dataesr/scientific-paragraphs-categorization huggingface dataset:
56, and 100k particles were extracted with a box size of 282, and a binning of 3. After 2D classification, 48k particles were selected and re-extracted without binning. To generate consistent figures, these particles were imported into cryoSPARC v3. 3. 1, and subjected to 2D classification. For each dataset, an ab initio reconstruction was computed using the filtered particles through the ab Initio Reconstruction job in cryoSPARC v3. 3. 1. Default reconstruction settings were used, aside from 500 extra final iterations in all, and C6 symmetry was imposed where noted. Details including the number of movies, and particles picked can be found summarized in Supplementary Table  S1.
Ice Thickness Measurements As an indirect measure of ice thickness, all images corresponding to each of the cryo-EM datasets were plotted according to their relative ice thickness, determined using the relative ice thickness parameter in the Manually Curate Exposure option, following patch CTF estimation (multi), in cryoSPARC v3. 3. 1. As a direct measure of ice thickness, frozen aliquots of cleaved CDC48A (following the optimized methodology) were further purified using SEC. Fresh sample corresponding to the peak hexameric fraction was used to prepare cryogenic grids under the conditions used for optimized datasets 3 and 4. Ice thickness was measured using the aperture limited scattering method of  Rice et al. (2018) , by measuring the beam intensity of the specimen vs. the beam intensity of the vacuum (λ = 322 nm) on a Titan Krios operated at 300 kV using 100 micron Objective Aperture, and equipped with Gatan's GIF/K2 detector.
We observed a sufficiently dense distribution of particles throughout grid regions. However, the particles did not appear homogeneous in size, and shape and differed markedly from the NS-EM images (Figure  1F ). We collected a dataset of 1,242 movies on these grids and performed image processing to compute 2D class averages (Figure  1G ). The 2D classes corroborated the presence of significant structural heterogeneity, apparently as a result of CDC48A hexamer dissociation. The strong dissociation of the CDC48A hexamer in cryo-EM was unexpected because intact hexamers prevailed in SEC, SAXS, and NS. The cryo-EM-specific hexamer dissociation warranted further optimization of our workflow, and protocols for the preparation of the protein sample, and cryo-EM grids.
Ice Thickness Influences Complex Integrity and Particle Orientation First, we wanted to investigate whether particle dissociation was caused primarily by our handling of the sample. To this end, we cryogenically prepared grids using a slightly altered Vitrobot protocol (see Methods), and used them for a subsequent round of cryo-EM screening at the Astbury Centre for Structural Molecular Biology (ACSMB), University of Leeds, United Kingdom. Inspection of the images confirmed that hexamers largely dissociated in optimally thin ice regions, evidenced by an increase in background noise, and a reduction in visible hexamers. However, when screening regions of the grids that had thicker ice we noted the substantially increased prevalence of intact hexamers (Figure  2A ). Collecting a dataset of 1,311 movies from thick ice and processing the data to the stage of 2D classification revealed only ~50,000 particles.
The computed 2D class averages reveal a proper distribution of particle orientations, with top and bottom views of CDC48A indicated by red and orange boxes, respectively, a few side views of CDC48A with a crab-like organization in blue and intermediate particle orientations in green (Figure  2B ). The particle integrity and isotropic orientation of this dataset was also reflected in the template-free ab initio reconstructions with and without imposed C6 symmetry (Figure  3A ). The maximum resolution of images proposed through CTF estimation in cryoSPARC ranges from 4 to 6 Å (Figure  4 ), indicating that these images are not suitable for computing a high resolution 3D reconstruction of CDC48A. Therefore, we concluded that capturing particles in thicker ice maintained the integrity of the hexamers, and provided a more homogeneous distribution of particle orientations.
We reasoned that the hexamer dissociation was lesser in thick ice because of a reduced surface to volume ratio, and hence a lesser exposure of proteins to the destabilizing air-water interface. To test this effect, we used the spray-on method integrated into the Chameleon grid preparation instrument (operated at eBIC, Diamond Light Source), instead of Vitrobot blotting. The faster spray-on method is expected to limit the number of interactions of the protein with the air-water interface prior to vitrification, and thus preserve the integrity of proteins  (Klebl et al. , 2020). Additionally, self-wicking grids are used in the Chameleon, eliminating the blotting step that may disrupt sample integrity  (Wei et al. , 2018). The collected dataset comprised 1,472 movies from optimal thin ice regions (Figure  2C ).
We extracted about 250,000 particles. The resulting 2D class averages of CDC48A revealed mainly top and bottom views of a hexameric CDC48A (Figure  2D ). Thus, although the integrity of the quaternary state was maintained through the use of the Chameleon, unfortunately, only ~2% of the extracted particles displayed the side-view orientation, resulting in a poor quality of the ab initio reconstruction (Table  1 ; Figure  3B ). Therefore, thin-ice collection on Chameleon-prepared grids provided a remedy to particle dissociation but presented the problem of preferred orientation. The ice thickness of this dataset was estimated to be the thinnest of all datasets recorded. Surprisingly, the resolution of images ranged from 3 to 8 Å (Figure  4 ). Using the method developed by Rice et al. (  2018 ), the holes of cryo-EM grids presenting the thinnest ice have a thickness of 21.
42 ± 3. 17 nm. As the diameter of CDC48A is expected to be around 16 nm, we can speculate that the shape and surface charge distribution of CDC48A, and the thickness of ice are certainly the main parameters explaining the preferential orientation of particles in this dataset. Because our access to the Chameleon was limited, we were not able to test thick-ice conditions on this instrument. In conclusion, using the standard Vitrobot protocol, we could either observe a dissociated complex in optimally thin ice or an intact complex in thicker ice at the cost of resolution. Conversely, blot-free vitrification by the Chameleon preserved the sample integrity but introduced a strong preferential orientation due to the asymmetrical shape of our complex that compromises its 3D reconstruction. To be able to have a rapid workflow using our inhouse Vitrobot, we decided to explore optimizing the ice thickness jointly with improving the intrinsic stability of the protein sample.
ATP is a cofactor for CDC48A, and each monomer binds two adenosine phosphate nucleotides, one each in D1 and D2 domains. The presence of ATP or ADP introduces large structural changes in p97  (Banerjee et al. , 2016). TABLE 1 | Quantifying the preferential orientation bias. As a proxy for determining the degree of preferred orientation bias in the datasets, the number of particles that belonged to classes representing a complete side view of CDC48A was divided by the number of particles in all good classes, for each dataset. Values of 9. 0 ± 1. 0% correspond to an even orientational distribution. Hence, the production of CDC48A molecules without this cofactor allows the preparation of homogeneous apo, ATP or ADP-loaded samples for structural and functional analysis. In conclusion, the amended purification protocol produced a purer, fresher, and more homogenous sample.
Combining an Improved Sample With Thicker Ice Yields Sufficient Data Quality Because our initial cryo-EM work had shown disassembly and preferential orientation in thin ice for our hexameric CDC48A sample, we first investigated the behavior of our biochemically optimized protein sample in thin ice. In line with the improvements we saw in NS-EM, the optimized sample preparation increased the integrity of the sample in cryo-EM, as homogeneous and well-dispersed hexameric particles were observed under thin ice conditions following Vitrobot blotting. However, our screening of thin ice grid regions revealed a high degree of preferential orientation with particles oriented primarily in a top or bottom view. The first movies recorded from grid regions with thick ice showed a homogeneous distribution of orientations, but at the cost of high-resolution information.
Therefore, we decided to start from thick ice that displays a wide distribution of particle orientations, and record datasets at different ice thicknesses to identify the optimal compromise between particle resolution, and orientation. In line with our previous observations, we did not record movies in holes presenting the thinnest ice. Using the image grayscale as a proxy for ice thickness, we collected four separate datasets from grid regions of decreasing ice thickness and processed each dataset individually. Utilizing CTF parameter estimation in cryoSPARC, the relative ice thickness and CTF fit resolution was compared for each dataset (Figure  4 ). The estimated relative ice thickness obtained did not allow us to discriminate between the ice thickness of optimized datasets 1, 2 or 3, but shows clearly that dataset 4 has a thinner mean ice thickness (Figure  4A ).
In a second round of sample preparation using the same type of cryo-EM grids and preparation conditions, we directly measured the ice thickness according to the method of  Rice et al. (2018). This indicated three different ice thicknesses for the holes used to collect the optimized datasets: 100. 48 ± 6. 24, 66. 99 ± 8. 27, and 30. 60 ± 8. 57 nm. The estimated CTF fit resolution revealed that datasets 1, 2, and 3 contain a majority of images with resolution ranging from 5 to 6 Å, with dataset 3 additionally containing a minority of images with resolution around 3. 5 Å (Figure  4B ). The estimated CTF fit resolution of the majority of images in dataset 4 range from 3 to 4 Å, indicating that this dataset is suitable to reach a final 3D reconstruction of CDC48A between 3 and 4 Å. According to our measurements, we presume that dataset 4 should have an ice thickness around 30-40 nm, while datasets 1 and 2 certainly correspond to holes having an ice thickness near 100 nm.
Dataset 3 should certainly represent a mixture of images recorded in holes having ice thicknesses ranging from 30 to 100 nm, explaining why we have two populations of images within the distribution of estimated CTF fit resolution. Additionally, 2D class averages and ab intio reconstructions were computed for each dataset (Figure  6 ; Figures  3D-F ). As expected from thick ice, all particle orientations were present as revealed by the top, bottom, side, and intermediate views of CDC48A seen in the 2D class average and further confirmed by the template-free ab initio reconstructions. The hexameric organization of CDC48A is perfectly observed in reconstructions without symmetry imposed, in which the double ring structure is evident from the side view. The reconstructions with the applied symmetry reveal a homogeneous organization of CDC48A presenting all expected structural features of CDC48A.
In our case, the method of hole selection was manual and involved visual inspection of the ice thickness or its "grayness" in images. However, it should be possible to automate screening holes according to a specific range of this parameter, as implemented via scripting in Gatan's DM software  (Rheinberger et al. , 2021). Given that the movie recording speed is steadily increasing, a second option would be a "shoot first, ask questions later" approach, where data is recorded on holes of varying ice thickness, and the most appropriate ice thickness is automatically selected using analysis software such as IceBreaker  (Olek et al. , 2022). Ice thickness-based filtering/selection of micrographs is also implemented in cryoSPARC through the "Curate Exposures" job  (Punjani et al. , 2017). Although the increased ice thickness reduced the maximal resolution, the achievable resolution was, in our case, suitable for an in-depth structural, and molecular analysis (to be published later).
Owing to the substantial advances of AI-based structure prediction algorithms in 2021  (Baek et al. , 2021; Jumper et al. , 2021) , we argue that the resolution limit for addressing a functional, and biological question with cryo-EM can be lower without loss of information when confidently predicted domain structures can replace density-based ab initio structure modeling. There are, of course, also other, more sophisticated ways of achieving a more homogeneous particle orientation  (Drulyte et al. , 2018). In our case, we could have combined the Chameleon spray-on method  (Razinkov et al. , 2016; Dandey et al. , 2018; Wei et al. , 2018) , which preserved the hexameric state in thin ice, with recording a singleparticle dataset on a tilted stage  (Tan et al. , 2017). However, access to the Chameleon is still limited for most groups.
ACKNOWLEDGMENTS We would like to thank the  Imaging and Characterization core lab  and the  Supercomputing Laboratories at KAUST  for the use of their resources and support. We thank  Miriam Weckener  and  Yuriy Chaban  for data collection at eBIC,  Diamond Light Source. We acknowledge the  Research Complex at Harwell  for access to their cryo-EM facilities and we are especially thankful to  Miriam Weckener  for preparing grids using the Chameleon. We would like to express our gratitude for access and support in the cryo-EM work carried out at the  Astbury Centre for Structural Molecular Biology, University of Leeds, United Kingdom ; especially to  Rebecca F. Thompson ,  Emma Hesketh , and  Daniel Maskell  for their great support for sample preparation and data collection. We are grateful to  ETH Zurich  for access to the computational resources at ScopeM and Euler cluster.
The main performance parameters of the TC devices in the linear regime are reported in Table  2  as well as transfer and outputs characteristics in Figure  7. Table  3. Structural and electronic properties of DN4T and isoDN4T benchmarked with those of structurally related thienoacenes. All transfer integrals mentioned with (ADF) have been calculated using the Amsterdam Density Functional package with a PW91 functional and a TZP basis set. Data with a (*) correspond to measurements/calculations performed in this work: transfer integrals were calculated using the Amsterdam Density Functional package with a B3LYP functional and DZ basis set, and the reorganization energy of DNBDT was calculated with the Gaussian package, MP2 functional, and 6-31(d,p) basis set. Mobility values mentioned with (SC) have been measured on single crystals.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under the  Marie Sklodowska-Curie Grant  No  811284. Y. H. G. is thankful to the  Belgian National Fund for Scientific Research (FNRS)  for financial support through research projects  BTBT  ( No 2. 4565. 11 ), Phasetrans  ( No T. 0058. 14 ), Pi-Fast  ( No T. 0072. 18 ), 2D  to  3D (No 30489208 ), and  DIFFRA (No U. G001. 19 ). Financial supports from the  Fédération Wallonie-Bruxelles  ( ARC No 20061 ) are also acknowledged. P. S. acknowledges financial support from the  Labex  project  CSC  ( ANR-10LABX-0026 CSC ) within the  Investissement d'Avenir program  ( ANR-10-IDEX-0002-02 ), the  International Center for Frontier Research in Chemistry , and the  Institut Universitaire de France (IUF).
| Characterization Crystal structure and phase purity were probed by laboratory XRD pattern using a Bruker D8 Advance, with Cu-K α radiation (40 kV and 30 mA, λ = 1. 54053 Å) and LynxEye rapid detector, over a 2θ-angular interval from 10 up to 100 with step size of 0. 02 and step time of 6 s. The diffraction patterns were compared with structural data available at the Inorganic Crystal Structure Database (ICSD, FIZ Karlsruhe, and NIST). No trace of secondary phase was found in any of the samples investigated here. High-angular resolution synchrotron X-ray diffraction (SXRD) was performed in the beamline MSPD of the CELLS-ALBA Synchrotron (Barcelona, Spain). [28] The pattern was recorded in the high-resolution setup with an incident energy of 38. 07 keV (λ = 0. 32566 Å). The powder sample was placed in a glass capillary, which was rotating along the collection time to better average the diffracted signal.
Laboratory XRD and SXRD data were evaluated by Rietveld refinement method using the Fullprof software. [29] The microstructures of both powders and ceramics were examined using an Inspect F50 fieldemission scanning electron microscope (FEI, Netherlands) at 5 kV coupled to an Energy dispersive Xray (EDS) spectrometer, as summarized in Supporting Information. Raman spectra at ambient condition were collected in a HR800 Evolution spectrometer from Horiba Jobin-Yvon, equipped with a Peltier-cooled charge-coupled device (CCD), 1800 gr mm À1 diffraction gratings, and Olympus microscope (50Â objective LD, NA = 0. 35). As an excitation source, an Nd:YAG laser operating at 532 nm was used in a quasi-backscattering geometry with a power below 1 mW. All the spectra were further corrected by the Bose-Einstein thermal factor prior to the fitting procedures by Lorentzian profiles (intensity, position, and linewidth) within the software PeakFit.
[30] lectrical characterizations were performed by means of impedance spectroscopy using a Solartron frequency response analyzer (model 1260) coupled with a dielectric interface model 1296, at a frequency range between 0. 5 Hz and 1 MHz. For such measurements, ceramic pellets' polished surfaces were carefully painted with platinum paste in order to form parallel plate capacitors. The ac amplitude was defined after signal-to-noise ratio tests to ensure the results within Ohmic regime. The spectral data were adjusted using the software ZView 2. 9c. [31] etails on the electrical studies in the aforementioned ceramics are presented in Supporting Information.
[46]  Nevertheless, more five modes could be clearly identified, which was an evidence for the symmetry lowering process. Indeed, the incorporation of La 3+ (r = 1. 36 Å, CN = 12) ions at Ba 2+ sites (r = 1. 61 Å, CN = 12) may promote structural instabilities, which may induce the octahedral tilting mechanism. I G U R E 4 (a) After a detailed literature revision,  [18, 47]  we have concluded that the most plausible space group to index the BLCN unit cell is the monoclinic I2=m (S. G. #12), being derived from the aristotype cubic phase Fm3m through an out-of-phase tilting as represented by the Glazer system a 0 b À b À. [17] Such assumption was also confirmed by the software SPuDS. [48] The structural details are summarized in Table  3  together with the vibrational modes predictions using the group theory on the factor group 2=m.
ACKNOWLEDGEMENTS Financial support from the  Brazilian agencies FAPESP  (Grant number:  13/07793-6 ) is gratefully acknowledged. J. A. A. thanks the  Spanish Ministry of Science, Innovation, and Universities  for funding the project  MAT2017-84496-R. We thank the  CELLS-ALBA (Spain)  for making all the facilities available for the synchrotron radiation experiments. ORCID João Elias F. S. Rodrigues https://orcid. org/ 0000-0002-9220-5809Renilton  C. Costa https://orcid. org/0000-0003-2129-2930Paulo S. Pizani https://orcid. org/ 0000-0002-5914-1671Antonio  C. Hernandes https://orcid. org/ 0000-0002-7556-8050José  A. Alonso https://orcid. org/ 0000-0001-5329-1225.
, 2017; Zegarra-Moro et al. , 2002). Progression to CRPC with ongoing AR signaling is because of several factors, one of which is the expression of constitutively active AR splice variants (AR-Vs) that lack the C-terminal AR LBD but carry the N-terminal and DNA binding domains  (Ware et al. , 2014). Developing inhibitors to the AR NTD is technically challenging because it is intrinsically disordered and lacks rigid pockets for drugs to bind. Several attempts to directly target this AR domain have met major setbacks  (Schweizer et al. , 2018). We have recently reported the development of a compound, A4B17, that disrupts the BAG1L-AR NTD interaction and inhibits the proliferation of AR positive prostate cancer cells  (Kuznik et al. , 2021). In this report, we present data to support a dual function of A4B17 in inhibiting AR activity.
, 2001)  (Figure  S1E ). This suggests that the HSC70/HSP70 and AR binding sites on the BAG domain are non-overlapping and could therefore be targeted separately. To determine whether the interaction of BAG1L with the AR NTD and its effect on AR dynamics target a distinct subset of AR-regulated genes, we reanalyzed our previous RNA-seq datasets in the control and BAG1L KO cells  (Cato et al. , 2017)  by calculating the fold change in androgen-mediated expression of each gene using a fold difference cutoff of 1. 5. The expressed genes were plotted as scatterplot matrix with LNCaP control vehicle (EtOH) versus DHT-regulated genes plotted on the x-axis and LNCaP BAG1L KO EtOH vs DHT-regulated genes plotted on the y-axis (Figure  S2A ). We then performed gene ontology analysis using the Broad Institute Molecular Signature Database (MSigDB)  (Liberzon et al.
A4B17 attenuates the expression of androgen response genes We next used the small molecule A4B17 that we have recently reported to disrupt the BAG1L-AR NTD interaction  (Kuznik et al. , 2021)  to determine whether it would inhibit AR action similarly to what we reported for the BAG1L KO cells. RNA-seq experiments were first carried out in LNCaP cells treated with either vehicle, DHT, A4B17, or A4B17 + DHT and Gene Set Enrichment Analysis (GSEA) was performed to identify pathways that are enriched in each dataset. We found that the Hallmark Androgen Response pathway is enriched in the DHT dataset, but significantly attenuated in the A4B17 + DHT dataset (Figure  3A ). We further found that the Hallmarks Fatty Acid Metabolism and Cholesterol Homeostasis were also significantly attenuated in the A4B17 + DHT dataset when compared to the DHT only treatment.
Because lipid biosynthetic pathways are major AR targets  (Han et al. , 2018; Zadra et al. , 2019) , our finding that they are downregulated by A4B17 provides significant evidence of AR antagonistic properties of A4B17. Quantitative RT-PCR experiments confirmed A4B17-mediated downregulation of the classical AR targets (e. g KLK3 and FKBP5) as well as genes in the redox and metabolic pathways (MICAL1, SAT1, DUOX1, and GNMT) (Figure  3B ). We next compared our A4B17 RNA-seq dataset with the differential regulated genes derived from the BAG1L KO matrix (Figure  S2A ) and found in the heatmap that a number of genes, especially those involved in redox homeostasis, were regulated similarly between A4B17 and BAG1L KO datasets (Figure  3C ). As further proof that A4B17 functions through BAG1L, we carried out quantitative real-time PCR studies on a select number of genes identified in the heatmap to show that the inhibitory action of A4B17 was absent or impaired in the BAG1L KO cells compared to the control cells (Figure  3D ).
After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated. Library preparation and sequencing were carried out by Novogene Europe, Cambridge, UK. Gene set enrichment analysis was performed using GSEA Software v4. 1. 0 (Mootha et al. , 2003; Subramanian et al. , 2005)  and the data deposited at the GEO repository under accession number GSE159606. RNA-seq studies with LAPC-4 cells were carried out after treating the cells with vehicle (DMSO) or 5 mM A4B17 or enzalutamide. RNA preparation and sequencing was performed as described above by Novogene Europe. The raw gene expression was normalized with DESeq2  (Love et al. , 2014)  and the data deposited at the GEO repository under accession number GSE182278. Differential gene expression was determined for enzalutamide or A4B17 against vehicle samples (p(FDR)%0.