Workflow Process Research Articles

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records

BackgroundExtracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.MethodsIn our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories.ResultsThe sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption.ConclusionsThe findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Streamlining Document Processing: Basis foran Online Document Management System

This study addresses the critical need for an online document management system aimed at streamlining document submission, approval, and storing processes among the faculty and staff of Saint Michael College of Caraga (SMCC) located in Nasipit, Agusan del Norte, Philippines. By utilizing a mixed-methodology approach that integrates qualitative and quantitative techniques, the shortcomings and challenges of the current manual system were assessed through online survey and interviews. The research respondents, who were selected by convenience sampling, were primarily college instructors with 1 to 5 years of work experience. The investigation revealed significant inefficiencies in the current system, identifying key issues such as error-prone processes and delayed communication. The weighted mean scores indicated high level of agreement among the respondents for the proposed system that can potentially enhance process workflow, restructure communication channels, and promote transparency within the institution. The recommendations also include prioritizing primary functions such as user authentication, backup and recovery, and user training. Overall, it can be concluded the proposed online DMS can offer substantial improvements in document management practices, aligning with global trends and best practices in educational administration thus providing a potential model for other institutions facing similar challenges

Submarine cable positioning using a residual convolutional neural network based on magnetic features

Accurate positioning is important for improving the efficiency of repairing submarine cables and reducing the related repair costs. The magnetic anomaly produced by a submarine cable can be used to estimate its vertical and horizontal positions. A novel approach using magnetic data for estimating the position of submarine cables based on the 1D residual convolutional neural network (RCNN) is investigated. Infinitely long ferromagnetic cylinder models with different parameters are used to generate data sets for model training and testing. Tests on noisy synthetic data sets show that the developed 1D RCNN method can capture detailed features related to the magnetic source position information, which is more accurate than the conventional Euler method in estimating the position of submarine cables. The developed 1D RCNN method has also been successfully applied to processing field data. Furthermore, the processing workflow of our 1D RCNN method is less noise-sensitive compared with the conventional Euler method. The proposed 1D RCNN method and its workflow open a new window for estimating the position of submarine cables using magnetic data.

Navigating disparities: Insights from a colorectal cancer screening program in federally qualified community healthcare centers.

1537 Background: Colorectal cancer (CRC) is the second leading cause of cancer deaths in Georgia. Despite evidence for the effectiveness of CRC screening, disparities in access are prevalent in underserved populations. Georgia’s Federally Qualified Health Centers (FQHCs) struggle with low screening rates (42.8%), partly due to Georgia’s lack of Medicaid expansion, poor access to health services in rural areas, and deficiencies in coordinated strategies. The Georgia Colorectal Cancer Control Program (GCRCCP) was funded by the Centers for Disease Control and Prevention to implement evidence-based interventions (EBIs) in qualifying FQHCs across South Georgia. Here, we assess the program's impact on clinic-level screening rates (SR). Methods: Fourteen clinics with 12,159 CRC-eligible patients (ages 50-75) were enrolled from East Georgia Healthcare Center (EGHC) and Albany Area Primary Health Center (AAPHC). FQHC clinics had to have a SR below 60% to participate in the program. In addition to the EBIs (provider assessment and feedback, provider reminders, patient reminders, reducing structural barriers), the GCRCCP integrated provider education using the ECHO Model and navigation through a nationally recognized, evidence-based patient navigation program. We compared baseline (2020 and 2021) and follow-up data (2022) to determine the average change in SR over time, overall, and by demographic sub-groups. Results: The overall change in SR for the 14 clinics was +6.7%, from a baseline average of 45.3% to 52% in 2022. The average change in SR for EGHC clinics (n=9) was +11.5% (from 32.2 to 43.7%). Women had a higher SR (44.9%) than men (41.8%), but both realized an increase in SR, +10.5% and +9.1%. Non-Hispanic Black Americans (NHBA) experienced a higher increase in SR, +13.7% (from 37.2 to 50.9%) than their Non-Hispanic White (NHW) counterparts (+7.5% (from 29.8 to 37.3%)). The uninsured vs. insured followed a similar pattern (+15.3% (from 17.8 to 33.1%)) vs. +2.1 (from 45.9 to 48%). The average change in SR for AAPHC clinics (n=5) was +1.8% (from 58.4 to 60.2%). There was a decline in SR for men, -8.6% (from 64 to 55.4%), and an increase for women, +3.8% (from 58.2 to 62%). NHBA had a decrease in SR, -0.8% (from 61.2 to 61%), while NHW Americans had an increase in SR, +0.2% (from 53.8 to 54.2%). The uninsured and insured groups realized minimal increases of +0.6% (from 46 to 46.6%) and +0.8% (from 62 to 62.8%), respectively. Conclusions: The implementation of EBIs and patient navigation show varied potential to reduce disparities in accessing CRC screening in underserved populations. FQHC clinics that sustain implementation activities and workflow processes can benefit communities long-term and improve health equity.

The restoration, preservation and mastering of the Lantz Cartoons

This paper describes the workflow and approval process for bringing Lantz Cartoons from the Universal Pictures vault into a DolbyVision HDR workflow, and preserving film and audio elements as part of the Universal Pictures initiative to preserve and restore its 112-year-old library through digitisation. The paper describes the unique processes undertaken in accomplishing a request to master the films for a broadcast distribution deal and how the dedicated professionals used their technical knowledge, workflow expertise and understanding of library history to preserve, restore and master 139 short films to fulfil the sale. The paper details the challenges of creating a viable budget for the work and demonstrates the significance of technology in protecting historically essential films.

Timeliness of breast cancer care: Real-world experience at a tertiary care and a public safety-net hospital.

e13712 Background: Advances in breast cancer diagnostics and therapeutics have led to early cancer detection and an overall decrease in morbidity and mortality rates; nevertheless, cancer care disparity still exists and highlights areas of unmet need in cancer care delivery. Cancer navigator programs have been used as a strategy to reduce disparities arising from social determinants of health. We compared the timeliness of breast cancer care at UCLA Medical Center (UCLA), a tertiary care academic hospital, and at Olive View-UCLA Medical Center (OVMC), a safety-net hospital. This study highlights the benefits of cancer care navigation and streamlined workflow processes in bridging disparity gaps. Methods: In this retrospective cohort study, we examined patients diagnosed with breast cancer between 2018 and 2019 at UCLA and OVMC. A total of 100 patients were identified at each site. Demographics data, including age, race, ethnicity, pathology, and staging, were collected. For time interval analysis along the breast cancer care timeline, the mean number of days from the initial imaging date (i.e. either screening or diagnostic mammogram, with the latter if the patient presented with signs and symptoms of breast cancer) was compared with each diagnostic and therapeutic time point. These included time between screening and diagnostic mammograms (if available), time to biopsy, time to MRI, time to surgery, and time to treatment. Results: Mean age (years) at diagnosis was 62.0 and 57.5 at UCLA and OVMC, respectively (p=0.024). Racial distribution showed 60% White, 14% Asian, 7% Black, 7% Mixed, and 4% Other at UCLA, compared to 72%, 9%, 5%, 0%, and 0% at OVMC, respectively (p=0.01). Ethnicity comparison showed 11% Hispanic/Latino at UCLA versus 52% at OVMC (p<0.001). Breast cancer stage at diagnosis was not significantly different between sites. The time interval (days) from screening to diagnostic mammogram was significantly longer at OVMC compared to UCLA (35.1 versus 14.6, p=0.007); however, no statistically significant differences were observed from time to biopsy (27.3 versus 29.5, p=0.651), time to MRI (54.7 versus 49.9, p=0.703), time to neoadjuvant chemotherapy (73.4 versus 48.7, p=0.236), time to surgery (97.2 versus 106.2, p=0.523), time to adjuvant chemotherapy (158.6 versus 139.5, p=0.4), and time to radiation therapy (192.9 versus 198.6, p=0.796) between UCLA and OVMC, respectively. Conclusions: Our study shows that the timeliness of breast cancer care at UCLA and OVMC was overall comparable. This result can be attributed to the implementation of cancer navigator programs at OVMC as well as streamlined breast cancer detection workflow developed in multi-disciplinary collaboration across numerous departments. We believe this workflow can be expanded to all tumor types and be shared with other safety net facilities in order to bridge gaps in cancer care delivery for oncology patients.

Impact of the integration of remote clinical oncology pharmacists into routine precision medicine workflows on biomarker testing rates within The US Oncology Network community-based practices.

e13813 Background: The US Oncology Network implemented a centralized, remote clinical oncology pharmacist review program (ClinReview) into select community-based practices. Initially, pharmacists were integrated into a precision medicine workflow process to increase biomarker testing rates among patients receiving adjuvant treatment for non-small cell lung cancer, with recommendations achieving a 100% acceptance rate. Following this success, metastatic prostate cancer patients were incorporated into the established workflow, following updated guideline recommendations for both germline and somatic testing and the 2023 approvals of PARP inhibitors in the first-line treatment setting. With previously reported testing rates only reaching 60%, we sought to evaluate the impact of the pharmacist integration into the precision medicine workflow on biomarker testing in metastatic prostate patients. Methods: Over a 4-month period, ClinReview remotely identified patients with metastatic prostate cancer during normal order review. The EMR was reviewed to assess germline and somatic testing status. If testing was not ordered, the pharmacist sent a recommendation to the treating provider. Initial testing rates for germline and somatic testing, BRCA+ status and provider acceptance rates for testing recommendations were collected. Additional data was collected utilizing a de-identified database. While additional data collection is ongoing, descriptive statistics were utilized to analyze data and the impact of the pharmacists’ interventions. Results: Across 9 practices, 142 patients were initiated on treatment for metastatic prostate cancer. At the time of initial assessment, germline and somatic testing was not ordered in 50% and 37% of patients, respectively. Pharmacists recommended additional testing in 61 patients (43%) with a 73% provider acceptance rate. Findings showed equitable testing rates across racial demographics. Pharmacist workflow integration enhanced combined germline and somatic testing rates, demonstrating an improvement from a baseline of 57% up to 77% currently. Conclusions: Community oncology practices seek to provide high-value care, including offering the most current evidence-based precision medicine testing. These findings highlight the continued need for improved somatic and germline testing in metastatic prostate cancer. However, the implemented precision medicine pharmacist workflow demonstrates the positive impact a clinical pharmacist can have on both testing rates and the potential to improve therapeutic offerings within this growing treatment space. [Table: see text]

Inpatient outcomes of patients with new solid tumors admitted to a comprehensive cancer center.

e23056 Background: Hospitalized cancer patients demonstrate multiple challenges including management of acute illnesses, establishment of the cancer diagnosis, creation of a goal concordant care plan, and post-hospital disposition. Yet, there has been limited research regarding characteristics and outcomes of new to hospital oncology patients. Methods: We performed a retrospective chart review of patients with suspicion or confirmed solid tumor cancer diagnosis who were admitted without an established MD Anderson Cancer Center oncologist. We assessed length of stay, ICU utilization, 30-day readmission rate, inpatient mortality, and discharge disposition. We compared the results to our group’s established oncology patients. Statistical analysis was performed using Chi-squared testing. Results: Between November 2022 and January 2023, 201 unique new patients were reviewed out of 1635 admissions. Of those 103 (51.2%) were males, median age of 60 (range: 22-79), White including Hispanics 118 (59%), African Americans 46 (23%), others 33 (18%). Of the study population, 88 (43.8%) did not have a confirmed cancer diagnosis, of which we established a cancer diagnosis in 68 (77.3%) and ruled out cancer in 8 (4%). Of the 113 (56.2%) diagnosed at outside facilities, 63 (55.8%) were treatment naive and 22 (19.4%) had their cancer diagnosis revised. Of 175 patients which had staging information, 150 (85.7%) were stage 4. During the index admission, 45 (22.4%) patients had cancer-directed therapy. The average and geometric mean length of stay (days) for new patients was 12.1 and 8.8, respectively, compared to the established group of 8.2 and 6.1 (p < 0.001). Inpatient mortality was higher in new patients (10.5% vs 6.8%, p < 0.001) and they were more likely to be referred to hospice services (17.4% vs 8.3%, p < 0.001). Furthermore, hospice referral was higher in the unestablished, previously treated group compared to treatment naive patients (28% vs 13.9%, p = 0.003), however inpatient mortality was similar (12% vs 9.9%, p = 0.63). ICU stay, 30-day readmission rate, discharge to home vs placement were similar in new and established patients (Table 1). Conclusions: Admitted new oncology patients typically present with advanced disease and had longer length of stay, mortality rates, and hospice referrals compared to established patients. Further research is needed to identify and improve care barriers to new inpatients, focusing on improving workflow process and timely goal concordant care discussions. [Table: see text]

AlarmGPT: an intelligent alarm analyzer for optical networks using a generative pre-trained transformer

The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.

Rollout of a statewide Australian telestroke network including virtual reality training is associated with improved hyperacute stroke workflow metrics and thrombolysis rate

BackgroundTelestroke networks aim to address variability in both quality and access to stroke care in rural areas, by providing remote access to expert stroke neurologists. Implementation of telestroke requires adaptation of workflow processes and education. We previously developed virtual reality (VR) workflow training and documented acceptability, utility and feasibility. The effects on acute stroke treatment metrics have not been previously described.AimsThe overall aim was to improve hyperacute stroke metrics and shorten the time-to-reperfusion therapy administration in rural settings.MethodsThis study applies a natural experiment approach, collecting stroke metric data during transition from a pre-existing pilot to a statewide telestroke service at five rural hospitals. Pre- and post-intervention data included baseline patient demographics and assessment, diagnosis, and treatment delivery metrics. The primary study outcome was door-to-decision time (thrombolysis and endovascular thrombectomy). Secondary outcomes included door-to-computerized tomography time, door-to-thrombolysis time and proportion of patients receiving thrombolysis or thrombectomy treatment. Usage data relating to the VR stroke workflow training of interprofessional healthcare professionals was automatically captured via Wi-Fi. Statistical comparisons of clinical metrics between the pre- and post-intervention time periods, defined as the timeframes before and after VR deployment, were performed.ResultsA total of 2,683 patients were included (April 2013–December 2022); 1910 pre- and 773 post-intervention. All acute stroke time metrics significantly improved post-intervention. The primary outcome, door-to-decision time, decreased from 80 min [56–118] to 54 min [40–76; P < 0.001]. Secondary outcomes also improved, including door-to-thrombolysis time (90 min [68–114] vs. 68.5 min [54–90]; P < 0.001) and proportion of patients thrombolysed (11 vs. 16%; P < 0.001). The proportion of patients transferred for thrombectomy was unchanged (6 vs. 7%; P = 0.69). Seventy VR sessions totaling 15 h 39 min of training time were logged. VR training usage varied across sites (3–31 sessions per site).ConclusionsDelivery of a multi-factorial intervention including infrastructure, funding, education and training (with VR workflow training) as part of a state-wide telestroke rollout was associated with improved acute stroke treatment metrics. Additional work is required to identify the contribution of each intervention component on clinical outcomes and to increase training uptake and sustainment.

The impact of kit, environment, and sampling contamination on the observed microbiome of bovine milk.

In low-microbial biomass samples such as bovine milk, contaminants can outnumber endogenous bacteria. Because of this, milk microbiome research suffers from a critical knowledge gap, namely, does non-mastitis bovine milk contain a native microbiome? In this study, we sampled external and internal mammary epithelia and stripped and cisternal milk and used numerous negative controls, including air and sampling controls and extraction and library preparation blanks, to identify the potential sources of contamination. Two algorithms were used to mathematically remove contaminants and track the potential movement of microbes among samples. Results suggest that the majority (i.e., >75%) of sequence data generated from bovine milk and mammary epithelium samples represents contaminating DNA. Contaminants in milk samples were primarily sourced from DNA extraction kits and the internal and external skin of the teat, while teat canal and apex samples were mainly contaminated during the sampling process. After decontamination, the milk microbiome displayed a more dispersed, less diverse, and compositionally distinct bacterial profile compared with epithelial samples. Similar microbial compositions were observed between cisternal and stripped milk samples, as well as between teat apex and canal samples. Staphylococcus and Acinetobacter were the predominant genera detected in milk sample sequences, and bacterial culture showed growth of Staphylococcus and Corynebacterium spp. in 50% (7/14) of stripped milk samples and growth of Staphylococcus spp. in 7% (1/14) of cisternal milk samples. Our study suggests that microbiome data generated from milk samples obtained from clinically healthy bovine udders may be heavily biased by contaminants that enter the sample during sample collection and processing workflows.IMPORTANCEObtaining a non-contaminated sample of bovine milk is challenging due to the nature of the sampling environment and the route by which milk is typically extracted from the mammary gland. Furthermore, the very low bacterial biomass of bovine milk exacerbates the impacts of contaminant sequences in downstream analyses, which can lead to severe biases. Our finding showed that bovine milk contains very low bacterial biomass and each contamination event (including sampling procedure and DNA extraction process) introduces bacteria and/or DNA fragments that easily outnumber the native bacterial cells. This finding has important implications for our ability to draw robust conclusions from milk microbiome data, especially if the data have not been subjected to rigorous decontamination procedures. Based on these findings, we strongly urge researchers to include numerous negative controls into their sampling and sample processing workflows and to utilize several complementary methods for identifying potential contaminants within the resulting sequence data. These measures will improve the accuracy, reliability, reproducibility, and interpretability of milk microbiome data and research.

3D VSP Imaging Using DAS Recording of P- and S-Waves in Vertical and Lateral Well Sections in West Texas.

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P-P reflection and P-S volumes derived from the well's vertical section. The Wolfcamp formation can be seen in two 3D volumes (P-P and P-S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north-south direction and 1500 ft (460 m) in the west-east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) -8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes.

RANSAC-Based Planar Point Cloud Segmentation Enhanced by Normal Vector and Maximum Principal Curvature Clustering

Abstract. Planar feature segmentation is an essential task for 3D point cloud processing, finding many applications in various fields such as robotics and computer vision. The Random Sample Consensus (RANSAC) is one of the most common algorithms for the segmentation, but its performance, as given by the original form, is usually limited due to the use of a single threshold and interruption of similar planar features presented close to each other. To address these issues, we present a novel point cloud processing workflow which aims at developing an initial segmentation stage before the basic RANSAC is performed. Initially, normal vectors and maximum principal curvatures for each point of a given point cloud are analyzed and integrated. Subsequently, a subset of normal vectors and curvature is utilized to cluster planes with similar geometry based on the region growing algorithm, serving as a coarse but fast segmentation process. The segmentation is therefore refined with the RANSAC algorithm which can be now performed with higher accuracy and speed due to the reduced interference. After the RANSAC process is applied, resultant planar point clouds are built from the sparse ones via a point aggregation process based on geometric constraints. Four datasets (three real and one simulated) were used to verify the method. Compared to the classic segmentation method, our method achieves higher accuracy, with an RMSE from fitting equal to 0.0521 m, along with a higher recall of 93.31% and a higher F1-score of 95.38%.

Supernatant fluid from endobronchial ultrasound-guided transbronchial needle aspiration for rapid next-generation sequencing

There is an increasing demand to optimize the workflow and maximize tissue available for next-generation sequencing (NGS) for non-small cell carcinoma. We looked at transbronchial needle endobronchial ultrasound-guided bronchoscopy with transbronchial needle aspiration samples and evaluated the performance of supernatant (SN) fluid processed from a dedicated aspirate collected for NGS testing. Nineteen samples were collected and processed using a new workflow. Five aspirates were collected in formalin. One additional dedicated pass was collected fresh and centrifuged. The resulting cell pellet was added to formalin for cell block (CB) processing. DNA and RNA were extracted from concentrated SN for targeted testing using the Oncomine Precision Assay (Thermo Scientific, Waltham, MA). NGS results from the corresponding CB samples were used as "controls" for comparison. Thirty-one mutations were detected in SN (Table1). The most frequently mutated genes were TP53 (35%), EGFR (23%), KRAS (13%), CTNNB1 (6%), and ERBB2 (6%). There was 100% concordance between the mutations detected in SN and corresponding CBs with comparable variant allele frequencies. Turnaround time of NGS results was 1 day for SN compared to 4-10 days for CB. We were able to demonstrate the usefulness of SN for reliable rapid molecular results. We successfully incorporated the workflow for tissue handling and processing among our clinical, cytopathology, and molecular teams. Molecular results were available at the same time as the cytologic diagnosis, allowing for timely reporting of a comprehensive diagnosis. This approach is particularly useful in patients with advanced disease requiring urgent management.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

Evaluation of the Efficiency of a Digital Workflow for Cataract Planning in Patients with Astigmatism.

To evaluate the efficiency and associated costs of a digital cataract workflow system and manual cataract workflow system for patients, with astigmatism presenting for cataract surgery in Brisbane, Australia. Sixty patients with bilateral cataract requiring toric intraocular lenses (IOL) were sequentially assigned to a manual cataract workflow (n = 30) or digital workflow (n = 30) using EQ Workplace (SW v1.7.0) running on FORUM (SW v.4.2.1.66) (Carl Zeiss Meditec, Jena, Germany). Each step of preoperative data acquisition and analysis was timed. Steps in each workflow were divided into presurgical planning time and total workflow time, the latter including the time required to input toric data into CALLISTO eye (Carl Zeiss Meditec). Secondary outcomes included staff costs within each workflow. Median presurgical planning time using a digital workflow process was 6.51 ± 0.65 minutes, and using a manual workflow process, 12.32 ± 0.56 minutes (p < 0.001). Similarly, median total workflow time using a digital workflow process was 6.93 ± 0.57 minutes and using a manual workflow process, 13.49 ± 0.47 minutes (p < 0.001). Evaluating the staff remuneration during presurgical planning and the operating costs associated with running EQ Workplace, there was a cost-reduction of 35% per patient when using the digital cataract workflow process. Using a digital cataract workflow process is more efficient and provides staff cost-savings compared to a manual workflow process when planning for toric IOL implantation.

LC-MS/MS software data processing and review workflow improvements for clinical research

LC-MS/MS software data processing and review workflow improvements for clinical research

Orbital Prosthesis Rehabilitation in Biomedical Engineering by Means of Computer Vision-Photogrammetry and 3D Prototyping

Eye loss may be caused as a result of eye trauma, accidents, or malignant tumors, which leads the patient to undergo surgery to remove the damaged parts. This research examines the potential of computer vision represented by Structure from Motion (SfM) photogrammetry in fabricating the orbital prosthesis as a noninvasive and low-cost technique. A low-cost camera was used to collect the data towards extracting the dense 3D data of the patient facial features following Structure from Motion-Multi View Stereo (SfM-MVS) algorithms. To restore the defective orbital, a Reverse Engineering (RE) based approach has been applied using the similarity RE algorithms based on the opposite healthy eye to rehabilitate the defected orbital precisely. Following quality assurance and best-fitting statistical analysis, the digital model of the restored eye was converted into a physical model using 3D prototyping. This is later used to fabricate the mold for casting medical-grade silicone to obtain the final orbital prosthesis. The results show the power of SfM photogrammetry by offering a high-accuracy model of 0.048 mm and 0.186 mm relative errors acquired in the horizontal and vertical directions, respectively. These results boost the RE implementation in medicine to reconstruct the patient's damaged eye by mirroring the image of the healthy eye using RE algorithms. Therefore, the margin matching results claim perfect data capture settings and successful data processing workflow as designed in the first place. Consequently, one can claim this approach effectively rehabilitates maxillofacial deformities as an alternative to invasive restoration approaches. The presented approach provided a low-cost and safe workflow that avoids the patient the risks of exposure to harmful rays or magnetic fields available in other sensors.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2 library: Sishen formula as a case

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.