dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, some gene set projection methods, such as Pathifier, iPAS, or GRAPE  35  , require a reference panel to define the scores so they are not suitable for settings such as in epidemiology -where all samples are healthy and small differences are expected -or in some clinical trials -where all individuals are cases and present unique features. NetActivity R/Bioconductor package implements our model trained with GTEx project 22 data based on 1,518 GO  17, 18  biological process terms and KEGG  [19] [20] [21]  pathways. This package enables users to compute GSAS in new datasets and currently is the best approach to define representative, robust, and interpretable GSAS from individual gene expression values. NetActivity generates scores with high replicability and independent of the population, can be applied to RNA-seq or gene expression array data, and has good biological reproducibility, as it can replicate the same biological results in datasets generated with different techniques.
NetActivity training can be used to gain insights into gene regulatory programs. Genes with low weights after training are considered less relevant for the gene set function. Thus, this information could be extracted from the model to remove less relevant genes and refine the gene sets. NetActivity also enables integrating different gene set collections in the same model. We trained our main model using GO biological processes terms and KEGG pathways, expecting to cover most of the human tissue functionalities. Nonetheless, NetActivity models can accommodate additional gene sets representing other biological functions  36  , gene regulatory networks  37  , disease transcriptomic signatures  36, 38  , drug perturbation signatures  3938, 39  , or even custom gene sets. As shown in the main model, GSAS replicability can help us define a list of gene sets with independent biological functions.
For example, if the model includes two gene sets sharing a high proportion of genes, they are very likely to represent the same unique biological function. As such, the corresponding GSAS will highly depend on the initialization of the weights. Based on this idea, we can remove gene sets until all included gene sets are robust, to identify an independent list of gene set functions (potentially from different collections). Finally, large gene sets had lower GSAS robustness, possibly because they do not represent a single biological function. NetActivity advances in the integration of gene expression results and datasets obtained from different platforms. NetActivity returns gene set activity scores for 1,518 gene sets, independent of the platform; and the data preprocessing ensures that the GSAS are on the same scale.
Thus, NetActivity facilitates performing large meta-analyses including datasets from different platforms, as we exemplified in a meta-analysis of nine public prostate cancer datasets generated with three platforms. Our meta-analysis demonstrated that GSAS reflect consistent biological processes that are associated with the phenotypes of interest. In our case, the top identified gene sets were relevant for prostate cancer progression, such as the hepatocyte growth factor receptor signaling pathway 28. Hence NetActivity overcomes the current limitations of meta-analyses combining different gene expression platforms, such as having different features measured in different platforms 1 or having the gene expression measured on different scales. NetActivity standardization achieves similar statistical properties to preprocessing gene expression data with quantile normalization and log2 transformation, an approach successfully applied to run a meta-analysis using effect estimates in Down Syndrome  40.
NetActivity also has some advantages over QuSAGE  41  , a method to perform meta-analysis based on gene set activity scores. As QuSAGE does not include any previous information about the relationships between genes, GSAS might represent overexpression of different genes in different datasets. In addition, QuSAGE has not been shown to be capable of integrating gene expression results from different platforms. Thus, NetActivity appears as the bestperforming method to perform meta-analyses of multiple gene expression platforms. NetActivity GSAS are also highly interpretable. Specifically, based on NetActivity weights, researchers can identify which genes are driving the GSAS and check the expression of these genes. Thus, in the meta-analyses of prostate cancer, we explored two top gene sets whose GSAS were driven by ESM1, and by SMC6, CDT1, and RAD18.
Interestingly, ESM1 and CDT1 have been previously associated with prostate tumor progression  42, 43. Similarly, the top gene set whose GSAS was different between resistant and sensitive metastatic prostate samples to abiraterone treatment was driven by KNSTRN, ECT2, and RCC2 genes, three genes previously associated with prostate cancer progression  [44] [45] [46]. Therefore, NetActivity identifies gene sets associated with a condition, as well as the specific genes that drive this gene set scores, enabling researchers to conduct experimental validation of their results. This validation cannot be performed with commonly used gene set projection methods, such as GSVA, as they do not report the genes that drive the score computation. In addition, NetActivity can improve the interpretability of standard gene expression analysis.
Therefore, further analyses and experiments are required to better characterize each gene set function. Finally, extending the model to new gene sets requires training the whole model in a large dataset. This requirement might not be feasible if we aim to define gene sets specific to rare conditions. One solution could be to fine-tune the model with the new dataset using techniques from transfer learning. More gene sets could also be included by adding new nodes in the encoder and initializing the weights with the pre-trained ones. Further studies are required to ascertain the validity of these approaches.
Training of the main model We trained a model based on GO (Gene Ontology) biological processes  17, 18  and KEGG pathways  [19] [20] [21]  using the 9,662 samples from GTEx v8  22. Count and phenotype data were downloaded using recount2  47  R package and count data was transformed using Variance Stabilizing Transformation (VST) from DESeq2 48 R package. Next, we selected the 19,423 coding genes, based on GENCODE v33 annotation  49  , and standardized the data at the gene level (each gene has a mean expression of 0 and a standard deviation of 1). We used 80% of the samples for training and 20% for validation, preserving this proportion for the different GTEx tissues (brain tissues were considered as one tissue). In total, 7,729 samples were used for training and 1,933 samples for validation.
Gene set representativity We evaluated whether the gene set activity scores contained the information present in the full gene expression matrix. First, we computed the variance of the original gene expression matrix explained by the GSAS in GTEx and TCGA samples using redundancy analysis from the vegan R package (https://CRAN. R-project. org/package=vegan). TCGA RNA-seq data was downloaded using TCGAbiolinks 50-52 R package and the effect of the sequencing center was removed with ComBat-seq  53  R package, protecting for project id and sample type (tumor vs normal). Data was transformed using VST from DESeq2.
Replicability We evaluated whether we obtained the same results when performing the same analysis for different projection methods (NetActivity, GSVA, and hipathia) and individual gene expression in different datasets of the same disease. We compared the gene expression between prostate samples with low or high Gleason scores, a histological marker of tumor progression. We computed the gene set scores using NetActivity, GSVA, and hipathia, based on VST values for TCGA-PRAD and the original gene expression values for GEO-PRAD. All models in PRAD were adjusted for tumor subtype, age, and ancestry, while models in GSE169038 were adjusted for ancestry and decipher classification. Individual gene differential expression analysis was run using raw counts and DEseq2 R package for PRAD and original gene expression values and limma 60 R package for GEO-PRAD.
In both cases, we used all available genes for differential expression analysis and false discovery rate (FDR) computation. Then, GEO-PRAD features were mapped to ENSEMBL ids, and genes annotated to the same ENSEMBL id were selected. The differential expression analysis for NetActivity and GSVA gene set scores was run using limma R package. For hipathia gene set scores, the differential expression analysis was run using the Wilcoxon test implemented in hipathia R package. Finally, we computed the correlation between the differential expression estimates in both datasets: (1) genes -log2FC from DESeq2 and logFC in limma; (2) NetActivity and GSVA -logFC in limma; and (3) hipathia -U-statistic.
Meta-analysis of prostate cancer We run a meta-analysis of differences in gene expression due to cancer progression in prostate cancer to show the applicability of NetActivity. We included nine datasets generated with three platforms: RNA-seq, Illumina HumanHT-12 array, and Affymetrix Human Exon 1. 0 ST Array (Sup Table  4 ). We preprocessed GSE46691 and GSE21034 CEL files with the rma algorithm from oligo  61  Bioconductor package, summarizing expression values into core genes. Gene probes were mapped to ENSEMBL ids using huex10sttranscriptcluster. db Bioconductor package. We normalized count data from GSE183019 and GSE201284 using VST from DESeq2 R package, while we used normalized gene expression values from GEO for GSE141551, GSE70768, GSE70769. Gene SYMBOL identifiers were converted to ENSEMBL ids with org.
Hs. eg. db Bioconductor package (https://bioconductor. org/packages/org. Hs. eg. db) in GSE141551, GSE70768, GSE70769, GSE183019 and GSE201284. Pre-processing of TCGA-PRAD and GSE169038 was defined in the previous section. In all datasets, NetActivity was used to standardize gene values and to compute GSAS. In each dataset, we run a differential expression analysis between samples with low (<8) or high (>=8) Gleason scores with limma, adjusting for the most relevant covariates in each dataset (Sup Table  4 ). We regressed out the first 2 surrogate variables from GSE183019, computed with sva  62  Bioconductor package, to correct a clustering of the samples not related to any biological variable. Meta-analysis of effect estimates was run with METAL  63. To interpret the results, we run a differential expression analysis using the standardized gene values computed in the previous sections, limma, and the relevant covariates defined in the previous section.
Application to metastatic prostate cancer We applied NetActivity to the RNA-seq data from the PROMOTE study  29, 30  available at dbGAP database with accession number phs001141. v2. p1. PROMOTE study aims to identify the molecular mechanisms associated with abiraterone therapy resistance in metastatic castration-resistant prostate cancer. Samples were taken after 12 weeks of abiraterone acetate/prednisone treatment and the primary outcome was determined by the time-to-treatment-change (TTTC), i. e. , the time the patients were under the therapy before they changed to another alternative due to disease relapse or progression. We evaluated the association between the TTTC with the gene expression from RNAseq data and the activity scores computed from it by NetActivity. We select the 47 samples from bone metastatic tissue, the tissue with the largest number of samples.
Original gene Symbols were updated to the newest version based on the HGNG HUGO Gene Nomenclature Committee (https://ftp. ebi. ac. uk/pub/databases/genenames/hgnc/archive/monthly/tsv/hgnc_complete_set_20 22-10-01. txt, accessed the 07/10/2022) and mapped to ENSEMBL ids using AnnotationsDbi (https://bioconductor. org/packages/AnnotationDbi) and org. Hs. eg. db R packages. Count expression data was normalized using VST from DESeq2 R package, before computing GSAS with NetActivity. We explored the effect of log10(TTTC adjusted) (for simplicity from here on referred to as TTTC) on gene expression, using the transformed gene expression matrix or GSAS. Differential gene expression analysis was performed with DESeq2 R package, while the association of GSAS with TTTC was estimated using a linear model with the limma R package.
Additionally, we used a linear regression model to estimate the effect of the gene expression of the top gene and the whole set of genes for each of the significantly activated pathways on the TTTC. We calculated the adjusted R 2 of these models and compared them with the model computed with the activity scores for those same pathways. For calculating the differentially expressed genes we used the standard differential expression analysis of DESeq2 R package using Benjamini and Hochberg's method for multiple test correction. Overrepresentation enrichment analysis was performed using the function enrichGO from the R package clusterProfiler  64, 65  R package with size of gene sets to test between 10-5000, using either all genes or just genes included in NetActivity.
B: Consistency of gene set score definitions in different datasets. Technology: consistency of the gene set score definition in two datasets from prostate cancer generated with RNA-seq and gene expression microarray. Tissue: consistency of the gene set score definition in two datasets from prostate cancer and breast cancer generated with RNA-seq. C-F: Differences in gene expression between Gleason low and high using 4 approaches and 2 datasets (TCGA-PRAD: PRAD samples; GEO-PRAD: GSE169038). C: Differential expression of genes. logFC: differential expression estimates from limma. log2FC: differential expression estimates from DESeq2.
The 19,423 genes included in the main model were selected and the data was standardized at the gene level. 334 prostate cancer samples from TCGA that contained Gleason information 56. We classified the 334 samples from PRAD in 88 samples with high Gleason (Gleason > = 8), and 246 with low Gleason (Gleason < 8). TCGA-BRCA are 1,066 well-characterized breast cancer samples from TCGA 57. TCGA-PRAD and TCGA-BRCA were generated with RNA-seq. GEO-PRAD are 1,131 prostate cancer samples from GSE169038 58 generated with Affymetrix Human Exon 1. 0 ST Array. We downloaded GSE169038 with GEOquery R package 59 , which consisted of 1,152 prostate cancer samples from European and American ancestries. After discarding samples with a very initial prostate cancer stage (Gleason score < 3), we had 1,131 samples, 115 with high Gleason (>= 8), and 1,016 with low Gleason (< 8).
To prepare the data for NetActivity, we mapped GSE169038 features to ENSEMBL ids, using the platform annotation. If multiple features were mapped to the same ENSEMBL id, the feature with more probes was selected. NetActivity was then used to standardize the data and compute the scores. We computed GSAS with NetActivity, GSVA, and hipathia in TCGA-PRAD, TCGA-BRCA, and GEO-PRAD. We compared the GSAS definition between TCGA-PRAD and GSE169038 (same tissue, different technology -RNA-seq vs Affymetrix Human Exon 1. 0 ST Array), and between TCGA-PRAD and TCGA-BRCA (different tissue, same technology).
Introduction In 2021, sensational progress was made in protein structure prediction with AlphaFold2, the artificial intelligence system developed by DeepMind  (Jumper et al. , 2021). These predictions became freely available in the AlphaFold Protein Structure Database (AlphaFold DB), created by EMBL-EBI, which presently includes more than 200 million predictions  (Tunyasuvunakool et al. , 2021)   (Varadi et al. , 2022). The reliability of these predictions is astonishing, and-even more critical-the reliability is estimated at the level of each single amino acid through the predicted local distance difference test (pLDDT), enabling users to identify structures' moieties that might be uncertain, e. g. , conformational disorder. The importance of this precise accuracy estimate has been documented recently in a survey of AlphaFold2 applications in structural and molecular biology  (Akdel et al.
Data selection All experimental structural data were taken from the Protein Data Bank (PDB)  (Bernstein et al. , 1977)   (Berman et al. , 2000)  (wwPDB Consortium, 2019). Only X-ray crystal structures refined at a resolution better than (or equal to) 1. 5 Å and determined in the 90-110 K temperature range were retained. Care was taken to discard multi-model refinements and structures where more than 5% of the atoms are not protein or water atoms. Then, pairwise percentage of sequence identity was limited to 40% with CD-HIT  (Li and Godzik, 2006)   (Fu et al. , 2012) , to avoid redundancy. Computational models were taken from the AlphaFold Protein Structure Database  (Tunyasuvunakool et al. , 2021)   (Varadi et al. , 2022). They were identified through the sequence database accession codes, reported in the DBREF of the PDB files, which allow to identify the computational model with exactly the same sequence of the experimental structure.
AlphaFold2 is a powerful tool for predicting protein three-dimensional structures. There are only a few cases where the predicted structure is very different from the experimental one. For example, in ten cases with Delta-α > 30 °, most of them have large Delta-ds (Figure  1D ). However, in seven of them (PDB: 1vf8, 3b4n, 3om0, 3uci, 6ac5, 6h20, and 6jk4), the average pLDDT values of the residues bridged by the ChB are <90, indicating that side-chains might not be predicted reliably, or even <50, suggesting that predictions should be treated with caution. Only three predicted structures (PDB: 3soj, 4kl3, and 6ya1) have pLDDT >90, suggesting that models have high accuracy, despite Delta-α >30 °. Figure  2C  shows one of the rare examples where AlphaFold DB models seem to be completely inadequate.
Simulation results The simulation results of the CAE and normal states of the spiking patterns of TC, CT, and RT neurons with the tracking results of the AFTSMC for suppression of the oscillatory pattern of the CAE are provided in this section. The schematic of the closed-loop control system through adaptive DBS using AFTSMC is indicated in Fig. 2. To implement the controller, 15 fuzzy estimators are used to approximate the nonlinear dynamics of the plant embedded in the control input. The simulation is performed on the desktop computer (containing an Intel (R) Core (TM) i7-9700K @ 3. 60 GHz CPU and 32 GB DDR4 RAM) in MATLAB R2020a Simulink (64-bit) under Windows 10. The sampling period of 0. 001 ms is used for updating the adaptive values of the FLS estimator and the controller. The mean running time of the closed-loop system for the total period of 2000 ms simulation is about 25 min.
The covered solid angle is determined by the sample-to-detector distance (D) [Fig. 6(a) ]. 33 CoNi medium entropy CCA alloy samples were tempered in air either 600 ○ C for 10 min or to 800 ○ C for 1 h. GE-XAS measurements were performed at the Cr K-edge (5. 989 keV) on both samples. The sequence for obtaining the depth-resolved XAS data is the following: (1) we select a Cr-Kα fluorescence line, (2) we plot the intensity of this line vs an exit angle (angular intensity profile), (3) we construct a 3D plot of all angular intensity profiles for each incident x-ray energy around the Cr K-edge (XAS data), and (4) we plot and compare the different as-normalized XAS data. The results of these steps (1-4) are displayed in Fig. 6(b). At 600 ○ C for 10 min, there is a clear difference in Cr oxidation state (CrO 3 on top of Cr-metal); at 800 ○ C for 1 h, only one layer of Cr 2 O 3 is measured.
Consequently, there is a need for element specific techniques to understand ordering/disordering phenomena, which is indeed offered by the x-ray absorption spectroscopy. The high flexibility of the BAMline allowed us to perform multi-element EXAFS studies on compositionally complex samples. The ability to perform rapid beamline alignments and to change the beamline energy within a few minutes resulted in a relatively efficient use of the instrument, even when several elements had to be studied in one experiment. EXAFS spectra for all five elements in five-component high entropy alloys were collected within a single beam time. For data analysis, we performed simultaneous Reverse Monte Carlo (RMC) modeling of all spectra, which allowed us to obtain not only average interatomic distances and thermal parameters but also characteristic distances for each of the 14 element pairs (Fig.
The first step for a successful experiment is to confirm the beamline's capabilities, such as energy range, flux, resolution, and polarization. To help with this, we have started to install a digital twin on the base of the XRT x-ray tracer for enhanced monitoring, simulation, and optimization. 69 The XRT (XRayTracer)  70  is a Python software library for ray tracing and wave propagation in an x-ray regime. It can model synchrotron sources, beamlines, and their elements with high accuracy and flexibility. When the implementation is finished, the digital twin based on XRT can simulate the behavior of the used x-ray optics and detectors under different conditions and configurations. Figure  18  illustrates the beam size impact on energy resolution dE/E, essential for XANES measurements. For example, with a Si111 DCM and 1 mm vertical aperture, dE is 1.
To support the user with this task, a program based on Bayesian optimization with Gaussian processes with a user-friendly Graphical User Interface (GUI) was developed. Bayesian optimization  70  is a method for finding the optimal value of a computationally expensive, or time-consuming to evaluate, black-box function by using a probabilistic model to guide the search. Gaussian processes are a common choice for modeling the function as they provide a flexible and nonparametric way to represent uncertainty over functions. 73 Gaussian processes are defined by a mean function and a covariance function, which encode prior assumptions about the function's behavior. Bayesian optimization can handle noise, complex functions, and high-dimensional inputs and provide confidence intervals but is computationally expensive for large datasets.
The training data were generated by convolving input images with the projected mask pattern, and careful selection of training input data was shown to be crucial for achieving good results. The trained network was able to reconstruct images in milliseconds, which is significantly faster than the conventional methods that take a few minutes up to an hour. However, significant effort and time are required to produce adequate training samples and to train the network. We proposed to overcome these challenges by training ANNs on simulated data generated by Monte Carlo methods, as we used this approach to improve SR-XRF data analysis. 75  new motorized x-ray zoom lens, developed by the Karlsruhe Institute of Technology's Institute for Microstructure Technology (KIT/IMT),  76  will be integrated and tested at the BAMline.
ACKNOWLEDGMENTS We would like to express our deepest gratitude to  HZB  for providing access to synchrotron radiation at  BESSY  and for fostering our collaboration over two decades. We would also like to thank all the users who have used the BAMline and all the former staff and the master and Ph. D. students who have contributed tirelessly to the work on the BAMline. Their contributions have had a significant impact on the success of our research and have enabled us to continue to push the boundaries of our knowledge. We thank  Dr. Heinrich Riesemeier  for his dedication and many years of support and contribution to further developments at the BAMline. It is with great sadness that we dedicate this manuscript to the memory of  Dr. Uwe Reinholz  ( 1956-2022 ), an esteemed colleague and scientist.