dataesr/scientific-paragraphs-categorization huggingface dataset:
10 Nikon Plan objective. Images were acquired using a 16-bit Pixelfly CCD camera (PCO AG, Germany). Because of a low level of fluorescence emission, NAD(P)H images were acquired every 600-800 ms as 4x4 binned images (effective spatial resolution of 348x260 pixels). (http://software. broadinstitute. org/gsea/msigdb/index. jsp) and used in Gene Set Enrichment Analysis (http://software. broadinstitute. org/gsea/index. jsp). For GSEA, we used a ranking based on score -log10(pvalue) * sign (fold change). We calculated p-values comparing two consecutive time points with t-test in control and epileptic animals separately (e. g. when comparing ZT3 and ZT7, we used ZT3 as our reference time point). All values can be found in corresponding tables S14 to S19. A large wealth of information is available to evaluate the circadian regulation of different pathways in controls and their modification in TLE.
30 than during day-time represented by ZT11. 30 (one way ANOVA P=0. 0171, post hoc Bonferroni P<0. 05). Both groups and timepoints were compared using a two-way ANOVA, which revealed significant differences between the two groups at the time-points ZT11. 30 and ZT23:30 (two-way ANOVA, followed by unpaired t-test *P<0. 05;**P<0. 01). Tables S1 to S4. Results of transcriptome (S1) and proteome (S2) analyses for control and TLE (S3 and S4) animals, respectively. Tables  S5 to S13. Enriched terms reported from DAVID bioinformatics for oscillating genes found in control condition alone, epileptic condition alone, and oscillating in both conditions for 0. 05 (S5 to S7), 0. 01 (S8 to S10) and 0. 001 (S11 to S13) p-values, respectively. Tables  S14 to S25. Raw data corresponding to figures S3 to S14 for Gene Ontology Molecular Function (GO_MF), Cellular Component (GO_CC), Biological Process (GO_BP), BIOCARTA, KEGG and REACTOME.
Acknowledgments: This work was supported by  Inserm , and the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  no602102  (EPITARGET). WL and SB are supported by the  Niedersachsen-Research Network on Neuroinfectiology (N-RENNT) of the Ministry of Science and Culture of Lower Saxony in Germany. KJD and KL are supported by the  Polish Ministry of Science and Higher Education  grants  888/N-ESF-EuroEPINOMICS/10/2011/0  and  W19/7. PR/2014.
S1 ) were obtained at 3. 4-Å resolution for Δrox1 and at 2. 8-Å resolution for cox5ab and revealed a CIII dimer flanked by either one or two copies of CIV in the III 2 -IV 5B  1 or the III 2 -IV 5B 2 SC. Apart from the change in CIII:CIV stoichiometry, the overall interaction of CIII and CIV 5B appeared to be very similar to that in the III-IV 5A SCs reported previously  (19, 20). As previously described for the III 2 -IV 5A 2 SC (  19 ), the resolution of CIV 5B was lower than that of the CIII dimer, particularly for those subunits at the periphery of the SC. Thus, for each dataset, we followed the same procedure of using subtracted experimental particle images and performing focused refinement for each CIV 5B and the CIII of the III 2 -IV 5B  1 Δrox1 SC (Materials and Methods). This approach effectively increased the resolution of CIV 5B and CIII to 3.
This density was at  a relatively low resolution, in the range of 7 to 10 Å, precluding assignment. We thus collected a second dataset of the same sample, and by increasing the number of aligned particles, we were able to increase the local resolution in this region to 4 to 5 Å (SI Appendix, Fig. S4A ). This allowed us to confidently assign the new density to the C-terminal fragment of Rcf2 (residues 107 to 205; Appendix, Fig. S5 ), which comprises a short N-terminal α-helix (residues 107 to 114), followed by a transmembrane helixhairpin-helix motif in the IMM (residues 118 to 175) and a final C-terminal α-helix in the IMS (residues 182 to 203). This assignment is further supported by a previous report showing stable association of a C-terminal fragment of Rcf2 with CIV after proteolytic processing of the full-length protein  (28).
Nonetheless, these data suggest that Rcf2 is not specific to the III 2 -IV 1 SC. In our previous study of the Cox5Acontaining SC (purified from the Δcox5b strain), mass spectrometry analysis detected the presence of Rcf1 and Rcf2 in the final protein sample used for structure determination, even though neither protein was detected in the final map  (19). Reanalysis of this dataset with focused refinement of both III 2 -IV 5A  2 and III 2 -IV 5A 1 SCs (80% and 20% of particles, respectively) using a mask that includes Rcf2 led to the observation of additional density (SI Appendix, Fig. S6 D-F ) where the transmembrane helices of Rcf2 were found in the Cox5Bcontaining SCs. Overall, our model of Rcf2 fits well in these extra densities, but again, more data are needed to completely model and identify the new protein within the Cox5A-containing SCs.
s -1 ) = O 2 consumption gradient (M. s -1 ) × 4/[CIII or CIV] (M) for SCs or CIV, respectively. The data presented are the average of several measurements performed on different SC preparations. Error bars were calculated from the SD from the mean. Cryo-EM. Grids for cryo-EM (UltrAuFoil R1. 2/1. 3; Quantifoil) were prepared using a Vitrobot Mark IV (Thermo Fisher Scientific) after 1:1 or 1:3 dilution of purified Δrox1 III 2 -IV 5B  1 SC (2. 9 μM CIII; 0. 6 μM CIV) or cox5ab III 2 -IV 5B 2 SC (6. 9 μM CIII; 2. 9 μM CIV) in 50 mM Hepes, 150 mM NaCl, and 0. 05% GDN, pH 7. 2 (12-to 14-s blotting time, 4 °C, and 100% humidity), as described previously  (19). Data were collected with a Titan Krios microscope (Thermo Fisher Scientific) operated at 300 kV and equipped with a Quantum energy filter (Gatan), using EPU software (Thermo Fisher Scientific).
Two datasets were recorded for the Δrox1 III 2 -IV 5B 1 SC, one at the Electron Bio-Imaging Centre (eBIC; Diamond Light Source, Oxfordshire, UK) and one at Birkbeck College, in otherwise similar conditions so that the particles from the two datasets could be merged (see below). A post-GIF K2 Summit direct electron detector (Gatan), operating in counting mode at a nominal magnification of 130,000× (pixel size of 1. 048 Å), was used for image collection, and an energy slit with a width of 20 eV was used during data collection. A total of 4,140 micrographs were collected at eBIC (dose rate of 5. 14 e/Å 2 /s on the specimen and a total dose of 56. 4 e/Å 2 fractionated over 40 frames; nominal defocus range set from -1. 6 μm to -3. 6 μm) and 2,981 micrographs were collected at Birkbeck College (dose rate of 5.
14 e/Å 2 /s on the specimen and a total dose of 51. 4 e/Å 2 fractionated over 40 frames; nominal defocus range from -1. 5 μm to -2. 7 μm). The cox5ab III 2 -IV 5B 2 SC data were collected at eBIC in "superresolution" mode using a post-GIF K3 Summit direct electron detector (Gatan) operating in counting mode at a nominal magnification of 81,000× (pixel size of 1. 085 Å). An energy slit with a width of 20 eV was used during data collection. A total of 3,634 micrographs were collected with a dose rate of 22. 4 e/pixel/s on the specimen and a total dose of 56. 4 e/Å 2 fractionated over 40 frames and a nominal defocus range set from -1. 5 μm to -2. 8 μm. Image Processing. The two Δrox1 III 2 -IV 5B  1 SC datasets were processed separately until particles were picked. In both cases, MOTIONCOR2 (44) was used for frame alignment and exposure weighting, while CTFFIND4.
1  (45)  was used to estimate the contrast transfer function (CTF) parameters. Micrographs with excessive specimen drift, overfocus, or ice defects were removed manually. Reference-free particle picking was performed with Gautomatch v0. 53 (written by Kai Zhang; https://www. mrc-lmb. cam. ac. uk/kzhang/ Gautomatch/), using a 384-Å 2 box size. This resulted in the selection of 44,268 particles from 3,648 micrographs in the first dataset and 44,968 particles from 2,541 micrographs in the second dataset. Particles were combined, resulting in a final particle number of 89,236, and 3D classification was performed in Relion (46) using a map generated from homogeneous refinement of the first dataset in cryoSPARC2  (47). This revealed a 3. 41-Å resolution map, based on the Fourier shell correlation (FSC) gold standard.
To improve resolution of the SC, we used a particle subtraction approach, as described previously  (19). In brief, a mask was generated around CIII (or CIV) and used to subtract density from the particles, resulting in a new set of particles that was used in focused refinement to improve local resolution on both complexes (SI Appendix, Figs. S2  and S4 A  and C ). For real-space refinement, the two maps (CIV 5B at 3. 41 Å and CIII at 3. 29 Å) were merged using UCSF Chimera  (48). Frame alignment and CTF estimation of the cox5ab III 2 -IV 5B 2 SC dataset were performed using MOTIONCOR2 in Relion and CTFFIND4. 1 within the cryoSPARC v2 interface, respectively. The superresolution micrographs were binned 2 × 2 (1. 085 Å/pixel) during frame alignment. An initial set of 892 particles was picked manually and then classified into 50 representative 2D classes, 12 of which were then used as templates for automatic particle picking in cryoSPARC v2.
This resulted in three maps, one for the core of the SC (covering CIII and part of the two CIV monomers as well) and one each for the two monomers of CIV (CIV 5B -a and CIV 5B -b), with resolutions of 2. 80, 2. 83, and 3. 01 Å, respectively (SI Appendix, Figs. S3 and S4 B and D). For real-space refinement, the maps were then merged using UCSF Chimera. For the CIV 5A within the Δcox5b III 2 -IV 5A  1 and III 2 -IV 5A 2 SCs, data collection, frame alignment, and CTF estimation of micrographs have been reported previously  (19). For the present work, the micrographs were further processed in cryoSPARC v2. A total of 98,968 particles were picked, of which 57,132 were selected from the 2D classification for ab initio reconstruction, including particles of both III 2 -IV 2 and III 2 -IV 1 SCs. The generated ab initio models together with their particles  (44, 195  for the III 2 -IV 2 SC and 12,937 for the III 2 -IV 1 SC) were further used for nonuniform refinement.
To generate focused refined maps on the CIVs, we followed the same procedure as above for signal subtraction, followed by refinement with a CIV mask that included Rcf2. The resulting maps obtained for CIV 5A within the III 2 -IV 1 SC (4. 1 Å) and for CIV 5A -a and CIV 5A -b within the III 2 -IV 2 SC (3. 09 and 3. 24 Å, respectively) are shown in SI Appendix, Fig. S6 E  and F. Model Building. The two individual maps after focused refinement corresponding to CIII and CIV in the Δrox1 III 2 IV 5B  1 SC were aligned on the initial map and merged in Chimera using the "vop maximum" command. The dimeric CIII and one monomer of CIV from the previously determined yeast III 2 IV 5A  2 SC (PDB ID code 6HU9)  (19)  were fitted into this merged map using Chimera, and this model was further improved through several iterations of model building in Coot  (49)  and real-space refinement in Phenix  (50).
Respiratory rate ratios, calculated from the ratio of the state 3 over state 4 oxygen consumption rates measured, ranged from 1. 4 to 2. 0, depending on preparations. The ADP/O values presented here are the average of the ADP/ O T and ADP/ΔO values determined after ADP addition. Error bars are calculated from the SD from the mean. Data Availability. All relevant data are included in the paper and/or are available from the corresponding author on reasonable request. Cryo-EM maps have been deposited in the Electron Microscopy Data Bank under accession nos. EMD-10317 (CIII 2 ) and EMD-10318 (CIV 5B ) for Δrox1; EMD-10340 (III 2 IV 5B 2 SC), EMD-10335 (CIV 5B -a), and EMD-10334 (CIV 5B -b) for cox5ab; and EMD-10375 (CIV 5A -a) and EMD-10376 (CIV 5A -b) for Δcox5b. The coordinates of the atomic models of the CIV 5B -containing SCs built from a combination of EM maps have been deposited in the Protein Data Bank under ID codes PDB ID 6T15 (Δrox1 III 2 IV 5B 1 ) and 6T0B (cox5ab III 2 IV 5B 2 ).
ACKNOWLEDGMENTS. We thank the  Diamond Light Source  and  Dr. A. Howe  for access to and support from the cryo-EM facilities at the  UK National  electron bio-imaging center (eBIC; Proposal  EM14704-36 ), funded by the  Wellcome Trust , the  Medical Research Council UK , and the  Biotechnology and Biological Sciences Research Council. We also thank  Dr. N. Lukoyanova  from the  EM laboratory at Birkbeck  for cryo-EM data collection, as well as  Dr. D. Houldershaw  and the computer support group at  Birkbeck. This work was supported by the  Medical Research Council UK (Career Development Award   MR/M00936X/1 , to A. M. ) and  Wellcome Trust  grants to the  Birkbeck EM  facility ( 202679/Z/16/Z  and  206166/Z/17/Z ).
-10318 ( CIV 5B  ) for Δrox1;  EMD-10340  ( III2IV 5B 2 SC ),  EMD-10335  ( CIV 5B -a ), and  EMD-10334  (CIV 5B -b) for cox5ab; and  EMD-10375  ( CIV 5A -a ) and EMD-10376 (CIV 5A -b) for Δcox5b. The coordinates of the atomic models of the CIV 5B -containing SCs built from a combination of EM maps have been deposited in the Protein Data Bank under PDB ID codes  6T15  ( Δrox1 III 2 IV 5B 1  ) and  6T0B  ( cox5ab III2IV 5B 2 ).
In the current study, we sequenced the transcriptome of the gray matter of dlPFC in SZ and controls. As we found that the differentially expressed genes were enriched in Liver X Receptor/Retinoid X Receptor (LXR/RXR)mediated lipid metabolism genes, we next investigated whether SZ has a genetic link with lipid metabolism. We indeed identified shared genetic etiology between SZ and among other acylcarnitines, very-low-density lipoprotein (VLDL) lipids, and fatty acid metabolites. Finally, exploratory analyses of structural magnetic resonance imaging (MRI) data were in accordance with a lower lipid content of the dlPFC gray matter in SZ patients as compared to controls and correlated with reduced cognitive performance. Thus, distortions in lipid homeostasis play a key role in the cognitive symptoms of SZ.
Samples and RNA sequencing Human post-mortem dlPFC brain tissue from four chronic SZ patients and four control individuals was obtained from the Dutch Brain Bank (Amsterdam, The Netherlands). Sample size was based on tissue availability. Sections of 300 µm were obtained in a cryostat (Leica) at -15 °C and two to three punches were collected from different places in the gray matter and in the white matter using a 2. 00 mm punch needle (Harris). Punches were frozen on dry ice and stored at -80 °C until RNA isolation using RNeasy lipid tissue mini kit (74804 Qiagen). Isolated RNA was sent for quality control, RNA sequencing, and bioinformatics data analysis to BGI Genomics. Agilent 2100 Bio Analyzer was used to determine RNA quality and RNA integrity numbers of all RNA samples were 6. 7 or higher.
RNA sequencing was performed using BGISEQ-500 platform generating 6. 71 Gb bases per sample. Using hierarchical indexing for spliced alignment of transcripts or HISAT, clean reads were mapped to the reference genome UCSC HG38 with an average of 92. 06% mapped reads. Gene expression levels (fragments per kilobase of transcript per million mapped reads) were calculated using RSEM and NOIseq algorithms were then used to determine genes differentially expressed in SZ patients and controls. Significantly differentially expressed genes (probability > 0. 8) were used for analysis with the Ingenuity Pathway Analysis software package (Qiagen). RNA sequencing and RNA-sequencing data analysis were performed by researchers that were blinded for disease state. RNA-sequencing data are freely available through https://doi.
Melting temperature was used to check whether a single PCR product was generated and the take off and amplification values of the housekeeping genes (Ppia and Gapdh) were used to determine the normalization factor with GeNorm 38 after which normalized mRNA expression levels were calculated. qPCR data were analyzed using Levene's test for equality of variances and twotailed independent samples T-tests in SPSS Statistics 21. Individual data points and means were plotted using Graphpad Prism 4. Researchers were blinded for disease state during qPCR analysis.
Shared genetic etiology Two SZ genome-wide association studies (GWASs) and four metabolite GWAS datasets were used to calculate shared genetic etiology between SZ and metabolite levels. We first calculated shared genetic etiology between 561 metabolites and SZ using previously published SZ GWAS data that was obtained from 33,426 SZ patients from European ancestry  39. We then replicated the calculation using a second SZ GWAS dataset, namely the GWAS data from 36,989 SZ patients as provided by the Psychiatric Genomics Consortium  40  , which includes the same patients from European descent, but also includes individuals with East-Asian ancestry. The metabolite GWAS data were obtained from Rhee et al. 41  including 268 metabolite GWASs, Draisma et al. 42  including 129 metabolite GWASs, Kettunen et al.
43  including 123 metabolite GWASs, and Ahola-Olli et al. 44  including 41 cytokine GWASs, and included 2076, 7478, and 24,925 participants of European decent, and 2019 Finnish participants, respectively. Shared genetic etiology was calculated using the freely available program PRSice version 1. 23  45  with PLINK version 1. 9 and based on the method of Johnson et al. 46. Metabolite GWAS data were taken as base samples and SZ GWAS data as the target sample, and correlation results were weighted by the SZ group size. Using PRSice, single-nucleotide polymorphisms (SNPs) were clumped to remove linkage disequilibrium (LD) with an LD threshold of 0. 1, a distance threshold of 250 kb, and the 1000 Genomes Project data as genotype reference  47. A range of SNP significance thresholds was used (p T < 0.
Analysis of the dlPFC gray-matter MP-RAGE signal and correlation with task-switching accuracy The Consortium for Neuropsychiatric Phenomics made available an MRI dataset including 125 healthy individuals (median age = 28 years old, 53% female) and 50 individuals (median age = 37. 5 years old, 76% female) diagnosed with SZ or schizoaffective disorder. This dataset includes a T 1 -weighted magnetization prepared-rapid gradient echo (MP-RAGE) sequence (repetition time = 1. 9 s, echo time = 2. 26 ms, field-of-view = 250 mm, matrix = 256 × 256, slice thickness = 1 mm, 176 slices), as well as cognitive behavioral data from the task-switching test. For details on the dataset, see ref. 48. For all MRI analyses, open source code was used. The MP-RAGEs were corrected for B 0 /B 1 inhomogeneities using the N4 algorithm.
A study-specific template of the MP-RAGE scans was created in the common space between the scans with an iterative diffeomorphic warp estimate using the ANTS package  49. The template was diffeomorphically registered to the MarsAtlas  50. A segmentation of the dlPFC was extracted from the atlas and projected to each individual scan. The dlPFC regions of interest were corrected at the individual level with a gray-matter mask made with FSL-FAst and the output was visually verified. The average MP-RAGE signal in the dlPFC gray matter of SZ patients and controls was examined. Two linear models were fitted including the average left and right gray-matter dlPFC MP-RAGE signal as the dependent variable and age, sex and group as the independent variables. A retrospective motion-estimate (Average Edge Strength) was also calculated with the homonymous Matlab toolbox  51  and entered as an independent variable.
The analyses were repeated for data acquired at both 3T scanners (Trio, Siemens Healthineers). We then utilized a linear model to analyze the correlation between dlPFC gray-matter MP-RAGE signal and accuracy in the taskswitching test in SZ patients accounting for age and motion. For details on the task-switching test, see ref. 48. Individual data points and means were plotted using Graphpad Prism 4. Researchers were blinded for disease state during data analysis.
Shared genetic etiology between SZ and lipid metabolism To further investigate the role of lipid metabolism in SZ, we analyzed the shared genetic etiology between SZ and 514 circulating metabolites, including amino acids, nutrients, organic compounds, cytokines, growth factors, and lipids. Following Bonferroni correction (Table  2  and Supplementary Table  S4 ), we found significant overlap between genetic risk for SZ and 35 metabolites (Table  2  and Supplementary Table  S4 ; p < 0. 05) using the results of the 2018 SZ GWAS study published by the Bipolar and Schizophrenia working group of the Psychiatric Genomics Consortium  39. Using a second 2014 SZ GWAS dataset provided by the Psychiatric Genomics Consortium  40  , the genetic association between SZ risk and 25 of the 35 metabolites was replicated, and 21 additional metabolites that share genetic etiology with SZ were identified (Table  2  and Supplementary Table  S5 ).
Lipid content of dlPFC gray matter is lower in SZ than in controls and correlates with reduced accuracy in the taskswitching test We further investigated the effect of the disrupted lipid homeostasis in SZ dlPFC gray matter using a publicly available dataset from the Consortium for Neuropsychiatric Phenomics. This dataset contains amongst others structural MRI scans and performance in the taskswitching cognitive test of 50 SZ patients and 125 control individuals  48. From this dataset, we analyzed the T 1weighted MP-RAGE signal. The macromolecular pool in the brain consists mainly of lipids, as illustrated by the typical gray-white matter contrast obtained in T 1weighted MRI scans. The T 1 inversion pulse saturates the free-water pool and the macromolecule pool. Following the saturation, the macromolecular pool quickly relaxes and subsequently accelerates the relaxation of the freewater pool in a process termed magnetization transfer.
Cholesterol can be transported out of the cell via HDLlike particles. Based on our transcriptomic study and the shared genetic etiology between SZ and several lipidrelated metabolites, we conclude that lipid homeostasis involving fatty acid oxidation and cholesterol efflux, production, and transport may well play a role in SZ. Using a publicly available dataset  48  , we show that the T 1 -weighted MP-RAGE signal is significantly decreased in SZ dlPFC gray matter. The T 1 -weighted MP-RAGE signal creates contrast between gray and white matter, which is thought to be due to magnetization transfer effects, where increased lipid content results in increased signal. Our results are therefore consistent with a decreased lipid content in the gray matter of the dlPFC of SZ patients. This conclusion does not take into account the added effects from differences in spin density or in the relaxation rate of the free-water pool itself to the MP-RAGE signal.
Data sources and variable measurement To examine the link between environmental performance and market share, this study focused on leading U. S. -based green firms identified in the list of the top green firms in Newsweek's green ranking. Data for the study were gleaned from two databases, namely, Newsweek's ranking of green firms and Compustat, both of which have been utilized by earlier studies and recognized as reliable and robust databases (e. g. Shimshack, 2015). The green score of each firm ranges between 1% and 100%, and firms are ranked based on the green score they achieve. See Appendix 1 for a detailed explanation as to how firms' green performance is measured by Newsweek.
Dependent Variable: Market share data were obtained from the Compustat database. Following earlier studies (e. g. Rego, Morgan, & Fornell, 2013) , market share for each sample firm for each year was operationalized as the percentage of sales revenue as compared to total sales revenue in the firm's industry at a 4-digit SIC level  (Rego et al. , 2013). Following previous precedent, this study used both the current value of the dependent variable as well as the one-year lagged value  (Dixon-Fowler et al. , 2013).