dataesr/scientific-paragraphs-categorization huggingface dataset:
Following temperature shift, 0-and 20 min samples of the cdc45 depleted strains were subjected to Cal-ChIP-Seq. ratio of average cohesin levels before and temperature shift from À100 kb to +60 KB relative to all 16 centromeres is plotted. Also see Figure  5 -figure supplement 2C and D. (F) The percentage of cohesin that remains on DNA upon Scc2 inactivation (after temperature shift) along the entire chromosome four is shown. The median cohesin level along the entire chromosome 4 (dotted line) is marked with arrowheads. Data from SCC2 wild type cells arrested in lateG1 (Figure  2A ) is shown in grey. Data from scc2-45 cells arrested in late G1 (Figure  2A ) is shown in yellow. Data from Cdc45 depleted scc2-45 cells (Figure  5E ) is shown in turquoise. Data from scc2-45 cells arrested in G2 (Figure  2B ) is shown in green.
Data analysis, alignment and production of BigWigs Unless otherwise specified, data analysis was performed on the Galaxy platform. Quality of reads was assessed using FastQC (Galaxy tool version 1. 0. 0) and trimmed as required using 'trim sequences' (Galaxy tool version 1. 0. 0). Generally, this involved removing the first 10 bases and any bases after the 200 th , but trimming more or fewer bases may be required to ensure the removal of kmers and that the per-base sequence content is equal across the reads. Reads shorter than 50 bp were removed using Filter FASTQ (Galaxy tool version 1. 0. 0, minimum size: 50, maximum size: 0, minimum quality: 0, maximum quality: 0, maximum number of bases allowed outside of quality range: 0, paired end data: false) and the remaining reads aligned to the necessary genome(s) using Bowtie2 (Galaxy tool version 0.
Visualisation of ChIP-seq profiles The resulting BigWigs were visualised using the IGB browser. To normalise the data to show quantitative ChIP signal the track was multiplied by the samples' occupancy ratio (OR) and normalised to 1 million reads using the graph multiply function. In order to calculate the average occupancy at each base pair up to 60 kb around all 16 centromeres, the BAM file that contains reads uniquely aligning to S. cerevisiae was separated into files for each chromosome using 'Filter SAM or BAM' (Galaxy tool version 1. 1. 0). A pileup of each chromosome was then obtained using samtools mpileup (Galaxy tool version 0. 0. 1) (source for reference list: locally cached, reference genome: SacCer3, genotype likelihood computation: false, advanced options: basic). These files were then amended using our own script (chr_position.
Data and software availability Scripts All scripts written for this analysis method are available to download from https://github. com/naomipetela/nasmythlab-ngs (Petela, 2019 ; copy archived at https://github. com/elifesciences-publications/nasmythlab-ngs). Chr_position. py takes mpileups for S. cerevisiae chromosomes and fills in gaps, with each position in the chromosome added given a read depth of 0. Filter60. py reads the files produced by Chr_position. py and takes the read depth for all positions 60 kb either side of the CDEIII for all chromosomes, produces an average for each position and multiples it by the OR. The OR should be derived from the reads aligned in the appropriate bam files  (Hu et al. , 2015).
Acknowledgements Maria Demidova  conducted initial experiments that this study expanded on. We are grateful to  Tomo Tanaka  and  Seiji Tanaka  for supplying reagents. We thank all members of the  Nasmyth group  for valuable discussions, technical assistance and critical reading of the manuscript. This work was funded by the  Wellcome Trust Senior Investigator Award , Grant Ref  107935/Z/15/Z  and  Cancer Research   UK Programme Grant , Grant  Ref 26747  to KN. BH is funded by ( 202062/Z/16/Z ).
In order to better understand the mechanical properties of crystalline materials, we performed a large-scale exploration of the elastic properties of 13 621 crystals from the Materials Project database, including both experimentally synthesized and hypothetical structures. We studied both their average (isotropic) behavior, as well as the anisotropy of the elastic properties: bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and linear compressibility. We show that general mechanical trends, which hold for isotropic (noncrystalline) materials at the macroscopic scale, also apply "on average" for crystals. Further, we highlight the importance of elastic anisotropy and the role of mechanical stability as playing key roles in the experimental feasibility of hypothetical compounds.
Introduction The last decade has seen an incredible acceleration of the research into the links between structure and properties of crystalline materials, and in particular of several families of framework materials, including zeolites, metal-organic frameworks (MOFs), supramolecular frameworks, and dense inorganic materials. These have been studied by both experimental and theoretical methods, oen in synergy, in order to provide a broad characterization of the properties of materials, and the links between their structure and properties-with the ultimate goal being to develop and design novel materials with targeted properties, or to identify as-yet unidentied properties of interest in known materials. 2] [3] [4] [5] [6]  These databases are oen classied according to the materials' class or chemical family: zeolites, zeolitic materials, metal-organic frameworks, etc.
Recently, a major development in the area of inorganic crystalline solids is the launch of the Materials Project (MP) database,  7, 8  a part of the Materials Genome Initiative whose goal is to apply high-throughput computing to map the properties of "all known inorganic materials". This takes the form of an open database, which can be used both for data mining and interactive exploration, as well as open-source packages for developing analysing materials and their properties. The current release of the Materials Project database contains data derived from quantum mechanical calculations for more than 133 000 known and predicted inorganic compounds, and this number is rapidly growing. The data contained in the MP database include crystal structure, energy/formation enthalpy, electronic band structure, X-ray absorption spectra, synthesis descriptions extracted from (and linked to) the relevant publications, elastic tensors, piezoelectric constants, etc.
A detailed description of the computational methods used to develop the database are available in ref. 9, for the interested reader. This initiative is proving popular, and more than 40 000 users have registered to the MP database, where they can interact online through the MP website (https://www. materialsproject. org/), with no knowledge of programming required to run simple queries, or through a REpresentational State Transfer (REST) application programming interface (API). One of the goals of the Materials Project is to accelerate the analysis and to allow users to access and interpret computational data. The existence of a well-documented public API allows this, and makes it possible to interface the MP database with other soware. In this vein, our group has recently worked on the integration of an online application, named ELATE, allowing the analysis and the visualization of elastic tensors.
10 The ELATE application interfaces two way with the Materials Project: you can search for compounds in the MP database from within ELATE, and you can use ELATE to analyse and visualise the elastic properties of compounds from the Materials Project website. We targeted elastic properties because they are key to the process of screening for physical and chemical properties, in order to ascertain the mechanical stability of materials during phases of discovery and design. 11, 12 Indeed, elastic constants are a linear response property, and as such can be characterized with comparatively cheap computational cost, and they provide a rst-order approximation of the full response of materials to external mechanical constraints. In addition, they offer great potential for applications in mechanical energy storage,  13  mechanochemistry,  14  and geophysics.
15 espite the importance of elastic properties and mechanical stability of materials in screening for applications,  16  it has only been characterized for a small fraction of all known inorganic compounds. Full experimental determination of the elastic constants, usually through single-crystal Brillouin scattering,  17, 18  is far from being a routine characterization, and thus predictive computational methods have been used as a complement. In 2015, Jong et al. calculated at the density functional theory (DFT) level the elastic properties for 1181 inorganic compounds,  19  and uploaded the resulting data to the Materials Project database. This effort continued, using increasingly available high performance computing (HPC) resources, and at the time of writing the MP database contains elastic information for 13 621 inorganic compounds.
27 In this work, we report for the rst time a mechanical properties investigation for 13 423 inorganic compounds, calculated at the quantum chemistry level and available in the Materials Project database. We provide rst an analysis of the mechanical behavior of these materials in the isotropic approximation, looking at directionally-averaged properties such as bulk modulus and Young's modulus. We then perform a full tensorial analysis of their properties, looking at the shear modulus, Young's modulus, Poisson's ratio, and linear compressibility. We show that general mechanical trends, which hold for isotropic (noncrystalline) materials at the macroscopic scale, also apply "on average" for inorganic crystals. Further, we highlight the importance of elastic anisotropy and the role of mechanical stability as playing key roles in the experimental feasibility of hypothetical inorganic compounds.
29, 30 These elastic constants can be calculated as second derivatives of the energy with respect to unit cell parameters: C ij ¼ 1 V v 2 E v3 i v3 j (1) where the energy for each strained conguration can be evaluated through quantum chemistry calculations. In the present study, we did not perform any explicit calculations of elastic constants, but used the second-order elastic tensors provided in the Materials Project database for 13 621 inorganic crystalline structures. 2] [33]  All the data was obtained through the Materials Project API and the pymatgen package. 34 sing these elastic tensors, we performed calculations of the physically meaningful quantities such as bulk modulus (K), shear modulus (G), linear compressibility (b), and Poisson's ratio (n) through tensorial analysis, as described in Marmier et al.
Elastic properties in the isotropic approximation As stated above, the Materials Project database contains secondorder elastic tensors, calculated through quantum-chemical methods, for 13 621 inorganic crystalline compounds to date. This tensor takes the form of a 6 Â 6 symmetric matrix of second-order elastic constants, from which more physically meaningful properties can be calculated. The bulk modulus K is the simplest mechanical property of material; it is the inverse of the compressibility, and quanties the resistance of the structure (measured by its volumetric strain) when it subjected to a isotropic stress (such as a hydrostatic pressure). The shear modulus G, named also the rigidity modulus, represents the resistance of a material under the inuence of an opposing pair of shear stresses acting parallel to the material surface, see Fig.
1. G is a directional quantity, but for both K and G average values can be computer in the isotropic approximation, based on three different averaging schemes: Voigt,  36  Reuss, 37 and Hill. 38 Voigt averaging assumes a uniform strain in a polycrystalline sample, while Reuss averaging assumes uniform stress; the Hill method corresponds to the arithmetic mean of the other two, and is considered the most accurate in a wide range of experimental conditions. Other average quantities available directly in the MP database are the elastic anisotropy and the average Poisson's ratio (n) in the isotropic approximation-we refer the reader to the ref. 19 for details on their calculation. To ensure the mechanical stability of a material, the values of K and G are required to be positive. 39 From the original elastic properties information for 13 621 inorganic compounds contained in the MP database, we rst applied stability criteria on K and G as a lter and found that 1312 structures (around 9% of materials in the database) were failing that test, i.
e. computed to be mechanically unstable. This reects the need for curation of computational data before it can be integrated into databases, or used for analysis. Moreover, we found some materials with reported values of K and G unphysically high. Given that diamond is one of the stiffest inorganic compounds, with K ¼ 530 GPa and G ¼ 440 GPa, we ltered out a handful of materials with K and G values larger than 10 3 GPa. (A list of the 13 materials presenting K or G values larger than 10 3 GPa is available in ESI, † with respective elastic properties values in the isotropic approximation. ) We thus obtained a list of 12 296 crystalline compounds, of which 8335 have been experimentally synthesized and 3961 are hypothetical structures. We have then used the ELATE soware package, 10 available both as an open source Python module (available online at https://github.
63. These ndings follow the same trend identied  in the work of Jong et al. on a smaller database of 1181 inorganic structures. 19 If we focus separately to the subsets of synthesized (8050) and hypothetical (3714) structures, presented in Fig. 3 , we identify a clear difference: while the correlation is the same on average, the relationship is much more strictly obeyed in experimentally known materials (R 2 ¼ 0. 97) than in hypothetical structures (R 2 ¼ 0. 55). The same trends are also observed with different averaging schemes, Reuss and Voigt, as presented in ESI, † and appear to be a generic property of inorganic compounds. The difference observed between known and hypothetical structures is very signicant, as it indicates that the "parameter space" of hypothetical structures is much wider than that of experimentally feasible materials.
2  that n tends to become negative with very small values of bulk modulus, relatively irrespective of the value of shear modulus-while for the highest K values (more than 100 GPa), all materials present in the Materials Project database display strongly positive Poisson's ratio. The overall trend observed is the same for the synthesized and to the hypothetical inorganic structures cases (see Fig. 3 ). We nd that, from the 11 764 compounds studied, only 75 materials exhibit negative average Poisson's ratio, i. e. only 1% of the structures. Auxeticity is thus a rare thing in inorganic solids in general, as has been established for zeolites, for examples. 25 hose split relatively evenly between 45 synthesized and 30 hypothetical inorganic compounds. Their structures are presented in the ESI, † along with their material ID and a summary of their elastic properties.
If we take n ¼ 0. 3, the linear law would then be K ¼ 2. 2G, plotted in Fig. 2  and 3. This line actually lies close to the data points corresponding to materials with average Poisson's ratio close to 0. 3. This an interesting link between the statistical behavior of a large number of anisotropic crystals, and the known behavior of isotropic materials-a conclusion similar to that reached by de Jong et al. on a smaller dataset. 19 ooking further into the distribution of elastic properties, we plot in Fig. 4  the distribution of average elastic moduli, and split it between experimentally synthesized and hypothetical structures in Fig. 5 -corresponding distributions for the Reuss and Voigt averaging schemes can be found in the ESI. † We see that K and G do not exceed 600 GPa for the selected inorganic compounds, and that shear modulus values fall mostly in the 10  to 100 GPa range (red-shaded area), which accounts for 90% of materials.
Looking for anomalous properties Auxetic materials. Auxetic materials, characterized by a negative Poisson ratio (n < 0) in one or more directions of space, are a rare class of materials. 48 They have attracted a widespread interest due to their exciting potential applications in extremely precise sensors, tougher ceramics and highperformance armor. 40, 49, 50 As discussed above, analysis of the Materials Project elastic data in the isotropic approximation reveals only a limited number of inorganic materials with auxeticity (121 structures). When we analyzed these 126 structures further, we found out that only 75 are mechanically stable, highlighting the need for a full tensorial analysis of elastic     51  The authors successively predicted 29 new materials with negative Poisson's ratio, and grouped structures with |n| # 0.
26 NLC has been quite reported in some zeolites, MOFs structures, and other framework materials. 11,52-54 NLC materials have a broad range of potential applications in designing pressure sensors and articial muscles. 55, 56 e have analyze the Materials Project database to determine the minimal and the maximal values of linear compressibility for every materials. We nd that b min values range from À1890 TPa À1 to 2000 TPa À1 range, and the b max values from 0. 5 TPa À1 to 5073 TPa À1. We nd that 357 inorganic compounds exhibit negative linear compressibility (meaning b min < 0), accounting for 3% of the mechanically stable structures. The full list of the 357 NLC materials ID is available in the ESI. † Moreover, more than 50% of NLC materials are experimentally synthesized (238 structures), and most of them have not been mechanically characterized-and their NLC behaviour has not been discussed in previous literature.
Finally, we conclude that although NLC is indeed a rare phenomenon, there are more NLC structures than completely auxetic ones. This present large-scale screening of an inorganic materials database gives hope in the search for structures with anomalous mechanical properties, and will help scientists to identify materials with specic targeted properties-as well as inform a clear strategy to accelerate the discovery and design of novel structures with anomalous elastic behaviour. which hold for isotropic (noncrystalline) materials at the macroscopic scale, also apply "on average" for inorganic crystals. By going beyond the isotropic approximation, we have highlighted the signicance of the anisotropy of elastic properties, which can only be studied by a tensorial analysis of the full second-order elastic stiffness matrix.
25 This better understanding of the rarity of various anomalous properties in crystals is a necessary rst step to enable the design of new mechanical metamaterials, based not on a designed micro-or macro-structure, but on a nanoscopic crystalline framework. 27 xperimental and computational characterization of framework materials has, so far, taken place in a very step-by-step fashion, where a few materials are characterized in depth in each study. 18, 57, 58 Based on the knowledge gained in this study, we now look to apply high-throughput screening methods to larger databases of materials,  59  in order to accelerate discovery of mechanical metamaterials based not on their complete elastic characterization, but on descriptors such as their structural motifs, composition, topology, etc.
A detailed comparison between BindsNet and the other available frameworks can be found in their paper. In this paper, we propose SpykeTorch, a simulation framework based on PyTorch which is optimized specifically for convolutional SNNs with at most one spike per neuron. SpykeTorch offers utilities for building hierarchical feedforward SNNs with deep or shallow structures and learning rules such as STDP and R-STDP  [43, 44, 45, 46]. SpykeTorch only supports time-to-first-spike information coding and provides a non-leaky integrate and fire neuron model with at most one spike per stimulus. Unlike BindsNet which is flexible and general, the proposed framework is highly restricted to and optimized for this type of SNNs. Although BindsNet is based on PyTorch, its network design language is different.
In contrast, SpykeTorch is fully compatible and integrated with PyTorch and obeys the same design language. Therefore, a PyTorch user may only read the documentation to find out the new functionalities. Besides, this integrity makes it possible to utilize almost all of the PyTorch's functionalities either running on a CPU, or (multi-) GPU platform. The rest of this paper is organized as follows: Section 2 describes how SpykeTorch includes the concept of time in its computations. Section 3 is dedicated to SpykeTorch package structure and its components. In Section 4, a brief tutorial on building, training, and evaluating a DCSNN using Spyke-Torch is given. Section 6 summarizes the current work and highlights possible future works.
Time Dimension Modules in SpykeTorch are compatible with those in PyTorch and the underlying data-type is simply the PyTorch's tensors. However, since the simulation of SNNs needs the concept of "time", SpykeTorch considers an extra dimension in tensors for representing time. The user may not need to think about this new dimensionality while using SpykeTorch, but, in order to combine it with other PyTorch's functionalities or extracting different kinds of information from SNNs, it is important to have a good grasp of how SpykeTorch deals with time. SpykeTorch works with time-steps instead of exact time. Since the neurons emit at most one spike per stimulus, it is enough to keep track of the first spike times (in time-step scale) of the neurons. For a particular stimulus, SpykeTorch divides all of the spikes into a pre-defined number of spike bins, where each bin corresponds to a single time-step.
Package Structure Basically, SpykeTorch consists of four python modules; (1) snn which contains multiple classes for creating SNNs, (2) functional that implements useful SNNs' functions, (3) utils which gathers helpful utilities, and (4) visualization which helps to generate graphical data out of SNNs. The following subsections explain these modules. 1 0 1 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 ∞ ∞ 3 1 0 2 3 2 Time Features Features t = 0 t = 1 t = 2 t = 3 Spike Times Spike-Wave Tensor ∞ Figure  1 : An example of generating spike-wave tensor from spike times. There are three feature maps, each constitutes a 2 × 2 grid of neurons that are going to generate a spike-wave representing a stimulus. If the maximum number of time-steps is 4, then the resulting spike-wave is a four-dimensional tensor of size 4 × 3 × 2 × 2.
snn Module The snn module contains necessary classes to build SNNs. These classes are inherited from the PyTorch's nn. Module, enabling them to function inside the PyTorch framework as network modules. Since we do not support error backpropagation, the PyTorch's auto-grad feature is turned off for all of the parameters in snn module. snn. Convolutional objects implements spiking convolutional layers with two-dimensional convolution kernel. A snn. Convolutional object is built by providing the number of input and output features (or channels), and the size of the convolution kernel. Given the size of the kernel, the corresponding tensor of synaptic weights is randomly initialized using a normal distribution, where the mean and standard deviation can be set for each object, separately. A snn.