CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Automating the Illumina DNA library preparation kit for whole genome sequencing applications on the flowbot ONE liquid handler robot.

Whole-genome sequencing (WGS) is currently making its transition from research tool into routine (clinical) diagnostic practice. The workflow for WGS includes the highly labor-intensive library preparations (LP), one of the most critical steps in the WGS procedure. Here, we describe the automation of the LP on the flowbot ONE robot to minimize the risk of human error and reduce hands-on time (HOT). For this, the robot was equipped, programmed, and optimized to perform the Illumina DNA Prep automatically. Results obtained from 16 LP that were performed both manually and automatically showed comparable library DNA yields (median of 1.5-fold difference), similar assembly quality values, and 100% concordance on the final core genome multilocus sequence typing results. In addition, reproducibility of results was confirmed by re-processing eight of the 16 LPs using the automated workflow. With the automated workflow, the HOT was reduced to 25 min compared to the 125 min needed when performing eight LPs using the manual workflow. The turn-around time was 170 and 200 min for the automated and manual workflow, respectively. In summary, the automated workflow on the flowbot ONE generates consistent results in terms of reliability and reproducibility, while significantly reducing HOT as compared to manual LP.

Slimmable transformer with hybrid axial-attention for medical image segmentation.

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better; the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning.

Effect of different fabrication workflows on the passive fit of screw-retained bar splinting two interforaminal implants: a parallel blinded randomised clinical trial.

BACKGROUND: To clinically compare the effect of the conventional and the digital workflows on the passive fit of a screw retained bar splinting two inter-foraminal implants. METHODS: The current study was designed to be a parallel triple blinded randomised clinical trial. Thirty six completely edentulous patients were selected and simply randomized into two groups; conventional group (CG) and digital group (DG). The participants, investigator and outcome assessor were blinded. In the group (CG), the bar was constructed following a conventional workflow in which an open top splinted impression and a lost wax casting technology were used. However, in group (DG), a digital workflow including a digital impression and a digital bar milling technology was adopted. Passive fit of each bar was then evaluated clinically by applying the screw resistance test using the "flag" technique in the passive and non passive situations. The screw resistance test parameter was also calculated. Unpaired t-test was used for intergroup comparison. P-value < 0.05 was the statistical significance level. The study protocol was reviewed by the Research Ethics Committee in the author's university (Rec IM051811). Registration of the clinical trial was made on clinical trials.gov ID NCT05770011. An informed consent was obtained from all participants. RESULTS: Non statistically significant difference was denoted between both groups in all situations. In the passive situation, the mean ± standard deviation values were 1789.8° ± 20.7 and1786.1° ± 30.7 for the groups (CG) and (DG) respectively. In the non passive situation, they were 1572.8° ± 54.2 and 1609.2° ± 96.9. Regarding the screw resistance test parameter, they were 217° ± 55.3 and 176° ± 98.8. CONCLUSION: Conventional and digital fabrication workflows had clinically comparable effect on the passive fit of screw retained bar attachments supported by two dental implants.

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.

3D printing vs traditional workflow for the fabrication of mandibular implant overdentures: study protocol for a mixed-methods cross-over RCT.

BACKGROUND: Complete tooth loss is a significant global oral health issue, particularly impacting older individuals with lower socioeconomic status. Computer-assisted technologies enhance oral healthcare access by the elderly. Despite promising in vitro reports on digital denture materials, evidence from randomized clinical trials (RCTs) is lacking to verify their performance. This cross-over RCT will investigate whether 3D-printed implant-retained mandibular overdentures (IMO) are more satisfactory for edentulous seniors than those made through traditional methods. METHODS/DESIGN: We will recruit 26 completely edentulous participants (any sex/gender) based on the following eligibility criteria: age ≥ 60 years, no tooth extraction in the past 12 months, two implants in the lower jaw, and need for new dentures in both jaws. Each participant will receive two denture pairs, either manufactured by 3D printing or traditionally, to be worn in a random order. A timeline of 3 months with each denture pair will be considered for outcome assessment (total: 6 months). Patient satisfaction with dentures will be measured by the McGill Denture Satisfaction Questionnaire. We will evaluate other patient-reported outcomes (including oral health-related quality of life) as well as clinician-assessed quality and cost. At the end of the trial, participants will choose which denture pair they wish to keep and interviewed about their experiences with the 3D-printed IMO. The quantitative and qualitative data will be incorporated through an explanatory mixed-methods strategy. A final quantitative assessment will happen after 12 months with the preferred IMO to assess the long-term performance and maintenance needs. DISCUSSION: This mixed-methods RCT will explore patient experiences with 3D-printed IMOs, aiming to assess the potential for altering clinical practice and dental public health policies. Our results will inform policies by showing whether 3D printing offers comparable outcomes at lower costs, facilitating greater access to oral care for the elderly. TRIAL REGISTRATION: ClinicalTrials.gov, NCT06155630, Registered on 04 December 2023. https://classic. CLINICALTRIALS: gov/ct2/show/NCT06155630.

Global O-glycoproteome enrichment and analysis enabled by a combinatorial enzymatic workflow.

A comprehensive analysis of site-specific protein O-glycosylation is hindered by the absence of a consensus O-glycosylation motif, the diversity of O-glycan structures, and the lack of a universal enzyme that cleaves attached O-glycans. Here, we report the development of a robust O-glycoproteomic workflow for analyzing complex biological samples by combining four different strategies: removal of N-glycans, complementary digestion using O-glycoprotease (IMPa) with/without another protease, glycopeptide enrichment, and mass spectrometry with fragmentation of glycopeptides using stepped collision energy. Using this workflow, we cataloged 474 O-glycopeptides on 189 O-glycosites derived from 79 O-glycoproteins from human plasma. These data revealed O-glycosylation of several abundant proteins that have not been previously reported. Because many of the proteins that contained unannotated O-glycosylation sites have been extensively studied, we wished to confirm glycosylation at these sites in a targeted fashion. Thus, we analyzed selected purified proteins (kininogen-1, fetuin-A, fibrinogen, apolipoprotein E, and plasminogen) in independent experiments and validated the previously unknown O-glycosites.

In-Depth Retinal Sensitivity Assessment With the MP3 Type S Microperimeter: A Methods Study.

Purpose: Retinal sensitivity is frequently listed as an end point in clinical trials, often with long working practices. The purpose of this methods study was to provide a new workflow and reduced test time for in-depth characterization of retinal sensitivity. Methods: A workflow for the MP3-S microperimeter with detailed functional characterization of the retina under photopic, mesopic, and scotopic conditions was evaluated. Grids of 32 and 28 test positions for photopic/mesopic and scotopic, respectively, were tested in 12 healthy individuals and compared with an established 68-point grid for test time, mean sensitivity (MS), and bivariate contour ellipse area (BCEA). Results: The mean test time (range; ±SD) was 10.5 minutes (8.4-14.9; ±2.0) in the 68-point grid and 4.3 minutes (3.8-5.0; ±0.4) in the 32-point grid, which was significantly different (P < 0.0001). The mean of difference in test time (±SD; 95% confidence interval) was 6.1 minutes (±2.0; 4.6-7.6). MS and BCEA were significantly correlated between grids (r = 0.89 and 0.74; P = 0.0005 and 0.014, respectively). Mean test time of subjects who underwent the full protocol (n = 4) was 2.15 hours. Conclusions: The protocol suggested herein appears highly feasible with in-depth characterization of retinal function under different testing conditions and in a short test time. Translational Relevance: The protocol described herein allows for characterization of the retina under different testing conditions and in a short test time, which is relevant due to its potential for patient prognostication and follow-up in clinical settings and also given its increasing role as a clinical trial end point.

Standardised Versioning of Datasets: a FAIR-compliant Proposal.

This paper presents a standardised dataset versioning framework for improved reusability, recognition and data version tracking, facilitating comparisons and informed decision-making for data usability and workflow integration. The framework adopts a software engineering-like data versioning nomenclature ("major.minor.patch") and incorporates data schema principles to promote reproducibility and collaboration. To quantify changes in statistical properties over time, the concept of data drift metrics (d) is introduced. Three metrics (dP, dE,PCA, and dE,AE) based on unsupervised Machine Learning techniques (Principal Component Analysis and Autoencoders) are evaluated for dataset creation, update, and deletion. The optimal choice is the dE,PCA metric, combining PCA models with splines. It exhibits efficient computational time, with values below 50 for new dataset batches and values consistent with seasonal or trend variations. Major updates (i.e., values of 100) occur when scaling transformations are applied to over 30% of variables while efficiently handling information loss, yielding values close to 0. This metric achieved a favourable trade-off between interpretability, robustness against information loss, and computation time.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Sample Preparation and Phosphopeptide Enrichment for Plant Phosphoproteomics via Label-Free Mass Spectrometry.

Phosphopeptide enrichment is the main bottleneck of every phosphorylation study. Therefore, in this chapter, a general workflow tries to overbridge the hurdles of plant sample handling from sample collection to protein extraction, protein solubilization, enzymatic digestion, and enrichment step prior to mass spectrometry. The workflow provides information to perform global proteomics as well as phosphoproteomics enabling the researcher to use the protocol in both fields.

Serum micro-RNAs with mutation-targeted RNA modification: a potent cancer detection tool constructed using an optimized machine learning workflow.

RNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool.

An On-Farm Workflow for Predictive Management of Paralytic Shellfish Toxin-Producing Harmful Algal Blooms for the Aquaculture Industry.

Paralytic shellfish toxins (PSTs) produced by marine dinoflagellates significantly impact shellfish industries worldwide. Early detection on-farm and with minimal training would allow additional time for management decisions to minimize economic losses. Here, we describe and test a standardized workflow based on the detection of sxtA4, an initial gene in the biosynthesis of PSTs. The workflow is simple and inexpensive and does not require a specialized laboratory. It consists of (1) water collection and filtration using a custom gravity sampler, (2) buffer selection for sample preservation and cell lysis for DNA, and (3) an assay based on a region of sxtA, DinoDtec lyophilized quantitative polymerase chain reaction (qPCR) assay. Water samples spiked with Alexandrium catenella showed a cell recovery of >90% when compared to light microscopy counts. The performance of the lysis method (90.3% efficient), Longmire's buffer, and the DinoDtec qPCR assay (tested across a range of Alexandrium species (90.7-106.9% efficiency; r2 > 0.99)) was found to be specific, sensitive, and efficient. We tested the application of this workflow weekly from May 2016 to 30th October 2017 to compare the relationship between sxtA4 copies L-1 in seawater and PSTs in mussel tissue (Mytilus galloprovincialis) on-farm and spatially (across multiple sites), effectively demonstrating an ∼2 week early warning of two A. catenella HABs (r = 0.95). Our tool provides an early, accurate, and efficient method for the identification of PST risk in shellfish aquaculture.

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

An Optimized Genotyping Workflow for Identifying Highly SCRaMbLEd Synthetic Yeasts.

Synthetic Sc2.0 yeast strains contain hundreds to thousands of loxPsym recombination sites that allow restructuring of the Saccharomyces cerevisiae genome by SCRaMbLE. Thus, a highly diverse yeast population can arise from a single genotype. The selection of genetically diverse candidates with rearranged synthetic chromosomes for downstream analysis requires an efficient and straightforward workflow. Here we present loxTags, a set of qPCR primers for genotyping across loxPsym sites to detect not only deletions but also inversions and translocations after SCRaMbLE. To cope with the large number of amplicons, we generated qTagGer, a qPCR genotyping primer prediction tool. Using loxTag-based genotyping and long-read sequencing, we show that light-inducible Cre recombinase L-SCRaMbLE can efficiently generate diverse recombination events when applied to Sc2.0 strains containing a linear or a circular version of synthetic chromosome III.

Workflow interruptions in an era of instant messaging: A detailed analysis.

INTRODUCTION: The complex practice environment and responsibilities incumbent on diagnostic radiologists creates a workflow susceptible to disruption. While interruptions have been shown to contribute to medical errors in the healthcare delivery environment, the exact impact on highly subspecialized services such as diagnostic radiology is less certain. One potential source of workflow disruption is the use of a departmental instant messaging system (Webex), to facilitate communications between radiology faculty, residents, fellows, and technologists. A retrospective review was conducted to quantify the frequency of interruption experienced by our neuroradiology fellows. MATERIALS AND METHODS: Data logs were gathered comprising all instant messages sent and received within the designated group chats from July 5-December 31, 2021, during weekday shifts staffed by neuroradiology fellows. Interruptions per shift were calculated based on month, week, and day of the week. RESULTS: 14,424 messages were sent across 289 total shifts. The 6 fellows assigned to the main neuroradiology reading room sent 3258 messages and received 10,260 messages from technologists and other staff. There was an average of 50 interruptions per shift when examined by month (range 48-53), and 52 interruptions per shift when examined by day of the week (range 40-60). CONCLUSION: Neuroradiology fellows experience frequent interruptions from the departmental instant messaging system. These disruptions, when considered in conjunction with other non-interpretative tasks, may have negative implications for workflow efficiency, requiring iterative process improvements when incorporating new technology into the practice environment of diagnostic radiology.

A single workflow for multi-species blood transcriptomics.

BACKGROUND: Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. RESULTS: Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. CONCLUSIONS: Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Trueness of Crown Preparation Dies in Dental Models: An In Vitro Assessment of Digital and Analog Workflows.

PURPOSE: To assess crown die trueness using additive manufacturing (AM) based on intraoral scanning (IOS) data and compare it with stone models. MATERIALS AND METHODS: Crown dies with four finish line types- equigingival shoulder (SAE), subgingival shoulder (SAS), equigingival chamfer (CAE), and subgingival chamfer (CAS)-were incorporated into a reference model and scanned with a coordinate measurement machine (CMM; n = 1 scan). Trios4 (3Shape) scans generated a second reference dataset (IOS; n = 10 scans). Using scans, crown dies were produced with two different 3D printers (MAX UV385 [Asiga] and NextDent 5100 [3DSystems]; n = 10 per system). Stone dies were created from conventional impressions (n = 10). Specimens were digitized with a laboratory scanner (E4, 3Shape). Trueness was evaluated with Geomagic Control X (3DSystems). Data analysis was done using Shapiro-Wilk, Levene, ANOVA, and t tests (α < .05). RESULTS: All crown dies fell within the clinically acceptable trueness range (150 µm). IOS exhibited significantly lower (P < .05; Δ ≤ 21.7 µm) or similar trueness compared to stone models. Asiga dies demonstrated similar and NextDent significantly lower marginal trueness than IOS (P < .05; Δ ≤ 57.3 µm). Most AM margin areas had significantly lower trueness than stone (P < .001; Δ ≤ 57.2 µm). Asiga outperformed NextDent (P < .001). Shoulder trueness surpassed chamfer in optical scans (P = .01). Finish line design and gingiva location did not have a significant impact on AM and stone models (P > .05). CONCLUSIONS: Combining IOS and AM achieves clinically acceptable crown die trueness for single molar teeth. The choice of AM device is critical, with Asiga outperforming NextDent. Finish-line design has an impact on optical scans. Finish-line design and marginal gingiva location have little effect on AM trueness.