Correlating Transcription Initiation and Conformational Changes by a Single-Subunit RNA Polymerase with Near Base-Pair Resolution.

We provide a comprehensive analysis of transcription in real time by T7 RNA Polymerase (RNAP) using single-molecule fluorescence resonance energy transfer by monitoring the entire life history of transcription initiation, including stepwise RNA synthesis with near base-pair resolution, abortive cycling, and transition into elongation. Kinetically branching pathways were observed for abortive initiation with an RNAP either recycling on the same promoter or exchanging with another RNAP from solution. We detected fast and slow populations of RNAP in their transition into elongation, consistent with the efficient and delayed promoter release, respectively, observed in ensemble studies. Real-time monitoring of abortive cycling using three-probe analysis showed that the initiation events are stochastically branched into productive and failed transcription. The abortive products are generated primarily from initiation events that fail to progress to elongation, and a majority of the productive events transit to elongation without making abortive products.

Multicenter phase 2 study of oral azacitidine (CC-486) plus CHOP as initial treatment for PTCL.

Peripheral T-cell lymphomas (PTCL) with T-follicular helper phenotype (PTCL-TFH) has recurrent mutations affecting epigenetic regulators, which may contribute to aberrant DNA methylation and chemoresistance. This phase 2 study evaluated oral azacitidine (CC-486) plus cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) as initial treatment for PTCL. CC-486 at 300 mg daily was administered for 7 days before C1 of CHOP, and for 14 days before CHOP C2-6. The primary end point was end-of-treatment complete response (CR). Secondary end points included safety and survival. Correlative studies assessed mutations, gene expression, and methylation in tumor samples. Grade 3 to 4 hematologic toxicities were mostly neutropenia (71%), with febrile neutropenia uncommon (14%). Nonhematologic toxicities included fatigue (14%) and gastrointestinal symptoms (5%). In 20 evaluable patients, CR was 75%, including 88.2% for PTCL-TFH (n = 17). The 2-year progression-free survival (PFS) was 65.8% for all and 69.2% for PTCL-TFH, whereas 2-year overall survival (OS) was 68.4% for all and 76.1% for PTCL-TFH. The frequencies of the TET2, RHOA, DNMT3A, and IDH2 mutations were 76.5%, 41.1%, 23.5%, and 23.5%, respectively, with TET2 mutations significantly associated with CR (P = .007), favorable PFS (P = .004) and OS (P = .015), and DNMT3A mutations associated with adverse PFS (P = .016). CC-486 priming contributed to the reprograming of the tumor microenvironment by upregulation of genes related to apoptosis (P < .01) and inflammation (P < .01). DNA methylation did not show significant shift. This safe and active regimen is being further evaluated in the ALLIANCE randomized study A051902 in CD30-negative PTCL. This trial was registered at www.clinicaltrials.gov as #NCT03542266.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities.

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Computer-assisted biosynthetic capacities elucidation accelerates our ability to interpret microbial interactions, allowing us to understand better and establish a balance within ecosystems.

Enhanced Production of Nitrogenated Metabolites with Anticancer Potential in Aristolochia manshuriensis Hairy Root Cultures.

Aristolochia manshuriensis is a relic liana, which is widely used in traditional Chinese herbal medicine and is endemic to the Manchurian floristic region. Since this plant is rare and slow-growing, alternative sources of its valuable compounds could be explored. Herein, we established hairy root cultures of A. manshuriensis transformed with Agrobacterium rhizogenes root oncogenic loci (rol)B and rolC genes. The accumulation of nitrogenous secondary metabolites significantly improved in transgenic cell cultures. Specifically, the production of magnoflorine reached up to 5.72 mg/g of dry weight, which is 5.8 times higher than the control calli and 1.7 times higher than in wild-growing liana. Simultaneously, the amounts of aristolochic acids I and II, responsible for the toxicity of Aristolochia species, decreased by more than 10 fold. Consequently, the hairy root extracts demonstrated pronounced cytotoxicity against human glioblastoma cells (U-87 MG), cervical cancer cells (HeLa CCL-2), and colon carcinoma (RKO) cells. However, they did not exhibit significant activity against triple-negative breast cancer cells (MDA-MB-231). Our findings suggest that hairy root cultures of A. manshuriensis could be considered for the rational production of valuable A. manshuriensis compounds by the modification of secondary metabolism.

Effect of Stress Signals and Ib-rolB/C Overexpression on Secondary Metabolite Biosynthesis in Cell Cultures of Ipomoea batatas

Ipomoea batatas is a vital root crop and a source of caffeoylquinic acid derivatives (CQAs) with potential health-promoting benefits. As a naturally transgenic plant, I. batatas contains cellular T-DNA (cT-DNA) sequence homologs of the Agrobacterium rhizogenes open reading frame (ORF)14, ORF17n, rooting locus (Rol)B/RolC, ORF13, and ORF18/ORF17n of unknown function. This study aimed to evaluate the effect of abiotic stresses (temperature, ultraviolet, and light) and chemical elicitors (methyl jasmonate, salicylic acid, and sodium nitroprusside) on the biosynthesis of CQAs and cT-DNA gene expression in I. batatas cell culture as a model system. Among all the applied treatments, ultraviolet irradiation, methyl jasmonate, and salicylic acid caused the maximal accumulation of secondary compounds. We also discovered that I. batatas cT-DNA genes were not expressed in cell culture, and the studied conditions weakly affected their transcriptional levels. However, the Ib-rolB/C gene expressed under the strong 35S CaMV promoter increased the CQAs content by 1.5-1.9-fold. Overall, our results show that cT-DNA-encoded transgenes are not involved in stress- and chemical elicitor-induced CQAs accumulation in cell cultures of I. batatas. Nevertheless, overaccumulation of RolB/RolC transcripts potentiates the secondary metabolism of sweet potatoes through a currently unknown mechanism. Our study provides new insights into the molecular mechanisms linked with CQAs biosynthesis in cell culture of naturally transgenic food crops, i.e., sweet potato.

Heterogeneity of Integrin αIIbß3 Function in Pediatric Immune Thrombocytopenia Revealed by Continuous Flow Cytometry Analysis.

Immune thrombocytopenia (ITP) is an autoimmune condition primarily induced by the loss of immune tolerance to the platelet glycoproteins. Here we develop a novel flow cytometry approach to analyze integrin αIIbß3 functioning in ITP in comparison with Glanzmann thrombasthenia (GT) (negative control) and healthy pediatric donors (positive control). Continuous flow cytometry of Fura-Red-loaded platelets from whole hirudinated blood was used for the characterization of platelet responses to conventional activators. Calcium levels and fibrinogen binding were normalized to ionomycin-induced responses. Ex vivo thrombus formation on collagen was observed in parallel-plate flow chambers. Platelets from all ITP patients had significantly higher cytosolic calcium concentration in the quiescent state compared to healthy donors (15 ± 5 nM vs. 8 ± 5 nM), but calcium increases in response to all activators were normal. Clustering analysis revealed two subpopulations of ITP patients: the subgroup with high fibrinogen binding (HFB), and the subgroup with low fibrinogen binding (LFB) (8% ± 5% for LFB vs. 16% ± 3% for healthy donors in response to ADP). GT platelets had calcium mobilization (81 ± 23 nM), fibrinogen binding (5.1% ± 0.3%) and thrombus growth comparable to the LFB subgroup. Computational modeling suggested phospholipase C-dependent platelet pre-activation for the HFB subgroup and lower levels of functional integrin molecules for the LFB group.

A new network representation of the metabolism to detect chemical transformation modules.

BACKGROUND: Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RESULTS: RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. CONCLUSIONS: This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

E-cadherin is under constitutive actomyosin-generated tension that is increased at cell-cell contacts upon externally applied stretch.

Classical cadherins are transmembrane proteins at the core of intercellular adhesion complexes in cohesive metazoan tissues. The extracellular domain of classical cadherins forms intercellular bonds with cadherins on neighboring cells, whereas the cytoplasmic domain recruits catenins, which in turn associate with additional cytoskeleton binding and regulatory proteins. Cadherin/catenin complexes are hypothesized to play a role in the transduction of mechanical forces that shape cells and tissues during development, regeneration, and disease. Whether mechanical forces are transduced directly through cadherins is unknown. To address this question, we used a Förster resonance energy transfer (FRET)-based molecular tension sensor to test the origin and magnitude of tensile forces transmitted through the cytoplasmic domain of E-cadherin in epithelial cells. We show that the actomyosin cytoskeleton exerts pN-tensile force on E-cadherin, and that this tension requires the catenin-binding domain of E-cadherin and αE-catenin. Surprisingly, the actomyosin cytoskeleton constitutively exerts tension on E-cadherin at the plasma membrane regardless of whether or not E-cadherin is recruited to cell-cell contacts, although tension is further increased at cell-cell contacts when adhering cells are stretched. Our findings thus point to a constitutive role of E-cadherin in transducing mechanical forces between the actomyosin cytoskeleton and the plasma membrane, not only at cell-cell junctions but throughout the cell surface.

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

Duvelisib plus romidepsin in relapsed/refractory T cell lymphomas: a phase 1b/2a trial.

PI3K-δ inhibitors have shown impressive activity in lymphoid malignancies but have been hampered by autoimmune and infectious toxicities, leading to market withdrawals. We previously demonstrated activity of the PI3K-δγ inhibitor duvelisib in T cell lymphomas (TCLs) that was associated with inflammatory adverse events. As reported here, we conducted a phase 1b/2a study of duvelisib in combination with either romidepsin (n = 66) or bortezomib (n = 32) in patients with relapsed/refractory TCL and found that the addition of romidepsin, but not bortezomib, appeared to increase efficacy while attenuating PI3K inhibitor-driven toxicity. The primary endpoint of the study was to determine the safety and maximum tolerated dose of duvelisib, which was 75 mg twice daily when combined with romidepsin versus 25 mg twice daily when combined with bortezomib. The most common adverse events were neutropenia (42%, 25/59) and fatigue (37%, 22/59) in patients treated with duvelisib and romidepsin and diarrhea (48%, 11/23) and neutropenia (30%, 7/23) in patients treated with duvelisib and bortezomib. Duvelisib and romidepsin resulted in less grade 3/4 hepatotoxicity (14%, 8/59) compared to 40% (14/35) in our previous study with duvelisib monotherapy. This was associated with reductions in circulating inflammatory mediators and myeloid cell inflammatory gene expression. Secondary endpoints of overall and complete response rates were 55% (35/64) and 34% (22/64) for patients treated with duvelisib and romidepsin and 34% (11/32) and 13% (4/32) for patients treated with duvelisib and bortezomib. Among patients with peripheral T cell lymphomas (PTCLs), overall and complete response rates of duvelisib and romidepsin were 56% (27/48) and 44% (21/48), respectively, with exploratory analyses showing increased response rates in patients with a follicular helper T cell subtype. These findings support further development of combined PI3K and histone deacetylase (HDAC) inhibition in TCLs and suggest a unique strategy to enable PI3K inhibitor-based combinations for additional patient populations. ClinicalTrials.gov identifier: NCT02783625 .

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients.

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry.

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub ( https://github.com/zmahnoor14/MAW ). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi.

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.

Biosynthesis of Functional Silver Nanoparticles Using Callus and Hairy Root Cultures of Aristolochia manshuriensis.

This study delves into the novel utilization of Aristolochia manshuriensis cultured cells for extracellular silver nanoparticles (AgNPs) synthesis without the need for additional substances. The presence of elemental silver has been verified using energy-dispersive X-ray spectroscopy, while distinct surface plasmon resonance peaks were revealed by UV-Vis spectra. Transmission and scanning electron microscopy indicated that the AgNPs, ranging in size from 10 to 40 nm, exhibited a spherical morphology. Fourier-transform infrared analysis validated the abilty of A. manshuriensis extract components to serve as both reducing and capping agents for metal ions. In the context of cytotoxicity on embryonic fibroblast (NIH 3T3) and mouse neuroblastoma (N2A) cells, AgNPs demonstrated varying effects. Specifically, nanoparticles derived from callus cultures exhibited an IC50 of 2.8 µg/mL, effectively inhibiting N2A growth, whereas AgNPs sourced from hairy roots only achieved this only at concentrations of 50 µg/mL and above. Notably, all studied AgNPs' treatment-induced cytotoxicity in fibroblast cells, yielding IC50 values ranging from 7.2 to 36.3 µg/mL. Furthermore, the findings unveiled the efficacy of the synthesized AgNPs against pathogenic microorganisms impacting both plants and animals, including Agrobacterium rhizogenes, A. tumefaciens, Bacillus subtilis, and Escherichia coli. These findings underscore the effectiveness of biotechnological methodologies in offering advanced and enhanced green nanotechnology alternatives for generating nanoparticles with applications in combating cancer and infectious disorders.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Draft genome assembly and sequencing dataset of the marine diatom Skeletonema cf. costatum RCC75.

Diatoms (Bacillariophyceae) are a major constituent of the phytoplankton and have a universally recognized ecological importance. Between 1,000 and 1,300 diatom genera have been described in the literature, but only 10 nuclear genomes have been published and made available to the public up to date. Skeletonema costatum is a cosmopolitan marine diatom, principally occurring in coastal regions, and is one of the most abundant members of the Skeletonema genus. Here we present a draft assembly of the Skeletonema cf. costatum RCC75 genome, obtained from PacBio and Illumina NovaSeq data. This dataset will expand the knowledge of the Bacillariophyceae genetics and contribute to the global understanding of phytoplankton's physiological, ecological, and environmental functioning.

The LOTUS initiative for open knowledge management in natural products research.

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

MAYGEN: an open-source chemical structure generator for constitutional isomers based on the orderly generation principle.

The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generation efficiently, exhaustively and isomorphism-free, the structure generator needs to ensure the building of canonical graphs already during the generation step and not by subsequent filtering. Here we present MAYGEN, an open-source, pure-Java development of a constitutional isomer molecular generator. The principles of MAYGEN's architecture and algorithm are outlined and the software is benchmarked in single-threaded mode against the state-of-the-art, but closed-source solution MOLGEN, as well as against the best open-source solution PMG. Based on the benchmarking, MAYGEN is on average 47 times faster than PMG and on average three times slower than MOLGEN in performance.

Description and Analysis of Glycosidic Residues in the Largest Open Natural Products Database.

Natural products (NPs), biomolecules produced by living organisms, inspire the pharmaceutical industry and research due to their structural characteristics and the substituents from which they derive their activities. Glycosidic residues are frequently present in NP structures and have particular pharmacokinetic and pharmacodynamic importance as they improve their solubility and are often involved in molecular transport, target specificity, ligand-target interactions, and receptor binding. The COlleCtion of Open Natural prodUcTs (COCONUT) is currently the largest open database of NPs, and therefore a suitable starting point for the detection and analysis of the diversity of glycosidic residues in NPs. In this work, we report and describe the presence of circular, linear, terminal, and non-terminal glycosidic units in NPs, together with their importance in drug discovery.

DECIMER-Segmentation: Automated extraction of chemical structure depictions from scientific literature.

Chemistry looks back at many decades of publications on chemical compounds, their structures and properties, in scientific articles. Liberating this knowledge (semi-)automatically and making it available to the world in open-access databases is a current challenge. Apart from mining textual information, Optical Chemical Structure Recognition (OCSR), the translation of an image of a chemical structure into a machine-readable representation, is part of this workflow. As the OCSR process requires an image containing a chemical structure, there is a need for a publicly available tool that automatically recognizes and segments chemical structure depictions from scientific publications. This is especially important for older documents which are only available as scanned pages. Here, we present DECIMER (Deep lEarning for Chemical IMagE Recognition) Segmentation, the first open-source, deep learning-based tool for automated recognition and segmentation of chemical structures from the scientific literature. The workflow is divided into two main stages. During the detection step, a deep learning model recognizes chemical structure depictions and creates masks which define their positions on the input page. Subsequently, potentially incomplete masks are expanded in a post-processing workflow. The performance of DECIMER Segmentation has been manually evaluated on three sets of publications from different publishers. The approach operates on bitmap images of journal pages to be applicable also to older articles before the introduction of vector images in PDFs. By making the source code and the trained model publicly available, we hope to contribute to the development of comprehensive chemical data extraction workflows. In order to facilitate access to DECIMER Segmentation, we also developed a web application. The web application, available at https://decimer.ai , lets the user upload a pdf file and retrieve the segmented structure depictions.