Workflow Specification Research Articles

Abstract A060: Identification of tumour-specific variants from pleural effusion cell-free DNA as diagnostic markers of cancers affecting the lungs

Abstract Background: Pleural mesothelioma (PM) can be difficult to diagnose due to a combination of the growth pattern of the tumour, the invasive nature of the biopsy procedure and the frailty of the patient. Some patients with PM develop a build-up of fluid around their lungs as their first clinical symptom. This fluid can make breathing difficult and is routinely drained as part of standard care. Currently, a cytological test can be performed to identify if PM cells are present in the fluid, however, while this is a specific test, it lacks sensitivity. We examined if (a) cell-free DNA (cfDNA) originating from tumours affecting the lung could be detected in this fluid and (b) if this cfDNA could be determined as coming from PM compared to non-small cell lung cancer (NSCLC), due to changes in genes that only occur in these types of tumours. Methods: Using the QIAamp MinElute ccfDNA Kit, we were able to isolate sufficient concentrations of cfDNA from pleural effusions (n=80) derived from patient samples procured by our colleagues at Southmead Hospital with either PM or NSCLC to perform gene panel sequencing. Using the QIAseq Targeted DNA Pro Human Lung Cancer Research Panel, with additional booster primers covering BAP1 and SETD2, we performed the library preparation in-house. Data analysis was carried out using the QIAGEN CLC Genomics Workbench, with the specific workflows for the gene panels and results were filtered by QC, proportion of variant reads and variant read count. Data were compared to expected variant frequencies published in TCGA. Results: Tumour-specific cfDNA was isolated from pleural effusions in the majority of samples tested. In a proportion of these, variants indicative of a specific type of tumour (PM or NSCLC) were readily detectable, at slightly lower than the expected frequencies than reported in TCGA. Conclusions and future work: This approach can act as a 'rule-in' clinically diagnostic test for the presence of PM or NSCLC in a higher proportion of people that can currently be identified through cytology testing. These individuals could be spared from receiving an invasive biopsy to diagnose their tumour. We are currently working on (i) increasing the sensitivity of this approach to identify more cases and (ii) if a bioinformatic method can identify tumour-specific copy number alterations from the gene panel sequencing data. Citation Format: Connie MacKinnon, Dr Colette Mustard, Dr Nicole Brace, Dr Beth Sage, Jenny Symonds, Colin Nixon, Dr Anna Bibby, A/Prof Antonia Pritchard. Identification of tumour-specific variants from pleural effusion cell-free DNA as diagnostic markers of cancers affecting the lungs [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A060.

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation

Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typically, evaluations focus on accuracy metrics, and other parameters such as perceived utility and safety are routinely ignored. Here, we report our finding from the implementation and clinical evaluation of OSAIRIS, an open-source AI model for radiotherapy image segmentation that was carried out as part of its development into a medical device. The device contours OARs in the head and neck and male pelvis (referred to as the prostate model), and is designed to be used as a time-saving workflow device, alongside a clinician. Unlike standard evaluation processes, which heavily rely on accuracy metrics alone, our evaluation sought to demonstrate the tangible benefits, quantify utility and assess risk within a specific clinical workflow. We evaluated the time-saving benefit this device affords to clinicians, and how this time-saving might be linked to accuracy metrics, as well as the clinicians' assessment of the usability of the OSAIRIS contours in comparison to their colleagues' contours and those from other commercial AI contouring devices. Our safety evaluation focused on whether clinicians can notice and correct any errors should they be included in the output of the device.We found that OSAIRIS affords a significant time-saving of 36% (5.4 ± 2.1 minutes) when used for prostate contouring and 67% (30.3 ± 8.7 minutes) for head and neck contouring. Combining editing time data with accuracy metrics, we found the Hausdorff distance best correlated with editing-time, outperforming dice, the industry-standard, with a Spearman correlation coefficient of 0.70, and a Kendall coefficient of 0.52. Our safety and risk-mitigation exercise showed that anchoring bias is present when clinicians edit AI-generated contours, with the effect seemingly more pronounced for some structures over others. Most errors, however, were corrected by clinicians, with 72% of the head and neck errors 81% of the prostate errors removed in the editing step. Notably, our blinded clinician contour rating exercise showed that gold standard clinician contours are not rated more highly than the AI-generated contours.We conclude that evaluations of AI in a clinical setting must consider the clinical workflow in which the device will be used, and not rely on accuracy metrics alone, in order to reliably assess the benefits, utility and safety of the device. The effects of human-AI inter-operation must be evaluated to accurately assess the practical usability and potential uptake of the technology, as demonstrated in our blinded clinical utility review. The clinical risks posed by the use of the device must be studied and mitigated as far as possible, and our ‘Mystery Shopping’ experiment provides a template for future such assessments.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.

Software data objects application integrity modeling in medication dispensing errors predictions using machine learning algorithms in workflow-based pharmacy software systems

The study focuses on using the software objects created at each stage of the workflow-based systems (and in our article we can focus on the pharmacy-based medication dispensing software systems) to predict the anomalies or potential issues in that software object constructed at each stage of the workflow using the historical issues recorded in that specific workflow stage using linear regression, decision trees, and neural network algorithms. By utilizing historical data and integrity metrics, the research aims to forecast potential failures and maintain the reliability of software applications. The focus is on employing algorithms such as linear regression, decision trees, and neural networks to enhance predictive accuracy and facilitate informed decisions or recommendations to the workflow users (pharmacists) about the potential medication dispensing error the software objects constructed at that state or workflow could cause and advise to take necessary corrections.

Abstract P321: Team-Based Care: A Winning Strategy for Managing Hypertension

Hypertension is a serious health condition that can lead to heart disease. Despite available methods for managing hypertension, blood pressure (BP) control in the US is declining, highlighting a gap between expert recommendations and real-world practice. Our clinic initiative aimed to create a specialized workflow and team, which included resident physicians, pharmacists, and health counselors focused on underserved communities. We also provided blood pressure cuffs and encouraged patients to monitor their blood pressure at home. This approach, coupled with the 'Knowing Your Numbers' campaign, has significantly improved outcomes, promoted continuity of care, and built resilience within care communities for vulnerable populations. 110 patients with poorly controlled hypertension were provided with BP monitors to track their blood pressure. Demographic data and office BP readings before and after the monitors were distributed were collected from patient charts. The results were analyzed using descriptive statistics and paired t-tests. The study included 58% female participants with an average age of 59. The racial distribution was 51% black, 35% white, 10% unreported/unknown, 2% Asian, and 2% American Indian/Alaska native. Most participants (70%) reported feeling empowered in their healthcare. Female participants who received a blood pressure cuff and engaged in shared decision-making experienced a significant reduction in their systolic BP by 17.6 mm Hg (P<0.05) and diastolic BP by 7.9 mm Hg (P<0.05). In comparison, males saw a reduction in their systolic BP by 10.6 mm Hg (P<0.05) and diastolic BP by 7.1 mm Hg (P<0.05). The average weight for both genders falls under the obese category, with females having an average BMI of 32.4 and males having an average BMI of 32.6. Providing loan-free blood pressure monitoring cuffs for clinic patients or blood pressure machines through insurance can improve patient engagement and health outcomes. Removing cost barriers can significantly impact in-office blood pressure reduction, monitoring, and patient satisfaction. This initiative highlights the importance of implementing patient-centered approaches to healthcare that address the financial and logistical barriers that can hinder effective hypertension management. Future research is needed to confirm these findings, but this study supports implementing similar programs to improve patients' health and well-being.

Clustering-based multi-objective optimization considering fairness for multi-workflow scheduling on clouds

Distributed computing, such as cloud computing, provides promising platforms for orchestrating scientific workflows' tasks based on their sequences and dependencies. Workflow scheduling plays an important role in optimizing concerned objectives for distributed computing, such as minimizing the makespan and cost. Many researchers have focused on optimizing a specific single workflow with multiple objectives. Currently, there are few studies on multi-workflow scheduling, with most research focusing on objectives such as cost and makespan. However, multi-workflow scheduling requires the design of specific objectives that reflect the unique characteristics of multiple workflows. On the other hand, clustering-based approaches have garnered significant attention in the field of workflow scheduling over distributed computing resources due to their advantage in reducing data communication among tasks. Despite this, the effectiveness of clustering-based algorithms has not been extensively studied and validated in the context of multi-objective multi-workflow scheduling models. Motivated by these factors, we propose an approach for multiple workflows' multi-objective optimization (MOO), considering the new defined metric, fairness. We first mathematically formulate the fairness and define a fairness-involved MOO model. Then, we propose an advanced clustering-based resource optimization strategy in multiple workflow runs. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

Children and adolescents at risk for seclusion and restraint in inpatient psychiatric treatment: a case control study

To reduce coercion in acute inpatient child and adolescent psychiatric units, a better understanding of individuals at risk for seclusion and/or restraint (S/R) is needed. We report data on the proportion of patients secluded/restrained and factors associated with higher risk of S/R. Identifying preventative mechanisms through risk stratification upon inpatient admission can aid the training of mental health professionals, and support shaping specific workflows for at-risk populations for example by joint crisis plans or post-coercion review sessions. Methods: A case-control study included all admissions (n = 782) to a department of child and adolescent psychiatry within 36 months between 2019 and 2022. Data on age, sex, out of home care, primary and comorbid ICD-10 diagnoses, length of stay, prior/multiple admissions were compared between admissions with and without S/R using chi square tests for categorical and t-tests for continuous variables. Uni- and multivariate binary logistic regression models were computed. Results: The overall proportion of S/R was 12.8% (n = 100). Females (p = 0.001), patients in out of home care (p < 0.001), with prior admission (p < 0.001), Post-traumatic stress disorder (PTSD; p < 0.001) and Borderline personality disorder (BPD; p < 0.001) were at a significantly higher risk of S/R. Length of stay in days (OR 1.01), out of home care (OR 3.85), PTSD (OR 6.20), BPD (OR 15.17), Attention deficit hyperactivity disorder (ADHD)/conduct disorder (OR 4.29), and manic episode/bipolar disorder (OR 36.41) were significantly associated with S/R in multivariate regression. Conclusions: Child and adolescent psychiatric staff should consider risk factors when using coercive measures. Patients with PTSD and/or BPD are the most vulnerable subgroups. Training of professionals and clinical practice need to be adapted in order to prevent the use of S/R and its potential hazards.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

BackgroundIn recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked.MethodsWe systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it.ResultsA standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm’s suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance.ConclusionsThe establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

Optical Genome Mapping for Applications in Repeat Expansion Disorders.

Short tandem repeat (STR) expansions are associated with more than 60 genetic disorders. The size and stability of these expansions correlate with the severity and age of onset of the disease. Therefore, being able to accurately detect the absolute length of STRs is important. Current diagnostic assays include laborious lab experiments, including repeat-primed PCR and Southern blotting, that still cannot precisely determine the exact length of very long repeat expansions. Optical genome mapping (OGM) is a cost-effective and easy-to-use alternative to traditional cytogenetic techniques and allows the comprehensive detection of chromosomal aberrations and structural variants >500 bp in length, including insertions, deletions, duplications, inversions, translocations, and copy number variants. Here, we provide methodological guidance for preparing samples and performing OGM as well as running the analysis pipelines and using the specific repeat expansion workflows to determine the exact repeat length of repeat expansions expanded beyond 500 bp. Together these protocols provide all details needed to analyze the length and stability of any repeat expansion with an expected repeat size difference from the expected wild-type allele of >500 bp. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Genomic ultra-high-molecular-weight DNA isolation, labeling, and staining Basic Protocol 2: Data generation and genome mapping using the Bionano Saphyr® System Basic Protocol 3: Manual De Novo Assembly workflow Basic Protocol 4: Local guided assembly workflow Basic Protocol 5: EnFocus Fragile X workflow Basic Protocol 6: Molecule distance script workflow.

Distributed Petri nets for model-driven verifiable robotic applications in ROS

AbstractVerifying industrial robotic systems is a complex task because those systems are distributed and solely defined by their implementation instead of models of the system to be verified. Some technologies mitigate parts of this problem, e.g., robotic middleware such as the Robotic Operating System (ROS) or concrete solutions such as automata-based specification of robot behavior. However, they all lack the required modeling depth to describe the structure, behavior, and communication of the system. We introduce an improved version of our previous model-driven approach based on Petri nets, integrating these three aspects of ROS-based systems. Using a formal modeling language enables verification of the described system and the generation of complete system parts in the form of ROS nodes. This reduces testing effort because the specification of component workflows and interfaces remains formally proven, while only changed implementations have to be revalidated. We extended our previous approach with novel model transformations, which considerably improved our approach’s performance and memory requirements. We evaluate our approach in a case study involving multiple industrial robotic arms and show that the structure of and communication between ROS nodes can be described and verified.

Large language model-based code generation for the control of construction assembly robots: A hierarchical generation approach

Offline programming (OLP) is a mainstream approach for controlling assembly robots at construction sites. However, existing methods are tailored to specific assembly tasks and workflows, and thus lack flexibility. Additionally, the emerging large language model (LLM)-based OLP cannot effectively handle the code logic of robot programming. Thus, this paper addresses the question: How can robot control programs be generated effectively and accurately for diverse construction assembly tasks using LLM techniques? This paper describes a closed user-on-the-loop control framework for construction assembly robots based on LLM techniques. A hierarchical strategy to generate robot control programs is proposed to logically integrate code generation at high and low levels. Additionally, customized application programming interfaces and a chain of action are combined to enhance the LLM's understanding of assembly action logic. An assembly task set was designed to evaluate the feasibility and reliability of the proposed approach. The results show that the proposed approach (1) is widely applicable to diverse assembly tasks, and (2) can improve the quality of the generated code by decreasing the number of errors. Our approach facilitates the automation of construction assembly tasks by simplifying the robot control process.

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility of the results. First, we discuss some of the most used tools, including QuPath, HistoQC, and HistomicsTK, and provide an updated overview of machine learning approaches and their application in pathology. Later, we report how these tools may simplify the automation of WSI analyses, also reducing manual workload and inter-observer variability. A novel aspect of this review is its focus on open-source tools, presented in a way that may help the adoption process for pathologists. Furthermore, we highlight the major benefits of these technologies, with the aim of making this review a practical guide for clinicians seeking to implement machine learning-based solutions in their specific workflows. Moreover, this review also emphasizes some crucial limitations related to data quality and the interpretability of the models, giving insight into future directions for research. Overall, this work tries to bridge the gap between the more recent technological progress in computer science and traditional clinical practice, supporting a broader, yet smooth, adoption of machine learning approaches in digital pathology.

Application of Robotic Stereotactic Assistance (ROSA) for spontaneous intracerebral hematoma aspiration and thrombolytic catheter placement

BackgroundSpontaneous intracerebral hemorrhage (ICH) accounts for up to 20% of all strokes and results in 40% mortality at 30 days. Although conservative medical management is still the standard treatment for ICH patients with small hematoma, patients with residual hematoma ≤15 mL after surgery are associated with better functional outcomes and survival rates. This study reported our clinical experience with using Robotic Stereotactic Assistance (ROSA) as a safe and effective approach for stereotactic ICH aspiration and intra-clot catheter placement. MethodsA retrospective analysis was conducted of patients with spontaneous ICH who underwent ROSA-guided ICH aspiration surgery. ROSA-guided ICH surgical techniques, an aspiration and intra-clot catheter placement protocol, and a specific operative workflow (pre-operative protocol, intraoperative procedure and postoperative management) were employed to aspirate ICH using the ROSA One Brain, and appropriate follow-up care was provided. ResultsFrom September 14, 2021 to May 4, 2022, a total of 7 patients were included in the study. Based on our workflow design, ROSA-guided stereotactic ICH aspiration effectively aspirated more than 50% of hematoma volume (or more than 30 mL for massive hematomas), thereby reducing the residual hematoma to less than 15 mL. The mean operative time of entire surgical procedure was 1.3 ± 0.3 h, with very little perioperative blood loss and no perioperative complications. No patients required catheter replacement and all patients’ functional status improved. ConclusionsWithin our clinical practice ROSA-guided ICH aspiration, using our established protocol and workflow, was safe and effective for reducing hematoma volume, with positive functional outcomes.

Enhancing Business Operations Through Microlearning, BPM and RPA

Abstract This paper aims to investigate the impact of emerging technologies such as microlearning, Business Process Modelling (BPM) and Robotic Process Automation on business operations. In the first part, we did a literature review and defined the key concepts underlying these technologies. Microlearning caters to the modern learner by offering short, effective learning bursts that align well with the fast pace of today's work environments. BPM accelerates the process of delivering learning content specific to microlearning modules by optimizing specific workflow. RPA contributes by automating routine tasks using bots, improving the learning experience by providing instant materials, support and feedback. In the second part, we conducted a bibliometric research with the help of VOSViewer, focusing on an analysis of the co-occurrence of keywords specific to the 3 technologies, highlighting research trends and patterns, the impact of research in terms of citation analysis, but also research gaps on a topic. A future perspective was outlined for the implementation of these technologies in business environments, analyzing their impact and presented a section with case studies, highlighting common challenges and offering possible solutions. Summarizing all this, it can be said that the synergy between these 3 technologies has obvious advantages such as increasing productivity, optimizing administrative processes, reducing costs, and improving learning experiences, but it can also pose implementation problems specific to adopting new technologies in a constantly changing environment.

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

Single-tube Ptprc SNP genotyping of JAXBoy (CD45.1) and C57BL/6J (CD45.2) mice by endpoint PCR and gel electrophoresis

Allelic variation at the Ptprc gene, which encodes the pan-leukocyte marker CD45/Ly5, is commonly exploited to track hematopoietic reconstitution by flow cytometry in mixed bone marrow chimera transplant experiments. Historically, this was accomplished using bone marrow from C57BL/6 (Ptprcb/CD45.2/Ly5.2) and congenic B6.SJL-PtprcaPepcb/Boy (Ptprca/CD45.1/Ly5.1) mice. Recently, the Jackson Laboratory directly CRISPR-engineered the Ptprca allele in C57BL/6J mice. This new isogenic strain, termed JAXBoy, differs from wild-type C57BL/6J mice by two nucleotides, compared to the biologically significant 37 megabase (Mb) SJL interval retained in B6.SJL-PtprcaPepcb/Boy/J mice. Currently, Ptprc/CD45 variants are identified by flow cytometry or allele-specific real-time PCR, both of which require specialized workflows and equipment compared to standard genotyping of endpoint PCR products by gel electrophoresis. Here, we employed allele-specific oligonucleotides in conjunction with differential incorporation of a long non-specific oligo 5′-tail to allow for simultaneous identification of the Ptprca and Ptprcb alleles using endpoint PCR and gel electrophoresis. This method allows for integration of Ptprc genotyping into standard genotyping workflows, which use a single set of thermocycling and gel electrophoresis conditions. Importantly, the strategy of primer placement and tail addition described here can be adapted to discriminate similar single- or multi-nucleotide polymorphisms at other genomic loci.

Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO) for Early Design of Helicopter Primary Parameters

The design of a helicopter involves multiple disciplines and a large number of technical parameters with conflicting objectives, leading to the challenges of multidisciplinary coupling in design and optimization. In the context of the application of Model-Based Systems Engineering (MBSE) in complex product design, how to bridge the gap between MBSE and Multidisciplinary Design Analysis and Optimization (MDAO) to accelerate the early design process of helicopters remains an urgent problem to be addressed. The paper proposed an optimization framework that links MBSE and MDAO models for the design optimization problem of helicopters. On the MBSE side, the design optimization problem of helicopters was defined through the descriptive SysML model. On the MDAO side, the specific domain analysis model and optimization analysis workflow were established. The integration of design data was achieved through stereotype plugins and design components, bridging the gap between MBSE and MDAO, and providing accurate engineering data for system design verification. The paper provided an optimization case of a helicopter, with the optimal mission performance of the helicopter as the objective function. Through MDAO analysis, the design optimization of fuel consumption, take-off weight, and rotor dynamics parameters was completed, achieving an integrated process of system design and optimization.

Computer-assisted multimodal analysis research: A technical review of GRAPE-MARS

Multimodal (discourse) analysis is about analyzing and theorizing individual semiotic resources (e.g., language, image, audio, space, gesture, facial expression) and inter-semiotic relations to make meaning of multimodal phenomena in context. Conducting multimodal analyses can be challenging because of its complexity. Computer-based software can support multimodal analysis by automating or semi-automating stages of annotating and analyzing, thus reducing the extensive time and labor required. The designed affordances of the software are aimed at helping researchers establish links between low-level features of multimodal texts and higher-order multimodal semantic meanings by following specific workflows. Amongst many popular multimodal transcription tools (e.g., ELAN, CLAN, ChronoViz), a new open-source multimodal annotation software for video analysis, GRAPE-MARS has been recently launched. This technical review will first describe the organization and affordances of GRAPE-MARS by illustrating a multimodal analysis of a video via this tool. We will then highlight the principal functionalities that make this software efficient in supporting multimodal analysis. Finally, the limitations and future possible applications of GRAPE-MARS will be discussed.

Quantitative measurements of radiation-induced fibrosis for head and neck cancer: A narrative review.

To provide a comprehensive summary of the different modalities available to measure soft tissue fibrosis after radiotherapy in head and neck cancer patients. PubMed, Scopus, and Web of Sciences. A search was conducted using a list of medical subject headings and terms related to head and neck oncology, radiation fibrosis, and quantitative measurements, including bioimpedance, MRI, and ultrasound. Original research related to quantitative measurement of neck fibrosis post-radiotherapy was included without time constraints, while reviews, case reports, non-English texts, and inaccessible studies were excluded. Discrepancies during the review were resolved by discussing with the senior author until consensus was reached. A total of 284 articles were identified and underwent title and abstract screening. Seventeen articles had met our criteria for full-text review based on relevance, of which nine had met our inclusion criteria. Young's modulus (YM) and viscoelasticity measures have demonstrated efficacy in quantifying neck fibrosis, with fibrotic tissues displaying significantly higher YM values and altered viscoelastic properties such as increased stiffness rate-sensitivity and prolonged stress-relaxation post-radiation. Intravoxel incoherent motion offers detailed insights into tissue changes by assessing the diffusion of water molecules and blood perfusion, thereby differentiating fibrosed from healthy tissues. Shear wave elastography has proven to be an effective technique for quantifying radiation-induced fibrosis in the head and neck region by measuring shear wave velocity. There are various modalities to measure radiation-induced fibrosis, each with its unique strengths and limitations. Providers should be aware of these implications and decide on methodologies based on their specific clinical workflow. Step 5.

Implementing a Hospitalist Comanagement Service in Orthopaedic Surgery.

➤ Hospitalist comanagement of patients undergoing orthopaedic surgery is a growing trend across the United States, yet its implementation in an academic tertiary care hospital can be complex and even contentious.➤ Hospitalist comanagement services lead to better identification of at-risk patients, optimization of patient care to prevent adverse events, and streamlining of the admission process, thereby enhancing the overall service efficiency.➤ A successful hospitalist comanagement service includes the identification of service stakeholders and leaders; frequent consensus meetings; a well-defined standardized framework, with goals, program metrics, and unified commands; and an occasional satisfaction assessment to update and improve the program.➤ In this article, we establish a step-by-step protocol for the implementation of a comanagement structure between orthopaedic and hospitalist services at a tertiary care center, outlining specific protocols and workflows for patient care and transfer procedures among various departments, particularly in emergency and postoperative situations.