dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity.
This can be done using a controlled vocabulary, i. e. , ontologies allowing functional annotations, and bioinformatic tools that are designed to query this information stored in dedicated databases, e. g. , KEGG  (Kanehisa and Goto, 2000)  and HMDB  (Wishart et al. , 2018). Biological processes and/or molecular functions can then be ranked using a statistical test (e. g. , Fisher exact test or hypergeometric test) according to their probability to be represented more frequently than it would occur by random chance  (Kankainen et al. , 2011). The rationale behind this strategy is that the metabolites belonging to a metabolic pathway involved in the manifestation of a specific metabolic signature are expected to be modulated simultaneously. This is a rather strong hypothesis that may not be fulfilled in a real biological context and this type of analysis has therefore some limitations  (Marco-Ramell et al.
These biological links between genes and metabolites constitute valuable information that can be used to infer metabolic networks from genomes, following a systems biology approach. Major improvements of the sequencing technology and databases have indeed enabled the reconstruction of genome-scale metabolic networks of several model organisms  (Yilmaz and Walhout, 2017). It is, however, to be noted that network curation and validation remain mandatory to correct for false positive or missing reactions, and guarantee an adequate stoichiometric balance. Various metrics can then be applied to highlight specific topological features of interest, such as path lengths, degree centrality and clustering coefficient, which allow hubs and modules of topological importance to be highlighted  (Lacroix et al.
Chronic Kidney Disease Dataset Plasma samples collected during four prospective observational monocentric CKD studies performed at a tertiary hospital (Geneva University Hospitals, Geneva, Switzerland) were considered. CKD severity stages were defined according to the glomerular filtration rate (GFR) criterion KDIGO guidelines  (Levey et al. , 2020). A first cohort of patients was recruited to assess the impact of different stages of CKD severity (3b-5) on plasma metabolites  (Gagnebin et al. , 2019). A second and third one explored the benefits offered by kidney transplantation, as well as the potential impact of kidney donation on healthy living donors (LKD)  (Gagnebin et al. , 2020b). Finally, the last one was designed to investigate plasmatic metabolites patterns of patients with end-stage renal disease (ESRD) undergoing regular hemodialysis (HD)  (Gagnebin et al.
, 2015). QC samples were injected for system conditioning (15 first injections in each batch), while QCs and dQCs were analyzed regularly during the sequence (every 6 samples). The UPLC system was coupled to a maXis 3G Q-TOF highresolution MS from Bruker (Bruker Daltonik GmbH, Bremen, Germany) with an electrospray ionization source working in positive (ESI+) mode for RPLC and aHILIC, or negative (ESI-) mode for RPLC and ZICpHILIC. The instrument was operated using the following parameters: capillary voltage of -4. 7 kV for ESI+ and 2. 8 kV for ESI-, nebulizing gas pressure of 2. 0 bar, drying gas temperature of 225 • C for RPLC and 200 • C for aHILIC and ZICpHILIC and a flow rate of 8. 0 L min -1. Data acquisition between 50 and 1,000 m/z was performed in profile mode at a rate of 2 Hz. In-run automatic calibration was achieved using formate adducts in the 90-1,247 m/z range and the quadratic plus high-precision calibration algorithm (Bruker Daltonics).
Data Processing and Analysis Raw data processing was performed using Progenesis QI 2. 3 (Non-linear Dynamics, Waters, Newcastle upon Tyne, United Kingdom). QCs and dQCs were used to monitor and control data acquisition quality, remove unreliable signals, and correct for within-batch drifts and between-batch effects. A filtering procedure was carried out to remove unreliable signals by applying a threshold of 50% for the dQC/QC ratio relative standard deviation (RSD) and a dQC/QC ratio between 0. 2 and 0. 8. LOESS regression was used for intra-and inter-batch normalization based on QC samples. Finally, the position of the QCs was assessed separately for each data block using Principal Component Analysis (data not shown). Metabolite annotation was achieved using an in-house database containing experimental data from more than 900 authentic standard compounds measured in different chromatographic conditions.
Briefly, level 1 annotation was achieved by matching m/z values, retention times, and isotopic patterns, while MS/MS spectra and collisional cross-section values were considered for confirmatory purpose. Cytoscape 3. 8. 2 was used to generate circular layout graphs. NetPCA was computed after unit variance scaling using the NetPCA Python package  (Codesido et al. , 2020). Clustering was computed under the MATLAB 9. 5 environment (The MathWorks, Natick, United States). MetExplore  (Cottret et al. , 2010)  and MetExploreViz  (Chazalviel et al. , 2018)  were used with the Recon3D human metabolic reconstruction  (Brunk et al. , 2018)  for mapping identified metabolites, pathway over-representation analysis, network visualization and evaluation of carbon transfer reaction paths. A more detailed description of the data processing and analysis workflow, as well as the dataset, are available as Supplementary Material.
Metabolomic Dataset Structure In total, 393 blood samples were collected from the predefined cohorts of patients grouped into different categories according to their renal status: 56 healthy control volunteers (CTRL), 69 CKD patients at intermediate stage (ICKD), 35 patients with ESRD undergoing HD (HD), 42 ESRD undergoing kidney graft (KG) and 24 healthy LKD (DV). Repeated measures were carried out for three groups: (i) HD: before (preHD) and after (postHD) blood dialysis on the midweek session, (ii) KG: before (preKG), 1 week (postKG1), and 1 month (postKG2) after the graft, (iii) DV: before (preDV), 1 week (postDV1) and 1 year (postDV2) after the transplantation procedure. All samples went through our standard analytical workflow already presented elsewhere  (Pezzatti et al. , 2020). Briefly, generic sample preparation was first carried out to cover a large chemical diversity of compounds and each sample was analyzed using four LC-HRMS setups, i.
e. , RPLC+, RPLC-, aHILIC+, and ZICpHILIC-. Raw data processing involving baseline correction, peak picking, adduct deconvolution and retention time alignment was carried out with Progenesis QI, while subsequent filtering and normalization based on QC samples  (Broadhurst et al. , 2018; Pezzatti et al. , 2020)  was performed using in house scripts. Metabolite annotation was then performed by matching reference values measured with standards in the same analytical conditions, and a scoring strategy was implemented to remove compounds annotated in more than one LC-MS setup. For that purpose, a peak quality score based on intensity, shape, and retention time was used to select the best analytical information  (Pezzatti et al. , 2019). In the present work, annotation was restricted to metabolites matching entries from our in-house database.
This strategy led to a set of 218 compounds annotated at level 1 for each sample. These identified metabolites were spread over four blocks of variables corresponding to the different LC-MS setups as follows: 88 in RPLC+, 38 in RPLC-, 27 in aHILIC+, and 65 in ZICpHILIC-. A schematic diagram of the dataset structure composed of 40 data matrices involving the five different groups of individuals, the four blocks of variables,.
Circular layout graphs corresponding to the connections between (A) groups of observations and (B) blocks of variables are presented in Figure  2. Using this representation, blocks of data associated with the same individuals appear clearly (data matrices #1-4 for CTRL and #5-8 for ICKD), while tensorial substructures generated by repeated measures during the longitudinal follow-ups form larger clusters (data matrices #9-16 for HD, #17-28 for KG, and #29-40 for DV). Moreover, the four subsets of metabolites measured using the different LC-MS setups were also clearly visible. A two-component NetPCA model was considered to investigate the main metabolic variations in the dataset. From the distribution of the groups on the score plot (Figure  3 ), it appeared that the first component, summarizing 27.
A simplified version of the network was obtained by removing this information, and a single node was considered for each metabolite in the case of multiple occurrences in different compartments. This network was composed of 109 pathways involving 4,095 metabolites, 5,389 reactions and 3,099 enzymatic complexes. A subset of 134 metabolites were successfully mapped on Recon3D using MetExplore Metabolite Identifier Matcher module, corresponding to 61% of the pool of compounds identified in the samples.
Although the coverage of some parts of metabolic networks or chemical families still needs to be improved and/or refined (e. g. , lipids), the current databases already allow a fine assessment of the interconnectivity of the different metabolic pathways and their topology. This allows to put into perspective the differences observed between samples characterizing specific clinical or experimental situations, and to go further in the biological interpretation of the regulatory networks governing phenomena of interest. In addition, the annotation of metabolic networks is being actively carried out thus offering continuous improvements to grasp the complexity of the metabolism. With this aim in sight, the association of chemometrics and bioinformatics in a common workflow will certainly play a central role in the future of metabolomics.
FUNDING This work was partly supported by the research fund of the  Department of Internal Medicine of the University Hospital  and the  Faculty of Medicine of Geneva ; this fund received an unrestricted grant from  AstraZeneca Switzerland. BP was partly funded by the  Marie Heim-Vötglin  Grant of the  Swiss National Science Foundation  (SNSF nos. PMPDP3_171352  and  PMPDP3_186203 ). This work was supported by the  French Ministry of Research and National Research Agency as part of the French MetaboHUB , the  National Metabolomics and Fluxomics Infrastructure  (Grant  ANRINBS-0010 ).
e. the unifying transfer framework presented in this paper), and hence they can be run with a single computer code (e. g. FullSWOF_1D, or any other shallow water software through a coupling with the unifying transfer model equations). Note that several works are limited to the case of model equilibriums  (Beuselinck et al. , 2002; Sander et al. , 2002). In this article, as an extra challenge, our goal is not only to catch the equilibrium, but also to get the evolution toward this equilibrium: starting from given initial conditions, the transitory solution towards the equilibrium is expected to be simulated.
Implementation FullSWOF (for "Full Shallow Water equations for Overland Flow") is a free open-source software (GPL-compatible licence) that solves the shallow water equations and gives the evolution of a shallow layer of water in space and time  (Delestre et al. , 2017). Written in C++, it implements the finite-volume numerical method. It is developed by IDP (ex-MAPMO) and INRAE. There exists a one-dimensional version (for one-dimensional flows) where the quantities (such as the water height) depend only on one space dimension x and time, and a two-dimensional version, to simulate more general flows, where functions depend on the two space dimensions x and y and time. Currently, the transfer model framework (1) is integrated in the one-dimensional version of FullSWOF (FullSWOF_1D version 2.
00. 00) with a splitting method, meaning that, at each time step, the evolution of the water is computed first, giving the water height h and the water discharge q in space, and then the transfer equations (1) are solved. As implemented, the transfer equations (1) could also be used as a library by another flow software. In fact, for any other software giving the evolution of water height and discharge as functions of time, one can extract the resolution of the transfer model framework and make the coupling easily. For details about the implementation, see Appendix A. In the following, the calculations are done using FullSWOF_1D. The parameters files are not fully detailed here but are available in the source code repository of FullSWOF_1D (https://sourcesup. renater. fr /projects/fullswof-1d/), either in the Benchmarks folder (for bedload transport in river) or in the Examples folder (for the other three cases).
Discussion The first point we want to underline in this discussion is the correctness of the results and the comparison with exact solutions. The writing of a code from a set of equations that models physical phenomena can be affected by two types of errors: either a bug or an issue in the numerical scheme used to solve the equations. In both cases, it negatively affects the results. However, this may easily go unnoticed:   assessing that the results are faithful to the equations is not a trivial task. The use of (quasi-)analytical solutions allows to verify the proper functioning  (Delestre et al. , 2013). As shown in the present paper, this approach has been applied successfully to the unifying transfer equations as implemented in FullSWOF_1D. Such a verification was previously carried out to check that FullSWOF_1D accurately solves the shallow water equations.
FullSWOF_1D is routinely tested via a benchmarking script using ten analytical solutions of the shallow water equations taken from the library SWASHES  (Delestre et al. , 2013). Following the implementation of the unifying transfer equations, the exact solution of  Lajeunesse et al. (2013)  was added to the list of benchmarks. These analytical solutions, representing a diversity of flow conditions, enable users and developers to assess the quality of the numerical results (see https://sourcesup. renater. fr/projects/fullswof-1d/). On the one hand, they allow developers to check that code changes do not decrease the simulation quality. On the other hand, they allow users to check that FullSWOF_1D (and the unifying transfer equations) runs faithfully with their computer and compiler. We believe such an approach should be carried out as a standard procedure before any scientific results be reported.
We present here Mn distribution maps and valence based on synchrotron nanoXRF mapping and XANES in six nannofossil species which we have established with the objective to determine whether Mn has implication in nannoplankton photosynthesis chemistry (e. g. Mn-pool, Mn-waste or light shielding) or is incorporated in calcite during diagenesis. The samples and nannofossil species were selected in order to compare data from different i) ages (i. e. , Middle Jurassic, Paleocene, and Quaternary), ii) localities with different environmental settings (i. e. , Portugal, US, South Atlantic, Western Pacific and Norwegian sea), iii) morphologies and structures (i. e. , five placoliths and one asterolith). We selected specimens larger than 4 µm in diameter to overcome the limited spot size at the ID21 beamline (see section 2.
For both standards and nannofossils, Mn K-edge XANES was collected in the 6. 52-6. 60 keV energy range with 0. 5 eV step and 50 ms per step. For each standard, 10 XANES spectra were collected. For each nannofossil, 50 XANES spectra were collected. The spectra for each standard hereafter correspond to the average of all XANES spectra. The spectra for D. falcatus and W. britannica spot C correspond to the average of all XANES spectra per nannofossil; for D. striatus 48 XANES spectra were considered whereas for H. carteri and W. britannica spots A-B-D 10 XANES spectra were considered for the normalization. The normalization was performed with Demeter 0. 9. 21 software. The exclusion of some XANES spectra for the normalization in some samples was done to limit the noise around average. This was a trade-off between statistics and radiation damage to the samples.
Data Availability All data presented here are deposited in PANGAEA repositories in open access. D. striatus and W. britannica analyzed at 17 keV (ESRF project EC811) are in 10. 1594/PANGAEA. 913811. All nannofossils analyzed at 7. 5 keV (ESRF project ES113) and presented in this study are in repository 10. 1594/PANGAEA. 913806. Mn-edge XANES data (ESRF project ES113)nannofossils and standardspresented in this study are in repository 10. 1594/PANGAEA. 913188. All nannofossils analyzed during ESRF project ES113 and not presented in this study are in repository 10. 1594/PANGAEA. 913810.
Methods: We developed SMS text messages using the Tavoca platform. These messages informed patients of their vaccine eligibility and allowed them to indicate their interest in scheduling an appointment via a specific method (email or phone) or indicate their lack of interest in the vaccine. We tracked the status of these messages and how patients responded. Messages were sent to patients aged 65 years and older (N=30,826) at a nonprofit health care system in Washington, DC. Data were collected and examined from January 14 to May 10, 2021. Data were analyzed using multivariate multinomial and binary logistic regression models in SAS (version 9. 4; SAS Institute Inc). Results: Approximately 57% of text messages were delivered to patients, but many messages received no response from patients (40%).
However, the COVID-19 vaccination effort was unprecedented in its scale, and many health care systems across the United States were faced with challenges of outreach, equity, scheduling, and administration. With no blueprint for this type of vaccination campaign, health care systems-particularly those with large diverse patient populations-had to quickly design and launch outreach efforts to patients  [2]. These efforts were further complicated by ongoing challenges with patient distrust of medical institutions, vaccine hesitancy and access barriers, and interoperability of health record databases  [3, 6]. Outreach to patients was complicated by out-of-date or incorrect demographic information, which was necessary for determining vaccine eligibility of each patient. Other complications included the absence of coordination between adjacent health care systems, as patients had the opportunity to be vaccinated at other health care sites, but these vaccination records were not shared among health care systems.
Data Collection and Management Patient information was extracted from the electronic health record and scheduling systems. Data were extracted and analyzed from January 14 to May 10, 2021. During this stage of the vaccine rollout, only the Pfizer and Moderna COVID-19 vaccines were available to patients. For this analysis, patients who shared the same phone number (n=14,419) were excluded, as there was no way to accurately determine which of the multiple patients associated with one phone number responded to the text message (Table  1 ). Confirmed text messaging interaction data were available for 30,826 patients who had unique phone numbers, which were analyzed for this study (Table  1 ). The demographic characteristics of the excluded patients were similar to those of the patients included in the study sample.
Statistical Analysis First, descriptive statistics were assessed to determine sample characteristics. Next, multivariate regression models were used to assess the association between patient demographics (priority zip codes, race or ethnicity, age, and sex) and outcomes of interest. Multivariate multinomial regression models were used for the first outcome of interest (text message status) and multivariate logistic regression for the other outcomes (preferred communication method and appointment scheduled). It was not possible to analyze vaccine uptake in this study owing to sample size limitations. Data were analyzed using SAS (version 9. 4; SAS Institute Inc).
Introduction The initial 2001 study demonstrating the ability of PROteolysis TArgeting Chimeras (PROTACs) to hijack the ubiquitin proteasome system (UPS) offered a new chemical biology approach to control protein function, which continues to impact drug discovery today. 1  From their humble beginnings as chimeric peptides, PROTACs have evolved into potent small molecules that can degrade a variety of intracellular and transmembrane proteins in cells, mice, and recently humans  [2] [3] [4] [5] [6] [7]  (clinical trials NCT04072952 and NCT03888612). The success of PROTACs helped launch the field of Targeted Protein Degradation (TPD), which has recently moved beyond the proteasome. For example, Lysosome Targeting Chimeras (LYTACs) harness the lysosomal degradation pathway to induce degradation of extracellular proteins  8  and the Macroautophagy Degradation Targeting Chimera (MADTAC) platforms, AUTACs and ATTECs hijack the autophagy pathway, thus potentially enabling the targeted destruction of entire organelles and protein aggregates.
37  Aberrant STAT3 activity has been linked to the incidence of several cancers including Fig. 2  A non-extensive timeline of ''hard to drug'' proteins degraded with PROTACs. The insights gained from these studies will help the TPD community tackle other proteins like KRAS G12D and c-Myc. FRS2a PDB: 2MFQ; BAF PDB: 6LTJ; STAT3 PDB: 1BG1; KRAS G12C PDB: 6UTO. leukemias and lymphomas. 38  STAT3 activation has long been held to be dimerization dependent, which is facilitated by phosphorylation of Tyr705. 39 pTyr705 of one monomer is recognized by the SH2 domain of the other monomer. STAT3 ligands that bind to the SH2 domain have been discovered, but have low utility as inhibitors since monomeric STAT3 can still translocate into the nucleus and regulate gene expression. 40, 41  Additionally, these ligands have only modest selectivity for STAT3 over the SH2 domains in STAT1 and STAT4 (B20 fold).
47, 55  Zeng et al. used a flow cytometry-based assay to identify a compound capable of degrading stably overexpressed GFP-KRAS G12C, named XY-4-88. Interestingly, XY-4-88 did not induce degradation of endogenous KRAS G12C, suggesting that degradation of the GFP-KRAS G12C fusion protein was due to ubiquitination of GFP and not KRAS G12C. Our lab has also been interested in degrading KRAS G12C with PROTACs. Based on data from Zeng et al. , we opted to test degraders that recruited VHL and used the synthetically tractable MRTX849 as the KRAS G12C warhead. 49, 56  We synthesized a small library of PROTACs and identified LC-2 as the first PROTAC capable of degrading endogenous KRAS G12C. 57  LC-2 induces rapid, sustained degradation in both homozygous and heterozygous KRAS G12C-expressing cell lines with maximal degradation (D Max ) values ranging from 40-90% and DC 50 (concentration at which 1/2D Max was reached) values in the high nanomolar range (0.