dataesr/scientific-paragraphs-categorization huggingface dataset:
An important feature of the phenome-wide map is the exploration and discovery of co-clusters of related genes and phenotypes. So far, few community detection algorithms in bipartite networks could be run in a reasonable amount of time. One fast and precise algorithm is the biLouvain algorithm  10  which maximizes bipartite modularity, an extension of the modularity measure found in unipartite network clustering algorithms. The biLouvain creators compared their algorithm against five state-of-the-art bipartite community detection algorithms. In their evaluation, they conclude that biLouvain is always close or equal to the maximum bipartite modularity achieved by any of the five methods while being consistently one of the fastest algorithms for the large real-world datasets tested. Table  1  displays various examples of gene-phenotype co-clusters confirming known genetic associations and also suggesting unsuspected etiological links between phenotypes.
We note that this represents only a small fraction of interesting co-clusters we chose to highlight in our paper. For example, many epidemiological and genetic studies have suggested shared loci between migraine and CAD, and one study identified gene PHACTR1 as the strongest shared locus between the two disorders  29. However, some coclusters might also consist of phenotypes being observed as a consequence of another phenotype. For example, we observed lipid-lowering medications in the same co-cluster as lipid disorders. There are numerous limitations to our study that deserve mention. First, some phenotypes are highly correlated in UKBB, and therefore, colocalized signals were sometimes redundant in our phenome-wide map. However, this redundancy provided some internal replication of the strongest colocalized signals between Neale and SAIGE association datasets, while sometimes complementing signals found in one dataset but not in the other due to different criteria of case and control definition (ICD-10 codes versus PheCodes)  30.
Fortunately, the bipartite network and its characteristics are gaining more attention in the network literature, and future methodological developments will expand the range of tools and analyses that could be performed in this type of networks. Sixth, the first version of biPheMap currently utilizes Neale v1 GWAS summary statistics and GTEX v7 data. Recently, more GWAS summary statistic data (e. g. , Pan-UK Biobank  33  ) and more recent versions of GTEX (e. g. , GTEX v8  34  ) have been published. Furthermore, additional large-scale phenome-wide association datasets originating from exome sequencing data (e. g. , genebass  35  ) have become available. We expect subsequent versions of biPheMap will incorporate these resources as they become publicly available over time. Seventh, while we recognize that the GWAS of many binary (case-control) phenotypes in UKBB are underpowered due to the reduced number of cases for many of them -hence reducing the power to detect colocalization loci-our study was more focused at identifying the strongest shared links between genes and phenotypes in a single population-based cohort.
Nonetheless, to increase the number of colocalization loci, an alternative route is to include GWAS summary statistics from other consortia (which usually include more cases than UKBB), in addition to a larger gene expression level dataset, such as the whole blood eQTLGen consortium  23. Our colocalization analyses using larger case-control GWAS and/or eQTL datasets independently confirmed many colocalized loci found in the biPhe-Map while uncovering additional ones that were missed due to the lower number of cases in Neale and SAIGE for CAD, schizophrenia, and T2D. We note that although the eQTLGen whole blood sample size is much larger than the GTEx whole blood sample size, the identified cis-eQTLs are based on a meta-analysis of 37 different cohorts and therefore heterogenous cell typecomposition effects might persist.
Methods Datasets. This study uses two resources: (a) the UK Biobank (UKBB) project; and (b) the Genotype-Tissue Expression (GTEx) project. The UKBB project is a prospective EHR-linked cohort with deep genetic and rich phenotypic data collected on ~500,000 middle-aged individuals (aged between 40 and 69 years old) recruited from across the United Kingdom 1. Ethics approval for the UK Biobank project was obtained from the North West Centre for Research Ethics Committee (11/NW/0382) and all participants provided written informed consent. The GTEx project is a resource database and associated multi-tissue bank aimed at studying the relationship between genetic variation and gene expression in different human tissues  [5] [6] [7]. Colocalization method. We integrated multiple association datasets to assess whether two association signals, one from a genome-wide association study (GWAS) on a phenotype, and the other from expression quantitative trait locus (eQTL) analysis in a tissue, overlap in such a matter that they are consistent with a shared causal gene.
Asymptotic Bayes factors are averaged across three different values of the prior variance term (0. 01, 0. 1, and 0. 5)  39. We defined a colocalized signal using a posterior probability for H 4 (PPH4) ≥ 0. 80, as described previously  38. GWAS and eQTL summary statistics. coloc2 requires both GWAS summary data and eQTL association summary data. For GWAS data, we used two sets from the UKBB project. The first set of results are GWAS association test statistics publicly available from the Neale lab (Round 1 in 2419 phenotypes). We selected variants with minor allele frequency (MAF) > 0. 1%. More details on the data quality control and the full list of phenotypes can be found at www. nealelab. is/uk-biobank. We further used a second set of UKBB GWAS association statistics computed by the SAIGE testing method  40.
In total, 1403 case-control phenotypes (PheCodes) were available. We selected variants with MAF > 0. 1%. Full datasets and list of PheCodes can be downloaded at https://www. leelabsg. org/resources. For eQTL association signals, we used data from Analysis V7 of the GTEx project available at https://www. gtexportal. org/home/datasets. We restricted our study to the list of 48 tissues (from 620 donors) having a sample size of at least 80. Cis-eQTLs with MAF > 1% were considered as input for coloc2 (detailed in https://storage. googleapis. com/gtex-public-data/Portal_ Analysis_Methods_v7_09052017. pdf available on the GTEx Portal). Before running coloc2 on the Neale GWAS data, we performed stringent quality control. First, we removed results from phenotypes related to cause of death (since these phenotypes generally had very low number of cases), and we also removed case-control phenotypes with less than 1250 cases (or controls), except phenotypes showing prior gene/locus association as reported in the NHGRI-EBI GWAS catalog (https://www.
Colocalization with disease case sample size larger than UKBB. In order to assess the effect of using GWAS summary statistics with case sample size in various diseases larger than in UKBB, we downloaded three GWAS meta-analysis case-control datasets as generated by three different consortia: coronary artery disease from CARDIoGRAMplusC4D (60,801 cases and 123,504 controls)  24  , schizophrenia from the Psychiatric Genomics Consortium (36,989 cases and 113,075 controls)  25  , and type 2 diabetes from DIAGRAM (55,005 cases and 400,308 controls, after excluding UKBB participants)  26. We ran coloc2 by combining each of these three GWAS datasets with the same GTEX cis-eQTL dataset used before (all 48 tissues). PANTHER enrichment analysis. To test if biLouvain co-clusters were enriched with some functional gene classes, we selected 183 co-clusters consisting of 10 genes or more, and input them into the online PANTHER enrichment analysis tools  13, 14.
Acknowledgements We thank  Dr. Benjamin Neale 's and  Dr. Seunggeun Lee 's group for generously sharing  GWAS summary statistics in the UK Biobank. We would also like to thank the donors and their families for making organ and tissue donations to the  GTEx  project. This work was supported in part through the computational resources and staff expertise provided by  Scientific Computing at the Icahn School of Medicine at Mount Sinai. R. D. is supported by  R35GM124836  from the  National Institute of General Medical Sciences  of the  National Institutes of Health ,  R01HL139865  and  R01HL155915  from the  National Heart, Lung, and Blood Institute of the National Institutes of Health. D. M. J. is supported by  T32HL00782  from the  National Heart, Lung, and Blood Institute of the National Institutes of Health.
Data availability All colocalized results analyzed in this study are available through a R Shiny app called biPheMap at https://rstudio-connect. hpc. mssm. edu/biPheMap/. This research has been conducted using the UK Biobank Resource under Application Number "16218". UK Biobank data is available to researchers upon approval of an application form at https:// www. ukbiobank. ac. uk/. The GTEx Analysis V7 dataset can be freely downloaded at https://www. gtexportal. org/home/datasets. At the time of our colocalization analysis, we utilized Round 1 of Benjamin Neale's lab GWAS summary statistics in the UK Biobank. The Round 1 results are no longer accessible and has since been replaced by a more recent Round 2, which can be freely downloaded at http://www. nealelab. is/uk-biobank. Seunggeun Lee's lab GWAS summary statistics in the UK Biobank using SAIGE can be freely downloaded at https://www.
Code availability R codes to run coloc2 are available at https://github. com/Stahl-Lab-MSSM. The biLouvain algorithm may be downloaded and installed by following instructions at https://github. com/paolapesantez/biLouvain. The PANTHER classification system is available at http://www. pantherdb. org/. R software and packages (https://cran. r-project. org/) were used to analyze the data and generate the figures, except Fig. 1 , which contains graphical elements from a free version of Canva (https://www. canva. com/en/).
Observational datasets The observational data from the ACLOUD campaign used in this study are summarized in Table  1. Figure  2a  shows detailed measurements of the clouds in the target area as obtained with the Microwave Radar and radiometer for Arctic Clouds radar onboard P5 (MiRAC;  Mech et al. , 2019). Because of the doubling-back between C5 and C6, the same cloud structure appears three times, once in each panel. Cloud top height varies significantly along the flight track in this area, which is typical for broken convective cloud fields. The maximum cloud top height is at approximately 1. 4 km, which is consistent with the thermal inversion height visible in the DS04 profile (see Fig. 3a ) and the Airborne Mobile Aerosol Lidar (AMALi;  Stachlewska et al. , 2010)  measurements (also included in Fig.
There is a notable gap in wind measurements between 1. 6 -2. 5 km, where samples have been removed after quality checks. z [km] LES DS04 0 50 100 RH [%] 0 2 4 q v [g kg 1 ] 10 0 10 u [m s 1 ] 10 0 10 v [m s 1 ] a) b) c) d) e) Cloud measurements are of key importance for this study, given its focus on understanding interactions between microphysics, turbulence and aerosol in mixed-phase convective clouds. In particular, the observational data on hydrometeor occurrence and mass as collected by the P5 and P6 are useful for evaluating LES experiments. While the MiRAC and AMALi data onboard P5 provide information about cloud top heights, vertical structure, and liquid water path, the Nevzorov probe onboard P6 provides in-situ samples of cloud liquid water content. The LaMP CIP probe provides information on ice water content using  Brown and Francis (1995)  mass diameter relationship on non-spherical particles only.
DALES The simulations in this study are carried out with the Dutch Atmospheric Large Eddy Simulation model ((DALES,  Heus et al. , 2010). DALES has been successfully applied to simulate observed turbulent/convective boundary-layers and clouds in many climate regimes, including the tropics (Vilà-Guerau de Arellano et al. , 2020), the subtropics (  Van der Dussen et al. , 2013; de Roode et al. , 2016; Reilly et al. , 2020) , mid-latitudes  (Neggers et al. , 2012; Corbetta et al. , 2015; Van Laar et al. , 2019)  and high latitudes  (de Roode et al. , 2019; Neggers et al. , 2019; Egerer et al. , 2020; Neggers, 2020a, b). The code of DALES is open source and maintained online at https://github. com/dalesteam/dales. The governing equations, numerical aspects and the various subgrid physics packages are described in detail by  Heus et al.
Following Van Laar et al. (2019), a combination of analyses (available every 12 hours) and short-range forecasts (available every 3-hours) is used, effectively yielding a four-dimensional dataset of the atmospheric state variables {θ l , q t , u, v} at 3-hourly temporal resolution and 0. 1x0. 1 degree spatial resolution. In this study these are calculated at 3-hourly points along the back-trajectory, a method previously adopted by  Neggers et al. (2019). The forcing profiles are time and height dependent. Horizontal advective forcings are represented as prescribed advective tendencies, calculated through horizontal averaging within a 0. 5 • × 0. 5 • -wide column around the location. The tendency due to large-scale subsidence relies on a prescribed profile of pressure velocity that acts on the evolving vertical structure in the LES.
Forcings in the momentum equation include the Coriolis term and the pressure gradient term, in combination expressed as the departure of the model wind from the prescribed geostrophic wind. The latter is calculated from the pressure field. Given these time-and height-dependent forcing profiles at the trajectory points, timeaveraging is then applied over 12 hours to obtain the composite forcing dataset used to drive the LES. These profiles are described in more detail in Appendix B), and are characterized by a persistent low-level subsidence, a weak advective cooling and moistening tendency above 1 km height, and negligible geostrophic forcing of the wind. The DS04 dropsonde profiles are used as initial state, amalgamated with the composite large-scale model data. Where available, the sonde data is averaged onto the vertical grid of the composite ECMWF forcings.
It seems particularly suited to investigate interactions between aerosol, hydrometeors and turbulent https://doi. org/10. 5194/acp-2021-888 Preprint. Discussion started: 5 November 2021 c Author(s) 2021. CC BY 4. 0 License. dynamics. This motivates the further testing of this method for other cold cloud regimes, such as clouds at high latitudes and/or the tenuous cloud regime. Another follow-up activity is to find out if joint-pdf analyses of purely observational datasets, for example high-frequency in-cloud measurements of multiple variables by aircraft, exhibits the same fingerprints. This is for now considered a future research topic. We hope the results obtained in this modeling study can provide a useful context for this effort. The obtained results and insights also have a bearing on Arctic Amplification.
Acknowledgements. We gratefully acknowledge the funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Projektnummer  268020496 -TRR 172 , within the  Transregional Collaborative Research Center "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes , and  Feedback Mechanisms  (AC) 3 ". We thank  ECMWF  for providing access to the large-scale model analyses and forecasts fields used to drive the LES experiments. We gratefully acknowledge the  Regional Computing Centre of the University of Cologne (RRZK)  for granting us access to the CHEOPS cluster. The  Gauss Centre for Supercomputing e. V. (www. gauss-centre. eu) is acknowledged for providing computing time on the  GCS Supercomputer JUWELS at the Jülich Supercomputing Centre (JSC)  under projects CHKU28 and VIRTUALLAB.
Code and data availability. The modified version of DALES with extension for mixed-phase microphysics with interactive CCN (dales4. 3_sb3) used in this model study is marked as release https://doi. org/10. 5281/zenodo. 5642477 and for the licensing information please see the main DALES GitHub page: https://github. com/dalesteam/dales. The files containing the ACLOUD RF20 case configuration, as well as the main model output, are available at https://doi. org/10. 5281/zenodo. 5642505 (last update October 2021).
Morphometrics All trait measurements were made with an ocular micrometer installed on an Olympus SZX16 stereomicroscope at a magnification of 150x by F. B. All measurements were made in µm using a pin-holding stage, permitting rotations around X, Y, and Z axes. Morphometric data are given in µm and provided in Supplementary File S1 and is also available at https://figshare. com/articles/dataset/Temnothorax_morph_data/19596454/1, accessed on 14 April 2022. Measured traits are defined as in  [24]  and abbreviated as follows: CL: maximum cephalic length in median line; the head must be carefully tilted to the position with the true maximum. Excavations of hind vertex and/or clypeus reduce CL. CWb: maximum width of head capsule, measured just posterior to the eyes. EL: maximum diameter of the eye.
Data Analyses Data handling and statistical analyses were performed using R software (ver. 4. 0. 1) [25]. We used principal component analysis (PCA) to reduce the number of variables representing morphological variation, retaining the first two axes for visualization. In a preliminary analysis step to test if infection affected morphological traits and whether the direction and magnitude were similar among species and traits, we fitted a linear regression model with measurements of morphological traits as the response variable, and infection status, species, and names of traits (as defined in the Morphometrics section) as predictors, with control to their 2-and 3-way interactions. From the analysis of variance tables, we identified whether or not interaction terms were significant, which would indicate that (1) infection affects different morphological traits differently, and/or (2) the effect of infection differs between species.
Admittedly, our approach is an intermediate to these approaches since we used phenotypic data directly (rather than from images) for species identification, while still relying on a machine learning approach [29] as the high-dimensional and structured nature of complex phenotypic, data is substantially more challenging to model using classical statistical methods. The model matrix was built so that predictors were the morphological traits, infection status, and the interactions between each trait and infection. The topology of the neural network (NN) consisted of three hidden layers, each with ten hidden units. Model fitting was done using backpropagation via gradient descent, utilizing logistic activation functions on the NN nodes, and crossentropy loss function. (The codes for the used R implementation are available at github: https://github.
com/zradai/R/tree/master/MachineLearning, accessed on 14 April 2022). We applied a 10-fold cross-validation to assess how well the fitted models can generalize to data that they were not acquainted with. In the cross-validation iterations, we used stratified random re-sampling to train the models to keep the same relative proportions of species in the training data sets across model fitting. Finally, to quantify model performances, we calculated the classical performance measures of classification algorithms (namely: accuracy, precision, sensitivity, specificity), as well as the Matthews correlation coefficient, from the predictions of the models on the test data (i. e. , on data which were not involved in model training). Subsequently, we estimated the putative uninfected trait values for infected specimens by considering the species-specific distributions of trait values, separately for infected and uninfected ants.
First, we estimated mean and standard deviation of trait value distributions with maximum likelihood, separately for infected (µ inf , σ inf ) and uninfected (µ un , σ un ) phenotypes within species. Then, we calculated the standardized distance from the mean infected trait value for each infected specimen as d = (yµ inf )/σ inf , with y the given infected specimen's observed trait value. Afterward, we calculated the predicted wild-type trait value as p = d × σ un + µ inf + s, with s the contrast parameter representing the difference between wildtype and infected mean trait values, i. e. , s = (µ unµ inf ). The script is written in R and is available in Supplementary File S2 and at https://github. com/zradai/R/blob/master/published_ research_analyses/Morphological_reconstruction_2022/R_analyses_Csosz_et_al_2022.
Introduction Macromolecular crystallography emerged in the 1950s with the first structure determination of a protein, myoglobin  (Kendrew et al. , 1958). At that time, room-temperature macromolecular crystallography (RT-MX), requiring the use of several crystals in order to overcome radiation damage, was the norm until the quest for the structure determination of a ribosome demonstrated the advantage of collecting diffraction data at cryogenic temperature  (Hope et al. , 1989). Here, the slowing down of radiation damage meant that complete diffraction datasets could be collected from a single crystal and, as a consequence, RT-MX fell out of favour except for niche experiments such as Laue studies in the context of timeresolved crystallography  (Moffat, 2019)  or the investigation of the conformational landscape of enzymes  (Fraser et al.
This was achieved by recording full oscillation datasets on a series of 88 single crystals, having first synchronized the start of data collection on the X-ray detector with the illumination of the crystal with a blue LED used for actinic purposes. Time-resolved datasets were constructed by the hierarchical clustering analysisdirected merging of 15-image wedges corresponding to the same 63 ms time segment during the data collection. In this way, we obtained structural snapshots showing the progressive conversion of the resting state population of a crystal into a photostationary equilibrium, reached with a time constant of 1. 4 s with the chosen illumination conditions, composed of approximately two thirds photoadduct and one third resting state. This method, termed TR-SOX (time-resolved serial oscillation crystallography) resulted in a time resolution of 63 ms which could, in principle, be increased or decreased by varying the number of merged images.
The large difference in timescale between the build up and decay of the photoadduct (microseconds versus seconds) prompted us to investigate the relaxation of the photostationary equilibrium using a classical crystallography approach, i. e. by recording full datasets from single crystals at various time points, up to 20 min after the end of crystal illumination. Combining X-ray crystallography with in crystallo UV-Vis absorption spectroscopy, we then monitored the relaxation of the photoadduct population, and obtained similar in crystallo time constants from the two methods. Unexpectedly, we observed that, after a time corresponding to twice the relaxation time constant, the space group of the crystals changed from P4 3 2 1 2 to P2 1 2 1 2 1 , suggesting that the in crystallo relaxation of the photoadduct population leads to a phase transition.