dataesr/scientific-paragraphs-categorization huggingface dataset:
Adapting SVD for MISC with Changing Unit Cell Parameters The DED map are calculated (Supplementary Methods) in the CCP4 le format  74  and cover the entire unit cell of the crystal. The maps are represented by a three-dimensional (3D) array with m x , m y and m z grid points for each unit cell axis, respectively. Each 3D grid point (voxel) contains the magnitude of the difference electron density at that given position (Extended Data Fig. 3 a ). In such a DED map, positive features indicate regions where atoms have shifted away from their position in the reference model. Negative features are then found on top of the atoms in the reference model. Most of the map contains only spurious noise except in ROIs such as the active sites where larger structural changes are expected due to the binding or dissociation of a ligand (Extended Data Fig.
3 b ). The noise within the majority of the difference map would interfere with the SVD analysis. To avoid this, a ROI was isolated individually for each subunit and an SVD performed only on the DED within. When multiple active sites are present, each active site can be investigated separately. Extended Data Fig. 4  shows a ow chart of the steps required to prepare the data matrix A. The steps are described in detail below. Step 1: The coordinates of the atoms of the amino acid residues and the substrate of interest are speci ed in a particular subunit. This de nes the ROI. For the present work, four different ROIs were de ned, one for each subunit A to D, respectively, and investigated separately. Step 2: A mask is calculated that covers the selected atoms plus a margin of choice (Extended Data Fig.
If the voxel size changes, the DED value assigned to each voxel position will also change which will skew the SVD analysis. If the voxel numbers change, the SVD algorithm will fail as it requires that all the maps are represented by arrays of identical sizes. Accordingly, both conditions, (i) a constant voxel size and (ii) a constant number of grid points in the masked volume must be ful lled when the unit cell changes. Step 3: In order to ful ll (i) the total number of grid points in the DED map is changed proportionally to the unit cell change. When the volume of the ROI is not changed, condition (ii) is automatically ful lled, and a suitable data matrix A can be constructed. However, when the unit cell parameters change, the ROI is also changing position. This must be addressed in addition.
Step 4: A box is chosen that will cover the density that was just masked out (Extended Data Fig. 3 d ). The box will include the ROI which is saved as a new map. The size of the box must be large enough such that the ROIs can be covered at all time points. The box must be calculated with reference to a stable structure (usually the protein main chain). As the protein chain displaces as a result of the change of the unit cell, the box will also move accordingly to cover the correct ROI (Extended Data Fig. 3 e ). As mentioned, the DED within the moving box can be used to evolve the mask that de nes the nal ROI as indicated in step 2. Step 5: All m voxels in the evolved mask are converted to a one-dimensional (1D) column array, a vector in high (m) dimensional space. How the conversion is achieved does not matter as long as the same convention is applied to all the n maps.
Acknowledgement This work was supported by  NSF -STC-1231306  (BioXFEL). P. F. was supported by  NSF BioXFEL STC  grant  NSF-1231306  Biology with X-Ray Lasers, the  NIH  grant  R01GM095583  and the  ASU Biodesign Center for Applied Structural Discovery. A. O. was supported by the  US Department of Energy, O ce of Science, Basic Energy Sciences  under award  DE-SC0002164  (underlying dynamical techniques), and by the  US National Science Foundation  under awards  STC-1231306  (underlying data analytical techniques) and DBI-2029533 (underlying analytical models). K. A. Z. was supported by the  Cornell Molecular Biophysics Training Program  ( NIH   T32-GM008267 ). We acknowledge funding from  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF ; the  Cluster of Excellence "Advanced Imaging of Matter " of the  Deutsche Forschungsgemeinschaft  ( DFG)  - EXC 2056 - project  ID 390715994 ; the  Helmholtz Association Impulse and Networking  fund -project  InternLabs-0011  " HIR3X "; the  German Federal Ministry of Education and Research (BMBF)  -project  05K18CHA.
Data Availability Data has been deposited in the Coherent X-ray Imaging Database (CXIDB) with accession code 209. The structure factors and the re ned coordinates of XFEL structure of BlaC mixed with sulbactam for 15ms, 30 ms, and 240ms, and the cryo-soaked structure have been deposited in the PDB as entries 8EBI, 8EBR, 8EC4 and 8ECF, respectively.
Singular value decomposition A kinetic analysis was performed by the application of the singular value decomposition (SVD) to the Xray data  14  using the DED omit maps (for data collection and difference map calculation, see the Supplementary Information, SI). A region of interest (ROI) was determined that corresponds roughly to the volume of an individual active site selected from the four subunits in the asymmetric unit. The ROI covers the volume occupied by the SUB and TEN ligands and the entire side chain of Ser70. It touches the terminal atoms of residues Lys 73, Glu168, Thr239, Asn172, Arg173, Ser128, Ser102 and Gln112 (from B/D) in the case of subunits A/C. Difference electron density values found in grid points (voxels) of the ROI were assigned to a m-dimensional vector. N = 6 of these vectors were obtained for measured Δt misc and assembled to a m x n dimensional matrix A, called the data matrix.
The identification of differentially methylated regions (DMRs) to compare conditions is one of the main analyses performed when investigating DNA methylation patterns. Many tools with various approaches have been developed for whole-genome methylation datasets that have been reviewed and benchmarked by several studies  (14) (15) (16) (17). Most approaches first rely on the identification of single differentially methylated CpGs (DMCs) and then aggregates them into DMRs. One of the first approach developed to identify DMRs is BSmooth  (18) , which applies local likelihood smoothing to overcome potential biases due to low coverage. It requires replicates to calls DMCs using a t-test and group the ones above a specific threshold into DMRs. RADmeth  (19)  is based on a beta-binomial regression approach.
Adjusted p-values are also provided as well as p-values from a Mann-Whitney U test. Although many tools have been developed to identify DMRs, the regions identified differ in terms of number, level of DNA methylation differences and region boundaries and there is limited overlap between DMRs from different approaches. Here, we present MethyLasso, a new segmentation approach to analyze DNA methylation patterns and identify differentially methylation regions from whole-genome datasets. MethyLasso models DNA methylation data using a regression framework known as generalized additive model. It relies on the fused lasso to estimate regions in which the methylation is constant. We apply MethyLasso on data from a single condition to identify LMRs, UMRs and DMVs based on DNA methylation levels independently of CpG content as well as PMDs.
MethyLasso general framework MethyLasso models DNA methylation data using a nonparametric regression framework known as a Generalized Additive Model. It relies on the fused lasso method  (26)  to segment the genome by estimating regions in which methylation is constant. This model is adapted from the one applied for Hi-C data in Binless normalization  (27). For a single whole genome DNA methylation dataset, the resulting data takes the form of a N-dimensional vector of the observed methylated fraction y=M/C from methylated counts M (program argument -mC), and total sequencing coverage C (program argument --cov). y is given a normal likelihood with unknown true methylation fraction β. A 1D fused lasso prior is then placed on β, resulting in a minus log posterior proportional to the L1-penalized weighted least squares target.
MethyLasso benchmarking DNA methylation datasets We used whole genome Bis-seq datasets publicly available in the NCBI gene expression omnibus (GEO) repository from human embryonic stem cells (ESH1) and fetal lung fibroblasts cells (IMR90) (28) (GEO accessions GSM429321, GSM429322, GSM429323 GSM432685 and GSM432686 for ESH1 one replicate, GSM432687, GSM432688 and GSM432689 for IMR90 replicate one, and GSM432690, GSM432691 and GSM432692 for IMR90 replicate two), primary human colorectal cancer and normal cells (29) (GEO accessions GSM1204465 and GSM1204466 respectively with three replicates each), and mouse hematopoietic stem cells (HSC) and multipotent progenitor populations (MPP) (30) (GEO accessions GSM1274424, GSM1274425 and GSM1274426 for HSC three replicates and GSM1274433, GSM1274434 and GSM1274435 for MPP three replicates).
Identification of low-methylated regions (LMRs), unmethylated regions (UMRs) and DNA methylation valleys (DMVs) MethyLasso is a method that first analyzes DNA methylation patterns in each experimental condition independently. It relies on a fused lasso approach to segment the genome by estimating regions in which the methylation is constant (see Methods). It fits the model to DNA methylation data, segments the data and calls different regions such as LMRs (10 to 50% methylation), UMRs (0 to 10% methylation) or DMVs (UMRs larger than 5 kb) according to specific thresholds (Figure  1A ) (see Methods). One advantage over MethylSeekR is that MethyLasso can integrate replicates when available to call each set of regions only once per condition. On data from human IMR90 fibroblast cells  (28) , MethyLasso identified 29,914 LMRs, 28,308 UMRs including 240 DMVs where MethylSeekR identified more LMRs but less UMRs in both replicates independently (Figure  1B  and Supplementary Figure  1  for other datasets).
Identification of differentially methylated regions (DMRs) MethyLasso can also identify DMRs in DNA methylation data from different conditions. It fits the model to DNA methylation differences across conditions, segments the data and calls DMRs according to specific thresholds such as by default a mean methylation difference above 10% and a minimum of 4 CpGs (see Methods). It also annotates the DMRs using the previous segmentation from condition one to condition two e. g. LMRtoUMR, (nothing)toPMD, fromDMV(to nothing). To evaluate the performance of MethyLasso in identifying DMRs, we compared it to six other approaches: Defiant  (21) , DSS  (20) , DMRcate  (23) , Dmrseq  (22) , Radmeth  (19)  and Metilene  (24) , using Bis-seq datasets from human colon cancer compared to healthy samples  (29).
Defiant DMRs also have sharp boundaries but the median methylation difference within DMRs is less homogenous. In contrast, the methylation differences at the DMR boundaries of DSS, DMRcate and Radmeth have a bell shape and the CpGs just outside their DMRs still have methylation differences higher than 10% (our threshold), indicating that their boundaries are less accurate. DMRcate and Dmrseq DMRs only have small shifts and CpGs in DMRs have lower DNA methylation differences indicating that their boundaries are not accurate. This might be due to the fact that it smooths the data before calling DMRs and identifies mainly large DMRs. For Metilene, which is also a segmentation-based approach, it's DMRs boundaries are well defined (Figure  3E ). Importantly, we obtained similar results when identifying DMRs in completely different datasets: human ESH1 versus IMR90 cells  (28)  with big DNA methylation differences, or mouse hematopoietic stem cells (HSCs) versus multipotent progenitors (MPPs) (30) that show small changes in DNA methylation (Supplementary Figure  4 ).
DISCUSSION MethyLasso is a new approach to analyze DNA methylation patterns from whole-genome Bisseq datasets. It mostly differs from other tools as it is based on a segmentation of the DNA methylation patterns. MethyLasso can analyze data from each condition independently and can integrate replicates to identify LMRs, UMRs and DMVs by performing a segmentation of the DNA methylation levels, and PMDs by performing a segmentation of the DNA methylation variation. It differs from MethylSeekR mainly by defining regions of interest based on their DNA methylation levels matching their definition (LMR or UMR) rather than based on their CpG content (Figure  1D, E ). Additionally, we show that MethyLasso performs better at defining the region's boundaries since it does not smooth methylation levels (Figure  1H, I ).
With hardware differentiation, a given program has to target multiple kinds of computing resources. This means the code has to encompass the complexity of this hardware. Portability is often antinomic to performance, and programmers have to couple their program with multiple runtimes specific to a given kind of resource and sometimes fixed hardware. In such a changing landscape, the stability of the runtime's Application Programming Interface (API) is becoming of paramount importance. Not only may your program not compile, but you also need to assess the presence of some parallel features before using them -feature sets linked to the chosen runtime. For example, the OpenMP standard is released before being implemented in the various compilers at different paces, and programmers are required to read the documentation before choosing a feature.
In this paper, we pose the question of how to guide both end users and developers through the increasingly complex landscape of supported features and versions. To address this, we propose leveraging the Parallel Computing Validation System (PCVS) runtime, an HPC-oriented massively parallel testing and reporting framework with unique features suited for this task. Specifically, we implement a set of generated tests that cover exhaustively the MPI runtime interfaces (C, Fortran mpif. h, use mpi, and use mpi_f08 to pinpoint levels of support in the various MPI runtime implementations. The code is available as open source, and the results are publicly distributed at the https://mpicheck. pcvs. io/ URL, which we plan to maintain and enhance in the future by pulling new versions from the Spack package manager.
PARALLEL COMPUTING VALIDATION SYSTEM The Parallel Computing Validation System (PCVS) is a testing framework that was originally developed to test the MPC MPI runtime  [15]. As a result, it was designed with specific requirements to enable recompilation and parallel execution of tests. Coupled with the JCHRONOSS scheduler  [2] , it enables the massively parallel execution of test suites. Recently, PCVS has been completely rewritten from Perl to Python in order to provide a richer set of features, particularly on the analysis side and JCHRONOSS has now been embedded, also in Python. In this section, we provide more details on PCVS to highlight what makes it unique and specific in the HPC testing field. We first describe how PCVS can be used to run tests from a simple YAML syntax. Then, we detail how tests are executed in parallel on a supercomputer.
Furthermore, this YAML description can be generated from any script using standard output, allowing for the handling of more complex scenarios that may not fit PCVS's standard semantics. Such YAML node is called a "Test Expression". The PCVS runtime takes Test Expressions (TE) as input and generates a set of tests by combining the test parameters in a way that is compatible with the target execution environment. This process is guided by a machine profile, which defines the available resources on the target system. The resulting test set is the intersection of the TE specifications with machine profile. This approach allows test suites to be scaled dynamically from a single laptop to a large supercomputer without the need for manual editing of the test parameters. PCVS is designed to work with different runtime environments and provides a way to add wrapper commands, such as srun and mpirun, to execute distributed jobs.
However, some runtime environments may not support certain combinations of test parameters, such as a number of nodes greater than the number of MPI processes. To address this, PCVS allows users to define filter plugins that can remove impossible test combinations after the TE is evaluated. The runtime provides standard plugins for mpirun, and users can customize them by editing the configuration in the form of a Python script. To summarize how PCVS handles tests, it can be noted that PCVS manages test compilation, allowing a test suite to be run independently of the target system. Additionally, PCVS can be coupled with the Spack package manager to compile and run tests, runtimes, and compilers. The ability to retarget a test suite to different systems at various scales is also a key feature for test-suite portability.
To implement collaborative test suites and efficient result sharing, PCVS relies on a Git repository called a "bank". This method of sharing takes advantage of UNIX file-system or SSH-based access control, which simplifies access control to potentially sensitive application or platform-dependent data. Teams can share a single test repository and accumulate results over a long period. Git is particularly suited to handling change histories, so it is easy to track over time and assign responsibility for a given test-suite run. On the storage side, PCVS relies on a hierarchy of JSON files matching the run test-suite tree. Each JSON file is mapping a test and its path is the exact test name it had during execution. Each JSON file contains identification (names, id, command line), its results raw information like the return code, the elapsed time or the output streams from the program (possibly truncated using a configurable threshold) and extra data, either attached to PCVS or as requested by the TE (tags, artifacts, metrics).
Listing 2: Example output for bank listing command $ pcvs bank show mpicheck Projects contained in bank ' / p a t h / t o / bank ' : -mpich : 1 distinct testsuite ( s ) * mpich / 1 9 4 dc6e6a7cb78e56e1a59 : 4 run ( s ) -openmpi : 1 distinct testsuite ( s ) * openmpi / 1 9 4 dc6e6a7cb78e56e1a59 : 5 run ( s ) It is therefore possible to build a history of the test results for later query thanks to Git's logging capabilities on the temporal axis. In practice, a test-suite can be directly submitted to a bank after its execution, but it is also possible to work with regular archives for the case of disconnected networks. As depicted in Listing 3 PCVS comes with a Python interface that we call the PCVS Domain-Specific Language to consume theses banks. It allows a high level analysis of the multiple test suites, including bank exploration, test listing and naturally individual test inspection; including captured values and artefacts.
Thanks to these facilities it is then much easier to extract trends or develop custom analysis to add them to the validation pipeline. Listing 3: Sample PCVS DSL python script extracting metadata from a given test serie bank = dsl. Bank ( " / p a t h / t o / bank. g i t " ) serie = bank. get_serie ( " mpich / 1  9   One aspect provided by the Git-inspired architecture is the possibility of having a centralized repository where all results are pushed. This repository does not require a server per se and is simply backed up by the file system. In such a configuration, scripts or even monitoring daemons can probe this repository using the DSL to generate higher-level validation metrics without having to develop specific analyses during each measurement. This allows for the deployment of dedicated monitoring and validation tools at the team level with simple scripts.
For example, a simple crontab calling a Python script leveraging the DSL language could be used to send an email to the developer team, summarizing or warning about potential issues. Similarly, for metric tracking and visual reporting, it is easy to implement a Prometheus exporter consuming the performance database (updating only on push) to feed a Grafana dashboard for continuous display. Overall, this file-based storage with the availability of a Python consumer interface is facilitating the interoperability of the test-suite results. Such analyses may then be used as validation attributes for later execution of this same test.
MOTIVATION This paper introduces PCVS, a testing framework that provides comprehensive handling of recompilation and test environment, which we refer to as retargeting. We demonstrate how this feature facilitates the validation of runtimes with diverse sources and constraints. To demonstrate the capabilities of PCVS, we developed a systematic testing framework for the MPI standard API using the Spack package manager. Our framework runs exhaustive symbol tests to assess the presence of specific features in a given version. We present the test suite results in the PCVS reporting server, which uses Python Flask to provide a user-friendly browsing experience. The MPI test suite is designed to ensure the correct implementation of MPI functions and facilitate their implementation. With the large count support, hundreds of functions were added to MPI, and they need to be present to fully support version 4.
0. However, MPI runtimes currently do not support all MPI 4. 0 features, and it is unclear if all features will be released at once. Therefore, end-users need to know which implementation can run a code using the latest features. This paper is structured as follows: we first present related work, followed by a general presentation of our testing infrastructure. In the second part, we describe the tests we developed for MPI. Finally, we present the test results derived from these test suites for various versions of the respective runtimes.
Moreover, OpenMP is a "pragma" language, meaning that if the "pragma" is ignored, the code may remain totally valid but sequential. Consequently, if the runtime does not provide target support and warnings are overlooked during the compilation, the code may forget to use GPUs. Although PCVS was originally designed to validate MPI implementations during their continuous integration cycle by running several tests and benchmarks at scale, this paper focuses on a smaller test set that targets standard support levels for MPI. This means that we do not validate features by themselves, but we simply check if the runtime allows us to compile and run a program depending on a given symbol in the MPI case. In the rest of this section, we will further describe our MPI test suite. Finally, we will summarize our testing infrastructure, outlining the reporting side and how the results are generated from periodic tests.
mpicheck Test Generation As shown in Figure  3 , we used the binding tools included in the MPI standard itself to validate the MPI interface. Since MPI 4. 0, several parts of the standard document are generated through Python scripts. This means that there is a common description available for all MPI functions and symbols. We used this data, made available by the MPI Forum document editors (although not publicly at the time of writing), to generate systematic symbol tests for all MPI functions in MPI 4. 0 with high fidelity to the standard. We also manually added functions that were deprecated by previous standards to achieve good MPI coverage. Additionally, we manually labeled each function with the versions of MPI in which it is present using the "label" capability in PCVS. These labels define meta-test groups, allowing for cross-validation of test results.