Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance

Renal pseudoaneuryms and pulmonary embolism: A unique manifestation of complications following blunt renal trauma

This case report presents a unique manifestation of complications in a 71-year-old man following blunt renal trauma. Initially, computed tomography (CT) revealed a traumatic left kidney laceration. Hematuria ceased quickly after ureteral stent placement. One week later, hematuria reoccurred while the patient was treated for pulmonary embolism. Multiphase CT revealed two renal pseudoaneurysms as the underlying cause. Renal pseudoaneurysms are commonly associated with surgery or inflammation and rarely seen after trauma. Selective angiographic embolization successfully stopped hematuria. Thereafter, the patient was hemodynamically stable to continue therapeutic thrombolysis. After discharge, the patient remained symptom-free and had an unremarkable follow up assessment

Primary urethral squamous cell carcinoma: a unique manifestation of a penile tumor

This case report describes a unique manifestation of a primary urethral squamous cell carcinoma (SCC) as the underlying pathology in an 80-year-old male patient who underwent partial penectomy due to an enlarging penile mass. Persistent pain in the right knee was discovered to be a pathologic fracture using magnetic resonance imaging. Computed tomography-guided biopsy confirmed metastatic SCC. Whole-body positron emission tomography revealed systemic dissemination to multiple sites. Orthopedic knee replacement was performed in combination with local radiotherapy. Palliative chemotherapy was rejected due to poor performance status. Primary urethral SCC is rare and an uncommon cause of advanced penile cancer. These findings could be of great interest to clinicians for two reasons. First, a tumor's appearance can be misleading. Consequently, histological work-up in accordance with clinical guidelines is necessary for accurate diagnosis. Second, a more comprehensive investigation is required when clinical symptoms persist despite the use of conventional treatment. Our case is an instance in which persistent pain masked the presence of downstream metastasis. We believe that these aforementioned points are of significant clinical importance and present a salient learning opportunity

Mutations in MAST1 Cause Mega-Corpus-Callosum Syndrome with Cerebellar Hypoplasia and Cortical Malformations

Corpus callosum malformations are associated with a broad range of neurodevelopmental diseases. We report that de novo mutations in MAST1 cause mega-corpus-callosum syndrome with cerebellar hypoplasia and cortical malformations (MCC-CH-CM) in the absence of megalencephaly. We show that MAST1 is a microtubule-associated protein that is predominantly expressed in post-mitotic neurons and is present in both dendritic and axonal compartments. We further show that Mast1 null animals are phenotypically normal, whereas the deletion of a single amino acid (L278del) recapitulates the distinct neurological phenotype observed in patients. In animals harboring Mast1 microdeletions, we find that the PI3K/AKT3/mTOR pathway is unperturbed, whereas Mast2 and Mast3 levels are diminished, indicative of a dominant-negative mode of action. Finally, we report that de novo MAST1 substitutions are present in patients with autism and microcephaly, raising the prospect that mutations in this gene give rise to a spectrum of neurodevelopmental diseases

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance