Custom Workflows Research Articles

Building an End-to-End Hiring Process in Salesforce: Automating Recruitment with Custom Objects, Approval Processes, and Lightning Components

The process of hiring employees involves numerous steps that can be complex and time-consuming for HR teams. In this paper, we explore how Salesforce can be customized to streamline the hiring process end to end, from the moment a candidate submits an application to the final approval and onboarding. Using custom objects, automation, and integrations within Salesforce, we will develop an efficient system to track interview stages, manage interviewer feedback, and automate approval processes, resulting in a smoother, more organized hiring process. The paper includes key custom fields, workflow automation, and Apex code examples to showcase the technical implementation. Keywords Salesforce, Hiring Process, Automation, Interview Stages, Custom Objects, Workflow, Process Builder, Apex, HR Solutions, Recruiter

NEURD offers automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

StreamSAXS: a Python-based workflow platform for processing streaming SAXS/WAXS data.

StreamSAXS is a Python-based small- and wide-angle X-ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user-friendly tool for analysis of both batch data files and real-time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug-in framework, which enables developers to extend the built-in workflow tasks. Another feature is the support for both already acquired and real-time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large-scale acquisition systems for end-to-end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios.

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

A reliable knowledge processing framework for combustion science using foundation models

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

An untargeted analytical workflow based on Kendrick mass defect filtering reveals dysregulations in acylcarnitines in prostate cancer tissue

BackgroundMetabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and β-oxidation of fatty acids. ResultsIn the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square–discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites. SignificanceHydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.

First report of strawberry polerovirus 1 in strawberry in Italy.

Strawberry (Fragaria × ananassa Duch.) was introduced in Sicily (Italy) in the 1930s in the small town of Maletto, on the slopes of Etna volcan, where it's currently cultivated in a total area of 30 ha. The French cv. 'Madame Moutot', appreciated for its unique flavor and intense fragrance, was there propagated vegetatively and after decades, the distinctive 'Etna ecotype' originated by adaptation to the peculiar environmental conditions of the area (Milella et al., 2006). In May 2023, in a 0.5 ha "Etna ecotype" strawberry field, virus-like symptoms were observed in approximately 50% of the plants. Symptoms included severe dwarfing, leaf cupping and chlorotic spotting which lead to decline of infected plants. To investigate the etiology of the disease, leaf samples were collected from eight symptomatic plants for analysis by High-Throughput Sequencing (HTS). To this aim, total RNAs were extracted by using the RNeasy PowerPlant Kit (Qiagen, Germany). The RNAs were pooled, depleted of ribosomal RNA (QIAseq FastSelect; Qiagen), and a library was prepared according to the Illumina DNA Prep Kit. Sequencing on a NextSeq2000 instrument at Leibniz Institute DSMZ (Braunschweig, Germany) generated 31,149,784 of paired-end reads (150 nt), which were further analyzed in Geneious Prime version 2023.2 (Biomatters) using a custom workflow for virus discovery and genome assembly. Analysis of the assembled contigs by local BLASTn and BLASTp alignments against a custom plant virus database of NCBI nuclear-core (NC) reference sequences assigned a number of contigs to accession NC_025435, strawberry polerovirus 1 (SPV-1). Reconstruction of the virus genome by assembly of contigs and reads alignment resulted in a nearly complete genome sequence of SPV-1 (GenBank Acc. No. OR989958) showing by BLASTn 98.69% identity to the SPV-1 NC reference sequence, and 98.99 % identity with an isolate from the Czech Republic (GenBank Acc. OL421571). To confirm the presence of SPV-1 in each sample, RT-PCR using specific primers designed in this study SPV-1-CP-1F (5'-TCGAGATACGTCTAGAACTGCAA-3') and SPV-1-CP-1R (5'-GAGAGGCCCCTTCTACCTATTTG-3') targeting the entire 623 bp coat protein (CP) gene was performed. Amplicons of the expected size were obtained in five samples and Sanger-sequenced. The resulting sequences shared 99.85% - 100% of identity to the HTS - derived sequence (GenBank Acc. No. OR989958) through BLASTn analysis. Strawberry mottle virus (SMoV), strawberry mild yellow edge virus (SMYEV) and strawberry crinkle virus (SCV) were detected in the same library in addition to SPV-1 and then confirmed by RT-PCR using specific primers (Martin & Tzanetakis 2013). Strawberry polerovirus 1, related to the genus Polerovirus in the family Solemoviridae, was first reported in strawberries in Canada (Xiang et al. 2015) and was thereafter detected in the United States (Thekke-Veetil & Tzanetakis 2016), Argentina (Luciani et al. 2016), and Nepal (Kuwak et al. 2022). To date, the virus has been reported in Europe only in the Czech Republic (Franova et al. 2021). To our knowledge, this is the first report of SPV-1 in strawberry plants in Italy. Although the correlation between SPV-1 and strawberry decline (SD) is still uncertain (Xiang et al. 2015) transmission of the virus via aphids has recently been demonstrated (Franova et al. 2021). Our report let to hypothesize that its dissemination in Europe can be considered as increasing.

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data.

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts.

Cell shape is a powerful readout of cell state, fate and function. We describe a custom workflow to perform semi-automated, 3D cell and nucleus segmentation, and spherical harmonics and principal components analysis to distill cell and nuclear shape variation into discrete biologically meaningful parameters. We apply these methods to analyze shape in the neuromast cells of the zebrafish lateral line system, finding that shapes vary with cell location and identity. The distinction between hair cells and support cells accounted for much of the variation, which allowed us to train classifiers to predict cell identity from shape features. Using transgenic markers for support cell subpopulations, we found that subtypes had different shapes from each other. To investigate how loss of a neuromast cell type altered cell shape distributions, we examined atoh1a mutants that lack hair cells. We found that mutant neuromasts lacked the cell shape phenotype associated with hair cells, but did not exhibit a mutant-specific cell shape. Our results demonstrate the utility of using 3D cell shape features to characterize, compare and classify cells in a living developing organism.

KServe inference extension for an FPGA vendor-free ecosystem

KServe inference extension for an FPGA vendor-free ecosystem

Custom Workflow for the Confident Identification of Sulfotyrosine-Containing Peptides and Their Discrimination from Phosphopeptides.

Protein tyrosine sulfation (sY) is a post-translational modification (PTM) catalyzed by Golgi-resident tyrosyl protein sulfo transferases (TPSTs). Information on sY in humans is currently limited to ∼50 proteins, with only a handful having verified sites of sulfation. As such, the contribution of sulfation to the regulation of biological processes remains poorly defined. Mass spectrometry (MS)-based proteomics is the method of choice for PTM analysis but has yet to be applied for systematic investigation of the "sulfome", primarily due to issues associated with discrimination of sY-containing from phosphotyrosine (pY)-containing peptides. In this study, we developed an MS-based workflow for sY-peptide characterization, incorporating optimized Zr4+ immobilized metal-ion affinity chromatography (IMAC) and TiO2 enrichment strategies. Extensive characterization of a panel of sY- and pY-peptides using an array of fragmentation regimes (CID, HCD, EThcD, ETciD, UVPD) highlighted differences in the generation of site-determining product ions and allowed us to develop a strategy for differentiating sulfated peptides from nominally isobaric phosphopeptides based on low collision energy-induced neutral loss. Application of our "sulfomics" workflow to a HEK-293 cell extracellular secretome facilitated identification of 21 new sulfotyrosine-containing proteins, several of which we validate enzymatically, and reveals new interplay between enzymes relevant to both protein and glycan sulfation.

B-020 Fully Automated and Scalable cfDNA Extraction Solution from up to 10 mL Samples

Abstract Background The use of circulating, cell-free DNA (cfDNA) is rapidly evolving in the clinical landscape, becoming one of the fastest growing areas in recent years. It offers tremendous clinical potential as a screening method for tumor, cancer, disease progression, treatment response, and more. Circulating DNA is found in low quantities and often requires large sample input volumes to isolate the less abundant cfDNA to sufficient amounts needed for downstream analysis. The existing methods for extraction are often silica spin column-based, which involve an extensive amount of hands-on work, and they require large volumes of binding buffers, making it unsuitable for extractions involving sample volumes as large as 10 mL. These existing workflows impose certain limitations in terms of the sample volume and number of samples that they can process. Methods To tackle this necessity, Omega Bio-tek has developed a fully automated solution using Mag-Bind® cfDNA Kit (M3298) to extract cfDNA from up to 10 mL volumes from 48 samples in 2.75 h when integrated on Hamilton’s Microlab® STAR™ platform. Results The system is scalable and can extract from sample volumes ranging from 1–10 mL without any hardware modification or additional, expensive accessories on the Hamilton workstation. Here, we elucidate the workings of this automated protocol using Omega Bio-tek’s Mag-Bind® cfDNA Kit and compare its performance to a manual extraction protocol from 4 mL of plasma processed using the same kit. We also present additional solutions using the Mag-Bind® cfDNA kit to showcase extraction of fragments as small as 50 bp with minimal genomic DNA contamination. Conclusion Omega Bio-tek’s custom workflows provides the customers with different protocols to fit their different application needs. Thus, this kit offers researchers and scientists the flexibility to use the same kit for not only large volume sample processing but also for recovering short cfDNA fragments.

Intranasal insulin treatment partially corrects the altered gene expression profile in the hippocampus of developing rats with perinatal iron deficiency.

Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. To objective was to assess the effects of intranasal insulin administration intranasal insulin administration on the hippocampal transcriptome in a developing rat model of perinatal FeD. Perinatal FeD was induced using low-iron diet from gestational day 3 until postnatal day (P) 7, followed by an iron sufficient (FeS) diet through P21. Intranasal insulin was administered at a dose of 0.3 IU twice daily from P8 to P21. Hippocampi were removed on P21 from FeS control, FeD control, FeS insulin, and FeD insulin groups. Total RNA was isolated and profiled using next-generation sequencing. Gene expression profiles were characterized using custom workflows and expression patterns examined using ingenuity pathways analysis (n = 7-9 per group). Select RNAseq results were confirmed via qPCR. Transcriptomic profiling revealed that mitochondrial biogenesis and flux, oxidative phosphorylation, quantity of neurons, CREB signaling in neurons, and RICTOR-based mTOR signaling were disrupted with FeD and positively affected by intranasal insulin treatment with the most benefit observed in the FeD insulin group. Both perinatal FeD and intranasal insulin administration altered gene expression profile in the developing hippocampus. Intranasal insulin treatment reversed the adverse effects of FeD on many molecular pathways and could be explored as an adjunct therapy in perinatal FeD.

ModEx: a general purpose computer model exploration system.

We present a general purpose visual analysis system that can be used for exploring parameters of a variety of computer models. Our proposed system offers key components of a visual parameter analysis framework including parameter sampling, deriving output summaries, and an exploration interface. It also provides an API for rapid development of parameter space exploration solutions as well as the flexibility to support custom workflows for different application domains. We evaluate the effectiveness of our system by demonstrating it in three domains: data mining, machine learning and specific application in bioinformatics.

G:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

Morphoscanner2.0: A new python module for analysis of molecular dynamics simulations.

Molecular dynamics simulations, at different scales, have been exploited for investigating complex mechanisms ruling biologically inspired systems. Nonetheless, with recent advances and unprecedented achievements, the analysis of molecular dynamics simulations requires customized workflows. In 2018, we developed Morphoscanner to retrieve structural relations within self-assembling peptide systems. In particular, we conceived Morphoscanner for tracking the emergence of β-structured domains in self-assembling peptide systems. Here, we introduce Morphoscanner2.0. Morphoscanner2.0 is an object-oriented library for structural and temporal analysis of atomistic and coarse-grained molecular dynamics (CG-MD) simulations written in Python. The library leverages MDAnalysis, PyTorch and NetworkX to perform the pattern recognition of secondary structure patterns, and interfaces with Pandas, Numpy and Matplotlib to make the results accessible to the user. We used Morphoscanner2.0 on both simulation trajectories and protein structures. Because of its dependencies on the MDAnalysis package, Morphoscanner2.0 can read several file formats generated by widely-used molecular simulation packages such as NAMD, Gromacs, OpenMM. Morphoscanner2.0 also includes a routine for tracking the alpha-helix domain formation.

NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows.

Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.

QuNex-An integrative platform for reproducible neuroimaging analytics.

Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

Improving Communication of Peer-Review Conference Outcomes: A Practical Experience

The aim of this work was to describe the design and implementation of a more robust workflow for communicating outcomes from a peer-review chart rounds conference. We also provide information regarding cycle times, plan revisions, and other key metrics that we have observed since initial implementation. A multidisciplinary team of stakeholders including physicians, physicists, and dosimetrists developed a revised peer-review workflow that addressed key needs to improve the prior process. Consensus terminology was developed to reduce ambiguity regarding the priority of peer-review outcomes and to clarify expectations of the treating physician in response to peer-review outcomes. A custom workflow software tool was developed to facilitate both upstream and downstream processes from the chart rounds conference. The peer-review outcomes of the chart rounds conference and resulting plan changes for the first 18 months of implementation were summarized. In the first 18 months after implementation of the revised processes, 2294 plans were reviewed, and feedback priority levels assigned. Across all cases with feedback, the median time for the treating attending physician to acknowledge conference comments was 1 day and was within 7 calendar days for 89.1% of cases. Conference feedback was acknowledged within 1 day for 74 of 115 (64.3%) cases with level 2 comments and for 18 of 21 (85.7%) cases with level 3 comments (P=.054). Contours were modified in 13 of 116 (11%) cases receiving level 2 feedback and 10 of 21 (48%) cases receiving level 3 feedback (P < .001). The treatment plan was revised in 18 of 116 (16%) cases receiving level 2 feedback and 13 of 21 (61%) cases receiving level 3 feedback (P < .001). We successfully implemented a workflow to improve upstream and downstream processes for a chart rounds conference. Standardizing how peer-review outcomes were communicated and recording physician responses allow for improved ability to monitor conference activities.