dataesr/scientific-paragraphs-categorization huggingface dataset:
89. 3187. 2890; Email: prokisch@ helmholtz-muenchen. de. acid) and solubilized with 10 μL 20% digitonin (Serva) or 5 μL 20% DDM. Samples were supplemented with 2. 5 μL 5% Coomassie G250 in 500 mM aminocaproic acid and 5 μL 0. 1% Ponceau S in 50% glycerol. Equal amounts of sample protein were loaded on top of a 3% to 18% acrylamide gradient gel (dimension 14 × 14 cm). After native electrophoresis in a cold chamber, blue-native gels were fixed in 50% (v/v) methanol, 10% (v/v) acetic acid, and 10 mM ammonium acetate for 30 minutes and stained with Coomassie (0. 025% Serva Blue G, 10% [v/v] acetic acid). Mass spectrometry of assembled RCCs resolved by BNE. Procedures and parameters for mass spectrometry and data analysis are summarized at the PRIDE-proteomics identification database (https:// www.
ebi. ac. uk/pride/) with the identifiers PXD021385, PXD021386, PXD021500, PXD022340, PXD022339, and PXD021548. pSILAC metabolic labeling. For pSILAC metabolic labeling, medium was changed into medium containing 13 C 6 , 15 N 4 -L-arginine and 13 C 6 , 15 N 2 -L-lysine (Silantes) for 0, 6, 8, 10, and 12 hours. Mitochondrial membranes were solubilized with either digitonin (Patient runs 1, 2, 4, and HEK293 run, as per Supplemental Table  10 ) or DDM (Patient run 3, as per Supplemental Table  10 ), protein complexes resolved by BNE and the band(s) representing either the supercomplex and complex V or the individual complexes, after digitonin and DDM solubilization respectively, were used for mass spectrometry. For protein turnover the ratio of heavy intensity-based absolute quantification (IBAQ)/sum of light and heavy IBAQ was calculated.
1. 423. The choice of statistical test was determined by visual inspection of the distribution of the data. Where assumptions of normality and equal variance were met, the parametric 2-sided Student's t test was used to compare the means of 2 groups. When multiple comparisons were made to a control, the P values were corrected with Dunnett's test. Categorical data were analyzed by Fisher's exact test. The statistical test is stated in the respective figure legend. All reported P values are 2-tailed with Bonferroni's correction for multiple testing. P ≤ 0. 05 was considered statistically significant. The P values are annotated in the figures by *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001, and ****P ≤ 0. 0001. Study approval. This study was in accordance with the ethical principles of the Declaration of Helsinki and received local ethical committee approval from each center for their respective patient(s).
Acknowledgments This study was supported by a  German Federal Ministry of Education and Research (BMBF, Bonn, Germany)  grant to the  German Network for Mitochondrial Disorders  (mitoNET,  01GM1906D  to IW and HP and  01GM1906A  to TK); by the  German BMBF  and  Horizon2020  through the  E-Rare  project  GENOMIT  ( 01GM1920A  to HP, TK, JM, and CL) and the  ERA PerMed project PerMiM  ( 01KU2016A  to HP and DG); by the  Italian Ministry of Health  ( GR-2016-02361449  to LC and by the  "Ricerca Corrente " funding to VC); by the  Deutsche Forschungsgemeinschaft (DFG)  ( SFB 815/Z1  to IW); by the  Cardio Pulmonary Institute (CPI)  of the  DFG  ( EXC2026  and  TRR267-Z02  to IW); by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC, Oxford, United Kingdom ; to PCI); and by the  Austrian Science Fund (FWF)  (I 4695-B to JAM).
Reference ab initio CCSD(T) and SAPT calculations: PSI4 83 was employed for density fitted FNO-CCSD(T)  84  and density fitted SAPT2+(3)δMP2  [85] [86] [87]  computations using the frozen core approximation. 9] [90]  MP2/CBS(at,aQ) indicates the standard 2-point extrapolation following Halkier et al. 91 SAPT2+(3)δMP2 calculations also employed the jun-cc-pVTZ basis set. Both, CCSD(T) and the mentioned SAPT theory level are highly accurate QM methods if appropriate basis sets are used and are labelled as 'gold standard' for the evaluation of non-covalent interactions. 87, 92 The CCSD(T) data were used to validate the DHDF-D3 approach (see below). SAPT calculations were used to decompose the interaction energy into various components. QM DHDF-D3 single point energy computations: Energies were computed using the doublehybrid density functional approximation DSD-BLYP-D3  93  with D3(BJ)  94  correction, the minimally-augmented ma-def2-QZVPP  76, 95  basis set (we use the abbreviation DHDF-D3 for DSD-BLYP-D3/ma-def2-QZVPP in following sections) and the ORCA program.
AMBER FF single point energy computations: The in-house program bff  99  was used for calculating AFF interaction energies on QM-optimized geometries of DME…nucleobase and deoxyribose…nucleobase models. Note that for interaction energies, only the non-bonded potential of the AFF is needed. The AFF interaction energy was calculated as 𝐸 where the first term is the electrostatic potential describing the pairwise Coulomb interactions between all intermolecular atomic pairs  and  by using  8 and  4 partial atomic charges and  84 interatomic distance. The 332 factor takes care of the conversion to kcal. mol -1 from charges in atomic units and distances in Ångström. The second term is the Lennard-Jones (LJ) potential composed of a repulsive ( 84 @>/ ) and an attractive (- 84 @? ) part. The LJ parameters are represented by the  ;<= (84) optimal interatomic distance and by the  84 potential well depth.
Simulations were done in Na + excess salt conditions of 0. 2 M NaCl resulting from the addition of 15 Na + and 5 Cl -. 112  Ions were placed using AmberTool's leap module that uses basic electrostatic mapping. MD simulations of the r(ggcacUUCGgugcc) tetraloop hairpin were initiated from the NMR structure (PDBid: 2KOC  113  ) and data were taken from our previous work. 40 Simulations were performed with the AMBER OL3 (i. e. , ff99bsc0χOL3)  114  RNA force field with modified LJ parameters for phosphate oxygens, 31 associated dihedral adjustments,  115  and the external gHBfix potential. 37 Trajectory snapshots were saved every 10 ps for both Z-DNA and r(UUCG) tetraloop. Equilibration protocols and further details to r(UUCG) production runs are provided in the Supporting Information. For Z-DNA, the whole 6 µs trajectories were analyzed.
In contrast, we used only the initial 4. 5 μs (MD1), 2. 4 μs (MD2) and 2. 8 μs (MD3) segments extracted from 3x10 μs trajectories of r(UUCG) tetraloop. 40 In these segments the majority of the signature interactions of the r(UNCG) tetraloop is preserved. 40 The remaining MD segments were not analyzed since the r(UUCG) tetraloop denaturates and deviates significantly from the canonical structure. Further, the ribose…guanine lp…π contact is totally lost because the guanine nucleobase flips out of the loop. Additional software: Structures were visualized by Molden 116 and VMD 117 ; figures were prepared with PyMOL 118 , Jmol  119  and VMD; graphs were prepared by Gnuplot  104  and Xmgrace, MD analyses were performed using CPPTRAJ 120 obtained from AmberTools18 106.
For instance, the AFF energies become positive below 2. 7 Å, while the QM energies are still around -3. 0 kcal. mol -  at this distance. AFF minIEd surfaces are too repulsive and shallow compared to the DHDF-D3 reference. DME…purines. The global minimum of the DHDF-D3 minIEd surface for both purine nucleobases is located in the middle of the pyrimidine rings at 2. 97 Å (G) and 2. 95 Å (A) (Figure  4  and Table  S2 ). The surface remains shallow towards the C2 and C6 atoms. A secondary minimum is located above the imidazole rings at 3. 04 Å (G) and 3. 01 Å (A). For G, the location of the global minIEd minimum is in agreement with PDB database analysis of NpG Z-like steps that show that the O4' atom is mainly distributed above a region located between the C2 and C6 atoms and the pyrimidine ring centroid.
4, 17 The data is also consistent with a PDB database analysis taking into account (deoxy)ribose…nucleobase stacking contacts between nonconsecutive nucleotides. 5 he AFF minIEd surfaces recover some features of the DHDF-D3 surfaces, i. e. , the surface minima are located close to the centroid of the pyrimidine ring (Figure  4 ). However, the minima lie at ≈3. 10 Å, a notable deviation of 0. 13 Å (A) and 0. 15 Å (G) from the DHDF-D3 surface minima (Figure  4  and Table  S2 ). Overall, the AFF minIEd surfaces are too repulsive over the whole surface area and are shallower than the DHDF-D3 minIEd surfaces. This is likely a consequence of the simple form of the AFF non-bonded potential that is unable to capture short-range electronic-structure effects like polarization, charge-penetration, and exchangerepulsion.
The minima are located close to the C2 atom and not close to the ring centroid as one would expect from the purine nucleobases data (Figure  4 ). This dissimilarity may result from the absence of the pyrimidine/imidazole junction that allows for smaller atomic volumes of the purine C4/C5 carbon atoms when compared to the "equivalent" pyrimidine C6/C5 atoms due to the presence of the electron-withdrawing nitrogen atoms of the imidazole ring. For instance, the minimum distance of approach to the C5 atom in pyrimidines is ≈3. 22 Å while it is close to 3. 15 Å (G) and 3. 09 Å (A) in purines (see AIM polarizabilities α(0) explain shapes of minIEd surfaces and Figure  6 ). Interestingly, our DHDF-D3 results are in agreement with a PDB database study of (deoxy)ribose…nucleobase stacking that shows that the O4' atom is mostly positioned above the C2 atom of a pyrimidine nucleobase.
Further evaluation using either a larger dataset or following a different approach, like obtaining AIM electron density-based LJ parameters unique for each atom prior to MD simulation as proposed by Kantonen et al. 122  , is needed to precisely parametrize LJ radii values for sp 2 carbon atoms. It can be noted that earlier we calculated α(0) polarizabilities for the indole rings  17  and that the values for the two junction carbons are large and close to those calculated for pyrimidine C5 and C6 nucleobase atoms.
Data and Software Availability The datasets generated during the current study are contained within the manuscript or Supporting Information. Raw trajectory data are available from the authors on request. The in-house program used for restrained geometry optimizations can be downloaded from https://github. com/hokru/xopt (Xopt) and the QM software coupled to Xopt used for energy and gradient calculations can be purchased from https://www. turbomole. org/ (Turbomole version 7. 3). The software used for DHDF-D3 single-point calculations for surface scans is freely available after registration at https://orcaforum. kofo. mpg. de/app. php/portal (ORCA version 4. 0. 1), the software used for CCSD(T) calculations and SAPT analysis is freely available at https://psicode. org/installs/v14/ (PSI4 version 1.
4). The software used for the electrostatic potential maps calculations further used for generating RESP charges by Antechamber can be purchased from http://gaussian. com/ (Gaussian 09). Antechamber itself, as well as CPPTRAJ used for analysis of MD trajectories, is a part of freely available AmberTools found at https://ambermd. org/GetAmber. php. The MD software AMBER can be purchased from the same website. The in-house program used for single-point AFF calculations for surface scans can be downloaded from https://github. com/hokru/BrnoFF (bff). A program used for calculations of AIM polarizabilities is freely available at https://www. chemie. unibonn. de/pctc/mulliken-center/software/dftd4 (dftd4). The used visualization programs are freely available at https://www3. cmbi. umcn. nl/molden/ (Molden), https://www.
ks. uiuc. edu/Research/vmd/ (VMD version 1. 9. 3), http://pymol. org/ (PyMOL version 2. 5. 0) and http://jmol. sourceforge. net/download/ (JMOL version 14. 6. 4). The software used for preparing graphs is freely available at http://www. gnuplot. info/ (Gnuplot version 4. 6) and http://plasma-gate. weizmann. ac. il/Grace/ (Xmgrace version 5. 1. 22).
Second, we compile qualitative reports from social media, news articles, scientists, and published manuscripts, describing seemingly lockdown-related responses of nature, encompassing 406 media reports and 471 observations from 67 countries. Third, we map the direction and magnitude of responses from wildlife, the environment and environmental programs, using data collected before and during lockdown provided by scientists, representing replicated observations across large geographic areas. We collated data from 84 research teams that maintained or accessed existing monitoring programs during the lockdown period, reporting 326 responses analyzed using a standardized analytical framework. We accounted for factors including autocorrelation and observation bias using mixed-effects statistical models, and selected the most robust available baselines for each study to report lockdown-specific effect sizes (see methods).
Materials and methods Here we interpret data and qualitative observations that represent a non-random sample of available information comprising diverse response variables. Thus, we make inferences about the geographic scope of observations and focus on what integrated understanding can be gained from considering the evidence of both positive and negative effects of the lockdown and their linkages. From diverse data sources and analyses, we compiled a high-level view of how the lockdown influenced four major categories of responses or shifts in (1) human mobility and activity, (2) biodiversity threats, (3) wildlife responses, and the (4) social structures and systems that influence nature and conservation (described in further detail in Appendix 1, Table  A1 ). In brief, human mobility and activities included recreational activities such as park visits and boating, commuting, and activities related to industry, such as shipping.
Human mobility data Data on government responses to COVID-19 across countries and time were retrieved from the Oxford COVID-19 Government Response Tracker  (Hale et al. , 2021) , which also reports where the restrictions on internal movement apply to the whole or part of the country. The global population under confinement of internal movement was calculated by adding up the population of countries where the restriction is general, and 20% of the population of countries where the restriction is targeted, as an estimate of the fraction of the population affected. Population data by country corresponding to year 2020 have been obtained from the Population Division of the Department of Economic and Social Affairs of the United Nations  (United Nations, 2018). Note that the data about restrictions contain missing information for some countries and dates.
Therefore, the calculated number of human confinement does not take into account the population of countries with missing information and may thus underestimate the actual number of humans under restriction. Changes in human mobility data were recorded by a number of agencies globally, and combined, describe how the lockdown affected movements on land, at sea, and in the air. Data on the restriction of individuals in residential areas and to parks were derived from Google Community Mobility Reports (https://www. google. com/covid19/mo bility/). Data on driving were obtained from the Apple Maps Mobility Trends Report (https://www. apple. com/covid19/mobility). Marine traffic and air traffic data were derived from exactEarth Ltd. (http:// www. exactearth. com/), and OpenSky Network (https://openskynet work.
org/) respectively. Google Community Mobility Report data are based on anonymized data representing how long users stay in different types of localities, and are aggregated to regional scales (usually country). Each regional mobility report reflects a percentage change over time compared to a 5-week baseline  (Jan. 3 to Feb. 6, 2020). Similarly, Apple Maps Mobility Trends Reports are based on Apple maps user data and aggregated by region to reflect the percent change in time Apple maps users spent driving relative to a baseline (Jan. 12, 2020). The percent change in the responses of human mobility through time allows identification of extreme inflections related to human behavior. For Google and Apple data, we extracted the overall mobility trends for each country until May 1st, which was selected from a sensitivity test and before relaxation of confinement measures were introduced in most countries.
We further excluded within-country variations in mobility, and removed all countries with extensive data gaps and countries that did not show a response to lockdown. The first step to quantifying the effect due to the lockdown on community mobility (residential and parks) and driving data was identifying the date of greatest change in each time-series (data and script files are here: https://github. com/rjcommand/PAN-Environmen t). Because each country had differing lockdown dates and multiple types of lockdown, we identified critical transition dates which best explained the change in mobility for each country. To do so, we used Generalized Additive Models (GAM  (Wood, 2011) ) on daily mobility levels in each country, using the Oxford Covid-19 Government Response Tracker database of country-level containment policies (C1-C7) to define a variable for the before and after lockdown periods, running up to 15 models per country depending on the number of different kinds of lockdown measures imposed.
From these models, we selected the lockdown date that explained the greatest amount of change. We manually identified the confinement dates in cases where the models did not converge or when multiple unexplained inflection points were detected (N = 10 countries). Percent change was calculated as the mean percentages after implementation of the confinement measure selected from the models. For marine traffic mobility, satellite AIS (S-AIS) data for April 2019 and 2020 were obtained from exactEarth Ltd. (http://www. exactearth. com/), a space-based data service provider which operates a constellation of 65 satellites to provide global AIS coverage at a high-frequency rate (< 5 min average update rate). The latest upgrade in the constellation entered into production in February 2019 and S-AIS coverage was equivalent for both periods  (exactEarth Ltd.
, pers. comm. ). Values represented the monthly number of unique vessels within grid cells of 0. 25 × 0. 25 degrees. We calculated the vessel density as the number of vessels per unit area, considering the difference of cell size across the latitudinal gradient  (March et al. , 2021). Grid cells from the Caspian Sea and with <10% ocean area were removed from the analysis, based on the GADM Database of Global Administrative Areas (version 3. 6, https://gadm. org/). Further quality control procedures were provided in more detail in a complementary publication. We calculated the percentage change in marine traffic density between April 2019 and April 2020 per country and Exclusive Economic Zones (EEZ, Figs. S6 & S7) using a Generalized Linear Model (GLM  (R Core Team, 2020; Pinheiro et al. , 2021) ).
For air traffic mobility, data were downloaded from the OpenSky network (https://openskynetwork. org). OpenSky uses open-source, community-based receivers to receive air traffic data from around the world and makes these data available in an online repository. The online database consists of latitude and longitude of departure and landing for all flights detected where receivers are available. Data are limited in some areas, including Africa and parts of Asia. We downloaded daily data for 129 countries where data were available in April 2019 (1,302,282 flights) and the same period in April 2020 (316,609 flights, when most countries included in the analysis had imposed international travel restrictions) to compare the total volume of traffic departing from, or arriving to, all countries where data were available for both years.
Reports that listed several impacts (e. g. , independent observations, species, or locations) were entered as multiple lines. Following entry to our dataset, each observation was assigned an effect score from 0 to 10 (as described in Appendix 1, Table  A2 ) to distinguish between observations with ephemeral effects with unknown impacts from those that will have widespread or persistent outcomes with strong effects in positive or negative directions. Qualitative data were recorded for all continents, except Antarctica, representing 67 countries. Non country-specific observations were also included, representing 20% of all anecdotes. The majority of countries were represented by fewer than five observations (51 countries), while South Africa submitted approximately one third of the total observations (total = 297).
This high representation in South Africa was a known bias due to the use of African birding forums to collect citizen science data which were organized to communicate and engage widely as lockdown measures were implemented. Similarly, other known biases included high relative representation of charismatic species and those that were easily observed during lockdown by humans (e. g. , giant pandas and garden birds). Most reports were gathered from English sources, however, over 100 observations were translated from Italian, and another 50 and 10 were from Spanish and Afrikaans, respectively. We interpreted our results in this context by focusing on the inferences that can be made in spite of these biases, and in combination with the empirical data. See Appendix 3 (Table  S3 ) for the full dataset.
Empirical data We further assembled a global network of scientists and managers to download, interpret, and analyze quantitative information investigating the negative, neutral, and positive effects resulting from the lockdown. We made use of ongoing monitoring programs for comparisons before, during, and after the lockdown confinement period, or in similar time windows in previous unaffected years. Seven example scripts were provided to represent different types of considerations for analyses for each team to match with the types of response data, biases, references, study durations, and complexity (covariates, spatial and temporal autocorrelation, and random effects) (available in Appendix 2). The core author team further consulted on the analysis of each dataset to ensure consistency across studies.
e. , the reference was the post-confinement period (8 studies). For instance, litter accumulation at two locations was measured from the strict lockdown in April 2020, and over two months as restrictions eased. Spatial comparisons between areas impacted by the lockdown with unaffected sites were also included to detect lockdown related effects. These unaffected sites were considered as reference areas after evaluation by the relevant research teams who contributed the data (2 studies). The rationale for each study design and selection of the baseline period is reported in Table  A4  and A5 (Appendices 4  and 5 ), and was reviewed by the core analysis team to ensure the baseline period comprised a suitable reference for the given response of interest. Total percent changes were calculated as the difference between the response coefficient (attributed to the lockdown) relative to the reference coefficient.
For instance, if we observed a 400% increase in a response during the lockdown, this translates to an effect which was 4 times greater. We used Generalized Linear, Additive Mixed (GAMM  (Wood, 2004 )) or Linear Mixed-Effects (LME  (Pinheiro et al. , 2021) ) models, as best suited for each data type. Suitability was based on the distribution of the response data, fit of the statistical data, and the covariates that needed to be accounted for to estimate the appropriate coefficients. In brief, for each dataset, we quantified percentage change from expected or typical values, as well as an effect size in the form of a t-statistic standardized by sample size  (Bradley et al. , 2019). Datasets and results summary tables for each analysis of human mobility and empirical datasets are deposited in a GitHub repository, filed under each contributing author's name: https://github.