dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolic modeling Genome sequencing Cell pellets (equivalent to 1e 10 CFU) were obtained by centrifugation for 10 min at 5000 g from strain cultures in broth medium. DNA was extracted using DNeasy midi kits (Qiagen) according to the protocol described in  (Falentin et al. , 2010). Genomes were sequenced by Illumina NovaSeq 2 x 150 pb and PacBio Sequel (Eurofins Genomics, Constance, Germany). Genomes were assembled using Unicycler  (Wick et al. , 2017a,b)  on the Galaxy Genotoul bioinformatics platform (Toulouse, France). Genome sequences were integrated in the MicroScope platform hosted at Genoscope (CEA, Evry, France) for automatic annotation according to  (Vallenet et al. , 2019). Annotated genomes are available at ENA (Cambridge) under accession number ENA: PRJEB54980. Genome-scale metabolic model reconstruction.
40 For P. freudenreichii , production upper bounds were evaluated by computing per capita production flux from the metabolite dosages and the growth data in the pure culture experiments, assuming a constant flux (see Supplementary material B. 7). The initial condition, the inoculum and nutritional environment composed of milk compounds, amino acids, and co-factors, was defined as specified in  (Cao et al. , 2021) , after unit conversion (See supplementary material for a list of metabolites). A cell mass of × 10 -12 g/CFU was applied for biomass unit conversion. Numerical implementation The dynamical system is solved by a semiimplicit Euler scheme. At time step n, after computation of F j the total flux of compound j as defined by the set of GEMs, F j = i∈B µ i,j (c ex min,i , c ex max,i )(b n , m n ) b i , we computed b n+1 i = b n i + ∆t * F b i m n+1 j =        m n i + ∆t * F j if F j > 0(explicit case) m n j /(1 -∆t * F j /m n j ) else (implicit case) This scheme guaranties solution positivity at all times.
The dFBA problems (3)-(  4 ) and parameter inference is solved using cus-tom Python scripts and the scientific libraries numpy v. 1. 19. 5  (Harris et al. , 2020 ), pandas v. 0. 25. 3 (McKinney et al. , 2010 ), and scipy v. 1. 5. 3 (Wes McKinney, 2010). During dFBA, the FBA is solved using a defined lexicographic order  (Gomez et al. , 2014).
Inference of metabolic interactions In order to predict metabolic exchanges likely to occur in the microbial consortium, MICOM v0. 32. 0  (Diener et al. , 2020b)  and SMETANA v1. 2. 0  (Zelezniak et al. , 2015)  were performed on the milk medium. Additionally, the prediction of metabolic complementarity between the three metabolic models grown in milk was performed with Metage2Metabo v. 1. 5. 0  (Belcour et al. , 2020). All metabolites predicted by the tools were classified into higher level categories: compounds identifiers were mapped to the Metacyc v. 25. 5 database  (Caspi et al. , 2018) , and the ontology of the compounds was used. The list of predicted cross-feeding interactions is available in Supplementary Table  S7. A subset of putative bacterial interactions were selected among the ones predicted by SMETANA, according to their score (SMETANA score), for which a threshold was set at 0.
5. The relevance of selected exchanged metabolites was assessed using metatranscriptomic data (RPKM and replicated means). For each metabolite, reactions producing (in the producer species) and consuming (in the consumer species) the metabolite were extracted from gene-protein-reactions associations. In order to evaluate the putative expression of these genes, the expression values were compared to 43 the overall expression of genes in each species, separated into quartiles.
Visualization and statistical analyses Plots were generated using matplotlib version 3. 3. 4  (Hunter, 2007). Metabolic maps for pathways of interest were generated with Escher  (King et al. , 2015)  for Python v. 3. 6. 9 (Van Rossum and Drake, 2009) 2 , after running an FBA on each model. Fluxes of reactions of interest for each species were normalized by the maximal flux value of the targeted pathways in the corresponding species.
Code and data availability Code for metabolic simulation and metabolic models is available in https://forgemia. inra. fr/tango/ta Genomes of the three bacterial strains and their annotations are available in ENA under the accession number PRJEB54980. Metatranscriptomic data is available in ENA under the accession number PRJEB42478. Annotated genomes, RNA reads and RPKM are deposited in https://entrepot. recherche. data. gouv. fr/privateur 29dd-4d71-b25e-b775d9cc39dc. Biochemical dosage data and metabolomic data are available in  (Cao et al. , 2021). Then, the decile were color coded from highest deciles (dark red, highest expression in this micro-organism at this time point) to lowest decile (light red, low expression). The displayed genes were manually selected from the list of possible interactions raised by SMETANA: for a selected interaction metabolite, genes involved in production (in the donor), and in consumption (in the receiver) were retained for analysis.
The authors acknowledge the  Galaxy Genotoul bioinformatics platform (Toulouse, France) , the MicroScope platform hosted at  Genoscope (CEA, Evry, France) , and the GenOuest bioinformatics core facility (https://www. genouest. org) for providing the computing infrastructure. CF, DJS and SL were supported by the  French National Research Agency (ANR) France   2030   PEPR Agroécologie et Numérique MISTIC   ANR-22-PEAE-0011. CF and SL were supported by the  Inria Exploratory Action SLIMMEST. We are grateful to the  INRAE STLO Dairy Platform  (https://www6. rennes. inrae. fr/plateforme_lait_eng/) for providing help and support (doi: 10. 15454/1. 557240525786479E12 ), to the  CNIEL  for partial funding and fruitful discussions on the interpretation of the results (TANGO Project).
Simple Summary: The natural history of isolated lower-limb superficial vein thrombosis (SVT) in cancer patients is not well understood. To address this gap, we analyzed data from the RIETE registry, which included 110 cancer patients with isolated SVT, 1695 cancer patients with isolated deep vein thrombosis (DVT), and 1030 non-cancer patients with SVT. Compared to cancer patients with DVT, cancer patients with lower-limb SVT received lower doses of anticoagulants, had similar rates of subsequent pulmonary embolism (PE), DVT or SVT, were less likely to have metastases and had a lower mortality rate. However, cancer patients with lower-limb SVT had a higher rate of subsequent PE, DVT or SVT than non-cancer patients with SVT. Additionally, in cancer patients with lower-limb SVT, almost all recurrences and bleeding occurred within the first three months.
Second, the length of SVT and its proximity to the sapheno-femoral junction was not available in some patients. Third, the lower proportion of patients with SVT than DVT in cancer patients may reflect under-reporting of SVT due to an underestimation of the severity of this disease. Fourth, our patient sample may not be representative of the cancer population as most oncologists are not involved in the RIETE registry. Nevertheless, our cohort is the largest cohort of patients on this topic and contributes brings data that could help to improve the treatment of isolated SVT in cancer patients. Finally, the treatment of our patients was quite variable, with methods ranging from LMWH to VKAs or DOACs, and the different treatments could have influenced the outcomes in addition to the differences between SVT or DVT.
, 2016) , which are often performed at infrequent intervals. Wearable technologies and other technical devices make it increasingly possible for researchers to access granular information about daily activities, from walking and sleeping to device interaction (e. g. sleep mat to monitor sleep patterns, gait speed and laterality monitoring devices). These can potentially provide a far more thorough understanding of treatment effect, as well as allowing for better detection of adverse events and side effects  (Kaye et al. , 2021). Such detailed datasets can also potentially be combined with a 'n of 1' approach, allowing researchers to evaluate the effects of interventions on an individual basis  (Wood et al. , 2020). By analogy, outcomes in preclinical studies should aim to capture cognition, function, mobility and activities, for example via 24/7 cage-monitoring technology  (Ho et al.
This repurposing approach is not commonly followed, especially by the pharmaceutical industry, and existing viable drugs might be dismissed  (Bath and Wardlaw, 2015). Preclinical 'platform' trials, including MAMS trials described earlier, would offer a valuable complementary approach to drug testing to help determine potential modes of action of repurposed drugs that showed promise in clinical trials, and could help design better compounds. The process of bedside-to-bench can also be informed by analyses of electronic health records. This approach was used for COVID-19 by the UK Longitudinal Linkage Collaboration (https://ukllc. ac. uk/) and in AD  (Fang et al. , 2021). We can also use large longitudinal research registry datasets for VCI research, associating the outcomes of the diseases for at-risk individuals with potential factors such as lifestyle, risk factors or medication (see Panel 2 for useful resources).
, 2017a)  and merging of previously published guidelines into a more rigorous approach via the RIGOR guidelines  (Lapchak et al. , 2013). The same principles can apply to the VCI field, including having a central database of standardised protocols for behavioural testing, surgical procedure, and ex vivo experiments which would permit consistency of protocols across institutes, and facilitate meta-analyses. To address the issue of transparent reporting, and facilitate reproducibility, the ARRIVE criteria (Animal Research: Reporting of in vivo Experiments) were published in 2010  (Kilkenny et al. , 2010)  and updated in 2020 (Percie du  Sert et al. , 2020). This includes careful definition of the independent experimental unit in the study (e. g. the animal/cage) and the study design including the control groups included.
Building a SVD community forum Perhaps a disconnect between clinical and preclinical research in SVD and VCI is contributing There is a growing need for a centralised database of information on SVD models. For example, one such database is Alzheimer Research Forum (https://www. alzforum. org/), an online community resource of specific knowledge to promote communication, research, collaborative and multidisciplinary interactions  (Clark and Kinoshita, 2007). No such database existed for SVD/VCI at the time of the workshop, but has now been started by the UK DRI (see Panel 2). It so far includes 14 models, and will become a very valuable resource for research into vascular contributions to neurodegeneration. Interested researchers are invited to submit data on animals not yet represented in the database (contact Sarmi Sri, s.
uk/) Vascular ECR community (https://ukdri. ac. uk/news-and-events/from-bench-to-bedsidebridging-the-gap-between-discovery-research-and-the-clinic-in-vascular-research) Vascular models database (to be launched early 2024) VISTA Cognition (https://www. virtualtrialsarchives. org/vista-cognition/) StrokeCOG consortium (http://www. strokecog. ie/) MultiPART (Multicentre Preclinical Animal Research Team) (https://cordis. europa. eu/project/id/603043/reporting).
Further structure-based iterative optimization resulting in inhibitors shown in Fig. 1d  was driven by bioisosteric replacement of the fragments bound to subpockets A-C, scaffold-hopping, de novo design, and medicinal chemistry and was facilitated by SZMAP/ GamePlan  37  , vBrood  38  , and MOE software  39. The computational analysis in Fig. 1e , f was facilitated by combining two X-ray fragments found in subpockets A-C in PDB:6FP4 and PBD:6FTC via a short CH 2 CH 2 linker in a putative chimera molecule. Since all the fragments crystallized in subpocket C contained HEPE moiety with an additional polar substituent, it was tempting to think that a similar polar moiety should be placed in subpocket C. We noticed, however, that the bottom part of subpocket C in the vicinity of the fragments found in the X-ray structures is formed by the hydrophobic portions of D325, Y479, A481, H538, G483, V469, T471 and is likely to prefer bulky hydrophobic substituents.
While subpockets B and C are rather large, subpocket A is narrow and restricted in depth by FAD at the bottom of the pocket, limiting the choice of modifications of the inhibitor in subpocket A. Although the SZMAP and GamePlan analyses (Fig. 1e, f ) suggested placement of polar and non-polar substituents in multiple locations, including those that were relatively remote, we intentionally limited the modifications to those in proximity or to the putative chimera molecule itself to maximize the potential for further improvements during structure-activity relationship (SAR) studies. By advancing d The TGR inhibitors designed in this study. e Gameplan non-polar hypotheses (magenta sticks) generated for chimera molecule (green) built using the X-ray fragments found in subpockets A-C in PDB ID 6FP4 and PBD ID 6FTC and connected by a short CH 2 CH 2 linker to facilitate calculations and analysis.
TGR inhibitors bind to the doorstop pocket To gain additional insights into the mechanism of inhibition, we conducted a series of structural studies. Our attempts to obtain X-ray co-crystal structures of TGR and inhibitors resulted in structures with no detectable density of the inhibitors, possibly owing to the limited solubility of the compounds in the crystallization conditions. To increase aqueous solubility of the compounds to facilitate structural studies, the cycloheptyl substituent in TGR uncompetitive inhibitor 8 was replaced with a sugar moiety, resulting in compound 9, a noncompetitive inhibitor and thus capable of binding the enzyme in absence of NADPH. However, co-crystallization trials failed again. We, therefore, used alternative approaches and have determined the structure of a high molecular weight TrxR subfamily member using cryo-EM, demonstrating the feasibility of this methodology to study this protein subfamily of importance in several human diseases (PDB ID 8A1R, EMD-15084) (Fig.
Non-covalent inhibitors of TrxR class of redox proteins may have broad application One of our recently synthesized TGR inhibitors compound 10 with IC 50 = 18. 6 μM for TGR is even more potent against TrxR from Brugia malayi (BmTrxR; IC 50 = 2. 5 μM) and is a weak inhibitor of TrxR from Plasmodium falciparum (PfTrxR; IC 50 = 32. 5 μM) (Table  1  and Supplementary Table  20 ). Both BmTrxR and PfTrxR are validated drug targets against lymphatic filariasis and malaria, respectively  56, 57. These results and the differences in the composition of the doorstop pockets (Supplementary Fig. 27 ) suggest that selectivity for individual TrxRs can be attainable, which may result in lower toxicity. Fig. 5  | Proposed mechanism of inhibition for the compounds described in this study. a structural superposition between TGR in complex with 9 (in grey, PDB ID 8A1R) and TGR in complex with NADPH (in cyan, PDB ID 2 × 99).
b structural superposition between TGR in complex with 9 and human GR in complex with NADP + (in yellow, PDB ID 3DK4). In both panels the FAD cofactors belonging to the different enzymes are in green sticks, while the two conformers of 9 are in pink and in magenta sticks. c hypothetical mechanism of inhibition for the noncompetitive (9) and uncompetitive (3, 7, and 8) inhibitors, considering the possibility that the destabilized NADP + -TGR(H) reduced forms of the enzyme may specifically accommodate uncompetitive inhibitors due to the presence of conformational states more suitable for compound binding. EH 2 and EH 4 represent the reduced species with 2 and 4 electrons, respectively, populated during the enzymatic reduced half-reaction; EH 4 is the species competent for substrate reduction in the oxidative half-reaction.
Computer-aided molecular design, synthesis, and characterization All the computer-aided molecular design studies were performed in biological assembly (i. e. , dimers) PDB:6FP4 and PDB:6FTC were downloaded from PDB. The proteins were subjected to the Structure Preparation procedure followed by addition of atoms using the Protonate 3D application  71  with all the default settings in MOE. The of the resulting structure was minimized utilizing AMBER14:-EHT forcefield in MOE  72, 73  with all the settings set default until the RMS gradient reached 0. 001 kcal/mol/ Å 2. The resulting ligand-proteins complexes were aligned to match the position of the co-crystallized small molecule fragments. At this step, the water found in the X-ray structures were removed. The co-crystallized fragments found in sub-pockets A-C and the protein were used as input for the SZMAP, Gameplan, and vBrood applications and visualization in MOE.
To facilitate the calculations and analysis, an artificial chimera molecule was built using the X-ray fragments found in subpockets A-C in PDB:6FP4 and PDB:6FTC and connected by a short CH 2 CH 2 linker. The resulting complex was additionally minimized as described above. The default settings for the SZMAP and Gameplan applications were used in all the calculations. SZMAP was used for analysis of energetics of stabilizing and destabilizing effects of water. The szmap command with keyword -stbl resulted in complex, apo, and ligand grids. Additional grids were generated using -results_set max keyword. To breakout the region displaced by the ligand, the grid_comp command was run on the results of the previous step. WaterColor and Water-Orientation VIDA extensions were applied to the results of SZMAP calculations to visualize the regions that favor polar and non-polar substituents.
Gameplan was used to generate a set of hypotheses for polar and non-polar ligand modifications. All the settings in Gameplan were set default. The fragment library for the vBrood application was prepared using the CHOMP application to fragment the ChEMBL database 77 version CHEMBL25  78. In the CHOMP application, no additional parameters were used except for the minimal required to run it (i. e. -in and -out). The total number of fragments in the resulting database was 17,697,078. vBrood was run to replace the piperazine portion of the molecule using the default settings in vBrood. The synthesis and characterization of compounds 1-12 and PRP are given in Supplementary Methods, Supplementary Figs. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 , and Supplementary Tables 1-14.
A Shimadzu LC20AD, Ultra Performance Liquid Chromatography (UPLC) system (Shimadzu Corporation, Kyoto, Japan) equipped with Shimadzu 8040 triple quadrupole (QqQ) mass analyzer (Shimadzu Corporation, Kyoto, Japan) with an electrospray ionization source (ESI) operated in the positive charge mode for the quantification of 1 and 2. Instrument control and data acquisition was achieved via LabSo-lutioFns software (Shimadzu Corporation, Kyoto, Japan).
Instrument control and data acquisition was achieved via LabSolutions software (Shimadzu Corporation, Kyoto, Japan). Evaluation of compound 2 stability under the biochemical assay conditions with recombinant TGR TGR assay solutions (1 ml each) were prepared as described in the main manuscript. Compound 2, NADPH, and incubation time was varied as following: 1) TGR and 2, no preincubation, 2) TGR, 2, and NADPH, no preincubation, 3) TGR and 2, 15 min preincubation, 4) TGR, 2, and NADPH, 15 min preincubation, 5) TGR and NADPH followed by 15 min preincubation, 2 was added after 15 min preincubation, 6) TGR and 2 followed by 15 min preincubation, NADPH was added after 15 min preincubation. Each of the reaction mixtures were terminated by the addition of 250 µl of ethyl acetate, extracted with 1 ml of methyl tertbutyl ether.
The final 3D map has an average resolution of 3. 6 Å at FSC = 0. 143. The crystal structure of TGR (PDB ID: 2V6O)  32  was docked manually in the cryo-EM map using COOT  91, 90. Map local anisotropic sharpening and real space refinement was carried out with Phenix 92 , while manual model building was done with COOT. After several cycles of refinement and model building, inhibitor was placed into the cryo-EM map.
Acknowledgements Oncomelania hupensis subsp. hupensis, Chinese strain, infected with S. japonicum, Chinese strain, and  Biomphalaria glabrata , strain NMRI, infected with S. mansoni, strain NMRI, were provided by the  NIAID Schistosomiasis Resource Center for distribution through BEI Resources, NIAID ,  NIH. We are grateful to  Dr. Guy Schoehn  ( Univ. Grenoble Alpes, CEA ,  CNRS ,  Institut de Biologie Structurale, Grenoble, France) ,  Prof. Beatrice Vallone  ( Sapienza University of Rome, Italy ) and  Dr. Linda C. Montemiglio  ( IBPM, National Research Council, Italy ) for helpful discussions of the cryo-EM studies. We acknowledge the  Elettra-Sincrotrone Trieste (Italy)  for support in X-ray data collections and the  European Synchrotron Radiation Facility  for provision of microscope time on CM01.
The study was funded in part by  US NIH/NIAID   R33AI127635  to F. A. , P. A. P. , G. R. T. and D. L. W. This work benefited from access to  Research Resources Centre and UICentre at University of Illinois at Chicago  and used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The  IBS Electron Microscope facility  is supported by the  Auvergne Rhône-Alpes Region , the  Fonds Feder , the  Fondation pour la Recherche Médicale  and  GIS-IBiSA. The IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ).
M. A. has been supported by  MIUR -Ministero dell'Istruzione Ministero dell'Università e della Ricerca (Ministry of Education, University and Research)  under the national project  FSE/FESR -PON Ricerca e Innovazione 2014-2020  ( N°AIM1887574 , CUP: E18H19000350007). M. A. stay in IBS (Grenoble, France) was funded by  Instruct-ERIC  (proposal number: 16046), part of the  European Strategy Forum on Research Infrastructures (ESFRI)  and supported by national member subscriptions by the proposal APPID1673. We acknowledge  OpenEye/Cadence  for providing us with an academic license for the software used in these studies.
Data availability The cryo-EM data generated in this study have been deposited in the PDB and in the EM data bank under accession codes 8A1R and EMD-15084, respectively. The X-ray-derived data small molecular fragments in complex with TGR used in this study to design the compounds here described are available in the PDB under accession codes 6FTC, 6FMU, 6FMZ, 6FP4, 2X99, 2X8G, 2X8H, 6ZST, 6ZP3, 6ZLP, 6ZLB, 7B02, 7NPX, 2V6O, 6RTJ, 6RTO, 6RTM, and 3H4K. The X-ray-derived data of human GR in complex with NAPD + used in this study to formulate the inhibition mechanism of the compounds depicted in Fig. 5  is available in the PDB under accession codes 3D4K. The ChEMBL25 data used in this study are available in the ChEMBL25 database here [https://doi. org/10. 6019/CHEMBL. database. 25]. Source data are provided with this paper.