dataesr/scientific-paragraphs-categorization huggingface dataset:
Because high-throughput screening is performed on the scale of hundreds of thousands or even millions of structures, there is a need for faster computational methods to predict the materials' properties, to be able to analyze larger and larger databases. In the eld of adsorption, several measures have been proposed in order to study the performance of a material for the adsorption of a specic guest molecule. The most accurate, but also the most computationally expensive one, is the grand canonical Monte Carlo (GCMC) simulation. On the other end of the scale, the use of simple geometric descriptors (pore size, pore volume, and surface area) has also been proposed as proxies for various adsorptionrelated metrics. As the eld of computational chemistry is turning more and more towards machine learning (ML), the development of a wide range of rapidly calculable descriptors  17  is an exciting research focus especially to speed up computational screenings.
Benchmark Before describing the core components of our surface sampling algorithm, we briey present the other simulation tools used in the article, for comparison and benchmarking purposes. We used the RASPA2 soware for calculations such as Widom insertion and surface area calculations. 20 For the Voronoi sampling technique, we used the Zeo++ soware to determine the positions of the Voronoi nodes. 21 All the calculations were performed by considering the CoRE MOF 2019 (ref. 22) structures as rigid. In order to speed up the surface sampling, our algorithm exploits the symmetry of the material, looping over all symmetry-inequivalent atoms. Since the space groups (and symmetry operations) of the nanoporous structures in the CoRE MOF database were not specied, we used a Python script based on the Spglib library to determine them.
23 The unique atoms are dened according to the symmetry determined by the aforementioned Python script. All simulations are performed for xenon adsorption on structures of the CoRE MOF 2019 database at innite dilution and at 298 K. Krypton adsorption has also been considered in order to see the viability of the method in prediction of Xe/Kr selective materials (see full details in ESI † Section S4). Adsorption at another temperature (600 K) was tested using the nal algorithm, and the results are presented in ESI † Section S2. Other databases have also been briey explored to test the robustness of the proposed algorithm. A subset of a hypothetical MOF database, the ToBaCCo database,  24  has been screened (Section S6. 1 †), because it contains very different MOFs according to the diversity analysis of Moosavi et al.
25  (for instance, the pores are larger). An amorphous material database (containing 205 structures) has also been screened and the algorithm identied some top materials for xenon adsorption (Section S6. 2 †). Widom insertion 19 is implemented in RASPA2. It consists in randomly inserting a single molecule inside an existing molecular system to measure an interaction energy. In adsorption simulations, these interaction energies of the randomly inserted adsorbate are typically used to determine values of the Henry constant K H and of adsorption enthalpy DH ads at the zero-loading limit. The surface area calculation implemented in RASPA2 follows a very standard "rolling ball" algorithm 26 based on hard spheres with sizes determined by the sigma value of the Lennard-Jones potential. First, a probe-molecule samples the spherical surface of the atoms of the framework.
Therefore, we can improve this method if we manage to sample preferentially points with the most negative interaction energies. To achieve this improvement we need to identify the characteristics of the adsorption sites that will have the highest weight ("count the most") in the nal average, while avoiding sampling parts of space where points will have marginal contribution. The Voronoi sampling and the surface sampling presented in this article are examples of biased sampling methods, that follow this idea. The change in the sampling technique can dramatically improve the computation time required, and even a slight improvement in computational efficiency can have a nonnegligible impact when dealing with datasets containing thousands of structures. This article focuses on biased sampling techniques to speed up adsorption enthalpy calculations.
As a proof of concept, we only consider monoatomic adsorbates (in our tests, we used xenon) or adsorbates that can be modeled as a sphere (which is common in molecular simulations of species such as CH 4 ). However, the methodology can be adapted to rigid polyatomic adsorbates, where the algorithm would need to be adapted by sampling the rotational degree of freedom of the adsorbed molecule. In all the comparisons in this paper, we chose to take a Widom insertion simulation with 100 000 cycles (a very large number) as a ground truth or reference for the adsorption enthalpy values of every structure of the CoRE MOF 2019 database. 22.
28 s (measured as an average over all the structures of the CoRE MOF 2019 database), on a typical workstation (a single Intel Xeon Platinum 8168 core at 2. 7 GHz). While a simple Python code for the energy calculation took around 27 s per structure, we benchmarked that a C++ optimized implementation can perform the Voronoi sampling in around 0. 4 s. We only need to remember that this method takes a few hundred milliseconds per structure, while a Widom insertion needs approximately hundreds of seconds per structure. A Voronoi sampling is therefore 2 to 3 orders of magnitude quicker than a full sampling of the pore space. This preliminary study identied a fast method for adsorption enthalpy calculations that can be widely used in screening procedures, but has limited accuracy for quantitative prediction.
90 kJ mol -1 and 0. 66 kJ mol -1 considering all the structures from the database. Moreover, there is no noticeable difference in RMSE when considering the structures with a pore size above 3. 7 Å (as determined by the largest cavity diameter, or LCD). Unlike Voronoi sampling, this method gives a consistent accuracy across all the structures of the database with a lower error. The fact that the RMSE error is below 1 kJ mol -1 is quite promising, and validates our intuition that this new sampling technique can be an intermediate between to the two previous methods (Voronoi and Widom). Aer proving the good accuracy of the method, we are now exploring the computation time required. We see in Fig. 3  that the method reaches an RMSE below 1. 0 kJ mol -1 very quickly for an average CPU time of 1.
If one wants to use it on a completely different database under very different conditions, then one can either choose a default value that works ne (e. g. l = 1. 4) or one can optimize the parameter on a small diverse sample of the unseen data. 3. 3. 3 Rejection conditions. As shown above, our algorithm has better accuracy than Voronoi sampling, but its initial implementation was several times slower, which could make it unsuitable for screening applications in high-throughput workows, where the number of structures to be screened can reach one million or more. To reduce the computational expense, we thought of rejecting the points with little contribution to the nal enthalpy, i. e. , the largely positive interaction energies that would vanish in the exponential of the Boltzmann average.
The performance of our algorithm, including the two new features (sampling sphere scaling and rejection criterion) is illustrated in Fig. 7 , where we can see the improvement brought about by each feature and how it compares to reference simulations. All CPU times are calculated using the smallest possible number of sampling points so that the respective algorithms reach convergence. With the implementation of a rejection condition, we nd that surface sampling is even quicker than Voronoi sampling. Moreover, the increase in the size of the sampling sphere makes the surface sampling much more accurate, reaching an RMSE of 0. 33 kJ mol -1 and an MAE of 0. 21 kJ mol -1. The ideal set of parameters, determined for porous materials from the CoRE MOF 2019 database, is (l = 1. 6, m = 0. 85) in order to combine the lowest error and smallest computational cost.
Furthermore, as aforementioned in the section on rejection conditions, it is possible to trade-off a bit of accuracy for faster simulations especially in high-throughput screenings where speed is extremely important. Similarly, in the case of xenon, the cost of increasing the sphere size is around 10 to 20%. On very large databases, one could consider that this increase in the required computational time is not worth the accuracy improvement, and one could decide to keep a smaller sampling sphere. If this method is transposed to different molecular systems, its parameters should be tested on the specic database and adsorbate of interest. 3. 4. 2 Calculation of the Henry constant and surface area. The main goal of our sampling algorithm is to calculate adsorption enthalpy at the zero-loading limit.
Conclusions and perspectives In the present article, we described a novel algorithm for the high-speed calculation of adsorption enthalpy in nanoporous materials that takes a unique approach to reduce the sampling necessary. This new algorithm is based on the core principle of dimensional reduction, from a volume problem to a surface one. The algorithm is proven to be signicantly faster than the reference Widom insertion (random sampling of porous space). Moreover, the error associated is found to be in the order of 0. 4 kJ mol -1 , tested throughout the entire CoRE MOF 2019 database, for xenon adsorption. Even when compared to existing very fast sampling techniques such as Voronoi sampling, this surface sampling technique requires similar CPU time, combined with a better accuracy. Based on these results, this algorithm has important potential for applications in the current computational analysis workows of material databases, such as high-throughput screening studies.
For instance, this algorithm can be used to get a fast approximation of the low-loading adsorption enthalpy of a molecule inside nanoporous materials. This cheap evaluation of enthalpy can be used to screen out the structures with little affinity with the targeted adsorbate molecule. It can also be used as a thermodynamic descriptor for selectivity prediction in a machine learning model, as performed by Simon et al. 14  The computational speed-up brought about by this novel methodology can also enable the screening of materials databases at a larger scale in the future. We note, moreover, that the speed of our method resides in the sampling technique itself, rather than in the actual energy calculation. While we have benchmarked it in this work for a simple Lennard-Jones interaction potential, this sampling technique could equally be used to speed up samplings of space based on more expensive modeling strategies, including polarizable force elds or density functional theory (DFT) calculations.
CBX8 binding sites on chromatin in mouse embryonic stem cells are accessible Given the porous structure of PRC1 C8 -condensed chromatin in vitro (Fig. 1 ) and the dynamic diffusion of PRC1 C8 within condensates (Fig. 2b ), we next wished to probe for the accessibility of PRC1 C8 -bound chromatin in cells. We carried out the Assay for Transposase Accessible Chromatin (ATAC-seq) in differentiated mESC, combined with ChIP-seq for CBX8 and H3K27me3. We used differentiated mESC, because CBX8 is expressed at very low levels in pluripotent mESC and is upregulated during retinoic acid-induced cell differentiation (  23  and Fig. 4a ). The DNA-loaded Tn5 used in ATAC-seq experiments forms a dimeric complex of approximately 130 kDa with an estimated hydrodynamic radius 32 of 4. 6 nm (based on PDB code 1MUH  33  ).
Cryo-electron tomography data collection and processing The data was collected with a Titan Krios electron microscope (Thermo Fisher) at 300 keV acceleration voltage using a Gatan K2 Summit camera (Gatan). A tilt series was acquired ranging from -60 to 60 degrees with 3 degree increments and a nominal defocus of -2. 5 μm. A dose symmetric collection scheme was followed as described previously  47. The pixel size was 1. 32 Angstrom, the total dose per tomogram was 144. 32 e/A 2. Four frames were collected per tilt (dose per frame 0. 88 e/A 2 ). The movies were motion corrected using motioncor2  48. The motion corrected images were combined into stacks and further processed with Imod 49 version 4. 9. 9. Imaging artefacts were identified and removed with Imod's Ccderaser function. Tilts were aligned using the gold fiducial markers and the final aligned stack was binned by a factor of 4.
The pixel size of the template was adjusted using emClarity to match the unbinned pixel size of the tomogram  50. The template was then subjected to a low-pass filter of 30 Angstrom and binned by a factor of four to match the binned tomogram. Template matching was done with the Matlab implementation of Dynamo 52 using the "dynamo_match" function. The template was masked with a tightfitting mask with smooth edges. The scanning range was set to 360 degrees with 10 degree steps. In-plane rotation was also scanned over 360 degrees with 10 degrees steps. Particles that passed a cross-correlation cut-off of 0. 17 (Tomogram #1) or 0. 19 (Tomogram #2) were selected for further analysis. Obvious false positives were removed manually. The particles were cropped from the tomogram with a box size of 36 pixels.
Nucleosome position and orientation was refined over three rounds of subtomogram averaging  52. As an initial template for subtomogram averaging, we used an average from all cropped particles after template matching, masked with a tight-fitting mask with smooth edges generated in the Dynamo mask editor. Specific settings for the different rounds of subtomogram averaging are detailed in Table  2. The resulting average structure of a nucleosome from the subtomogram averaging was then used to populate a volume the size of the tomogram with nucleosomes at the determined positions and orientations. The graphic depiction of the final model (Figure  1G ) was generated using the Dynamo Matlab implementation  52.
28 Angstrom/pixel. Next, the table including nucleosome positions was used by the Dynamo function 'dtchimera' to generate a chimera cmd script that placed a pdb structure of a single nucleosome (PDB 1KX4  54  ) at the position and orientation defined for each nucleosome in the table. At the end of this process, each of the nucleosomes within the tomogram section is represented by the pdb coordinates of the nucleosome template. The resulting model was saved as a pdb file and used as input for 3V  26. Varying probe radii ranging from 2 to 20 nm were used.
Hydrodynamic radius calculation using protein structures The hydrodynamic radii of various proteins (Figure  1I ) were calculated using the HullRad  32  web server (http://52. 14. 70. 9/). The PDB codes for the structures used are: 6LTJ (BAF  37  ), 1MUH (Tn5 33 ), 7O4J (PolII-PIC  39  ), 6C24 (PRC2. 2  55  ), 8GXS (PolII-Mediator 56 ). 6LTJ, 6C24, 8GXS and 7O4J are only partial models as not all residues were assigned, therefore the calculated hydrodynamic radius represents an estimate of the minimal complex size, while the actual size of these complexes could be larger.
Analysis of nucleosome-nucleosome orientation Nucleosome-nucleosome orientation was classified into face-face, face-side and side-side, as described in  22. The orientations were calculated from the output table of the template matching and subtomogram averaging process in Dynamo 52 using the Matlab script 'calculate_orientation. m'. The nucleosome-nucleosome orientation plot (Fig. S1A ) was generated using the 'plot_edges' function from the python notebook NCP_orientation_analysis. ipyn. Both scripts are available on Github (https://github. com/MichaUckelmann/Chromatin-Structure-Analysis).
Fluorescence recovery after photobleaching (FRAP) Samples and plates were prepared and images were recorded as described above for chromatin condensation assays. 1 μM PRC1 C8 with an N-terminal GFP tag was used. The FRAP experiments were set up with the NIS-Elements software (Nikon). Regions of interest (ROIs) were defined and a single image was recorded before bleaching. Then the ROI was bleached using 488 nm (GFP) and 561 (Cy5) lasers. The bleaching time and laser power was set so that approximately 80 % of fluorescence signal within the ROI was quenched. Recovery was measured over 424 seconds, recording a total of 13 images. The data was analysed with ImageJ, the mean pixel intensity of the bleached ROI was quantified for each timepoint.
Crosslinking mass spectrometry (XL-MS) Reconstituted chromatin was dialysed overnight against 1 litre of XL buffer (25 mM HEPES-KOH pH 7. 5 at 20 °C, 150 mM NaCl, 1 mM DTT). The chromatin was combined with PRC1 C8 in a 1:1. 5 molar ratio (Nucleosomes:PRC1 C8 ). Specifically, the nucleosome equivalent concentration of the chromatin array was calculated from the measured DNA concentration assuming 18 nucleosomes per DNA molecule. 1. 5 μM PRC1 and 1 μM nucleosome equivalent concentration of chromatin arrays were then combined in XL buffer and incubated for 30 minutes at room temperature. Crosslinking was done as described before  57. The bis(sulfosuccinimidyl)suberate (BS3) crosslinker was added to a final For the data analysis, Thermo raw files were analysed using the pLink 2. 3. 4 search engine  58  to identify crosslinked peptides, searching against the sequences of RING1b, BMI1, CBX8, H2A, H2B, H3 and H4.
The default settings for searches were used. N-terminal acetylation and methionine oxidation were used as variable modifications and carbamidomethyl on cysteines as a fixed modification. False discovery rates of 1% for peptide spectrum match level were applied by searching a reverse database. Crosslinked peptides were further analysed using the crissrosslinkeR package  59. Specifically, peptides were retained by crissrosslinkeR only if they passed a p-value cutoff of 0. 05 or were present in at least two of three replicates. Subsequent visualisation of retained peptide was carried out with xiNET  60.
Data processing for Chip-seq The reads were quality-trimmed and adapters were removed using Trim Galore!, a wrapper script to cutadapt  64  , in paired end mode and using default settings. The reads were then aligned to the mouse mm10 genome build using bowtie2  65  with the option "very-sensitive". The data was reduced to only properly paired reads using "samtools view" with the flag "-f 3". PCR duplicates were removed using the RemoveDuplicates function from Picard Tools. Read mates were fixed using samtools fixmate. BigWig files were calculated using BamCoverage with CPM normalisation. Peaks were called with Macs2 66 callpeak function, using the input sample as control. For the CBX8 ChIP-seq data set, default settings with a q-value cut-off of 0. 05 were used. For H3K27me3 ChIP-seq data set, broad mode was used with a q-value and broad cutoff of 0.
Data processing ATAC-seq ATAC-seq data was processed as described previously  68. Specifically, reads were qualitytrimmed and adapters were removed using Trim Galore!, a wrapper script to cutadapt  64  , in paired end mode using default settings. The reads were then aligned to the mouse mm10 genome build using bowtie2  65  with the option "very-sensitive". Reads were sorted and indexed using Samtools. Mitochondrial reads were removed using a python script from Harvard Bioinformatics (available at https://github. com/harvardinformatics/ATAC-seq). The data was reduced to only properly paired reads using "samtools view" with the flag "-f 3". The library complexity was estimated and the data sets were subsampled to reach a similar complexity as described previously  68. PCR duplicates were removed using the RemoveDuplicates function from Picard Tools.
Read mates were fixed using samtools fixmate. BigWig files were calculated using BamCoverage with CPM normalisation For peak calling the bam files were converted to BEDPE files and the Tn5 shift was corrected by running a bash script provided at https://github. com/reskejak/ATAC-seq (bedpeTn5Shift. sh). Files were then converted to minimal bed format and peaks were called using Macs2 66 callpeak function in broad mode with broad-cutoff set to 0. 05. Peaks overlapping with ENCODE blacklisted regions  67  were removed. Consensus peaks for each condition were defined as the intersect of peaks from both biological replicates. Venn diagrams were generated using the ChIPPeakAnno package  69.
Acknowledgements:Fig. 1. 1 Fig. 1. The molecular structure of PRC1-chromatin condensates is porous and accessible to macromolecules. a, Schematics introducing the workflow. b, SDS-PAGE of purified PRC1 C8 complex that includes RING1b, BMI1 and MBP-tagged CBX8. c, Size exclusion chromatography of the purified PRC1 C8 using a HiLoad Sephacryl 300 16/60 column. d, In vitro ubiquitylation assay comparing PRC1 C8 to a RING1b-BMI1 heterodimer. Samples in lane 2 and 3 included E1, E2, ubiquitin and ATP. Ubiquitylation is detected by western blot using an anti-H2A antibody. e, Chromatin condensates induced by the PRC1 C8 complex and the individual proteins, visualised by confocal (left and centre) and phase contrast (right, from independent experiments) microscopy. CBX8 is GFP-labelled and chromatin is Cy5 labelled.
CD226 has been reported to participate in the rescue of CD8 + T cell dysfunction. In this study, we aimed to assess the prognostic value of CD226 in tumor-infiltrating lymphocytes (TILs) derived from colorectal cancer (CRC) liver metastases treated with chemotherapy and radical surgery. TILs from 43 metastases were isolated and analyzed ex vivo using flow cytometry. CD155 and CD3 levels in the tumor microenvironment were assessed by immunohistochemistry. Exploration and validation of biological processes highlighted in this study were performed by bioinformatics analysis of bulk RNA-seq results for 28 CRC liver metastases pretreated with chemotherapy as well as public gene expression datasets. CD226 expression contributes to the definition of the immune context in CRC liver metastases and primary tumors.
CD226 on CD8 + T cells was not specifically coexpressed with other immune checkpoints, such as PD1, TIGIT, and TIM3, in liver metastases. Multivariate Cox regression analysis revealed CD226 expression on CD8 + T cells to be an independent prognostic factor (p = 0. 003), along with CD3 density at invasion margins (p = 0. 003) and TIGIT expression on CD4 + T cells (p = 0. 019). CD155 was not associated with the prognostic value of CD226. Gene expression analysis in a validation dataset confirmed the prognostic value of CD226 in CRC liver metastases but not in primary tumors. Downregulation of CD226 on CD8 + TILs in the liver microenvironment was restored by IL15 treatment. Overall, CD226 expression on liver metastasis-infiltrating CD8 + T cells selectively contributes to immune surveillance of CRC liver metastases and has prognostic value for patients undergoing radical surgery.
We hypothesized that CD226 is a marker of nonexhausted T cells that maintains potent cytotoxic activity and increases survival rates when expressed by TILs in liver metastases. To test this hypothesis, TILs from 43 liver metastases of 39 patients were isolated and thoroughly analyzed ex vivo using multiparametric flow cytometry and immunostaining. Findings related to CD226 and CD155 were validated in public gene expression datasets of primary CRC and liver metastases and by using a local RNA-seq dataset of 27 liver metastases from CRC treated with chemotherapy.