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The introduction of Large Language Models (LLM) to the chatbot landscape has opened intriguing possibilities for academic libraries to offer more responsive and institutionally contextualized support to users, especially outside of regular service hours. While a few academic libraries currently employ AI-based chatbots on their websites, this service has not yet become the norm and there are no best practices in place for how academic libraries should launch, train, and assess the usefulness of a chatbot. In summer 2023, staff from the University of Delaware's Morris Library information technology (IT) and reference departments came together in a unique partnership to pilot a low-cost AI-powered chatbot called UDStax. The goals of the pilot were to learn more about the campus community's interest in engaging with this tool and to better understand the labor required on the staff side to maintain the bot. After researching six different options, the team selected Chatbase, a subscription-model product based on ChatGPT 3.5 that provides user-friendly training methods for an AI model using website URLs and uploaded source material. Chatbase removed the need to utilize the OpenAI API directly to code processes for submitting information to the AI engine to train the model, cutting down the amount of work for library information technology and making it possible to leverage the expertise of reference librarians and other public-facing staff, including student workers, to distribute the work of developing, refining, and reviewing training materials. This article will discuss the development of prompts, leveraging of existing data sources for training materials, and workflows involved in the pilot. It will argue that, when implementing AI-based tools in the academic library, involving staff from across the organization is essential to ensure buy-in and success. Although chatbots are designed to hide the effort of the people behind them, that labor is substantial and needs to be recognized. [ABSTRACT FROM AUTHOR]

The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in scanning probe microscopy. We explore the concept of the "seed effect," where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy on PbTiO3 thin films, we illustrate the impact of the "seed effect" and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques. [ABSTRACT FROM AUTHOR]

This clinical case outlines a comprehensive digital workflow for a minimally invasive multidisciplinary treatment. The process utilizes one open-source software for digital wax-up and one low-cost software to address esthetic concerns related to teeth misalignment. The patient's function was stabilized with a digitally made occlusal splint. The application of the described digital workflow technique, incorporating open-source, lowcost, and closed software, played a pivotal role in attaining a straightforward and predictable outcome with minimally invasive treatment. Furthermore, the continual evolution of technology contributes to the growing precision of dental procedures. The presented digital workflow helped formulate a predictable treatment plan, replicate a diagnostic digital wax-up, and achieve precise teeth alignment. This approach satisfactorily addressed the patient's esthetic concerns, providing an outstanding approximation of the definitive result. [ABSTRACT FROM AUTHOR]

Purpose: To validate an innovative 3D volumetric method of evaluating tissue changes proposed by Lee et al in 2020 by comparing the results of this method--in which the scanned peri-implant surfaces were transformed, visualized, and analyzed as 3D objects--to the results reported by an existing method based on calculation of the mean distance between measured surfaces. The null hypothesis was that there was no statistically significant difference between the two methods. Additionally, the present study evaluated peri-implant tissue changes 5 years after single implant placement in the esthetic zone. Materials and Methods: Both methods were applied to 11 oral implant site casts (6 maxillary central incisor sites, 5 maxillary lateral incisor sites) taken from 11 patients at crown placement and at follow-up examinations 5 years later. The methods are based on digital workflows in which the reference and 5-year casts are scanned and the resulting STL files are superimposed and analyzed for three regions of interest (mesial papilla, central area, and distal papilla). The volumetric changes reported by the Lee et al method and the mean distance method were calculated and compared using the Spearman rank correlation coefficient (P < .01) and the Wilcoxon signed-rank test (P < .05). Results: The correlation between the two sets of measurements was very high (Spearman rank correlation coefficient = 0.885). The new volumetric method indicated a mean volume loss of 2.82 mm3 (SD: 5.06), while the method based on the measurement of mean distance showed a mean volume loss of 2.92 mm3 (SD: 4.43; Wilcoxon signed-rank test result: P = .77). No statistically significant difference was found. The two methods gave equivalent results, and the null hypothesis was accepted. Conclusions: The new volumetric method was validated and can be considered a trustworthy tool. [ABSTRACT FROM AUTHOR]

Purpose: To compare the treatment time of digital and conventional workflows for single-implant crowns, as well as prostheses made of polymer-infiltrated ceramic-network (PICN; Vita Enamic, Vita Zahnfabrik) and lithium disilicate (LS2; n!ce, Straumann). Materials and Methods: A total of 40 patients who needed a single-implant crown in posterior regions were considered and randomly divided into digital workflows (n = 20) that used an intraoral scanner (IOS; iTero Element 5D, Align Technologies) and conventional workflows (n = 20) that used polyether impressions (3M ESPE Impregum Penta). Then, each group was again distributed into two subgroups based on the crown material used: PICN (n = 10) and LS2 (n = 10). Treatment time was calculated for both digital and conventional workflows. Analysis was done at a 5% confidence interval (P < .05). An independent two-sample t test was used to compare treatment time between the groups. The Kruskal--Wallis test was used to compare clinical try-in time among subgroups. Any of the implant crowns that had to be remade in each subgroup were evaluated using the Fisher exact test. Results: The entire process of digital and conventional workflows required 104.31 ± 20.83 and 153.48 ± 16.35 minutes, respectively. Digital workflows saved 39.2% more time than the conventional protocol for the single-implant crown treatment (P < .0001). Conclusions: Both digital and conventional workflow protocols can achieve a successful outcome for single-implant monolithic crowns in posterior areas. The digital protocol yielded greater timesaving over the conventional procedure in data acquisition and laboratory steps, while the time for clinical try-in and delivery were similar. [ABSTRACT FROM AUTHOR]

Next-generation sequencing (NGS) has proven clinical utility on disease management and serves as an important tool for genomic surveillance. Currently, hurdles surrounding its implementation, namely the complex and demanding analytical workflows, have impeded its widespread use in many laboratories. To address this challenge, the UCLA Molecular Microbiology and Pathogen Genomics Laboratory evaluated the performance of the Tecan MagicPrep NGS system, a commercial automated solution for library preparation for clinical whole-genome sequencing assays, against the Illumina Nextera DNA Flex Library Prep. Using 35 unique organisms (28 bacteria and 7 fungi) for various clinical applications, including microbial identification and genomic characterization, we compared the quantity and quality of the prepared libraries and the resulting sequences, and concordance of the overall results. We also assessed the impact of its implementation on laboratory workflow. The MagicPrep NGS produced higher library concentrations with smaller sizes, and correspondingly, higher molarity. Quality metrics of the sequences, however, demonstrated no significant impact on the overall results, producing 100% concordance with the reference method. Importantly, workflow analysis showed 5 hours less hands-on time per run with more flexibility. This evaluation study indicates that performance of the MagicPrep NGS is comparable to the Nextera DNA Flex with the added benefit of improving workflow efficiency and reducing labor for performing routine clinical microbial whole-genome sequencing tests.

Digital or CAD/CAM workflows and protocols are being increasingly utilized because of their improved efficiency and reproducibility. For the fabrication of complete dentures, digital workflows can reduce treatment time and clinical visits while enhancing the reliability and reproducibility of the laboratory phase and materials. However, establishing centric relation (CR) and vertical dimension of occlusion (VDO) in a reproducible way is still a challenging step for complete denture fabrication in both analog and digital workflows. This clinical report describes a digital workflow using an individualized gothic arch tracing device (GATD) with open-source software for the fabrication of complete dentures. With this workflow, clinicians can offer customized solutions according to patient rehabilitation, with good reproducibility using gothic arch tracing to be implemented in the digital workflow. [ABSTRACT FROM AUTHOR]

Purpose: To describe the use of work process modelling to optimise the organisation of the demand for oral health treatment in primary care units in Brazil. Materials and Methods: The oral health care routine was at first described as the “AS IS” model, which was evaluated by the oral team professionals, rearranged, and further described as the “TO BE” model described using a business process management modelling tool. The significant increase in the demand of patients due to restrictions offered by the dental service in addition to non-urgent treatments being avoided by patients during COVID-19 pandemic was also considered. Results: Structuring the work processes in a visual way using modelling tools was useful to picture the entire treatment process and adjust when needed. The use of the managerial tool was useful to understand and reorganise the workflow of organising the demand and ultimately improve the efficiency of the resources. The use of such managerial tools helped oral health professionals to efficiently rearrange their tasks and set priorities to meet their needs. Conclusions: With the use of management tools, each unit can readjust its structures and ways of working, aiming to improve the quality of public health care services provided to patients. [ABSTRACT FROM AUTHOR]

The use of conventional scan bodies (SBs) with an intraoral scanner (IOS) to capture the position of a complete arch of dental implants has proven to be challenging. The literature is unclear about the accuracy of intraoral scanning techniques using SBs that are connected vertically to multiunit abutments (MUAs) for numerous adjacent implants in the same arch. Recently, there has been a paradigm shift from vertical SBs to horizontal SBs, which are positioned perpendicular to the long axis of the MUAs. Most IOSs available today can capture these horizontal SBs, called scan gauges (SGs), with better accuracy and consequently acquire the position of multiple adjacent implants using an effective scan path, thus reducing stitching and the number of images. The key to implementing this novel technology is to strategically arrange the SGs to optimize horizontal overlap of multiple adjacent SGs without touching each other. By superimposing two high-resolution intraoral scans of the SGs, an artificial intelligence (AI) algorithm is employed to produce a calibrated digital best-fit model on which a passive complete-arch prosthesis can be designed and fabricated. The advantages and disadvantages of SBs and SGs are discussed, and a case report using a digital workflow is presented. [ABSTRACT FROM AUTHOR]

This paper delves into the intricate relationship between the evolving field of genomic medicine and healthcare providers' legal and ethical responsibilities, with a particular focus on the duty to warn in the context of genetic risks. Through a critical examination of landmark cases such as Pate v. Threlkel and Safer v. Estate of Pack, this study underscores the expanding scope of healthcare providers' duties to include not just the patient but potentially at-risk family members as well. It highlights the legal, ethical, and practical challenges that arise when balancing patient confidentiality with the need to prevent harm through the disclosure of genetic information. The descriptive portion of the paper outlines the current legal precedents and the ambiguity surrounding healthcare providers' responsibilities. The prescriptive portion proposes the establishment of comprehensive, clear guidelines to support healthcare providers, particularly genomic counselors, in navigating these complexities. This includes recommendations for policy changes and the development of protocols that respect patient autonomy while addressing the preventive potential of genomic medicine. Through an analysis of legal frameworks and ethical considerations, this paper argues for a nuanced approach that equitably balances individual rights with the collective good, ultimately advocating for a future where genomic medicine is integrated into patient care in an ethically responsible and legally sound manner.

With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature. [ABSTRACT FROM AUTHOR]

Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances. [ABSTRACT FROM AUTHOR]

Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy ab initio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g., the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse and, therefore, require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile atomic simulation environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks. [ABSTRACT FROM AUTHOR]

This paper explores the potential of language models such as ChatGPT to transform library cataloging. Through experiments with ChatGPT, the author demonstrates its ability to generate accurate MARC records using RDA and other standards such as the Dublin Core Metadata Element Set. These results demonstrate the potential of ChatGPT as a tool for streamlining the record creation process and improving efficiency in library settings. The use of AI-generated records, however, also raises important questions related to intellectual property rights and bias. The paper reviews recent studies on AI in libraries and concludes that further research and development of this innovative technology is necessary to ensure its responsible implementation in the field of library cataloging. [ABSTRACT FROM AUTHOR]

The traditional configuration of four maxillary implants to restore full-arch maxillary fixed implant prostheses is not without pitfalls. Intraoperative variability, such as bone quality and quantity, may lead to unexpected intraoperative challenges that may not allow for execution of the original treatment plan. Additionally, extensive maxillary sinus pneumatization may inhibit posterior spread and lead to a shortened dental arch or excessive cantilever forces. The PATZi protocol is a systematized algorithm for maxillary full-arch implant treatment planning that addresses such intraoperative surprises and achieves full arch length in the planned prosthesis. PATZi also assigns a numbering classification to each implant configuration, which leads to uniform communication and grading of surgical difficulty, as well as predictable planning for developing future salvage plans. By using the PATZi protocol as a guideline, the surgeon may improve composite torque value and anteroposterior (AP) spread, eliminate distal cantilever forces, and ultimately improve the predictability of the prosthetic treatment plan. [ABSTRACT FROM AUTHOR]

The purpose of this case series was to assess the application of a novel selfcutting, tapered implant (Straumann BLX) in combination with a digital integrated prosthetic workflow for immediate placement and restoration. Fourteen consecutive patients who required replacement of a single hopeless maxillary or mandibular tooth in the esthetic area and met the clinical and radiographic criteria for immediate implant placement were treated. All cases followed the same treatment procedure of extraction and immediate implant placement. Immediate temporization with full contoured screw-retained provisional restorations was performed using an integrated digital workflow. After implant placement and dual-zone bone and soft tissue augmentation, connecting geometries and emergence profiles were finalized. The average implant insertion torque was 53.2 ± 14.9 Ncm (range: 35 to 80 Ncm), allowing immediate provisional restoration in all cases. Final restorations were delivered 3 months after implant placement. An implant survival rate of 100% was observed at the 1-year postloading follow-up. The results of this case series suggest that immediate placement of novel tapered implants and immediate provisionalization using an integrated digital workflow can result in predictable and functional and esthetic results for the immediate transition of failing single teeth in the esthetic area. [ABSTRACT FROM AUTHOR]

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called Texera. The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

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.

We created a concise nurse-driven delirium reduction workflow with the aim of reducing delirium rates and length of stay for hospitalized adults. Our nurse-driven workflow included five evidence-based daytime sunrise interventions (patient room lights on, blinds up, mobilization/out-of-bed, water within patients reach and patient awake) and five nighttime turndown interventions (patient room lights off, blinds down, television off, noise reduction and pre-set bedtime). Interventions were also chosen because fidelity could be quickly monitored twice daily without patient interruption from outside the room. To evaluate the workflow, we used an interrupted time series study design between 06/01/17 and 05/30/22 to determine if the workflow significantly reduced the units delirium rate and average length of stay. Our workflow is feasible to implement and monitor and initially significantly reduced delirium rates but not length of stay. However, the reduction in delirium rates were not sustained following the emergence of the COVID-19 pandemic.

Background: Microbes produce diverse bioactive natural products with applications in fields such as medicine and agriculture. In their genomes, these natural products are encoded by physically clustered genes known as biosynthetic gene clusters (BGCs). Genome and metagenome sequencing advances have enabled high-throughput identification of BGCs as a promising avenue for natural product discovery. BGC mining from (meta)genomes using in silico tools has allowed access to a vast diversity of potentially novel natural products. However, a fundamental limitation has been the ability to assemble complete BGCs, especially from complex metagenomes. With their fragmented assemblies, short-read technologies struggle to recover complete BGCs, such as the long and repetitive nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS). Recent advances in long-read sequencing, such as the High Fidelity (HiFi) technology from PacBio, have reduced this limitation and can help retrieve both accurate and complete BGCs from metagenomes, warranting improvement in the existing BGC identification approach for better utilization of HiFi data. Results: Here, we present HiFiBGC, a command-line-based workflow to identify BGCs in PacBio HiFi metagenomes. HiFiBGC leverages an ensemble of assemblies from three HiFi-tailored metagenome assemblers and the reads not represented in these assemblies. Based on our analyses of four HiFi metagenomic datasets from four different environments, we show that HiFiBGC identifies, on average, 78% more BGCs than the top-performing single-assembler-based method. This increase is due to HiFiBGC's ensemble assembly approach, which improves recovery by 25%, as well as from the inclusion of mostly fragmented BGCs identified in the unmapped reads. Conclusions: HiFiBGC is a computational workflow for identifying BGCs in long-read HiFi metagenomes, implemented majorly using Python programming language and workflow manager Snakemake. HiFiBGC is available on GitHub at https://github.com/ay-amityadav/HiFiBGC under the MIT license. The code related to the figures and analyses presented in the manuscript is available at https://github.com/ay-amityadav/HiFiBGC_analyses. [ABSTRACT FROM AUTHOR]

Background: The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Results: Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. Conclusions: The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Purpose/Aim Materials and Method Results Conclusion For edentulism, an implant supported removable complete overdenture (IOD) is an attractive solution to restore patients' chewing capacity, aesthetics, and self‐esteem, however, treatment is expensive and time consuming.To estimate the decline in costs for digitally designed and CAD/CAM fabricated IODs (3D‐IODs) compared to conventionally fabricated IODs (C‐IODs) at comparable general health related quality of life (GHRQoL).A randomized crossover study enrolled 36 fully edentulous patients, in whom six maxillary implants were placed together with two mandibular implants, if not already present.At the start of the study, a set of C‐IODs and 3D‐IODs was fabricated for each patient. All patients wore each IOD‐type for 1 year: first the 3D‐IOD and the second year a C‐IOD, or vice versa. At all three‐time points patients general QoL was assessed using the EQ‐5D‐5L questionnaire as well as the SF‐36 from which the SF‐6D was obtained, to research the anticipation of no significant difference.To enable cost consequence analysis (CCA), both costs made within healthcare and patient costs were assessed. Subsequently, a budget impact analysis (BIA) was performed to demonstrate the potential savings.No differences in general GHRQoL were seen between C‐IOD (M = 0.840, SD = 0.177) and 3D‐IOD (M = 0.837, SD = 0.156) (paired t‐test (N = 31): p = 0.880).With respect to the total costs for a complete IOD, however, the digital approach showed a reduction in initial total costs of 14.2% (€4700.33 vs. €4030.61: p < 0,001), in treatment time of 41.1% (309 vs. 182 min: p < 0.001), and in number of treatment sessions of 47.1% (5.68 vs. 3.0: p < 0.001). For repairs for an IOD in both the upper and lower jaw, the C‐IOD and 3D‐IOD scored similar for treatment time as well as additional costs.Implementing a 3D workflow in the production of IOD's supplies patients with a high‐quality 3D‐IOD at lower costs.Trial Registration: NL‐OMON44248 https://onderzoekmetmensen.nl/en/trial/44248 [ABSTRACT FROM AUTHOR]

Predicting crystal density of energetic materials accurately critically impacts assessment of their detonation potential, with higher density directly improving energetic performance. This new approach will help in density prediction to enable accelerated computational material discovery. By refining Politzer's approach using rigorously validated B3LYP quantum calculations and Multiwfn's precise structural elucidation, our optimized protocol substantially enhances predictive accuracy, marking a significant leap over traditional approaches. This streamlined and efficient framework spearheads high-fidelity virtual screening to transformatively expedite the development of novel, potent energetic candidates. Moreover, this research accentuates the decisive impact of refined computational techniques on elucidating structure–property relationships early-on, paving the way to accelerated development of promising energetic materials. Future work may concentrate on expanding training datasets to further improve model robustness and computational efficiency. Overall, this transformative density prediction workflow delivers a more precise approach to fundamentally advanced energetic material innovation. [ABSTRACT FROM AUTHOR]

Men who have sex with men (MSM) who use dating applications (apps) have higher rates of engaging in condomless anal sex than those who do not. Therefore, we conducted a two-arm randomized controlled trial to evaluate the effectiveness of an interactive web-based intervention in promoting safer sex among this population. The intervention was guided by the Theory of Planned Behavior and co-designed by researchers, healthcare providers, and MSM participants. The primary outcome was the frequency of condomless anal sex in past three months. Secondary outcomes included five other behavioral outcomes and two psychological outcomes. This trial was registered on ISRCTN (ISRCTN16681863) on 2020/04/28. A total of 480 MSM were enrolled and randomly assigned to the intervention or control group. Our findings indicate that the intervention significantly reduced condomless anal sex behaviors by enhancing self-efficacy and attitudes toward condom use among MSM dating app users, with the effects sustained at both three and six months. [ABSTRACT FROM AUTHOR]

Background: Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted. Results: To address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards. Conclusions: The MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual. EB_9RbKd3CYdEq3zCTHKaq Video Abstract [ABSTRACT FROM AUTHOR]

Objectives This study aimed to establish a comprehensive human erythrocyte antigen (HEA) frequency data set for Koreans. It also sought to develop a mobile app that facilitates the calculation of the frequencies of specific antigen-negative red blood cell units and the average number of units required for antigen typing. Methods Human erythrocyte antigen frequencies were compiled from large-scale blood donor data and 5 previous papers. Based on the collected data, we developed a mobile calculator app for HEA frequency and evaluated its usability. Results Human erythrocyte antigen frequency data for 20 blood group systems, including the ABO, Rh, MNS, Duffy, Kidd, and Diego systems, were established. The app was designed to enable users to select the desired phenotype from a drop-down menu and display the calculated frequency at the bottom. The number of units required for antigen typing to find 1 compatible red blood cell unit was also displayed. Five users participated in app evaluation and rated the functionality and information categories highly. In quizzes prompting users to calculate frequencies using the app, all participants provided correct answers, confirming the app's user-friendly functionality. Conclusions This app, which encompasses comprehensive HEA frequency data, is expected to find multiple uses in transfusion medicine, including optimizing blood bank workflow and defining rare blood groups in Korea. [ABSTRACT FROM AUTHOR]

Background Test consolidation and total laboratory automation (TLA) were implemented in a core laboratory with a high volume of specimens in a medical center in Taiwan to reduce the costs of laboratory services and improve laboratory workflow and performance. Methods Using a retrospective research approach, 5 stat and 7 routine tests were used to analyze the in-laboratory to report turnaround time (IR-TAT). Mean, SD, medium, 90th percentile, outlier percentage of IR-TAT, full-time equivalents, productivity, tube touch moment (TTM), and financial impact were determined and compared pre- and post-TLA. Results The mean IR-TAT of overall stat chemical tests for inpatient and outpatient were 32.8% and 11.9% reductions, respectively. The productivity of each medical technologist increased by 32.4% per month, and there was a reduction of 5 medical technologists compared with the number required to complete the same tests before consolidation. The TTM of staff per year post-TLA decreased by 74.1% tube touches. Conclusion The efficiency of laboratory services was improved by consolidation to the core laboratory along with TLA implementation coupled with logic rules such as delta-check and autoverification. Effectiveness was improved as measured by an increase in productivity, labor reduction, staff safety, and cost reduction. [ABSTRACT FROM AUTHOR]

Electronic legal deposits in Saxony-Anhalt have been collected, archived and made accessible in a well-established workflow for some years now. The operating system is currently being converted to the use of repository software DSpace, to which media that have already been archived, will be migrated. Once the changeover is complete, further adjustments in terms of automation and collection profile will be addressed. [ABSTRACT FROM AUTHOR]

In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the "annotate entire image, then slice" workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery. [ABSTRACT FROM AUTHOR]

In addition to anatomical loss, removal of the full or portion of the orbit results in a facial deformity and psychological distress for the patient. This article details a practical case of prosthetic rehabilitation using digital workflow for an orbital deformity caused by post-COVID-19 Mucor mycosis. The main goal of this case study was to create a maxillofacial orbital prosthesis that is well-retained, simple to use, and accurate in terms of appearance. The study addresses the problems involved in fabricating the orbital prosthesis, particularly the unique dimensions and form of the defect, replicating the natural skin tone, and accomplishing retention by the most prudent and patient-friendly approach. Through this article, a digitised algorithm, using photogrammetry technique for facial scan, is suggested for fabricating the prosthesis. [ABSTRACT FROM AUTHOR]

Close-up facial images captured at short distances often suffer from perspective distortion, resulting in exaggerated facial features and unnatural/unattractive appearances. We propose a simple yet effective method for correcting perspective distortions in a single close-up face image. We first perform 3D GAN inversion using a perspective-distorted input facial image by jointly optimizing the intrinsic and extrinsic camera parameters and the face latent code. To address the ambiguity inherent in this joint optimization, we develop starting from a short distance, optimization scheduling, reparametrizations, and geometric regularization. Re-rendering the portrait at a proper focal length and camera distance effectively corrects perspective distortions and produces more natural-looking results. We also incorporate a workflow to handle full images rather than limiting our method to cropped faces. Our experiments show that our method compares favorably against previous approaches qualitatively and quantitatively. We showcase numerous examples validating the applicability of our method on in-the-wild portrait photos. Our code is available at https://github.com/lightChaserX/DisCO. [ABSTRACT FROM AUTHOR]

With the rising importance of mental health screenings in nontraditional settings, dental offices offer an important opportunity to provide these services. This feasibility study examined mental health screenings and referral to treatment procedures in dental practices. A total of 17 dentists, dental hygienists, and dental office staff members (dental office personnel [DOP]) from 5 dental practices enrolled 36 patients in the study. Patients completed 4 validated mental health screening instruments. In addition to training in study procedures, DOP also were trained on how to review patient screening measures and provide appropriate mental health follow-up (ie, provide a list of area mental health resources) when necessary. Twenty-two patients (61.1%) met the study threshold for follow-up on any mental health screening form. DOP provided appropriate follow-up for all 22 patients, which included providing a list of area mental health resources. DOP reported that the mental health screening procedures did not disrupt the workflow in 93% of patient encounters. Overall, DOP reported minimal disruption in their workflow. The findings suggest the viability of incorporating regular mental health screenings as part of the regular dental patient workflow. This study also highlights the potential for the scalability and impact of screening procedures in dental practices. This feasibility study highlights the potential of dental offices becoming an avenue for screening patient mental health concerns and providing area resources, thus enhancing comprehensive patient care and overall patient well-being. [ABSTRACT FROM AUTHOR]

Purpose The establishment of a new outpatient pharmacy provided a strategic opportunity to repurpose and convert an existing outpatient pharmacy into a closed-door mail-order pharmacy within a health system. This article describes the steps taken to successfully make this change and evaluates the impact. Summary The mail-order pharmacy conversion project was divided into 3 phases: phase 1 (before conversion) from July through August 2022, phase 2 (conversion) from October through November 2022, and phase 3 (after conversion) from December 2022 through February 2023. Phase 1 included standardizing workflows with standard operating procedure (SOP) development, improving automation, determining staffing ratios, gathering baseline staff engagement data, and identifying primary and secondary outcomes of interest. Phase 2 encompassed SOP implementation and training of mail-order team members. Phase 3 involved evaluating available pharmacy floorspace, marketing mail-order services, and the second distribution of the staff engagement survey. The measured outcomes of this project were total prescription volumes, increase in total revenue, and staff engagement. Data collection was completed in phase 3. Conclusion The existing outpatient pharmacy was successfully converted to a closed-door pharmacy, and the associated prescription volume increased. Developing a strategic action plan to establish SOPs, calculate staffing performance metrics, and identify opportunities for growth and engaging frontline team members were essential to the success of this project. [ABSTRACT FROM AUTHOR]

Introduction: Extracorporeal cardiopulmonary resuscitation (ECPR) is associated with improved outcomes in select populations, however, crisis resource management (CRM) in this setting is logistically challenging. This study evaluates the impact of ECPR simulation on self-perceived confidence and collaboration of intensive care unit team members. Methods: This is a prospective observational study analyzing data obtained between July 2018–December 2019. This study focused on non-surgical members of critical care team consisting of pediatric intensivists, resident physicians, registered nurses, respiratory therapists. Participants were expected to perform cardiopulmonary resuscitation (CPR) during the ECPR event, participate in code-team responsibilities and provide ancillary support during cannulation. Pre- and post-simulation surveys employed the Likert scale (1 = not at all confident, 5 = highly confident) to assess self-perceived scores in specified clinical competencies. Results: Twenty-nine providers participated in the simulation; 38% had prior ECPR experience. Compared to mean pre-study Likert scores (2.4, 2.4, 2.5), post-simulation scores increased (4.2, 4.4, 4.3) when self-evaluating: confidence in assessing patients needing ECPR, confidence in participating in ECPR workflow and confidence in performing high-quality CPR, respectively. Post-simulation values of > 3 were reported by 100% of participants in all domains (p <.0001). All participants indicated the clinical scenario and procedural environment to be realistic and appropriately reflective of situational stress. Additionally, 100% of participants reported the simulation to improve perceived team communication and teamwork skills. Conclusion: This study demonstrated preliminary feasibility of pediatric ECPR simulation in enhancing independent provider confidence and team communication. This self-perceived improvement may establish a foundation for cohesive CRM, in preparation for a real life ECPR encounter. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to provide recommendations in order to facilitate communication between dental professionals and surgeons who are collaborating in the field of dentofacial esthetics. Clinical Considerations: Smile esthetics are beyond the scope, both of the surgeons who are collaborating with facial esthetics and of the dentists, as a wide range of treatment options from both sides is available. It can be difficult for the surgeon or the dentist that first comes in contact with the patient to conduct an individualized global treatment plan, in order to find out how the various phases of the treatment can be sequenced, as a workflow for an efficient interaction between facial surgery and dentistry still does not exist in the scientific literature. Conclusions: Facial cosmetic procedures and dental treatment have to be planned as individual elements of the whole dentofacial esthetic rehabilitation. The treatment has to be initiated with the design of the smile and the intraoral mock‐up, followed by the required surgical interventions, and to be finished with the delivery of the definitive dental restoration. Clinical Significance: Dentofacial esthetics require comprehensive communication between surgeons and dentists. Following the proposed recommendations, an individualized interdisciplinary treatment plan can be conducted, defining the role of each specialty. [ABSTRACT FROM AUTHOR]

Time‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties. [ABSTRACT FROM AUTHOR]

Even in the current era of hematology analyzer automation and peripheral equipment, quality control sample measurement remains a manual task, leading to variability in quality control data and increased workload. In this study, we evaluated the performance of quality control measurement using the BT-50 Transportation Unit (BT-50, Sysmex, Kobe, Japan), equipped with a scheduled automatic quality control function, to ensure measurement accuracy and streamline the workflow of hematology testing. We evaluated the automatic measurement performance of quality control samples using the BT-50 for six representative blood test parameters: WBC (white blood cell), RBC (red blood cell), HGB (hemoglobin), HCT (hematocrit), PLT (platelet), and RET% (reticulocyte percent). We evaluated the equivalence and compared measurement accuracy between the BT-50 and the manual method. We then compared the variability to other laboratories and confirmed the stability of quality control samples. We also evaluated changes in workflow and staff resources before and after the introduction of the BT-50. The quality control measurement results for the BT-50 and the manual method were found to be equivalent for all six parameters. The variability measured by the BT-50 was lower for some parameters compared to the manual method. Furthermore, the workflow was streamlined by reducing manual processes, resulting in increased efficiency. We confirmed the performance of quality control measurements using the schedule function of the BT-50. Introducing the BT-50 reduced the operator's workload, improved operational efficiency, and promoted the standardization of quality control measurements. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the differences in endovascular thrombectomy (EVT) outcomes of patients treated for acute ischaemic stroke (AIS) during business versus off-business hours. Methods: A single-centre retrospective cohort study of patients with AIS treated with EVT from February 1, 2015, to May 31, 2021, was performed at a comprehensive stroke centre (CSC). Patients were divided into business (Monday to Friday, 8 AM-5 PM) versus off-business hours groups. The primary outcome was functional neurological disability, scored using the modified Rankin Scale (mRS) at 90 days. Secondary outcomes included the rate of successful reperfusion and procedural workflow time delays. Differences in proportions were assessed using Fisher's exact and Chi-Square tests as appropriate. For continuous variables, differences in medians between groups were assessed using Mann-Whitney U tests. Results: A total of 676 patients were included, with 399 patients (59%) comprising the off-business-hour group. No significant differences were seen in age, sex, ASPECTS score, or NIHSS at arrival. Off-business hours strokes had a longer delay between CSC arrival to groin puncture (minutes: 81 vs 44, P <.0001) and between imaging to groin puncture (minutes: 67 vs 32, P <.0001) compared to the business hours strokes. There were no differences in the rate of successful reperfusion (mTICI ≥2b) between groups (82% vs 83%, P =.61). At 90 days, 65% of patients in both groups had an mRS ≤2 (P =.91). Conclusion: Despite workflow delays in initiating EVT during off-business hours, there were no differences in the rate of successful reperfusion or functional outcomes. [ABSTRACT FROM AUTHOR]

Understanding more about the discharge of care orders is vital—whether a care order remains in place has significant implications for children and their families and for local authorities. While there has been comprehensive research about the process and outcomes of care proceedings, much less is known about the discharge of care orders—particularly how, why and when care orders are ended and the differences between applications that are granted and those that are not. The present study combined data from an anonymized administrative data on discharge applications, a detailed analysis of children's e‐records and qualitative interviews with family justice professionals to create the first detailed profile of discharge applications across England and Wales. This paper reports, for the first time, the number of discharge applications and outcomes across England and Wales, highlighting regional as well as between‐country variation. Drawing on data from children's e‐records and interviews with professionals, highlighting how and why local authorities are more likely to submit discharge applications, and to have applications granted, than parents. Recommendations are made for how to adapt professional practice and policy around discharge applications to better meet the needs of children and families. [ABSTRACT FROM AUTHOR]

Scientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized. [ABSTRACT FROM AUTHOR]

Workflow scheduling poses a significant challenge due to the large scale of workflows and heterogeneity of algorithms in cloud-fog environment. Also, these nature-inspired algorithms have limitations, such as being trapped in local optima, leading to failure in achieving a global optimal solution. Since, workflow scheduling is used to enhance the optimization of task allocation and execution within cloud-fog computing. Therefore, various metaheuristic algorithms have been recommended by researchers for addressing scheduling problems. Our paper used a hybrid algorithm "particle swarm optimization-whale optimization algorithm" (PSO-WOA) to overcome the abovementioned issues. When applied individually, the PSO becomes trapped in the global optimum due to the presence of a high-dimensional search space, while the WOA, which lacks flexibility in search spacing, becomes trapped in local optima. The proposed PSO-WOA algorithm overcomes the constrained exploration of search spaces observed in the population-based WOA algorithm and efficiently mitigates the challenge of becoming trapped in local optima. The simulation was performed using the WorkflowSim toolkit, and the results were tested for several test cases on five scientific workflows with varying numbers of tasks and iterations. The results illustrate that the hybrid PSO-WOA approach produced superior outcomes when compared with state-of-the-art algorithms (ACO, PSO, WOA, GA and BH-FWA) for both total execution cost (TEC) and total execution time (TET). After analysis of results, two workflows i.e., montage and epigenomics stood apart from others in terms of time and cost. Statistical analysis was done with Wilcoxon and Friedman tests and were found to be significant. In summary, this study provides a comparative analysis of enhancing both TET and TEC in a hybrid algorithm along with its impact on performance metrics. [ABSTRACT FROM AUTHOR]

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers. [ABSTRACT FROM AUTHOR]

The article shares guidance for developing effective interpersonal communication in the health care profession. Topics discussed include components of communication, communication scenarios between a radiologic technician and physician and between a laboratory scientist and operating room personnel, significance of feedback to improving workflow, role of effective communication in health care, and integration of interpersonal communication in the professional curriculum.

Purpose: A routine preoperative assessment is considered both ineffective and inefficient. Despite the widespread application of lean thinking in healthcare, there is little evidence of successful experiences in preoperative admissions in order to reduce "No value added" activities. A conceptual framework reporting the drivers (clinic, tools, innovation, organization, and governance) and impacts (patient, efficiency, sustainability, time, learning and growth) was developed. Methodology: Drawing on the experience of an Italian high complexity hospital, this paper analyzes the case study by reporting evidence on how to implement lean in preoperative assessment and how to evaluate the positive results obtained. Results: Applying lean principles, the identification of value improved the appropriateness of care by creating 40 personalized pathways; the value stream resulted in a reduction of "No Value Added Time" from 37% to 28%, chest X-rays from 41% to 14% and cardiac visits from 49% to 37%; the pursuit of continuous flow through innovation contributed to increase the use of digitalization; the new pull organization helped to reduce the average time spent per year by 1.5 h; the continuous improvement was ensured through the governance of results. Conclusion: The proposed framework should be used to improve the quality of care in preoperative admissions by adopting the lean drivers for successful implementation and reporting the impacts. [ABSTRACT FROM AUTHOR]