of members within this rectangular section of PPS, corrected for radial incompleteness.
3 corresponds to an apparent magnitude limit V ∼ 18, and a stellar mass limit log M ∼ 9. 5 (using the relations of  Bell & de Jong 2001). The sample we use for the Σ(R) determination is ∼1 mag fainter that of  Postman & Geller (1984) , corresponding to a stellar mass log M ∼ 9. 1. The field morphological fractions change significantly with stellar mass, but not below log M ∼ 9. 5, so the morphological fractions of  Bamford et al. (2009)  are applicable to our sample. The statistics are good enough to allow fitting 47/42/33 of the 67 individual cluster E/S0/S surface number density profiles. Using the Bayesian information criterion BIC = -2 ln L max + (ln N d ) N p , (1) where N d , N p are the number of data and free parameters, respectively, we establish that the projected NFW model provides a better fit than the King model to 84, 78, and 67% (respectively 80, 63, and 56%) of the E, S0, and S galaxy surface density profiles for the Reg (respectively Irr) cluster set.
Conclusions We use the WINGS data set to investigate the spatial and velocity distribution of cluster galaxies of three morphological classes, E, S0, and S. We do so by building stack clusters, using two different stacking normalization parameters, one based on velocity dispersion, the other on cluster richness. Our results do not depend on the stacking parameter. We build two stacks, one for regular (Reg) clusters and another for (Irr) clusters with significant evidence for substructures according to the DS test. We determine the surface number density and velocity dispersion profiles Σ(R) and σ(R), of cluster E, S0, and S galaxies to an unprecedented accuracy. We fit Σ(R) with both NFW and King models, and obtain marginally better fit results for the NFW model, independently of the morphological class and in both Reg and Irr clusters.
Acknowledgements. We wish to thank the anonymous referee for her/his useful comments that have helped to improve this manuscript. The work of A. C. is supported by the  STARFORM Sinergia Project  funded by the  Swiss National Science Foundation. This research has made use of the X-Rays Clusters Database (BAX) which is operated by the  Laboratoire d'Astrophysique de Tarbes-Toulouse (LATT) , under contract with the  Centre National d'Études Spatiales (CNES). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. A. B. acknowledges the hospitality of the  Institut d'Astrophysique de Paris. The work of A. B. has been financially supported in part by the  PRIN INAF 2014 : " Glittering kaleidoscopes in the sky: the multifaceted nature and role of Galaxy Clusters ", P.
To investigate the regulation of Daam1-MIC by SRRM3/4 (Fig. 1i ), we used data from human HEK293 cells overexpressing human SRRM4 (GEO: GSE112600   2b ). Visualization was performed using the PyMoL program. To validate the inclusion of Daam1-MIC through RT-PCR assays in mouse and zebrafish (Fig. 1h  and throughout the manuscript), total RNA was extracted using the RNeasy Plus Mini kit (Qiagen, 74136), treated with DNase using TURBO DNA-freeTM Kit (Thermo Fisher Scientific), and reverse-transcribed with SuperScript III Reverse transcriptase (Invitrogen, 18080044) and oligo(dT)20 as advised by the manufacturer. GoTaq G2 Flexi DNA Polymerase enzyme (Promega, M7806) was used to amplify Daam1-MIC's flanking exons and PCR assays were performed with 25 cycles, 54°C annealing temperature and 120 sec extension.
All pipette solutions were adjusted to pH 7. 2-7. 3 with K-OH or Cs-OH. Membrane currents and voltages were acquired with Multiclamp 700B amplifiers, digitized (Digidata 1550B) and controlled by pClamp 10. 7 (Molecular Devices Corporation, Sunnyvale, CA, USA) software. Membrane intrinsic properties of CA1 pyramidal cells were determined by passing hyperpolarizing and depolarizing current steps (1 s, with 10 pA increments from -100 to 140 pA) in CC. Synaptic responses in CA1 were evoked by monophasic current (50 μs duration) stimulation of the Schaffer collateral fibers (SCs) with an extracellular bipolar tungsten electrode via isolated current stimulator (DS3) that was set to deliver monophasic currents of 50 μs duration. Membrane currents and voltages were acquired with Multiclamp 700B amplifiers, digitized (Digidata 1550B) and controlled by pClamp 10.
For LTP experiments, the stimulus intensity was adjusted to elicit 50% of the maximum response signal and kept constant throughout the experiment. Data was stored through an acquisition system (Axon Instruments) and the software pClamp 10. 7 was used to display fEPSP and measurements of fEPSP slopes. After recording stable baseline responses for 30 min, LTP was induced by a single train of theta burst stimulation (TBS; 10 bursts of 5 pulses at 100 Hz, with an interval of 200 ms between bursts). Potentiation was measured for 1 h after LTP induction at 0. 033 Hz. Changes in the fEPSP slope were calculated in relation to the baseline fEPSP responses during the last 10 min before TBS, and the time course of LTP values was then normalized to this baseline.
Log2 fold changes between KO and WT were shrunk using apeglm method (Zhu, Ibrahim, and Love 2019) and used for plotting KEGG pathways with the bioconductor package pathview  (Luo and Brouwer 2013). Gene expression was transformed with variance stabilizing transformations (VST) as implemented in DESeq2 and used as input for the Gene Set Variation Analysis (Hänzelmann, Castelo, and Guinney 2013) on KEGG pathways. Gene sets were considered differentially expressed if p-value was lower than 0. 05 in a linear model modeling for genotype. RNA-seq data have been deposited in the Gene Expression Omnibus (GEO), and is available at the GSE219244 accession number.
Acknowledgments We thank  Kyung-Min Noh  for providing the mESC line,  Miguel Valverde  and  Francisco Muñoz  for their help and feedback with the calcium imaging experiments, and members of the  Irimia and Dierssen groups  for constant scientific discussion and feedback. We also thank the  CRG Genomics, Protein Technologies, Tissue Engineering and Advanced Light Microscopy Units  for the RNA sequencing, protein purification, blastocyst injection and microscopy services, respectively.
Results Cognitive impairment and reduced density of dendritic spine were observed in miR-195a knockout mice. Sema3D was identified to be a direct target of miR-195 and a possible contributor to age-associated neurodegeneration as Sema3D levels showed age-dependent increase in rodent brains. Injection of Sema3D-expressing lentivirus caused significant memory deficits while silencing hippocampal Sema3D improved cognition. Repeated injections of Sema3D-expressing lentivirus to elevate cerebral Sema3D for 10 weeks revealed a time-dependent decline of working memory. More importantly, analysis of the data on the Gene Expression Omnibus database showed that Sema3D levels were significantly higher in dementia patients than normal controls (p < 0. 001). Over-expression of homolog Sema3D gene in the nervous system of Drosophila reduced locomotor activity and lifespan by 25%.
Animal studies First, miR-195a KO and WT mice were used to investigate the role of miR-195 in cognitive functions, neurogenesis, and brain senescence, with operators being blinded to the genotype during testing. Cognitive functions of miR-195a KO mice and WT were assessed by the Morris Water Maze (MWM) test, Y-maze test and open field test (OFT). Neurogenesis ability was evaluated by quantifying SOX2 positive neural stem cells (NSCs) in the dentate gyrus (DG) and subventricular zone (SVZ). The senescence-associated β-galactosidase (SA-β-gal) activity and p16 INK4a /p19 Arf expression were used to assess brain senescence. To explore lifespan of miR-195a KO mice, we used the historical life-span data on wildtype C57BL/6 mice to compare with that in miR-195a KO mice due to the limited space and resources, so the magnitude of lifespan reduction needs to be interpreted with caution  [17].
Selection and analysis of gene expression datasets The Gene Expression Omnibus (GEO) database (as of December 2019) was queried for human microarray gene expression datasets in response to neurodegenerative diseases and aging. We first focused on gene expression data on the hippocampus (Table  1 ) and then on the cortex & cerebellum (Table  2 ). For the hippocampus data, the specific search terms used were: "neurodegeneration", "dementia", "cognitive impairment" and "postmortem brain", with the retrieved datasets being filtered based on the following criteria: 1) raw data available and derived from human hippocampal tissue; 2) for neurodegenerative disease datasets, at least one control group (normal subjects) and one diseased group; and 3) Sema3D should be detected in the microarray results.
For the Sema3D expression data on cortex and cerebellum, the retrieved datasets were filtered based on the following criteria: 1) raw data available and derived from cortex and cerebellum tissue; 2) Alzheimer's disease dataset; 3) at least 25 dementia subjects; and 4) detection of Sema3D in the microarray results. Table  S2  summarizes  all retrieved datasets along with the reasons for their inclusion or exclusion from our analyses. After filtering, four microarray datasets from Alzheimer's disease (AD) were retained for further analysis (Table  2 ). Lastly, transcriptome dataset analysis and fold change of Sema3D calculations were performed as mentioned above.
Template identification and protein homology modeling The structure of human Sema3D was not available in the RCSB Protein Data Bank (http:// www. rcsb. org/), so BLASTP was used to identify homologs in this Databank. The X-ray crystal structure of Sema3A from Mus musculus (PDB code is 4GZ8), which has 60. 04% sequence identity to Sema3D, was used as the template during the SWISS-MODEL tool  [19, 20]. Optimization of the predicted model of Sema3D was confirmed via VERIFY 3D and PROCHECK programs available in the Structural Analysis and Verification Server (SAVES) (http:// nihse rver. mbi. ucla. edu/ SAVES), where the VERIFY 3D program examined the compatibility of a protein 3D model with any amino acid sequence (1D), and the PRO-CHECK program evaluated the stereochemical quality of the protein secondary structure  [21].
Structure-based virtual screening using molecular docking The ZINC database contains commercially available molecules for virtual screening  [22]  to identify potential molecules against a specific protein target. Xanthofulvin is a Sema3A inhibitor, and analogs with a minimum of 75% similarity to xanthofulvin were selected from the ZINC database with a total of 950 compounds meeting this criterion and being downloaded as our ligand library. Xanthofulvin has been reported to interfere with the binding between Sema3A and PlexinA receptor  [23]. Among all the possible interactions between Sema3 and PlexinA family members, only Sema3A-PlexinA2 complex has been revealed to possess the x-ray structure (PDB code: 4GZA)  [19]. Based on the vital residues in the interface of Sema3A-PlexinA2 complex, we predicted the docking site on Sema3D, and to validate this, virtual screening was firstly performed by docking the compound from our ligand library to the binding site of Sema3D using iGEMDOCK software  [24]  to rank ligands, with the docking program being run at accurate docking (very slow) option (population size (n = 800), generations (g = 80) and number of solutions (s = 10)) respectively, to predict the interaction between the ligands and the binding site of Sema3D.
For this program, in iGEMDOCK scoring function option, the scoring function of GEMDOCK along with ligand hydrophobic, electrostatic preference and intra-energy (both was set to 1) were all chosen. The best conformation with the lowest binding energy was selected from the docking screening results. The interactions of Sema3D-ligand complex conformations including bond lengths and hydrogen bonds were analyzed using Pymol and Swiss-PdbViewer v4. 0 software  [25]. We expected that the virtually screened compound could block Sema3D signaling transduction.
B The control mice significantly improved their learning after 4-day practices but Sema3D-overexpressing mice failed to show significant improvement after 5-day practices. C Escape latency and D frequency were measured in the memory trials. These two types of mice had significant differences in memory over the 14-day test. E Sema3D-overexpressing mice significantly lost short-term memory on day 14 in the novel object recognition test (n = 4/group). F The Y-maze test was performed to measure spatial working memory in mice for 10 weeks (n = 14/each group). A lower percentage of alteration indicates worse spatial working memory. Data in Fig. 2B -F are presented as mean ± SEM. **p < 0. 01 and ***p < 0. 001. #p < 0. 05 G The Y-maze test measuring spatial working memory was used to assess the effect of siRNA-Ctrl or siRNA-Sema3D injected into miR-195a KO mice aged 12 months (n = 7/each group).
The tests for siRNA-Sema3D and siRNA-Ctrl mice were conducted on the same experiment day. Two-way repeated measures ANOVA showed siRNA-Sema3D significantly improved spatial memory (*p < 0. 05). H Locomotor function was determined using the total travel distance, with longer distance indicating better locomotor function database. Although the hippocampus is often damaged in the early stage of dementia, other brain regions also become involved as the disease progresses; accordingly, we analyzed Sema3D levels in both hippocampus and neighboring brain regions. Firstly, we analyzed hippocampal Sema3D expression in patients with cognitive impairment, and based on the filtering criteria described in the Methods section and Figure  S3A , there were six available GEO datasets containing human hippocampal gene expression profiles with data on Sema3D expression (Supplementary Table  S2 ).
These six datasets included one for normal aging, one for dementia according to Clinical Dementia Rating (CDR), three for Alzheimer's disease (AD), and one for frontotemporal lobar degeneration with ubiquitin-positive inclusions (FTLD-U) (Table  1 ). Additionally, Sema3A expression was also analyzed as presented in Table  1. Hippocampal Sema3D levels had an agedependent increase in normal subjects (GSE11882, Table  1 ), which is consistent with the finding from our rodent brain samples (Fig. 2A ). Among the three AD datasets, hippocampal Sema3D levels were significantly higher in the subjects with more severe AD in two datasets (GSE1297 and GSE48350) (Table  1 ) while also being higher in subjects with more severe clinical dementia (GSE84422) and FTLD-U (GSE13162) (Table  1 ). Taken together, these data indicated that a high Sema3D level is a common characteristic in dementia patients and also in normal aging subjects, which suggest Sema3D as a contributor to the development of dementia and brain aging.
Furthermore, we analyzed Sema3D levels in the cortex and cerebellum. In the GEO database, there are more data on AD than other neurodegenerative diseases. To exclude less reliable studies, we arbitrarily used at least 25 dementia subjects in a study as a selection criterion. According to the filtering criteria described in Figure  S3B , 7 GEO datasets were filtered for Sema3D analysis (Supplementary Table  S2 ). Among these 7 datasets, three studies had data on the prefrontal cortex, two of which showed a significantly higher Sema3D level in the AD patients (p < 0. 001) and one smaller study yielded an odd ratio of 1. 41 without reaching significance level of 0. 05 (Table  2 ). Only one dataset had Sema3D in the cerebellum and one in the visual cortex, both of which included more than 100 AD samples and showed a significantly higher Sema3D level in AD patients (p < 0.
Protein homology modeling for Sema3D structure We first explored Sema3D structure in order to identify the docking site for potential inhibitors. As none of the homo sapiens Sema3 family members have known protein structures, the X-ray crystal structure of Mus musculus Sema3A (PDB code: 4GZ8) was used as the template to predict the Sema3D structure in humans by homology modeling  (19). The sequence identity between Mus musculus Sema3A and Homo sapiens Sema3D is 60. 04% (Figure  S5A ). The quality of the predicted Sema3D structure has good integrity according to the Ramachandran plot indicating 83. 2% of residues in the most favorable region and 15. 5% of residues in the allowed region (Figure  S5B ).
K108, H216, and R404 were on Sema3A; K122, S233, and K422 were on Sema3D. B Cytotoxicity of CHOV20191024 was assessed in human neurons (SY5Y) and the total cell number was calculated at 48 h-post-treatment. Quantitative data are presented as mean ± SEM from three independent experiments. C CHOV20191024 rescued cell viability of Sema3D-treated SY5Y cells that were simultaneously treated with Sema3D and CHOV20191024 for 48 h. The MTT assay was conducted to determine cell viability. Quantitative western blot data are on the right (mean ± SEM) from three independent experiments. N. S. no significance; *p < 0. 05. D CHOV20191024 reversed Sema3D-induced signaling cascade in SY5Y cells treated with Sema3D and CHOV20191024 for 24 h. Phosphorylation of the PI3K/Akt/mTOR signaling pathway was measured by western blot.
E CHOV20191024 rescued Sema3D-induced autophagy dysfunction in SY5Y cells treated with Sema3D and CHOV20191024 for 72 h. Autophagy-associated proteins including mTOR, p62, Beclin-1, and LC3-II / I were measured by western blot. For Fig. 4D  and E , quantitative data are presented as mean ± SEM from three independent experiments. *p < 0. 05 and **p < 0. 01 Sema3D to the Plexin receptors; additionally, as these inhibitors are unlikely to interfere with other Sema3 receptors such as Nrp1 and VEGFR2 based on the protein sequence alignment, and according to docking simulations, the five most promising inhibitors were identified on the basis of empirical binding energy (kcal/ mole). Among these potential inhibitors, compound CHOV20191024 and Sema3D complex was stabilized by three polar contacts.