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A collaboration between the Linked Art II project and Participatory Knowledge Practices in Analogue and Digital Image Archives (PIA) has led to a reconfigurable Python-based workflow to transform cultural heritage data, initially photographic collections, into Linked Open Usable Data (LOUD), as a foundation for varied participatory interfaces supporting scholarship and beyond. Motivation PIA, led by the University of Basel and the Bern Academy of the Arts, aims to encourage participation from scholars and the wider public through three collections from the photographic archives of the Swiss Society for Folklore Studies (SSFS): The Atlas of Swiss Folklore, Ernst Brunner, and Kreis Family. PIA aims to create multiple interfaces reflecting diverse perspectives by deploying community-developed LOUD specifications such as the International Image Interoperability Framework (IIIF) and Linked Art, an RDF application profile (JSON-LD) based on CIDOC-CRM to describe object-based cultural heritage. In collaboration with the University of Oxford via the Linked Art II project, PIA has transformed their cultural heritage collection data into Linked Art using templates, which encapsulate the data characteristics and cataloguing practices. A Linked Art API will provide an additional entry point, as a means of conveying semantically enriched events and as a benchmark against other collections leveraging this model. Python-based Workflow To generate Linked Art files for the combined PIA collection a data transformation workflow has been created through which PIA templates were encoded in Python for a given Linked Art API entity endpoint, currently: DigitalObject HumanMadeObject and Set. The workflow, described in a use case example with a photograph from the Ernst Brunner collection, provides a three-step software process for transforming data into Linked Art. Query: The first Python script extracts collection data from the (legacy) PIA JSON API. YAML front matters are used for script variables. The filepath for the relevant .yaml file is specified as a script argument throughout the workflow. For our example, the script queried data for all images looking for DigitalObject entities (one of the variables) and the object's metadata that are stored come specifically from https://json.participatory-archives.ch/api/v1/images/12033} Map: Templates are used to map collection data to an intermediate JSON data format. The intermediate JSON data format means that the later transformation script that creates Linked Art JSON-LD does not necessarily need to be modified if a new data source is introduced. Transform: The intermediate JSON data format is transformed to Linked Art with Python functions that define ‘patterns’ (for example classified_as) for representing different aspects of photographic collection data as Linked Art. Future Work The SSFS will migrate its database into the DaSCH Service Platform (DSP) and amend their data model which will affect PIA, requiring an upgrade of its infrastructure and APIs. The workflow for creating Linked Art representations will have to be reconfigured and repurposed with different data sources. After the migration of the SSFS database, the PIA team will investigate remaining issues in the GitHub repository regarding the correct mapping of Linked Art entities and attribution of IDs. The team will work to ensure that the appropriate Linked Art modelling is achieved through the workflow. Conclusion The reconfigurable Python-based workflow is able to transform cultural heritage data, initially photographic collections, into LOUD, as a foundation for varied participatory interfaces supporting scholarship and beyond. The adaptability and extensibility of the workflow allows for potential future transformations of data from other collections to Linked Art. Acknowledgements This work has been supported by the PIA research project which is funded by the Swiss National Science Foundation (SNSF) as part of the Sinergia funding scheme (contract no. CRSII5_193788) and by the Linked Art II project at the University of Oxford (Principal Investigator: Dr. Kevin R. Page, Oxford e-Research Centre) funded by the UK Arts and Humanities Research Council (AHRC project reference AH/T013117/1)., This poster is related to the extended abstract that is published as part of the DH2023 Book of Abstracts. All versions are identical, only the malfunctioning QR code was replaced., {"references":["Newbury, David (2018): 'LOUD: Linked Open Usable Data and linked.art', in 2018 CIDOC Conference. CIDOC Annual Conference, Heraklion, Greece: International Council of Museums, pp. 1–11. [01.04.2023].","Page, Kevin R. / Delmas-Glass, Emmanuelle / Beaudet, David / Norling, Samantha / Rother, Lynn / Hänsli, Thomas (2020): 'Linked Art: Networking Digital Collections and Scholarship', in DH2020 Book of Abstracts. Digital Humanities 2020, Online, pp. 504–509. [01.04.2023].","Raemy, Julien Antoine / Demleitner, Adrian (2023): 'Implementation of the IIIF Presentation API 3.0 based on Software Support: Use Case of an Incremental IIIF Deployment within a Citizen Science Project', in Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. Cham: Springer International Publishing (Lecture Notes in Computer Science). Unpublished paper presented at the International Conference on Digital Heritage, Limassol, Cyprus, November 7-11, 2022.","Sanderson, Robert (2018): 'Shout it Out: LOUD'. EuropeanaTech Conference 2018, Rotterdam, the Netherlands, 15 May. [01.04.2023].","Sanderson, Robert (2019): 'Keynote: Standards and Communities: Connected People, Consistent Data, Usable Applications', in 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL). 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Urbana-Champaign, Illinois, USA: IEEE, p. 28. DOI: 10.1109/JCDL.2019.00009."]}

This workshop aims at supporting researchers interested in creating a workflow in the SSH Open Marketplace, to share best practices methods with the community. Selected participants will be supported by the members of the Editorial Board of this discovery portal to write and document their research scenarios.

OPERAS Innovation Lab provides targeted guidance for scholars seeking to disseminate their outputs in an innovative way. This poster presents three case studies of workflows for innovative scholarly communication: 1) toolkit/anthology; 2) interdisciplinary online journal; 3) software services. The workflows will serve as blueprints for SSH scholars.

A blueprint for establishing a workflow for generating research data in humanities research projects using the example of the project Exil.Trans. FAIR criteria as well as Linked Open Data principles and ontology-based modeling are addressed.

In this paper we will present publication workflows developed by the DHd Data Steward for publishing conference papers of the DHd annual conferences and illustrate how the collaboration with the DHd community is an essential part of the improvement of the publication strategy within the DHd association. More precisely, we will introduce the DHd Task Force "DHd Abstracts", which is working on improving the submission and publication processes community-driven.

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.

Significance: Early detection of epithelial cancers and precancers/neoplasia in the presence of benign lesions is challenging due to the lack of robust in vivo imaging and biopsy guidance techniques. Label-free nonlinear optical microscopy (NLOM) has shown promise for optical biopsy through the detection of cellular and extracellular signatures of neoplasia. Although in vivo microscopy techniques continue to be developed, the surface area imaged in microscopy is limited by the field of view. FDA-approved widefield fluorescence (WF) imaging systems that capture autofluorescence signatures of neoplasia provide molecular information at large fields of view, which may complement the cytologic and architectural information provided by NLOM. Aim: A multimodal imaging approach with high-sensitivity WF and high-resolution NLOM was investigated to identify and distinguish image-based features of neoplasia from normal and benign lesions. Approach: In vivo label-free WF imaging and NLOM was performed in preclinical hamster models of oral neoplasia and inflammation. Analyses of WF imaging, NLOM imaging, and dual modality (WF combined with NLOM) were performed. Results: WF imaging showed increased red-to-green autofluorescence ratio in neoplasia compared to inflammation and normal oral mucosa (p

Background One of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics. Methods We conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating. Results Our findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because: (a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use. (b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital. (c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow. (d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration. (e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority. Conclusions For the optimal actualization of EHRs’ collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.

The rapid dispersion of new psychoactive substances (NPS) presents challenges to customs services and analytical laboratories, which are involved in their detection and characterization. When the seized material is limited in quantity or of a complex nature, or when the target substance is present in very small amounts, the need to use advanced analytical techniques, efficient workflows and chemo-informatics tools is essential for the complete identification and elucidation of these substances. The current work describes the application of such a workflow in the analysis of a single blotter paper, seized by Swedish customs, that led to the identification of a lysergic acid diethylamide (LSD) derivative, 1-butyl-lysergic acid diethylamide (1B-LSD). Such blotter paper generally contains an amount in the range of 30&ndash, 100 ug. This substance, which is closely related to 1-propionyl-lysergic acid diethylamide (1P-LSD), seems to have only recently reached the drug street market. Its identification was made possible by comprehensively combining gas chromatography with mass spectrometry detection (GC&ndash, MS), liquid chromatography coupled with high-resolution tandem MS (LC&ndash, HR-MS/MS), Orbitrap-MS and both 1D and 2D nuclear-magnetic-resonance (NMR) spectroscopy. All the obtained data have been managed, assessed, processed and evaluated using a chemo-informatics platform to produce the effective chemical and structural identification of 1B-LSD in the seized material.

Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of Deep Learning, or deep neural networks, to this problem has received a great deal of attention. This paper reviews the literature on learned image reconstruction, summarising the current trends, and explains how these new approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these new techniques can be understood from a Bayesian perspective, providing useful insights. The paper also provides a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications - where data may be sparse, fast imaging critical and priors difficult to construct by hand - that Deep Learning will have the most impact. With this in mind, the paper concludes with some indications of possible future research directions.

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

A blind quality control (QC) program was successfully developed and implemented in the Toxicology, Seized Drugs, Firearms, Latent Prints (Processing and Comparison), Forensic Biology, and Multimedia (Digital and Audio/Video) sections at the Houston Forensic Science Center (HFSC). The program was put into practice based on recommendations set forth in the 2009 National Academy of Sciences report and is conducted in addition to accreditation required annual proficiency tests. The blind QC program allows HFSC to test its entire quality management system and provides a real‐time assessment of the laboratory’s proficiency. To ensure the blind QC cases mimicked real casework, the workflow for each forensic discipline and their evidence submission processes were assessed prior to implementation. Samples are created and submitted by the HFSC Quality Division to whom the expected answer is known. Results from 2015 to 2018 show that of the 973 blind samples submitted, 901 were completed, and only 51 were discovered by analysts as being blind QC cases. Implementation data suggests that this type of program can be employed at other forensic laboratories.

Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows., Journal of Physics D: Applied Physics, 54 (29), ISSN:0022-3727, ISSN:1361-6463

Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Molecular image-guided surgery has the potential for translating the tools of molecular pathology to real-time guidance in surgery. As a whole, there are incredibly positive indicators of growth, including the first United States Food and Drug Administration clearance of an enzyme-biosynthetic-activated probe for surgery guidance, and a growing number of companies producing agents and imaging systems. The strengths and opportunities must be continued but are hampered by important weaknesses and threats within the field. A key issue to solve is the inability of macroscopic imaging tools to resolve microscopic biological disease heterogeneity and the limitations in microscopic systems matching surgery workflow. A related issue is that parsing out true molecular-specific uptake from simple-enhanced permeability and retention is hard and requires extensive pathologic analysis or multiple in vivo tests, comparing fluorescence accumulation with standard histopathology and immunohistochemistry. A related concern in the field is the over-reliance on a finite number of chosen preclinical models, leading to early clinical translation when the probe might not be optimized for high intertumor variation or intratumor heterogeneity. The ultimate potential may require multiple probes, as are used in molecular pathology, and a combination with ultrahigh-resolution imaging and image recognition systems, which capture the data at a finer granularity than is possible by the surgeon. Alternatively, one might choose a more generalized approach by developing the tracer based on generic hallmarks of cancer to create a more "one-size-fits-all" concept, similar to metabolic aberrations as exploited in fluorodeoxyglucose-positron emission tomography (FDG-PET) (i.e., Warburg effect) or tumor acidity. Finally, methods to approach the problem of production cost minimization and regulatory approvals in a manner consistent with the potential revenue of the field will be important. In this area, some solid steps have been demonstrated in the use of fluorescent labeling commercial antibodies and separately in microdosing studies with small molecules. (C) The Authors.

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1–3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community’s data analysis tasks.

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations. ISSN:1467-5463 ISSN:1477-4054

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community. The Science Foundation Ireland (grant number SFI/12/RC/2289). peer-reviewed

The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large—up to whole genome in size—and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.

Although the interrelations between urban microclimates and energy demand have been acknowledged, few workflows integrate microclimatic boundary conditions to predict energy demand in parametric morphological studies. This paper helps bridge this gap by introducing a novel workflow which brings together energy and microclimatic modelling for a synergetic assessment at the block scale. The interrelation between form, energy and urban microclimatic conditions is explored here in the climatic context of Tel Aviv by coupling Envimet and EnergyPlus. The potential of this coupling is explored in three different block typologies, each tested for four different density scenarios focusing on the cooling demand on a typical hot day. Results show the substantial increase of as high as 50% in cooling demand when the microclimatic weather data is taken into account and indicate the potential to capitalize on new computational tools which allow to quantify the interrelations between urban form, microclimate and energy performance more accurately.

Objective Healthcare institutions worldwide are moving to electronic health records (EHRs). These transitions are particularly numerous in the US where healthcare systems are purchasing and implementing commercial EHRs to fulfill federal requirements. Despite the central role of EHRs to workflow, the cognitive impact of these transitions on the workforce has not been widely studied. This study assesses the changes in cognitive workload among pediatric nurses during data entry and retrieval tasks during transition from a hybrid electronic and paper information system to a commercial EHR. Materials and methods Baseline demographics and computer attitude and skills scores were obtained from 74 pediatric nurses in two wards. They also completed an established and validated instrument, the NASA-TLX, that is designed to measure cognitive workload; this instrument was used to evaluate cognitive workload of data entry and retrieval. The NASA-TLX was administered at baseline (pre-implementation), 1, 5 and 10 shifts and 4 months post-implementation of the new EHR. Results Most nurse participants experienced significant increases of cognitive workload at 1 and 5 shifts after “go-live”. These increases abated at differing rates predicted by participants’ computer attitudes scores (p = 0.01). Conclusions There is substantially increased cognitive workload for nurses during the early phases (1–5 shifts) of EHR transitions. Health systems should anticipate variability across workers adapting to “meaningful use” EHRs. “One-size-fits-all” training strategies may not be suitable and longer periods of technical support may be necessary for some workers.

Five years ago our department migrated from a film-based imaging environment to a PACS-based environment. We discovered that our reliance on paper tracking forms for clinical history and dictation information was hindering our practice. Integrating dictation with PACS was one of three key components we needed to free ourselves from the tyranny of paper, the other two being an online worklist and an online patient history. We discuss our evolution to a (mostly) paperless reading room environment, our implementation, general performance, and future development plans, focusing on integrated dictation.