dataesr/scientific-paragraphs-categorization huggingface dataset:
Statement of Need Reconstructing Hs from the heavy atom coordinates can be done using standard geometric rules respecting stereochemistry. In the first NMRlipids project  (Botan et al. , 2015) , H reconstruction was performed using a program called g_protonate from the software GROMACS version 3. *  (Berendsen et al. , 1995). However, g_protonate has been removed from GRO-MACS version 4. * or higher. So, there was a need to find other solutions. Currently, there are many programs in the field of chemoinformatics that are able to build Hs such as OpenBabel  (O'Boyle et al. , 2011)  or other proprietary softwares. It is also possible to use pdb2gmx from the GROMACS software  (Abraham et al. , 2015)  to build Hs, but it is not initially intended for that since its main purpose is to build a topology.
Many of these solutions remain workarounds where one uses a sophisticated software for just doing one basic task. Using these open or proprietary software usually has a slow learning curve. Moreover, it is not always easy to use them in the Unix command line when they have complicated graphical interfaces, thus complicating their use in automatic pipelines of trajectory analyses. Here, we propose the buildH software to meet this need. buildH is very light and usable in the Unix command line or as a Python module, making it a tool of choice to integrate in analysis pipelines or Jupyter notebooks. It can be easily extended or customized if needed. buildH is currently widely used in the NMRlipids project IVb dealing with the conformational plasticity of lipid headgroups in cellular membranes and protein-lipid complexes  (Bacle et al.
Overview buildH is a Python software that enables automatic analyses of order parameter calculations from UA trajectories of lipids. The software has the following features: • It reads a single structure or a trajectory of lipids in a UA representation. • It reconstructs the aliphatic Hs using standard geometric rules. • From the reconstructed Hs, it calculates and outputs the order parameters on each requested C-H bond. • Optionally, it outputs a structure (in pdb format) and a trajectory (in xtc format) with all reconstructed Hs. Beyond order parameter calculations, the trajectory with Hs can be used for any further analyses (e. g. precise molecular volume calculation). buildH has been natively developed for a use in the Unix command line. It possesses a minimum number of options, making it easy to use.
It is also possible to utilize it as a Python module, which may be convenient in some cases. To reconstruct H atoms, buildH uses standard geometric rules. These rules require so-called helper atoms. For example, the reconstruction of the two Hs of a CH2 on carbon C i , requires two helpers which are C i-1 and C i+1 , that is, the two neighbors of C i in the chain (note that helpers can also be other heavy atoms such as oxygen or nitrogen). The list of helpers used for the reconstruction of each H is written in a json file. Many json files are already present on the buildH repository representing the major lipids: phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylserine (PS) for the polar heads and palmitoyl, myristoyl, oleoyl for the aliphatic chains, as well as cholesterol.
Major UA force fields are also represented  (Berger (Berger et al. , 1997) , GROMOS-CKP  (Piggot et al. , 2012) , CHARMM-UA  (Lee et al. , 2014) ). In case a user wants to analyze a lipid which is not present in buildH, a step-by-step documentation guides the user in the process of creating and supplying his/her lipid description as a json file. All structure and trajectory read / write operations are handled by the MDAnalysis module  (Gowers et al. , 2016; Michaud-Agrawal et al. , 2011). Mathematical vector operations are performed by Numpy  (Harris et al. , 2020)  and accelerated with Numba  (Lam et al. , 2015) , leading to very decent performances. For example, the reconstruction of all Hs and order parameter calculation on a trajectory of 2500 frames with 128 POPC molecules can be handled in approximately 7 minutes using a single core Xeon processor at 3.
60 GHz, whereas it was almost 30 minutes long without Numba. buildH has been implemented with good practices of software development in mind  (Jiménez et al. , 2017; Taschuk & Wilson, 2017) : • version controlled repository on GitHub https://github. com/patrickfuchs/buildH, • open-source license (BSD-3-Clause), • continuous integration through tests, • and documentation https://buildh. readthedocs. io/. Some notebooks are provided in the GitHub repository to explain how buildH works and how to analyze the data produced. In case of trouble, any user can post an issue on GitHub. buildH is available in the Python Package Index (PyPI) as well as in the Bioconda repository. The current version 1. 6. 0 of buildH is archived in the Zenodo repository (https://zenodo. org/record/5356246) and in the Software Heritage archive (swh:1:dir:4c63d5ca3497726a1e54ac152ce1667d7c004d2b).
This paper investigates the behaviour of exposed column base-plates with four outer anchor bolts subjected to monotonic loading. The results of six full-scale tests on steel column base-plate connections subjected to uni/ biaxial bending moment are presented. The influence of the base-plate thickness and the bending moment axis orientation on the connection response is thoroughly investigated. In addition to standard measurements, Digital Image Correlation has been used to closely monitor the evolution of the contact area that develops in the tensile and compressive zones. Furthermore, a detailed finite element model has been developed using the software Abaqus and used to explore additional orientations of the bending moment axis. This numerical model allows to investigate the evolution of the anchor bolt forces, the contact pressure distribution and the lever arm, that strongly influence the bending resistance and rotational stiffness.
The following inclinations of the bending moment axis are considered: 0 • (strong-axis), 45 • and 90 • (weak-axis). The effect of the axial force is not investigated in this paper. Digital Image Correlation is used to follow the base-plate deformation and thus the modification of contact area in tensile and compressive zones and therefore the evolution of prying effect. In addition, a sophisticated finite element model has been developed in Abaqus to simulate the behaviour of the six connections tested and to get informations that are not accessible. To extend the scope of experimental tests, additional inclinations of the bending moment axis have been considered. Key data obtained numerically are the anchor bolt force, contact pressure distribution and thus position of the centre of compression.
First, we applied it to publicly available data obtained by profiling a cancer cell line reference ctDNA mixture with a barcode-equipped capture assay (Illumina, San Diego, CA; Fig  2E ). 10  This data set includes 447 germline or somatic variants (19 classified as [likely] pathogenic by ClinVar). Second, to demonstrate the validity of LAVA for other variant callers, we adapted it to predict the sensitivity of VarDict for the assay A2 data set (Data Supplement). 33 For both analyses, predicted and observed sensitivities were strongly correlated, confirming that LAVA works for different assays, laboratories, and variant callers. To demonstrate how this can reduce false negatives, we used the rsctDNA data (Fig 1B ) to mimic a clinical situation where variants must be detected above a specified VAF (eg, corresponding to a treatment threshold).
We verified the accuracy of model-predicted sensitivities for the clinical samples (Data Supplement) and then reclassified standard variant calls using these predictions (Fig  3C ). To assign a high-confidence negative status, one must specify the VAF down to which detection is required (minimum required VAF). We used 0. 5%, as recommended for EGFR T790M. 38, 39 Negative calls whose LOD was sufficient to rule out a variant ≥ 0. 5% were thus labeled negative and the remainder equivocal. We then evaluated the LOD-aware results against the ground truth, classifying a negative result as a false negative if the confirmed VAF was ≥ 0. 5%. All positive calls for confirmed variants were counted as true Top: VAFs, detection status, and predicted LOD for 447 confirmed variants in cancer reference ctDNA mixtures processed using the Illumina TruSight Tumor 170 assay.
Statistics Statistical analysis was performed using MedCalc (version 18. 5, 64 bits, MedCalc Software, Ostend, Belgium). Results are expressed in mean ± standard deviation (SD) in case of normal distribution (Shapiro-Wilk test). Differences in categorization rate between the three classifications were compared using the Chi squared test, where each classification was compared separately to the other two classifications with the number of categorized and non-categorized waveforms used as inputs. A p-value < 0. 05 was considered significant.
The quadrotor thrust and rotational controller was implemented on its onboard microcontroller (Atmel AVR 8-bit, ATmega-1284, running at 20 MHz), based on the inertial sensors of the robot, the rest of the control law was implemented on an external PC (Core i7, 16GB RAM, running Ubuntu 14), communicating with the robot using a pair of Zig-Bee transceiver chips. An Omega. 3 haptic device (Force Dimension, Nyon, Switzerland) was used as the master device. We used the force/torque ATI sensor (ATI Industrial Automation, Apex, NC, USA) embedded inside the object to measure the applied force by the aerial manipulator. The software was implemented in the ROS, and all control loops ran at a frequency of 100 Hz.
Light Intensity and Thermal Distribution in an Open Environment The tip of the endoscope was firmly placed in close contact with the light detector of the lux meter. Light intensity was recorded when the source output was stable. The initial power was set at 5% and then tested to 100% in 5% increments. Both light sources were studied with each of the four endoscopes. Seven thermocouple sensors were used to measure the axial thermal distributions for each of the endoscopes. Three sensors were placed 0. 5, 1, and 2 cm in front of the tip of the endoscope along the light axis, and the remaining four were placed at the tip of the endoscope, 1 cm to the rear of the tip, 1/4 shaft length rear of the tip, and 1/2 shaft length rear of the tip (Figure  1A ). The sensors' data collection cycle was adjusted to 1 s.
Statistical Analysis Light intensity (lux), power setting (%), time (s), and temperature ( • C) were measured. The ambient temperature during the experiments was 25 ± 3 • C. Values are presented as means and standard deviation or ratios. Statistical analyses were conducted with WPS Office (Kingsoft Office Corp. , Beijing, PRC) and GraphPad Prism8 (GraphPad Prism Software, San Diego, CA, USA), with a significance level represented as * P < 0. 01. This work did not contain animal/human studies. No IRB (institutional review board) approval was required.
The Lagrangian Particle Image Velocimetry (LAPIV) method was firstly proposed in  Yang et al. (2019)  as a prototype approach to achieve the goal of accurate and efficient reconstruction of 3D Eulerian velocity field of fluid flow from multi-view particle images. After validating against synthetic datasets, the prototype has already shown significant advantages in revealing more small scale flow structures than other stateof-the-art Eulerian velocity estimation methods, such as TomoPIV  (Scarano, 2013)  and VIC#  (Jeon et al. , 2019). However, at this early stage, LAPIV can not be easily applied to other datasets. In the current work, we focus on extending LAPIV to operational search by incorporating several essential and wellestablished paradigms: multi-resolution, warping, and proxy regularization.
The resulting system to solve for the regularized flow remains light-weight compared to  Lasinger et al. (2019) , or Gesemann (2020), whose optimization methods are quite complex since they anchor either the image or the particle field as the data. As for LAPIV, the regularized flow always occupies the same grid as the proxy flow. Both are to be refined in agreement with the multi-resolution approach. We have participated in the 1st LPT challenge after a slight modification of the original algorithm, as the challenge emphasized LPT rather than the Eulerian velocity reconstruction. As evaluated by the challenge, our method has achieved state-of-the-art performance at medium-high particle density levels both for twopulse datasets (PPP <= 0. 08) and time-resolve datasets (PPP <= 0. 12). We have also done quantitative tests against the same synthetic datasets depicting the wake flow of a turbulent cylinder at Re=3900, produced by a Large Eddy Simulation  (Parnaudeau et al.
, 2008). Table  1  summarizes the RMSE results of LAPIV compared to TomoPIV, STB-bin and VIC# implemented in Davis 10. 0. 5. LAPIV yielded the minimum error among all methods. Finally, we have applied LAPIV to an impinging jet flow  Yang and Heitz (2021)  at Re=1250. The Q criteria of the instantaneous velocity field are shown in figure  1  with color-coded by the velocity magnitude. In this study, we have shown, after integrating those essential techniques such as multi-resolution and proxy regularization, LAPIV can yield competitive, if not better, results compared to other state-of-the-art approaches facing versatile datasets. Methods Resolution RMSE (N x , N y , N z ) (U err /U ∞ ) TomoPIV  (145, 45, 24)  0. 1517  45, 24)  0. 2439 VIC# (145,45,24) 0. 09141 LAPIV (151,48,27) 0. 07799 Table  1 : Summary of results obtained by TomoPIV, STB binning, VIC# (all three methods are implemented in Davis 10.
Exposome-wide interaction study reveals interactions underlying many vQTLs In stage two, we conducted exposome-wide interaction tests for the 182 vQTL-biomarker pairs. We used the PHESANT program to produce 2,380 filtered and cleaned exposures, including physical, lifestyle, psychosocial, medical, and other types of traits (Supp. Table  S7 ; see Methods). For each vQTL-biomarker pair, we tested for GEI effects involving the index variant and biomarker with each of 2380 exposure variables as potential effect modifiers, first stratified by ancestry and then meta-analyzed. Using a conservative multiple testing adjusted significance threshold (p < 0. 05 / 182 / 1,156. 2 effective exposures = 2. 38×10  -7  ) we identified 846 significant interaction effects at 54 of the 182 vQTL-biomarker pairs, altogether representing 34 loci (Fig.
3 , Supp. Table  S8 ). The 846 GEIs were unevenly distributed across biomarkers, with greater than one hundred for ALT, TG, and AST and none for albumin, creatinine, cystatin C, total bilirubin, and urea. Many of the participating exposures were from highly correlated categories such as anthropometric measures. This dense correlation structure means that many resulting interactions may reflect the same biological phenomenon, but also provides a unique opportunity to isolate the specific relevant exposure with greater precision. Returning to the WGHS dataset, we chose to replicate specifically those interactions involving BMI as an exposure, since (1) many significant GEIs implicated BMI or another anthropometric trait (84%), (2) BMI is an objective and standard quantitative measurement that is easily compared across studies, and (3) BMI has strong biological links to the majority of biomarkers assessed in this study.
Future work should incorporate more ancestry-balanced datasets, explore heterogeneity of interaction effects across ancestries, and pursue follow-up of some of the many ancestry-specific vQTLs and interactions described here. The vQTLs and GEIs described here have been made publicly available in the AMP Common Metabolic Disease Knowledge Portal (https://hugeamp. org/research. html?pageid=UKB-vQTL-GxE). These catalogs can generate hypotheses for future research and enable lookups to better characterize findings from GWAS and other genetic studies. Our findings extend previous proofs-of-concept of the vQTL strategy to the realm of cardiometabolic biomarkers, establish a set of loci to be prioritized for further GEI analysis, and highlight specific GEIs that can inform cardiometabolic precision medicine strategies.
UK Biobank genetic data UKB is a large prospective cohort with both deep phenotyping and molecular data, including genomewide genotyping, on over 500,000 individuals ages 40-69 living throughout the UK between 2006-2010  30. Genotyping, imputation, and initial quality control on the genetic dataset have been described previously  31. We removed individuals flagged for failing UKBiLEVE genotype quality control, heterozygosity or missingness outliers, individuals with putative sex chromosome aneuploidy, individuals with self-reported vs. genetically inferred sex mismatches, and individuals that had withdrawn consent by the time of analysis. Additionally, we subset to a group of unrelated samples by including only those that were used for genetic principal components analysis (PCA) during central genetic data preprocessing  31.
Furthermore, only genetic variants with minor allele frequency (MAF) > 0. 005 in the full sample were included in the present analysis (in addition to subsequent ancestry-specific MAF filters). For the vQTL and GWAS analyses, imputed genotypes within 0. 1 of an integer value (0, 1, or 2) were converted to hard-calls using PLINK2  32  and all other values were set to missing. Work was conducted on genetic data release version 3, with imputation to both Haplotype Reference Consortium and 1000 Genomes Project (1KGP), under UK Biobank application 27892. This work was conducted under a Not Human Subjects Research determination (NHSR-4298 at the Broad Institute of MIT and Harvard). UKB samples were grouped into four primary ancestry groups: West African (AFR), East Asian (EAS), European (EUR) and South Asian (SAS).
Using 1000 Genomes project (1KGP) phase 3 as the training dataset, probabilistic Gaussian mixture models were built to represent normally-distributed subpopulations within the overall population, assigning data points to the multivariate normal components that maximized the component posterior probability. Ten-fold cross-validation was used with different initialization states and clustering was evaluated using the adjusted Rand index score. The model with the highest adjusted Rand index score for each ancestry was used to cluster individuals in UKB into ancestry subgroups based on their 1KGP projected PCs and self-reported ethnicities (field 2100).
Serum biomarkers We focused on 20 serum biomarkers related to cardiovascular disease and metabolism, including but not limited to lipids, liver enzymes, glycemic parameters, and kidney function markers (see Supp. Table  S2 ). Blood samples were collected at the baseline visit for the majority of participants, and specific biomarkers were measured using colorimetric, enzymatic, and immunoassays (details available at: https://biobank. ctsu. ox. ac. uk/crystal/crystal/docs/serum_biochemistry. pdf). Phenotype data processing and other downstream analyses were conducted using R version 3. 6. 0 33 unless described otherwise. Biomarker values were pre-processed separately within each ancestry group, and followed a modified version of the procedure described by Sinnott-Armstrong and colleagues; see original manuscript for detailed description and online code  34.
Due to the substantial correlation between the 20 cardiometabolic biomarkers, a smaller number of "effective" biomarkers was calculated to inform multiple hypothesis testing correction. Using a previously-described approach  35  , preprocessed biomarkers were collected into a single dataset across ancestries and principal components analysis was performed (prcomp function with standardized variables). The number of effective biomarkers was then calculated from the principal component variances ߣ (equal to the eigenvalues of the biomarker covariance matrix) as ܰ ெ, ൌ ሺ∑ ఒ ೖ ሻ ೖసభ మ ∑ ఒ ೖ మ ೖసభ. Variance-QTL analysis vQTL analysis was performed using the vQTL module from the OSCA suite  36. Differential variance tests used the median-based Levene's test, which is equivalent to a one-way analysis of variance (ANOVA) for absolute deviations from the median biomarker value.
Preparation of exposure variables In order to conduct the GEI analysis in an exposome-wide manner, we needed to collect and clean a large set of relevant exposure variables. For this purpose, we used a program for automated phenotype preprocessing initially developed for phenome-wide association studies (PHEnome Scan ANalysis Tool, or PHESANT  1  ) and later expanded (https://github. com/astheeggeggs/PHESANT). This PHESANT procedure relies on files describing variable input types (continuous, integer, categorical single [one choice], and categorical multiple [multiple choice]) and data coding schemes. Based on this input, it then conducts automated pre-processing of the phenotypic dataset, including removal of variables with excessive missingness or insufficient heterogeneity, conversion of categorical variables to ordinal and binary variables, and inverse-rank normal transformation of continuous variables.
As originally applied, the PHESANT pipeline processed a comprehensive set of phenotypes appropriate for phenome-wide association studies. We modified the set of included variables to better reflect the space of traits and exposures that may participate in GEIs. For example, sex does not make sense to use as an outcome, but may be an important characteristic that modifies genetic associations with blood biomarker levels. Specifically, in comparison to the PHESANT pipeline referenced above, we additionally included sex, age, and assessment center, and 24-hour recall-based dietary assessments and excluded blood-based assays, hospital records, and cancer and death registers. After running PHESANT on the full UK Biobank dataset, the output dataset contained 2,380 variables for analysis. This corresponded to 1,156.
1 effective exposures tested based on the same procedure used above for determining the number of effective biomarkers. These exposures included physical (e. g. , BMI), lifestyle (e. g. , dietary behaviors), psychosocial (e. g. , neuroticism score), medical (e. g. , statin use), and other types of traits. Based on the data dictionary available from UKB and additional manual curation, phenotypes were placed into one of eight exposure categories. Exposure group summary counts and variable examples are available in Supp. Table  S7.
Anthropometric exposure and lipid subfraction analysis Anthropometric exposures were defined as those with a "Level 3 Category" of "Anthropometry" in the UKB data dictionary. Initially, GEI summary statistics from the EWIS were collected for this set of exposures with TG as the outcome and including nine genetic variants passing the EWIS significance threshold for at least one of these 31 exposures. These nine variants were manually annotated to likely causal genes based on known lipid biology. To create a set of anthropometric summary variables for GEI analysis, PCA was performed on this set of exposures in the European ancestry subset. The first two principal components were determined to represent body mass and body fat based on inspecting of trait loadings. These two components were extracted and used for GEI testing.