Scientific Reports

Pig vocalizations convey information about the emotional states of individuals, varying with arousal and valence. Studies show that different call types reflect distinct emotional contexts and social interactions for the receivers. However, little is known about the brain mechanisms behind the perception of conspecifics vocalizations. This study used BOLD fMRI to explore how pigs brains respond to emotionally varied vocalizations, with the aim to identify activity in regions linked to emotion, reward, and social processing. Eight healthy 2-month-old pigs underwent auditory brainstem response (ABR) testing and BOLD fMRI to assess brain responses to pig vocalizations with different hedonic valence. Sounds were delivered via MRI-compatible earphones, and imaging was performed on a 1.5T scanner. Data were analyzed using voxel-based and ROI-based statistics in SPM12 with small volume correction (SVC). Due to hearing anomalies or MRI artefacts, only 5 pigs were included in the final analysis. Functional MRI revealed that vocalizations activated regions of the auditory pathway and the left amygdala (pFWE at peak < 0.05 after SVC for all), with specific differences between positive and negative sounds. Clusters of activated voxels covering part of hippocampal areas, caudate nuclei and putamen were found with both positive and aversive vocal sounds. Limbic regions, including the amygdala and insula (p<0.05), as well as the right hippocampus after SVC (pFWE = 0.015) were uniquely engaged during the perception of negative conspecific vocalizations, indicating distinct processing based on emotional valence. This study shows for the first time that piglets brain can process and differentiate emotional vocalizations from other pigs, even under general anesthesia. Positive and negative vocal sound playbacks activated distinct brain regions related to hearing, emotion and reward. These findings highlight pigs cognitive and emotional processing of vocal cues. This study is part of a wider research program aimed at developing the fMRI protocol with acoustic stimulation in juvenile pigs.

Cognitive maps encode spatial relationships between locations and support flexible navigation. However, how these mental representations form in the absence of visual experience remains unclear. Here, we introduce a multisensory virtual navigation paradigm that allows to track the temporal dynamics of non-visual cognitive map formation. Sixteen early blind (EB), 17 late blind (LB), and 29 sighted controls (SC) learned the layout of a tactile maze. Participants repeatedly performed virtual pointing (estimating directions between locations) and navigation (reaching locations) tasks, which measured cognitive maps across multiple stages of learning. This method also enabled algorithmic inference of cognitive maps, providing insights into how mental distortions are progressively corrected. Although there were no group differences in average navigation performance, EB showed slower knowledge accumulation compared to LB and SC. In addition, both EB and LB had difficulties translating cognitive maps into first-person perspectives, resulting in reduced pointing and cognitive map accuracy. Yet, cognitive map accuracy improved progressively in all groups and a subset of EB and LB achieved expert-level performance with high navigation and pointing precision. In sum, this study provides a scalable framework for tracking alterations in cognitive map formation in blindness and other neurological conditions. Importantly, it demonstrates that cognitive map formation in the absence of vision is experience-dependent and trainable. Spatial disadvantages often observed in EB and LB thus do not reflect cognitive deficits but result from adaptive behavioral strategies constraining the use of allocentric cognitive maps.

Caveolinopathies caused by CAV3 mutations present with heterogeneous clinical phenotypes ranging from asymptomatic hyperCKemia to limb-girdle-type muscular dystrophy. Although prior imaging studies have described commonly affected muscles, structured modeling of muscle involvement patterns in caveolinopathy has not been established. We analyzed whole-body skeletal muscle computed tomography imaging in eight patients with pathogenic or likely pathogenic CAV3 variants, comprising 14 imaging study samples. Fat infiltration across 43 muscles was graded using modified Mercuri scores. Computational multivariate analysis,including principal component analysis, clustering, and pseudotime modeling,was applied to characterize severity staging and distribution patterns. A statistically supported, stage-dependent continuum of muscle involvement was identified. Most samples demonstrated a distributed limb-girdle-predominant pattern with coordinated progression across muscle clusters. In contrast, one patient (three samples in longitudinal series) exhibited a compartment-restricted thigh-dominant pattern characterized by early posterior and medial thigh involvement. Rectus femoris showed consistent stage-dependent progression, while greater medial gastrocnemius involvement was associated with advanced severity. None of the patients exhibited clinical evidence of rippling muscle disease. These findings suggest that integrating semi-quantitative imaging with computational modeling may provide an objective framework for characterizing muscle involvement patterns in CAV3-related myopathy.

Tumor habitat imaging aims to capture intratumoral heterogeneity by grouping voxels with similar radiomic properties into spatially coherent subregions. However, radiomic features are known to be sensitive to small variations in image acquisition and processing, which can affect the stability of the resulting habitat maps. Feature repeatability is usually evaluated using test-retest scans, but such data are rarely available in clinical practice. To overcome this, we adopted an image perturbation framework, which simulates test-retest conditions by applying small, controlled changes to a single image. In head and neck cancer (HNC), where imaging is further complicated by complex anatomy, dental artifacts, and variability in tumor delineation, dedicated stability analyses are still missing. In this study, we evaluated how the repeatability of radiomic features affects habitat stability in 390 oropharyngeal cancer patients (discovery cohort). For each patient, 11 perturbed CT volumes were generated using small in-plane rotations, sub-voxel translations, and tumor-adaptive Gaussian noise. Ninety-three radiomic features were extracted from each image set, and their repeatability was assessed using the lower confidence limit of the intraclass correlation coefficient (ICC-LCL), grouped into poor, moderate, good, and excellent categories. Tumor habitats were then generated using K-means clustering (H = 3) for each feature subset, and habitat stability was measured by the Dice similarity coefficient (DSC) between habitat maps obtained from original and perturbed images. Overall, 48.4% of features were poorly repeatable and only 6.5% reached the excellent category, with first-order features being more stable than texture-based ones. Habitat stability followed a clear monotonic trend with feature repeatability: the median DSC was 0.93 for habitats generated from excellent features, 0.84 for good features, 0.75 for moderate features, and dropped to 0.41 for poorly repeatable features. Habitats generated using all features (without any repeatability-based filtering) yielded an intermediate median DSC of 0.52. All pairwise comparisons between feature subsets were statistically significant (p < 0.001). To evaluate the generalizability of these findings, the analysis was repeated in an independent external validation cohort of 372 oropharyngeal cancer patients treated at the H. Lee Moffitt Cancer Center. The stability classification showed substantial feature-level concordance between the discovery and validation cohorts (overall agreement 67.7%, quadratic-weighted Cohen's kappa = 0.78), with no feature shifting by more than two stability classes. The habitat-stability hierarchy was fully preserved in the validation cohort (median DSC of 0.87, 0.73, 0.69, and 0.39 for excellent, good, moderate, and poor features, respectively; all pairwise p < 0.001). These results show that selecting features with higher repeatability clearly improves the spatial consistency of habitat maps in HNC and support the use of perturbation-based stability analysis as a routine step in habitat imaging studies.

Irritable Bowel Syndrome (IBS) is a highly prevalent, commonly diagnosed gastrointestinal disorder of gut-brain interaction (DGBI) that causes substantial physical, psychological, and financial burden. The role of abnormal motility and altered autonomic nervous system function, and their interplay, remains to be fully understood. Here we present a non-invasive method using body surface electrical recordings to concurrently quantify meal-response colonic motility and heart rate variability (HRV). We demonstrate the practical utility of this new technique in a pilot study comparing colonic motility and autonomic nervous system (ANS) function in IBS patients (n=14) and healthy controls (HC; n = 22). The study protocol included a 2-3 hr body-surface electrical recording with 60-90 minutes each of pre- and post- meal epochs. Colonic motility was markedly increased in the subset of IBS patients experiencing moderate-to-severe symptoms during the study, compared to IBS no or mild symptom groups and healthy controls. HRV metrics in IBS patients showed substantial baseline shifts with decreased vagal and increased sympathetic input, with blunted autonomic meal responses compared to HC. Newly introduced dynamic trajectory maps revealed pronounced colon motility-vagal dysregulation in high symptom IBS patients but not mild or no symptom groups. These results indicate altered autonomic-motility interaction as a potential mechanism of symptom genesis in IBS patients. This technology platform offers an easy-to-apply, non-invasive tool for larger scale investigations of gut and autonomic nervous system function in healthy and gastrointestinal disease cohorts.

Contactless assessment of cardiopulmonary function remains an unmet need, with current approaches relying either on subjective clinical examination or on resource-intensive imaging. We evaluated a novel multipoint airborne ultrasound surface motion camera (SMC) designed to map thoracic vibration patterns without contact and to extract clinically relevant information through data-driven analysis. In a prospective observational study, clinically characterised participants underwent short-duration acquisitions during natural breathing and externally induced oscillations. The resulting signals were transformed into spatially and frequency-resolved maps and analysed using machine learning models to discriminate healthy individuals from patients with respiratory or cardiac disease. The approach proved feasible in a clinical setting and achieved excellent discrimination between healthy individuals and respiratory patients (area under the receiver operating characteristic curve (AUC) 0.90 {+/-} 0.07), including in patients with subtle abnormalities not detected by pulmonary function testing. Discrimination between healthy individuals and cardiac patients ranged from acceptable to excellent (AUC 0.76-0.90 depending on subgroup), with the highest performance observed in aortic stenosis. Model interpretability analyses revealed spatial and spectral patterns consistent with the known physiological organisation of lung mechanics and cardiac auscultation areas, supporting a structure-function relationship between recorded signals and underlying processes. These findings indicate that thoracic vibration transmission encodes spatially and spectrally organised information that can be captured without contact and exploited through explainable data-driven modelling. While the results require confirmation in larger populations, this approach may represent an operator-independent, low-burden extension of bedside assessment, with potential applications in early detection, triage, and monitoring of cardiopulmonary disease.

Recent studies have shown that implementing explicit social cues, such as gaze, facial expressions, and gestures, in artificial agents can improve impressions of these agents. However, humans may also use implicit physiological cues, such as facial coloration and cardiac information, in social perception. The present study examined whether subtle skin color changes reflecting pulse signals enhance the perceived human likeness of artificial agents, and whether this effect depends on agent type, signal type, observers interoceptive sensibility, and their awareness of the skin color changes. Participants observed morphed face stimuli created from artificial agents and human faces and judged whether each stimulus appeared human-like or robot-like. In Experiment 1, skin color changes based on human-derived pulse wave signals enhanced perceived human likeness for a highly human-like agent, but not for a less human-like agent. In Experiment 2, perceived human likeness was enhanced not only by pulse-based skin color changes but also by sinusoidal skin color changes matched to the pulse wave signal in terms of mean amplitude and number of peaks. In addition, participants with higher scores on some subscales of the Multidimensional Assessment of Interoceptive Awareness (MAIA), a subjective measure of interoceptive sensibility, tended to notice the skin color changes. However, neither observers interoceptive sensibility nor their awareness of skin color changes directly explained the enhancement of perceived human likeness induced by skin color changes. These results suggest that subtle skin color changes reflecting pulse wave information may function as implicit dynamic cues signaling embodiment or biologicalness in artificial agents, thereby contributing to perceived human likeness.

While microbiome is increasingly recognized as crucial for human health, translating this knowledge into effective healthcare and preventive strategies remains challenging. Many studies focus on identifying changes in microbiome composition associated with disease and evaluating the potential of such disease-associated microbial profiles as biomarkers for disease diagnosis. Under the hypothesis that microbiome dysbiosis may reflect physiological alterations present long before disease onset, in this work, we analyse the potential of disease-specific microbial signatures not as a diagnostic tool when the disease is already present, but as a means of health assessment in the general population. Moreover, instead of trying to define a single health measure, we believe it is necessary to consider several ways in which the microbiome departs from health, according to different disease-related physiological changes. To evaluate our assumptions, we designed a two-stage study: the identification of disease-specific microbial signatures (discovery stage) and, subsequently, the study of their distribution in the general population to assess associations with general health (external validation stage). Specifically, in the discovery phase we characterized 16 disease-specific bacterial signatures from large public microbiome data using a compositional data analysis methodology. In the second phase, we quantified these microbial signatures in the Lifelines-DMP cohort, a large population-based cohort, and evaluated their association with self-reported health status. Results indicate that most disease-specific microbial signatures associate with health status, supporting our assumption that microbial composition can capture physiological alterations before disease onset, and highlighting the importance of considering multiple ways in which microbiome departs from a healthy state. These findings reaffirm the potential of microbial information as an additional tool in preventive medicine.

During the COVID-19 pandemic, limited testing capacity and reporting delays complicated epidemic surveillance and decision-making in Mexico. We calibrated \textit{covidestim}, a Bayesian nowcasting model, to estimate the total SARS-CoV-2 infections from reported cases and deaths using Mexican surveillance data. Disease-progression distribution priors were calibrated using Mexico City records and validated through comparisons with national seroprevalence surveys, hospitalization data, and annual reported severe-case rates across all states. Using the reconstructed estimates of active infections, we implemented an event-based risk framework that quantifies the probability of encountering at least one infectious individual in gatherings of different sizes. This probability was subsequently translated into a four-level epidemiological traffic-light indicator and computed at both state and municipality levels. The resulting estimates revealed substantial spatial heterogeneity that is obscured by state-level aggregation, particularly in states with marked differences between urban and rural municipalities. To evaluate consistency with public-health indicators, we compared the proposed risk classification with the official Mexican epidemiological traffic-light system, considering interpretable gathering sizes relevant to public-health decision making. Weekly reports derived from this framework were delivered to policymakers in the State of Queretaro in Mexico, as an anticipation tool for school reopening and public-space management. This demonstrates that this Bayesian reconstruction of infections combined with event-based risk metrics can provide an interpretable and generalizable municipality-level complement to routine surveillance systems, particularly in regions with limited testing capacity and heterogeneous local transmission dynamics.

Background: Midurethral sling placement is often performed during prolapse repair to treat or prevent stress urinary incontinence. However, some women experience persistent or new-onset stress or urgency urinary incontinence after surgery. It is unclear how prolapse repair, with or without a concomitant midurethral sling, alters urethral morphology and support, and how these changes relate to urinary continence outcomes. Objectives: To compare postoperative urethral morphology (dimensions, angles, shape) and support (position, mobility) after transvaginal prolapse repair with vs without a concurrent midurethral sling, and to explore associations between postoperative urethral characteristics and urinary outcomes (stress, urgency symptoms). Study Design: This ancillary analysis used magnetic resonance imaging and urinary outcome data from the Defining Mechanisms of Anterior Vaginal Wall Descent Study conducted across 8 clinical sites within the United States Pelvic Floor Disorders Network. Eighty-two women (median age, 65 years) underwent transvaginal prolapse repair (vaginal mesh hysteropexy or vaginal hysterectomy with uterosacral ligament suspension) with or without a concurrent midurethral sling between April 2013 and February 2015. Postoperative imaging at rest and during strain was performed 30-42 months after surgery (or earlier if they chose reoperation) between June 2014 and May 2018. Prolapse recurrence, defined as descent beyond the vaginal introitus during strain, was recorded. The urethra was segmented from postoperative scans to create 3-dimensional models for measuring urethral diameters, length, surface area, volume, angles, shape (principal component scores from a statistical shape model), position, and mobility (rest-to-strain displacement). Preoperative and 24-48-month postoperative urinary continence outcomes were assessed using validated questionnaires: the Urogenital Distress Inventory, Urinary Impact Questionnaire, and the Incontinence Severity Index. Comparisons of urethral and urinary outcomes by (1) midurethral sling and (2) stress urinary incontinence were made using Wilcoxon rank-sum tests, principal component analysis, and multivariate models as appropriate. Associations between urethral and urinary outcomes were evaluated with Spearmans rank correlation. Results: Forty-six women (22 hysteropexy, 24 hysterectomy) were in the sling group, and 36 (19 hysteropexy, 17 hysterectomy) were in the no-sling group. Among the 48 women without prolapse recurrence (28 sling, 20 no-sling), those with a sling (vs without) had larger urethral dimensions (all P<.03), a more anterior-superior position of the proximal urethra (indicating better bladder neck support) (P=.04), a straighter urethral shape (P=.006), and reported less bothersome postoperative stress incontinence (P=.02). Overall, 14 women (17%) experienced postoperative stress incontinence. Stress urinary incontinence was linked to a more acute proximal urethral sagittal angle (more aligned with axial plane) (P=.01), and a lower proximal urethra position (P=.04) and mid-urethra position (P=.03). Poorer stress and urgency urinary outcomes were associated with a shorter urethral length (P=.01), a more posterior-inferior urethral position (all P<.05), increased C or S-shaped urethral concavity (P=.008; P=.006), and smaller rest-to-strain displacement of the proximal (P=.03) and distal (P=.009) urethra. Conclusions: Urethral morphology and support differed with concomitant midurethral sling (vs no sling) and stress urinary incontinence after vaginal surgery. Urethral characteristics were also associated with postoperative urinary symptoms. Urethral configuration may influence urinary outcomes and could be considered during prolapse and stress urinary incontinence repairs.

BackgroundThe pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL-deficient knees, yet grading remains subjective, as evidenced by poor inter-observer reliability, particularly for Grade 2. Since low-grade (Grade 1) versus high-grade (Grades 2/3) PS is the threshold for recommending lateral extra-articular augmentation, performing the test in awake clinic patients limits grading reproducibility and introduces variability in surgical decision-making. Existing methods to quantify the pivot shift usually require examiner-performed testing under general anaesthesia. No prior approach has ascertained PS grading from a separate patient-performed functional movement. PurposeTo evaluate the feasibility of a machine learning (ML) classifier, trained on kinematic ultrasound bone-tracking signals acquired during a patients sit-stand-sit (SSS) knee movement, to predict their PS grade, and to clinically validate its ability to differentiate low versus high-grade PS. MethodsUltrasound bone-tracking kinematic data were collected during SSS manoeuvres in 23 ACL-injured patients using the GATOR device, and ground truth PS grades (0-3) were assigned under general anaesthesia by fellowship-trained orthopaedic sports surgeons. From the data collected, Leave-one-out cross-validation (LOOCV) was used to train the ML classifier. Clinical SSS data from 6 ACL-deficient patients was used for independent held-out validation of their low-grade (Grade 1) versus high-grade (Grade 2/3) PS. Multiple deep learning architectures (XceptionTime, InceptionTime, FCN, ResNet, ResCNN) and training strategies (including mixup augmentation and supervised contrastive learning) were tested. Performance was measured by one-versus-rest (OVR) AUC under LOOCV and by AUC (low vs high grade PS) from the held-out patient sessions. ResultsThe ML classifier achieved a maximum OVR AUC of 0.928 {+/-} 0.084 under LOOCV. Classifier performance increased with pivot-shift severity: Grade 3 was identified most reliably (AUC ~0.81; sensitivity 0.70-0.80), whereas Grade 2 remained the most challenging boundary (sensitivity 0.20-0.75 across configurations). For the clinically relevant binary classification of low-versus high-grade pivot shift, the classifier generalised well to a completely unseen patient cohort (AUC 0.889; accuracy 0.860; sensitivity 0.850; minimum-class sensitivity 0.767). ConclusionThe study demonstrates that kinematic ultrasound bone-tracking during sit-stand-sit contains transferable information about rotational instability severity in ACL-deficient patients, and represents the first reported approach to predict pivot shift grade from a patient-performed functional movement. The strong cross-validation performance confirms that the signals contain meaningful PS grade-discriminative information, but larger datasets targeting 50-100 sessions per grade will be required to achieve patient-level generalisation and advance this novel rotational instability assessment tool toward full clinical adoption. Level of EvidenceLevel IV, diagnostic feasibility study.

AIMS Cardiometabolic risk factors may impair health by altering the autonomic modulation of the cardiovascular system, a physiological process described by heart rate (HR) circadian oscillations. However, the impact of cardiometabolic health determinants on HR circadian oscillations remains scarcely characterized in real-world, population-based settings. To address this, we applied digital health technologies to investigate how cardiometabolic health determinants shape HR circadian oscillations in a real-world cohort of individuals free of cardiometabolic diseases. METHODS First, a 10-fold cross-validation of a model was performed, aiming at mitigating wearables measurement error caused by motion artifacts. This process was informed by 10,056 epochs of concurrent wearable-derived and polysomnographic HR assessment, yielding an average 1.3 bpm reduction in wearables measurement error. We subsequently applied this model to over 2 million 1-minute epochs of HR data, derived from 7-day continuous actigraphic recordings of 245 individuals free of cardiometabolic disorders. Functional-on-scalar regression modelling and both parametric and nonparametric analyses characterized HR circadian profiles and their relationships with demographics, lifestyle, chronotype, sleep health, and chronic insomnia diagnosis. A 6-dimension sleep health index was calculated. RESULTS Sex, chronotype, and sleep health predominantly shaped HR circadian oscillations. In detail, females consistently showed higher HR across the 24 hours. Moreover, chronotype was associated to a phase shift in HR circadian profiles, with later timings corresponding to eveningness. Notably, sleep health impacted HR circadian oscillations in a dose-dependent fashion: each additional impaired sleep dimension was associated with a 1.2 bpm HR increase during nighttime, alongside reduced circadian robustness and delayed oscillation timings. Finally, the earlier occurrence of morning HR peaks served as a digital biomarker of insomnia (80% specificity, 74% sensitivity). CONCLUSIONS This work provides a digital health framework to characterize HR circadian oscillations in free-living populations and supports its clinical utility in capturing the autonomic disruptions related to cardiometabolic health determinants.

Feeding behavior in blood-sucking insects relies on gustatory evaluation to decide on sustained ingestion, yet quantifying this process from electromyogram (EMG) recordings is labor-intensive. Here we developed MyoRec, an automated computational framework employing machine learning to analyse EMG signals from the triatomine bug Rhodnius prolixus. Using recordings under appetitive and aversive conditions, a convolutional neural network detected ingestion events with 97.7% accuracy. Automated analysis revealed distinct feeding dynamics, with prolonged ingestion and higher pumping frequency under appetitive stimuli, compared to rapid feeding cessation under aversive stimuli. MyoRec substantially reduces analysis time while maintaining accuracy, providing a scalable tool to investigate how gustatory cues modulate feeding decisions in hematophagous insects.

Objective: To develop and validate a robust deep-learning model capable of fine-grained phase recognition in total hysterectomy, particularly the complex periuterine dissection phase. Design: Multicentre retrospective observational study. Setting: Japan. Sample: Surgical videos (n = 764) from 43 institutions. Methods: We developed a robust and generalisable deep-learning model for surgical phase recognition in total hysterectomy, applicable to laparoscopic and robot-assisted procedures. Overall, 1,591,334 still images were annotated across nine surgical phases. A convolutional neural network (Xception architecture) was trained on 200 cases using four-fold cross-validation, with institutional separation between training and testing sets. Main outcome measures: Model performance was assessed using accuracy, precision, recall, and F1 score. Subgroup analysis and logistic regression evaluated the association between background clinical factors and recognition accuracy. Results: The model achieved an overall phase recognition accuracy of 0.78 (95% CI: 0.74--0.80), with a precision of 0.75 (95% CI: 0.72--0.78) and a recall of 0.76 (95% CI: 0.74--0.78). Performance was consistent across laparoscopic and robot-assisted procedures and across most surgical phases. Accuracy plateaued after training on 120 cases. No clinical factors significantly impacted performance. Trends toward lower accuracy were observed for cases with cervical myoma and pouch of Douglas adhesions. Conclusions: This model demonstrated high accuracy across diverse institutions and patient backgrounds. Its potential applications include surgical education, real-time intraoperative support, and training efficiency enhancement.

The neurodegenerative disorder Alzheimers disease (AD) is widely known for biomarkers such as amyloid beta plaques and tauopathy, as well as functional differences in memory and cognitive ability. Despite this devastating functional impact, a large body of work only focuses on molecular biomarkers of AD. In this study, we investigate collective neural dynamics in vitro and assess how network-level properties differ between a well-established model of familial AD (FAD) and a newly developed in vitro accelerated model (acAD). The new model system reliably develops the key structural characteristics of AD in three weeks, but its calcium dynamics had not been characterized previously. Spontaneous network dynamics influences information processing as part of the internal network state. Here we measure this spontaneous activity of a network of hundreds of cells in each field of view. We find that the FAD model has a larger fraction of hyperactive cells, while the acAD model displays similar characteristics to healthy cells. Additionally, the FAD model has altered cooperation between cells, losing a proportion of highly correlated cellular activities, both for fast and slow coupling among cells. The acAD model is again consistent with healthy networks. Since the acAD model does not show the same spontaneous network dysfunction seen in FAD, it can enable measurements of changes in learning and memory associated with the plasticity, rather than the structure of the internal network state.

The increasing availability of portable ultrasound systems motivates exploration of novel approaches to respiratory signal assessment. In this in-vitro study, we investigate whether pulsed-wave (PW) Doppler ultrasound can capture structured spectral patterns from replayed lung sound recordings. Digitized respiratory sounds were replayed through a tissue-mimicking ultrasound phantom, generating 1,478 PW Doppler spectral images from recordings associated with healthy subjects and several externally labeled disease categories. Exploratory classification experiments using a ResNet-18 architecture demonstrated that these Doppler representations contain learnable differences under controlled conditions. These findings motivate further investigation into PW Doppler as a potential representation of respiratory acoustics.

Background: Before transcatheter aortic valve replacement (TAVR), patients with severe aortic valve stenosis are at an increased risk of developing fluid volume overload and heart failure, which is associated with subsequent adverse outcomes after TAVR. Purpose: To quantify fluid volume status as whole-body fast-exchange extracellular volume (FE-ECV) in patients undergoing TAVR compared to healthy reference values using photon-counting CT (PCCT). Methods: Consecutive patients referred for TAVR and healthy living kidney donor candidates, respectively, underwent PCCT including the pelvis. FE-ECV (mL) was quantified using venous hematocrit, injected iodinated contrast concentration and volume, and blood iodine concentration and urinary bladder excreted iodine mass quantified in iodine map regions of interest from the inferior vena cava and covering the urinary bladder, acquired at one time point 6-10 minutes after intravenous iodinated contrast administration. Results: The study included 156 subjects (healthy: n=51, age 47{+/-}9 years, 55% female; TAVR: n=105, age 78{+/-}6 years, 39% female). In healthy subjects, FE-ECV was 160{+/-}22 mL/kg lean body mass (LBM), 95% limits 116-204 mL/kg LBM, and was independent of age, sex, contrast agent type, and scan delay time after contrast injection (p>0.66 for all). Compared to healthy subjects, FE-ECV in patients referred for TAVR was higher (174{+/-}34 mL/kg LBM, p=0.01), with 19 patients (18%) exceeding the normal range. Conclusion: One in five patients referred for TAVR demonstrated increased FE-ECV, revealing a substantial prevalence of fluid overload detectable by single-time point late-phase PCCT iodine mapping.

Fixed larval specimens often shrink and curve, making length measurement labor-intensive. Although recent studies have demonstrated efficient fish-length estimation from images using deep learning, methods for estimating curved length remain limited. Furthermore, although deep learning is a powerful method for object detection in images, an essential step for length measurement, it requires preparing large amounts of training data, which can hinder practical implementation. In this study, we used a zero-shot model that requires no training to detect fish in an image. The curved length was then estimated using an image-processing approach that combines image thinning with Bezier curve approximation, and its accuracy was evaluated. We analyzed 1,040 larvae from five tuna species captured in stereomicroscope images. Manual measurements (notochord length or standard length; 1.5-8.5 mm) were conducted by two measurers and served as reference values. Fish regions were detected using GroundedSAM, and curved body centerlines were extracted through image thinning and approximated with Bezier curves. The curve length was used as the estimated body length. Estimation accuracy was assessed using bias and standard deviation between estimated and manual measurements. GroundedSAM detected all 1,040 fish, although there were 49 overdetections. Overdetection was caused by the double-detection of a single fish or by the misidentification of debris and light reflections as fish. Although the standard deviation of the differences between manual measurements and the image analysis-based (IAB) method was larger than the inter-measurer differences, the bias for [&le;]5 mm was comparable to or smaller than the inter-measurer bias. According to the strength-of-agreement criteria for the concordance correlation coefficient, the IAB method demonstrated substantial agreement in the [&le;]5.0-mm range. The IAB method accurately measured most curved tuna larvae without prior training, particularly in the [&le;]5.0-mm range. Combining the IAB method with manual remeasuring can improve the efficiency of curved-length measurement tasks.

Remote instruction is increasingly used to teach complex sensorimotor skills, yet conventional audio-video communication poorly conveys the fine-grained attentional cues that support expert guidance. This study tested whether real-time bidirectional gaze sharing enhances remote transfer of piano performance skill by restoring joint visual attention between teacher and learner. Twenty-seven conservatory-level pianists were randomly assigned either to a group, in which teacher and learner gaze positions were visualized during online instruction, or to a group receiving otherwise identical instruction without gaze cues. We recorded eye movements with wearable eye trackers and evaluated piano performance using a high-resolution key-motion sensing system. Real-time gaze sharing increased learners gaze-pattern similarity to a teacher, which was not evident in the control group. A parallel effect was observed for head-movement similarity. Critically, gaze sharing also reduced variability of the key-descending velocity at the moment of finger-key contact for the right-hand landing after a leap, a feature associated with unstable key-striking velocity. These findings exhibit that gaze information is not merely an auxiliary communication cue but a timing-critical coordination channel for remote motor instruction. By augmenting video-mediated pedagogy with shared attentional dynamics, the proposed system offers a framework for transmitting tacit, high-dexterity skills across distance.

Glabrous skin on the human palms and soles is specialised for mechanical interaction and is characterised by papillary ridges, an absence of hair follicles, and a thick stratum corneum. While gross morphological differences, such as the increased thickness of the plantar stratum corneum, are well-documented, the fine-scale structural features associated with individual ridges and whether they help explain differences in tactile perception remain poorly understood. Here, we employed optical coherence tomography, automated image segmentation, and 3D reconstruction to generate high-resolution models of the stratum corneum and viable epidermis across six sites on the hand and foot for 27 participants. Our morphological analysis revealed that while the stratum corneum was thicker on the foot sole than the hand, the thickness of the viable epidermis remained remarkably consistent across all sites. Furthermore, papillary ridge width increased in a distal-to-proximal fashion and was larger on the foot. We also found that internal ridge architecture was considerably more pronounced than the topography observable on the skin surface. Correlating these structural parameters with psychophysical measures, specifically absolute force and two-point discrimination thresholds, demonstrated distinct drivers for tactile sensitivity. Force thresholds were primarily governed by a combination of skin thickness and innervation density, and additionally on the foot by papillary ridge width and internal ridge depth. In contrast, spatial acuity was best predicted by innervation and papillary ridge width. These findings provide a precise quantification of glabrous skin morphology and offer new insights into the process of mechanotransduction by highlighting how tissue architecture might contribute to human tactile perception.