cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5044580
title: data for "An unbound proline-rich signaling peptide frequently samples cis conformations in Gaussian accelerated molecular dynamics simulations"

description: Disordered proline-rich motifs are common across the proteomes of many species and are often involved in protein-protein interactions. Proline is a unique amino acid due to the covalent bond between the backbone nitrogen and the proline side chain. The resulting five-membered ring allows proline to sample the <em>cis</em> state about its peptide bond, which other residues cannot do as readily. Because proline-rich disordered sequences exist as ensembles that likely include structures with the proline peptide bond in <em>cis</em>, a robust methodology to accurately account for these conformations in the overall ensemble is crucial. Observing the <em>cis </em>conformations of proline in a disordered sequence is challenging both experimentally and computationally. Nitrogen-hydrogen NMR spectroscopy cannot directly observe proline residues, which lack an amide bond, and computational methods struggle to overcome the large kinetic barrier between the <em>cis </em>and <em>trans </em>states, since isomerization usually occurs on the order of seconds. In the current work, Gaussian accelerated molecular dynamics was used to overcome this free energy barrier and simulate proline isomerization in a tetrapeptide (KPTP) and in the 12-residue proline-rich SH3 binding peptide, ArkA. We found that Gaussian accelerated molecular dynamics, when combined with a lowered peptide bond dihedral angle potential energy barrier (15 kcal/mol), allowed sufficient sampling of the proline <em>cis </em>and <em>trans </em>states on a microsecond timescale. All ArkA prolines spend a significant fraction of time in <em>cis</em>, leading to a more compact ensemble with less polyproline II helix structure than an ArkA ensemble with all peptide bonds in <em>trans</em>. The ensemble containing <em>cis</em> prolines also matches more closely to <em>in vitro</em> circular dichroism data than the all-<em>trans</em> ensemble. The ability of the ArkA prolines to isomerize likely affects the peptide’s ability to bind its partner SH3 domain, and should be studied further. This is the first molecular dynamics simulation study of proline isomerization in a biologically relevant proline-rich sequence that we know of, and a similar protocol could be applied to study multi-proline isomerization in other proline-containing proteins to improve conformational diversity and agreement with <em>in vitro</em> data.
DOI: 10.5281/zenodo.5639569
title: RNA dataset to train XGBoost model

description: This repository includes the RNA-seq dataset from 27 GBM samples, as published in this manuscript: <strong>Topographic mapping of the glioblastoma proteome reveals a triple axis model of intra-tumoral heterogeneity</strong><br> <em>Lam KHB, Leon AJ, Hui W, Lee SCE, Batruch I, Faust K, Koritzinsky M, Richer M, Djuric U, Diamandis P</em> <strong>(under review)</strong>
DOI: 10.5281/zenodo.5593516
title: RNA dataset to train XGBoost model

description: This repository includes the RNA-seq dataset from 27 GBM samples, as published in this manuscript: <strong>Topographic mapping of the glioblastoma proteome reveals a triple axis model of intra-tumoral heterogeneity</strong><br> <em>Lam KHB, Leon AJ, Hui W, Lee SCE, Batruch I, Faust K, Koritzinsky M, Richer M, Djuric U, Diamandis P</em> <strong>(under review)</strong>
DOI: 10.5281/zenodo.5639481
title: Swift-BAT Response Files for NITRATES: Forward Ray Tracings at IMX &gt; 0

description: These tarred files contain the forward ray tracings onto the BAT detector plane for source positions at IMX ranging from 0.0 to 1.8 and IMY ranging from -1 to 1 with a grid spacing of 0.002. The files are in a npy format and are designed for use in the NITRATES analysis.
DOI: 10.5281/zenodo.5639480
title: Swift-BAT Response Files for NITRATES: Forward Ray Tracings at IMX &gt; 0

description: These tarred files contain the forward ray tracings onto the BAT detector plane for source positions at IMX ranging from 0.0 to 1.8 and IMY ranging from -1 to 1 with a grid spacing of 0.002. The files are in a npy format and are designed for use in the NITRATES analysis.
DOI: 10.18739/a25t3g149
title: Maps of contemporary subsistence land use in rural Interior Alaska derived from predictive models

description: This geospatial dataset provides model-based predictive maps of subsistence land use in rural Interior Alaska. Subsistence harvesting of wild resources is crucial to the well-being of Alaska Natives and rural Alaskans. The development of these community and regional scale maps was intended to improve our understanding of the spatial extent and patterns of subsistence practices, to support communication among land users, and to facilitate predictions of the human impacts from changes in resource availability, climate and environment, land use, and policy. Logistic regression models for predicting subsistence land use were developed using publicly-available maps of documented subsistence use areas assembled by Alaska Department of Fish and Game for 30 communities (Neufeld et al. 2019). Separate models were used for remote communities and road-connected communities. The best-fit models of subsistence land use probability included the terms: distance to community, distance to main travel corridors (rivers for remote communities; roads and rivers for road-connected communities), distance to lakes (for remote communities only), and community population size. The models were applied to 64 rural communities throughout the Interior region to generate probability maps of subsistence land use at the community level (file names: probability_subsistence_*communitytype*_*CommunityName*.tif). A regional probability map of subsistence land use was constructed using the maximum probability value per pixel from community-level maps (file name: probability_subsistence_InteriorAK.tif). Maps of predicted subsistence use areas were created by classifying the community-level probability maps into used and unused areas (file name: classification_subsistence_InteriorAK.zip). Classification accuracy ranged from 83-86%. Results suggest a large spatial extent (353,771 square kilometers (km2)) of subsistence land use in Interior Alaska, comprising ~60% of the region’s land area. These data were produced as part of a study “Geospatial patterns and models of subsistence land use in rural Interior Alaska” by D. R. N. Brown, T. J. Brinkman, G. Neufeld, L. Navarro, C. L. Brown, H. S. Cold, B. L. Woods, and B. L. Ervin currently in review with Ecology and Society (Brown et al., in review).
DOI: 10.5517/ccdc.csd.cc27xfg2
title: CCDC 2082904: Experimental Crystal Structure Determination

description: Related Article: Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2021|Chem.Commun.|57|11513|doi:10.1039/D1CC04080D
DOI: 10.5517/ccdc.csd.cc27xfqb
title: CCDC 2082912: Experimental Crystal Structure Determination

description: Related Article: Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2021|Chem.Commun.|57|11513|doi:10.1039/D1CC04080D
DOI: 10.5517/ccdc.csd.cc27xfrc
title: CCDC 2082913: Experimental Crystal Structure Determination

description: Related Article: Zhifang Guo, Victoria L. Blair, Glen B. Deacon, Peter C. Junk|2021|Chem.Commun.|57|11513|doi:10.1039/D1CC04080D
DOI: 10.6084/m9.figshare.16920100
title: COVID-19 vaccine acceptability among health sciences students in Northwest Nigeria

description: Anonymized survey dataset of COVID-19 vaccine acceptability among health sciences students in Northwest Nigeria
DOI: 10.5517/ccdc.csd.cc27zz29
title: CCDC 2085341: Experimental Crystal Structure Determination

description: Related Article: Nourhan M. Ibrahim, Rabaa M. Khaled, Mohamed A. Ragheb, Krzysztof Radacki, Ahmad M. Farag, Ahmed M. Mansour|2021|Dalton Trans.|50|15389|doi:10.1039/D1DT02296B
DOI: 10.6084/m9.figshare.16920100.v1
title: COVID-19 vaccine acceptability among health sciences students in Northwest Nigeria

description: Anonymized survey dataset of COVID-19 vaccine acceptability among health sciences students in Northwest Nigeria
DOI: 10.5517/ccdc.csd.cc27zz5d
title: CCDC 2085344: Experimental Crystal Structure Determination

description: Related Article: Nourhan M. Ibrahim, Rabaa M. Khaled, Mohamed A. Ragheb, Krzysztof Radacki, Ahmad M. Farag, Ahmed M. Mansour|2021|Dalton Trans.|50|15389|doi:10.1039/D1DT02296B
DOI: 10.5517/ccdc.csd.cc2830m1
title: CCDC 2088272: Experimental Crystal Structure Determination

description: Related Article: Jinjiang Wu, Xiao-Lei Li, Léo La Droitte, Olivier Cador, Boris Le Guennic, Jinkui Tang|2021|Dalton Trans.|50|15027|doi:10.1039/D1DT02071D
DOI: 10.5517/ccdc.csd.cc2830n2
title: CCDC 2088273: Experimental Crystal Structure Determination

description: Related Article: Jinjiang Wu, Xiao-Lei Li, Léo La Droitte, Olivier Cador, Boris Le Guennic, Jinkui Tang|2021|Dalton Trans.|50|15027|doi:10.1039/D1DT02071D
DOI: 10.5517/ccdc.csd.cc284psx
title: CCDC 2089889: Experimental Crystal Structure Determination

description: Related Article: Taryn M. Golding, Mziyanda Mbaba, Gregory S. Smith|2021|Dalton Trans.|50|15274|doi:10.1039/D1DT02842A
DOI: 10.5517/ccdc.csd.cc284zv8
title: CCDC 2090170: Experimental Crystal Structure Determination

description: Related Article: Zong-Mei Yu, Sheng-Ze Zhao, Yu-Ting Wang, Peng-Yu Xu, Chun-Yan Qin, Yong-Hua Li, Xin-Hui Zhou, Shi Wang|2021|Dalton Trans.|50|15210|doi:10.1039/D1DT02394B
DOI: 10.5517/ccdc.csd.cc284zyc
title: CCDC 2090173: Experimental Crystal Structure Determination

description: Related Article: Zong-Mei Yu, Sheng-Ze Zhao, Yu-Ting Wang, Peng-Yu Xu, Chun-Yan Qin, Yong-Hua Li, Xin-Hui Zhou, Shi Wang|2021|Dalton Trans.|50|15210|doi:10.1039/D1DT02394B
DOI: 10.5517/ccdc.csd.cc284zzd
title: CCDC 2090174: Experimental Crystal Structure Determination

description: Related Article: Zong-Mei Yu, Sheng-Ze Zhao, Yu-Ting Wang, Peng-Yu Xu, Chun-Yan Qin, Yong-Hua Li, Xin-Hui Zhou, Shi Wang|2021|Dalton Trans.|50|15210|doi:10.1039/D1DT02394B
DOI: 10.6084/m9.figshare.14671596
title: Table S10

description: Functional annotation, corresponding counts, genes and taxonomy of the obtained contigs for every fraction from each individual.
DOI: 10.6084/m9.figshare.14671596.v1
title: Table S10

description: Functional annotation, corresponding counts, genes and taxonomy of the obtained contigs for every fraction from each individual.
DOI: 10.14278/rodare.211
title: Temperature‒dependent luminescence spectroscopic and mass spectrometric investigations of U(VI) complexation with aqueous silicates in the acidic pH‒range

description: In this study the complexation of U(VI) with orthosilicic acid (H<sub>4</sub>SiO<sub>4</sub>) between pH 3.5 and 5 with electrospray ionization mass spectrometry (ESI‒MS) and laser‒induced luminescence spectroscopy was comprehensively characterized. The ESI‒MS experiments performed at a total silicon concentration of 5∙10<sup>‒5</sup> M (exceeding the solubility of amorphous silica at both pH‒values) revealed the formation of oligomeric sodium‒silicates in addition to the UO<sub>2</sub>OSi(OH)<sub>3</sub><sup>+</sup> species. For the luminescence spectroscopic experiments (25 °C), the U(VI) concentration was fixed at 5∙10<sup>‒6</sup> M, the silicon concentration was varied between 1.3∙10<sup>‒4</sup> ‒ 1.3∙10<sup>‒3</sup> M (reducing the formation of silicon oligomers) and the ionic strength was kept constant at 0.2 M NaClO<sub>4</sub>. The results confirmed the formation of the aqueous UO<sub>2</sub>OSi(OH)<sub>3</sub><sup>+</sup> complex. The conditional complexation constant at 25 °C, log *β = ‒0.31± 0.24, was extrapolated to infinite dilution using the Davies equation, which led to log *β<sup>0</sup> = ‒0.06 ± 0.24. Further experiments at different temperatures (1 – 25 °C) allowed the calculation of the molal enthalpy of reaction Δ<sub>r</sub>H<sub>m</sub><sup>0</sup> = 45.8 ± 22.5 kJ∙mol‒1 and molal entropy of reaction Δ<sub>r</sub>S<sub>m</sub><sup>0</sup> = 152.5 ± 78.8 J∙K‒1∙mol‒1 using the van’t Hoff equation, corroborating an endothermic and entropy driven complexation process.
DOI: 10.14278/rodare.210
title: Temperature‒dependent luminescence spectroscopic and mass spectrometric investigations of U(VI) complexation with aqueous silicates in the acidic pH‒range

description: In this study the complexation of U(VI) with orthosilicic acid (H<sub>4</sub>SiO<sub>4</sub>) between pH 3.5 and 5 with electrospray ionization mass spectrometry (ESI‒MS) and laser‒induced luminescence spectroscopy was comprehensively characterized. The ESI‒MS experiments performed at a total silicon concentration of 5∙10<sup>‒5</sup> M (exceeding the solubility of amorphous silica at both pH‒values) revealed the formation of oligomeric sodium‒silicates in addition to the UO<sub>2</sub>OSi(OH)<sub>3</sub><sup>+</sup> species. For the luminescence spectroscopic experiments (25 °C), the U(VI) concentration was fixed at 5∙10<sup>‒6</sup> M, the silicon concentration was varied between 1.3∙10<sup>‒4</sup> ‒ 1.3∙10<sup>‒3</sup> M (reducing the formation of silicon oligomers) and the ionic strength was kept constant at 0.2 M NaClO<sub>4</sub>. The results confirmed the formation of the aqueous UO<sub>2</sub>OSi(OH)<sub>3</sub><sup>+</sup> complex. The conditional complexation constant at 25 °C, log *β = ‒0.31± 0.24, was extrapolated to infinite dilution using the Davies equation, which led to log *β<sup>0</sup> = ‒0.06 ± 0.24. Further experiments at different temperatures (1 – 25 °C) allowed the calculation of the molal enthalpy of reaction Δ<sub>r</sub>H<sub>m</sub><sup>0</sup> = 45.8 ± 22.5 kJ∙mol‒1 and molal entropy of reaction Δ<sub>r</sub>S<sub>m</sub><sup>0</sup> = 152.5 ± 78.8 J∙K‒1∙mol‒1 using the van’t Hoff equation, corroborating an endothermic and entropy driven complexation process.
DOI: 10.14278/rodare.363
title: Local void fraction and pressure drop data for horizontal annular flow through orifice

description: This dataset combines multiple measurements form air-water horizontal annular flow experiments in a pipe (case A) and a pipe with circular orifice with \(d^2 / D^2 = 0.6\) (case B). Measurements where taken at superficial Reynolds numbers of Re=25000 for the gas phase and Re =4090 for the liquid phase. The following data are included for each case: linear pressure drop between two points (case A), four points (case B) measured with split-range differential pressure transducers at 5Hz time-averaged local liquid volume fraction distribution in cylindrical and Cartesian coordinates reconstructed from X-ray microtomography projections reconstructed pipe axis coordinates and pipe radius Python code to calculate secondary validation parameters (e.g. film thickness distribution) from primary data The detailed 3D data is intended for validation of computational fluid dynamics codes based on phase-averaged variables such as the Euler-Euler approach.
DOI: 10.14278/rodare.361
title: Local void fraction and pressure drop data for horizontal annular flow through orifice

description: This dataset combines multiple measurements form air-water horizontal annular flow experiments in a pipe (case A) and a pipe with circular orifice with \(d^2 / D^2 = 0.6\) (case B). Measurements where taken at superficial Reynolds numbers of Re=25000 for the gas phase and Re =4090 for the liquid phase. The following data are included for each case: linear pressure drop between two points (case A), four points (case B) measured with split-range differential pressure transducers at 5Hz time-averaged local liquid volume fraction distribution in cylindrical and Cartesian coordinates reconstructed from X-ray microtomography projections reconstructed pipe axis coordinates and pipe radius Python code to calculate secondary validation parameters (e.g. film thickness distribution) from primary data The detailed 3D data is intended for validation of computational fluid dynamics codes based on phase-averaged variables such as the Euler-Euler approach.
DOI: 10.14278/rodare.646
title: Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

description: Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication (https://arxiv.org/abs/2010.04905).
description: This is only a limited set of the entire output data. The remainder of the data will be made available at a later point once approval from the collaborating research institution (Sandia National Laboratories) has been granted. The source code of the associated machine learning framework will also be published at that stage.
DOI: 10.14278/rodare.645
title: Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

description: Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication (https://arxiv.org/abs/2010.04905).
description: This is only a limited set of the entire output data. The remainder of the data will be made available at a later point once approval from the collaborating research institution (Sandia National Laboratories) has been granted. The source code of the associated machine learning framework will also be published at that stage.
DOI: 10.5281/zenodo.5639635
title: Fine-scale species occurrence data

description: Surveys of individual vascular plant species occurrence and cover in quadrats of 1x1 m on sub-Antarctic Marion Island.
DOI: 10.5281/zenodo.5639634
title: Fine-scale species occurrence data

description: Surveys of individual vascular plant species occurrence and cover in quadrats of 1x1 m on sub-Antarctic Marion Island.
DOI: 10.5281/zenodo.4563132
title: Black-Box and White-Box Test Case Generation for RESTful APIs: Enemies or Allies? - Supplementary package

description: This is the supplementary package for the paper entitled "Black-Box and White-Box Test Case Generation for RESTful APIs: Enemies or Allies?".
DOI: 10.5281/zenodo.4563131
title: Black-Box and White-Box Test Case Generation for RESTful APIs: Enemies or Allies? - Supplementary package

description: This is the supplementary package for the paper entitled "Black-Box and White-Box Test Case Generation for RESTful APIs: Enemies or Allies?".
DOI: 10.5281/zenodo.4495136
title: IDEA Dataset: Inter-parameter DEpendencies in web Apis

description: Dataset of inter-parameter dependencies found in real-world web APIs.
DOI: 10.5281/zenodo.4495135
title: IDEA Dataset: Inter-parameter DEpendencies in web Apis

description: Dataset of inter-parameter dependencies found in real-world web APIs.
DOI: 10.18739/a2sx64b20
title: (:unkn)
DOI: 10.18739/a2p55dh91
title: (:unkn)
DOI: 10.18739/a2jd4pq6c
title: (:unkn)
DOI: 10.18739/a2dr2p95z
title: (:unkn)
DOI: 10.18739/a2901zh2m
title: (:unkn)
DOI: 10.5281/zenodo.5639650
title: DRAM raw annotations file, file_s4

description: This file is for the manuscript "<strong>Variation in root exudate composition influences soil microbiome membership and function" </strong>for publication in Applied and Environmental Microbiology, and ASM Journal.
DOI: 10.5281/zenodo.5639649
title: DRAM raw annotations file, file_s4

description: This file is for the manuscript "<strong>Variation in root exudate composition influences soil microbiome membership and function" </strong>for publication in Applied and Environmental Microbiology, and ASM Journal.
DOI: 10.6084/m9.figshare.16912366.v1
title: antspymm helper data

description: multi-channel and time series example data for antsx processing
DOI: 10.6084/m9.figshare.16629118
title: Current Moments Weak Field Streamer Collision

description: Current Moment Data from Cases 1-6 (M), for my 2021 paper, along with the respective time of each data point. <br><br><br><br>
DOI: 10.5517/ccdc.csd.cc27y31c
title: CCDC 2083542: Experimental Crystal Structure Determination

description: Related Article: Fedor M. Miloserdov, Anne-Frédérique Pécharman, Lia Sotorrios, Nasir A. Rajabi, John P. Lowe, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2021|Inorg.Chem.|60|16256|doi:10.1021/acs.inorgchem.1c02072
DOI: 10.5517/ccdc.csd.cc27y33f
title: CCDC 2083544: Experimental Crystal Structure Determination

description: Related Article: Fedor M. Miloserdov, Anne-Frédérique Pécharman, Lia Sotorrios, Nasir A. Rajabi, John P. Lowe, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2021|Inorg.Chem.|60|16256|doi:10.1021/acs.inorgchem.1c02072
DOI: 10.5517/ccdc.csd.cc288mry
title: CCDC 2093670: Experimental Crystal Structure Determination

description: Related Article: Ying Lu, Tong Zhang, Yue-Xian Zhang, Xiao-Jing Sang, Fang Su, Zai-Ming Zhu, Lan-Cui Zhang|2021|Dalton Trans.|50|15198|doi:10.1039/D1DT02644E
DOI: 10.5517/ccdc.csd.cc288rbn
title: CCDC 2093781: Experimental Crystal Structure Determination

description: Related Article: Xiu-Gang Wu|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc288z1l
title: CCDC 2093989: Experimental Crystal Structure Determination

description: Related Article: Zhonghao Zhou|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2890sd
title: CCDC 2094043: Experimental Crystal Structure Determination

description: Related Article: Wolfram Feuerstein, Paul Varava, Farzaneh Fadaei-Tirani, Rosario Scopelliti, Kay Severin|2021|Chem.Commun.|57|11509|doi:10.1039/D1CC05161J
DOI: 10.5517/ccdc.csd.cc289rn0
title: CCDC 2094752: Experimental Crystal Structure Determination

description: Related Article: Wolfram Feuerstein, Paul Varava, Farzaneh Fadaei-Tirani, Rosario Scopelliti, Kay Severin|2021|Chem.Commun.|57|11509|doi:10.1039/D1CC05161J
DOI: 10.25345/c5tp17
title: MassIVE MSV000088301 - Comparison of analytical performances of targeted proteomics using parallel reaction monitoring, data-independent acquisition and data dependent acquisition
title: In this study, we investigated three mass-spectrometry-based quantitative methods, PRM, DIA, and DDA modes, with the goal of determining the analytical performance of targeted proteomic analysis of different candidate peptides. We applied the Skyline and Pycloud computational environments for spectral library generation and DIA, PRM, and DDA data analyses, respectively.
DOI: 10.5281/zenodo.5527140
title: All code generated for Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: The read_me file text file contains a list of all subfigures and the code used to generate them.
DOI: 10.5281/zenodo.5527139
title: All code generated for Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: The read_me file text file contains a list of all subfigures and the code used to generate them.
DOI: 10.5281/zenodo.5519364
title: Fig 1 ChR2 open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recording data for 120 s optogenetic stimulation experiments. Associated with Figure 1.
DOI: 10.5281/zenodo.5519363
title: Fig 1 ChR2 open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recording data for 120 s optogenetic stimulation experiments. Associated with Figure 1.
DOI: 10.5281/zenodo.5522796
title: Fig 1 eYFP open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recordings for eYFP open loop control data. Associated with Figure 1.
DOI: 10.5281/zenodo.5522795
title: Fig 1 eYFP open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recordings for eYFP open loop control data. Associated with Figure 1.
DOI: 10.5281/zenodo.5507513
title: Fig 2 ChR2 closed loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recording data for ChR2 closed loop experiments. Associated with Figure 2C,E.
DOI: 10.5281/zenodo.5507512
title: Fig 2 ChR2 closed loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

description: Sleep recording data for ChR2 closed loop experiments. Associated with Figure 2C,E.