dataesr/scientific-paragraphs-categorization huggingface dataset:
A hundred of independent models were generated and optimized with the method VTFM using conjugate gradients with a maximum iteration number of 300. The degree of the VTFM was set by the autosched module applying the long, thorough optimization schedule of autosched. slow to avoid knotted loop formation in the long, neighboring, missing loop regions of the template structures. Subsequently the models were refined by short molecular dynamics (MD) simulations (of 2 ns) and by simulated annealing using the predefined function, refine. slow of the refine module of MODELLER. Optimization of each model was repeated two times with an objective function cutoff of 10 6. Further, the loop refinement procedure employing the loopmodel module of MODELLER was performed. The loop refinement level was set by the predefined function, a.
RMSDs were calculated between the binding sites (binding residues and ligand) of the docking models and that of the highly resolved X-ray structure (PDB id: 5H8Q) presenting the separate ligand binding domain (LBD) complexed with E and G. The docking poses with lowest RMSDs and lowest binding free energies were selected as final conformations. The protonation states of the titratable residues were determined by PropKa 3. 1 and assigned after visual inspection. The aspartate residues in the binding sites were considered as ionized. The distances between each ligand and its binding residues were monitored during each simulation. The contacts between each ligand and protein were conserved throughout all the simulations, with the exception of one trajectory, where after 160 ns one of the E moved from its binding position and was partially unbound (not shown).
Systems modeling A pre-equilibrated 1-palmitoyl-2-oleoyl-D-glycero-3-phosphatidylcholine (POPC) bilayer consisting of 1,225 lipid molecules was packed around the TMD of hNMDAR using the Membrane Builder facility of CHARMM-GUI  [42]. Two generated models, hNMDAR and hNMDAR•G•E, were inserted into the membrane. The receptor was oriented with respect to membrane in a way that the charged side chains of the TMD were surrounded by either the lipid head groups or the water molecules. The membrane position relative to the receptor was determined based on the database of Orientations of Proteins in Membranes (OPM)  [43]. Both receptor-membrane systems were solvated with 226,708 TIP3P  [44]  water molecules resulting in 210. 125×210. 125×215. 824 Å 3 simulation box cells with a distance of at least 20 Å between protein surface and box face.
Data simulation analysis The MD trajectories were analysed (RMSDs, RMSFs, DSSPs, PCA, clustering and geometric measurements) with tools included in the GROMACS 5. 0. 4 package. Correlation matrices were calculated with the Bio3d R package  [47]. The RMSDs were calculated on 3 × 30,000 conformations generated over the 3 × 300 ns MD runs per system. Only the Cα atoms of hNMDAR were considered. According to the RMSD curves, the initial interval of each trajectory -15, 50, 85 ns for hNMDAR and 15, 20, 25 ns for hNMDAR•G•Ewere omitted as non-equilibrated. The truncated trajectories were merged for each system and used for the RMSF calculation respective the average structure, correlation analysis, PCA and clustering. To remove the overall translation and rotation, each frame of the merged trajectory was superimposed onto the Cα atoms of the reference structure that is the first conformation of the equilibrated merged trajectory.
The Gromos algorithm of Gromacs 5. 0. 4 was used for clustering analysis using a cut-off of 4. 5 A for the both states of hNMDAR. The middle structure of each cluster was considered as representative conformation. Visual inspection of molecular conformations was made with PyMOL and VMD. Graphs were generated using Grace, correlation matrix has been drawn with the Bio3d R package. To visualize the PCA eigenvectors, two extreme projections of each eigenvector along the trajectory on the averaged structure were calculated using Gromacs 5. 0. 4. A python script of PyMOL (Sean M. Law) was used to draw small arrows from the starting structure to the final structure. A python script (Pablo Guardado Calvo) of PyMOL was applied to calculate and represent rotation axes (big arrows) of domains between two extreme structures by aligning the structures and extracting the transformation matrix (T).
This work was supported by  Institut Farman  (http://farman. ens-paris-saclay. fr/), Ecole Normale Superieure (ENS) Paris-Saclay  (http:// www. ens-cachan. fr/), Centre National de la Recherche Scientifique  (CNRS, http://www. cnrs. fr/),  Agence National de Recherche  ( ANR-14-CE16-0016-03 ) and  GENCI -TGCC Curie  (Grant  2015-2016 t2015077381 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We analyze the impact of the different geophysical corrections available on the result. We also compute sea surface height bias and noise over both open ocean (>10 km away from coast) and coastal zone (within 10 km or 5 km coast-ward). The study shows that, in the Hong Kong area, after outlier removal, the ALES retracker performs better in the coastal zone than the other retrackers, both in terms of noise level and trend uncertainty. It also shows that the choice of the ocean tide solution has a great impact on the results, while the wet troposphere correction has little influence. By comparing short-term trends computed over the 2008. 5-2014 time span, both in the coastal zone and in the open ocean (using the Climate Change Initiative sea level data as a reference), we find that the coastal sea level trend is about twice the one observed further offshore.
e. , larger errors around 16°N than near the coast. (a) In parallel, in order to see the impact of the different altimetry products used here (thus of the different data processing) on the results, the linear 6. 5-year trends were also estimated along the Jason-2 pass from both the raw and filtered versions of the X-TRACK SLA time series. Results obtained from the filtered X-TRACK SLA are shown in Figure  9  as a function of latitude. From Figures  8  and 9 , we observe a significant spatial variability in the regional sea level trends. The CCI map (Figure  8a ) displays values of -6 to -4 mm/yr over the open ocean which tend to increase to +2 to +4 mm/yr near the coast. Figure  8b  illustrates how Jason 2 pass 153 captures part of this regional trend pattern, with values varying significantly from -2 mm/yr to +4 mm/yr along the track.
In X-TRACK (Figure  9 ), around 20°N, the linear trend is also relatively small (~2-3 mm/yr), in agreement with the CCI data. A change is observed near 19°N, corresponding to the along-track influence of a narrow trench (the bathymetry rapidly decreases to ~-2500 m and then recovers to ~-100 m). As in the CCI data, the X-TRACK trends increase towards the coast but the values obtained in the vicinity of HK are much larger than for the CCI trends: +8 to +9 mm/yr against +2 mm/yr. The sea level trend at the last point of the filtered X-TRACK SLA is: +8. 6 ± 2. 2 mm/yr. However, gridded altimetry products, such as in the CCI data set, have too low resolution to capture near-shore sea level variations. This also explains why the trends of the gridded CCI data have larger uncertainty than the unsmoothed X-TRACK data.
In parallel, in order to see the impact of the different altimetry products used here (thus of the different data processing) on the results, the linear 6. 5-year trends were also estimated along the Jason-2 pass from both the raw and filtered versions of the X-TRACK SLA time series. Results obtained from the filtered X-TRACK SLA are shown in Figure  9  as a function of latitude. From Figures  8  and 9 , we observe a significant spatial variability in the regional sea level trends. The CCI map (Figure  8a ) displays values of -6 to -4 mm/yr over the open ocean which tend to increase to +2 to +4 mm/yr near the coast. Figure  8b  illustrates how Jason 2 pass 153 captures part of this regional trend pattern, with values varying significantly from -2 mm/yr to +4 mm/yr along the track. In X-TRACK (Figure  9 ), around 20 • N, the linear trend is also relatively small (~2-3 mm/yr), in agreement with the CCI data.
A change is observed near 19 • N, corresponding to the along-track influence of a narrow trench (the bathymetry rapidly decreases to ~-2500 m and then recovers to ~-100 m). As in the CCI data, the X-TRACK trends increase towards the coast but the values obtained in the vicinity of HK are much larger than for the CCI trends: +8 to +9 mm/yr against +2 mm/yr. The sea level trend at the last point of the filtered X-TRACK SLA is: +8. 6 ± 2. 2 mm/yr. However, gridded altimetry products, such as in the CCI data set, have too low resolution to capture near-shore sea level variations. This also explains why the trends of the gridded CCI data have larger uncertainty than the unsmoothed X-TRACK data.
Solutions Derived from the Different Retrackers For each of the three cases defined in Section 2 and each retracker, we computed a spatially averaged 20-Hz SLA time series (see Section 4. 1. 1) as well as the associated 20-Hz noise level (estimated by the standard deviation of the 20-Hz SLA series). The results are shown in Figure  10. In all cases, ALES solution provides the lowest noise level after editing, and MLE4 is slightly less noisy than MLE3. Concerning the three experimental retrackers used in PISTACH, ICE3 has the lowest noise level, and RED3 is slightly less noisy than ICE1. Note that a problem was detected in some of the PISTACH SLA solutions: around cycle #150, both ICE3 and RED3 have much larger SLA than ICE1, the relative bias being about 0. 2 m. Comparison with the tide gauge sea level showed that ICE1 SLA roughly followed the tide gauge data, while ICE3 and RED3 data display large jumps.
8. We also computed a version without the DAC. Given the results obtained above, we concentrated on case 1 area. Different diagnostics are considered to evaluate the impact of the correction choice: (1) Secular trend and uncertainty (mm/yr) computed for different retrackers (results are shown in Table  7 ), and (2) Standard deviation of the SLA residuals with respect to the tide gauge data (results are shown in Table  8 ). Inspection of Tables  6  and 7  indicates that the impact of the wet tropospheric correction is rather low. For the trend estimates, the uncertainties are the same but the composite wet delay results are +0. 1-0. 2 mm/yr larger. Some values of the standard deviations of the differences w. r. t the tide gauge data increase, but some others decrease. The impact of the ocean tide correction appears much more important.
The use of FES tide solutions introduces slightly less residual errors than GOT 4. 8. FES2012 decreases the trend uncertainties, but the trend estimate seems too large (large difference with respect to the tide gauge). Correlations for different tide solutions with respect to the tide gauge data are also shown in Table  9. All ocean tide solutions lead to excellent correlations between altimetry-based and tide gauge-based sea level. FES 2012 gives the highest correlation, FES2004 the lowest. Remember that we did not correct the HK tide gauge data from the DAC. Assuming that the tide gauge and altimeter roughly capture the same dynamic atmospheric effects, we should obtain better statistics if no DAC correction is applied to altimetry. It corresponds to what is observed in Table  8. In conclusion, we note that the choice of the retracker and of the altimetry geophysical corrections, especially the tide correction, has a very significant impact on the estimated coastal sea level trend.
The results indicate that, in spite of the presence of large outliers in ALES (up to tens of meters), after outlier-editing, ALES performs better than MLE4 and MLE3, both in terms of noise level and uncertainty in the sea level trend estimate. We validated the coastal altimetry-based sea level by comparing with data from the HK tide gauge (located ~10 km away). Applying a 3-month smoothing, the standard deviation between the altimetry and tide gauge sea level time series is about 5 cm, which is quite encouraging given the complexity of the study area. Another interesting result is that the computed sea level trend within 5 km from the coast is about twice the trend estimated at larger distances from the coast. This result, based on merging the CCI open ocean-based sea level with the retracked coastal data, suggests that offshore of the HK region, the short-term trend significantly increases when approaching the coast.
Acknowledgments: This study is supported by the  National Key R & D Program of China  under contract # 2017YFB0502800  and # 2017YFB0502802 , and by the  Open Fund of State Key Laboratory of Remote Sensing Science  (Grant No. OFSLRSS201705 ). We are very grateful to  Fernando Nino  for providing us with Figure 2. Xi- Yu Xu  was funded by the  China Scholarship Council  (Grant No. 201604910329 ) during a one year visit in LEGOS. Jason-2 SGDR data are archived and maintained by AVISO. X-TRACK, ALES, and PISTACH data are generated and distributed by  LEGOS/CTOH ,  NOC  and CLS respectively. Special gratitude is also given to the three peer-reviewers, whose insightful comments have improved the manuscript significantly.
Finally, secondary contact is established on the range of 10,000 years ago (t C = 5) to present time (t C = 0; in this setting, both populations have expanded, but no gene-flow has occurred yet). Our simulations show that the molecular signal of secondary contact vanishes after approximately 10,000 years. To apply our results to a specific organism, parameters like effective population size, time of divergence and expansion rate need to be calibrated. In additional simulations, we found that the same summary statistics are distinctive for a wide range of parameter values, and also in datasets with fewer samples. However, reducing the number of loci increases the variance of the summary statistics considerably as it has been observed in coalescent models of single populations or isolation-migration models  (Felsenstein, 2006; Wang and Hey, 2010).
and S10. We also simulate new data with only 4 genomes per deme (fig. S11instead of 20). In addition, we compute the summary statistics for a dataset, where 20 genomes have been sampled from every 5th deme only(deme 5, 10,. , 50, 51, 56,. , 96), so the data consists of DNA sequences from 20 demes (fig. S12).
and S10. We also simulate new data with only 4 genomes per deme (fig. S11instead of 20). In addition, we compute the summary statistics for a dataset, where 20 genomes have been sampled from every 5th deme only(deme 5, 10,. , 50, 51, 56,. , 96), so the data consists of DNA sequences from 20 demes (fig. S12). A 0 1 time since sec. contact (t C ) 2 3 4 5 Directionality index Admixture coeff. 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 -0. 06 -0. 02 0. 02 0. 06 0. 002 0. 010 0. 050 0. 200 1. 000 30 35 40 45 50 barrier permeability (m B m) deme j B time since sec. contact (t C ) 0 1 2 3 4 5 LD center LD edges 0 1 2 3 4 0. 002 0. 010 0. 050 0. 200 1. 000 barrier permeability (m B m).
Long non-coding RNAs (lncRNAs) are an emerging class of regulatory RNA that may be implicated in psychiatric disorders. Here we performed RNA-sequencing in the rostral anterior cingulate cortex of 26 depressed suicides and 24 matched controls. We first performed differential lncRNA expression analysis, and then conducted Weighted Gene Coexpression Network Analysis (WGCNA) to identify co-expression modules associating with depression and suicide. We identified 23 differentially expressed lncRNAs (FDR < 0. 1) as well as their differentially expressed overlapping and antisense protein-coding genes. Several of these overlapping or antisense genes were associated with interferon signaling, which is a component of the innate immune response. Using WGCNA, we identified modules of highly coexpressed genes associated with depression and suicide and found protein-coding genes highly connected to differentially expressed lncRNAs within these modules.
Data processing and RNA-seq differential gene expression analysis As described previously  35  , quality control was first performed. Trimmed reads were aligned to the human hg19 reference genome, using TopHat2  36. We used a lncRNA-specific gene annotation from GENCODE (Release 19 (GRCh37. p13) Long non-coding RNA gene annotation), which contains 13,000 annotated lncRNA genes, to count reads aligning with lncRNA genes using HTSeq-Count  37. We first focused only on lncRNA gene expression to increase our ability and power to detect significantly differentially expressed lncRNAs using DESeq2 and to overcome normalization bias  38. While this approach can lead to increased assignment of sequenced reads to lncRNA gene features, we followed up these results with qPCR experiments using primers carefully designed to target RNA sequences specific for regions unique to these lncRNAs.
LncRNAs with an average of <20 reads per library were discarded from further analysis as they are more likely to be transcriptional noise. Fifteen genes were removed due to having sequenced reads accounting for >0. 4% of the total number of reads, which could skew the data normalization. These were as follows: BCYRN1, KCNQ1OT1, LINC00657, MALAT1, MEG3, MIAT, NEAT1, NEFL, OIP5-AS1, RMRP, RN7SL1, RPPH1, SNHG14, SNORD3A, SYN2. A principal component analysis (PCA) was performed to identify and evaluate effects of covariates. Age, brain pH, PMI, RIN, sex, and substance use disorder (SUD) status were controlled for in the differential lncRNA expression analysis. The top lncRNAs passing an adjusted p-value (FDR < 0. 1) were selected for further investigation. The expression of protein-coding genes was similarly characterized from our RNA-seq dataset using the Comprehensive gene annotation from GENCODE (Release 19, GRCh37.
High throughput qPCR We used Fluidigm's Biomark HD to validate lncRNA expression. Pre-amplified samples and primers were loaded onto a 96 by 96 integrated fluidic circuit (IFC) chip (Fluidigm, USA). Gene expression was quantified by the relative standard curve method  31  , and using the SsoFast EvaGreen Supermix (Bio-Rad, 1725210, USA). LncRNA expression was normalized against the geometric mean of 3 HKG's: ARHGEF12, B-ACTIN, and TUBA1A. Each of these 3 housekeeping genes was stable across conditions in both the RNA-seq and qPCR data (Supplementary Table  3 ). To assess their stability in qPCR data, each housekeeping gene was normalized to the geometric mean of the other 2 housekeeping genes. qPCR RT-qPCR was done to validate the differential expression of potential lncRNA cis-targets as described previously  31.
Weighted gene co-expression network analysis and identification of depression modules We used WGCNA  29  to identify co-expression modules using RNA-seq expression data from controls and depressed suicides combined. A 1-step network construction and module detection method was used along with the Dynamic Tree Cut method. A soft thresholding power of 5 was selected according to an analysis of network topology, the TOMType was set to the default parameter "unsigned", and the branch mergeCutHeight was set at 0. 4. Module detection was done in a single block with a maximum block size of 25,000 genes. We then searched for associations between modules and various sample traits. By default, the WGCNA package correlates a module eigengene with each external sample trait to identify significant associations.
TopGO gene ontology analysis To determine the functional significance of modules associated with depression and suicide, we performed gene ontology enrichment analysis on genes within each depression and suicide associated module using the topGO package  40. We chose to use the elim algorithm and Fisher test statistic to identify significantly enriched gene ontologies. The org. Hs. eg. db annotation package was used for gene mapping to GO terms and we specified a gene universe consisting of all the genes identified by our RNA-seq analysis.
qPCR statistical analysis Statistical analysis for qPCR data was done using the SPSS software. A general linear model was used to determine the significance of the Group factor (depressed suicides vs control) on gene expression while taking into account Sex, SUD, brain pH, PMI, age, and RIN as covariates. Some qPCR lncRNA expression data sets were log transformed, to ensure the normality of the data and equal variances, in order to carry out parametric statistical testing. Grubb's outlier test was used to identify sample outliers.
Identifying other potential lncRNA cis and trans targets LncRNAs have also been shown to regulate more distal gene targets through both cis and trans regulatory mechanisms  45. To identify such potential regulatory targets, we took a 2-step approach: first, we performed a WGCNA analysis to identify modules of co-expressed genes; then, we searched within each module for significant correlations between expression of lncRNAs and distal protein-coding genes. The WGCNA network was constructed using both protein-coding and lncRNAs genes (Supplementary Figure  4 ) and 10 distinct modules of co-expressed genes were detected (Supplementary Table  8 ). Among these, the Brown and Blue modules showed significant associations with depression and suicide (Supplementary Table  8 ). Interestingly, 6 of the previously validated differentially expressed lncRNAs were members of the Blue module and 1 was a member of the Brown module (Supplementary Table  9 ).
LncRNA potential associations with distal target genes LncRNA genes have been shown to produce isoforms with distinct functions carried out through cis or trans regulatory mechanisms  53. As such, a single lncRNA gene could regulate several target genes which may be distally located. Using WGCNA, we found that the Blue and Brown modules significantly associated with depression. GO analysis revealed an enrichment of protein-coding genes involved in cytoskeleton remodeling and in interactions between the cytoskeleton and the cell membrane (Table  2 ) within these modules. Importantly, several of these protein-coding genes were also associated with differentially expressed lncRNAs found within the Blue and Brown modules and some of these same genes were reported to have roles in the regulation of cell migration or in neural differentiation as well.
Finally, several lncRNA-associated protein-coding genes (such as TCF12 and FNBP1) are implicated in both neural processes involving cytoskeletal or membrane regulation as well as processing of immune signaling and function. Along with our findings that lncRNAs are associated with overlapping and antisense genes involved in IFN signaling, lncRNA associations with distal protein-coding genes also involved in immune and neurological processes implicate lncRNAs in regulating immune signals that may be significant players underlying depression and suicide. We would like to acknowledge that a higher threshold value for module construction and detection was used than the recommended default parameters suggested by the developers of WGCNA (see Methods and Materials). This was done to increase the power of detecting significant modules after multiple testing corrections.
Acknowledgements YZ is supported by research funds provided by G. T. P-E. L. is supported by scholarships from the  American Foundation of Suicide Prevention , the  Fondation Deniker , the  Fondation pour la Recherche Médicale , and the  UNAFAM ('Union nationale de  familles et amis de personnes malades et/ou handicapées psychiques'). G. T. holds a Canada Research Chair (Tier 1),  FRQS Chercheur National  salary award and a  NARSAD   Distinguished Investigator Award ; he is supported by grants  FDN148374 ,  MOP93775 ,  MOP11260 ,  MOP119429 , and  MOP119430  from  CIHR , by  NIH  grant  1R01DA033684 , by the  FRQS  through the  Quebec Network on Suicide ,  Mood Disorders , and  Related Disorders , and through an  investigator-initiated research grant  from  Pfizer. BioMark HD High Through -put RT-qPCR validation was completed with the help of Y.