Scanning Approach Research Articles

Abstract 2720: Engineering sweeping antibodies for the clearance of tumor-derived, pro-tumorigenic cytokines

Abstract Soluble, tumor-derived, pro-tumorigenic (STP) cytokines have been shown to contribute to cancer cell proliferation and drug resistance. But therapeutic targeting of STP cytokines by monoclonal antibodies is limited by high concentrations and persistent production of antigen. We hypothesize that the ability of sweeping antibodies (swAbs) to actively clear soluble antigens will overcome these limitations. SwAbs differ from conventional antibodies via (1) pH-dependent binding of antigen, and (2) increased binding to the neonatal Fc receptor (FcRn). SwAbs facilitate clearance of soluble antigens by releasing antigen in the low pH environment of FcRn-expressing cells. SwAbs are then recycled via FcRn for repeated clearance of antigen.We are developing swAbs against four STP cytokines contributing to cancer cell proliferation, survival, migration or intra-tumoral angiogenesis: hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), transforming growth factor-β (TGF-β) and interleukin 16 (IL-16). We selected four existing neutralizing antibodies against these cytokines with available crystal structures and generated a second antibody targeting interleukin 16 with high affinity (clone 3B6; KD = 13 nM) de novo using phage display. We are using bio-layer interferometry and custom recycling/clearance assays for antibody characterization and pre-clinical validation. To introduce pH dependency into the four antibodies with crystal structures, we performed targeted histidine mutagenesis of residues at the interface with the respective target antigens. For the de novo-generated interleukin-16 antibody, we performed an unbiased histidine scanning approach by mutating residues across all six complementarity-determining regions (CDRs), which resulted in slight increases in pH sensitivity. We then performed a second round of mutagenesis of the most pH-sensitive 3B6 variants by replacing additional residues considered developability liabilities with histidine residues. We found that this approach significantly increased expression and affinity at pH 7 of the resulting 3B6 variants but only modestly increased pH sensitivity. In contrast, replacing a single residue in the heavy CDR1 in clone 14.1, a pre-clinical anti-IL-16 antibody, resulted in substantially increased pH sensitivity (KD(pH=7) = 2 nM; KD(pH<6) = n.b.). We are currently performing pH-sensitivity screenings for the three additional STP-specific antibodies. Our findings indicate that the ability to introduce pH sensitivity is largely dependent on the parental antibody and that some antibodies may not be amenable to this process. In addition, we show that histidine mutagenesis can be an effective approach to enhance expressibility and binding, while also increasing pH sensitivity. This project will answer the question whether anti-STP cytokine swAbs are an effective approach to treat patients with cancer. Citation Format: Jillian M. Baker, Nevil J. Singh, Tim Luetkens. Engineering sweeping antibodies for the clearance of tumor-derived, pro-tumorigenic cytokines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2720.

Contributions of Hyperactive Mutations in Mpro from SARS-CoV-2 to Drug Resistance.

The appearance and spread of mutations that cause drug resistance in rapidly evolving diseases, including infections by the SARS-CoV-2 virus, are major concerns for human health. Many drugs target enzymes, and resistance-conferring mutations impact inhibitor binding or enzyme activity. Nirmatrelvir, the most widely used inhibitor currently used to treat SARS-CoV-2 infections, targets the main protease (Mpro) preventing it from processing the viral polyprotein into active subunits. Our previous work systematically analyzed resistance mutations in Mpro that reduce binding to inhibitors; here, we investigate mutations that affect enzyme function. Hyperactive mutations that increase Mpro activity can contribute to drug resistance but have not been thoroughly studied. To explore how hyperactive mutations contribute to resistance, we comprehensively assessed how all possible individual mutations in Mpro affect enzyme function using a mutational scanning approach with a fluorescence resonance energy transfer (FRET)-based yeast readout. We identified hundreds of mutations that significantly increased the Mpro activity. Hyperactive mutations occurred both proximal and distal to the active site, consistent with protein stability and/or dynamics impacting activity. Hyperactive mutations were observed 3 times more than mutations which reduced apparent binding to nirmatrelvir in recent studies of laboratory-grown viruses selected for drug resistance. Hyperactive mutations were also about three times more prevalent than nirmatrelvir binding mutations in sequenced isolates from circulating SARS-CoV-2. Our findings indicate that hyperactive mutations are likely to contribute to the natural evolution of drug resistance in Mpro and provide a comprehensive list for future surveillance efforts.

A Vulnerability Scanning Method for Web Services in Embedded Firmware

As the Internet of Things (IoT) era arrives, the proliferation of IoT devices exposed to the Internet presents a significant challenge to device security. Firmware is software that operates within Internet of Things (IoT) devices, directly governing their behaviors and functionalities. Consequently, the security of firmware is critical to shielding IoT devices from potential threats. In order to enable users to operate a device intuitively, firmware commonly provides a web interface. Consequently, this interface frequently serves as the primary attack goal in Internet of Things (IoT) devices, rendering them susceptible to numerous cyber-attacks. Unfortunately, web services have complex data interactions and implicit dependencies, and it is not easy to balance efficiency and accuracy during the analysis process, leading to heavy overhead. This paper proposes a lightweight vulnerability scanning approach, WFinder, designed explicitly for embedded firmware web services to perform vulnerability checks on backend binary files in firmware. WFinder uses static analysis to focus on identifying vulnerabilities in boundary binary files related to web services in firmware. Initially, the approach identifies boundary binary files and external data entry points based on front-end and back-end associativity features. Subsequently, rules are formulated to filter hazardous functions to narrow the analysis targets. Finally, the method generates sensitive call paths from the external data input points to the hazardous functions and conducts a lightweight taint analysis along these paths to uncover potential vulnerabilities. We implemented a prototype of WFinder and evaluated it on the firmware of ten devices from five well-known manufacturers. We discovered thirteen potential vulnerabilities, eight of which were confirmed by the CNVD, and assigned them CNVD identification numbers. Compared with the most advanced tool, SATC, WFinder was more efficient at discovering more bugs on the test set. These results indicate that WFinder is effective at detecting bugs in embedded web services.

Fully automated structured light scanning for high-fidelity 3D reconstruction via graph optimization.

Convenient and high-fidelity 3D model reconstruction is crucial for industries like manufacturing, medicine and archaeology. Current scanning approaches struggle with high manual costs and the accumulation of errors in large-scale modeling. This paper is dedicated to achieving industrial-grade seamless and high-fidelity 3D reconstruction with minimal manual intervention. The innovative method proposed transforms the multi-frame registration into a graph optimization problem, addressing the issue of error accumulation encountered in frame-by-frame registration. Initially, a global consistency cost is established based on point cloud cross-multipath registration, followed by using the geometric and color differences of corresponding points as dynamic nonlinear weights. Finally, the iteratively reweighted least squares (IRLS) method is adopted to perform the bundle adjustment (BA) optimization of all poses. Significantly enhances registration accuracy and robustness under the premise of maintaining near real-time efficiency. Additionally, for generating watertight, seamless surface models, a local-to-global transitioning strategy for multiframe fusion is introduced. This method facilitates efficient correction of normal vector consistency, addressing mesh discontinuities in surface reconstruction resulting from normal flips. To validate our algorithm, we designed a 3D reconstruction platform enabling spatial viewpoint transformations. We collected extensive real and simulated model data. These datasets were rigorously evaluated against advanced methods, roving the effectiveness of our approach. Our data and implementation is made available on GitHub for community development.

Why Violence Cannot be Prevented in Healthcare Settings in Türkiye?: A Retrospective Policy Analysis.

Preventive policies have been devised with the aim of curbing health-related violence, and their efficacy is believed to furnish evidence for their continued implementation, thereby enhancing their effectiveness. This study undertakes a retrospective analysis of these policies in the context of Türkiye. A comprehensive examination involved the review of 26 documents, employing a progressive scanning approach for data collection. This methodology encompassed the utilization of gray literature databases (OECD iLibrary and WHO Iris), extensive Google searches, thorough website scans, and consultations with subject-matter experts. Data analysis was meticulously conducted within the framework of the Health Policy Triangle. The findings reveal active participation from diverse stakeholders, including governmental bodies, political entities, professional organizations, and trade unions, in various preventive initiatives aimed at mitigating health-related violence. Notably, the adoption of legislation for health violence prevention, perceived as a paramount achievement, can be attributed to persistent efforts by both the media and other influential actors and stakeholders. These endeavors have sustained the topic's prominence on the policy agenda, positioning it as a promising source for the development of novel violence prevention and management strategies. This study underscores the necessity for a comprehensive investigation into the working conditions, personal rights, and wage policies of healthcare workers, in light of documented factors that frequently precipitate violence. Concomitantly, it advocates for the development of effective mechanisms to address these issues.

Evaluation of classical correlation functions from 2/3D images on CPU and GPU architectures: Introducing CorrelationFunctions.jl

Correlation functions are becoming one of the major tools for quantification of structural information that is usually represented as 2D or 3D images. In this paper we introduce ▪ open-source package developed in Julia and capable of computing all classical correlation functions based on imaging input data. Images include both binary and multi-phase representations. Our code is capable of evaluating two-point probability S2, phase cross-correlation ρij, cluster C2, lineal-path L2, surface-surface Fss, surface-void Fsv, pore-size P and chord-length p distribution functions on both CPU and GPU architectures. Where possible, we presented two types of computations: full correlation map (correlations of each point with other points on the image, that also allows obtaining ensemble averaged CF) and directional correlation functions (currently in major orthogonal and diagonal directions). Such an implementation allowed for the first time to assemble a completely free solution to evaluate correlation functions under any operating system with well documented application programming interface (API). Our package includes automatic tests against analytical solutions that are described in the paper. We measured execution times for all CPU and GPU implementations and as a rule of thumb full correlation maps on GPU are faster than other methods. However, full maps require more RAM and, thus, are limited to available RAM resources. On the other hand, directional CFs are memory efficient and can be evaluated for huge datasets – this way they are the first candidates for structural data compression of feature extraction. The package itself is available through Julia package ecosystem and on GitHub, the latter source also contains documentation and additional helpful resources such as tutorials. We believe that a single powerful computational tool such as ▪ presented in this paper will significantly facilitate the usage of correlation functions in numerous areas of structural description and research of porous materials, as well as in machine learning applications. We also present some examples as applied to ceramic, soil composite and oil-bearing rock samples based on their 3D X-ray tomography and 2D scanning electron microscope images. Finally, we conclude our paper with discussion of possible ways to further improve presented computational framework. Program summaryProgram Title: CorrelationFunctions.jlCPC Library link to program files:https://doi.org/10.17632/6gb9gfm3dw.1Developer's repository link:https://github.com/fatimp/CorrelationFunctions.jlLicensing provisions: MITProgramming language: JuliaSupplementary material: Numerous Jupiter notebooks with examples are available on the GitHub pageNature of problem: Correlation functions are invaluable universal statistical descriptors of structures used in numerous scientific fields such as astronomy, material science, rock and soil physics, hydrology and biology, to name just a handful of examples. While computational approaches are available in the literature for some functions, they are fragmented and are usually implemented in proprietary interpreted languages for CPU architecture alone.Solution method: We contribute an open source and cross-platform solution with well documented API for computation of all classical correlation functions from both 2D and 3D images on CPU and GPU architectures. The package computes correlation functions using two approaches: computation of correlation maps and computation along predefined directions. These two approaches can be thought of as an execution time - memory trade-off, but the choice may also depend on the application. The computations are based on a) fast Fourier transform with preprocessing steps such as cluster labeling or edge detection, and b) linear scan approach to evaluate correlation functions along predefined directions. Where justified, the algorithms can be executed on both CPU and GPU which results in high execution speed on modern hardware.

Model scan of factors in U.K. stock returns

We use the Bayesian model scan approach of Chib, S., X. Zeng, and L. Zhao. 2020. ‘On Comparing Asset Pricing Models.’ The Journal of Finance 75 (1): 551–577. https://doi.org/10.1111/jofi.12854, and Chib, S., L. Zhao, and G. Zhou. 2023. ‘Winners from Winners: A Tale of Risk Factors.’ Management Science to examine which are the best performing models in a set of 12 candidate factors in U.K. stock returns. We find that a five-factor model has the highest posterior probability across the whole sample period but the posterior probability is low. The best factor model outperforms traditional factor models using a number of metrics. However the best model performs poorly in pricing a set of anomaly portfolios.

Exploring a Novel Material and Approach in 3D-Printed Wrist-Hand Orthoses

This article proposes the integration of two novel aspects into the production of 3D-printed customized wrist-hand orthoses. One aspect involves the material, particularly Colorfabb varioShore thermoplastic polyurethane (TPU) filament with an active foaming agent, which allows adjusting the 3D-printed orthoses’ mechanical properties via process parameters such as printing temperature. Consequently, within the same printing process, by using a single extrusion nozzle, orthoses with varying stiffness levels can be produced, aiming at both immobilization rigidity and skin-comfortable softness. This capability is harnessed by 3D-printing the orthosis in a flat shape via material extrusion-based additive manufacturing, which represents the other novel aspect. Subsequently, the orthosis conforms to the user’s upper limb shape after secure attachment, or by thermoforming in the case of a bi-material solution. A dedicated design web app, which relies on key patient hand measurement input, is also proposed, differing from the 3D scanning and modeling approach that requires engineering expertise and 3D scan data processing. The evaluation of varioShore TPU orthoses with diverse designs was conducted considering printing time, cost, maximum flexion angle, comfort, and perceived wrist stability as criteria. As some of the produced TPU orthoses lacked the necessary stiffness around the wrist or did not properly fit the palm shape, bi-material orthoses including polylactic acid (PLA) inserts of varying sizes were 3D-printed and assessed, showing an improved stiffness around the wrist and a better hand shape conformity. The findings demonstrated the potential of this innovative approach in creating bi-material upper limb orthoses, capitalizing on various characteristics such as varioShore properties, PLA thermoforming capabilities, and the design flexibility provided by additive manufacturing technology.

Comprehensive metabolome characterization and comparison between two sources of Dragon's blood by integrating liquid chromatography/mass spectrometry and chemometrics.

Dragon's Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results beingbeneficial to quality control and thedifferentiated clinical application of DB.

Preparation, Crystal Structure, Physico‐chemical and Nonlinear Optical Properties of a New D‐π‐A Centrosymmetric Stilbazolium Framework of 4‐[2‐(3hydroxy‐phenyl)‐vinyl]‐1‐methyl‐pyridinium iodide (HMPI)

AbstractNew organic Stilbazolium cation Salt of 4‐[2‐(3 hydroxy‐phenyl)‐vinyl]‐1‐methyl‐pyridinium iodide (HMPI), which was obtained through solvent evaporation solution growth. The crystal structure was proved via the SC‐XRD technique. Its outcome reveals that the HMPI crystallised in a centrosymmetric pattern (P121/n1) and monoclinic crystal system. The compositional analysis was performed via 1H NMR and 13C NMR. The FT‐IR spectrum was used to determine the vibrational mode of the different molecular groups present in the material. The intermolecular interactions in the crystal structure were studied using Hirshfeld surface analysis. The molecular orbital energies (HOMO‐LUMO) and molecular electrostatic potential (MESP) were estimated using the Gaussian program with the density functional theory (DFT) method. The linear optical study revealed the transmittance percentage, lower cut‐off wavelength, and energy band gap for HMPI crystal. In the photoluminescence study, a green colour broad emission peak is observed at 521 nm. TG and DTA measured the thermal behaviour of grown HMPI crystal. Chemical etching and atomic force microscopy (AFM) experiments were used to report the surface properties of grown HMPI crystal. The Z‐scan technique, a He−Ne continuous wave laser at 632.8 nm used to measure the third‐order nonlinear optical susceptibility of the HMPI crystal. The Z scan approach reveals a saturable absorption and self‐defocusing nature of the title crystal.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.

Spatiotemporal distribution of sudden oak death in the US and Europe

Sudden Oak Death (SOD) is a devastating forest disease caused by Phytophthora ramorum, leading to rapid branch and leaf wilting. This study analyzed SOD data from various sources to study the spatiotemporal distribution of this pathogen globally. The spatial autocorrelation analysis shows that seven counties in the US have positive spatial correlations with high clustering values, whereas, the other six counties have negative correlations with low clustering values. In Europe, the regions with positive spatial correlations with high clustering values are located in England, Scotland, southern Finland, northern Germany, western France, and northern Spain, and many areas show negative correlations with low clustering values. The results of time series analysis reveal a clear seasonal pattern of pathogen incidence in the western US, with the peak of pathogen occurrences mainly in May and June. Furthermore, our spatiotemporal permutation scanning approach detects six clusters in the US and four in Western Europe during 2005–2006, 2012–2013, and 2021, providing insight into temporal dynamics and geographical hotspots of SOD. Our study also explored the impact of El Niño-Southern Oscillation (ENSO) on SOD for the first time, revealing a lag correlation up to 6 months. By improving our understanding of SOD spatiotemporal patterns and dynamics, we can better predict future trends and mitigate its impact. We recommend strengthening priority protection of host plants in areas where SOD outbreaks were clustered during La Niña events, and containing outbreak risks through effective detection, early warning and isolation of susceptible plants. This study provides a basis for global forest protection and disease prevention efforts to save susceptible plants.

US efficient factors in a Bayesian model scan framework

PurposeThe author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al. (2020), and Chib et al.(2022).Design/methodology/approachEhsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.FindingsThe author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data.Originality/valueThe author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.

Computational Complexity Reduction of HEVC by Early Termination of Transform Unit Partitioning Using Raster Scan Approach

Computational Complexity Reduction of HEVC by Early Termination of Transform Unit Partitioning Using Raster Scan Approach

Three-dimensional visualization and detection of early bruise in apple based on near-infrared hyperspectral imaging coupled with geometrical influence correction

Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN

Abstract This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human wrist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end optimally using a portable SENSE 2.0 3D scanner. A central composite design (CCD) matrix was developed for three input variables; light intensity (LI = 12–20 W/m2), capture angle (CA = 10°–50°), and scanning distance (SD = 10–20 inches) for executing the experimental runs. For accuracy evaluation, the wrist perimeter on the distal end was checked using CREO Parametric software for wrist perimeter error (WPE). Various AHML tools were developed using: response surface methodology (RSM), multi-objective genetic algorithm RSM, and multi-objective genetic algorithm neural networking (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach combined with the Bayesian regularization algorithm (trainabr) provided the best mutual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to LI = 12.001 W/m2, CA = 29.428°, and SD = 18.214 inch, with a significant percentage reduction of 55.83% in WPE. Executing 3D scanning of the human wrist over the optimized process parameters predicted by AHML tools will ensure the availability of precise scans for the rapid prototyping of customized orthotic devices in a reliable manner.

Taking Action Against Clinician Burnout Through Reducing the Documentation Burden With an Operating Room Supply Scanning Approach.

Taking Action Against Clinician Burnout Through Reducing the Documentation Burden With an Operating Room Supply Scanning Approach.

Methyl scanning approach for enhancing the biological activity of the linear peptidic natural product, efrapeptin C

Efrapeptin C is a linear peptidic natural product that exerts potent anticancer activity. The methyl scanning approach led to the discovery of a fully synthetic efrapeptin analogue with superior bioactivity and proteolytic stability.

Taking Action Against Clinician Burnout Through Reducing the Documentation Burden With an Operating Room Supply Scanning Approach.

Documenting surgical supply items in the operating room can be a burdensome task for circulating nurses because of manual input within the electronic medical record. This can lead to documentation fatigue and contribute to nursing burnout. The aim of this quality improvement project was to design and implement a supply item scanning process and evaluate the effect on intraoperative documentation completion time, room turnover time, picklist documentation accuracy, nurse satisfaction, and burnout. The sample included nine acute care hospitals throughout the United States, with 189 total circulating nurses and 31 718 procedures occurring during the study timeframe of 8 months. Results indicated that nurses were able to complete documentation on average 37.33 minutes sooner, and the operating room turnover time decreased by 1.88 minutes. Although nurses reported that their perceived picklist documentation accuracy did not improve, and the presence of new scanning technology did not influence their hospital employment decision, subjective feedback was mostly positive, with most responses citing the helpfulness of scanning for documentation. This study shows that an interdisciplinary team can effectively work to optimize documentation efficiency and performance improvement using a scanning intervention. Lessons learned through this process can translate into optimizations elsewhere in the electronic medical record.

Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space

This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space. The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network (CNN)-based models, i.e., U-Net (University of Freiburg, Germany) and DeepLabV3+ (Google, USA) models. Chain code technology, polyline approximation algorithm, and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces, including trace length, dip angle, density, and intensity. The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise, especially the DeepLabV3+ model. Furthermore, the quantization results further prove the reliability of extracting the fracture trace. As a result, a case study with two types of traces (i.e., on flat and uneven surfaces) demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.