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PurposeAn overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues.Design/methodology/approachThis paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material.FindingsTranskribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified.Research limitations/implicationsThe paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc.Practical implicationsOnly HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.Social implicationsThe increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals.Originality/valueThis is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.

Purpose An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. Design/methodology/approach This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. Findings Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. Research limitations/implications The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. Practical implications Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. Social implications The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. Originality/value This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.

Dividing eukaryotic cells package extremely long chromosomal DNA molecules into discrete bodies to enable microtubule-mediated transport of one genome copy to each of the newly forming daughter cells 1 – 3 . Assembly of mitotic chromosomes involves DNA looping by condensin 4 – 8 and chromatin compaction by global histone deacetylation 9 – 13 . Although condensin confers mechanical resistance to spindle pulling forces 14 – 16 , it is not known how histone deacetylation affects material properties and, as a consequence, segregation mechanics of mitotic chromosomes. Here we show how global histone deacetylation at the onset of mitosis induces a chromatin-intrinsic phase transition that endows chromosomes with the physical characteristics necessary for their precise movement during cell division. Deacetylation-mediated compaction of chromatin forms a structure dense in negative charge and allows mitotic chromosomes to resist perforation by microtubules as they are pushed to the metaphase plate. By contrast, hyperacetylated mitotic chromosomes lack a defined surface boundary, are frequently perforated by microtubules and are prone to missegregation. Our study highlights the different contributions of DNA loop formation and chromatin phase separation to genome segregation in dividing cells. Histone deacetylation at the onset of mitosis induces a chromatin-intrinsic phase transition that endows chromosomes with the physical characteristics necessary for their precise movement during cell division.

Plants intimately associate with diverse bacteria. Plant-associated bacteria have ostensibly evolved genes that enable them to adapt to plant environments. However, the identities of such genes are mostly unknown, and their functions are poorly characterized. We sequenced 484 genomes of bacterial isolates from roots of Brassicaceae, poplar, and maize. We then compared 3,837 bacterial genomes to identify thousands of plant-associated gene clusters. Genomes of plant-associated bacteria encode more carbohydrate metabolism functions and fewer mobile elements than related non-plant-associated genomes do. We experimentally validated candidates from two sets of plant-associated genes: one involved in plant colonization, and the other serving in microbe–microbe competition between plant-associated bacteria. We also identified 64 plant-associated protein domains that potentially mimic plant domains; some are shared with plant-associated fungi and oomycetes. This work expands the genome-based understanding of plant–microbe interactions and provides potential leads for efficient and sustainable agriculture through microbiome engineering. Comparative genomic analysis of 3,837 bacterial genomes, including new sequences from 484 root-associated isolates, identifies plant-associated gene clusters and plant-mimicking domains.

Non-small-cell lung cancer (NSCLC) represents approximately 80–85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Maladaptive proximal tubular (PT) epithelial cells have been implicated in progression of chronic kidney disease (CKD), however the complexity of epithelial cell states within the fibrotic niche remains incompletely understood. Hence, we integrated snRNA and ATAC-seq with high-plex single-cell molecular imaging to generate a spatially-revolved multiomic atlas of human kidney disease. We demonstrate that in injured kidneys, a subset of HAVCR1 + VCAM1 + PT cells acquired an inflammatory phenotype, upregulating genes encoding chemokines, pro-fibrotic and senescence-associated proteins and adhesion molecules including ICAM1 . Spatial transcriptomic and multiplex-immunofluorescence determined that specifically these VCAM1 + ICAM1 + inflammatory PT cells localised to the fibrotic niche. Ligand-receptor analysis highlighted paracrine signaling from inflammatory PT cells mediating leucocyte recruitment and myofibroblast activation. Loss of HNF4α and activation of NF-κβ and AP-1 transcription factors epigenetically imprinted the inflammatory phenotype. Targeting inflammatory tubular cells by administering an AP-1 inhibitor or senolytic agent ameliorated inflammation and fibrosis in murine models of kidney injury, hence these cells may be a tractable target in CKD. The complexity of epithelial cell states in the fibrotic niche in the context of chronic kidney disease remains incompletely understood. Here the authors integrate snRNA and ATAC-seq with high-plex single-cell molecular imaging to generate a spatially-revolved multiomic atlas of human kidney disease.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

MicroRNAs may act as diagnostic and prognostic biomarkers of chronic kidney disease and are functionally important in disease pathogenesis. To identify novel microRNA biomarkers, we performed small RNA-sequencing on plasma from individuals with type 2 diabetes, with and without chronic kidney disease. MiR-190a-5p abundance was significantly lower in the circulation of type 2 diabetic patients with reduced function compared to those with normal kidney function. In an independent cohort of patients with chronic kidney disease of diverse aetiology, miR-190a-5p abundance predicted disease progression in individuals with no or moderate albuminuria ( < 300 mg/mmol). miR-190a-5p expression in kidney biopsy tissue correlated with the level of miR-190a-5p in the circulation and with estimated glomerular filtration rate, tubular mass and negatively with histological fibrosis. Administration of a miR-190a-5p mimic in a murine ischaemia-reperfusion injury model in male mice reduced tubular injury and fibrosis and increased expression of genes associated with tubular health. Our analyses suggest that miR-190a-5p is a biomarker of tubular cell health, low circulating levels may predict chronic kidney disease progression independent of existing risk factors and strategies to preserve miR-190a-5p may be an effective treatment for restoring tubular cell health following kidney injury. Chronic Kidney Disease affects 1 in 10 people worldwide with prevalence continuing to rise, thus there is a need to identify novel biomarkers that can add value to existing clinical and biochemical risk predictors. Here the authors identify miR190a-5p as potential indicator of kidney health and disease progression in patients with chronic kidney disease.

We generated a global genetic interaction network for Saccharomyces cerevisiae , constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Inflammation disrupts tissue architecture and function, thereby contributing to the pathogenesis of diverse diseases; the signals that promote or restrict tissue inflammation thus represent potential targets for therapeutic intervention. Here, we report that genetic or pharmacologic Hedgehog pathway inhibition intensifies colon inflammation (colitis) in mice. Conversely, genetic augmentation of Hedgehog response and systemic small-molecule Hedgehog pathway activation potently ameliorate colitis and restrain initiation and progression of colitis-induced adenocarcinoma. Within the colon, the Hedgehog protein signal does not act directly on the epithelium itself, but on underlying stromal cells to induce expression of IL-10, an immune-modulatory cytokine long known to suppress inflammatory intestinal damage. IL-10 function is required for the full protective effect of small-molecule Hedgehog pathway activation in colitis; this pharmacologic augmentation of Hedgehog pathway activity and stromal IL-10 expression are associated with increased presence of CD4⁺Foxp3⁺ regulatory T cells. We thus identify stromal cells as cellular coordinators of colon inflammation and suggest their pharmacologic manipulation as a potential means to treat colitis.