Hand hygiene (HH) is essential for preventing healthcare-associated infections, yet conventional monitoring approaches primarily capture event occurrence and provide limited insight into procedural quality, timing, and individualized feedback. To address these limitations, we present a real-time HH training and assessment framework that combines deep-learning-based WHO step recognition with a protocol-aware decision-support engine deployed on both a Desktop LED display and a Mixed Reality (MR) headset. The system supports two complementary modes: Concurrent-Feedback Coaching (CFC), which provides real-time sequence guidance and corrective prompts, and Uncued Retention Assessment (URA), which evaluates unguided execution and summarizes detected steps and errors. To support real-time deployment, we retrained and evaluated YOLOv12+MV, TimeSformer, and TSM on four heterogeneous HH datasets (PSCUH, Jurmala, METC, and Kaggle). While several YOLOv12 variants achieved strong recognition performance, the compact YOLOv12-n+MV model provided the most favorable accuracy-efficiency trade-off for deployment, achieving F1-scores of 0.99, 0.87, 0.72, and 0.58 across Kaggle, Jurmala, METC, and PSCUH, respectively, with low computational cost. This lightweight recognizer was integrated with temporal majority voting and a protocol-aware controller to support stable closed-loop interaction on Desktop and HoloLens 2. We evaluated the framework in a controlled mixed-methods study with $N=20$N=20 participants using a $2\times 2$2×2 design (Desktop vs. MR; CFC vs. URA). Desktop yielded significantly faster, more temporally stable, and less error-prone HH performance than MR, whereas CFC reduced total completion time and URA reduced weighted mistake scores, indicating a speed-accuracy trade-off. Subjective results showed higher perceived usability for Desktop than for MR and for CFC than for URA. NASA-TLX further showed a higher workload for MR than Desktop across five subscales under counterbalancing, while URA increased perceived effort relative to CFC. Overall, these findings suggest that Desktop is more suitable when efficiency, stability, and lower workload are priorities, whereas CFC and URA can be selectively used to emphasize guided acquisition or independent recall within Desktop and MR HH training workflows.
This pilot randomized controlled trial evaluated whether a Gagné's model-based interactive desktop simulation program could reduce ageism and improve nursing students' attitudes toward older adult care. Hundred and twenty nursing students were randomized to an intervention group (n = 60) receiving eight sessions combining Gagné's instructional events, desktop simulations, case discussions, and structured reflection, or a control group (n = 60) receiving no intervention. Groups were comparable at baseline across all outcome measures (WEPS, FSA, PCOP-SV; all p > 0.05). Post-intervention, the intervention group demonstrated significantly greater improvement in willingness to work with older adults (WEPS), reduced ageist attitudes (FSA), and more positive perspectives on caring for older patients (PCOP-SV), with statistically significant group-by-time interactions across all three outcomes sustained at follow-up. A simulation-enhanced instructional program based on Gagné's model shows promise for improving attitudinal outcomes in gerontological nursing education. Findings are preliminary and should be interpreted cautiously given the pilot design and complete-case analysis. Trial Registration: ClinicalTrials.gov identifier: NCT0613947.
Three-dimensional (3D) virtual patients constructed from facial and intraoral scans often lack orientation information. The natural head position (NHP) is a standardized, reproducible posture re--ed for facial scanning. The present technique describes a workflow for registering the true horizontal plane in a desktop facial scanner using a 3D printed calibration device. The device calibrates the scanner and orients the facial scan with the patient in NHP, after which the NHP orientation is transferred to the virtual patient. Testing on a dental mannequin head demonstrated that this technique provides high repeatability and accuracy.
While simplistic volcano plot visualizations of multi-omics changes can highlight the most critical genomic, transcriptomic, or proteomic features, integrative frameworks combining literature evidence, pathway associations, and functional annotation remain limited. We present MagmaFlow, a cross-platform application offering three key capabilities: literature-based gene scoring, interactive pathway-to-volcano mapping, and synchronized cross-view updates. The literature module retrieves gene associations from PubMed via PubTator3, provides direct PubMed identifier (PMID) links, and ranks genes by context-specific relevance. The pathway module visualizes enrichment as multi-layer circle plots displaying cross-pathway membership, automatically synchronized with volcano selections. Interactive features include smart label positioning, drag-and-drop annotation, double-click gene targeting, and customizable styles for publication-quality figures. Thus, MagmaFlow transforms volcano plot analysis from static display into dynamic biological interpretation. To our knowledge, this is the first tool integrating artificial intelligence-powered literature contextualization and enrichment analysis to convert differential expression data into actionable insights.
Exposure-based cognitive behavioral therapy is among the least used evidence-based practices for anxiety disorders in routine care. Providers' negative beliefs about exposure (eg, fears of harm or intolerability) are a major barrier. Experiential methods can reduce these beliefs but are limited by accessibility, standardization, and fidelity. Virtual reality (VR) offers a scalable way to deliver standardized experiential practice. Guided by an "exposure to exposure" (E2E) framework, we conceptualized VR training as an exposure intervention targeting therapists' own anxious beliefs about exposure. This feasibility study examined a VR-based exposure training program (SET-VR) (1) to evaluate usability and effects on therapist learning targets (knowledge, self-efficacy, attitudes) and (2) to test whether a high-immersion head-mounted display (HMD) format provides added benefit over a lower-immersion desktop format. Eligibility included holding an active caseload. Eligible clinicians (ie, aged >21 years with an active caseload; n=41) completed a 4-hour didactic workshop on exposure and were randomized (1:1, blinded) to the desktop or HMD condition. In the experiential phase, therapists delivered 3 rounds of exposure with a virtual patient. They titrated exposure intensity (increase, decrease, continue as is) at fixed decision points based on state-dependent visual (character animations) and auditory (prerecorded verbalizations) cues reflecting the patient's distress. Exposure knowledge, self-efficacy, and beliefs about exposure were measured at baseline, post-didactic, post-experiential, and follow-up. Participants also rated the acceptability, usability, and authenticity of the program. Both groups (desktop and HMD) showed significant improvement in exposure knowledge (d=0.52, P=.006; d=0.58, P=.002), self-efficacy (d=0.88, P<.001; d=1.36, P<.001), and beliefs (d=0.61, P=.001; d=1.05, P<.001) from baseline to post-didactic training using binomial generalized estimating equations. There were no significant differences between the low- and high-immersion groups on any measure after didactics. Both groups demonstrated significant improvement in exposure self-efficacy (d=0.66, P<.001; d=0.93, P<.001) and beliefs (d=0.46, P<.01; d=0.66, P<.001) from post-didactic to post-experiential. Both groups gave positive ratings for acceptability, usability, and authenticity. No adverse events or side effects were reported. In this feasibility randomized controlled trial, an E2E-guided VR training program produced promising improvements in therapists' self-efficacy and negative beliefs about exposure beyond gains from didactic training alone. This work is innovative in testing immersion as a dose parameter while also applying an explicit framework (E2E) to target a key mechanism (ie, therapist beliefs) in the underuse of exposure therapy. Compared to prior VR training studies focused on skills and knowledge acquisition, our findings support the standardization of an emotionally engaging exposure practice context that shifts therapist-level mechanisms linked to actual delivery. The lack of clear advantages for HMD over desktop VR suggests that lower-immersion, more scalable implementations may provide a sufficient experiential "dose." Larger, more diverse trials are needed to confirm effectiveness and determine the real-world impact of VR-based exposure training on access to evidence-based care.
Use of virtual reality (VR) is increasing in education. A VR and simulation field trip was developed, allowing students to experience a walkthrough of a sterile pharmaceutical manufacture site using three modalities: a VR headset with 3D static tour, 2D static desktop tour and a 360° dynamic video tour. This study aims to understand student opinions, assess usability of the modalities used and investigate potential for a simulation to replace physical site visits. Data from undergraduate pharmacy and postgraduate pharmaceutical science students was gathered through an online survey. A system usability scale (SUS) score was calculated for each modality. Responses were coded and analysed using descriptive and inferential statistics and open-text responses were analysed using conventional content analysis. Data was collected from 82.3% (n = 79) of students who participated in the virtual field trip. Most students (97.4%, n = 76) across both courses were either extremely satisfied or satisfied with the session. Almost two thirds of students (64.9%, n = 50) ranked VR walkthrough highest, followed by 360° video walkthrough (19.5%, n = 15) and desktop walkthrough (15.6%, n = 12). Two out of three students (66.7%, n = 12) with previous on-site experience agreed the virtual field trip could replace a physical site visit. Open-text responses revealed advantages and drawbacks versus physical visits. All modalities displayed acceptable usability. Most students (91.0%, n = 71) would welcome wider use of VR and simulation in their course. Positive results obtained highlight potential for greater implementation of VR and simulation to support teaching of sterile manufacturing and increase student accessibility to manufacturing sites.
Genome-based bacterial taxonomy requires standardized and reproducible analytical workflows for species delineation and phylogenomic placement; however, the practical deployment of these workflows remains a significant barrier for experimental biologists and clinical scientists. Widely adopted tools such as Prokka, antiSMASH, and PhyloPhlAn underpin key steps in genome annotation, functional characterization, and phylogenomic reconstruction, but their practical deployment in routine laboratory settings, especially on Windows based systems, remains non trivial due to complex software dependencies and command line centric workflows. Existing solutions, including cloud-based platforms (e.g., Galaxy and KBase) and commercial software suites (e.g., CLC Genomics Workbench), partially alleviate these challenges but may also involve considerations related to data-privacy concerns, upload latency, storage quotas, shared computing resources, and recurring licensing costs. To address these limitations, we introduce TaxaScope, a graphical-interface-driven desktop workstation designed to support reproducible, genome-based bacterial taxonomy by integrating a curated set of community-validated tools for genome quality assessment, annotation, phylogenomic inference, genome relatedness estimation, and functional profiling within a unified local graphical user interface (GUI). By leveraging Docker- and Podman-based containerization behind a user-friendly frontend, TaxaScope provides version-locked, standardized execution environments across computing platforms without requiring manual dependency management or prior Linux expertise. We demonstrate the utility of TaxaScope through a comprehensive re-analysis of Pseudomonas putida KCTC 1751T, illustrating how standardized taxonomic workflows can be executed locally while automatically generating high-quality circular genome maps and interactive functional reports suitable for downstream interpretation and figure preparation directly from native tool outputs. Collectively, TaxaScope lowers the technical barrier to standardized and reproducible genome-based bacterial taxonomy by providing a private, locally controlled, containerized workflow that complements cloud-based and commercial infrastructures for routine taxonomic research. By providing a containerized and visualization-oriented desktop environment, TaxaScope facilitates the standardized execution of established genomic tools, thereby bridging the gap between complex bioinformatic workflows and consistent bacterial taxonomy.
We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information, experience metrics, habits, and preferences of computer usage were collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.
Phantom-based quality assurance (QA) of magnetic resonance imaging (MRI) coils is a standard practice for assessing signal-to-noise ratio (SNR), image uniformity, and magnetic field homogeneity. Manual slice selection and region-of-interest (ROI) placements introduce operator-dependent variability and workflow inefficiencies. To develop a unified, fully automated desktop-based workflow for MRI coil QA that eliminates manual slice selection and ROI placement. A Python desktop app was built to automate MRI coil QA workflows for body, torso, and head-and-neck coils. It takes DICOM image sets and raw free-induction decay data and performs slice selection, phantom detection, and fixed-geometry ROI placement automatically to calculate standard QA metrices: SNR, percent image uniformity, and frequency-domain magnetic field homogeneity. Thus, obtained results are directly saved in structured reports and plots. The automated workflow carried out all implemented QA tasks, without manual ROI placement, and consistently produced QA metrices for body, torso, head-and-neck, weekly QA, and magnetic field homogeneity analyses. The standardization of the outputs facilitated an objective evaluation of the coils and channel performance by eliminating the subjective bias. This study presents an automated MRI quality assurance workflow that operates through standardized testing methods and delivers consistent results while decreasing the impact of human error in standard clinical quality control procedures.
The Nwanedi River Catchment in Limpopo Province, South Africa, experiences seasonal salinity accumulation that constrains downstream water use and undermines agricultural productivity. This study develops a hydrology-based framework to define environmental flow requirements (EFRs) to mitigate salinity through flow-driven dilution and flushing. A desktop methodology integrating the Revised Desktop Reserve Model (RDRM), GIS-based catchment characterisation, stakeholder insights, and empirical water quality analysis was implemented. Results reveal a distinct downstream increase in salinity, with electrical conductivity (EC) reaching approximately 146.8 mS/m during the dry season. Modelled EFRs indicate that, under Environmental Management Class D, minimum low-flow allocations correspond to 18.7% and 21.0% of mean annual runoff for sub-catchments A80H and A80J, respectively. Monthly dry-season flow releases range between 0.35 and 1.05 Mm3, while maintaining an average residual volume of approximately 1 Mm3 to support agricultural demand. These environmental flows are derived from naturalised flow regimes and are assumed to re-establish the critical hydrological conditions necessary to dilute and mobilise saline baseflows. The findings, therefore, do not quantify direct reductions in salinity but demonstrate that maintaining these flow thresholds can recreate the hydraulic conditions required for salt flushing, particularly in downstream reaches where salinity accumulation is most pronounced. This study provides a preliminary, process-based foundation for developing adaptive water allocation protocols. It establishes a testable framework for future cause-and-effect analyses, in which controlled dam releases and systematic water-quality monitoring can be used to quantify the extent to which EFRs reduce salinity in semi-arid, data-scarce catchments.
Literature suggests that individual differences in tendencies toward gaming disorder (GD) may be associated with characteristics of the video games people prefer. We examined how game genre, multiplayer capability, and platform relate to GD tendencies assessed using both APA (DSM-5) and WHO (ICD-11) frameworks in a large international sample of gamers. We analyzed cross-sectional online data from 116,047 gamers. Participants completed validated measures of GD symptoms aligned with DSM-5 and ICD-11 and reported the genre, multiplayer capability, and platform of their currently preferred game. Associations were examined using bivariate and multivariate analyses. We additionally applied a Random Forest model to evaluate the predictive contribution of game-related variables to GD scores. GD levels were highest among participants preferring games with multiplayer capabilities, while positive associations with shooter and casual games were particularly evident among console users. In contrast, puzzle, platformer, and board games showed negative associations with GD scores. Players preferring desktop or laptop computers reported higher GD scores than those favoring consoles or small devices. In Random Forest models, game genre and multiplayer capability were modestly predictive of GD scores (R ≈ .08-.15) with predictive accuracy being higher for participants who used console and desktop computers and lowest among small-device users. Multiplayer capability and preferred genre were consistently related to GD tendencies across DSM-5 and ICD-11 measures. However, these variables alone offered limited predictive power, indicating that GD risk is only partly explained by game characteristics and likely depends on broader individual and contextual factors.
Fused deposition modeling (FDM) of polylactic acid (PLA) produces parts whose weak interlayer bonding and low as-printed crystallinity limit their tensile performance. This work used a Taguchi L9 orthogonal array with five replicates per cell (n = 5; N = 45 annealed specimens plus five non-annealed controls) to study how annealing temperature (70, 80, and 90 °C) and holding time (40, 60, and 80 min) change the tensile response of a commercial PLA grade (eSUN PLA+) printed on a desktop FDM machine. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) were used in parallel to measure total crystallinity, and XRD was deconvoluted to estimate the α'/α polymorph fractions; the DSC α'→α exothermic shoulder was used as an independent cross-check. Every annealed condition exceeded the non-annealed baseline ultimate tensile stress (UTS) of 39.75 ± 1.28 MPa. The optimum, 47.00 ± 0.97 MPa at 70 °C/60 min, gave an 18.2% gain. Total crystallinity rose from 8.6% (DSC baseline) to 41.8% (DSC, 90 °C/80 min), with DSC and XRD ranking the conditions consistently. ANOVA confirmed both temperature (30.0% contribution) and time (24.2%) as significant at α = 0.05. The new contribution is a combined strength-crystallinity-polymorph map for desktop FDM-printed PLA: the best-performing specimens are dominated by the disordered α' form, while the stiffer but weaker high-temperature specimens shift toward α. A partial least squares regression on all 50 specimens supports the polymorph-composition role beyond what total crystallinity alone explains. The practical conclusion is that moderate annealing just above the glass transition gives the best balance of crystal content, polymorph character, and geometric stability for FDM-printed PLA.
Accurate implant impressions or scans are critical to the success of complete arch implant-supported prostheses. Photogrammetry devices have recently gained popularity for their ability to deliver predictable scans immediately after complete arch implant placement. However, they must be used in combination with an intraoral scanner. This article describes a workflow for obtaining accurate scans during complete arch implant surgery without a photogrammetry device. A custom calibrator was designed as a part of the complete arch implant surgical guide. This calibrator was then 3-dimensionally printed and scanned with a desktop scanner. After implant placement during surgery, an intraoral scan is made with the calibrator positioned alongside the scan bodies. The intraoral scan is then calibrated using the previously obtained desktop scan, resulting in an accurate digital representation of the implant positions.
It remains unclear whether trimming sharp edges from scan body library files improves automatching accuracy, what magnitude of trimming is optimal, and whether any benefits are consistent across different scan body geometries. The purpose of this in vitro study was to evaluate the effect of scan body geometry and the edge trimming of scan body library files on the accuracy of matching between intraoral scans and library files. Two scan body geometries (angular and rounded) were evaluated with 3 library file designs: unmodified (U), 0.1-mm edge removal (E01), and 0.3-mm edge removal (E03). Ten scan bodies per geometry were scanned using a desktop scanner and an intraoral scanner. Library files were matched to the intraoral scan data in a computer-aided design software program to generate implant positions that were compared with reference positions derived from desktop scans. Deviations were measured as the linear distance between implant apices, angular difference between long axes, and root mean square surface deviation (n=10 per group). The data were analyzed by using mixed 2-way analysis of variance (α=.05). Edge trimming improved accuracy. For the angular geometry, linear deviation decreased from 118.4 ±21.7 µm (U) to 93.6 ±20.9 µm (E03; P=.001), angular deviation decreased from 0.33 ±0.12 degrees (U) to 0.24 ±0.11 degrees (E03; P=.043), and surface deviation decreased from 56.4 ±4.8 µm (U) to 44.0 ±6.9 µm (E03; P<.001). For the rounded geometry, linear deviation decreased from 91.2 ±12.1 µm (U) to 73.6 ±11.0 µm (E03; P=.020), and surface deviation decreased from 51.7 ±4.9 µm (U) to 39.1 ±5.1 µm (E03; P<.001). The rounded scan body showed lower linear deviation than the angular across all library designs (all P<.05). Edge removal in library files and the use of simpler rounded scan body geometry enhanced the accuracy of library automatching between intraoral implant scans and scan body library files.
Scaffolding authentic assessments through a Forensic Science programme are crucial for allowing HEI students the opportunity to actively repeatedly learn and improve their knowledge and skills. This is achieved through the common sequential forensic investigative cycles of investigative review, activity plan and design, through to processing simulated forensic crime scene(s), the resulting data processing, analysis, interpretation and professional report writing investigative stages. Authentic simulated crime scene activities need to be carefully constructed to ensure case realism, use current investigative practices, grounded in pedagogic theory and scaffolded for the appropriate student learning level, albeit being conscious of the need to be balanced with potential resource/funding limitations. This article details scaffolding of deliberately different simulated forensic crime scene investigations progressively through a UK HEI forensic science programme, from 'case' intelligence and desk-based studies, through increasingly complicated indoor/outdoor crime scene data collection/processing and professional report writing investigative stages. HEI academics have direct experience of these to give assessments real authenticity. The first case details an indoor commercial bar scene, with L4 first year undergraduate students tasked with investigating and virtually recording criminal evidence, scenes and producing a virtual resource for potential presentation in court. A second case details an outdoor simulated human remains scatter scene, with L5 second year undergraduate students tasked with investigating, recording and producing scaled sketches. A third case details a wildlife forensics scene, with L6 final year undergraduate students tasked with leading the investigation, recording and producing a professional report of illegal disturbance of a badger sett. A fourth case details an outdoor mass grave investigation scene, with L7 post-graduate taught students tasked with the complete multi-staged site investigation process, from desktop study through to field reconnaissance, non-invasive data collection, through to physical excavation, forensic recovery of human remains and associated material and a professional report to be generated. All assessments received very high student feedback and provided evidence that these resources were effective for their learning and understanding in a forensic science context. These types of authentic assessments of simulated crime scenes, whilst costly in terms of development time and staff resource, will assist HEI students with crucial experience and problem-solving skills needed in time restricted scenarios to mimic those they will face in future forensic science practitioner employment. Plentiful online resources and some cost-saving suggestions for colleagues if intending to construct similar authentic assessments are also included.
The growing global mental health (MH) burden, especially in underresourced communities, calls for innovative, scalable, and culturally responsive training approaches to expand care access and improve outcomes. Task-sharing has shown promise in addressing workforce shortages but is limited by training and supervision challenges. Traditional methods, such as role-playing and standardized patients, are resource-intensive and less scalable. Virtual simulations, including augmented reality (AR), present novel opportunities for immersive and interactive training. An AR-assisted training tool can enable culturally sensitive training while fostering empathy, communication skills, and confidence in handling nuanced MH scenarios. However, AR's usability and effectiveness for MH task-sharing training remain underexplored. This study aimed to assess the usability of an AR-assisted MH task-sharing training tool that uses virtual patient (VP) simulation and evaluate its potential to enhance training. Additionally, we developed design recommendations for future related XR-assisted clinical training tools. We conducted a formative, explorative sequential mixed-methods usability study. A convenience sample of 5 MH trainees or workers (ages 18-60 years; female: n=3, male: n=2; identifying as African American, Asian, and Hispanic) participated. Participants were recruited through a university-affiliated MH training program. The usability testing protocol included a semistructured prestudy interview, orientation to the AR headset (Magic Leap 2), a think-aloud user testing session, and a poststudy quantitative questionnaire and qualitative interview. Usability was assessed using a modified Post-Study System Usability Questionnaire (PSSUQ), which measures system usefulness, information quality, and interface quality. Data were analyzed using descriptive statistics and thematic analysis. The AR simulation was positively received by participants, demonstrating above-average usability. The overall mean PSSUQ score was 3.46 (SD 1.71), with subscale scores for system usefulness (mean 3.46, SD 1.77), information quality (mean 3.76, SD 1.73), and interface quality (mean 2.83, SD 1.59). Thematic analysis highlighted high realism fostered trainees' empathy toward the VP, while increasing immersion and interaction quality. Despite some hardware limitations and user discomfort that broke immersion, participants recognized the tool's potential and usefulness for training in various MH scenarios. Based on these findings, we proposed design recommendations across environmental context, training structure, VP behavioral realism (body language, voice, eye movement, and technical hardware considerations). This pilot study is among the first to evaluate AR-based VP simulation for training lay MH task-sharers, filling the technology and population gaps of prior VR- and desktop-focused simulation research. Preliminary empirical usability evidence from a validated instrument (PSSUQ) demonstrates above-average usability, and findings informed design recommendations for future research. These findings suggest AR-based training could provide a less resource-intensive solution for realistic MH training practice in underresourced settings. Further research includes larger sample sizes for inferential analysis, comparison studies with traditional methods, and generalizing to other MH conditions.
Recent advances in tissue clearing protocols such as DISCO, CUBIC, Clarity, FUnGI, and PEGASOS have revolutionized our ability to label and image intact 3-dimensional (3D) biological structures using fluorescence microscopy. The lactating mammary gland particularly benefits from clearing due to its high degree of tissue opacity. Cleared mammary gland images are strikingly beautiful and complicated but are difficult to fully interpret without developing a series of quantitative techniques and assays to analyze and compare them. These approaches will ultimately be as varied as the biology each scientist wishes to study. Here, we present one strategy based on a modular, hybrid, deep-learning approach and classical image processing that can segment and measure alveoli, cell nuclei and myoepithelial cells in intact mammary tissue. We have developed two original, three-dimensional (3D) U-shaped encoder-decoder networks (U-Nets), AlveoliNet and MyoNet, and combined these with CellPose3 nuclear instance segmentation and SlideBook/SlideBook Synergy binary mask operations. This approach can be used to easily score 100,000s of cells in intact tissue and differentiated glands at different developmental stages, genetic backgrounds, or treatments. We demonstrate the utility of this approach for quantifying the change in proportion of myoepithelial cells over the pregnancy-lactation transition, driven by endoreplication in the gland postpartum. We present a complete methods pipeline for other laboratories to utilize our approach in their own studies using standard desktop computers. Colin Monks is co-founder and co-President of Intelligent Imaging Innovations, Inc. (3i) and receives a salary from it.
MOrbVis is an open-source web application that visualizes molecular orbitals directly in the browser without precomputed Gaussian Cube files. Reading only a Molden file, it evaluates Gaussian-type basis functions (s through g shells) on a three-dimensional grid through WebGPU compute shaders. Benchmarks on five devicesfrom a smartphone to a desktop with an NVIDIA RTX 5090show that single-orbital evaluation on grids exceeding 106 points finishes within 100 ms, up to 3 orders of magnitude faster than the single-threaded CPU path. The tool requires no installation and is available at https://yasuaki-ito.github.io/morbvis/.
This article discusses how quantitative approaches to the history of chemistry gradually emerged in the first three decades of the postwar era, within the broader context of quantitative social and economic history, historical sociology of science, and quantitative science indicators. I suggest that influential publications in the 1960s, postulating logistic curves in the exponential growth of science and the role of a broad scientific community in producing and distributing scientific knowledge, stimulated subsequent quantitative research in the history of chemistry. With mainframe computers presenting technical difficulties in the foundational period to the mid-1980s, initial quantitative research usually involved sets of non-computerised data cards developed ad hoc for specific projects - here exemplified by databases including German and British chemists. Shifting to computerised databases during the 1980s and 1990s, as they confronted increasingly large amounts of data. Fortunately, simultaneous technological improvements, leading to more powerful desktop computers and more user-friendly software, greatly facilitated this transition. Early computerised databases may have been neither designed for wider use online, nor mutually compatible; without external support, they might not survive the careers of their creators. Preserving, integrating, and broadening access to such valuable raw data remains a crucial challenge for future scholarship.
Ambient clinical intelligence (ACI) systems use automatic speech recognition (ASR) to capture patient-provider conversations for downstream clinical documentation. However, many ASR evaluations are conducted under controlled conditions using specialized hardware. We evaluated how recording devices influence transcription performance of contemporary ASR engines applied to clinical dialogue. Thirty-five primary care encounters were re-enacted from transcribed conversations and recorded using five devices simultaneously: smartphone, laptop microphone, portable recorder, clip-on microphone, and a desktop microphone. Six ASR engines were evaluated using word error rate (WER), clinical concept extraction precision and recall, and sentence-level semantic similarity. Median WER ranged from 16.7% to 20.7% across engines. Engine choice produced larger variation in transcription performance than recording device, although device-related differences were statistically significant. Overall, contemporary ASR engines demonstrated relative robustness to consumer-grade recording hardware, suggesting that model selection may have greater impact on transcription performance than recording device configuration in real-world ACI deployments.